The Vulnerability of Coastal Communities to Water Pollution in Jakarta Bay: Integrating Livelihood and Biophysical Approaches

Amanda Putri

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

School of Physical, Environmental and Mathematical Sciences

University of New South Wales, Canberra

July 2018 PLEASE TYPE THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet

Surname or Family name: Putri

First name: Amanda

Abbreviation for degree as given in the University calendar: PhD

School: School of Physical, Environmental and Mathematical Sciences

Faculty: UNSW Canberra

Title: The Vulnerability of Coastal Communities to Water Pollution in Jakarta Bay: Integrating Livelihood and Biophysical Approaches

Abstract 350 words maximum: (PLEASE TYPE)

Jakarta Bay, located on the edge of Jakarta megacity, is suffering significant environmental degradation because of pressures from development and rapid urban growth. Water pollution of Jakarta Bay is a prominent environmental problem. Previous research has shown the serious impacts of water pollution on the biophysical system that has contributed to changes of the bay's ecosystem. The traditional fishing communities that rely on the bay's fishery resources have been seriously affected by the consequences of these changes. This research provides better understanding of the less studied aspects of water pollution in Jakarta Bay; that is, how it affects these fishing communities.

An integrated approach, that combines livelihood and biophysical analysis, was applied to investigate the impacts of water pollution on the traditional fishing community, their coping strategies and the factors that shape their vulnerability to water pollution. Three occupational groups represent vulnerable household types (traditional fishers, mussel farmers and informal workers) took part in interviews. Information from the participatory activities of these households (n = 294) produced new data about their livelihood characteristics (in the context of livelihood capital) and their knowledge of water pollution. An analysis of water quality (analysed statistically and spatially from government data) was combined with the results of participatory mapping to estimate each group's exposure to water pollution. Concurrently, a vulnerability assessment, was performed based on the elements of exposure, sensitivity and adaptive capacity.

This research contributes new insights about how groups in the community had been affected differently by and had adapted their livelihoods to the consequences of water pollution. This research has revealed that the fishing community was the most vulnerable to water pollution because of their high sensitivity and exposure to water pollution. This investigation of vulnerability contributes important evidence to guide management responses to improve the well-being of this fishing community. It became clear that an effective way to improve their livelihoods is to reduce their exposure and at the same time to build their capacity to adapt. Detailed recommendations for further research and management were developed to be a valuable source of information for researchers, policy makers and managers.

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ACKNOWLEDGEMENT

My PhD has been a challenging journey and an amazing one at the same time. It is a pleasure to thank those who made this thesis possible.

I would like to express my most sincere gratitude to my supervisor, Associate Professor Stuart Pearson. I value his insightful advice and warm encouragement that have contributed greatly to this PhD completion. His enthusiasm in every discussion was an enormous source of motivation. I also wish to thank my co-supervisors, Associate Professor Xiao Hua Wang and Dr. Nicolaas Warouw, for their continuous support and valuable comments in improving this thesis.

My gratitude also goes to Dr. rer. nat. Wiwin Windupranata from Bandung Institute of Technology and Dr. Widodo Pranowo from the Indonesian Ministry of Marine and Fisheries Affair. I am thankful for all the insightful discussions and their support back when I did my fieldwork in Jakarta.

I am indebted to the coastal communities of Muara Angke and Cilincing in Jakarta, especially the respondents, who, with their high enthusiasm, were willing to take part in this research and were very helpful during my fieldwork. Their generosity to share their insights and knowledge (and their delicious meals) has made this research possible. I would also thank the interviewees from the Indonesian Coalition of Fishers (KNTI), KIARA, Bogor Institute of Agriculture, University of Padjadjaran and DHI Indonesia who had given their valuable time and shared their expertise on Jakarta Bay.

This research had been supported by the Indonesian Endowment Fund for Education (LPDP), the University of New South Wales (UNSW) and the School of Physical, Environmental and Mathematical Sciences (PEMS) in Canberra. I would like to express my appreciation for their financial support that has made one of my dreams, to pursue doctoral degree, came true.

I would also like to extend my gratitude to the administration and academic staffs in the UNSW Canberra for their great support as well as the UNSW Statistical Centre in

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Kensington for their valuable statistical advice. My thanks also goes to my editor, Robin Brown, with his excellent support for improving the writing of this thesis.

Special thanks to all my colleagues in PEMS, especially Bobbi, Saiful, James, Anne, Pearly, Mba Diah, Mas Bambang, Amerita, Dustin, Julia, Shengnan and Solomon. Thank you for all those tea-room and late night chats. You have inspired me with your energy and perseverance.

I would like to express my deepest gratitude to my family in Indonesia, especially my parents. I would not have been where I am now without your never ending support, pray and blessing. Finally, to my number one supporter, Dimas, and our new bundle of joy and hope, Giri. I could not find a word that could describe how much grateful I am to have you two along this PhD journey. Bapi, thank you for always believe in me, for your understanding, love and selfless support. Giri, thank you for being there for Ibu during those sleepless nights, Nak!

ABSTRACT

Contents

Originality Statement i

Acknowledgements iii

Abstract v

Table of Contents vii

List of Figures xiii

List of Tables xvii

Abbreviations xix

Publications xxi

Chapter 1 Introduction 1

1.1. Research background 1

1.2. Research objectives and research questions 5

1.3. Research context 7

1.4. Thesis outline 9

Chapter 2 Literature Review 11

2.1. Water pollution and environmental degradation: the impacts on resource dependent communities 11

2.1.1. Water pollution and environmental degradation in coastal megacities 11

2.1.2. Water pollution and environmental degradation: Jakarta context 13

2.1.3. Traditional fishery sectors: under pressures from water pollution and environmental degradation 17

2.1.4. Traditional fishery communities in Jakarta: identifying the challenges 20

2.2. Frameworks for examining the impacts of water pollution on traditional fishing communities 24

2.2.1. Vulnerability concept: an approach to understanding the human- environmental system 24

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2.2.1.1. The onion framework of vulnerability 27

2.2.1.2. The BBC framework 28

2.2.1.3. Global environmental changes framework 30

2.2.1.4. Sustainable livelihood framework: linking vulnerability and

sustainability 31

2.2.2. Defining vulnerability elements: exposure, sensitivity and adaptive capacity 34

2.2.3. Putting vulnerability in context: livelihood vulnerability to water pollution 37

Chapter 3 Research Design 41

3.1. Introduction to study area: Jakarta Bay and coastal areas 41

3.2. Traditional fishers and mussel farmers groups 43

3.3. Framework for research 45

3.4. Methodologies 47

3.4.1. Water quality assessment 48

3.4.1.1. Data analysis on water quality 51

3.4.1.2. Developing a water pollution exposure map 52

3.4.2. Livelihood vulnerability assessment 55

3.4.2.1. Data collection: livelihood and local knowledge 57

3.4.2.2. Data analysis: livelihood and local knowledge 61

3.4.2.3. Measuring livelihood vulnerability index 64

Chapter 4: Water Pollution as a Stressor That Contributes to Shaping a

Community's Livelihood 69

4.1. Water quality assessment of Jakarta Bay 69

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4.1.1. Cluster analysis results of water quality 69

4.1.2. Water pollution exposure map 71

4.1.3. The occupational groups’ differential exposure to water pollution 73

4.2. Perceptions of water pollution and environmental changes 74

4.2.1. Environmental issues in Jakarta Bay as defined by the occupational groups 74

4.2.2. Water Pollution as understood by the occupational groups 78

4.3. Portraying changes in the environments 82

4.3.1. Historical timeline: describing the changes in the environments and fishery activities 83

4.3.2. Portraying the changes: traditional fishers group 85

4.3.3. Portraying the changes: mussel farmers group 87

4.4. Occupational groups’ perspectives on the impacts of water pollution and environmental changes 90

Chapter 5: Vulnerability of Resource-based Communities to Water Pollution 97

5.1. Livelihood characteristics of the occupational groups 97

5.1.1. Physical capital: infrastructure and public services 97

5.1.2. Social capital: utilising networks 100

5.1.3. Financial capital: diversification of livelihood sources 102

5.1.4. Human capital: formal and informal skills 105

5.1.5. Natural capital: land entitlement and fishery resources 107

5.2. The livelihood vulnerability to water pollution 108

5.2.1. Livelihood capital as resources for coping (Livelihood Capital Index) 109

5.2.2. Dependency on fishery resources (Sensitivity Index) 112

5.2.3. Exposure to water pollution (Exposure Index) 113

5.2.4. Livelihood Vulnerability Index 114

5.3. Adaptation strategies: coping with changing environments 114

5.3.1. Fishing behaviour: geographical shifts and harvest activities in traditional fishing communities 115

5.3.2. Social network support 118

5.3.3. Occupational expansion and extension 120

5.3.4. Occupational transformation 123

Chapter 6 Discussion 125

6.1. Integrative approach to understanding the exposure to water pollution and its societal impacts 126

6.1.1. Biophysical assessment in the context of livelihood vulnerability 126

6.1.2. Understanding the exposure of water pollution through a vulnerable community’s participation 129

6.1.3. The impacts of water pollution and environmental changes 133

6.1.3.1. Declining productivity 134

6.1.3.2. Changes in fishing location 135

6.1.3.3. Occupational-related coping strategies in fishing activities 136

6.2. Livelihood characteristics that shape vulnerability to water pollution 138

6.2.1. The traditional fishing community: their reliance on fishery resources, and their limited education and skills 139

6.2.2. Diversification: reduce vulnerability and sustain livelihoods 142

6.2.3. Social networks and the community livelihoods 148

6.2.4. Informal settlements and access to public services 154

6.3. A vulnerability perspective on traditional fishing community in Jakarta Bay 156

6.3.1. Improving resilience: adaptive capacity and accessibility to capital 156

6.3.2. Reducing exposure for more sustainable livelihoods 162

Chapter 7 Conclusion 167

7.1. Improved biophysical assessment of water quality 167

7.2. Understanding the impacts of water pollution better by using an integrated approach 169

7.3. Reducing the livelihood vulnerability: a two-pronged approach 171

7.3.1. The need to reduce exposure to water pollution 172

7.3.2. Capacity building to improve resilience 172

7.4. Future research 174

REFERENCES 177

APPENDIX

Appendix 1: Coordinates of the sampling sites in Jakarta Bay 211

Appendix 2: Water quality datasets summary 213

Appendix 3: Boolean operator for calculation of exposure index 217

Appendix 4: Questionnaire form 218

Group discussion guideline 232

Interview guideline 236

Appendix 5: Count of responses and standardised values for indicators of

vulnerability 238

Appendix 6: Proximity matrix and agglomeration table of cluster analysis 239

Appendix 7: Fieldwork activities 241

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List of Figures

Figure 2.1 Water pollution impacts on coastal fisheries 19

Figure 2.2 Impacts of water pollution on ecosystem service provisions and traditional fishing communities 23

Figure 2.3 Widening of the vulnerability concept 26

Figure 2.4 The onion framework of vulnerability 28

Figure 2.5 The BBC framework 29

Figure 2.6 Global environmental changes framework 30

Figure 2.7 Sustainable livelihood frameworks for vulnerability assessment 32

Figure 3.1 Area of study: Jakarta Bay and the fishing villages of Muara Angke and Cilincing 41

Figure 3.2 Traditional fisher’s vessel in Cilincing 44

Figure 3.3 Bamboo platforms for green mussel aquaculture in Bidadari Island 45

Figure 3.4 The research framework 47

Figure 3.5 The research sequence, methods and products 48

Figure 3.6 The locations of 23 sampling sites in Jakarta Bay and the river mouths (in blue) including labels for the three major rivers (Citarum, Angke and Ciliwung) 50

Figure 3.7 Examples of qualitative data analysis processes 64

Figure 4.1 Dendrogram of sampling sites in Jakarta Bay clustered by water quality parameters 70

Figure 4.2 Water pollution exposure map of Jakarta Bay 72

Figure 4.3 Map showing average distance to fishing grounds and green mussel farms in Jakarta Bay overlaid on water pollution exposure index map 74

Figure 4.4 Most important environmental issues in Jakarta Bay and coastal area as stated by the occupational groups 75

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Figure 4.5 A fisher removes solid waste that obstructs the boat's propeller (left) and a pile of litter along Cilincing River (right) 76

Figure 4.6 Occupational groups’ awareness on water pollution 79

Figure 4.7 Indicators of water pollution as perceived by the occupational groups 81

Figure 4.8 A traditional fisher swims among the floating litter in Cilincing (left) and a bag of water and sediment from Jakarta Bay acquired by a mussel farmer (right) 81

Figure 4.9 Occupational groups’ perception of water pollution sources 82

Figure 4.10 A traditional fisher with his shrimps (left) and blue manna crabs (right) 84

Figure 4.11 A map showing the location of past and current fishing areas and their exposure index 86

Figure 4.12 The surface view of green mussel platforms at Bidadari Islands (above) and an illustration of the platforms drawn by the green mussel farmers (below) 87

Figure 4.13 Dead green mussels (left) and the new blackish mussels (right) 88

Figure 4.14 Past and current green mussel farming areas, overlayed with the exposure index and reclamation development 89

Figure 4.15 Occupational groups’ perceptions on the impacts of water pollution 91

Figure 4.16 Irritated skin on the arm of a mussel farmer that he attributed to water pollution (left) and grilled fish prepared by fishers from their catch (right) 93

Figure 4.17 Summary of the occupational groups responses on the impacts of water pollution 94

Figure 4.18 Factors that cause decline in fisheries mentioned by traditional fishing groups 95

Figure 5.1 A wooden-plank house alongside the Kali Adem river in Muara Angke 97

Figure 5.2 Type of dwelling for each occupational group 98

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Figure 5.3 Travel time to nearest health facility for each occupational group 99

Figure 5.4 Ownership of health insurance for each occupational group 99

Figure 5.5 Access to clean water for each occupational group 100

Figure 5.6 Organisation membership for each occupational group 101

Figure 5.7 Households that received information about water pollution for each occupational group 101

Figure 5.8 Households with additional family members working for each occupational group 102

Figure 5.9 Households with additional jobs for each occupational group 103

Figure 5.10 Occupational groups' sources of credit 104

Figure 5.11 Main purpose of credit 105

Figure 5.12 Level of education for each occupational group 105

Figure 5.13 Households with additional non-fishery jobs 106

Figure 5.14 Agricultural land entitlements (other than occupied dwellings) for each occupational group 107

Figure 5.15 Awareness about the decline in fishery productivity 108

Figure 5.16 Standardised indicator values for the occupational groups 109

Figure 5.17 Capital (social, physical, financial, human, and natural capital) indices of the occupational groups 111

Figure 5.18 Livelihood capital index (LCI) of the occupational groups 112

Figure 5.19 Sensitivity index of the occupational groups 112

Figure 5.20 Water pollution exposure index for each occupational group 113

Figure 5.21 Livelihood vulnerability index for each occupational group 114

Figure 5.22 Geographical shifts of fishing activities of the traditional fishing group 115

Figure 5.23 Map showing geographical shifts in fishing locations for traditional fishers (arcs show average distance) 116

Figure 5.24 Geographical shifts of mussel farming for the mussel farmers group 116

Figure 5.25 Map showing geographical shifts in mussel farming locations (arcs represent average distance) 117

Figure 5.26 Summary of the harvest and ‘seeding’ cycle of green mussel (the numbers represent the months in a year). The photograph shows the size of green mussels at three to four months (top right) and at seven to eight months (bottom right) 118

Figure 5.27 Type of organisations joined by the occupational groups 119

Figure 5.28 A group of fishers hauls a broken boat ashore for repair (left) and a KUB sign at one of the base camps in Cilincing village (right) 120

Figure 5.29 Examples of the diversified income sources for fishers: the garage in Muara Angke of a boat mechanic, who was also a traditional fisher (left) and a motor tricycle owned by a traditional fisher (right) 121

Figure 5.30 Workers in a mussel processing (left) and at a fish processing workplaces (right) 123

Figure 5.31 Former traditional fishers and their new occupations: beside the garbage picker boat in Jakarta Bay (left) and net repairing for a large vessel (right) 124

Figure 6.1 Multi-scale key recommendations to improve water quality and reduce the communities' exposure to water pollution 165

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List of Tables

Table 2.1 Research on heavy metals contamination in the area of Jakarta Bay 16

Table 3.1 Water quality values used in the analysis to develop clusters for the map of water quality 53

Table 3.2 Elements and sub-elements of vulnerability and indicators measured 56

Table 3.3 Characteristics of the districts in which the fishing villages were studied and sample sizes of the research 60

Table 4.1 Median values of water quality parameters used in cluster analysis 71

Table 4.2 Exposure index of the five cluster groups 71

Table 4.3 Occupational groups’ perceptions on the most important environmental issues 78

Table 4.4 Historical timeline 83

Table 5.1. Seasonal activities calendar of the traditional fishers group 122

Table 6.1. Key actions and potential actors to improve the adaptive capacity and community's resilience to water pollution 158

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ABBREVIATIONS

ANZECC Australian and New Zealand Environment and Conservation Council

BBC Bogardi, Birkmann and Cardona Framework of Vulnerability

BOD Biological Oxygen Demand

BPLHD Badan Pengelola Lingkungan Hidup Daerah (Environmental Management Agency)

BPS Badan Pusat Statistik (Bureau of Statistics)

DFID Department for International Development

DKP Departemen Kelautan dan Perikanan (Department of Marine and Fisheries)

DO Dissolved Oxygen

FAO Food and Agriculture Organisation

GECF Global Environmental Changes Framework

HAB Harmful algae blooms

ICSU International Council for Science

IFAD International Fund for Agricultural Development

IPCC Intergovernmental Panel for Climate Change

KIARA Koalisi rakyat untuk keadilan perikanan (People Coalition for Justice in Fisheries)

KKP Kementerian Kelautan dan Perikanan (Ministry of Marine and Fisheries)

KUB Koperasi Usaha Bersama (Group Cooperatives)

LCI Livelihood Capital Index

LVI Livelihood Vulnerability Index

MEA Millennium Ecosystem Assessment

NCICD National Capital Integrated Coastal Development

NGO Non-governmental organisation

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PEMP Program Pemberdayaan Ekonomi Masyarakat Pesisir (economic empowerment program for coastal communities)

PIK Pantai Indah Kapuk

PNPM Program Nasional Pemberdayaan Rakyat (economic empowerment program)

PPNI Persatuan Perempuan Nelayan Indonesia (Indonesian Association of Women Fishers)

PRA Participatory Rural Appraisal

TSS Total Suspended Solid

SLF Sustainable Livelihood Framework

UN United Nations

UN-DESA United Nations Department of Economic and Social Affairs

UNDP United Nations Development Programme

UNEP United Nations Environment Programme

UNESCO United Nations Educational, Scientific and Cultural Organisation

UNISDR United Nations International Strategy for Disaster Reduction

WWF World Wildlife Fund

Publications

Papers:

1. Putri, A. & Pearson, S.G. 2014. Poverty and pollution impacts in Jakarta's fishing villages (vulnerability assessment and scenario of livability). In The 3rd International Conference on Sustainable Built Environment, Faculty of Civil Engineering and Planning, Islamic University of Indonesia and Research Institute for Human Settlements, Agency of R&D Ministry of Public Works, Indonesia, pp. 34-48. Presented at the 3rd International Conference on Sustainable Built Environment, Yogyakarta, 21- 22 October 2014.

2. Putri, A. Pearson, S.G. & Windupranata, W. 2015. Sustaining the environments- sustaining the livelihoods: Insights from the coast of Jakarta, Indonesia. In The Proceeding of ISIC 2015 Academic Conference, pp. 20-30. Presented at the 15th Indonesian Scholars International Convention, London, United Kingdom, 2-4 October 2015.

3. Pearson, S., Windupranata, W., Pranowo, S. W., Putri, A., Ma, Y., Vila-Concejo, A., Fernández, E., Méndez, G., Banks, J., Knights, A. M., Firth, L. B., Breen, B. B., Jarvis, R., Aguirre, J. D., Chen, S., Smith, A. N. H., Steinberg, P., Chatzinikolaou, E. & Arvanitidis, C. 2016. Conflicts in some of the world harbours: What needs to happen next? Maritime Studies, vol. 15, no. 1, pp., doi: 10.1186/s40152-016-0049-x.

Conference Presentations:

1. Putri, A. & Pearson, S.G. Poverty and pollution impacts in Jakarta's fishing villages, Australian Marine Sciences Association Conference: Investigating our Marine Nation, Canberra, 6-10 July 2014.

2. Pearson, S.G., Windupranata, W., Putri, A., Ma, Y., Vila-Concejo, A., Fernandez, E., Mendez, G., Banks, J. & Chen, S. How conflicts are managed in the Worlds Harbours; sharing what has been learnt, MARE Conference: VIII People and the Sea, Amsterdam, 24-26 June 2015.

3. Putri, A. & Pearson, S.G. Integrating local knowledge with biophysical spatial model to better understand water pollution impact in Jakarta Bay, Indonesia, Institute of Australian Geographers Conference: Exploring Geographic Connections, Canberra, 1-3 July 2015.

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4. Putri, A. & Pearson, S.G. Small-scale fishers at the edge of Jakarta: challenges and opportunities from a harbour megacity, Australian Marine Sciences Association Conference: Sharing Ocean Resources - Now and in the Future, Wellington, 4-7 July 2016.

5. Putri, A. & Pearson, S.G. Insights from the traditional fishing community of Jakarta: looking through vulnerability lens, Indonesia Roundtable Discussion, Canberra, 18 April 2017.

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Chapter 1

Introduction

1.1. Research background

Coastal areas provide people with natural resources (such as fisheries and renewable energy) and valuable spaces used for human settlement, transport and other industries and the landscape settings provide scenic and cultural values. Humans over time are attracted to populate, develop and exploit the resources and advantages of this land and sea interface. For these reasons, more than two-thirds of the world's megacities, that is, cities with populations of more than 10 million, are to be found along the coast (UN, 2016 and von Glasow et al., 2013). The density, size and growth of the population in coastal megacities, particularly in developing countries, are concurrent with increases in human needs and demands that, in turn, cause further development and exploitation of the resources in coastal areas (Bruns, 2013 and Wolanski, 2006). This transformation increases the need for and use of resources, and in doing so, it overwhelms the capability of coastal areas and ecosystems to sustain some of the key functions of coastal environments, particularly over the longer term (Cui and Shi, 2012). The environments of coastal megacities worldwide show similar symptoms of environmental degradation (Blackburn and Marques, 2013); cities such as Manila, Mumbai, Rio de Janeiro, Lagos, and Jakarta, all reporting serious water and air pollution, loss of natural habitats and the collapse of their coastal fisheries (Sekovski et al., 2012). The effects of such degradation are affecting the living conditions in these megacities and the well-being of their inhabitants. For instance, the destruction of barrier habitats, such as mangroves, and their replacement with man-made environments has removed natural protection for coastal communities from storm-surge or tsunamis, and has made the communities more vulnerable to natural disasters (Karlsson et al., 2015). The combination of degraded water quality and the loss of coastal habitats has serious consequences for coastal fisheries and tourism and, in time, for the people whose livelihoods depend on these industries (WWF, 2015). With the flow of people to coastal cities becoming a torrent, increased attention is being given to environmental

1 deterioration and its impacts on coastal megacities (Dsikowitzky et al., 2016 and von Glasow et al., 2013).

There is a consensus that coastal water pollution is one of the most concerning degradation symptoms faced by the coastal megacities. Water pollution's devastating consequences for coastal ecosystems are well known; the destruction of coastal habitats, reduction marine biodiversity and declining fishing catches (Mulugeta et al., 2007 and Seitz et al., 2013). The clear evidence of ecological toxicity from pollutants, excessive nutrient input from anthropogenic activities, and residual effects of heavy metals, all have lethal and sub-lethal effects on marine organisms (Lakshmanasenthil et al., 2013 and Rabotyagov et al., 2014). This problem of water pollution is more pronounced in coastal megacities where pressures from development is greater (UNEP, 2005), people are more vulnerable and the size and focus of government investment is generally insufficient (Georgeson et al., 2016). For instance, bay-side residents of Manila, a megacity, experience an ongoing decline in the quality of the city's coastal waters. Flows of untreated waste from domestic, agriculture and industrial sources have led to hypoxia and heavy metal contaminations that threaten the sustainability of the Bay ecosystems (Jacinto et al., 2011 and Sotto et al., 2014). In addition to the dominant influence of terrestrial activities, the rapid development along the coast (for instance, land reclamation) and multifarious commercial and industrial activities (for instance, industry, ports and tourism) in the megacities can accelerate, and render irreversible, the degradation of habitats and water quality (van Lavieren et al., 2011).

Jakarta, the capital city of Indonesia, with almost 11 million people, like other coastal megacities, is experiencing massive growth in population and development (BPS, 2015). As a corollary, alarming water pollution and significant physical transformation of the coastline are causing environmental damage to the Bay of Jakarta's natural landscapes. The coast of Jakarta has also concurrent natural and human-induced hazards such as floods, storm surge, land subsidence, sea-level rises, salt-water intrusion and the loss of natural habitats; all of which contribute to conflict between land users (Firman et al., 2011 and Pearson et al., 2016).

Located in the north edge of Jakarta (Chapter 3; Figure 3.1), the Bay and its 13 river systems have historically been convenient to be used for waste discharge. The bay is

2 estimated to receive each day up to 2000 cubic meters of untreated solid waste, which is carried by the river systems, with households the largest contributors (BPLHD, 2012a). By volume, most is domestic waste, followed by industrial and shipping waste, which contains organic compounds, excessive nutrients and heavy metals at levels that cause serious concerns (Putri et al., 2012 and Rinawati, 2012). Increased sedimentation and changes in water circulation that have been caused by land reclamation activities intended to provide more space for business, high-rise residential properties, and port facilities, as well as the plan to construct a giant sea wall, will all contribute to further deterioration in coastal water quality (Pranowo et al., 2014).

The detrimental impacts of chronic, poor water quality on the biophysical aspects of the bay's environment are well understood from numerous projects and decades of research. For instance, alarming levels of heavy metal contamination in farmed green mussels, several mass fish kills and harmful algae blooms were caused by poor and declining water quality (Riyadi et al., 2012; Suwandana et al., 2011 and Wouthuyzen et al., 2007). It is also evident that the once-rich ecosystems of Jakarta Bay have been degraded significantly in biodiversity terms partly because of heavy pollution. This was demonstrated by a comparative study in 2005, the results of which show 58 molluscs only were to be observed compared with 171 species listed during the period 1937 and 1938 (van der Meij et al., 2009). In another comparative study on coral-reef ecosystems in Jakarta Bay and the nearby Thousand Islands, Baum et al. (2015) noted that fish populations and diversity in Jakarta Bay were, respectively, around 80% and 50% lower lower, compared with those in the Seribu Islands and the differences were attributed to the effects of pollutants from mainland effluent.

The impacts of water pollution on ecosystems are not the only serious challenges because there is a connection between the ecosystems and the condition of natural resources and community livelihoods, particularly for those dependent on the resources (UN, 2015). Further adverse impacts of pollution are expected to affect the livelihoods of those traditional fishing communities that mostly depend on coastal fishing in Jakarta Bay. In 2014, there were about 6000 fishers in Jakarta, traditional and large-scale, which was half the number surveyed in 2009 (Sudin PPK, 2014). These Jakarta traditional fishers are among the two million Indonesian traditional fishers that

3 contribute 95% of national fishery production (FAO, 2012). In addition to contributing to fishery production, this sector plays an essential role generating income for the coastal communities, providing affordable protein source and food security, particularly for the coastal people of Jakarta who live on the edge of the economy (Listianingsih, 2008).

Like many traditional fishing communities in the world, particularly in developing countries (Jentoft and Eide, 2011), most of Jakarta's fishing communities are characterised as 'poor' communities that depend on coastal fishing as their main, if not sole, source of income and have few employable skill sets apart from fishing (Bengen et al., 2006 and Padawangi, 2012). There is little published research on these fishers of Jakarta Bay; such research on traditional fishers usually focuses on the livelihoods of the communities; the narrowing of options, abuse of their rights, restricted source of capital, social marginalisation, and poverty (Fauzi and Anna, 2010 and Kusumastanto and Wahyudin, 2012). Yet, such internal challenges or stressors are not the only livelihood obstacles for these people. Destructive environmental changes, such as water pollution, might force them to adapt and change their fishing activities and behaviours to sustain their livelihoods (Baum et al., 2016 and Weatherdon et al., 2016). Furthermore, water pollution is expected to put additional pressures on these already vulnerable communities, for example, by causing productivity to decline and market prices to fall because of contaminated or low-quality fish, and it has already brought strong policy responses, such as the banning of mussel farming (Anna and Fauzi, 2007 and Mustaruddin, 2013).

In contrast with established research on biophysical impacts in marine environments, studies on how water pollution affects traditional fishing communities have received much less attention. For example, the most recent research on Jakarta Bay's environmental degradation was to provide comprehensive insights on the impacts of water pollution on the ecosystems and fishing livelihoods (Breckwoldt et al., 2016). The presence of organic contaminants and heavy metals in the coastal sediments and in the water, as well as pathogens in primary fish commodities, was investigated in detail (see Marine Pollution Bulletin special issue on "Impacts of Megacities on Tropical Coastal Ecosystems - The Case of Jakarta, Indonesia", 2016). Yet, the impacts of water

4 pollution on the fishing communities were treated in a minor way only in the research program (Baum et al., 2016) compared with the many papers on the biophysical impacts of pollution. The research by Baum et al. (2016) took a monolithic approach to the communities and focused on comparing the perceptions of water pollution, use of natural resources, and the livelihood conditions of the fishing communities in Jakarta Bay and nearby Seribu Islands.

The issue of deteriorating water quality along the Jakarta coast is now an intractable natural resource management problem, as it is with other megacities. It threatens the sustainability of the natural environment and that of the communities that depend on fishing as their sole and often subsistence source of food and livelihoods. Studies of traditional fishers on the edges of these rapidly changing megacities are very important and timely. This empirical study on the water pollution impacts on the traditional fishing community in Jakarta Bay provides insights that contribute to the theoretical and practical understanding of natural resource management. This research seeks to enable decision makers to plan effective management responses to support these communities (and different socio-economic or occupational groups that may exist in the communities) to transform them or enable them to adapt to the stressors and sustain their livelihoods.

1.2. Research objectives and research questions

With the foregoing in mind, this research has three main objectives. The first is to contribute to a necessary deeper understanding of the consequences of water pollution for the livelihoods of traditional fishing communities. The second is to achieve better understanding of the vulnerability of these communities to the effects of water pollution as well as to obtain insights to the many factors that influence their susceptibility to water pollution. The third objective is to understand the communities' coping mechanisms in dealing with water pollution and their changing environments.

Achieving these objectives and disseminating the results is expected to provide useful knowledge for informing the decisions of the policy and decision makers who are to work to improve the environment, to manage natural resources and to formulate ways to support the sustainability of these communities' livelihood. In a wider context, this

5 research is to provide a crucial perspective on how past and ongoing physical developments in the urban areas of megacities affect the sustainability of a fishing community’s livelihood. This study of environments that support such communities in Jakarta is an important contribution that will help to fill a gap in the literature that has, in the past decades of development, focused on biophysical aspects, such as ecotoxicology, without appreciating the people, occupations and other aspects that make up the social systems of Jakarta Bay.

This research focuses on three different occupational groups of the coastal community of Jakarta Bay that are particularly vulnerable. They are the traditional fishers, the green-mussel farmers, which together comprise the traditional fishing community, and the informal workers group. The term 'traditional fishers' was chosen in preference to a more common term used in the literature, that is, 'small-scale fishers' (see, for example, Aguilera et al., 2015 and Bene et al., 2010) because it better describes the local context. These traditional fishers of Jakarta are different from modern or large-scale fishers with regard to the carrying capacity of their boats, the distance they travel to their fishing grounds and the technology they use (Retnowati, 2011; this will be explained in more detail in the Chapter 2). The traditional fishing community is the focus of this research because of an expectation of their deep understanding and rich knowledge in experiencing and observing directly the effects of water pollution and environmental changes in Jakarta Bay.

This research hypothesises that the traditional fishing community is more vulnerable to the impact of water pollution compared to the other observed occupational group. The thesis answers several research questions to achieve the research objectives:

a. How does water pollution in Jakarta Bay affect different occupational groups in the community? b. How have these different occupational groups responded and adapted to the adverse impacts of water pollution? c. How does the vulnerability to water pollution differ between the occupational groups in the community and what are the factors that shape that vulnerability? d. To what extent can the applied vulnerability framework explain the condition and the adaptive responses of the occupational groups?

1.3. Research context

This case-based, integrated research seeks to deepen understanding of water pollution and the impacts it has on traditional fishing community in Jakarta Bay, Indonesia. To achieve these objectives, the research incorporated local knowledge about people's livelihoods, and their perceptions and experiences of water pollution and environmental changes, by using various participatory methods (such as interviews, survey questionnaires and community workshops). Looking into the heterogeneity of the community helps undermine assumptions of a narrow response that would constrain future options (Gret-Regamey et al., 2013 and Kettle et al., 2014). It was important to get accounts from three different occupational groups to voice their expertise and experiences because it elicited more specific information and knowledge about their livelihood conditions, their perceptions of the environmental changes and other specific features that are valuable for developing appropriate management.

This research contributes to the concept of livelihood vulnerability through its investigation of the effects of water pollution as external stressor on different occupational groups in the community. This research, adapting Adger's (2006) framework of thinking of vulnerability, views some occupational groups to be susceptible to external stressor, as a result of their exposure to the stressor and its interaction with their sensitivity and their capacity to adapt to the stressor. Livelihood vulnerability assessment in this research is performed by using an integrative approach, that is, by combining biophysical and livelihood analysis. As will be demonstrated throughout this thesis, this integrative approach provides more opportunities for exploring appropriate methodology design, for obtaining better and more comprehensive problem insights about the socio-environmental relationship and for generating actionable knowledge and recommendations.

The empirical biophysical assessment, a theoretical approach accepted in science and government, of water quality data using statistical (cluster analysis) and spatial analysis were used to provide insights on the water quality of the bay in the form of water- pollution index map. The information derived from such an index map is most valuable for management and enforcement agents to help them identify area of interest and map the distribution of pollutant levels in Jakarta Bay. Furthermore this research contributes

7 to the discipline of oceanography by extending the applicability of the biophysical assessment of water quality in providing a framework and context for understanding the exposure of water pollution on different occupational groups. Concurrently, the livelihood assessment provides a comprehensive and deeper understanding of the factors that contribute to shaping these groups' susceptibility to water pollution and their coping strategies in dealing with it.

Although the vulnerability of traditional fishing community in Jakarta Bay might be subjected to various external stresses or shocks, this research focuses on their vulnerability to the effects of water pollution. This allows sufficient detail to provide household scale analysis and understanding of the impact of water pollution that then scales-up through social-economic structures and behaviours to shape adaptation patterns.

In Indonesia, most of the studies on coastal vulnerability have focused on the assessment of biophysical systems in more rural areas, on rising sea levels associated with climate change (such as change in ecosystems or infrastructures) and on human- environment systems (Joseph, et. al., 2013; Ristianto, 2011; and Rositasari, 2011). Other research on a national scale in Indonesia has been to assess the social vulnerability due to combined natural hazards at the district level (Siagian, et. al., 2013). Therefore, this research contributes by adding to the skills, knowledge and understanding of how the livelihoods of a traditional fishing community can be impacted by, and vulnerable to, water pollution in the context of a megacity. Finally, although some vulnerability studies have focused on extensive areas and multiple stressors (e.g. Bennett et al., 2014 and Cinner et al., 2011), the unique contribution of this research is to fill a wide gap in the more specific-spatial scale and specific-sector vulnerability literature which was identified as a research need by Hughes et al. (2012). This understanding provides a credible and robust foundation for adoptable and specific recommendations for community and policy leaders and planners. In the context of an assessment of livelihood vulnerability, achieving this understanding is important for distinctive geographical and livelihood characteristics are often critical in mitigating the level of vulnerability of different groups in the community (Turner et al., 2003).

1.4. Thesis outline

The thesis is organised into seven chapters.

Chapter 1 Introduction

The introduction provides the research background that identifies the imminent need to answer the research questions and fill gaps in the current state of knowledge. This chapter provides the brief description of the research context that shows the research niche in terms of the concepts and methods as well as the contribution this research makes to the current literature (these will be elaborated in more detail in the second and third chapter).

Chapter 2 Literature Review

This chapter provides a focused review of relevant research aspects and concepts used in this research. The synthesis encompasses reviews of traditional fisheries, the biophysical aspects of water pollution and environmental changes. More important, it describes the concepts of vulnerability and livelihood sustainability that provide a foundation for the development of appropriate and reliable research design and methods.

Chapter 3 Research Design

The third chapter describes the research design and demonstrates the considered way the stages and processes were performed during this research to answer the research questions. This chapter has descriptions of the study area (Jakarta Bay and coastal areas, and Jakarta fishing community) and the framework that underpins the logic of the research. The research methodology includes conceptual and detailed explanation of the methods used in collecting and analysing of biophysical and livelihood data, as well as in the livelihood vulnerability assessments.

Chapter 4 and 5 Results

The fourth and fifth chapters provide the results obtained from this research. Chapter 4 shows the results of the water quality assessment (water pollution index map) and the participatory activities (such as survey questionnaires and community mapping) that

9 allow an understanding of the water quality conditions, the exposure experienced by different occupational groups, the perception and knowledge about water pollution and its impacts on livelihood and occupational activities. Chapter 5 consists of the results of the livelihood and vulnerability assessments. It explores insights about the livelihood conditions and characteristics of the occupational groups, including their sensitivity and adaptive capacities, that then shape their vulnerability to water pollution, as well as their coping strategies in dealing with the adverse impacts of water pollution and environmental changes.

Chapter 6 Discussion

The discussion and synthesis of the main findings of this research are presented purposefully as a sense-making and implication-seeking opportunity. It puts the findings in the context of previous, related research to highlight the meanings of the findings, and the contributions and the limitations of the research. Detailed recommendations, in the academic, natural resource management and decision making contexts, were developed specifically from the knowledge of conceptual and empirical results discussed and presented in this chapter.

Chapter 7 Conclusion

The last chapter provides concluding remarks that show the essence of the key findings about societal impacts of water pollution, livelihood vulnerability and the community's adaptation strategies. The summary of management implications of the findings is also an important part of this chapter and shows how this research contributes to current knowledge and the development of solutions to improve the environmental conditions and achieve more sustainable livelihood of the community in Jakarta Bay. Several recommendations and prospective research opportunities are also proposed.

Chapter 2

Literature Review

This chapter consists of the synthesis of relevant knowledge that provides the context for understanding the research problems, identifies the appropriate frameworks for analysis and ensures the selected methods are appropriate. It is necessary, first, to put the current research into the context of water pollution and environmental degradation in coastal megacities and the impacts on the environments and on the communities, particularly traditional fishing communities. The necessary background on Jakarta Bay conditions and the livelihoods of the traditional fishing communities will also show critical gaps in the current literature. The second part of this chapter explains the concept of vulnerability and associated research, and the approaches used to study thimpacts of water pollution on traditional fishing communities with the perspective of coupled human-environment system. Putting vulnerability into this context will be another discourse that will shed light on the 'vulnerability of whom?' and 'vulnerability to what?' questions that are sometimes missed in research and policy making. This part also provides synthesis on how this particular research into water pollution and its impacts on resource-dependent communities is a valuable case study of a wicked problem in a human-environment system and in the challenges for the system's sustainability.

2.1. Water pollution and environmental degradation: the impacts on resource-dependent communities

2.1.1. Water pollution and environmental degradation in coastal megacities

The coastal waters and environments of megacities worldwide suffer severe environmental degradation to varying degrees. These megacities, defined as urban areas with over 10 million people (UN-DESA, 2011), have significantly altered landscapes and ecosystems (Young, 2011). The literature has repeated descriptions of how the growth and socio-economic development of megacities applies pressures to valuable ecosystem services. The pressures from excessive exploitation of coastal natural

11 resources, increased chemical loading and altered physical qualities of run-off, inadequate management of catchment areas and coastal zones, and weak governance and policy enforcement have caused environmental degradation (Bruns, 2013 and Sekovski et al., 2012). Wolanski (2006) reported the symptoms of environmental degradation such as declines in water quality and biodiversity, habitat destruction, ecosystem collapse (indicated by fish kills and reduction in fish catches), and ecosystem shifts (indicated by invasion of alien marine species) among megacities (for example, Jakarta, Tokyo, Mumbai, and Manila). As megacities grow and absorb outer urban areas to create more urban sprawl, the pressures and attraction for more urban dwellers appear to increase (Blackburn and Marques, 2013) at rates greater than the improvements being made in environmental management.

Although there is reasonably clear evidence about how urban development has supported the economic growth of coastal megacities (World Bank, 2010), adverse consequences for the environments are also emerging. Degradation of natural environments leads to concerns about the adverse economic consequences in the long- term. For example, it has been predicted that the continuing loss of biodiversity because of human activity will affect the associated ecosystem services that would have an annual cost nearly 7% of the worlds’ GDP in 2050, with current losses of EUR50 billion per year (European Commission, 2008). Trends in water quality decline and environmental degradation cause concern about the long-term impacts on the coastal and marine environments, their dependent communities, and on economic development (Alam et al., 2006). It has been estimated that, globally, waste water flowing directly into the oceans has affected an area of 245,000 square kilometers of marine ecosystems with adverse impacts on fisheries, primarily coastal fisheries, and on the livelihoods of resource-dependent communities (Corcoran et al., 2010).

Pollution of coastal waters is not the only major issue for coastal megacities (von Glasow et al., 2013) but it has become one of the most serious threats that, according to Binney and Tunny (2014), requires prompt attention and action beyond biophysical research that mostly focuses on identifying the sources and types of pollutants. They suggest seeking understanding of the impacts of polluted water on coastal environments and communities' sustainability.

2.1.2. Water pollution and environmental degradation: Jakarta context

Jakarta is a perfect example of a coastal megacity standing in between the two sides of economic progress and environmental damage; the two aspects of social transformation. Its growing economy, its increasing coastal metropolitan population, and its daily influx of people that swells its urban population by day to more than 27 million people (Abidin et al., 2011), have led the capital city and its coast to massive development ever since the Dutch colonial era, 400 years ago (Bengen et al., 2006). Since then, developments in all sectors, business and industry, trade and transport, entertainment and tourism, have reinforced the role of Jakarta as the generator of economic growth in Indonesia and the main contributor to the country’s Gross Domestic Product (BPS, 2014a).

Nevertheless, for decades these social and economic developments have added pressures of growing urbanisation populations, increased resource use and added waste that damaged the natural environments (Dua and Esty, 1997). The coastal areas have been one of the most exploited areas of Jakarta: the extensive removal of mangrove forests to make way for the construction of built environments, such as port facilities and high-rise block of flats, means that more than 80% of the original mangrove areas have been destroyed over the past 40 years (Pramudji, 2008). This has significantly affected the ecosystems’ capability to protect the shore areas, contributed to the changes in the current and sedimentation patterns, and reduced nursery areas for marine organisms; all of which has contributed to declines in fish stocks and biodiversity (Waryono, 2002). There are plans to expand Tanjung Priok international port and develop coastal areas by reclaiming land to create 17 new islands, and as well there is the plan to build the Great Garuda giant sea wall to solve the challenge of rising sea levels and coastal flooding (National Capital Integrated Coastal Development or NCICD; Perda DKI No.1 of 2012). These projects have raised questions about the long- term impacts of such mega-projects on the environmental sustainability and the continued existence of traditional fisheries (Sampono et al., 2012). Excessive extraction of ground water to enable massive urban developments and construction has exacerbated land subsidence in North Jakarta and has, in turn, increased the risks of floods and salt water intrusion (Abidin et al., 2011 and Ng et al., 2012). Other environmental problems caused by human activity, such as rises in sea level and the

13 destruction of coastal ecosystem (Firman et al., 2011) are additional threats for Jakarta's coastal communities (in addition to natural hazards, such as storm surges), particularly for the communities of poor people in the flood-prone areas along the coastline.

Jakarta Bay is probably the most polluted bay in Asia (Wolanski, 2006), and its problems caused by water pollution have defied solution and the complexity of its environmental challenges is a subject of international research concern. Reclamation and the cumulative impacts of other coastal activities, such as tourism and shipping, along with the increasing volume of liquid waste from agriculture, domestic households and industry that flows through the 13 Jakarta rivers into the bay, have all contributed to a deterioration in the bays’ water quality. The river water entering Jakarta Bay carries contaminants, such as sediments, nutrient residues, traces of heavy metals, domestic waste and harmful pathogens that are the constituents of pollution (BPLHD, 2012a). Biophysical studies on the effects and histories of these contaminants in the bays’ waters are relatively well established (as described below) and provide a robust foundation for understanding their impacts on marine organisms, of water quality trends and of contaminant sources.

Research on the impacts of water pollution on marine organisms, on changes in biodiversity, and on the coastal ecosystems of Jakarta Bay go back a long way. Verstappen (1998) showed that heavy pollution caused by increasing sedimentation and waste from urban and port activities has affected the coral reef cover within the bay. Van der Meij (2009) compared coral reef species in the near-shore area of the bay using biological specimens and historical data from 1920 with field observation in 2005. The results of his research revealed a significant decrease of 45% in the numbers of coral reef species, which was attributed partly to anthropogenic factors, such as pollution. The occurrence of harmful algae blooms (HAB), which indicate a high input of nutrients from the river systems, has resulted in massive fish, crustacean and mollusc kills in Jakarta Bay. Such events have been reported several times in 2004, 2005, and 2007 (Thoha et al., 2007 and Wouthuyzen et al., 2007). Discussions with several fishers of Marunda village revealed that these fish kills are now observed each year and reported to the Environmental Management Agency (BPLHD) to be investigated (Discussion with Marunda fishers, 2014). In the case of Jakarta Bay, hypoxia or conditions of very

14 low levels of dissolved oxygen due to the occurrence of harmful algae blooms (HAB), is expected to be the main cause of fish kills (Thoha et al. 2007). It is evident that there has been a rapid increase in the frequency and extent of hypoxia along the coastal zones of the world's major cities that has been caused by high inputs of nutrients from anthropogenic sources (Rabalais et al. 2009). These shifts in marine biological conditions, which indicate a collapse or change of state in ecosystem form and function, are attributed to significant increases in anthropogenic activities over the past decades.

Marine ecosystems exposed to heavy metal contaminants are a serious problem in Jakarta Bay. Numerous studies have contributed to understanding the sources, distribution, concentration and the impacts of heavy metals in the sediments, waters, and organisms in the Bay (see Table 2.1). Research on heavy metals contamination (Cu and Pb) in the sediments of Jakarta Bay shows higher concentrations of heavy metals close to the shore as well as in the sediments from the areas near the industrial zones, particularly in the east side of Jakarta (Riyadi, 2012; Table 2.1). High concentrations (above trigger level) of toxic heavy metals in the sediment brings the risk of absorption by benthic organisms, which could lead to bioaccumulation in the food chain. This is of serious concern, particularly in the coastal areas where aquaculture activities, such as mussel culture, take place.

Mussels are important bioindicators because they filter large volumes of water and accumulate contaminants. Higher contamination leads to declining mussel productivity, mortality and local extinction (Denil et al., 2017). Research in Jakarta Bay has found several harmful heavy metals (Hg and Pb) at much higher concentrations than the recommended standard maximum for environmental exposure, human exposure or consumption, in the water, and in fish and green mussel tissue samples (Table 2.1). The research into contamination resulted in 2010 in a ban on green mussel aquaculture in Jakarta Bay, which demonstrates the serious concern by the authorities of the risk to human health.

Table 2.1 Research on heavy metals contamination in the area of Jakarta Bay

Measured Standard Observed Heavy Reference Concentration Value Objects Metals (mg/l) (mg/l) Hutagalung (1987) Water sample Hg 0.023-0.027 0.001* Arifin et al. (2004) Sediment sample Pb 3.200-57.800 50*** Green mussel Putri et al. (2012) Pb 0.920-1.485 1.000** tissue Cu 10.800-107 65*** Riyadi et al. (2012) Sediment sample Pb 13-106 50*** Mustaruddin (2013) Water sample Hg 0.0131 0.001* Pb 0.0240 0.008*

Demersal fish Hg 0.680 0.500** Tissue Pb 1.185 1.000** Koesmawati and Green mussel Hg 1.510 1.000** Arifin (2015) tissue Sources: *Indonesian Ministry of Environment Decree: standard value for coastal waters **National Standardisation Agency: standard value for human consumption *** Western Australia Department of Environment and Conservation, the values indicate low trigger value (biological effects rarely occur below the threshold) Regular and continuing water quality measurements in the bay of the key biological, physical and chemical parameters have been taken by the Environmental Management Agency (BPLHD) to monitor the bay's water quality. These activities suggest the government understands the importance and functions of the bay and coastal area of Jakarta. In their annual report, BPLHD (2012b) stated that measurements of several water quality parameters have shown values that exceed the threshold determined by the Ministry of the Environment. Concentrations of several contaminants, such as ammonia, phosphates and phenols, particularly close to river mouths, are higher than the Indonesian standards maximum values for marine organisms. High concentrations of ammonia and nitrates are indicative of domestic and agricultural effluents, while the presence of phenol indicates wastes from industries and domestic sewage (ATSDR, 2008 and BPLHD, 2012b). At high concentrations (>0.01 mg/L) phenol has devastating and poisoning impacts on fish; ammonia and phosphate contribute to the eutrophic level of the water that could lead to HAB and fish kills (BPLHD, 2012b and Damar, 2003).

Measurement and evaluation of water and environmental qualities are necessary to provide essential scientific knowledge that is valuable for monitoring and as a baseline to develop interventions. However, with such changes in the waters and the ecosystems, it is also important to consider wider aspects, such as the socio-economic aspects, that define this problem. This is because the impact of water pollution and environmental degradation could devastate the coastal fishery sector and, as a corrollary, threaten resource-dependent communities, such as the fishing community (Baum et al., 2016).

2.1.3. Traditional fishery sectors: under pressures from water pollution and environmental degradation

Globally, the Food and Agricuture Organisation (FAO) estimates that more than 90% of fishers that work in capture fisheries are part of the traditional fisher group (FAO, 2015) who work in what is often referred to as small-scale fisheries. The definitions of small- scale fishers or traditional fishers are often contextual and varies among studies. For example, one way of diferentiating traditional fishers from large-scale fishers is based on vessel capacity or type, another way to define the categories depends on the purpose: subsistence or commercial (FAO, 2004). Traditional fishing contributes a source of food and affordable protein and, more important, livelihoods, particularly for poor coastal communities in developing countries (Chuanpagdee et al., 2006 and Jacquet and Pauly, 2008). One of the defining features of traditional fisheries is the employment chain that goes beyond the fishing practices, highlighted by the World Bank (2012), that the number of people involved in pre and post-harvest activities could be as many as triple the number of traditional fishers. To obtain relatively similar amount of catch, this labour-intensive sector employs 25 times more people than large-scale fisheries (Chuanpagdee et al., 2006).

Global research projects have made estimates of the number of traditional fishers (capture fisheries and aquaculture) that vary from 35 to 50 million people who produce approximately half to three-quarters of the world's fish production (Carvalho et al., 2011). The difficulty in estimating the number of traditional fishers and their contribution is caused by the limitations of official and other data, particularly in developing Asian countries (excluding Japan), Africa and South America (Pauly, 2006). Most traditional fishers operate in coastal areas only because of limitations in their

17 vessel's capacity and their fishing gear or technology, and therefore they are restricted to coastal fishing grounds, which are subject to anthropogenic stressors.

In addition to fishing-related stressors, such as overfishing and habitat destruction, major environmental stressors such as water pollution, invasive species, loss of habitats and climatic changes multiply threats to these ecosystems and especially for coastal fisheries (Nelleman, et al., 2008). Water pollution in the coastal area is recognised as a prominent stressor of the marine environment that is likely to force adaptation or transformation of fisheries sectors. The significant consequences of fish mortality, habitat degradation and reduced recruitment caused by water pollution affects fisheries productivity by reducing fish catches for equivalent efforts (Islam and Tanaka, 2004 and Zhang et al., 2012). Some pollutants, such as heavy metals, also pose contamination risks leading to a decline in the quality of fisheries products that, in the worst case, could result in ill health, deaths or for fisheries to close or to be banned (Willson and Kazmierczak, 2007). In a review paper on impacts of water pollution, Islam and Tanaka (2004) (see Figure 2.1) described the consequences of water pollution to be low quality fish, reduced recruitment, and fish kills that could force resource-dependent communities such as traditional fishers to make adjustment. Using bio-economic models, these consequences could be shown as decreased economic profits of fishery activities. For instance, Anna and Fauzi (2007) incorporated the pollution variable into the function of fishery production to estimate potential loss of demersal fish because of water pollution in Jakarta Bay. They showed that the net economic loss reached IDR700 million per year, which was relatively large considering the scale of traditional fisheries in the bay.

(Source: Islam and Tanaka, 2004, p. 641)

Figure 2.1 Water pollution impacts on coastal fisheries

Market loss in fisheries is not the only result from water pollution. Anna and Fauzi’s (2007) research did not analyse other costs, such as the extent of the suffering or non- commercial adjustments made by traditional fishers who use the bay as their fishing ground. Very little research has been done to obtain a more complete understanding of how water pollution affects the traditional fishers as their communities' livelihood is often solely reliant on fish resources and other ecosystem processes and outputs. This is despite the substantial evidence of the impacts of water pollution on marine and coastal environments. Measurements of the impacts of natural and anthropogenic hazards (such as water pollution) in the literature are indeed more focused on biophysical properties compared with the consequences of it in the complexity of human-environment links. There were much more developed methods and tools for assessing the biophysical properties and there was also the economic and political importance (Heijmans, 2008). Further, Heijmans (2008) used the example of responses to the 1997-1998 Indonesian forest fire hazards to show how various technological tools were applied to investigate

19 the cause of such fires and their impacts on the richness of forest biodiversity, yet the impacts of these disasters on the local people who had lost their source of livelihood were neglected. This limited scope of research results in serious gap in the understanding of human adaptation and behaviour patterns when dealing with a particular hazard (such as water pollution), which are probably essential for management and decision making purposes.

2.1.4. Traditional fishing communities in Jakarta: identifying the challenges

People living in Jakarta's coastal areas have preserved the stories of traditional fishing communities for decades. Nowadays, these resource-dependent communities are squeezed by the pressures and competitiveness of other developed sectors, such as industry, tourism, shipping, high-rise residential buildings, and more modern and sophisticated fishing industries. Living on the edge of the megacity, their daily living and experiences are buffeted by the challenges and opportunities of development progress and environmental damage. In 2014, there were around 6,000 fishers in Jakarta, a total that includes traditional and large-scale fishers (Sudin PPK, 2014). Given the local context of this research, the definition of traditional fishers used in this research is based on the Indonesian fisheries Act (Act of Fisheries No. 45 of 2009). The term 'traditional fishers' in this research is the term used to refer to those who fish using small capacity vessels (less than 5 gross tonnes) and operate within five nautical miles of the coast. This definition differentiates them from the set of large-scale fishers who use vessels of greater than 5 gross tonnes and who usually stay at sea for months well beyond the area of Jakarta Bay. The number of traditional fishers has almost halved since 2009 (Sudin PPK, 2014) although there has been no specific research to investigate the reason for this decrease. Similar trends of declining fisher numbers are observed in other poor, traditional fishing communities where decline in fisheries occurs (Cinner et al., 2009).

There are two million traditional fishers in Indonesia who supply 95% of the total of Indonesia’s fishery production, provide to an extended chain of livelihood sources for the post and pre-processing fisheries workers, and provide sources of affordable protein (BPS, 2012 and FAO, 2013). Considering these contributions, the continuation of these communities is very important for economic growth, for food security for the poor, and

20 from the perspectives of environmental and social justice (Coulthard et al., 2011 and Garcia and Rosenberg, 2010). Many studies of traditional fishers have recognised the similarities and characteristics that shape the livelihoods of these people and their communities: they are subject to reductions in their employment opportunities, to abuses of their rights, to eroding access to capital, to marginalisation and to poverty. Kusumastanto and Wahyudin (2012) argued that these issues result from multidimensional factors ranging from lack of access to basic services (food, health, education and infrastructure), lack of supporting facilities (information and technology as well as capital), and from few opportunties to contribute to development planning and decision making.

In the case of Jakarta Bay, the traditional fishers receive strong support from several interested stakeholders, such as aid agencies, non-government organisations (NGOs), and relevant government agencies, all of which work to mitigate the effects of some of those limitations. The United Nations, as an example, since 1996 has paid attention to the marginalised and poor communities of Jakarta Bay, including the traditional fishers group, when a program with a focus on providing alternative income generation was run (UNESCO, 2000). The empowerment efforts also come from NGOs, such as KIARA (People Coalition for Justice in Fisheries), which act to enable fishing communities to participate in development planning. KIARA, for instance, organised a workshop that brought some of the representatives of fishing communities to meet government agencies to discuss land reclamation and the Great Garuda sea wall project that have potential implications for fishing activities (personal communication: KIARA, 2014). The government's support for the community was channelled through various programs, such as PNPM (National Community Empowerment Program), which is funded partly by the World Bank, and the provision of professional and financial support for the fishing community through occupational organisations (Baker, 2012 and PNPM, 2013).

Such support is essential if the fishing communities are to cope with external stressors. For instance, a program to provide life insurance for fishers is valuable because fishing is an occupation exposed to high risk, such as extreme weather (Sharma, 2011). However, Quinn et al., (2011) stressed that all communities respond differently to many stressors and therefore specific or tailored support was required to help individual

21 groups to cope. In a study of rural community vulnerability in South Africa, they showed how the communities were more likely to seek support grants from the government during droughts and to adjust their food consumption when dealing with fluctuations of their staple food price. Therefore, to minimise the impacts of some stressors, such as water pollution or climate change, a community needs to be equipped with specific support that complements their existing strength, capacities and strategies. This research is to provide background information to enable more precise empowerment strategies for fishing community in Jakarta Bay through the identification of their capacities to adapt to water pollution.

Many international studies of traditional fishers have focused on investigating their contribution or role in the society, analysing the components that shape their livelihood, and, more recently, how climate change will affect them (Allison and Horemans, 2006; Badjeck et al., 2010; Bene, 2003; Bene, 2006; Cinner et al., 2015; Kusumastanto and Wahyudin, 2012; Mills et al., 2011). Allison and Horemans (2006) and Bene (2003), for instance, revisited the concept of poverty in traditional fishers and emphasised the importance of looking at the manifold factors that condition their poverty, such as vulnerability to shocks, lack of education and political marginalisation. In a study of climate change as stressor, Badjeck et al. (2010) and Cinner et al. (2015) showed how increased weather variability, such as more extreme storms and more periods of drought, affects the capability of the fishers to operate and reduces fisheries productivity and forces them to adapt and apply new strategies to maintain livelihoods. The problems caused by water pollution, however, have been overlooked and very little research has been done on how the pollution affects these resource-dependent communities (see Baum et al., 2016 for examples). Studying water pollution, one of the major environmental stressors (Nellemann et al., 2008), and how it affects fishing communities is important. It is particularly relevant in the context of fishing communities in a megacity, such as Jakarta (and other megacities), where degradation of water quality in the coastal areas has been identified to be among the serious environmental challenges (Apip et al., 2015 and Dsikowitzky et al., 2016) and its potential to affect the livelihoods of traditional fishing communities.

A rescoping, using the ecosystem goods and services framework (Figure 2.2), helps to illustrate how water pollution directly and indirectly affects traditional fishing communities.

(Source: adapted from MEA, 2005, p.vii) Notes: Solid red box includes the components of biophysical approaches to research the impacts of water pollution (although cultural services have not been examined). The box with dashed line includes less researched field connecting ecosystem services and drivers with the social impacts. Figure 2.2 Impacts of water pollution on ecosystem service provisions and traditional fishing communities

The ecosystem services provided by Jakarta Bay for the livelihoods and well-being of the communities might be reduced because of water pollution through disturbance to fishery productivities and fish quality (see Figure 2.1). It also reduces the communities’ health, constrains their cultural identity and freedom of choice to make a living from the common and traditional resource pool (MEA, 2005 and Nayak et al., 2014). The adverse impacts of water pollution on the ecosystem services elements, such as provisioning and supporting services (see Figure 2.2: solid red box), have been stressed by many biophysical studies (mentioned in Section 2.1.2) yet how the changes in the

23 availability and quality of the ecosystem services affect the traditional fishing communities is still little researched.

The challenge, however, is in developing appropriate frameworks and methods that include the biophysical aspects of water pollution as a anthropogenic hazard and take into account the livelihoods of the community members. This research, centred on Jakarta Bay (see the red boxes in Figure 2.2), uses a more comprehensive analysis to obtain a broader understanding of water pollution impacts that includes how it can affect livelihoods and their sustainability, and the manifestations of the communities’ coping strategies and adaptations of their livelihood and fishing behaviours (explained in more details in the following section). In a specific spatial and temporal context, it is important to study the impacts of water pollution on fishing communities in the north of Jakarta to gain knowledge that could be used to inform further research and to provide information for policy makers about the current state of water pollution, of traditional fishing communities and the vulnerability and sustainability of their livelihoods. Moreover, in a regional context, this will provide a crucial perspective on how past and ongoing physical developments in the urban areas of Jakarta could affect the sustainability of community livelihoods and, more important, the sustainability of the environments that actually supports the wider human-environment system of Jakarta. Identifying these issues and ensuring analytical capability is an important research task that contributes to what is currently an international analytic gap.

2.2. Frameworks for examining the impacts of water pollution on traditional fishing communities

2.2.1. Vulnerability concept: an approach to understanding the human- environmental system

Vulnerability is a concept that is derived from the hazard assessment school of thought (Birkmann, 2006). In the face of external hazards or stresses (for example, water pollution, climate changes, earthquakes, floods), hazard assessment is an important mitigation tool for individuals and communities to mitigate their effects (Berke et al., 2012). For most people this happens naturally and without deliberation but it could be more purposefully used to calculate and identify the potential losses that could occur

24 and to arrange mitigation plans to reduce future exposure of a human-environment system, such as through the improvements of technology utilisation (for example, multihazard mapping, sophisticated water-treatment plans, infrastructure modifications to anticipate floods) (Tate et al., 2010). Other research, however, has acknowledged that hazard-focused assessment is not adequate to minimise the adverse impacts of particular stresses or shocks (Menoni et al., 2012 and Gaillard and Mercer, 2012). This is because those human-environment systems exposed to stress possesses characteristics that play crucial roles in defining a system's susceptibility (Hilhorst and Bankoff, 2004). Vulnerability arose as a comprehensive concept that links the interactions between the exposure to hazards or stress and the human and the environmental characteristics (Oliver-Smith, 2008).

Reformulating and widening the vulnerability concept (Figure 2.3) with regard to the context of the system under study has not resulted in a common definition of this term. The concept started with a very narrow perspective that assigned vulnerability or the likelihood of being vulnerable as an internal factor in the suffering from loss or disturbance caused by particular hazards. It then developed into a broader framework that includes adaptive capacity (that could be defined as resources used to reduce the hazard's impacts), exposure (that illuminates the inclusion of external hazards or stresses as part of vulnerability), and coping capacity. This perspective was defined by Adger (2006) who considered vulnerability to be the undesirable state to which a system is susceptible from harmful pressures as a result of changes in environmental and social systems caused by disturbances and the lack of adaptive capacity. A similar definition of vulnerability comes from the IPCC (2001) that describes vulnerability as a function of the character and magnitude and rate of a hazard to which a system is exposed, of the system's sensitivity and adaptive capacity. These two definitions by Adger (2006) and the IPCC (2001), in practice, illuminate two key elements that define vulnerability. The first element is the internality of the observed system (this could be applied to a social system or to a biophysical system), that is, the characteristics of a system that define its capacity to cope with external stresses or shocks. Another element that defines vulnerability is the external hazards or stresses (that could be natural or human-induced) that are affecting and interacting with the system. The definition of vulnerability offered by International Strategy for Disaster Reduction's (UN/ISDR, 2004) encompasses

25 wider, multidimensional aspects and various themes of vulnerability where it represents the conditions determined by physical, social, economic, and environmental factors or processes, which increase the susceptibility of a community to the impact of hazards.

(Source: Birkmann, 2006, p.17)

Figure 2.3 Widening of the vulnerability concept

As a growing concept, vulnerability has been extensively used in various fields of sustainable development and livelihoods, natural resources, ecosystems and other forms of human-environment systems (DFID, 1999; Goklany, 2007; Huang et al., 2012; Hughes et al., 2012; McLaughlin, 2011; Bennett et al., 2014 and Colburn et al., 2016). Experience from the extensive use of the vulnerability concept underlines its multidimensionality, the scale-dependency of vulnerability and how the factors and elements that shape the vulnerability of a system are unique to that system and are varied (Birkmann, 2007). In the context of coastal communities, for instance, Bennett et al. (2014) investigated how different communities in the Andaman coast, Thailand, were vulnerable and had adapted to multiple socio-environmental stressors, which included, economic fluctuation and climate change. Other researchers for example, Colburn et al. (2016), meanwhile, focused on studying the social vulnerability to a single stressor (climate change) or performed a national vulnerability assessment (Cinner et al., 2011). Defining the type of external stressors, the scale and the

26 characteristics of the observed system is considered as one of the most crucial stages of vulnerability assessment because different systems (and at particular spatial and temporal scales) are impacted differently by a stressor (Hopkins, 2015). As contextual concepts, the results of a vulnerability assessment could be extended to develop specific actionable knowledge and strategies to reduce vulnerability through learning, management and collaborations (Baas and Ramasamy, 2008). In the following sections, several existing frameworks of vulnerability will be introduced. Each makes a contribution to the conceptual models and description of vulnerability and later to the vulnerability elements to offer guidance in performing the vulnerability assessment.

2.2.1.1. The onion framework of vulnerability

This onion framework of vulnerability divides the human-environmental system into three different spheres: natural events or hazard, economic and social sphere (Figure 2.4). In this framework, vulnerability occurs when the economic damage (that could be measured from the financial or infrastructure losses), caused by natural hazard, further impacts the social sphere. The degree of vulnerability experienced by specific communities will depend on how well it can cope. The framework suggests that there are different levels of capacity within the communities that are represented by the circles C1 to C3 (Figure 2.4). The inner circle, C3, is described as community with less or insufficient capacity that makes them more vulnerable to hazard and therefore exposure to the hazard may readily cause a disaster. The outer circles (C1 and C2), illustrate better equipped communities that are less vulnerable.

It is interestingly that this framework highlights how the impacts of hazard are identified beyond economic measurement. It underlines the important roles of social disutility, which could include trust, fear and other intangible impacts, as the hazard is experienced by vulnerable communities (Birkmann, 2006). The onion framework, however, focuses only on social vulnerability although most literature discussing the vulnerability concept acknowledges and encompasses other branches of vulnerability, such as the concept of environmental vulnerability (see the Environmental Vulnerability Index in Yoo et al., 2014).

(Source: Bogardi and Birkmann, 2004, p.76)

Figure 2.4 The onion framework of vulnerability

2.2.1.2. The BBC framework

The BBC framework, based on the work of Bogardi and Birkmann (2004) and Cardona (1999), is a comprehensive conceptual framework of vulnerability that emerged from the hazard-risk school of thinking (Figure 2.5). It is comprehensive in the way that it links vulnerability, risk reduction and the concept of sustainability. The framework acknowledges the three spheres of sustainable development: environmental, social and economic in assessing vulnerability. When a natural hazard strikes (t=1), it does not necessarily affect the social sphere only (this leads us to the disaster context) but will also affect the environments and the economic systems that the communities depend on. The risk of a hazard as a result of vulnerability is determined by the coping capacity of the exposed spheres or systems. The framework uses the coping capacities as the pro- active elements that represent the readiness or preparedness of the system to reduce its vulnerability in the face of hazards (t=0). Reactive feedbacks that occur after facing hazards (t=1) are considered to contribute to vulnerability reduction through hazard and

28 disaster mitigation. These two approaches to reducing vulnerability influence the intervention systems; they explicitly target risk reduction efforts.

The BBC authors argue that it is essential to integrate the vulnerability and risk reduction assessments with the sustainability perspective. In ensuring the long-term benefits of the environmental, social and economic spheres, the understanding of coping capacities and vulnerable elements of each sphere, as well as interaction between different spheres, is highly important so that appropriate measures to sustain and improve their susceptibility could be taken for specific hazards. However, the framework has a vague consideration of anthropogenic hazards. This type of hazard, such as water pollution, is equally damaging or harmful to the sustainability of the environments, societies and the economy and therefore emphasis on anthropogenic or human-induced hazards as the source of vulnerability and risk is also necessary (ICSU, 2005).

(Source: Birkmann, 2006, p.35)

Figure 2.5 The BBC framework

2.2.1.3. Global Environmental Changes Framework

The global environmental changes framework (GECF) was developed by Turner et al. (2003) and depicts vulnerability as the key concept that links the interaction of humans, their environments and external stressors (Figure 2.6). Vulnerability is described in the core layer of this framework suggesting the evaluation of vulnerability should be place- based. The importance of context when considering a vulnerability assessment was also asserted by Birkmann (2007). This is because the factors that shape the vulnerability of a system are unique and varied. The context-specific nature of vulnerability is further highlighted by describing how the interactions between changes in human and environmental conditions could contribute to shaping the characteristics of exposure to hazards or stressors. This part of the framework implies that the characteristics of exposure vary depending on different observed systems and types of stressors.

(Source: Turner et al., 2003, p. 8076)

Figure 2.6 Global environmental changes framework

The framework also highlights the importance that a broader understanding of the layers of the framework is required to provide a more complete knowledge of the observed system and the changes that characterise the vulnerability. To measure vulnerability, the

30 framework emphasises three elements that should be taken into account: exposure to hazards or stressors (as drivers or causes), sensitivity (as an internal factor), and resilience (as the consequences). These elements are influenced by, and interact with, external dynamic factors such as political-economic states, trends and state of nature, which operate on many scales and act together to shape the vulnerability. The framework integrates the term “resilience”, which refers to the coping and adaptation mechanisms of the system, to particular hazards or stressors that determine the system's responses.

2.2.1.4.Sustainable Livelihood Framework: linking vulnerability and sustainability

The sustainable livelihood framework (DFID, 1999; Figure 2.7) was originally developed from the sustainable rural livelihoods concept of Chambers and Conway (1991). The concept consists of two terms, the first is 'sustainability' that stands for the condition that often relates to the sustained use and management of resources (also commonly applied to a desired state in environments and ecosystems) without compromising their long-term availability for the benefit of future generations. The second term is 'livelihood', defined as capabilities, assets (tangible and intangible) and activities required for a means of living (Chambers and Conway, 1991). Integrating the concept of sustainability with livelihood implies the capabilities of maintaining a means of living by being able to cope and recover from stresses or shocks and at the same time being capable of providing opportunities for the future generations (adapted from Chambers and Conway, 1991).

The sustainable livelihood framework (SLF) describes how the sustaining conditions of the observed community are influenced by their exposure to vulnerability, the availability of assets or capital for livelihoods, the existing transforming structures and processes, and the livelihood strategies. On the left side of Figure 2.7, it can be seen that vulnerability is considered to be the external element (in the form of shocks, stressors or seasonalities) that could influences the availability and conditions of livelihood assets that are essential to sustain livelihoods.

(Source: DFID, 1999)

Figure 2.7 Sustainable livelihood frameworks for vulnerability assessment

This concept of livelihood assets or capital (the second element of SLF) is central to the framework. There are five key elements of livelihood capital: human, social, physical, financial, and natural capital. The concept of capital in the SLF depicts the multidimensional aspects of livelihood well-being and complements the human well- being analysis that used to focus on income and consumption as the proxies (Rakodi, 1999). Numerous studies have followed this livelihood capital approach where it is used to define the characteristics of the observed communities through various measurable indicators that represent each type of capital (Allison and Horemans, 2006; Allison and Ellis, 2001; Islam et al., 2014; Iwasaki et al., 2009 and Reed et al., 2013). The SLF suggests that communities that are equipped and possess more capital are likely to have greater opportunities to cope with external stresses but not so for communities with less capital. It also emphasises the contextual aspect of capital where the importance of different type of capital depends on the local setting (DFID, 1999). The inclusion of livelihood capital also illuminates the effort to integrate a people-centred perspective as one of the essential elements of the SLF. It emphasises how disaster is not solely dependent on biophysical exposure but, more important, it requires an understanding of social or community structures and characteristics and an acknowledgement of their

32 strengths and capability to deal with external stress and maintain their livelihoods (Heijmans, 2008).

The availability and accessibility of capital influence the livelihood strategies applied by households or communities to achieve favourable outcomes (see Figure 2.7). The livelihood strategies could be defined as the combination of activities that people choose to undertake using the available capital, which could include productive activities or investment strategies (Alinovi et al., 2010). The SLF illustrates that more sustainable livelihood can be achieved when there are 'positive' livelihood outcomes (such as increased income or well-being, reduced vulnerability and sustainable use of resources). In the SLF, transforming structures and processes play important roles in creating the pathways to achieve these desirable livelihood outcomes. Government and the private sector, as the transforming structures, could influence the availability and accessibility of assets for the community through the implementation of a variety of instruments (such as policy and laws). For instance, the establishment of supportive policies or aid programs to enable the access of poor people to soft loans for productive means or ownership of land could provide the communities with capital that can be used to cope in a time of hardship caused by external hazards or stresses (Dodman and Satterthwaite, 2008 and UNDP, 2012). For example, some regulations on fishing communities, such as bans on destructive fishing practices and restriction of species caught, were implemented to promote more sustain use of the resources and livelihood outcomes (Sulu et al., 2015). However, these activities can equally have direct consequences in constraining livelihood strategies (Serrat, 2017). In a study about coastal livelihood and governance in Andaman Coast, Thailand, Bennett and Dearden (2014) show how the restriction of fishing and aquaculture areas due to the establishment of Marine Protected Areas (MPA) resulted in social conflict between the resource-dependent community and the government. The structures and processes are also important in the context of vulnerability. They could contribute to reducing or exaggerating the source of vulnerability. For instance, in the events of anthropogenic environmental hazards, such as water pollution, government interventions (that could be stricter policies or regulations and law enforcement) could have a direct effect on the occurrence and magnitude of the hazard (Harlan and Ruddell, 2011 and Hosono et al., 2011).

The SLF helps to identify the key elements that have important role s in promoting more sustainable livelihoods in the context of vulnerability. Nevertheless, the SLF does not include an explicit explanation about what comprises the vulnerability itself and therefore further modification is required to ensure this framework is a good fit for the vulnerability assessment in practice through integration with the existing vulnerability frameworks that provide practical perspectives.

2.2.2. Defining vulnerability elements: exposure, sensitivity and adaptive capacity

The vulnerability frameworks are useful guidance for performing vulnerability assessment. Within the context of this research, the combination of frameworks developed by Turner et al. (2003) and DFID (1999) and the working definition of 'vulnerability' used by the Intergovernmental Panel on Climate Change IPCC (2001), serve as the most appropriate approaches to be applied in assessing the vulnerability of traditional fishing communities to water pollution. The framework and definition distinguish three essential elements that are required in constructing comprehensive vulnerability measurement, these are exposure, adaptive capacities and sensitivity. The GEC framework by Turner et al. (2003) also helps to identify and understand the processes that occur beyond the observed system and includes a useful perspective that casts adaptation strategies as a form of resilience (Birkmann, 2006).

Exposure

The exposure element in vulnerability studies is generally defined as the nature and degree to which a system experiences hazards (Adger, 2006). However, because of the context-specific nature of such studies, more specific definitions are often adopted. Hughes et al. (2012), in a study on country level vulnerability to food security, defined exposure as the degree to which a country’s coral reef fisheries are threatened by anthropogenic drivers. They used expert-based, exposure-score mapping based on several anthropogenic stressors on fisheries to calculate the exposure score for each country. Various other studies on the context of vulnerability to climate change used the IPCC working definition of exposure (IPCC, 2001), which is about the magnitude and duration of the climate-related hazards, such as frequency of floods, droughts and cyclones, or about climatic conditions, such as temperature and precipitation (see

Birkmann et al., 2015 and Hahn et al., 2009 for examples). In the context of this research, exposure is described best as the nature of water pollution, encountered by the communities, that is characterised by the spatial distribution and exposure area of the pollution.

The inclusion of biophysical analysis in defining the exposure element in vulnerability assessment, as described in the studies previously cited, represents a more top-down approach, where the knowledge gained is most valuable to obtain information about the characteristics of the hazards or stressors that are under examination. However, for assessing social vulnerability, human characteristics play an important role in defining the vulnerability, impacts and adaptation to stressors where these can only be understood through participative or more bottom-up approaches (Martinez et al., 2008). Eakin and Luers (2006) and Lee et al. (2016) asserted the importance of integrating knowledge from the biophysical and social science disciplines in complementary ways rather than contrasting them or trading them off in the assessment of social vulnerability. This will give a more holistic understanding of the impacts of hazards on the social systems. Therefore, to obtain comprehensive understanding of the impacts of water pollution on communities, it is necessary to apply a combination of appropriate methods that include "experts" water quality assessment and the assessment of communities' livelihoods and local knowledge of water quality and environmental changes.

The participatory rural appraisal (PRA) is a powerful way to gather local knowledge. Cavestro (2003) and Chambers (1994) define PRA as a set of methods (for example, historical timelines, participatory mapping and seasonal calendars) applied to learn about rural life and conditions from, with and by rural people. In socio-environmental research, including vulnerability studies, this method is often used because it provides an adjustable tool and learning platform that encourages knowledge exchange for researchers and communities through inclusive participation (Kettle et al., 2014 and Tiani et al., 2015). For example, in a study of the social vulnerability of a farming community in Tanzania, Africa, a community workshop as a part of a PRA exercise was one of the effective data collection tools that enabled the researchers to understand better the local agricultural activities and the livelihood strategies that were taken by the

35 community to cope with climate variability (Below et al., 2012). The integration of local knowledge in defining the exposure to hazards is expected to contribute to a better understanding of the nature of the exposure and to result in more relevant vulnerability assessments shaped for specific hazards and a local context and will have a greater chance they will meet people’s behaviour in coping with the hazard (Ahmed et al., 2012 and Kettle et al., 2014).

Sensitivity

Sensitivity is defined as the degree to which a system is modified or affected by external hazards or stressors (Adger, 2006). In the context of livelihood vulnerability, the element of sensitivity brings to the surface the importance of internal conditions that could increase communities’ exposure to hazards, in other words, contribute directly or indirectly to their vulnerability (IPCC, 2001). Very slight changes in the environment because of the occurrence of hazards can significantly affect communities with high sensitivity to such hazards. As an illustration, irrigation's high dependency on stored precipitation in agriculture could result in higher sensitivity in the context of climate change or climate variability compared with other agriculture sectors that rely on several water sources (such as private water companies and wells) (Martinez et al., 2008). In the assessment of vulnerability of traditional fishing communities, sole reliance on fish resources is seen as a form of sensitivity and disadvantage because of the higher risk of dealing with uncertainties in production and uncertainties of external factors such as water pollution, storms or other hazards (see examples from other studies such as Cinner et al., 2011 and Islam et al., 2014).

Adaptive Capacity

The broad definition of adaptive capacity, as defined by Adger (2004), is the ability or capacity of a system to modify or change its characteristics or behaviour to cope better with external hazards or stressors. For adaptive capacity, many studies adopt the basic livelihood assets, as described in the sustainable livelihood framework (DFID, 1999), which includes social, human, physical, natural and financial capital as guides to identify the indicators. For example, Allison and Horemans (2006) use these five capital indicators to discover the type of interventions needed to support sustainable livelihoods

36 of the small-scale fishers. In a study about the assessment of livelihood vulnerability to climate change, Hahn et al. (2009) applied a total of 20 indicators (such as access to health facilities and water sources, livelihood diversification and access to government assistance) to provide a picture of the socio-economic characteristics of the households and argued that this approach is best used to help quantify and predict the strength of a household in coping with hazards.

In the context of vulnerability assessment, according to Birkmann (2006), indicators can be seen as a 'variable which is an operational representation of a characteristic or quality of a system able to provide information regarding the susceptibility, coping capacity, and resilience of a system to an impact of an albeit ill-defined event linked with a hazard'. The process of selecting indicators that represent the elements of vulnerability, such as the adaptive capacity and sensitivity, is highly specific to the context. It depends on the availability of data and, obviously, on the characteristics of hazards and the communities that are being observed (Adger et al., 2004). It is important for the indicators representing vulnerability elements in this research to capture the characteristics of water pollution exposure, socio-economic features of fishing communities in the context of their sensitivity, and their adaptive capacity to water pollution. For example, access to credit (as a form of financial capital) is often seen to be the last resort for resource-dependent communities to support their income generating activities and even the survival of their livelihood (Motsholapheko et al., 2011) and therefore it is very important for this type of capital to be accounted for in the vulnerability assessment. Thoughtful and well chosen indicators will provide better understanding of the features that fundamentally shape the vulnerability and could act as tools in developing improved strategies to reduce vulnerability (Rygel et al., 2006).

2.2.3. Putting vulnerability in context: livelihood vulnerability to water pollution

Even without the external disturbance of water pollution, traditional fishing communities are already one of the most vulnerable groups among coastal communities. This is because of the high uncertainty of the catch from every fishing trip, high risks from a hazardous environment, often exacerbated by fishers having no health or life insurance, and in the limitation of their tenure and of access to land and sea resources and other livelihood components (Bene, 2006 and Colburn et al., 2016). Furthermore,

37 traditional fishing communities in developing countries are described as particularly vulnerable in terms of being marginalised in society, by politics, policy development and by decision making (Allison and Horemans, 2006 and Coulthard et al., 2011). This is true for traditional fishing community in Jakarta Bay, which was highlighted by Padawangi’s (2012) work that reposrted their lack of participation in development programs and in Jakarta’s master plan 2010-2030. Moreover, 95 % of the traditional fishers in Jakarta can be considered as 'specialised' fishers meaning that they depend on fishing for their main income (BPS, 2012). This specialisation is seen as a weakness because their dependency on fishing increases their exposures to risks (Bene, 2009), such as fish deaths and profit loses caused by inreasing marine pollution.

Several studies in other countries have successfully assessed the vulnerability of fishery-dependent societies, specifically because of climate change in national, regional and local scales and these studies have mostly resulted in useful and specific recommendations for reducing vulnerability. Cinner et al. (2011) developed a vulnerability index and strategies for action in response to risks of climate change impacts on coral reef fisheries based on their analysis of the exposure, sensitivity and adaptive capacity of 29 coastal societies in five countries (Kenya, Tanzania, Madagascar, Seychelles and Mauritius). Similarly, Hughes et al. (2012) applied a set of vulnerability indices to 27 countries, including Indonesia, to predict their vulnerability to food insecurity caused by climate change impacts on coral reef fisheries. In Indonesia, there has been very little research to examine specifically the vulnerability of fishing communities or individual fishers. Most of the studies on coastal vulnerability have focused on the assessment of biophysical systems (such as ecosystems or infrastructures) and human-environmental systems because of the potential effects of sea level rise with regards to land subsidence and climate change (Joseph et al., 2013; Ristianto, 2011; and Rositasari et al., 2011).

Water pollution in the developing world's coastal areas is already recognised as one of the prominent stressors of the marine environment (Nellemann et al., 2008). Therefore, there are risks in paying most attention to the effects of global climate change and neglecting another significant stressor in the context of coastal megacities. Managing pollution is likely to provide valuable experience and develop capabilities for coping

38 with other emerging problems of Jakarta. In addition, this integrated vulnerability research is expected to contribute to new and more holistic insights for coastal and natural resources management in Jakarta and other coastal megacities.

Hughes et al. (2012) asserted that the effects of social-environment interactions are different because of the nature of external stressors and social groups. For example, resource-dependent communities have particular adaptation patterns they use to deal with the impacts of climate change that would be different if they sought to adapt to water pollution. The adaptation patterns might vary within communities, for example, among different traditional fishing communities (such as between traditional fishers and aquaculture farmers). Therefore, specific-sector and scales of vulnerability research, such as this research, contributes to a necessary effort to improve the precision of understanding and prediction of water pollution’s impacts on households and on communities (Cinner et al., 2011 and Hahn et al., 2009). This research will provide credible and robust evidence to be the foundations for specific and adoptable recommendations for communities, for policy leaders and for planners.

Chapter 3

Research Design

3.1. Introduction to study area: Jakarta Bay and coastal areas

Jakarta Bay is a relatively shallow Bay with an average depth of 15 meters located in the northern part of Jakarta (Figure 3.1). It has a 40-kilometer shoreline and an area of 514 square kilometers that include the Seribu Islands (Thousand Islands) region (Fauzielly et al., 2012).

Figure 3.1 Area of study: Jakarta Bay and the fishing villages of Muara Angke and Cilincing

The bay's hydrodynamic conditions are affected by monsoons, river discharges and tides (Koropitan et al., 2009). The monsoons, west, east and transitional, are responsible for the magnitude and flow direction of the bay's current (Williams et al., 1997). Thirteen river systems discharge into the bay but three rivers; Citarum, Ciliwung and Angke, are considered the major contributors to shaping the morphology and water quality of the bay and its coastal regions (BPLHD, 2012a and Farhan and Lim, 2012; see Figure 3.1). Citarum (with a catchment area 6000 square kilometers) is the major source of fresh water and discharges a 137 cubic meters per second (van der Wulp et al., 2016). The other twelve rivers (with a total catchment area of around 2000 square kilometers) discharge a volume similar to that of the Citarum (Koropitan et al., 2009). These discharges carry increasing amounts of pollutants: they have contributed to a ten- fold increase in nitrate concentration during the past three decades that have lead to hyper-eutrophic conditions in the bay, particularly in the near-shore areas (Arifin, 2004; Damar, 2003; Koropitan et al., 2009; and Sidabutar, 2016).

Changes in water quality, indicated, for instance, by increasing trophic level of waters, are not the only changes occurring in Jakarta Bay. Natural ecosystems, such as mangroves and coral reefs, have had significant reductions in extent and biodiversity. Between 1960 and 2014, nearly 80% of the mangrove forests were transformed for aquaculture purpose and, mostly, for land reclamation (BPLHD, 2014 and Pramudji, 2008). The remaining 275 hectares of mangroves are located mostly in the western part of the bay. This massive transformation occurred despite the recognition of the invaluable roles of mangrove ecosystems play in protecting the coastal land from erosion and extreme waves, regulating pollutants and providing natural habitats for organisms (many of which are locally protected and endangered species), including fish in their juvenile stages (Sudin PPK, 2014). Transects on coral reefs in the north of the bay by Estradivari et al. (2009) show declines from 34% to 31% in just two years (2005-2007). In the areas nearest to Jakarta (Bidadari and Onrust Island), coral reefs were seriously degraded by anthropogenic factors and this was most clearly indicated by a very low diversity index (equal to zero) and only one surviving genus (Estradivari et al., 2009).

Despite these dramatic environmental changes, the bay and coastal areas of Jakarta continue to provide the geographies for various social and economic activities that together construct their multi-sectoral functions and character. The international port of Tanjung Priok (Figure 3.1) is in the central area of the coast and it is the main gateway for trades of goods and services, which are estimated to serve the needs of a quarter of the nations’ population (Bengen et al., 2006). The industrial areas adjacent to the port are home to chemical, textiles and food manufacturing. Major industrial areas on the east coast of Jakarta, known as the Marunda industrial integrated zone, are very close to Tanjung Priok, and to the fishing village and port of Cilincing (Figure 3.1). These activities take place next to the prime coastal and marine tourism attractions of Ancol, which is to the west of Tanjung Priok (Figure 3.1). Further developments of the coastal area include land reclamations (of an area of 2700 hectares) that is intended to allow for the expansion of port, business, residential and industrial facilities (Perda DKI No. 1 of 2012). These land reclamation projects, and the planned Giant Sea Wall, are all constituent parts of Jakarta’s spatial planning to 2030 and are a form of coastal protection from the threats of rising sea level.

3.2. Traditional fishers and mussel farmers groups

Among these bustling activities, Jakarta’s coastal areas still provide space for traditional fishing communities, which are concentrated (more than 97%) in two Jakarta municipalities, Penjaringan and Cilincing (KKP, 2013; see Figure 3.1). These traditional fishing communities are divided into two major occupational groups; one relying on capture fisheries (traditional and large scale) and the other on aquaculture. The traditional capture fishers in Jakarta operate with small boats with capacities less than 5 gross tonnes (Figure 3.2), use no navigation technology, and use various fishing gear, such as hooks, gill nets, lift nets, boat nets, trawls nets and traps (Ningrum, 2011). DKP Jakarta (2011) reported that there were over 540 of this type of vessels operating in Jakarta Bay. These vessels usually have one owner who employs a group of fishers as labourers. Their main commodities comprise small pelagic fish (mackerel, yellow-stripe scad, sardines, anchovies and blue-tail mullet), demersal fish (silver pomfret, groper, sword fish and red snappers), shrimp and blue manna crabs (Sudin PPK, 2014). In 2014, it was estimated that fishery production in Jakarta contributed around 20% (more than

200 thousands tons) of total fish production in the area of the Java Sea (KKP, 2015). However, most of these data are probably gathered from larger fishing vessels. Because of the lack of fish catch data from smaller ports, there were no specific data to show the contribution to total production made by traditional fishers.

Figure 3.2 Traditional fisher’s vessel in Cilincing

Land-based aquaculture in brackish ponds in the surrounding areas have more variety in commodities (shrimp, milkfish, Nile and Mozambique tilapia), but aquaculture in Jakarta Bay consists solely of green mussel farming. In spite of being banned by the Jakarta government in 2010 because of heavy metal contamination, there are more than 200 households (that is, 25% of the number of aquaculture households) that still maintain their green mussel farming activities (BPS, 2013). Green mussel farmers in Jakarta Bay also use small capacity vessels (less than five gross tonnes) to travel in small groups to where they harvest the naturally growing Perna viridis L. from bamboo platforms (Interviews, 2015; Figure 3.3) or from the rocks and built structures around the coast areas. Perna viridis is a filter feeder that to grow requires relatively shallow water (between three and ten meters deep) and mud-substrates (Prasetyo, 2009). Therefore, areas suitable for green mussel aquaculture are limited by water depths and are concentrated around the near-shore areas. There is no available historical data on green mussel productivity but research by Haryati et al. (2013) mentions the average production per farmer could reach 20 tons per year.

Figure 3.3 Bamboo platforms for green mussel aquaculture in Bidadari Island

3.3. Framework for research

As described in Chapter 2, the problems of water pollution and the very existence of traditional fishery sector represent the real socio-environmental challenges in Jakarta Bay and its coastal areas. In seeking better understanding of the interactions of this socio-environmental system, this research used the case study approach. Case studies provide opportunities to apply deeper contextual and place-specific approaches so that the researchers can provide insights that adequately represent complex, real-life phenomena (White et al., 2013 and Yin, 2014). The approach engages with stakeholders directly and ensures that experts, government and different groups of community (traditional fishers and mussel farmers) explain the problems in the context of their lived experiences. This is the only way to know how different groups are affected differently by water pollution and to understand how this is related to the characteristics of their occupations, their exposure to water pollution and the conditions of their livelihoods.

Case studies are some times criticised for lacking objectivity or that there is a risk of imposing biased or prejudicial frameworks to problems or that findings based on case studies might not be easily extended to new situations (Piekkari et al., 2010). In this research, these risks were managed by using multiple sources and combining multiple methods. These resources and methods were deliberately chosen to represent and explore the broad interests and knowledge types that provided valuable information and research insights to an area and to communities that are undergoing dramatic change. It also used well-accepted methods and existing datasets (for example, livelihood

45 assessments and biophysical data) so that the research and results developed can be considered with confidence and be adopted by researchers, governments and NGOs or extended in the future to other situations.

This research focused on two traditional fishing communities (traditional fishers and green mussel farmers) and included members of the group of informal sector workers (for example, mussel peelers, food stall owners, motorcycle-taxi drivers) who lived in the same area. The first two groups were selected to ensure their knowledge of water pollution and environmental changes, and their direct experiences in dealing with or adapting to the adverse impacts of water pollution, were considered in this research. All three groups that participated in this research will be referred to as occupational groups henceforth. Muara Angke and Cilincing (in the district of Penjaringan and Cilincing respectively) were specifically chosen for this research because they are centres of traditional fisheries activities and typify fishing communities in Jakarta (Figure 3.1).

Extending the insights from the literature to this research, it was clear that a livelihood vulnerability assessment was an appropriate tool to allow the measurement and evaluation of human or social impacts of water pollution. It is necessary that assessment goes beyond biophysical measurements that only characterise physical properties of a hazard or stressor (Duxbury and Dickinson, 2007), such as water pollution. Applying this vulnerability assessment approach allowed the researcher to confidently contribute to the development of new knowledge. This was achieved by integrating the methods of biophysical science, which included measurements of water quality, with those of the human sciences to gather local knowledge of pollution, other environmental changes, and information on households’ livelihoods. This integrative, multidisciplinary approach in this research was itself an important contribution to demonstrate the role of local knowledge in enriching the understanding of the impacts of water pollution and environmental changes, so that it could contribute to developing solutions for environmental and livelihood sustainability (McConnell et al., 2011). Figure 3.4 describes the research framework logic that connects the inputs, key processes and outputs required to deliver the insights, answer the research questions and obtain the desired outcomes of the research.

Figure 3.4 The research framework

3.4. Methodologies

The research framework (Figure 3.4) represents the formulation of research methodologies. The research was implemented using two approaches that were integrated to provide a more comprehensive understanding of water pollution impacts. The first was to apply a biophysical approach that involved the assessment of water quality data using statistical and spatial model analysis to obtain information on the state of water quality in the bay. This also contributed to estimating the exposure to water pollution by the occupational groups, which was one of the elements included in the livelihood vulnerability assessment. Previously, researchers had focused their attention on understanding the biophysical aspects of water pollution and its potential impacts (as explained in Chapter 2), yet less attention is put on defining or addressing the impacts on people who constitute the human system of Jakarta Bay.

A people-centred approach was incorporated in the light of the literature review, which suggested that hearing local people’s narratives was essential if these environmental

47 problems were to be defined. This approach used place-based and face-to-face methods for gathering information about livelihood conditions, local knowledge and adaptative strategies used by the occupational groups. The inclusion of the livelihood capital concept illuminated the people’s livelihood characteristics and their capacity for coping and adapting to water pollution. Local knowledge, which included people’s stated perception of water pollution and their behaviour in their livelihood and fishing activities, helped to identify factors that increased their vulnerability to water pollution. These factors included people's dependence on fishery resources (defined as the sensitivity element) and the spatial context of their nature of occupation (this was later integrated with biophysical assessments of water pollution to result in the integrated exposure element). The flowchart of this research describes the steps in performing the vulnerability and livelihood analyses (Figure 3.5).

Figure 3.5 The research sequence, methods and products

3.4.1. Water quality assessment

Using conventional scientific methods, water quality in Jakarta Bay has been studied extensively and is known to be poor (explained in Chapter 2; see section 2.1.2). Knowing how this environmental hazard could affect the selected occupational groups requires a good knowledge and understanding of water quality conditions, which can be learnt by measuring and analysing water quality properties. Using water quality

48 measurements, made regularly by government, this research did some further analysis to derive an exposure map that could be linked to the livelihood vulnerability of the occupational groups.

This research made use (with permission) of the water quality data gathered by the Jakarta Agency of Environmental Management (BPLHD) from fixed point locations for the years 2001 to 2013 (information on the coordinates of each point or site are in the Appendix 1). The sampling and measurements are performed two to four times a year so that each sampling event represents the changing monsoonal seasons that dominate the hydrodynamics of the bay. These sampling activities were designed as a management tool for monitoring the intensified coastal and inland-source effluent that were expected to significantly influence water quality in Jakarta Bay (BPLHD, 2013). The regular rectilinear grid of sample sites at 23 locations (the distance between each site was four to five kilometers) is arranged along four transects according to their proximity to the shore line of Jakarta (Interview with BPLHD staff, 2015; Figure 3.6). Zone A comprises seven sites (A1, A2, A3, A4, A5, A6, A7) and is farthest from the Jakarta shore line and these sites represent the offshore waters of the bay. Zone B and C comprise seven and five locations respectively (B1, B2, B3, B4, B5, B6, B7 and C2, C3, C4, C5, C6) that represent the waters from the middle areas of the bay. Zone D comprises four locations and is the closest zone to the coastline of Jakarta (D3, D4, D5, D6).

Figure 3.6 The locations of 23 sampling sites in Jakarta Bay and the river mouths (in blue) including labels for three major rivers (Citarum, Angke and Ciliwung).

Although there were several other sources of data that could be used, such as remotely sensed data, the direct and in situ measurements used in this research have some significant advantages. The first was the ability to take into account various physio- chemical parameters, which were not able to be covered by remote sensing data because of its detecting limitation (Mehta, 2015); second and more important, was the advantage field measurements have in increasing the legitimacy and applicability of this research by working with official government data (Davies, 2010). The important physical and chemical parameters used by the government were expected to be stressors for the coastal waters and ecosystems and to be suitable for use as thresholds for action (Indonesian Ministry of Environment Decree 51 of 2004 and ANZEC, 2000). These physical parameters included dissolved oxygen (DO), biological oxygen demand (BOD), total suspended solid (TSS) and turbidity. The chemical parameters included phosphates, nitrates, total ammonia, detergent, phenols, hydrogen sulphide (H2S), and several heavy metals such as mercury (Hg), copper (Cu), lead (Pb), and zinc (Zn).

3.4.1.1. Data analysis on water quality

Like water quality datasets globally (Fu and Wang, 2012), the Jakarta Bay data had a non-normal distribution, missing values, censored-data (values below detection limit) and extreme values. Therefore, descriptive statistical analysis (using Microsoft Excel and SPSS) was applied to the initial datasets to deal with these issues as well as to plan further analysis. There were 6348 samples provided by BPLHD of 14 parameters obtained from 23 sites during the years 2001 to 2013. The percentages of missing values for each parameter ranged from 0.4% (Cu) to 11.2% (phosphates). Although some researchers applied imputation methods to replace the missing values (Humphries, 2011), considering the limits to time and resource and the relatively low proportion of missing values, no imputation methods were applied. Future analysis of the water quality results is possible using more advanced techniques to meet different research objectives.

In this research, 'censored data' refers to values below the detection range or limit of the measurement techniques. Various techniques, such as maximum likelihood or parametric methods that involve regression techniques, have been applied to censored data (Helsel, 2012). However, for simplicity, and in the absence of the need or time for more advanced statistical methods, this research applied a simple substitution method by applying the detection limit values (Fu and Wang, 2012). Four parameters (turbidity, phosphates, total ammonia and H2S) were recorded to have censored data of less than 18% and therefore were still included in the analysis. Two parameters (Hg and Cu) had more than 25% of censored data and therefore were not included for further analysis to avoid bias in the results (Australian Department of the Environment, 2000).

The exclusion or inclusion of extreme values or outliers was also another important point that should be considered when analysing water quality. It was not possible to identify the causes of extreme values in these data sets (for example, whether it was a true event or caused by sampling or laboratory faults). Therefore, those values were retained and included for further analysis to avoid the risk that essential information on water quality be excluded without good reason (Harmel et al., 2014). Appendix 2 provides a summary of the data sets.

3.4.1.2. Developing a water pollution exposure map

This research developed a spatial model of water pollution in Jakarta Bay from water quality data to help understand the spatial distribution of contaminants in the bay. The model was developed from the analysis of descriptive statistics and cluster analysis of water quality data using SPSS and ArcGIS software. This spatial model was used, in the form of a water pollution exposure map, during further analysis of different occupational groups. The development of this exposure map was in three major stages: cluster analysis, multi-criteria analysis and knowledge integration using an overlay method.

The concept of clustering is based on iterative analysis of the similarities in multiple parameters that characterise a group of objects (in this case, the sampling sites) into one cluster while sequentially analysing the dissimilarity of that cluster with another group of objects that belongs to a second cluster and so on (Norusis, 2012). Clustering has been widely used in water quality analysis to produce classification of sites and water samples based on multi-parameters to discover distinct spatial or temporal patterns (Bhat et al., 2014). For instance, Zhao et al., (2012) applied cluster analysis to evaluate water quality of Baiyangdian Lake in China and found that the clustered sites were associated with their proximity to pollution sources. In this research, cluster analysis was used to detect spatial patterns in the water quality sites in Jakarta Bay. For cluster analysis, a subset of data from the complete pre-analysed data was selected to include the minimum values of DO and the upper quartile values of eleven parameters. This served as appropriate approach to capture the ecologically critical values (Ryan et al., 2011) that were meaningful in the context of this Jakarta Bay research. Cluster analysis was performed on a subset data (extracted from the entire dataset) and therefore it has limitations. Since the hydrodynamics of Jakarta Bay is influenced by monsoon season variations yet the subsampling was not designed to emphasise this seasonal variation in the results. More detail analysis for shorter period of time to capture the hydrodynamic effects of the monsoon could be done as necessary for future research. The data subset used for cluster analysis is shown in Table 3.1.

Table 3.1 Water quality values used in the analysis to develop clusters for the map of water quality

Notes: all unit is in mg/l except for turbidity, which is in NTU

The hierarchical cluster analysis is a robust and suitable way to analyse relatively small data sets (Norusis, 2012) such as those in this research. The method clusters sites that are the next most similar using multi-dimensional ‘distance’ calculated from the subset of water quality parameters. Before clustering, all parameters were standardised to avoid high values of any parameter dominating the formation of cluster. The hierarchical method is an agglomerative method so that clusters are formed progressively until all the sites clustered together in one large cluster. In this research, a combination of Euclidean distance (a multidimensional length of a line between two points in n-dimensional space defined by n parameters) and Ward’s linkage method (that optimises clustering by iteratively merging clusters to give the minimum increase

53 in total variance) was used in the light of suggestions from various published studies on water quality analysis (Dheenan et al., 2014; Ogwueleka, 2004; and Zhao et al., 2012). This combination of methods and measures was recognised as the best for continuous or interval data to be used for identifying the most distinctive features that form a group together and separate it from others (Tiri et al., 2015). For the purposes of analysis and validation, however, several combinations of other distance measures and linkage methods were attempted and these resulted in reasonably consistent cluster outcomes, suggesting that the analytical results were robust.

The number of clusters was chosen deliberately to provide a reasonable number of clustered sites. Although there are no theoretical rules for achieving the optimal numbers, knowledge of the characteristics and patterns in the Jakarta Bay water quality data helped in determining reasonable cluster numbers as suggested in the literature (Salvador and Chan, 2004). The cluster analysis output identified several groups where each group consisted of several sampling sites that shared similar characteristics.

A descriptive index for each cluster was calculated using the parameters of water quality that would represent the water pollution exposure value. Boolean operators were used to calculate this water pollution exposure index. Boolean is a multi-criteria analysis method that assumes classification of features to be made on a binary basis (Charabi and Gastli, 2011 and Szuster and Albasri, 2010) to provide means of quantification. In the context of this research, the binary numbers were 0 (zero) and 1 (one). The concentration values (from the data subset in the Table 3.1) were compared with the maximum or minimum standard values for water quality that result in the binary number of 0 (for values within the limits set) or the binary number of 1 (for values that exceed the limits set). Inspection of the subset data revealed that some parameters functioned as the determinants of the index, which included DO, TSS, turbidity and phosphates. This meant that the inclusion of other non-determinant parameters resulted in no difference for the calculation of the index (because the concentration values were all below or above the thresholds). Therefore, only the determinant parameters were retained to calculate the exposure index.

The binary values were aggregated for each site and then summed with values from the other sites of similar cluster group. The sum of these values were then averaged (using

54 the number of sites that belong to similar cluster group) to provide a result in the range from 0 (defines a cluster group with the lowest exposure to water pollution) up to the value of 4 (defines a cluster group with the highest exposure to water pollution). The averaged values were then standardised to provide an exposure value ranging from 0, which defined lowest exposure value, to 1, which defined the highest exposure value (further details of Boolean processes and exposure index calculations are available in Appendix 3).

To create a water pollution exposure map of Jakarta Bay, the exposure values were then mapped in grids over the sample sites using the ArcGIS software. The locations of cluster members (that consisted of several sample sites) were represented as an exposure area with a colour on the choropleth map showing its exposure index value. An overlay of this exposure map on the spatial information of fishing areas (derived from participatory mapping) provided powerful insights into the exposure risks of different occupational groups. Overlay is a common geographical information technique that usually involves multiple layers of data with the main purpose of integrating various layers to understand spatial interactions (Nath et al., 2000 and Quan and Lee, 2012). This integrated exposure map was not only useful in describing water pollution level spatially but, more important, it helped describes how different occupational groups could have been exposed to water pollution differently. This occupation-related exposure information was used to represent the exposure element in the vulnerability assessment. Other contributing factors to exposure are also acknowledged and discussed in both the results and discussion sections of this thesis (Chapter 4 and Chapter 6, respectively).

3.4.2. Livelihood vulnerability assessment

The livelihood vulnerability was analysed using the elements of adaptive capacity, sensitivity and exposure (as previously explained in Chapter 2) to obtain a clear understanding of relative vulnerability among the occupational groups (aggregated from household level). Each element was represented by indicators that together characterised and could be aggregated to give a quantitative estimate for the element.

The indicators of adaptive capacity were developed as estimates from the socio- economic features of the occupational groups gathered at the household level. There were fourteen (14) indicators of adaptive capacity developed that were based on five capital types (social, physical, financial, human and natural capital; details of the indicators are shown in Table 3.2). Elements and indicators from the literature were used wherever possible to ensure the contributions of this research could be readily understood in the light of other research. However, some adaptive capacity indicators were specifically developed for this research, such as ownership of health insurance, access to credit (other than through a middleman), awareness of water pollution and entitlement to agriculture land. These were added because of their importance in the local context and discovered through initial discussions and observations. According to Adger et al. (2004), localised indicators such as these often contribute to developing more realistic and meaningful measurements that capture characteristics of the observed systems in vulnerability assessments.

In terms of sensitivity, this research used the dependency on fisheries-based occupations as a key indicator. The indicator of exposure to water pollution, as described in the previous section, was based on an integration of biophysical indicators of water quality with spatial behaviour of the groups in performing their occupational activities. Table 3.2 shows the indicators (except for the exposure element that was explained in the previous section; see Section 3.4.1.2) and measures used to inform the vulnerability assessment in this research. It also links the choices of elements and indicators made here to the wider literature.

Table 3.2 Elements and sub-elements of vulnerability and indicators measured

What was measured? Indicators Literature (% of households) Social capital: Organisational actively involved in an Petzold (2015), membership organisation Motsholapheko et al. (2011) Access to information with access to Adapted from Shiferaw et information about water al. (2014) pollution Physical capital: Dwelling status with dwelling ownership Below et al. (2012) Access to health service with access to health Hahn et al. (2009)

What was measured? Indicators Literature (% of households) service less than 30 minutes Access to clean water with direct (in-house) Hahn et al. (2009) access to clean water Health insurance own health insurance Developed first in this research Financial capital: Working extension with additional family Hahn et al. (2009) members working Working expansion where the heads have Adapted from Colburn et al., additional jobs (2016) and Hahn et al., (2009) Access to credit with credit access other Adapted from than middlemen Motsholapheko et al. (2011) Human capital: Education level where the heads finished Hahn et al. (2009) senior high school Non-fishery based skills where the heads had Wedathanthrige et al. (2013) non-fishery additional jobs Awareness of water that aware on the Developed for purpose of pollution occurrence of water this research pollution Natural capital: Land ownership with land ownership (for IFAD (2012) agriculture purpose) Fishery resources who stated decline on Adapted from Abramovitz et fishery resources al. (2002) Sensitivity: Dependency on fisheries reported only fisheries- Cinner et al. (2011) and related work as a source Islam et al. (2014) of income Notes: Sub-elements of adaptive capacity are typed in bold; Indicator for sensitivity element

3.4.2.1. Data collection: livelihood and local knowledge

Livelihood information from the occupational groups, as well as responses on water pollution and environmental issues, were collected through questionnaire survey, group discussions and interviews. The benefits of such qualitative and quantitative information were to enable this research to provide reliable sources of information. The questionnaire gathered very useful information on people’s livelihood conditions,

57 knowledge and perception of the issues and allowed the research to respond to emerging opportunities. The occupational groups involved in this research were the traditional capture fishers, green mussel farmers and informal workers. The potential respondents were asked to identify their main income source and that became the parameter of the grouping. This initial filter question was necessary as the respondents often revealed that they relied on more than one job to secure their livelihood. The questionnaire survey was conducted in Indonesian and the data was gathered at the household level.

'Household' was defined as the social group that resided in the same place, shared the same meals and made joint or coordinated decisions over resource allocation and income pooling (Allison and Horemans, 2006). Among livelihood vulnerability research, this level of data collection was considered as the best scale to capture various aspects of livelihood that are often allocated within or embedded to a household (rather than an individual). This research also referred to the socio-economic census of the Indonesian Bureau of Statistics (BPS) that collected baseline information at the household level. In addition, this level is appropriate in terms of its ability to capture the heterogeneity (in terms of livelihood characteristics and different views and knowledge) that was expected within the occupational groups. In the context of vulnerability assessment, taking into account the importance of heterogeneity is essential in order to understand better the factors that shape their collective livelihood vulnerability as well as the factors that motivate different responses in dealing with water pollution and environmental changes.

The survey used snowball sampling combined with targeted sampling in the villages to get respondents due to the limited alternative ways of gathering data. There was no detailed demographic information about these occupational groups; most lived on boats and houses constructed informally on untenured land and some worked in an industry (green mussel farming) that had been officially closed. Snowball sampling is commonly used to access communities where a priori knowledge is limited (Atkinson and Flint, 2001). The sampling was conducted by making initial contact with known members of the community, that is, chiefs of the communities and leaders of occupational organisations. Then, having experienced the research themselves, they were asked to pass an invitation to other community members who might be interested or willing to

58 participate, and so on. The snowball technique can result in respondent homogeneity that can reduce or bias the information gathered (Cohen and Arieli, 2011). In this research, that risk of bias was reduced by selecting initial contacts who would represent the diversity that might exist in the community and in each occupational group. Cross- checks through multiple methods and observations during the field trip were also conducted to identify or reduce the bias further.

Before to the full survey, a pilot survey was performed with several fishers and mussel farmers in Jakarta Bay. This preliminary survey was important to enable improvements to the questionnaire (for example, checking for ambiguity, the time needed and ensuring proper translation from English to Indonesian). It helped to ensure that the questionnaire was understandable, reasonable and contextually appropriate (Thabane et al., 2010 and van Teijlingen and Hundley, 2001) so that the full survey was more effective and accurate information could be obtained.

A face-to-face questionnaire was administered to the groups of traditional fishers, green mussel farmers and informal sector workers in Muara Angke and Cilincing villages in February, March and April 2015. These two villages are in the district of Penjaringan and Cilincing respectively (details on the districts' characteristics and samples sizes are shown in Table 3.3). Although there were no specific demographic data on the numbers and types of informal workers, this research involved fishery-related workers (mussel peelers, vessel mechanics, and fish trade and processing workers who worked inland) and non-fishery-related workers (motorcycle-taxi drivers, small shop owners, street- food stall owners) who lived in the villages. The face-to-face survey procedure was necessary because illiteracy in these communities was relatively high. The sample size for traditional fishers and mussel farmers was 101 and 90 respectively, that is, almost 10% and 40% of the two target populations respectively (Table 3.3). Data saturation was used to define the cut-off point of the number of respondents (Fusch and Ness, 2015). The sample size was considered to be adequate to address the research questions and the increasing number of respondents at the cutoffs did not result in gathering new information.

The questions gathered responses across several major themes that included information on livelihoods (related to livelihood capital and other household or general

59 information), their occupational activities (questions on this theme were adjusted according to the type occupation) and on respondents' perception and knowledge of water pollution and other environmental issues. The questionnaires (in Indonesian and translated into English) are in Appendix 4.

Table 3.3 Characteristics of the districts in which the fishing villages were studied and sample sizes of this research

Notes: TF: Traditional fishers; MF: Mussel farmers; IW: Informal sector workers; NA: data not available (*BPS, 2014b; **BPS, 2013)

In addition to the questionnaire survey, two group discussions involving 22 community members were held for two occupational groups (traditional fishers and mussel farmers). It was expected that this would produce different views and experiences that were rooted in the nature of their occupation or activities. Participants who joined the discussions had completed the household-based questionnaire survey and volunteered to explore further the issues and to continue their participation in the research.

The group discussions were a part of the participatory rural appraisal (PRA) activities applied in this research. The PRA method provides a platform to take a bottom-up approach for engaging with communities. It allows communities to generate ideas, share insights and knowledge, which become valuable sources of information that are otherwise unavailable outside the communities (Chambers, 1994 and Newing et al., 2011). Various techniques are applied in PRA and researchers adjust pragmatically the relevant techniques to match the characteristics of the respondents and to achieve their research objectives (Cavestro, 2003). This research performed sequences of PRA exercises that included brainstorming, problem listing, creating seasonal calendars and timelines and participatory mapping (see Appendix 7). Some other informal activities, such as sharing meals, travelling in small boats to their workplaces and visiting households during floods, all provided additional opportunities for the community and

60 the researchers to understand the actual conditions and what was being said. Overall, these activities were to generate insights, ideas and to exchange knowledge of occupational activities, local history, perceptions of the water pollution and environmental changes, and adaptation strategies. Participatory mapping was considered to be one of the key activities during these group discussions. For this research, participatory mapping was a powerful tool to focus attention and to elicit local knowledge about resources, historical changes to specific or observed features in geographical framework (Puri, 2011). It was used to obtain information on past and current fishing activities and sites in Jakarta Bay, as well as discovering new, distinctive environmental insights, features and changes that were considered important by the participants.

During the fieldwork, there were interviews with specific stakeholders. Seven interviewees, that included experts from governments, universities, occupational organisations and NGOs, were enabled to contribute their valuable insights at semi- structured interviews. A set of an interview guide that outlined some specific open- ended questions, in a context suggested by Newing et al. (2011), allowed specific knowledge to be gained from these experts. This arrangement offered a more flexible style of dialogue and yet the structured process allowed interviewees to expound as they saw fit and interviewer to cover the research knowledge needed (Edwards and Holland, 2013). In this research, the interviewees were deliberately selected because of their knowledge and experience of fishing communities, water pollution and environmental issues in Jakarta Bay. These interviews were to provide more information about Jakarta Bay’s human-environment systems and prospects for sustainability. It also served as a cross-check on the other research methods and developed a pathway for sharing the results of the research progressively. The guidelines used in the group discussions and stakeholder interviews are in Appendix 4.

3.4.2.2. Data analysis: livelihood and local knowledge

Descriptive data analysis was performed on the quantitative and qualitative data (yielded from the questionnaire survey, group discussions and interviews) before further analysis was undertaken. For the questionnaire survey, there were 286 usable responses from a total of 294 surveys. Unusable samples resulted from withdrawal of consent by

61 several respondents for personal reasons (the most frequent reason given was that the person did not know about the issue) and these were omitted from further analysis. Missing values were analysed and affected a small portion of responses (less than 2%), except for one question on fisheries productivity (the percentage of missing values was 15%). To maintain the completeness of data, imputation was applied (Humphries, 2011). After careful consideration, following suggestions from Saunders (2006), data were imputed for missing values using the mode values with confidence that the numbers of missing data were acceptable and did not cause bias or influence the overall results. Limited time and resources prevented more advanced imputation methods being applied in this research and furthermore, as Saunders (2006) has suggested, more intensive methods will not result in significantly different analysis results provided there are small numbers of missing data.

Further analysis of quantitative data was performed at the occupational group level (aggregated from household level). The analysis (using Microsoft Excel and SPSS software) compared responses and examined the differences between occupational groups in terms of their livelihood characteristics as well as their perception and knowledge about water pollution and environmental changes. Non-parametric tests were used in the analysis because these required fewer assumptions about normality of their distribution and could deal with categorical and ordinal scale responses (Pallant, 2013), the characteristics of the data gathered in this research. Using the chi-square test of independence, questions with categorical responses were analysed for all three occupational groups. In addition to chi-square, the Mann-Whitney U test was applied for questions with scales responses (such as responses that used a Likert scale). These tests are widely used to compare responses between two (chi-square and Mann-Whitney U) or more (chi-square) different groups. In socio-environmental studies, many researchers applied these tests to seek differences on knowledge, views or experiences of particular topics between groups (occupations, ages or communities). For example, studies on coral reef and fisheries degradation in Mauritius and in the Caribbean that investigated the differences in perceptions about the conditions of fisheries and coral health between groups of fishers and divers to seek better management interventions (Bunce et al., 2008 and Johnson and Jackson, 2015) used this approach.

Some researchers argued that multiple comparison analysis or further post hoc tests were important when comparing more than two groups using the chi-square test (Sharpe, 2015). The main purpose of this multiple analysis is to avoid false positive errors where one rejects the null hypothesis where the significance result may occur by chance (adjustment of critical p-value is required in this case). However, in this research the chi-square test and inspections of the results were considered adequate for identifying the significant results and sources of differences between responses (Ludbrook, 2011) because of very small significance values and distinct proportions between categories and responses (p < 0.0001 for 95% confidence interval for all responses except one about water access that had a p-value of 0.033, which was still lower than critical p-value). It is also important to stress that although these statistical tests helped in identifying the distinct characteristics of the occupational groups, all the analysis results (included the non-significant ones) were taken into account and considered important in providing insights to the occupational group’s livelihood conditions and their local knowledge. Therefore, the results were analysed and included in guiding the discussion.

Qualitative data obtained from interviews, field notes and group discussions were analysed using coding or key-word categorisation methods in Excel. Each respondent was assigned an identity number (ID) and then transcriptions were made of the material provided (from audio recordings and written notes) on particular aspects or themes of research. Simple allocation of keywords identification or coding was done throughout the research (for details, see Figure 3.7). These methods were useful in identifying essential themes and patterns and where the results could test, complement or validate the results from quantitative data. The processes were performed in Indonesian and were translated into English once the analysis results were required for further analysis or reporting.

ID Transcription (Indonesian) Coding for themes using keywords

Figure 3.7 Example of qualitative data analysis processes

3.4.2.3. Measuring Livelihood Vulnerability Index

To generate a livelihood vulnerability index (LVI), several steps were taken: the calculation of indicators (for each element), standardisation of indicators and calculation of vulnerability element indices (this included calculation of livelihood capital index, sensitivity index and exposure index). The first quantification was performed on all the indicators of vulnerability elements (adaptive capacity and sensitivity; details of indicators and what were measured are shown in Table 3.2). Adaptive capacity consisted of five sub-elements of social, physical, financial, human, and natural capital where each was measured by several related indicators. Sensitivity was measured by a single indicator of fisheries dependency (Table 3.2). The responses of households (from the questionnaire survey) for each indicator were summarised (as a percentage) in the same occupation group. For instance, there were 41 (of 96) and 22 (of 90) heads of households from the traditional fishers group and mussel farmers group respectively who were active in organisations. These resulted in the percentage values of 43% and 24% respectively. These percentages were used as proxies to represent the values of the indicators at occupational group level and were subsequently used in the following calculations (standardisation and indices).

These percentages were standardised to obtain a value for each indicator that ranged between 0 and 1. This was performed using an equation that was adapted from the life expectancy index equation used in the Human Development Index calculation (UNDP, 2007):

( ) = (1) 𝑠𝑠−𝑠𝑠𝑚𝑚𝑚𝑚𝑚𝑚 𝑠𝑠𝑖𝑖 𝑂𝑂𝑂𝑂 𝑠𝑠𝑚𝑚𝑚𝑚𝑚𝑚−𝑠𝑠𝑚𝑚𝑚𝑚𝑚𝑚 where si(OG) is the standardised value for each indicator for occupational group, s is the total count of responses with regards to a particular indicator in percentage, and smin and smax are the minimum and maximum expected values of the responses count respectively. Equation (1) was applied for all indicators that had positive relation with the associated sub-elements. For example, participation of household level in the organisational activity had a positive relation with its associated sub-element (social capital) meaning it added to the value of the social capital. For indicators with this positive relation, a high count of responses contributed to a high standardised value, meaning that the indicators contributed more to the sub-element and vice versa. In this case, higher counts responses in the organisational activity contributed to a high degree of social capacity.

For the indicator with a negative relationship (in this case only fisheries decline as an indicator of natural capital), the inverse of equation (1) was used for the standardisation:

( ) = (2) 𝑠𝑠𝑚𝑚𝑎𝑎𝑎𝑎−𝑠𝑠 𝑠𝑠𝑖𝑖𝑖𝑖𝑖𝑖 𝑂𝑂𝑂𝑂 𝑠𝑠𝑚𝑚𝑚𝑚𝑚𝑚−𝑠𝑠𝑚𝑚𝑚𝑚𝑚𝑚 For the indicator with this negative relation, high counts of responses resulted in low standardised values and the indicator contributed less to the sub-element. For example, high responses on fisheries decline yielded lower standardised value, meaning that the decrease in fisheries reduced the natural capital. The count of the responses and standardisation values for all the indicators are shown in Appendix 5.

After this standardisation of the indicators, the index of each sub-element was calculated using the equal averaging technique:

( ) ( ) = 𝑛𝑛 (3) ∑𝑖𝑖=1 𝑠𝑠𝑖𝑖 𝑂𝑂𝑂𝑂 𝐸𝐸 𝑂𝑂𝑂𝑂 𝑛𝑛

65 where E(OG) is the index of a sub-element for an occupational group (social [SC], physical [PC], financial [FC], human [HC], natural capital [NC] indices), si(OG) represents the standardised values for indicators associated with the sub-element, and n is the total number of the indicators that made up the sub-element.

A composite index of livelihood capital index (LCI) for each occupational group was then estimated from the sub-element indices. This LCI was a quantification of the adaptive capacity element. Both of the sub-element indices and the LCI were calculated using the equal averaging method, meaning that all the indicators and sub-elements in the calculations were weighted equally.

These averaging steps and equal weightings could lead to bias by underestimating important elements that could dominate the shaping of occupational groups’ livelihood. However, because of time and resource constraints, the method was considered fit for the purpose and nature of this research. Consideration in future to check the weights applied to various elements is suggested with the subjects of the research and in consultation with people familiar with the local settings and socio-economic characteristics of the occupational groups. The LCI should always be viewed as an interim or provisional measure and aide to further discussions and analysis.

The LCI was calculated using this equation:

( ) ( ) ( ) ( ) ( ) ( ) = (4) 𝐸𝐸𝐸𝐸𝐸𝐸 𝑂𝑂𝑂𝑂 +𝐸𝐸𝐸𝐸𝐸𝐸 𝑂𝑂𝐺𝐺 +𝐸𝐸𝐸𝐸𝐸𝐸 𝑂𝑂𝑂𝑂 +𝐸𝐸𝐸𝐸𝐸𝐸 𝑂𝑂𝑂𝑂 +𝐸𝐸𝐸𝐸𝐸𝐸 𝑂𝑂𝑂𝑂 𝐿𝐿𝐿𝐿𝐿𝐿 𝑂𝑂𝑂𝑂 5 where LCI(OG) is the livelihood capital index for a specific occupational group and E(OG) represents the five different sub-elements of livelihood.

The sensitivity index was calculated in a more straightforward manner because it was developed based on one indicator only (the dependency on fisheries). Therefore, equation (3) was used in the calculation of the sensitivity index. Aggregation of sub- elements of livelihood into a composite index of LCI could result in the neglect of important details. Therefore, further analysis on the results was not only performed on the composite index, but also on the sub-element indices and the indicators. The details of the calculations of the sub-indices and the indices are shown in Appendix 5.

Finally, the livelihood vulnerability index for each occupational group (LVI(OG)) was calculated based on the livelihood capital index (LCI(OG)), sensitivity index (ESS(OG)), and the exposure index (EXP(OG)) using the following formula originally developed by Hahn (2009). Hahn’s work used the three elements of adaptive capacity (sub-elements included socio-demographic profile, livelihood strategies and social networks), sensitivity (defined by health, food and water), and exposure (defined by incidents of climate-related disasters) to compare district-level vulnerability index in Mozambique in the climate change context. This formula was derived from the extension of vulnerability definition as defined by the Intergovernmental Panel on Climate Change, a working definition which was also used in this research (as explained in Chapter 2).

( ) = ( ( ) ( )) × ( ) (5)

𝐿𝐿𝐿𝐿𝐿𝐿 𝑂𝑂𝑂𝑂 𝐸𝐸𝐸𝐸𝐸𝐸 𝑂𝑂𝑂𝑂 − 𝐿𝐿𝐿𝐿𝐿𝐿 𝑂𝑂𝑂𝑂 𝐸𝐸𝐸𝐸𝐸𝐸 𝑂𝑂𝑂𝑂 The calculation of this LVI was one approach in developing indices that involved aggregation of all relevant elements or proxies. The disadvantage of composing a single index of vulnerability is that it could eliminate or 'hide' details of the features that contribute significantly to the vulnerability itself (Rygel et. al., 2006). Despite the drawbacks, this index of vulnerability in this research was useful to compare the overall vulnerability among three different groups and to highlight promptly the elements that require most attention so that appropriate interventions could be considered and applied within the context of reducing livelihood vulnerability. In addition, subsidiary indices from the elements that shape the vulnerability were also created to allow the identification of vulnerability factors that could be different among groups. As shown in the following results and discussion, vulnerability and its elements indices that were produced are most useful for the insights produced rather than in the value of the indices. The aggregation was used to measure the extent of water pollution impacts on each occupational group and to investigate the features that contribute to their vulnerability. Greater insights may come from the deconstruction of these in the light of other approaches to researching the impact of water pollution on the traditional fishing communities of Jakarta Bay.

Chapter 4

Results: Water Pollution as a Stressor That Contributes to Shaping a Community’s Livelihood

The results of water quality analyses in this chapter include the cluster analysis of sites, maps of water pollution and exposure index. They provide essential information for understanding the problems of water quality in Jakarta Bay. These are followed by results of the questionnaire survey, the interviews, and the group discussions that provide valuable insights to the perceptions of occupational groups’(traditional fishers hereafter TF; mussel farmers MF; and informal workers IF) about water pollution and environmental changes. This combined knowledge of the biophysical analysis with the local knowledge complemented and enriched the understanding of water pollution and its impacts on the occupational groups’ livelihoods.

4.1. Water quality assessment of Jakarta Bay

Assessing the water quality of Jakarta Bay provided essential background information for this study. The results are the more formal perspective of government-managed science. This information helped in defining the exposure level to water pollution for the occupational groups and was later used in the livelihood vulnerability assessment.

4.1.1. Cluster analysis results of water quality

The cluster analysis was derived from a subset of water quality data (12 parameters) from 23 sampling sites in Jakarta Bay and are described in Chapter 3. Analysis was performed using a hierarchical cluster method and analysed at the five-cluster stage. The dendrogram (Figure 4.1) shows the sampling sites and the multidimensional (rescaled) distance as they were clustered (the agglomeration table that shows more details of the clustering processes and stages is in Appendix 6). In summary, the larger the distance between vertical lines illustrates greater dissimilarities between sampling points. For example, point C2 had the largest distance and was combined in the latter stage of clustering because of its dissimilarities relative to the other points.

Figure 4.1 Dendrogram of sampling sites in Jakarta Bay clustered by water quality parameters

The cluster formation was driven by the data and an inspection of Table 4.1 (median values of the water quality parameters) reveals the relative importance of each parameter in driving the group clustering (bold text in the Table 4.1). The first cluster consisted of five sampling sites A3, A4, A5, A6 and B3 that appeared to be driven by high concentrations of DO and low concentrations of other physical parameters (BOD, TSS, turbidity; see Table 4.1) and relatively low phosphate and detergent concentrations. The second cluster contained the sampling sites A1, A2, B2, B4, B5, B6, B7, C3, C4, C5 and D3 and seemed to be driven by relatively low concentrations of chemical variables such as nitrates and total ammonia. Two sampling sites A7 and B1 were grouped together in the third cluster. The grouping of these two points was apparently driven by the combination of high values of TSS and turbidity (physical parameters) as well as nitrates and hydrogen sulphate (chemical parameters). The fourth cluster that consisted of four sites C6, D4, D5 and D6, was apparently grouped by high values of BOD, total ammonia and phenols. The fifth group consisted of only one point,

C2, and this had a distinct characteristic of very low DO value combined with very high TSS and turbidity values, as well as relatively high values of nutrients (phosphates and total ammonia) and detergents.

Table 4.1 Median values of water quality parameters used in cluster analysis

Notes: All unit in mg/l except turbidity, which is in NTU The median values were calculated from the water quality data subset used in the cluster analysis (see Chapter 3); bold text shows the driving parameters (based on inspection) 4.1.2. Water pollution exposure map

Cluster analysis identified sampling points that could be reasonably grouped together in one cluster. However, it did not provide information on the variation of pollutant levels for the cluster groups. Further analysis, using Boolean multi-criteria analysis resulted in a calculation of a water pollution exposure index ranging from zero to one (0-1). The value of zero represented the lowest pollutant level. Exposure value indices for each cluster group are shown in Table 4.2. Based on the calculation, it was determined that sites in cluster 1 had the lowest exposure index (0.30), followed by cluster 2 (0.64), cluster 3 (0.75), cluster 4 (0.81), and cluster 5 with the highest (1.00).

Table 4.2 Exposure index of the five cluster groups

Cluster Sampling site(s) Exposure index

1 A3, A4, A5, A6, B3 0.30

2 A1, A2, B2, B4, B5, B6, B7, C3, C4, C5, D3 0.64

3 A7, B1 0.75

4 C6, D4, D5, D6 0.81

5 C2 1.00

Mapping water pollution exposure illustrates the spatial clustering of sample sites in their groups. Figure 4.2 shows five different pollutant zones based on sites clustered by water quality and characterised by the exposure index.

Citarum

Bekasi Tanjung Priok

Figure 4.2 Water pollution exposure map of Jakarta Bay

Cluster 1 contained sites located in the northernmost areas of the bay and showed the lowest water pollution exposure index (0.30). Cluster 2 covered most of the mid-shore areas of Jakarta Bay and had an exposure index of 0.64. Cluster 3 consisted of sites close to the river mouths and had a higher exposure index of 0.75 that indicated the influence from material from those rivers. This cluster zone was adjacent to the river mouths areas of Teluknaga in the northwest (B1) and Citarum in the northeast (A7). Cluster 4 was close to a port (Tanjung Priok) and to industrial areas (Marunda, Cakung and Bekasi) and it had the second highest exposure index with a value of 0.81. The highest exposure index (1.00) was cluster 5, which was in very close proximity to five river mouths (from west to east: Dadap, Kamal Muara, Cengkareng Drain, Adem and Angke rivers) and to the land reclamation areas (west to Adem River). The formation of clusters showed reasonable groupings of sampling sites that appeared to reflect the strong influence of land-based and coastal-based pollution inputs on water quality in Jakarta Bay. It suggested that the near-shore areas had relatively higher water pollution compared with the offshore areas.

4.1.3. The occupational groups’ differential exposure to water pollution

The interviews showed that the groups had different geographical ranges in performing their main occupation, which was confirmed by direct observations in the field. For the informal workers (IF), it was clear that their jobs did not require them to have direct contact with Jakarta Bays’ waters. The traditional fishers group (TF), who operated in the middle areas up to the northern parts of the Bay (the interviewees often mentioned Damar Island in reference to their maximum travel distance), said in the questionnaire that their average fishing distance was about 16 kilometers from the fishing ports (see Figure 4.3). Compared with the TF, the mussel farmers (MF) had a much more limited spatial range. Their mussel farms were bound to the shallow areas at an average distance of 5 kilometers from Muara Angke fishing port (Figure 4.3). This spatial information about where the TF and MF did their work was verified during community workshops and using Google Earth imagery.

Because of those differences, the occupational groups had different levels of exposure to water pollution. The water pollution index map was very useful in defining each occupational groups’ distinctive exposure to water pollution (Figure 4.3). Because the IF group did not perform their activities in the water, it was assumed that they had no exposure and were assigned a zero (0) value of exposure index. The TF group that had the greatest spatial range, were doing their fishing in the areas with the exposure index ranging from 0.40 to 0.75. Those areas included areas in the north with the lowest water pollution exposure and other areas with higher exposure in the northeast of the bay. Mussel platforms of the MF group were located in the areas with much higher exposure indexes that ranging from 0.66 – 1.00.

This information about the exposure index of the occupational groups (in relation to their occupational spatial range) was further used to assess their livelihood vulnerability to water pollution. This is explained in Chapter 5. The highest exposure index values for each occupational group were applied and so it may represent the worst case scenario of exposure to pollution. That means the MF group was considered to have the highest exposure to water pollution with the value of 1.00, followed by the TF group with an exposure index of 0.75 and the IF group with zero (0) exposure value. It is noteworthy that the exposure here is related to the occupational activities. Potential exposure to pollutants through other means (such as consumption) was not considered in the

73 calculation of vulnerability assessment but it is acknowledged and is dicsussed further in Chapter 6.

Figure 4.3 Map showing average distance to fishing grounds and green mussel farms in Jakarta Bay overlaid on water pollution exposure index map

4.2. Perceptions of water pollution and environmental changes

The process and result of overlaying the spatial information of the water pollution exposure index and local knowledge on the workspace of the occupational groups demonstrated the importance of knowledge integration. Therefore, the results of the occupational groups' perceptions and local knowledge about water pollution and environmental changes became the key to better understand how water pollution affects their livelihoods. These insights complemented the understanding of water pollution's impacts that have been predicted by biophysical science research and they provided more holistic perspectives on the groups’ vulnerability to water pollution.

4.2.1. Environmental issues in Jakarta Bay as defined by the occupational groups

Questionnaire responses showed that the members of the occupational groups were knowledgable and perceptive of environmental conditions in the Jakarta Bay and its coasts. This was shown, for instance, when the respondents were asked about the most

important environmental issues in Jakarta Bay and its adjacent coastal areas (Figure 4.4). The presence of waste and litter made up more than 60% of the most concerning environmental issues reported by the occupational groups (TF 22%, MF 25% and IF 15%; see Figure 4.4). Furthermore, the TF and MF respondents were more likely to mention waste and litter but more IF respondents answered 'don’t know' or more varied topic in their responses (27%) (Figure 4.4). These other responses included floods, clean water supply, air pollution and loss of natural environments. There appeared to be an association between type of occupation and perception on environmental matters (χ2 = 44.6, p < 0.05, n = 286).

Q: What would you say is the most important environmental issues in Jakarta Bay and coastal area today?

Figure 4.4 Most important environmental issues in Jakarta Bay and coastal area as stated by the occupational groups

The stronger stance on the problem of waste and litter observed in the responses of TF and MF groups might be related to their perceptions of the impacts of this issue on their occupation as described in the Section 4.2.2. Respondents’ understanding of 'waste' mainly referred to liquid waste that was perceived as dumped from factories, large vessels and the Muara Karang power plant. As for 'litter', this referred to solid waste that were easily found in the neighbourhoods of the communities (Figure 4.5 and Table 4.3). Some respondents reported that their local community was sometimes involved in various waste management programs, such as kerja bakti (neighbourhood cleaning activities), recycling and provision of garbage bins. These programs were often supported by local governments and non-government organisations (NGOs). However,

75 the volume of solid waste at the river mouths that came from upstream was overwhelming.

The traditional fishers described their frustration over this waste problem,

There were times when moving the boat out from the river mouth [Adem River in Muara Angke] seemed impossible because (solid) waste obstructed our way. During the fishing trips, our propellers were often broken (collided with drifting litter). (011)

We sometimes went fishing around Muara Karang and Pantai Mutiara. What we got from our nets was litter, such as plastic bags, not fish. (030)

We could see (solid) waste (along our way to and around) Damar Island [about 18 km north from Muara Angke port]. (144)

One time, we saw mattress, drawers and a wooden table (floating) in Adem River. (058)

Figure 4.5 A fisher removes solid waste that obstructs the boat's propeller (left) and a pile of litter along Cilincing River (right)

The term ‘water pollution’ was less often used than‘waste’ or ‘litter’. Only around 5% of TF and MF groups mentioned water pollution to be the most concerning issue (far fewer IF respondents mentioned water pollution; around 2%; see Figure 4.4). However, when they mentioned ‘waste’, this term was often used to describe their understanding and perception of water pollution, for instance, by subsequently emphasising how the

76 waste could cause fish kills and contribute to the decline in fisheries (see also Table 4.3 for more details on local language). As reported by the TF and MF respondents,

The waste (in Jakarta Bay) got worse since 2000…the company disposes of their waste during the wet season… (that was why) we had less fish. (262)

Many factories dumped their waste (into the rivers and Jakarta Bay)… If the waste came, the blue manna crab emerged (to the surface) to avoid the waste on the (sea) bottom. It (the waste) made the fish die. (283)

Coastal development was the second most frequently mentioned issue when respondents were asked about environmental issues (8% of respondents; see Figure 4.4). The term ‘development’, as used by respondents, referred to reclamation activities in Jakarta Bay and along the coasts. It was interesting that they mentioned ‘development’ and included it as one of the important environmental issues. The respondents (particularly from the TF and MF groups) often gave accounts of this development and the adverse impacts it has on their occupations and the environments. One fisher said,

Until 2009, we could find fish here very close, now we can't (find anymore) because of the developments [the respondent referred to Pantai Indah Kapuk (PIK), a residential area built on reclaimed land that is west of Muara Angke port]. (284)

Some respondents argued that the land reclamation contributed to the decline in fisheries by destroying fish habitats and obstructing fishing locations. The TF groups also complained about restrictions on fishing enacted in reclamation areas and the surrounding areas that were formerly their fishing locations.

Fish and mussels died (because) there was a lot of sand from the new island in (Pantai Indah) Kapuk. (282)

We lost our fishing locations...where we used to catch fish and blue manna crab... because of the developments (reclamations). (089)

Our fishing locations have changed (shifted) because of the developments (reclamation). Now we are prohibited from fishing in PIK [Pantai Indah Kapuk], it was good for mackerel. We could get into trouble if (we) get caught by the security guards (of PIK). (110)

Table 4.3 summarises several terms used by the occupational groups and highlights the gap in their understanding of environmental issues (local language is italicised).

Table 4.3 Occupational groups’ perceptions on the most important environmental issues

Terms used to describe Respondents’ Facts or what was observed in the environmental perceptions field issues limbah: a term used • 'waste' was • waste was one of the pollutants for liquid waste intertwined with that contributed to water ‘water pollution’ pollution • it referred to liquid • the cause of fish kills was waste, released from attributed to several factors, industrial plants, big including hypoxia (which was ships and power associated with harmful algal plants bloom (HAB)) and weak water • the waste was blamed circulation for the fish and • the cause of mussel kills needs mussel kills more research sampah: a term used • the term 'sampah' • litter as one type of pollutant that for solid waste intertwined with contributed to water pollution ‘water pollution • community behaviour observed • litter was carried by in the field, that is, dumping the rivers from household in the street and in upstream or inland rivers, insanitary sewage systems areas and it (latrines were emptied into the obstructed fishing rivers). activities In addition to litter from the rivers, these dumping activities contributed to water pollution but this seemed to be much less acknowledged by respondents pembangunan: a this term was used to coastal development resulted in term used to refere to describe environment adverse impacts that contribute to coastal development issue environmental issue

4.2.2. Water pollution as understood by the occupational groups

When asked specifically about water pollution, the TF and MF groups demonstrated strong and clear awareness and knowledge about water pollution compared with the IF group. For instance, when the respondents were asked whether they were aware of water pollution as an issue in Jakarta Bay (Figure 4.6), more than 95% of TF and MF groups reported that they are aware of water pollution compared with 61% of the IF group. There was a significant difference in the awareness of water pollution shown by the occupational groups (χ2 = 57.2, p < 0.05, n = 286). The proportions of members of

78 the TF and MF groups who answered ‘don’t know’ on that question were also much lower (around 3%) compared with the IF group (23%).

Q: Based on your knowledge and observation, is there any water pollution problem in the Jakarta Bay?

Figure 4.6 Occupational groups’ awareness on water pollution

Respondents who answered ‘yes’ for the question of water pollution awareness (n = 239) were then asked about what indicated water pollution to them. The respondents again demonstrated their knowledge and awareness of water pollution by describing physical or visible ‘parameters’ that, although different from formal science indicators, provided accessible and relevant parameters (Figure 4.7).

Almost half the respondents from the three occupational groups mentioned organisms- related indicators (that is, fish and mussel kills, vertical and offshore movement of marine organisms) as indicators of water pollution (Figure 4.7). The TF and MF groups were significantly more likely to associate water pollution with indicators that related to changes in organisms and physical water properties compared with the IF group (χ2 = 31.5, p < 0.05, n = 239). Members of the TF group reported that they found dead fish floating in the near-shore areas of Ancol, Gembong and Tanjung Priok during the period from February to March 2015. They mentioned that the fish kills, usually along the coast of Ancol and Marunda, were more frequent after the year of 2000.

After (the year) 2000, the waters got murkier, the smell got worse, there were dead fish and mussels. (233)

The waste came five or six times a year… (and when it happened) we found dead fish in Binaria Ancol along the way to Paljaya Gembong [eastern part of Jakarta Bay]. (004)

I remember I took a lot of dead fish when there was terrible waste pollution in 2012. We reported it to the local office, but there were many companies (that were suspected of the dumping), so we did not know where it came from [from which company], it usually happened in the wet season. (283)

The mussel farmers also reported mussel mortality as evidence that pollution was killing the green mussels. Dead green mussels were frequently spotted in the platforms, particularly after 2008. This was consistently reported as the main cause for the decline in mussel productivity,

For the last two months (March and April 2015), I always found dead mussels at least once a week (in the platform). (281)

The waste was not there (in Jakarta Bay), green mussels were abundant back then (end of 1980 and in the 1990s), now the mussels died because of the waste. (158)

Respondents from TF group also described how the organisms tended to move offshore to avoid water pollution,

The waste reduced the number of fish, (the waste caused) all the fish moved further (offshore), the fuel price was also increasing. (278)

If there is a waste, the fish will come to the surface or move further (offshore). (230)

Q: What do you observe in the Jakarta Bay that, in your opinion, indicates the water pollution problem?

Figure 4.7 Indicators of water pollution as perceived by the occupational groups

The second most frequently mentioned indicators were related to the changes in water properties that were physically observable, such as changes in the colour of the water (to red or black), oily water, increased water temperature, changes in odour and turbidity (TF 14%, MF 14%, and IF 8%; see Figure 4.7). Other indicators of water pollution, such as floating litter and dermatological problems were mentioned mostly by the IF group (10%) followed by TF (4%) and MF (2%).

Figure 4.8 A traditional fisher swims among the floating litter in Cilincing (left) and a bag of water and sediment from Jakarta Bay acquired by a mussel farmer (right)

There was a very frequently voiced opinion that industries were the main sources of water pollution and environmental degradation in Jakarta Bay. As previously emphasised in Table 4.3, some respondents argued that the industries along the coast of Jakarta Bay were responsible for dumping that caused the degradation of the bay.

Perceptions of the source of water pollution were significantly different between occupations (traditional fishing community and informal workers, where χ2 = 23.5, p < 0.05, n = 239). As seen in Figure 4.9, industrial waste was perceived to be the most significant source of pollution by almost 70% of respondents but it was mentioned more often by TF (27%) and MF (30%) than by IF (13%). This was also reflected in the quotes provided previously (in Section 4.2.1) in which TF and MF members' views about how the industrial waste or waste dumping from the industrial companies caused the fish kills.

The second most significant source of pollution identified by respondents was household waste (TF 8%, MF 3% and IF 5%), followed by other sources that included port and shipping activities, fishing and mariculture activities, rivers and nature.

Q: What would you say is the most significant source of pollution in Jakarta Bay and coastal area?

Figure 4.9 Occupational groups’ perception of water pollution sources

4.3. Portraying changes in the environments

The perceptions and knowledge of the occupational groups about changes in water quality and the environments of Jakarta Bay and its coasts provides insights to the changes in the environments that in turn have triggered changes in traditional fishery practices. The results shown here were elicited from groups participatory exercises, including brainstorming, timeline development and participatory mapping (Appendix 7).

4.3.1. Historical timeline: describing the changes in the environments and fishery activities

An historical timeline, as shown in Table 4.4, was derived from discussions and interviews with the TF and MF groups. The timeline synthesised the views of TF and MF groups that included information going back to the 1970s. These fishers and farmers described the significant changes to the landscape and the quality of the environments in Jakarta Bay and its coasts. The participants also described the dynamics of traditional fisheries in Jakarta Bay that related to the changes in fishing technology, fishing locations and types of main commodities.

Table 4.4 Historical timeline

Year Timeline synthesis 1970 • Industrial activities started at the beginning of the 1970s. Among the first established companies was the Asahi Glass industry in Ancol • The establishment of a local auction office in Muara Angke (TPI) in 1977 • Adem River was the main source of clean waters for traditional fishers and local communities in Muara Angke • Traditional fishers used sailing boats instead of boats with engine • Fishing areas were near-shore, the farthest was about four kilometers from fishing ports

1980 Development of Muara Karang areas (once swamps, brackish ponds and mangroves) into a high-end residential area

1990 • The end of the peak period for shrimp and blue manna crab (peak period was from 1970 to 1990) • This was also marked as the year when wild harvesting of blood clams (Anadara granosa) or kerang darah declined sharply • The start of green mussel farming 1996 Relocation of communities that resided on the Adem riverbanks

2000 Industrial activities intensified in Ancol, Marunda and Cilincing [localisation of primary/integrated industrial areas to Marunda and Cilincing as stated in Jakarta Governor Decree No. 101 of 2000]

2003 • Relocation of communities that resided on the Adem riverbanks. Some were relocated to Empang Block (a block of reclaimed land on the east side of the Adem River) and some others to Buddha Suci flats located in the inland part of Muara Angke

Year Timeline synthesis • Massive fish kills in Ancol

2013- Green mussel platform relocated (reclamation started) 2014 2015 • Reclamation project was started [as a part of the National Capital Integrated Coastal Development or NCICD project that included a plan for giant sea wall] • The green mussel farmers reported the presence of a new mussel species with blackish colour that they started to harvest from their platforms in early 2015 Note: words between [] are additional notes from the literature

The fishers recalled 1980 as the new era of coastal development in Jakarta Bay. It was marked by the development of Muara Karang as a high-end residential area. Since then, more industries have been established along the coasts of Ancol, Tanjung Priok and Cilincing. The period from 1970 to 1990 was considered to have been the peak period for shrimp and blue manna crab when fishers could harvest hundreds of kilograms per trip. During the fieldwork, fishers highlighted that shrimp productivity had declined significantly since 1990. As shown in Figure 4.10, after seven hours of sailing this fisher caught fewer than ten shrimps. Nowadays, fishers reported that they could only catch, on average two to four kilograms of shrimp. The decrease in blood clams harvests since the 1990s was followed by the starting of green mussel farming.

Figure 4.10 A traditional fisher with his shrimps (left) and blue manna crabs (right)

Participants in the timeline exercise also mentioned the dwelling relocations that took place for the first time in 1996. Relocations of fishers' and mussel farmers’ dwellings have occurred several times since then. These relocations were attempts by the local

84 government to manage the problems of informal settlements along the Adem riverbank. In 2003, the government built the Buddha Suci flats and some other flats to solve the problem of these informal settlements. Some fishers and farmers were relocated to the flats but many eventually came back to rebuild their dwellings near the river. Their main reason for doing this was that they were not able to pay the rent and utilities fees (for clean water, electricity and cleaning). Some fishers argued that the flats were too far from the river. which made getting to their boats difficult. Concern about the security of the boats and fishing gear was also raised as another issue.

More recent changes that have caused concern for these occupational groups related to land reclamation activities. Since the reclamation started in 2013, hundreds of mussel platforms have been relocated or removed. The groups reported how these continuing changes in Jakarta Bay landscapes had directly affected their fishing activities. In addition to changing fishing and farming locations, increased sedimentation and destruction of natural habitats were some of the other changes mentioned in relation to the land reclamations. Section 4.3.2 shows the experiences and perceptions of the TF and MF groups of the changes in the environments and their fishing activities. Furthermore, this local knowledge provides new insights to how the impacts of water pollution have different consequences for different groups, which, in turn, lead to the use of various coping strategies in relation to their fishing activities.

4.3.2. Portraying the changes: traditional fishers group

Through participatory mapping, the TF group were able to share the changes they had experienced in terms of their fishing locations. During the period from 1970 to 2000, the near-shore areas of Muara Angke, Ancol and Marunda were the main fishing areas, those marked by the red shading in Figure 4.11. The fishers often used Bidadari Island as a reference when they spoke of the farthest points for their fishing activities in that period. Currently, the TF group considers this red area to be a ‘degraded zone’ where they can rarely catch fish. The red zone shows the near-shore areas most influenced by inland and coastal-based activity. Significant changes to the natural habitats along the coast of Jakarta contributed to the degradation as well. For example, massive destruction of the mangrove ecosystem for the development of PIK and Muara Karang residential areas took place during the period from 1980 to 1990. Current land reclamation projects were said to be the cause of further destruction of fish habitats and

85 obstruction to fishing locations. Intensified shipping around the port of Tanjung Priok, tourism activities in Ancol, as well as industries in Cilincing and Marunda have added to the pressures on the environments of these near-shore areas. As seen on Figure 4.11, this degraded zone, demarcated by the TF, coincides closely with the areas marked as highly polluted zones (cluster 4 and 5 with exposure indices of 0.81 and 1.00 respectively).

Figure 4.11 A map showing the location of past and current fishing areas and their exposure index

After the year of 2000, the TF reported that their main fishing areas shifted offshore to areas around the Bidadari Islands, Damar Island and Karawang (the blue shaded areas in Figure 4.11). A fisher said:

(Now) many things happened (reclamation) so we have to go farther (fishing). I used to fish in Ancol (in 1995), that was the furthest I would go…now I have to go to Karawang (northeast of the bay). (165)

According to the cluster analysis, these new areas (blue shading; see Figure 4.11) were less polluted than the red-shaded area, particularly the area around Damar Island (exposure index 0.40).

4.3.3. Portraying the changes: mussel farmers group

At the community workshop, a group of mussel farmers drew an important illustration of a bamboo platform to show the researchers the productivity changes in the green mussel farming (see Figure 4.12). They explained that the peak period for green mussels was from 1990 to 2000 when a platform could yield ten tonnes per harvest. During this period, a five-meter bamboo platform leg (five meters being the distance from the surface of the water to the sea floor) that was in the water was usually covered from top to bottom with green mussels (as shown in the green box in Figure 4.12).

Figure 4.12 The surface view of green mussel platforms at Bidadari Islands (above) and an illustration of the platforms drawn by the green mussel farmers (below)

After 2008, conditions gradually changed. The farmers reported that their harvests halved because they could only find (on average) healthy, living mussels on the top two

87 meters of the bamboo poles (as shown in the red box on Figure 4.12). Mussels could no longer be found along the lower sections of the bamboo poles. If there were mussels attached to the lower sections, too often these mussels were dead (indicated by opened or emptied shells; see Figure 4.13). On a recent short trip (in May 2016) to Muara Angke with mussel farmers, the farmers reported the presence of blackish mussels attached to their platforms. This unidentified mussel was first found in the area at the beginning of 2015 and became a new commodity (Figure 4.13). Some mussel farmers complained about these changes to their farming activities,

In 1990, green mussels (the harvest) were good, the whole platform was full (of green mussels). Now, if I have seven-meter bamboo, only the top three meters are full with it (green mussels), the rest, four meters (to the bottom) is empty. In 2012, from 25 platforms (that we used) we could only harvest half of it (dead mussels or empty shells were found). (235)

The green mussels were often dead or very thin. We moved to new locations, more to the west in Dadap and Tangerang to avoid pollution. If the waste comes, for instance in the ninth or tenth months [September or October], we only get ten buckets (of mussels) [equal to 100 kilograms]. (015)

Figure 4.13 Dead green mussels (left) and the new blackish mussels (right)

Like the TF group, the mussel farmers emphasised how reclamation had affected their farming locations. New islands (D & C islands, see Figure 4.14), which were built adjacent to PIK, forced them to relocate their platforms. The development and operation of the cargo port in Marunda (started in 2015) also caused the end of mussel farming in the Cilincing area. During the period from 1990 to 2013, mussel farming took place closer to the shore (ranging from 500 to 5000 meters) in front of the PIK, Dadap, Muara

Angke and Marunda. Later, the farming area moved north and became concentrated around the Bidadari Islands. As seen in Figure 4.14, the mussel platforms were in areas with the highest index of water pollution.

Occasionally, some of the fishers and farmers picked green mussels that grew naturally on the surfaces of hard coastal structures along the coast of Jakarta Bay, such as in Ancol, Muara Karang (a power plant) or Tanjung Priok (see Figure 4.14). According to the farmers, these areas were prohibited to wild mussel pickers after 2008 (this assertion was confirmed during the group discussion). Nowadays, many of these wild pickers harvest green mussels from the hulls of big ships (they dive and pick the mussels from the hulls) that are waiting to moor in Jakarta (at Tanjung Priok, Nizam Zahman or Marunda).

Note: The number of points (marks) on the map are indicative and do not show exactly the position of any one mussel platform. Participants marked the points as an estimation of the platform positions Figure 4.14 Past and current green mussel farming areas, overlayed with the exposure index and reclamation development

4.4. Occupational groups’ perspectives on the impacts of water pollution and environmental changes

The different occupational groups’ perceptions of the implications of changes in water quality was explored further with several questions about the consequences of water pollution on several key aspects, such as their occupation, their health and on marine organisms (see Figure 4.15). The TF and MF groups expressed relatively strong views and agreed with the statement that water pollution had been affecting their occupations (97% and 100%, respectively, of respondents answered that they strongly agree or agree). A Mann-Whitney U test revealed no significant difference in the degree of agreement between the two traditional fishing occupations of the impacts of pollution on their occupations (TF [Md = 5, n = 92] and MF [Md = 5, n = 86], U = 3607, z = -1.4, p = 0.16). The impacts of water pollution were described by the respondents in terms of how it contributed to reduced productivity and forced the relocation of their fishing activities (explained in Section 4.3). The occupational groups provided clear insights into the consequences of pollution,

We got much fewer eel-tailed catfish (ikan sembilang) [this type of fish usually lives in brackish habitats], because the water was not good (anymore), because of the waste. (169)

(The effect of) waste (on fish) could be observed as far as two to three miles (three to five kilometers) from the shore. This has been affecting traditional fishers because we (could only) fishing nearby…and it is hard for us to get an inland job [non-fishery jobs] because we don’t have any experience. (271)

In contrast, only 33% of IF respondents either strongly agreed or agreed with the statement that water pollution has impacts on their occupation. The Mann-Whitney U test showed that the IF (Md = 3, n = 61) had a significantly different perception of the impacts on their occupation compared with the TF (Md = 5, n = 92, U = 705, z = -8.7, p < 0.05) and the MF (Md = 5, n = 86, U = 725, z = -8.0, p < 0.05). Some IF respondents who agreed with this statement explained how water pollution could indirectly affect their occupations because they had jobs around the fishing villages (they provided services, such as selling food and water, for the fishers and mussel farmers). They argued that water pollution contributed to the decline in fisheries. Furthermore, this decline affected the fishers and mussel farmers’ incomes that, in turn, led to decreased

90 demand for the services of members of the IF group. Some other IF respondents, who worked in mussel or fish processing workplaces, complained about the decline in catches, which threatened their incomes.

Q: Do you agree or disagree with the following statements: Water pollution in Jakarta Bay has impact on occupation/health/organisms.

n = 92 n = 86 n = 61

Figure 4.15 Occupational groups’ perceptions on the impacts of water pollution

The traditional fishing groups (TF and MF) were more likely to agree that water pollution affected marine organisms (Figure 4.15). More than 95% of the TF and MF groups either strongly agreed or agreed with the statement that water pollution affected organisms in Jakarta Bay compared with only 65% of the IF group. The Mann-Whitney U test showed a significant difference on this perception between TF (Md = 5, n = 92) and IF (Md = 4, n = 61), U = 8344, z = -5.5, p < 0.05 and between MF (Md = 5, n = 86) and IF (Md = 4, n = 61), U = 7471, z = -4.9, p < 0.05. There was no significant difference between stated perceptions of the TF and MF, U = 8417, z = -0.7, p = 0.48 for this question.

The proportion of IF respondents who answered 'strongly agree' and 'agree' to this statement was relatively higher (65%) than the statements about the impacts of water pollution on occupations (only 33%). This probably related to their being able to observe the fish kills that were often seen on the coasts. For TF and MF groups, their

91 opinion about the impacts of water pollution on marine organisms is more immediate, informed by direct experience of changes that affect their fishing locations as well as the quantity and quality of their catches. For instance, a fisher who used bottom traps to catch blue manna crab described a specific day,

On the 3rd of April 2015, there was waste, because I couldn’t get any crabs. If the crabs are trapped and there is waste, I could not sell the crabs, no one will buy them because the smell is bad. I prefer to just throw them away. (290)

Another fisher added,

The fish that we caught now was different because sometimes they smelled really bad and could not stay fresh for long, we only kept it for one day and it already got rotten. (008)

Surprisingly, responses to the statement about water pollution's impacts on their health were more varied (Figure 4.15). The MF respondents were more likely to state that water pollution affected human health (MF 56%, TF 40%, IF 48%). Results from the Mann-Whitney U test revealed a significant difference between the statements of MF (Md = 4, n = 86) and TF (Md = 3, n = 92), U = 7270, z = -2.9, p = 0.04. However, there was no significant difference between the traditional fishing community (TF and MF) and the IF on this statement of health impacts (Md = 3, n = 61) (U = 6690, z = -1.5, p = 0.13; U = 6636, z = -1.1, p = 0.27 respectively). This strong stance expressed by the MF was probably associated with the nature of their occupation that required them to regularly dive into the water. These farmers dived to a maximum depth of seven meters using self-assembled equipment (consisting of snorkels, goggles, head masks, basket nets and oxygen compressors). They reported some skin irritation and other illnesses that they linked to prolonged direct contact with polluted waters (Figure 4.16, left picture; this will be discussed in more detail in Chapter 6).

Water pollution in Jakarta Bay causes health risks. I got itchy (after swimming) and headache from accidentally swallowing the water. (015)

(Water pollution) causes illness, I had a headache, nausea and vomit one time (after swimming). (019)

Figure 4.16 Irritated skin on the arm of a mussel farmer that he attributed to water pollution (left) and grilled fish prepared by fishers from their catch (right)

More than 60% of TF group disagreed, strongly disagreed, or thought pollution had no effect on human health. This was the largest proportion of disagreement with the statement among all occupational groups (44% and 52% for the MF and IF, respectively). As one traditional fisher asserted,

When we found dead shrimps, crabs or fish, sometimes we took and eat it. It was fine (safe to eat). (029)

A mussel farmer also asserted,

I don’t think the pollution affects us (our health), like mercury, no problem. They, us, have been eating (green mussels) for years but we were okay (healthy). (256)

It was possible that the mechanisms of how pollutant contamination could pose health risks for human were less understood or considered less important by the respondents due to lack of alternative. This will be discussed in more detail in Chapter 6.

The diagram in Figure 4.17 helps to connect and illustrate the understanding of the occupational groups about water pollution impacts. That the possible effects on health of polluted water were much less understood or treated as less important by the TF and MF groups is shown in the red box (dotted lines) in the diagram. Low awareness of health risks was of concern. Questionnaire survey results showed that more than 95% of respondents from the TF and MF groups consumed some of their catch (fish or mussels; see the photograph in Figure 4.16). Green mussels were still available in the market despite the banning order issued in 2010 by the local government. This highlighted

93 other potential forms of pollutant exposure for the occupational groups in addition to the occupational exposure (exposure due to the geographical nature of occupation as explained in Section 4.1.3).

Figure 4.17 Summary of the occupational groups responses on the impacts of water pollution

Figure 4.17 helps to summarise what the TF and MF groups demonstrated: an excellent understanding of water pollution relating to its effects on environments and occupations (as shown in the right side of the diagram). This knowledge and understanding was developed directly from their experiences as traditional fishers and mussel farmers. The impacts of water pollution, in terms of the changes in quality and quantity of catches, as well as shifting fishing locations, were all considered more tangible impacts (compared with effects on human health) and more observable from their perspective.

They further linked the implications of those changes to increased uncertainties in terms of input in fishing activities (such as resources, and the effort required to reach the offshore fishing grounds) and the output of their activities (such as income).

The boats and nets (must be) improved, because now we have to go far for fishing. (191)

Back then (1985), we only needed to spread the net (tebar jaring) ten times and we could get more than enough (fish). Now, even if we do it 25 times, we could not get enough (fish). (053) 94

When there was waste, there would be less or no fish, so I earned less too. (284)

The TF and MF groups were also asked in an open-ended question for their views about other factors that contributed to the decline in fisheries (Figure 4.18). Their responses varied, which showed that they have a high awareness and understanding of the complexity of environmental challenges in Jakarta Bay and its coastal area. There seemed to be an association between type of occupation and perception on the factors of fisheries decline (χ2 = 18.1, p < 0.05, n = 186). Water pollution was considered to be the most important factor that contributed to the decline in fisheries and was more likely to be mentioned by the MF (58%) than by the TF (41%).

Figure 4.18 Factors that cause decline in fisheries mentioned by traditional fishing groups

Other important factors highlighted by the respondents were to do with regulations, such as those needed to prevent overfishing (they commented that no licence was required for commercial fishing in Jakarta Bay) and the continued practice of fishing using bottom- trawling gear that was prohibited by the government (Permen KP No. 2 of 2015). These regulatory factors were mentioned mostly by the TF (13% of TF compared with only 2% of MF). The TF respondents complained that overfishing occurred because there

95 were too many fishers in Jakarta Bay. The bottom trawl nets, known locally as pukat harimau were also mentioned by the TF group as a factor that contributed to fisheries decline. Bottom trawls are boats with fishing gear set up to trawl the ocean floor (it uses weights that drag the net accross the sea bed) and therefore damage important fish habitats, such as coral reefs.

Both groups frequently (25% of respondents from both groups) mentioned uncertainty and natural factors, including bad weather and variable harvest seasons, as causes of the decline in fisheries. During particular periods of the monsoon season, especially the west monsoon (December to January), heavy rains and high waves often hamper fishing. The MF group reported that damage to the mussel platforms caused by high waves and strong winds during the season had reduced production. Technical matters (such as limitations of boat capacity and fishing gear) and land reclamation were mentioned by 6% of respondents from both groups. However, these technical issues, along with the uncertainty, could be considered factors that are more related to the number of catches at a time (per effort) rather than a condition that could affect the number of fish or fisheries productivity in the long term.

Chapter 5

Results: Vulnerability of Resource-based Communities to Water Pollution

This chapter is a detailed account of empirical results concerning the characteristics of the occupational groups based on the livelihood capital approach that leads to a more comprehensive understanding of the factors that shape the groups’ vulnerability to water pollution. This chapter also reports the narratives of adaptation strategies used by the occupational groups as they cope with increased uncertainty caused by water pollution and environmental changes.

5.1. Livelihood characteristics of the occupational groups

5.1.1. Physical capital: infrastructure and public services

The four indicators used to describe physical capital are tenure of dwellings, access to health services, access to clean water and health insurance. Having legal tenure to a dwelling was an important issue for members of these communities. The fishing villages of Muara Angke and Cilincing were informal settlements. The dwellings in these informal settlements were built on the edge of the bay and in watershed areas (which were areas where housing was prohibited) and made of bamboo or wooden planks. Most of the houses had no access to clean water and no proper sanitation (see Figure 5.1).

Figure 5.1 A wooden-plank house alongside the Kali Adem river in Muara Angke 97

Despite the lack of these facilities, the traditional fishers (TF) and mussel farmers (MF) groups contended that having their houses near the river was essential so that they could have easier access to their boats and, more important, could ensure the security of their boats and fishing gear.

Responses showed that 75% and 68% of TF and MF groups respectively did not have legal tenure of the informal houses they lived in, compared with a lower percentage of informal workers (IF) (61%, see Figure 5.2). Yet, there was no significant difference observed between these occupational groups regarding their legal entitlement (χ2 = 9.4, p = 0.052, n = 286). Only 18% and 20% of TF and MF respectively rented houses legally and fewer had private ownership and had legal tenure to their houses (7% and 12% respectively). A higher proportion of the IF group (33%) lived in rented houses and a very small percentage (6%) owned their houses.

n=286

Figure 5.2 Type of dwelling for each occupational group

Access to a health service, one of the physical capital indicators, was measured by asking respondents for their estimate of the time required to reach the nearest health facility (see Figure 5.3). More than 90% of respondents from all three occupational groups lived close to a health service with a travel time of less than 30 minutes. As observed in the field, there were health clinics near these communities’ living areas but a small number of MF (2%) had to travel longer, more than 30 minutes, to reach a health facility. 4% and 6% of the TF and IF groups respectively did not know where the facilities were.

n=286

Figure 5.3 Travel time to nearest health facility for each occupational group

During this research, it was found that the number of community members living a short distance from a health facility had no correspondence with the proportion of people who had health insurance. More than 50% of the respondents, from all occupational groups, were not registered for health insurance (see Figure 5.4). The IF in particular showed the lowest percentage (27%) compared with the other two groups, TF and MF (38% and 41% respectively). Yet, there appeared to be no association between the type of occupation and access to health insurance (χ2 = 4.6, p = 0.102, n = 286).

n=286

Figure 5.4 Ownership of health insurance for each occupational group

Access to clean water for the three groups was measured as the percentage of households with direct access to clean water in their dwellings. Figure 5.5 shows similar low percentages for access to clean water in the TF and IF dwellings (25% and 23% respectively). However, households of the MF group had slightly higher proportions

99 than the other groups (39% had direct access to clean water). A chi-square test indicated that there was an association between the type of occupation and access to clean water (χ2 = 6.8, p = 0.033, n = 286). Most (more than 70% of the TF and IF households, and more than 60% of the IF households) did not have direct access and had to purchase clean water (for household purposes) from water sellers (tukang air keliling).

n=286

Figure 5.5 Access to clean water for each occupational group

5.1.2. Social capital: utilising networks

Social capital was represented by two indicators: organisation membership and access to information about water pollution. Of the TF group, 42% of the respondents were members of at least one organisation. Compared with the other two groups, that proportion was higher (24% for MF and 12% for IF, see Figure 5.6). A chi-square test indicated that there was a significant association between type of occupation and membership of an organisation (χ2 = 24.1, p < 0.05, n = 286). These traditional fishers were mostly members of KUB (Koperasi Usaha Bersama), an occupational organisation for fishers that was inaugurated by the government. These KUBs comprise 12-20 fishers, and each is headed by one chief. The KUBs have important roles in educating and supporting their members with various activities, such as fisheries-related training and help. In contrast, the mussel farmers did not have an equivalent advantage in terms of government support for a similar organisation or group because green mussel mariculture had been banned. However, as observed in the field, the mussel farmers recognised the potential benefits from such social networking and interaction. This encouraged them to establish informal organisations (kelompok), which were usually headed by mussel platform owners.

100

n=286

Figure 5.6 Organisation membership for each occupational group

Access to information about water pollution indicated whether people received reliable or prompt communication about risk from an authority or formal institution (for example, governments, research or academic institutions). However, most respondents (more than 65% for the TF and more than 75% for the MF and IF) had never received any information about water pollution in Jakarta Bay (see Figure 5.7). A small percentage of the respondents mentioned that their households had received notification of matters related to water pollution (polluted waters, contaminated seafood and waste management) from local governments and officials. No significant difference was observed between the type of occupation and level of access to pollution-related information (χ2 = 3.8, p = 0.15, n = 286).

n=286

Figure 5.7 Households that received information about water pollution for each occupational group

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5.1.3. Financial capital: diversification of livelihood sources

The three indicators of financial capital used in this research covered the diversity of financial safety nets used by the occupational groups. The indicators were work extension (additional family members working) and work expansion (work diversification or additional work) and credit access. Around half of the households in each occupational group had additional family members working, that is, the head of a household was not the sole income earner, as shown in Figure 5.8 (TF 56%, MF 47%, and IF 59%). There was no significant difference among different occupational groups on work extension (χ2 = 3.2, p = 0.21, n = 286). In many households, a spouse (usually the wife) and children already contributed to the income generation through various full-time or casual fishery-related jobs, such as mussel peeling and fish processing or non-fishery jobs, such as operating food stalls or small grocery stalls (warung) and scavenging.

n=286

Figure 5.8 Households with additional family members working for each occupational group

Work diversification, indicated by whether the heads of households have additional jobs to supplement their livelihoods, is shown in Figure 5.9. The TF and MF groups had a much higher percentage of households that had additional occupations (44% and 59% respectively) compared with the IF group (22%). There was a significant association between type of occupation and the tendency to have extra jobs (χ2 = 25.8, p < 0.05, n = 286), which were mostly fishery-related. More than 50% of the TF and MF groups had additional jobs, such as boat mechanic, mussel peeler, auction officer, fish or mussel

102 seller, fishing-net maker, brackish-pond farmer or fishing guide. The most common non-fishery additional occupation was seasonal farming in their hometown or village, particularly during the low fishing season. More than 90% of the respondents came from outside Jakarta (most commonly from Indramayu in West Java). For some of the TF and MF respondents, it was quite common to spend the low season in their home villages, either to do additional work or to take a break before the next fishing season.

n=286

Figure 5.9 Households with additional jobs for each occupational group

Access to credit was expeceted to be an important financial safety net in this community. Figure 5.10 shows that the proportion of TF and MF (41% and 33% respectively) that secured credit from middlemen (tengkulak) was higher than for the IF group (26%). However, a chi-square test indicated no significant association between the type of occupation and the sources of credit or loans (χ2 = 12.7, p = 0.12, n = 286). It is important, however, to underline the role of middlemen in the finances of the traditional fishing communities. These informal lenders can quickly provide cash for the TF and MF, but at high rates of interest. In many cases, these middlemen were the sole buyers for the fish and mussel products of the indebted fishers. They often set lower- than-market prices, which created an unfair trading system for the fishers. Family and friends were also an important source of soft loans by these occupational groups. Formal credit institutions, such as cooperatives and banks, on the other hand, were much less frequently mentioned by these groups (less than 1% of TF and IF had used this source of credit and only 1.3% of the MF used formal credit). The absence of

103 financial cooperatives and limited access to private and government banks were mentioned by respondents as being factors that hamper their access to credit.

Figure 5.10 Occupational groups' sources of credit

Furthermore, the purpose of the loans was investigated for all the households who accessed them (n = 213). There seemed to be an association between the type of occupation and the purpose of loans (χ2 = 10.9, p = 0.03, n = 213). Figure 5.11 shows that more than half of the TF and MF respondents were more likely to use credit to cover their occupational expenses (for example, for fishing trips and boat maintenance) compared with only 32% of the IF group. Most (54%) of the IF used their loans for daily needs (such as food) and 14% spent it for other needs (health and education). This credit pattern from a traditional fishing community (TF and MF) further highlighted their dependency on loans (particularly from middlemen) for maintaining their fishing activities.

104

Figure 5.11 Main purpose of credit

5.1.4. Human capital: formal and informal skills

Human capital was represented by three indicators: formal education, non-fishery skills and awareness of water pollution. Most of the respondents in all occupational groups had completed elementary-school (TF 58%, MF 62% and IF 58%) but less than 10% had graduated from senior high school (Figure 5.12). Low levels of education were linked to relatively high illiteracy and contributed to limited employment opportunities outside fisheries for these groups. There was no significant difference found between occupation and level of formal education (χ2 = 7, p = 0.32, n = 286).

n=286

Figure 5.12 Level of education for each occupational group

105

Having non-fishery skills were interpreted as increasing human capital and to be an advantage in highly uncertain and seasonal occupations, such as traditional fishers and mussel farmers. In times of hardship (for example, because of bad weather or during the fishing off season), households with non-fishery skills were able to cope by exploiting their other sources of income (this was also identified during group discussion). Limited education and few formal skills, however, hampered their efforts to work outside fisheries. Figure 5.13 shows that from 116 respondents who had additional jobs, most were still working in fishery-related jobs (57% for TF, 69% for MF and 50% for IF). A chi-square test revealed no association between type of occupation and non-fishery related skills (χ2 = 2.9, p = 0.24, n = 116).

Figure 5.13 Households with additional non-fishery jobs

Awareness and knowledge of water pollution and its impacts was assumed to be a form of human capital and was expected to vary between households, this being influenced by their living and occupation-related experiences. Results showed the TF and MF groups had similar knowledge and experience about water pollution that related strongly to the nature of their occupations, as previously shown in Chapter 4 (Section 4.3.2, Figure 4.5). The awareness of water pollution observed among the TF and MF groups was significantly greater compared with the IF (χ2 = 57.2, p < 0.05, n = 286). More than 95% of these people reported that they were aware of water pollution in Jakarta Bay, compared with 61% of the IF group.

106

5.1.5. Natural capital: land entitlement and fishery resources

A household's ownership of land for agricultural (land other than that used for a dwelling) and fishery resources were two indicators chosen to represent natural capital. Because more than 90% of the respondents came from outside Jakarta, entitlement to agricultural land was considered to be an important indicator of natural capital, particularly for the TF and MF groups, whose occupations were very much seasonal. In contrast to the very low percentage of respondents that had legal tenure of their dwelling (see Figure 5.2), the proportion of those with entitlement to land elsewhere was much higher in the three occupational groups. Figure 5.14 shows that around 40% of all the respondents had legal land entitlements in their hometown or village. These agricultural lands were highly important as sources of income and food in time of hardship (for example, in the fishing off season; see work diversification in Section 5.1.3). There seemed to be no association between the type of occupation and ownership of land (χ2 = 0.57, p = 0.75, n = 286).

n=286

Figure 5.14 Agricultural land entitlements (other than occupied dwellings) for each occupational group

The state of fishery resources was another important indicator of natural capital. When respondents from the traditional fishing community were asked whether there had been a decline in fishery productivity (compared with the time they first started their occupations), 60% of the TF and 71% of the MF agreed with the premise (no significant difference between groups: χ2 = 2.35, p = 0.13, n = 186; Figure 5.15). The other

107 respondents mentioned that they could catch or harvest the same amount but with much greater effort (no decline with increased effort). For the TF group, this meant going farther afield and taking a longer time. For the MF, this meant increasing the number of platforms they harvested from or finding wild mussels around the Jakarta Bay area. Although there were different views expressed by members of the TF and MF groups, their responses reflected that there was apparently a decline in quantity per unit effort that was difficult to verify because of the absence of data pertaining to past years' fish catches and related financial information.

n=186

Figure 5.15 Awareness about the decline in fishery productivity

5.2. The livelihood vulnerability to water pollution

The assessment of livelihood vulnerability to water pollution in this research was developed from the information about the occupational groups' livelihood capital and water pollution exposure (this latter part is discussed in Chapter 4). The associated indicators, described in the previous sections, represented different means of adaptive capacity (derived from the livelihood capital indicators), sensitivity (using dependency on fisheries as the indicator) and water pollution exposure. These are useful indicators to understand better the factors that shape the livelihood vulnerability of the occupational groups.

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5.2.1. Livelihood capital as resources for coping (Livelihood Capital Index)

The livelihood capital indicators were used as the proxies to characterise or represent each occupational group's capability to sustain their livelihood and to cope with the adverse effects of water pollution and other environmental changes. The indicators for each type of livelihood capital were quantified through the processes of standardisation and resulted in standardised indicator values (see Chapter 3; Section 3.3.2.3). This quantification was useful to summarise and compare the characteristics of occupational groups' livelihood (see Appendix 5 for details). The indicator values for each occupational group are shown in a web diagram (see Figure 5.16). The diagram shows the results of quantification and the summaries of livelihood capital indicators (on a scale of 0 to 1) for the occupational groups. These numbers are supported by narratives of the respondents, which provide a richer understanding that is needed to inform plausible actions (this is discussed in Chapter 6). The values close to zero indicate opportunities to improve livelihoods and reduce vulnerability. Values close to one indicate the strength of the occupational group in particular aspects of their livelihoods.

Figure 5.16 Standardised indicator values for the occupational groups

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The TF group had the highest scores for the two social capital indicators, which are organisation membership and information access (0.43 and 0.33 respectively) compared with the MF and IF groups.

The physical capital measures for all groups returned relatively low scores on dwelling ownership (TF 0.07, MF 0.12, IF 0.06) but very high scores for access to health services (TF 0.96. MF 0.98, IF 0.94). The TF and MF groups have higher scores for subscriptions to health insurance compared with the IF group (0.38, 0.41 and 0.27 respectively). For clean water access, the MF has the highest score of 0.39, compared with the other two groups (TF 0.25 and IF 0.23).

The scores for work expansion (heads of households with additional jobs), as a financial capital indicator, varied among the groups with MF and TF having much higher scores than the IF group (0.58 0.44 and 0.22 respectively). In contrast, the IF group had higher scores for the other two indicators of financial capital. The IF scores for work extension (additional family members working) and access to credit, from sources other than middlemen, were 0.59 and 0.74 respectively. The TF and MF groups' scores were 0.56 and 0.47 respectively for work extension and 0.59 and 0.67 respectively for credit access.

The scores for education level and non-fishery-based skills (human capital indicators) were very low for all groups (less than 0.1 and 0.2 respectively). However, the TF and MF groups have much higher scores on awareness of water pollution compared with the IF group (0.96, 0.94 and 0.61 respectively).

For the natural capital indicator of land ownership, the scores for TF and MF were relatively similar (0.45 and 0.44) and just slightly lower for the IF group (0.40). The score for fishery resources (derived from the inverse of the opinion that there is a decline in fishery occuring) had the average value (0.34) and was similar for all groups.

These indicator values for each type of capital were aggregated and averaged for each occupational group and the results are shown in Figure 5.17 (the capital index scale is from 0 to 1). Overall, all three occupational groups had relatively low sub-element indices (less than 0.50) for almost all types of livelihood capital, which indicates the importance of improving the availability and access to various forms of capital to reduce the groups' vulnerability to water pollution (this is explored in more detail in

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Chapter 6). Figure 5.17 also shows that the resource-dependent groups (TF and MF) had higher indices for all types of capital compared with the IF group. The TF group, in particular, had a much higher social capital index (0.38) compared with the MF (0.23) and the IF group (0.18). The TF also had the highest score on the human capital index (0.41), followed by the MF group (0.38), and the IF group (0.27). The MF group had the highest physical and financial capital indices (0.48 and 0.57 respectively), followed by the TF group (0.41 and 0.53) and by the IF group (0.38 and 0.52). Natural capital indices for all groups were only slightly different (TF and MF 0.39; IF 0.37).

Figure 5.17 Capital (social, physical, financial, human, and natural capital) indices of the occupational groups

The composite livelihood capital index (LCI) for each occupational group was aggregated and averaged from the capital or sub-element indices (see Figure 5.18). Overall, the LCI scores for all occupational groups were low; less than 0.5 (on a scale of 0 to 1). Surprisingly, the traditional fishing groups (TF and MF) showed higher LCI (0.44 and 0.41 respectively) compared with the IF (0.37) indicating these groups might be widely seen as the poorest of the poor, yet they may have greater capacity to cope with an external stressor, such as water pollution (this is discussed later in Chapter 6).

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Figure 5.18 Livelihood capital index (LCI) of the occupational groups

5.2.2. Dependency on fishery resources (Sensitivity Index)

Fishery resources in Jakarta Bay, including fish and green mussels, were highly important for the livelihoods of the traditional fishing communities as well as their neighbouring communities. As Figure 5.9 shows, around half of the TF and MF groups (56% and 41% respectively) were solely dependent on fishery resources. In addition, more than 50% of the TF and MF groups who performed additional work were still involved in fishery-related jobs. Therefore, it could be said that the dependency of this community on fishery resources was high and has contributed to an increase in their livelihood vulnerability to water pollution.

Figure 5.19 Sensitivity index of the occupational groups

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The scores of dependency, or sensitivity index, for each occupational group were derived from information on work expansion (see Figure 5.19). The TF group had the highest sensitivity index (0.56), followed by the MF group (0.42) and the IF group (0.33) (on a scale of 0 to 1).

5.2.3. Exposure to water pollution (Exposure Index)

As described in Chapter 4, the integration of water quality analysis with the participative approach led to a better understanding that the degree of water pollution exposure was different for each occupational group because of the inherent geographical condition of the occupations. Using the worst-case scenario, the MF group had the highest exposure score (1.00), followed by the TF (0.75), whereas a score of zero (0) was assigned to the IF group (on a scale of 0 to 1). Figure 5.20 shows the exposure index for each occupational group. The highest exposure index result for the MF group was related to the high level of water pollution in the near-shore areas where the group farmed mussels. The TF group had a lower exposure index compared with the MF because their fishing was over a wider geographical area. They preferred to go to areas offshore where the water pollution is lower. Because the IF group had little or no direct contact with Jakarta Bays’ waters in their occupational activities, a score of zero was assigned to the group. It is noteworthy that this exposure index was developed based only on the geographical nature of the occupation. However, the IF groups' exposure to pollution can also occur through, for example, consumption of contaminated seafood. This will be discussed in more detail in the Chapter 6.

Figure 5.20 Water pollution exposure index for each occupational group

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5.2.4. Livelihood Vulnerability Index

The livelihood vulnerability index to water pollution for each of the occupational group was calculated from the livelihood capital index (LCI), sensitivity index and exposure index. Figure 5.21 shows the livelihood vulnerability index that ranges accross a scale from 0 (minimum) to 1 (maximum). A very high level of exposure to water pollution contributed to the high livelihood vulnerability index (LVI) result for the MF group (0.62) as shown in Figure 5.21. The TF group had the LVI of 0.59 and IF, the least vulnerable group, had an LVI of 0.44. This LVI index, along with its elements' indices, is best used to spot quickly the sources of vulnerability and opportunities for development with regard to the groups' vulnerability to water pollution. The narratives supporting these indices, however, are also important and should certainly be taken into account to obtain a more holistic understanding of their vulnerability (this is discussed in more detail in Chapter 6).

Figure 5.21 Livelihood vulnerability index for each occupational group

5.3. Adaptation strategies: coping with changing environments

The adaptation strategies, which sustain their livelihoods in the face of the changing conditions of fisheries and the increased uncertainty because of water pollution and environmental changes, were used by the TF and MF households. The strategies included adaptation in fishing activities (geographical shifts and harvest time adjustments), the use of social networking, diversification of livelihood sources (work

114 extension and expansion) and occupational transformation. These reflect the adaptive use of livelihood capital and occupational characteristics that existed in each group.

5.3.1. Fishing behaviour: geographical shifts and harvest activities in traditional fishing communities

The TF and MF groups reported they had deliberately chosen to shift where they fished (see Figure 5.22). More than 40% of the TF group reported they had moved further offshore to catch a sufficient quantity of fish for sale in the market. A fisher described these changes since 1995,

Back then, we could get 300 kilograms of fish within a distance of two hours sailing from port [eight kilometers], now we can only get 10 to 20 kilograms and have to go further to get that much. (269)

Figure 5.22 Geographical shifts of fishing activities of the traditional fishing group

Before 2000, the average distance for these 'moving fishers' was two kilometers; the current average distance was 17 kilometers (see Figure 5.23). A fisher in Muara Angke clearly made the link to pollution that had forced changes in productivity and locations through time, saying,

We had to get away from the pollution ...(fishing) further offshore. We could get 100 kilograms even now... (but we have to go further) [in 1977 he fished only one kilometers away, now he has to go more than 20 kilometers to the north]. (119)

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Just over half of the TF group had not changed their fishing areas and had always fished in the offshore areas. They travelled an average distance of 17 kilometers or outside the zone they marked as the degraded fishing zone to secure their catch (see Chapter 4; Figure 4.11).

Figure 5.23 Map showing geographical shifts in fishing locations for traditional fishers (arcs show average distance)

The spatial pattern was slightly different for the MF group. More than 60% of MF respondents reported that they had not moved from their usual farming locations but 37% reported that they had to move ('moving farmers'; see Figure 5.24).

Figure 5.24 Geographical shifts of mussel farming for the mussel farmers group

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As described before (see Chapter 3; Section 3.1.2), the green mussels’ growth is limited by depth, types of substrates and water turbidity, and so these MF groups could only erect their mussel platforms in the near-shore areas of Jakarta Bay. Therefore, the possible geographical shift in this group (for ‘moving farmers’) was not as great as for the TF group. Previously, the average distance of their platforms from the port was four kilometers and their current average travel distance was six kilometers (see Figure 5.25).

The locations for mussel farming during the period from 1990 to 2008 stretched from the areas around Muara Angke port to Bidadari Islands. Current locations are centred on the areas of Bidadari Island, which are on the east side of Jakarta Bay (see Figure 5.25). The reason for this geographical shift was not only because of declining water quality but the land reclamation activities forced them to relocate their platforms (see also Chapter 4; Figure 4.14).

Bidadari Islands

Figure 5.25 Map showing geographical shifts in mussel farming locations (arcs represent average distance)

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For the MF group, another important adaptation to pollution was a change in their production system. The MF group had changed the time of their harvesting cycles to maintain and increase productivity. The main time for harvest and ‘seeding’ (here 'seeding' means erecting the platforms because green mussels grow naturally on the legs or poles of the platforms) was, before 2008, usually in April or October every year, that is, before the beginning and by the end of west monsoon season (see Figure 5.26). By this time, the green mussels had been growing for seven to eight months and grown to an optimum size, ranging from eight to ten centimeters. The MF reported that after 2008, significant change was needed in the harvesting cycle because the mussels rarely survived to their optimum harvesting age. The MF preferred harvesting the mussels early (after only three to four months) to avoid the risk of mussel kills caused by water pollution. As a consequence, the optimum market size and weight of the mussels for market was not reached and the farmers harvested smaller and lower value mussels.

5.3.2. Social network support

As well as using their knowledge and experience to adjust their occupational activities to cope with the effects of water pollution, the TF and MF groups make use of the available capital for additional support. One of the most important was support from

118 social networks. The forms of social network in these communities included involvement in organisations and informal support from families and neighbours. Despite low rates of membership of occupational groups in the local organisations (see Figure 5.6), these played important roles, particularly in supporting fishery activities and the livelihoods of the TF and MF groups. Compared with those two groups, the IF group members were involved in more varied organisations apart from those related to their occupations; organisations such as those to do with religion and politics. Sixty per cent of respondents reported that they benefited by joining such organisations, which gave them support on technical and professional aspects of their occupations (such as training that related to fisheries, provision of fishing boats, machinery and fishing gear). Less than 10% of respondents mentioned financial support, social relations, and opportunities to engage with local communities, as the advantages of joining the organisations and 8% mentioned they received no benefits from organisations.

Figure 5.27 Type of organisations joined by the occupational groups

Figure 5.27 shows that members of the TF and MF groups who were involved in organisational activities were all members of occupational organisations such as Koperasi Usaha Bersama (KUB). This KUB system was initiated by the Ministry of Marine and Fisheries and the North Jakarta government and it was intended to include all traditional fishers as members. It was reported that there were 54 KUBs in North Jakarta and each has 12 to 20 members. Although there were no official data available, the number of traditional fishers who were not affiliated with these formal organisations were significant. Fishers’ groups often set up informal organisations that provided

119 members with support in the way of loans, social interaction and technical support related to fishing activities. Members of the KUBs, however, had easier access to various formal technical and livelihood support services that are often provided by the government or aid organisations through the KUB network. As a chief of one of the KUBs said during the interview,

Our members have life insurance and savings from Bumiputera [a private bank, currently known as MNC Bank International], it was (because of) new rules from the North Jakarta Department of Fisheries (for members of KUB)…all 13 (number of members) of us have our own boats. (143)

Figure 5.28 A group of fishers hauls a broken boat ashore for repair (left) and a KUB sign at one of the base camps in Cilincing village (right)

5.3.3. Occupational expansion and extension

Occupational expansion and extension were essential adaptation strategies that were used to diversify income sources observed particularly for the TF and MF groups. Occupational expansion refers to jobs that were additional to their main occupations (as traditional fishers or mussel farmers) or were performed during the fishing off season. For example, as shown in Figure 5.26, the off season for mussel farming is during the west monsoon season (December to March) when high waves and bad weather hampers harvesting. The mussel kills, triggered by changes in water quality at this time, further affect the mussel farmers' productivity. Therefore, MF members said they took additional work (such as being a mechanic for boats and fishing gear, or as a driver of motor tricycles used for deliveries or for passengers). These other jobs were important sources of income that enabled them to cope better in the face of increased uncertainty in their usual occupations (see Figure 5.29). 120

Similarly, the activities of the TF group were affected by the monsoon seasons and were sensitive to changes in the environment (see Table 5.1). During the west monsoon (paceklik barat from January to March) this group had to rely on other employment because bad weather would prevent them fishing more than twice a week on average. Many members of TF and MF groups spend their time in their hometowns during this off season where they do some agricultural work in their own or in rented paddy or maize fields. Towards the end of the west monsoon season (April), the fishing season for the TF started when they could catch 15 to 20 kilograms fish on average for each trip. The fishers considered this quantity very small compared with their catches decades ago where they could catch hundreds of kilograms in each trip but now, to get more fish, they had to go further offshore.

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Table 5.1. Seasonal activities calendar of the traditional fishers group

Month Season Activities Notes 1 West monsoon • Fixing gear, equipment and • Going fishing two 2 (paceklik barat) boats (if necessary) times a week on 3 • Alternative jobs: average scavenger, agricultural jobs • High waves and in their hometown, boat wind, peak of wet mechanics, motor taxi or season motor tricycle drivers • Yield 5 kg of fish per trip on average 4 Transition/end of • Fishing • Harvest season 5 west monsoon (especially for 6 (peneduh barat) Indian mackerel, the 7 main commodity) • Mild waves, wind and weather • Yield 15 to 20 kg of fish per trip on average 8 East monsoon • Fishing • Non-harvest season 9 (musim timur) • Alternative jobs • Good weather 10 • Yield 5 kg of fish 11 per trip on average 12

Another source of income that crucially supports the livelihoods of the communities in Muara Angke and Cilincing villages is from those family members who work in the neighbourhoods, mostly in fishery-related occupations. Many women and children were involved in income-generating activities, such as mussel peeling and fish processing (to produce salted and dried fish). As seen in Figure 5.30, these supplementary enterprises might have as many as 35 people working in a mussel peeling workplace, particularly during the harvest season. A traditional fisher in an interview emphasised this growing reliance on these additional sources of income,

After (the year of) 2000, my wife had to work so we can keep up (with our life)…we could not depend only from my income as a (traditional) fisher (because fewer fish were being caught). (118)

Considering the highly seasonal nature of these extra jobs, however, some family members looked for opportunities to work outside the fishery sector. Because of limited education and few formal skills, most worked in the informal sectors, for instance, as

122 household help or as cleaners in the nearby middle to high class residential areas, as scavengers or as owners or workers at small shops or food stalls (warung).

Figure 5.30 Workers in a mussel processing (left) and at a fish processing workplaces (right)

5.3.4. Occupational transformation

Occupational transformation is an important adaptive strategy for the TF and MF groups and it involved leaving their main occupations and moving to another. However, it was observed in the field that most of the transformations were to work that was still related to fisheries. Many of the past traditional fishers and past mussel farmers were working for owners of large vessels, for example, as net repairers, cleaners, vessel mechanics and as night guards. Some chose other occupations outside fisheries that required minimal education, such as security guards, drivers, labourers and casual staffs for the boats that gathered garbage. These former fishers and mussel farmers argued that income from fishing and mussel farming was not enough to maintain their standard of living anymore. Some former fishers explained the reasons for changing their jobs,

When rains come, the waste will follow (polluting the bay), make the fish run away or die… I quit fishing in 2009 because there was no fish to be caught. (287)

I was a fisher (since 1975) but I quit three months ago because I did not get enough money (from fishing), the (bay) environment is not good enough (anymore). I used to have my own boat but decided to sell it. (165)

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This transformation reflected the attitude of these people toward the future of fisheries in Jakarta Bay. The pessimism of these ex-fishers or farmers was also affirmed by the TF and MF respondents who had children. When asked whether they wanted their children to work in the fisheries in Jakarta Bay, around 90% of these respondents said they would prefer their children to have work other than in fisheries. If water pollution and environmental degradation continue in Jakarta Bay, this occupational transformation is likely to be more important as adaptive strategies for the traditional fishers, mussel farmers and the members of their households.

Figure 5.31 Former traditional fishers and their new occupations: beside the garbage picker boat in Jakarta Bay (left) and net repairing for a large vessel (right)

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Chapter 6

Discussion

This chapter provides a synthesis of the main findings, which are discussed in the context and in the light of previous related work in order to elaborate the meanings of the findings and to highlight the contributions of this research and its limitations. The first part of this chapter shows how this research contributes in providing different perspectives on water pollution as a socio-environmental problem instead of merely a biophysical problem. Although a biophysical assessment of water is still important in defining its quality or condition (as is shown in this chapter), inclusion of vulnerable groups in the community to voice their knowledge and opinion through participative methods is equally important to better understand how these groups see the problem in their livelihoods, how they are adapting to and can be affected differently by water pollution. The vulnerability assessment used in this research allowed a comparison to be made of livelihood characteristics between the different groups. This enabled specific recommendations to be identified based on the needs of each group, so that management options to improve the resilience of each group can be formulated more effectively.

A two-pronged approach for management options, by taking into account the efforts to reduce the exposure to water pollution and build the capacity of each group at the same time, is considered to be the key in reducing their vulnerability to water pollution and achieving more sustainable livelihoods and to improve environmental conditions in Jakarta Bay. These findings are to provide insights and a source of information that can contribute not only to the development of solutions for Jakarta Bay's problems but also to other socio-environmental issues, particularly in the context of coastal megacities, environmental degradation and traditional fishing communities. For clarity and simplicity, the term 'traditional fishing community' is used in this chapter to refer to two different groups, that is, the traditional fishers and the green mussel farmers.

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6.1. Integrative approach to understanding the exposure to water pollution and its societal impacts

6.1.1. Biophysical assessment in the context of livelihood vulnerability

Water quality analyses have provided essential spatial information on the biophysical condition of Jakarta Bay’s water (Chapter 4; Section 4.1; Figure 4.2). These analyses have enabled the identification of the areas of interest that can be used as a baseline to equip managers with relevant information to manage water quality in Jakarta Bay.

An exposure index for Jakarta Bay was developed from the combination of twelve physical and chemical parameters to provide spatial insights to the water pollution problem, as defined by government standard. It is noteworthy that there are very few studies and little research regarding the development of a water quality index for Jakarta Bay (see Yuliana et al., 2012 and Sachoemar and Wahjono, 2007). They used biological parameters (living organisms) to develop their index, which is often considered to be more viable in economic and prevalence terms compared to physio-chemical measurements (Parmar et al., 2016). The Agency of Environmental Management (BPLHD) developed a diversity index (based on phytoplankton and benthos distribution) for Jakarta Bay based on the regular water quality monitoring (BPLHD, 2001-2013). Yet their analysis of physio-chemical parameters was still on an individual basis, even though a multi-parameter analysis can provide more comprehensive insights on the water quality and ecological conditions (Fan et al., 2017). The development of a physio-chemical water pollution index in this research is one of the important contributions of this research and it can be used to further explore the potential of the governments' water quality data and to complement the current bio-indicators index. The combination of those two approaches to indexing could be a powerful tool and provide a preliminary measure before further investigations are taken to define the driving parameters, to discover the sources of contamination and to contribute to the formulation of control and management.

The results from the analysis of water quality data suggest that regular and continuing water monitoring is required and that further management action is essential if control of the sources of pollutants is made an important goal. It is not the intention and scope of this research to investigate in detail the pollutant sources, yet the results identified several areas of interests where the extent of pollutants was considered high (in terms of

126 the government's standard). These are particularly in the near-shore areas where influence from river mouths is relatively high, in the areas close to coastal areas where port and industries are located (southeast of the bay), and around the reclamation areas (see Chapter 4; Figure 4.2). This research found that the near-shore areas in the southwest of Jakarta Bay (Muara Angke, Muara Baru and Sunda Kelapa) had the highest exposure index values, followed by the southeast areas (Marunda and Bekasi) (see Chapter 4, Figure 4.2 and Table 4.2). Consistent with those findings, Sindern et al. (2016) and Takarina (2010) reported high concentrations of heavy metals (Cr, Cu, Pb, and Zn) in the sediments of Jakarta Bay in these areas. These areas of high exposure (particularly along the coasts of Muara Angke, Cilincing and Marunda) also had histories of fish kills associated with hypoxia and harmful algal blooms (Thoha, 2007 and Wouthuyzen et al., 2007). Some of the patterns of high levels of pollutants (in the southeast areas of the bay from Tanjung Priok to Bekasi and in the northeast around Citarum river; see Chapter 4; Figure 4.2) are also relevant to the work of Farhan and Lim (2012) where they measured the total suspended solid (TSS) and turbidity using satellites images. This research (see Chapter 4; Table 4.1) and that of Farhan and Lim (2012) found that high concentrations of TSS and high turbidity contributed to poor water quality in the eastern area of the bay, particularly near the Citarum River. Furthermore, using cluster analysis, this research identified the southwest area (around Muara Angke and Bidadari Island) as the area with the highest pollution index (dominated by high TSS and turbidity) although Farhan and Lim's work had considered it to be polluted at a low to medium degree. This difference might be attributed to the years (1989 and 2008) when the satellite images used in their research were captured, which were before the reclamation activity that took place in the southwest area. Even though turbidity and TSS do not pose direct toxic impacts, these findings stress the importance of assessing their impacts and monitoring the progress of reclamation work adequately to minimise the adverse socio-environmental impacts. Further work is needed to provide managers with ways to reduce sedimentation from the nearby rivers.

The development of a water pollution exposure map provides a means to identify spatial variations of water pollution across different areas of the bay. It is the most conventional and top-down way to understand the biophysical impacts of water pollution. As described above, the mapping outputs of such an approach are informative for governments or decision makers who can use the information to set goals for

127 monitoring, evaluation and regulation. However, in the context of this research, such biophysical assessment has additional value because the results can be used to provide useful insights and estimations of the level of exposure experienced by different occupational groups (see Chapter 4; Figure 4.2; Figure 4.3). The importance of understanding the nature of exposure through biophysical assessments is also consistent with other social vulnerability studies on climate change and the hazards thereto. Those studies showed how specific biophysical assessments of the geographical landscapes or characteristics were essential in defining communities' varying exposure to floods based on their geographic position (Binita et al., 2015 and Kazmierczak and Cavan, 2011). In another study in Florida, Frazier et al. (2010) used a numerical simulation to identify spatial variations of inundation risk, which was then combined with the analysis of socio-economic conditions to define the social vulnerability of different communities.

For Jakarta Bay, the exposure index map demonstrates the importance of what Baum et al. (2008) describe as place-specific exposure or ‘geography of exposure’ in vulnerability assessment. This research supports such approaches and contributes to the body of literature that demonstrates exposure is indeed place-specific and is closely associated with the biophysical characteristics of the places where exposure occurs. The implication is that different people or groups in the same community might be exposed to different degrees of exposure (in this case, because of their occupations) that subsequently leads to variations of their vulnerability. Furthermore, the composite exposure index developed in this research should be seen as an interim measure to provide an initial insight to water quality conditions that might inform further examination, various policy responses (such as enforcement of laws and regulations) or additional research. Further advanced and more sophisticated statistical analyses might be required by some audiences and adjusted to specific purposes. However, the value of this efficiently developed index, its ability to engage and inform and its spatial resolution, was very useful in this research and provide a meaningful descriptive tool for the exposure to water pollution experienced by the community, as described in the Section 6.1.2.

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6.1.2. Understanding the exposure of water pollution through a vulnerable community’s participation

It is very clear that understanding a community's lived experience of the exposure to and societal impacts of environmental hazards (this is explained in Section 6.1.3), such as water pollution, can only be understood by integrating the top-down and bottom-up approaches. The participation of the vulnerable groups provided new knowledge of their conditions and occupational activities, their perceptions and experiences in dealing with water pollution (see Chapter 4; Section 4.1.2, 4.2, 4.3 and 4.4). The use of this integrative approach generates benefits greater than either approach on its own (Buttler et al., 2015 and Regmi et al., 2014) by providing a means to better understand the complex social-environmental problems. In a work about climate change adaptation, Kettle et al. (2014) emphasised the importance of integrating climate-related science with local knowledge to understand specific community's adaptation patterns. In the case of this research, results from the more top-down approach (water quality assessments) provided preferred sources of information in the form of water pollution index map for the decision makers and management so that appropriate plans could be developed to manage the water pollution problem in Jakarta Bay. The participatory (bottom-up) approach provided new insights to the local contexts of water pollution, and different potential of exposure and impacts of such pollution for various groups in the community. As will be shown in the following section, this research adds evidence in support of the importance of integrating those two approaches. This research found that although biophysical sciences were essential as predictive tools and to understand the nature of water pollution, the local knowledge proved necessary to gain an understanding of the exposure, impacts and specific adaptation options that were found among different groups in the community.

In what could be seen, from economic or social data (such as government-based data), as an homogenous traditional fishing community, there were very different degrees of exposure to water pollution and its consequences among the groups in this community. The exposure to water pollution in this research is mainly associated with the spatial aspect of occupational activities. Overall, the traditional fishing community (the fishers and mussel farmers) was estimated to have higher exposure to water pollution compared with the non-fishing occupation who worked on land (informal workers). The mussel farmers were observed to have the highest exposure to water pollution because their

129 aquaculture was in the areas with the highest exposure index, followed by the traditional fishers who performed their activities in the less polluted (offshore) areas (see Chapter 4; Figure 4.3; Chapter 5; Figure 5.20). In the wider context of fishing communities, this finding on the exposure variations is consistent with other studies. Colburn et al. (2016) estimated that the exposure to climate change impact, that is, sea level rise, was experienced differently by various fishing businesses because of the differences in where such businesses operated (distance from the coast) and the types of commodity that they produced. For example, a seafood business that specialised in a high value commodity, such as scallops, and was in close proximity to the sea would be exposed more to the slightest rise in sea level (such as 30 centimeters) compared with those businesses further inland and that had a lower-valued commodity. In another study of social vulnerability, Joseph et al. (2013) predicted that brackish pond-fishers had a higher exposure to sea level rise than did the capture fishers because their occupational activities and equipment were heavily influenced by, and more likely to be affected by changes of sea level.

It is useful for this research to find that occupational-related exposure could also occur through bathing and unintended ingestion of polluted waters when fisher and mussel farmers perform their occupational activities (for instance, a mussel farmer spends two to four hours underwater on a typical fishing day). For mussel farmers, using minimal or unsuitable diving equipment could also expose them to higher health risks (see Chapter 4; Section 4.4). The mussel farmers’ greater awareness of the effects of working in polluted water on their health might be related to their prolonged and regular direct contact with polluted waters (see Chapter 4; Figure 4.15). Skin diseases, headaches and gastro-related illness were among the symptoms reported that are associated with the exposure to polluted waters (see Chapter 4; Section 4.4; Figure 4.16). Many studies have emphasised how occupational divers have a higher health risk from working in polluted waters. For instance, Schijven and Husman (2006) emphasised that occupational divers were more prone to faecal-related contamination (that can be caused by ingesting polluted water) compared with occasional or sport divers. Fleisher et al. (2010) concluded that the risk of the illnesses, particularly skin diseases, is higher for swimmers compared with non-swimmers in micro bacteria- contaminated coastal waters. It was beyond the context of this research to make further investigations about the health risks or health outcomes from water pollution exposure

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(for example, analyses that take into account frequency and length of exposure) on the traditional fishing community. Further medical and toxicological studies are suggested for the future and would complement this research. More important, explicitly linked environmental and human health studies could provide a better understanding of the effects of exposure to various pathogens or pollutants on the different groups. This could be linked to discovering specific and effective ways to prevent and minimise the exposure.

In addition to occupation-related exposure, consumption of contaminated seafood from Jakarta Bay is another form of exposure for the community. For example, the existence of green mussel farming in the most polluted areas (see Chapter 4; Figure 4.14, cluster 5 area) raises the concern of heavy metal contaminants entering the food chain for humans. This concern is supported by biotoxicity studies in Jakarta Bay. Putri et al. (2012) found that the green mussel samples collected from around the port of Muara Angke and the Bidadari Islands were contaminated by lead (Pb). Cordova et al. (2012) also found that the green mussels in Muara Angke displayed malformation, which was attributed to biotoxicity caused by excessive amounts of Hg, Cd, and Pb, amounts that increased with the age of the mussels. Unfortunately, progress in biophysical studies of mussel or seafood contamination in Jakarta Bay does not go hand in hand with the progress of studies on the effects on human health. This is a concern considering the consumption of the catch by members of traditional fishing communities and that at times when the catches are poor seafood becomes the primary source of subsistence food (see Chapter 4; Section 4.4). Furthermore, because seafood from Jakarta Bay is also available in other markets, the potential exposure to contaminants could extend to the wider community (including the informal workers group of Jakarta Bay). Koesmawati and Arifin (2015) raised this concern about heavy metals contamination (mercury and arsenic) of the green mussels and the need for stricter regulations and their monitoring to lessen the risk to human health from the consumption of green mussels from Jakarta Bay.

The risks of contaminant and pollutant exposure on humans is indeed seen as a very serious problem by the government and in 2010 they responded by issuing a ban on green mussel farming. However, it is clear that the ban is rather a simple legal response applied by the government. Making a policy that relied on scientific, biophysical assessments without adequately considering the socio-economic consequences of the

131 ban has led to the ban's lack of success and ongoing exposure risk. As clearly seen during the fieldwork, green mussels are still one of the main fishery commodities in the area, and a source of affordable protein for the fishing community (see Chapter 4; Section 4.3.3; Chapter 5; Section 5.3.3). This unsuccessful banning might be attributed to the lack of consultation with the community and the neglect of local context or conditions in the decision making. This local context can be related to the community’s poor understanding of the health risks posed by heavy metals contamination, their high dependence on mussel farming and their inability to exercise much choice or find alternatives. The lack of understanding was reflected, for example, by the diversity of responses to the question about the impact of water pollution on health, which were in contrast with much stronger responses about the occupational impacts (see Chapter 4; Figure 4.15). The respondents demonstrated their knowledge of health risks from pollutants to some extent; when it related to more direct experiences, such as skin diseases. The impacts of consuming possibly contaminated seafood tended to be denied or neglected (see Chapter 4; Section 4.4). It is possible that the mechanisms of eating seafood that is contaminated by heavy metals are much less understood by the community or when they are understood, the lack of opportunities for food or employment outside this sector outweigh the alternatives and perceived health risks. Risky behaviour has been reported in other poor fishing communities where their choices are few and, in the short-term, urgent needs (for example, food) become a priority. For instance, in the rural setting of mangrove fisheries in Sundarban, Bangladesh, there are tigers that often injure and even caused deaths, but this has not stopped the poor fishers from fishing (Islam and Chuenpagdee, 2013). Destructive fishing practices, using poison or catching undersize fish, were also common among the fishers in Sundarban and such practices have also been observed in other fishing communities, despite that such practices were illegal and will cost them in the future (Islam and Chuenpagdee, 2013 and Kittinger et al., 2013). An important area for future research is to investigate local contexts and conditions, such as lack of information or plausible alternative occupations, that prevent the effective implementation of laws and regulations in Jakarta Bay. Considering the lack of information on health risks, studies that investigate the link between exposure to pollutants and health are necessary so that the community can be shown reasonable evidence and have a good understanding of the consequences of pollutants, which can help them to make decisions to avoid the risks from eating contaminated seafood.

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This finding on the consequences of a lack of an effective ban highlights the importance of incorporating local conditions and the needs of the community in decision making, particularly to solve complex socio-environmental problems that might affect the most vulnerable groups in the community. This view is also supported by other studies. For example, in a case study of environmental risks in multicultural regions of Brooklyn, Corburn (2003) showed how ethnicity played a major role in determining people's diet, which, in turn, affected the risk to their health from eating contaminated fish caught in the nearby river. He further argued that close observation and communicating with the community was the only way to understand such socially specific information. For Jakarta Bay, the results show how reliance on biophysical assessment alone in decision making has lead to less effective implementation. To solve the problems of water pollution and pollutants, thorough biophysical assessment and understanding are indeed required. However, as the research demonstrates, the decision makers should take into account the local conditions and challenges that might affect the available options, such as those conditions that hamper the enforcement of bans, and the need for communities to be able to adapt effectively to the decision. For instance, information dissemination programs to increase the community's awareness of water pollution and concomitant health risks from contamination are one way of achieving change. Such programs are needed because the results of this research show that more than two-thirds of respondents have never received any information about water pollution. Equally important is to consider better the socio-economic challenges that must be overcome, such as the unavailability of other livelihood options, particularly for those households that rely solely on green mussel farming. The consideration of these local contexts and challenges will contribute to better design and implementation of regulations and other management instruments that are to reduce the community's exposure to pollutants.

6.1.3. The impacts of water pollution and environmental changes

Similar to improving in the understanding of exposure to water pollution, more complete understanding of the societal impacts of water pollution could be achieved only by integrating the views of vulnerable people with other narratives. Better understanding of how water pollution affects the traditional fishing community (the traditional fishers and mussel farmers) is another important contribution this research has made and it partly fills the gap in the current literature, which is focused more on

133 the assessment of water quality or on the biophysical impacts of polluted water (see Chapter 2; Section 2.1.2).

Traditional fishing community should be considered as one of the key stakeholders. Their involvement and understanding in assessing fisheries, environmental conditions and the impacts of water pollution should be taken into account. People’s knowledge should contribute as a valuable source of social science data and complement more conventional science (such as the analysis of water quality deployed to describe the problem) and could be used to inform further research and develop new, better and more effective management (Anticamara and Go, 2016; Blythe et al., 2013; and Muallil et al., 2014). The participation of vulnerable stakeholders’ can provide new meaning to the exposure and impacts of water pollution, as well as insights to the community's resilience and the occupational coping strategies that were more diverse than expected within sub-groups of the community. These insights suggest different management approaches are required to lessen the impacts of water pollution on these different groups.

6.1.3.1. Declining productivity

The results suggest that land-sourced water pollution has contributed to the changes in productivity and fishing locations (Chapter 4; Figure 4.17). Around two-thirds of traditional fishers and mussel farmers experienced declines in productivity (Chapter 5; Figure 5.15) and many said they had intensified their efforts to maintain their livelihoods. Almost 60% of the respondents described pollution as the main cause of this decline (see Chapter 4; Figure 4.18). However, their stated opinions of the causes of the decline in Jakarta Bay’s fisheries went beyond water pollution as the sole cause. The respondents demonstrated deep knowledge as they acknowledged other factors that have led to the decline; they mentioned overfishing because of the increasing number of fishers, destructive fishing methods (bottom trawling), and land reclamation. Some respondents also mentioned uncertainty (weather, fishing seasons and fate), and limited fishing gear and boats as contributing factors. The latter, however, could be seen as the factors that are more likely to affect the short-term (seasonal or daily) catches instead of causing the long-term decline in fisheries (see Chapter 4; Figure 4.18).

This strong awareness of the traditional fishing community about water pollution and its impacts is in line with the recent findings by Baum et al. (2016) from research on

134 traditional fisheries and environmental pressures in Jakarta Bay and Thousand Islands. This awareness is unsurprising considering their historical knowledge and their direct experiences of the changes in the environment and their fishing activities (see Chapter 4; Table 4.4). For instance, the significant decline in the number of blood clams (Anadara granosa) and shrimp, as well as the fish and mussels kills, might have directly shaped their livelihoods and perception of water pollution. Observed changes in marine organisms’ population, physiology or abundance associated with anthropogenic pressures (including water pollution) are consistent with various studies in Jakarta Bay (Dsikowitzky et al., 2016; Sachoemar and Wahjono, 2007; and van der Meij, 2010) and in estuarine or coastal regions elsewhere in the world (Corcoran et al., 2010; von Glasow et al., 2013; and Wolanski, 2006). In Jakarta Bay, increasing volumes of waste from land-based domestic sources and industries, along with other anthropogenic factors (including intensive coastal developments, overfishing and destructive fishing) have contributed to the decline in species numbers of demersal fish, benthic communities and corals (Arifin, 2004a; Cleary et al., 2014; and van der Meij, 2010) as also acknowledged by the fishers and mussel farmers.

6.1.3.2. Changes in fishing location

Traditional fishers and mussel farmers, without reference to water quality data from government agencies, also described the near-shore area of Jakarta Bay (areas within five kilometers of the fishing ports) as a degraded zone where they could no longer perform their fishing activities (see Chapter 4; Figure 4.11). The community’s perception of these more polluted areas was associated with their direct experiences and from observing perceivable indicators of water pollution, such as fish and mussel kills, changes in water colour and odour, turbidity and reduced catches (see Chapter 4; Figure 4.7). These evaluative remarks made about this degraded zone are also in agreement with the water pollution index map (see Chapter 4; Figure 4.11). The results verify other studies that highlight the degradation in the near-shore area of Jakarta Bay. For example, Arifin (2004) showed that the waters within five kilometers of the coastline of Jakarta have higher nutrients (phosphates, nitrates, nitrites, and ammonia) and lower dissolved oxygen, which are characteristic of the greater influence of land-based pollution. Estradivari et al. (2009) also found a very low coral diversity (only one genus of coral survived) in the near-shore area of Bidadari Island (five kilometers from the

135 coast) and that the trends of coral biodiversity, coverage and colony numbers decreased towards the shore.

Degraded environments and declining inshore fisheries have forced the fishers to go to offshore fishing locations where water quality and the marine environment are better (see Chapter 4; Section 4.4; Figure 4.3). Even though their boat capacity and fishing technology could limit or challenge their fishing in the deeper parts of the bay (see Chapter 4; Section 4.4), this changing spatial behaviour was crucial to maintain income from fisheries and to sustain their livelihood. This was especially clear for those who relied solely on fishing for their livelihood. Although some studies argued that fisher’s mobility in exploring new fishing locations, or adjusting to seasonal variability, are paths to cope with uncertainty (Fauzi and Anna, 2010 and Sievanen, 2014), this was not entirely true for traditional fishers in Jakarta Bay. On one hand, shifts in fishing locations could be seen as a coping strategy (see Chapter 5; Section 5.3), yet on the other hand, it was also associated with additional pressures on the fishers in terms of the costs and time required. For example, the formerly near-shore fishers complained about the risks and that the distances were exceeding the limits of their boats and gear as they travelled to fish in the deeper part of Jakarta Bay. In addition, spatial shifts to offshore locations increased the pressure on the offshore environments of Jakarta Bay. Baum et al. (2016) predicted that further development in Jakarta Bay (particularly land reclamation and the construction of the Giant Sea Wall) could force traditional fishers to travel even further offshore to find new fishery resources and put more pressure on areas already occupied by other fishers. This research found that this is already taking place (see Chapter 4; Section 4.3.2).

6.1.3.3. Occupational-related coping strategies in fishing activities

The implications of the changes in productivity and fishing locations highlight the challenges from increased uncertainty faced by the traditional fishing community. As was also found in this research (see Chapter 4; Figure 4.18), even without water pollution it is the nature of the occupation of fishing to hold a substantial degree of uncertainty and for fishers to accept risks because of their working environment (Bene et al., 2007). As also highlighted by Sievanen (2014), environmental variability (seasonal changes and changes in fish abundance) is considered ‘normal’ by traditional fishers. For Jakarta Bay's fishers, the monsoon season is one of those normal factors that

136 causes fluctuations in their productivity and subsequently their coping strategies (see Chapter 5; Table 5.1); these coping strategies are explained in more details in the next section of this chapter.

This research contributes new insights about how environmental stressors, such as water pollution, are forcing the community to explore new fishing strategies to adapt and survive in an industry subject to decline in fisheries and other changes (see Chapter 4; Figure 4.17). More awareness and knowledge about water pollution have helped the traditional fishing community to better equip and prepare this community for finding strategies or solutions in relation to their fishing activities (see Chapter 5; Figure 5.16). This was shown, for instance, by the adoption of shorter harvest cycles for green mussels to avoid the mussel kills that they suspected to occur because of changes in water quality (see Chapter 5; Section 5.31; Figure 5.26). The extended travel for traditional fishers to offshore and less polluted areas of the bay was also likely to be related to their perception and knowledge about water pollution, fish distribution patterns and behaviours that they develop through years of experiences (see Chapter 5; Figure 5.23). This prominent role of new knowledge by resource-dependent communities is consistent with Sievanen’s (2014) study in a broader context of environmental variability where he argued that knowledge could be considered as a key resource that enables a community to cope more flexibly with the continual changes embedded in the fishing occupation.

In relation to the occupational characteristics, the adaptations applied by the mussel farmers were different from those by the traditional fishers. Reliance of the green mussel farmers on the near-shore and shallow areas limited their options in facing the impacts of water pollution. To cope with the pollution, they were using protective suits and adjusting their harvest cycle. Adjustment to shorten the time for harvest (from seven to eight months to three to four months) was considered necessary to avoid the risk of mussel kills (see Chapter 5; Section 5.3.1). Yet, this shorter harvest time led to smaller mussels being produced that in the end affected the market price and the farmers' income.

Decision makers and managers need to acknowledge these differences in occupational strategies applied by different groups in the same community when develop specific approaches to support different coping strategies. The advances of fishing technology

137 might help in supporting the fishers to improve their fishing activities in offshore areas, but it is unlikely to be an effective way to help mussel farmers to adapt to water pollution. Relocation of the mussel platforms, from the areas now known to be the most polluted to less polluted sites, seems to be a better management action if green mussel farming is to be maintained in Jakarta Bay. This relocation should be accompanied by studies of the suitability of new areas (from the biophysical perspective) and careful consideration of the capacity of the mussel farmers (for example, in terms of the amount of investment required to erect new platforms). Ignoring or undermining their distinct occupational needs can lead to ineffective management and subsequently contribute less to supporting the sustainability of their fishing activities and livelihoods. Not only differences in terms of occupational-related coping strategies but several distinct livelihood characteristics were also observed among the groups that further influenced their capability to be resilient and build more sustaining livelihood in the face of environmental stressors, as detailed in Section 6.2.

6.2. Livelihood characteristics that shape vulnerability to water pollution

The results suggest that there were several determinants of livelihood characteristics that affect households' vulnerability to water pollution. Several of these characteristics were shared between the occupational groups (such as the availability and access to physical capital). Some others were more distinctive of the traditional fishing community, such as the reliance on fishery resources, diversification of occupation, social networking and environmental knowledge (this was discussed in the previous section). These livelihood characteristics, developed as indicators and capital indices, helped identify and inform possible solutions to reduce the sources of the vulnerability.

Selecting and using well-established and relevant indicators and integrating those with local context are measures expected to increase the legitimacy of the indices and the vulnerability assessment. For example, the inclusion of health insurance as one of the indicators of physical capital was based on the empirical observations and concerns raised in the interviews. During the pilot visits to the villages, it was found that, despite being in a very close proximity to health facilities, these people expressed concerns about paying for good health services. This confirms the importance of spending resources to build a reliable understanding of the local setting before defining the appropriate indicators to be used in the analysis of livelihood vulnerability (Adger et al.,

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2004 and King et al., 2014). King et al. (2014) further stressed that performing a livelihood analysis to represent well-being is indeed complex and requires the use of context-dependent indicators and theoretical as well as participatory-derived indicators so that the many dimensions of livelihoods can be captured. Therefore, it is important that iterative work be done and that any replication of this research in the future considers and adjusts to local context in redefining the most relevant indicators for any case studies. The indices of vulnerability and its elements developed in this research were supported by previous research and by added valuable narratives. These narratives contribute a more meaningful context to the indices and help explain the elements that shape the vulnerability from the perspective of the affected groups in the community, as can be seen in the following section.

6.2.1. The traditional fishing community: their reliance on fishery resources, and their limited education and skills

This research found that the traditional fishing community was more vulnerable to water pollution compared to the land-based workers (informal sector workers) as was to be expected. Specifically, the green mussel farming group was found to be the most vulnerable of all with a livelihood vulnerability index (LVI) score of 0.62, followed by the traditional fishers with 0.59 and the informal workers with 0.44 (see Chapter 5; Figure 5.21). Higher vulnerability for this resource-dependent community (compared with the informal workers group) were associated with their higher exposure to water pollution (high exposure index; as explained in the previous section) and their higher dependence on fishery resources (as indicated by high sensitivity index) (see Chapter 5; Figure 5.19 and 5.20).

This view that the fishing community is more vulnerable compared with other occupational groups because of its greater exposure to external stressors and its reliance on fishery resources, is supported by other studies. From the perspective of natural stressors, Badjeck et al. (2013) highlighted that the location of fishing communities (in open and low-lying coastal areas) already predisposed them to greater exposure to stressors or hazards, such as storms and tsunamis. Within a broader context of fish- dependent communities, Bene and Friend (2011) found that the combination of high exposure to uncontrolled external stressors (for example, bad weather, destruction of fishing properties) and sensitivity (that is, an inability to diversify their livelihood

139 option) became the source of vulnerability for small-scale inland fisheries in the Volta and Mekong basins, and rendered these fishing communities more vulnerable compared with the other socio-economic groups. The 6000 fishers and mussel farmers of Jakarta are also vulnerable to these natural stressors (such as storm surge and flooding). However this research contributes new insights about how a more controllable anthropogenic stressor, that is, coastal water pollution of a megacity, acted as a multiplier to households already under pressure and often who were already vulnerable.

Dependence on fisheries was observed among the traditional fishers and mussel farmers of Jakarta Bay (see Chapter 5; Section 5.2.2) despite the proximity of Muara Angke and Cilincing to the growing and busy Jakarta where there are many more employment opportunities existed compared with elsewhere in Indonesia. Around half of the respondents (fishers and mussel farmers) relied solely on fisheries for their livelihoods and the other half had additional jobs that also involved fisheries (see Chapter 5; Section 5.13). This heavy reliance on the fishery resources was, according to respondents, related to limited non-fishery skills and little formal education. Even though there was no significant difference found between the fishing community and informal workers in terms of their skills and education level (see Chapter 5; Section 5.1.4), this perception of their limited human capital might result in more serious long- term consequences for the livelihoods of the fishing community if the decline in fisheries continue. Severe and systemic limitations to their human capital have caused a pessimistic attitude among some of fishers and mussel farmers about pursuing non- fishery occupations.

It is hard (for fishers) to find other jobs (non-fishery) because we don’t have any experience. (271)

Being a fisher is an easy job (especially) for unemployed (people), it doesn’t require us to have a degree. (151)

Similar patterns of low human capital possessed by the members of traditional fishing community and how this hampers their entry to other jobs is also confirmed in the literature. Low standards of education, along with negligible access to productive assets, such as ownership of land, contribute to narrow the livelihood options of these communities (particularly in developing countries) and make the communities more

140 vulnerable to changes caused by environmental variability and natural stressors (FAO, 2006). In a comparative study of fishing communities in the Pacific and African islands, Mulyila et al. (2012) showed how limited education was associated with high reliance on fisheries. Pressures on fisheries contributed to resource conflicts because of increasing competition. This Jakarta Bay study showed that the inability of some fishers and mussel farmers to expand their occupational skills beyond fisheries and to pursue other sources of livelihood was one of the factors that increased their sensitivity and vulnerability to water pollution. Such a high and complete reliance on fishery resources could lead to further hardship as the decline in resources continues. Yet, according to people interviewed, a very limited range of additional or alternative sources of income were available or accessible.

For some fishers and mussel farmers, their poor education and limited skills seemed to be the reasons they chose those occupations and why they persisted. However, as the fishery resources continue to degrade they have been forced to be more creative and adaptive. In some cases, they diversified their income sources by taking additional jobs, counting on other family members to help and accessing loans from middlemen to obtain additional income and to meet their needs (this will be explained in the next section).

Aside from those who persisted in fisheries, there were others who saw that leaving fisheries to be the more reasonable choice. These ex-fishers and ex-farmers, who still live on the coast of Jakarta Bay, had decided to take other informal jobs that required minimum skills and education (see Chapter 5; Section 5.3.4). They had become workers in fishery-related jobs, such as net makers or boat mechanics, or worked in non-fishery jobs, such as driving. Their new jobs were considered more promising and provided a more reliable income than fishing. It can be seen that even though being a fisher was considered as ‘easy jobs’ (where people with low education and skills could easily do), declining fisheries and high uncertainties in these occupations were reflected in most respondents' pessimistic attitude towards this sector's future. This was dramatically illustrated, for example in their responses when asked about their children's future. Despite that most of the fishers and mussel farmers (around 70%) had inherited their occupations from their parents, more than 85% of them were reluctant to see their children enter the fishery sector (see Chapter 5; Section 5.3.4). These findings reveal the stark contrast between images of traditional fishery-related workers that are described as

141 professions that offer satisfaction, enjoyment and cultural identity in several cases (Johnson et al., 2014 and Sharma, 2011). For, in the face of declining fisheries and increased uncertainties, those values are insufficient to keep people working and living as fishers.

For households, improving human capital through education, particularly for the younger generations of fishers, mussel farmers and their children, was highly important and relevant. For this community, to support the expansion of livelihood opportunities outside fisheries, the improvements will be essential. This is because it is clear that the fishers and farmers in Jakarta Bay were keener for their children to pursue opportunities outside fisheries (see Chapter 5; Section 5.3.4). Therefore, in spite of their own limited formal education, they were eager to invest capital in equipping their children with a better education. In a wider perspective on primary producers, the role of formal education in influencing the structural changes in the community of developing countries was observed. Bhandari (2013), in a study of rural farmers in Nepal, showed that the more educated individuals were the more likely to abandon farming jobs because they had wider employment options that could provide better income and stability. This pattern was also found in the rural farming households of Shanghai, where education and age were among the key determinants that motivated livelihood transitions from farming to off-farm occupations (Liu and Liu, 2016). Promoting and supporting formal education (particularly for younger generations), in addition to developing non-fishery skills in young adults, might be essential steps that, in the long term, will contribute to reducing their reliance on fishery resources and to provide the households with wider opportunities. As for the fishers and the mussel farmers, improvement in basic literacy skills and ready-to-use non-fishery skills could be a good investment that may help the community to diversify their sources of livelihood. Subsequently, they may cope better with increased uncertainty and sustain their livelihood (as explained in the following section).

6.2.2. Diversification: reduce vulnerability and sustain livelihoods

Informal credit loans, additional jobs and additional family members working were important means of income diversification already found in the Jakarta Bay fishing community. According to the fishers and mussel farmers, their capability to diversify livelihood and income sources was essential. They were already exposed to relatively

142 large fluctuations of income because of the seasonal nature of their occupations, so these coping mechanisms were familiar. The combined impacts of water pollution and other environmental factors (such as overfishing and habitat destruction) not only add to income fluctuation, but they appeared to multiply the subsequent impacts on the living conditions of this vulnerable community (see Chapter 5; Section 5.3.3). For instance, massive fish kills, coupled with fluctuation in productivity, additional costs because of the greater distances to fishing locations and declining quality of their catches, all of which contributed to income loss. For many fishers and mussel farmers, these additional and sustained financial losses are beyond their ability to cope, which forces them to sell their limited assets to maintain their livelihood. They admit to shifting capital in its various forms to survive but their capital pool was shallow and subject to natural leakage because it was built on declining fisheries, and aggravated by deteriorating water quality on the edge of a megacity. A mussel farmer put it succinctly,

Many (mussel farmers) have to sell things when the waste comes, because there are no mussels, we sell things to keep living. (251)

Not only do they lose assets, their vulnerability to water pollution also affects their household capabilities and aspirations to pursue livelihood objectives. Pollution limits their ability to secure adequate food, to proper health services and to education for their children. A chief of the community, who was also a fisher, described vividly how this water pollution problem affects the households’ livelihood conditions,

Nowadays, to have the (fishers’) children go to high school requires a tremendous effort, because, you see, looking for fish is harder… There are no fish in Jakarta Bay now, just litter… the bigger impact (of water pollution) is actually on the economic and social aspects, one of the most important is about the (fishers') children’s (future), their parents are not able to give them a good education. (A1)

Relying on loans from middlemen or tengkulak to cope in the hard times seemed to be a common practice within the observed community, regardless of their occupational groups (see Chapter 5; Section 5.1.3). These informal providers played important roles in supporting the livelihood of the community by providing necessary financial flows required for daily and other essential needs (for example, education and health expenses) during times of hardship. As for the traditional fishing community, the

143 middlemen' role was even more important by providing them with the money required to cover their occupational expenses, in particular during the fishing off season when production was limited. However, the relations between the fishing community and middlemen were often one-sided because many fishers ended with large debts (from high interest rates or unfair pre-purchased fish trade systems). Respondents often mentioned that the decline in productivity, longer range and duration of fishing trips and increasing fuel prices had contributed to the income loss that then made it more difficult for them to repay the debts.

Loans from middlemen are found to be a part of trading systems in many fishing societies, particularly in the traditional fishing sectors. In Bangladesh (where they are known as dadon), loans from these middlemen were often required not only for fishing expenses but also for daily expenses (Islam and Chuenpagdee, 2013). Similar to the findings of this research, on the south coast of Java (Indonesia), a middlemen system (known as bakul) weakened the bargaining position of the fishers by lowering the selling price of fish for the fishers who were already in debt (Rudiawan et al., 2012). The absence of fair trade systems and reliance on middlemen loans for financial capital exposed the resource-dependent community to additional deprivations. The strong role of lenders within the community highlights the need to provide a formal scheme of soft loans, such as micro-credit, that actually could be established through cooperatives, semi-formal or other formal organisations (see Chapter 5; Figure 5.10). This is particularly important for the traditional fishing community members who borrow money mostly to meet their occupational needs (see Chapter 5; Figure 5.11). However, considering the widespread and rooted existence of these middlemen in this community, a specific approach involving the incorporation of the middlemen' roles into the financial systems, instead of removing them completely from the system, might be more realistic. As suggested by Crona et al., (2010) in a study about traditional fisheries in East Africa, it was necessary to consider the middlemen as another occupational group whose roles' could not be neglected in understanding fisheries governance. So far, there is a very limited body of published research available that specifically addresses this middlemen-traditional fishers relation for Jakarta Bay communities and this makes a case for further research. This relationship is complex because, for example, sometimes the middlemen handle the marketing and operations of fish landing and marketing, own the boats and equipment that ensures fishers' dependency on the system (Pomeroy,

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2016). This was also observed in Jakarta Bay. In a study on Bajau middlewoman in Kalimantan, Indonesia, Pauwelussen (2015) demonstrated how the traditional fishers were bounded to the system beyond fisheries-trade matter; the middlewoman ensured daily and important expenses (such as education and wedding costs) and sometimes provided protection to allow illegal fishing. Understanding these relations, the trade systems and cultural backgrounds through deeper research and scenarios of interventions would be valuable for stakeholders, in particular NGOs and governments as they develop solutions to manage and incorporate the role of middlemen and create fairer fisheries trading systems that benefit the fishing community.

Increasing the number of household members in employment (other than the heads of households) was also a key coping strategy to support households' livelihoods (see Chapter 5; Section 5.3.3). This pattern was observed not only in the traditional fishing community, but also in the informal workers group (see Chapter 5; Figure 5.8). One of the most popular employment opportunities for these household members was in the post-harvest processing of green mussels, which provided jobs for all members of the households and contributed an important social and financial support for the households. The mussel workshop was usually owned by someone who employed a group of mussel farmers (often comprising six or seven mussel farmers) to harvest the mussels from their own platforms or from rented platforms. The workforce at one workshop could reach thirty-five to forty workers (employees, who were mostly, but not limited to, the wives and children of the fishers and mussel farmers); there was clearly an employment multiplier in this value-adding work chain. The structure of the workforce and its employment multiplier effect were previously observed in a traditional fishing community (Baker, 2012) and interpreted to reflect the strength and the empowerment function of the traditional fisheries sector. As the findings suggest, this sector's sustainability is affecting its direct dependents, (such as the fishers and mussel farmers) and also other members of the community (mainly women and children) who are relying on the pre and post-processing sector.

One of the most distinctive diversification patterns found in the traditional fishing community was the occupational expansion or additional jobs. As shown by the findings, there were significantly more fishers and mussel farmers that had additional jobs compared to the informal sector group (work expansion indicator scores were 0.58 and 0.44 for fishers and mussel farmers compared with only 0.22 for informal workers; 145 see Chapter 5; Figures 5.9 and 5.16). This reasonably reflects the seasonal nature of their occupations. One of the interesting findings with regard to additional income was the pattern of some fishers and mussel farmers to be involved in agricultural jobs during the fishing off seasons, usually from January to March (see Chapter 5; Section 5.1.3; Table 5.1). The urban setting of Jakarta makes it almost impossible to find such jobs nearby; therefore, these fishers and mussel farmers would go back to their hometowns (for example, Indramayu and Cirebon in West Java) to work on their own land or work for other land owners. Most of the fishers and farmers in Jakarta Bay (in this case, around 90% of respondents) had migrated from other regions and this might explain why some of them still had legal land entitlements in their hometowns (these lands were mostly used for subsistence farming; see Chapter 5; Section 5.1.5).

Similarly, a study about climate change impacts on fisheries in the rural areas of India discovered agriculture labouring to be one of the most important coping strategies implemented by many fishers' wives to support the households in the face of falling fishery catches (Salagrama, 2012). Although it is apparently common for resource- dependent communities to switch between jobs or to have additional income sources (Anticamara and Go, 2016 and Badjeck et al., 2010), these findings in Jakarta show a complicated pattern of fisheries, agricultural jobs and land ownerships. This is an important insight that shows the diversity and strength of a fishing community in coping with changing environments and this dynamism should be considered in guiding aid or policy programs. Future research is recommended to investigate further this fishing- farming pattern for Jakarta Bay fishing communities. The results can be used to inform the policy makers in promoting the agricultural sectors (for example through improved access to farming lands and creation of a local market for the harvests) and encouraging the necessary agricultural skills that could provide the fishers' households with an alternative occupation to fisheries or as supplementary to the main occupation.

The serious indirect consequences of water pollution, that is, more uncertainty in terms of income, has forced further reliance on additional or alternative income sources (that is, from the additional jobs, more household members working as well as money loans) to fulfil their daily and occupational needs, and to meet other essential needs, such as for health and education (see Chapter 5; Section 5.3.3). This income diversification acts as a mechanism for hedging and reducing risk (either health, life or financial risk) and coping with the natural changes and dynamic conditions of fisheries (see Chapter 5; 146

Figure 5.9). That diversification is crucial in mitigating risk successfully in economic terms, was also emphasised by Finkbeiner (2015) who, in her study of traditional fisheries dynamics in Baja, California Sur, identified the great importance of diversification in stabilising incomes, particularly in the face of increasing uncertainties from external environmental stressors, such as climate change and fisheries decline.

However, the immediate challenge found by this research is that a large portion of the fishers and mussel farmers (more than 50% of respondents with additional jobs), as well as their family members, were still dependent of fishery-related occupations (such as mussel peelers and boat mechanics). In a situation where the fishery resources are declining and the prospects of recovery are poor, these households are very vulnerable to fisheries collapse, as well as to natural disasters and rapid policy changes. Therefore, there is an urgent need for fishers and mussel farmers to develop their skills outside fisheries while at the same time extending their current, necessary skills and education of household members so that they have a wider set of options to sustain their livelihoods.

In agreement with these findings, other studies have emphasised the urgency of creating more opportunities and developing the capacity of the traditional fishing communities to generate non-fisheries alternative and regular income and to spread the risks of income loss. A community development project for a Sri Lankan fishing community similarly found that members of fishing household (wives and children) should be the essential part of livelihood diversification programs because they often had spare time and the potential to reduce their households' reliance on fishing (Wedathanthrige et al., 2013). These other studies emphasised the importance of taking local conditions into account first in efforts to empower community so that the diversification would provide sustainable sources of income for the households. For example, they encouraged home gardening to build food security in times of hardship.

In the case of Jakarta Bay, empowerment programs, designed to encourage and attract this community to consider alternative livelihoods, were initiated by several development organisations (local and international) and the governments. Local NGOs, such as Kalyanamitra and Wahana Visi, for instance, are known for providing training in practical handcraft and driving skills for the fishing communities (this includes fishers and their wives) in Muara Baru and Cilincing villages (Padawangi, 2012). The

147 government, through the PEMP program (economic empowerment program for coastal communities), also provided a self-help program and was making soft credit arrangements for entrepreneurial activities (for example, opening food or small grocery stalls, establishing small industries, such as a cracker industry). Through this PEMP program, the government intends to encourage traditional fishers to expand their livelihood options. However, the requirement to provide assets as collateral for loans (in the form of land, houses, houses titles or other goods) has restricted access to such opportunities (Aisyah et al., 2010).

Therefore, discovering the livelihood conditions and the needs of the community through prior participative research and discussions, such as this study exemplifies, becomes the first step in designing empowerment programs that can be implemented successfully. Pilot research programs are needed because the availability and access to capital, as well as needs and aspirations were clearly different between groups in the community. Prior consultation with the community will help ensure that the programs are accessible, benefits those in need and avoid perverse outcomes. For instance, establishing household industries that involve training and coaching should also be accompanied with provision of basic education for literacy and also marketing links, considering the high level of illiteracy and limited networks of the community. Proper training, monitoring and evaluation are also important. Involving the members of the community will help ensure that such programs are sustainable and contribute to improvements in the livelihoods of the community.

6.2.3. Social networks and the community livelihoods

Supportive social networks, informal and formal, were important supports for the community member's daily life and occupational activities (see Chapter 5; Section 5.3.2). This is particularly true for the traditional fishing community in Jakarta Bay, whose occupations are associated with greater risks and uncertainties and whose activities require higher (and costly) maintenance and resources (for instance, boats, nets, fuels and other fishing equipment). With water pollution and other environmental stressors increasing their hardships and challenging their livelihoods through declining returns from fishing and through increasing costs, the role of social networks is expected to become more important. Good social networks mean easier access to various forms of support, including, but not limited to, reliable sources of finance. One

148 clear example was the reliance of most of the respondents to soft loans from their friends and extended family members (see Chapter 5; Figure 5.10). However, other examples of strong solidarity observed in the Jakarta Bay community was manifested in various ways other than financial support; from meal sharing and social gatherings to occupational activities (such as providing free services for fishing boats and equipment). Furthermore, this research showed that strong social cohesion not only benefited members of the resource-dependent community but others (including the informal workers) who lived in the neighbourhood. For example, an informal worker described how his fisher or mussel-farming neighbours often gave him some part of their catches,

Sometimes I just ask for some fish from fishers if I do not have money (to buy fish or food) and they will give it to me happily. (077)

The importance of social cohesion from this informal networking is consistent with other studies of traditional fishing societies. The cohesion, experienced as part of their support systems, can reduce the community’s burden and vulnerability, in terms of daily and occupational matters (Islam, 2011 and Marin, 2015). A study of the role of credit in a traditional fishing community in Rio de Janeiro found a similar pattern: loans from relatives or friends were important for fishers and used for small yet continuing needs for boat and gear maintenance (Haque et al. 2015). Another study explained how support from family members lessens the burden of expensive dowry for poor fishers in Bangladesh and highlighted how the partnership among colleagues on fishing trips increases the sense of security when dealing with unfortunate events (Islam and Chuenpagdee, 2013).

In addition to this informal networking, the occupational organisations acted as more formal networking platforms and played an important role in supporting fisheries activities and livelihoods. The traditional fishing community, particularly the fishers, was indeed more likely to be involved in these formal occupational organisations compared with informal workers (see Chapter 5; Figure 5.6; Figure 5.16; the indicator score for organisational involvement by fishers was the highest 0.43). In addition to providing its members with opportunities to socialise informally, the members also received some additional benefits that the non-members could not access. These organisations (KUB/Koperasi Usaha Bersama) are literally translated as cooperatives,

149 but function as small groups of fishers (with twelve to twenty members) that are supervised directly by the Ministry of Marine and Fisheries and the North Jakarta government. The structure of these cooperatives is different to those often found in traditional fishing communities where one major cooperative usually operates in an area or at a fishing village that serves all the fishing community (Amarasinghe and Bavinck, 2011 and Cinner and Bodin, 2010).

These KUBs seem to be one of the most effective means of communication, outreach and support that connect fishers with the governments, non-government organisations (NGO) and research and academic institutions. The benefits for the organisations’ members were reported in interviews and most related to professional support, such as provision of fishery-related training, fishing equipment (including machines and fishing gear), financial support and insurance or saving schemes. These occupational organisations in resource-dependent community were highly valued, particularly by the members who had directly benefited. For instance, some fishers, members of KUBs, described how they were provided with life insurance and access to a saving scheme that otherwise could be difficult to have (see Chapter 5; Section 5.3.2). Such schemes were reportedly valuable, considering the high-risk nature of their occupations and low rates of saving, and they can minimise the consequences and hardship from unexpected incidents or events (illness, death or fatal injuries from fishing activities). In addition, a saving scheme was reportedly useful for coping with short-term fluctuations in income, which are exaggerated by declining fisheries. It is therefore important for the government to engage more actively in ensuring that such supporting schemes are available and accessible for the traditional fishing community in Jakarta Bay. This can be done by establishing more KUBs. Similar to findings of this research, other studies of traditional fisheries have directed attention to protecting this type of organisation in the context of risks, protection of life and occupational injuries and assets (fishing vessels and gear) and access to reliable financial services (Farrugio, 2014 and Islam and Chuenpagdee, 2013). For example, soft loans with no interest, provided by local cooperatives for fishers in Pameungpeuk, Indonesia, provided additional safety nets for the fishers' households during the low season (Rudiawan et al., 2012).

Despite their proven success in enabling access to financial and professional support, these KUBs have challenges. This research identified some challenges that, if addressed, could improve the functioning of KUBs. For instance, some fishers reported 150 that there were occasions where the chief of the group misappropriated the aid funds that were entrusted to him to be distributed among the members. Others complained that the government rules that required them to register, initiate the group and manage the administrative matters made it difficult for some of the members to participate because they were illiterate. In addition, there was competition between groups because the more active were the members of a KUB, the higher the chance they would be provided with funding from the government. The implication of this is that sometimes the aid or support provided was unevenly distributed and rewarded more capable groups that indicated the presence of elite capture. In a study about local fishing groups in Cirebon, Indonesia, Mala (2016) also found similar problems. Her research showed that power and connections played important roles in the distribution of aid to the fishing groups. Heads of fishing groups with close connections to government and were usually local community figures, had gained easier access to the government support compared to the other groups whose chiefs had no local influence.

Another problem raised was about the mismatched aid received by the members. For instance, a fisher mentioned that once in the past, the government had provided a group of fishers with a boat engine. However, the engine's specifications made it unsuitable for their boats. This situation was a result of a lack of consultation with fishers. Another fisher, showing his understanding of the problem of inappropriate government intervention, complained,

..the aids received (from the governments) through the KUBs seemed to be given away without a proper research. How can it be, they gave us a compressor (laughed), what should we do with it? … ended up we sold it and shared the money among us (member of KUBs).

During the research, it was also found that several members of these organisations (including those who received the benefits) were not traditional fishers. In one case, fishers preferred to retire after the government had provided them with a new boat and instead they employed a group of fishers, who were not members of the group, to do the fishing.

The lack of government consultation with the community meant it failed to identify their needs and concerns accurately. It also showed a lack of monitoring of the

151 organisational structures and management of programs that might contribute to perpetuate these problems in subsequent programs. This led to lower trust and engagement in these programs and might explain the relatively low involvement of the traditional fishing community (see Chapter 5; Figure 5.6). Transparency in managing distributed aid is important to avoid internal conflict that may lead to distrust amongst the members of organisations (Wibowo et al., 2016), as also observed in this research. One of the fishers described their past experience with the organisation, which demonstrates another case of elite capture and made him withdraw his membership,

The chief used our behalf (the fishers) to get some (government) support, but in reality it never got through to us, there was no monitoring (by the government). (211)

To overcome these challenges and optimise the functioning of the organisations, it is necessary to implement better two-way communication and monitoring to help learns the needs of the community more precisely. Doing this will also improve trust, strengthen the relations and ensure the sustainability of the organisations and its long- term benefits for the members and their communities. Although the lack of consultation that led to aid being wasted was evident in this case of Jakarta Bay, future research is still required to better understand the monitoring and evaluation processes of the KUB. Further research on the elite capture is also important as such process is often unavoidable in a decentralised management (Cohen, 2013) and, as found in this research, affected the vulnerable members of community. Such research would be useful to learn in more detail about the factors that contribute to improving the effectiveness of these organisation, the leadership and the involvement of the fishing community.

The role for occupational organisations in the traditional fishing community in Jakarta Bay has expanded from narrow occupational or economic-related issues into providing a platform for group aspirations. This is slightly different from what is often found in many occupational organisations or fishing cooperatives in Indonesia where such organisations provided more economic support, such as food or other basic needs during the off season (Agunggunanto, 2011 and Dahuri, 2005). One of the most discussed drawbacks of traditional fisheries is the socio-economic and political marginalisation of the communities (Bene and Friend 2011; Hauck, 2011; and Sharma, 2011). Fishing

152 communities are made to feel left behind in development and progress. In Jakarta Bay, for instance, the vast infrastructure developments (that include islands reclamation, high-rise and expensive residential blocks and shopping malls, modern ports and tourism spots) adjacent to the two fishing villages were planned without reference to the community and there is no direct connection that might lead to improving the socio- economic situation of the community or in the environmental conditions of the villages and the bay. Instead, developments, such as land reclamation, obstruct and further degrade the natural resources and adversely affected the traditional fishing community (through the loss of fishing grounds and mussel farming areas and from increased sedimentation and pollutants; Chapter 4; Section 4.2.1) without mitigation, compensation or solutions. There is no evidence that the developers or governments considered possible adverse effects or the sustainability of the community's livelihood and the natural resources they rely on. As similarly observed in many other traditional fishing communities (Rocklin, 2016), this research found that the lack of participation by the fishing community of Jakarta in development and decision making epitomises and contributes to their community marginalisation.

However, in Jakarta Bay, the fishers' willingness to share their aspirations and be involved in the management and decision making was bridged by the occupational organisations, usually through the support and initiative of the local NGOs. One of the strongest facilitators for these traditional fishers is KIARA (Fisheries Community Coalition for Justice) which often assists and encourages the community to connect and communicate with the governments through various fishers-government meetings and events. For example, KIARA, in cooperation with the Indonesian Association of Women Fishers (PPNI), formulated recommendations that urged the government to explicitly acknowledge women fishers in the Fisheries Act and to provide more support for creative economic empowerment intended to benefit these women fishers (Personal communication. KIARA, 2014).

The occupational organisations and NGOs have very important roles; they provide fishers' with institutional representation for management decision making (Murshed-e- Jahan et al., 2014), which helps to ensure the community's voices are heard. Moreover, these occupational organisations act as platforms to provide opportunities and encouragement for the fishing community to be more active and involved in planning and decision making, particularly those that could affect their livelihoods and the 153 sustainability of fishery resources on which this community depends. This is necessary considering the changing and increasing demand made on the available land and water of Jakarta Bay made by commercial interests that can lead to conflicts of interest among its many different users (Pearson et al., 2016).

6.2.4. Informal settlements and access to public services

Land tenure is a critical issue that contributes to the vulnerability of the community because their lack of secure physical capital excludes them from public service facilities, such as clean water and proper sanitation. Very low rates of home ownership were found among all occupational groups (indicator score for dwelling ranged around 0.10 for all occupational groups; see Chapter 5; Figure 5.16), but of most concern was the traditional fishing community (the traditional fishers and mussel farmers groups) that mostly lived in what is officially an informal settlement along the river mouths and deltas at Cilincing and Muara Angke. Less than 20% of respondents from these fishers and mussel farmers groups said they lived in legally recognised dwellings (see Chapter 5; Section 5.1.1). Living in this informal settlement and in dwellings that are without legal status has rendered them more vulnerable to sudden changes (for example, forced relocations have occurred several times), made them less financially secure (with no legal title, their housing cannot be used as collateral) and more susceptible to adverse environmental conditions (for example, the lack of a proper sanitation system posed higher health risks to residents). Aside from these limitations, the informal dwellings of the traditional fishers and mussel farmers were important in supporting their occupations because they provide shelter for living and were often transformed into workspaces for the pre and post-processing fishery activities.

In 2003, in an effort to regulate and solve the problem of informal settlements, the government relocated the fishers in Muara Angke to nearby flats (Bunda Suci). However, before long they started to reject living in flats and move back to their informal houses. During the interviews, some respondents mentioned that several factors hampered their efforts to adapt to the flats and the new 'vertical lifestyle'. The issue raised most often was their financial inability to cover the regular expenses (rent, utilities, maintenance) because of their irregular income. The attraction of self identity and living culture of this traditional fishing community was highly valued. Their individual freedom, their perception of being close to the marine environment and their

154 occupations meant they required a relatively open living space close to the sea or river. To support and accommodate their main and supporting activities, whether fishing, mariculture, or pre and post-processing work, these lifestyle values and needs could not be met by living in the flats. Therefore, living farther from the sea in apartments was beyond their readiness to adapt and beyond their financial capacity or resources.

Governments and developers wanting to relocate this fishing community will have a complex transformation to manage. A reclamation project (National Capital Integrated Coastal Development/NCICD) includes plans to relocate and resettle the traditional fishers to a reclaimed area in the outer part of Jakarta Bay. Further thought, consideration and strong support from all stakeholders (especially governments) will be required because there are many occupational, socio-economic, cultural, as well as environmental aspects that will affect the success of such a relocation. For example, the fishers might need to adapt to new fishing locations in the outer part of the bay and will need further support to adjust to the fishing boats and gear suitable for deep-water fishing. Providing suitable and tenured settlement spaces for the traditional fishing community should be one aspect of the proposed solutions that could contribute to maintaining and sustaining their livelihood. Further work by social scientists accompanied with inclusive consultation with and participation by the community are required for any solutions that can encompass socio-economic, cultural and ecological aspects of a complex transformation. Those considerations are important to increase the likelihood of the fishing community continuing with fishing occupations and, at the same time, to expand their potential to obtain an adequate income, as well as to have a healthy environment for living and working.

Lack of sanitation and clean water in the areas of informal houses contribute to the poor living conditions that, in turn, expose the individuals and the community to higher health risks. Exposure to health risk is more serious because fewer than half of the respondents were registered for health insurance (see Chapter 5; Figure 5.5). Most of those who had insurance were the traditional fishers and mussel farmers who probably benefited from information provided through their active involvement in the cooperative organisations (KUB). Although many other studies include the availability of and proximity to health-care services or facilities as an important feature of physical capital (Bhandari, 2013; Hahn et al., 2009; and Motsholapheko et al., 2011), in this study, the ownership of health insurance was found to be a better indicator of health services. In

155 this case, health insurance would be most valuable where it can reduce the burden of health service costs from illnesses related to unhealthy living conditions. For instance, in addition to the pollutants that come from the water catchment areas, the open sanitation installed in the informal houses (usually placed over the rivers) has severely affected the water quality in the neighbourhood, contributing to the poor condition of the rivers and the bay. The primitive sanitation is also likely to be a local source of water-borne diseases, which are harmful for the community, particularly because the already heavily polluted rivers are usually used for daily activities (such as washing clothes and dishes and swimming). For the traditional fishers and mussel farmers, such insurance can be much more important because of their constant exposure to pollutants and their higher occupational health risks (as previously explained, see Section 6.1.2).

6.3. A vulnerability perspective on traditional fishing community in Jakarta Bay

As previously described, this research seeks to contribute to a deeper understanding of how the livelihoods and sustainability of the traditional fishing community in Jakarta Bay are affected by water pollution. The results from the vulnerability assessment sheds the light on the importance of a two-pronged approach, one that would improve the adaptive capacity of the community and reduce exposure to water pollution at the same time. This approach demonstrates the empirical contribution this research makes to the livelihood sustainability and vulnerability concepts by addressing the issue of water pollution impacts on the community. It offers a more holistic perspective and is most useful for the decision makers and managers to develop solutions to lessen the societal impacts of water pollution, reduce community vulnerability and help the households to sustain their livelihood.

6.3.1. Improving resilience: adaptive capacity and accessibility to capital

Improving adaptive capacity is essential to reduce the vulnerability of the traditional fishing community and make them more resilient to the pressures from water pollution and other external stressors. Increased capacity is hoped to improve the livelihoods and well-being of the community and leads to more sustainable and desirable livelihoods. The use of a livelihood capital assessment to represent the community's capacity helped identify the multidimensional aspects of livelihood well-being, such as proper housing, clean water, health insurance and facilities, and credit. This research shows that limited

156 availability and access to basic services are indeed among the major challenges to the living conditions of this community (as also shown in the previous section of this chapter; see also Chapter 5; Section 5.1.1 and 5.1.4). This is commonly observed in traditional fishing communities elsewhere, particularly in developing countries (Solaymani and Kari, 2014). Therefore, the fulfilment of basic amenities is a necessary part of the set of solutions to sustain the livelihoods of this community. In their study of Kenyan traditional fishers, Cinner et al. (2015) echoed this necessity to improve a community's capacity and they suggested financial support (such as credit access and training in non-fishery skills) and investment in infrastructure (such as water and electricity supplies and health services), so that the fishers could cope better with the impacts of changing environments and maintain their livelihood in times of hardship.

Any efforts to improve the adaptive capacity of this community should consider the complex nature of their livelihoods and well-being, deliberately shifting from the narrow income or economic views to the more multidimensional nexus of livelihood aspects. This is in line with other studies that showed productivity optimisation alone is insufficient to be a long-term solution for improving fishers' livelihood and well-being (Allison et al., 2011 and Jentoft and Midre, 2011). This new paradigm becomes crucial when considering current trends in the environments of the traditional fisheries in Jakarta Bay, which are ever degrading. Any form of support that seeks to maximise the fisheries productivity only, perhaps through provision of new fishing boats, engines or fishing equipment, will be less effective and possibly will increase the speed of the ecosystem collapse. It is no exaggeration that the sustainability of the traditional fishing community and the environment of Jakarta Bay requires a more fundamental transformation.

New insights from the livelihood analysis of Jakarta Bay communities identified in this research include key actions for capacity building and transformation that require immediate attention (based on the livelihood capital diagrams, See Chapter 5; Section 5.2.1). Table 6.1 presents the synthesis of recommendations developed through this research, along with a lists of potential agents or actors with important roles in initiating and supporting these actions. Several specific actions (shown in red, Table 6.1) are highly recommended for the traditional fishing community (traditional fishers and mussel farmers groups). These include training to give them non-fishery skills and to establish formal financial institutions that can give support to fishery activities. These

157 actions are expected to improve the resilience of these most vulnerable groups in coping with the adverse impacts of water pollution and other external stressors.

Table 6.1. Key actions and potential actors to improve the adaptive capacity and community’s resilience to water pollution

Potential actors Key actions Gov NGOs Res LC Human capital: Provide basic education training to improve literacy √ √ Provide non-fishery skills training* (including the promotion √ √ √ and support for agricultural jobs) Invest in formal education for children in the household* √ √ √ Disseminate water pollution-related information (this should √ √ √ √ include water pollution and health education)

Social capital: Facilitate the establishment of more KUBs and social groups √ (including for the mussel farmers) Improve regular monitoring, evaluation,and communication of √ √ √ the KUBs performance and the engagement of their members Facilitate interorganisational communication to minimise √ √ √ conflicts Encourage participation for the fishing community in the √ √ √ √ decision making through the KUBs

Financial capital: Extend micro-credit and other financial services, particularly √ √ for the fishing community Improve the functioning of fishing-related supplies and √ √ √ √ services (for example, soft loans, health and life insurance,

and saving schemes) Establish ecocompensation or other wealth redistribution to √ √ reflect costs and benefits of coastal developments (this could

involve the private sector as well)

Physical capital: Invest in the resettlement of the community (this should √ consider dwellings and supporting facilities for fishery- activities, such as pre and post-processing work areas) Improve and maintain the current communal amenities √ √ √ √ Develop and provide easier access to basic infrastructures and √ √ services (especially sanitation, clean water and health

insurance)

158

Potential Actors Key Actions Gov NGOs Res LC Natural capital: Assess and improve the regulations for waste dumping, waste √ √ water management and the bay’s development planning (include reclamation) Improve monitoring and enforcement of waste dumping √ √ Develop a spatial use zonation for Jakarta Bay (through √ √ √ √ inclusive participation) Notes: *These key actions are expected to improve and influence the financial capital as well as reduce the sensitivity of the community, by shifting their reliance from fishery resources to more diverse livelihood options; The recommendations highlighted in red are particularly important for the fishers and mussel farmers.

This traditional fishing community is close to the bustling part of a rapidly developing and well developed megacity (see Chapter 3; Figure 3.1). For instance, the fishing village of Muara Angke is very close to centres of business, high-rise residential areas and a port that connects Jakarta and the Thousand Islands (a major tourist attraction). Yet, this community does not have access to the city's amenities, particularly for basic services, such as adequate and secure housing and sanitation, health insurance, access to clean water and more opportunities to pursue employment options outside fisheries industries. This geographical setting in Jakarta Bay is starkly different from most traditional fishing communities in remote or rural settings and far from development that makes these rural fishing communities marginalised in terms of their accesses to public services, participation in development processes and livelihood options (Bene, 2006 and Islam and Chuenpagdee, 2013). Islam (2011) further emphasised that spatial remoteness prevents the fishers to build their non-fishery skills and, therefore, hampers their effort in expanding their livelihood alternatives. In many of the developing countries of Asia and Africa, fishing communities are struggling with geographical conditions that limit their access to markets, to education and to basic services, such as electricity and health services (Bene and Friend, 2011).

The living conditions of the Jakarta fishing community brings its own challenge that contributes to poor accessibility. For instance, the absence of legal tenure makes it difficult for proper permanent infrastructure to be installed, such as a water supply or a sanitation system. Improving and increasing the current communal amenities (such as communal water supply, water vendors and communal toilets) is therefore important in providing short-term yet less expensive solutions. Participation by the community members and interventions from governments or aid agencies are crucial to ensure that

159 these communal services are well maintained, affordable and able to accommodate all members of the community. For example, the government could collaborate with water vendors and subsidise or set a standard price for water for households. In a similar context of settlements of urban poor in developing countries, Duflo et al. (2012) proposed that micro-solutions (such as the communal services to overcome the problems of inadequate basic amenities) are often more feasible in informal settlements where legality is a main issue and large investment in infrastructure is not available. However, in an agricultural community in a remote area of Nepal, Gentle and Maraseni (2012) dwelt on the importance of long-term solutions that indeed required much more investment yet generated more significant positive impacts for the local community. They pointed out that large investments in infrastructure, by connecting the remote areas with the district's capital, had caused a chain of effects that contributed to more sustainable livelihoods because the community was able to obtain adequate sources of food and essential commodities as well as more markets for their agricultural produce. In the case of Jakarta Bay fishing villages, large investments in infrastructure and restructuring the informal settlements seems to be desirable. However, further research should consider the feasibility of the investment and restructuring as well as the appropriate processes to manage the land tenure, this to be done in consultation with the fishing communities and the other stakeholders (for example, governments, private sector businesses, such as developers) to ensure that such a transformation becomes a part of the long-term solutions to improve the livelihoods of these communities.

For the traditional fishing community, in addition to the problems of access to basic infrastructures and services, equally important is their access to the bay area and the fishery resources that are source of their livelihoods. Considering the public benefits, the multi-sectoral interests, strategic issues and high values of Jakarta Bay, regulations that are to manage the bay's spatial use are highly recommended to ensure the preservation of access for fishery activities (and other sectors). Such planning and regulation can be a valuable starting point for discussions and to provide fundamental guidance to manage the spatial utilisation in a way that minimises conflicts, to ensure that various sectors (including the traditional fishers) are accommodated, to control and ensure further development is in accord with the designation of the areas, and, most important, to promote more sustainable use of the space and resources in Jakarta Bay.

160

Currently, with no clear regulations to manage the spatial use of the coastal and water areas of the bay, its use by traditional fishing communities is threatened by other activities (see Chapter 4, Section 4.3). Some near-shore locations for fishing and mussel farming, for instance, were transformed into lands that forced the fishers and farmers to move further offshore and spend more efforts, reduced their incomes and increased their occupational risks. So far, there has been no zonation developed for the coastal waters despite the rapidly growing number and intensity of activities affecting the landscape of Jakarta Bay and its ecosystems’ functionality. At the time this thesis was being written, the detailed zonation plan for Jakarta Bay (Zonation Spatial Plan for Coastal Area and Small Islands of the Jakarta Special Capital Region/Rencana Zonasi Wilayah Pesisir dan Pulau-pulau KecilRZWP3K) was still underway (Mungkasa, 2015 and Personal communication KKP 2017). The lack of clear policy or of regulations to manage the complex values, uses and outcomes of even a simple designation of the bay's spatial use contributes to the resource use conflicts (in terms of access, space and coastal and marine resources) and subsequently affects the access to fishery resources for the already marginalised fishing communities.

In a review of marine spatial planning, Jones et al., (2016) found that one of the major challenges in the development of marine and coastal spatial planning was the lack of bottom-up processes or participation from stakeholders that often leads to serious discrepancies in the planning, implementations and outcomes. For the traditional fishing community, addressing this participation problem is essential considering they are unable to engage in or are excluded from decision making. This was clearly shown to be so for Jakarta Bay's fishers, for example in their pessimistic attitude to reclamation projects as well as their experience from past developments, which they associated with environmental degradation and declining fisheries (see Chapter 4; Section 4.2.1; Table 4.4). The on-going conflict about reclamation involved the fishing community and the private sectors because it caused a restriction of fishing activities in particular areas also demonstrated their disadvantage in dealing with the political and economic systems and how, in multi-sectoral, commercial competition, they would be prevented from access to public space and natural fishery resources in Jakarta Bay (see Chapter 4; Section 4.2.1).

As one of the most directly affected groups by any coastal spatial and development plans, the inclusion of local fishers in the discussions and the encouragement of more

161 participation are highly recommended in the literature (Yates and Schoeman, 2013). This Jakarta Bay research shows that the possibility of using participative methods could be a powerful tool to integrate local knowledge and to include the communities' voices in the planning. For example, in this research, participative workshops quickly empowered local expertise and helped the community to identify and discuss the threats, not only of water pollution, but also other intractable problems that they thought required attention. Using local knowledge, for example, by engaging community members to take part in participatory mapping, allowed the community to identify important fishing and aquaculture areas and other features of value (such as degraded coastal areas and past fishing grounds; see Chapter 4; Figure 4.11 and 4.14), which is important information for spatial planning of Jakarta Bay.

This research shows that ensuring the ongoing availability of livelihood capital (such as basic services) and the accessibility to the capital (such as healthy marine resources) is fundamental for the sustainability of the communities. On the one hand, as demonstrated in this research, engaging actively with the communities to measure their capacity as well as to identify their needs and limitations through participatory livelihood analyses was effective because it did provide a deeper understanding of their views and livelihood conditions. This knowledge and information would be most valuable for decision makers in developing better empowerment methods to build the capacities of different groups in the community. However on the other hand, the findings also show that the combination of socio-economic and political marginalisation, institutional and legal constraints, might be some of the structural and enduring factors that limit community members' efforts in having or accessing particular forms of capital. It is beyond the scope of this research to provide a more comprehensive analysis of these structural issues. However, it is clear that better understanding of the transformations to the system, structures (roles of the governments and multi-sectoral cooperation) and the processes (regulations and policies) are necessary as the essential parts in the solutions to overcome these issues of availability and accessibility and should be addressed in ongoing research.

6.3.2. Reducing exposure for more sustainable livelihoods

The findings show that the traditional fishing community has the capacity to withstand and adapt, yet it is most vulnerable to the consequences of water pollution (see Chapter

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5; Section 5.2; Figure 5.18 and 5.21). Therefore, any efforts to improve the sustainability of the community's livelihood should be complemented with efforts to reduce their exposure to external stressors, such as water pollution. This case study in Jakarta Bay adds new evidence to other studies (Allison et al., 2011; Allison and Horemans, 2006; and Bene, 2009) that show how traditional fishing communities, although not always the poorest might still be the most vulnerable because they are exposed more to external stressors and shocks (that include natural, anthropogenic and economic-related shocks). In their vulnerability study of a fishing community in Bangladesh, Islam et al. (2014) showed a community highly exposed to extreme shocks, such as flood, storms and climatic changes, which directly affected their fishing activities and livelihoods and made them one of the most vulnerable groups in the community. The combination of high exposure and limited options served to increase the community's susceptibility to external stressors or shocks and their potential to fall into poverty (Bene and Friend, 2011). This Jakarta Bay research contributes an empirical understanding of this phenomenon and adds to the published knowledge of livelihood vulnerability. It shows how a community, especially traditional fishing communities, is not only vulnerable to natural hazards (such as floods, extreme weathers and storm surges) that cause destruction of assets and loss of lives. In addition, anthropogenic hazards, such as pollution, are also a source of vulnerability and put additional pressures on their livelihoods. This is highly relevant because Jakarta is a coastal megacity where pressures from environmental stressors, such as water pollution, are high, worsening and affecting the livelihoods of local communities.

Following the insight that environmental stressors have adverse consequences for the livelihood and the well-being of this community, it is very important for decision makers to pay more attention to these stressors or anthropogenic hazards and develop management responses that support the affected communities. In contrast to natural hazards; the cause, occurrence and variables that define the source of the anthropogenic hazards (including water pollution) are more predictable compared with natural hazards (Omoboye and Festus, 2014). Controlling and regulating the sources of pollution therefore can make a difference and positive contribution to reduce the exposure on the community. For Jakarta Bay, the combination of good policy instruments (such as creating and enforcing effective regulations) and effective waste management is recommended to control the pollution and improve water quality and the environment.

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However, it should be noted that the complexity of water quality control in Jakarta Bay is the result of connections between activities and interests that often lie in different spatial and temporal scales. Knowing at what scale to pursue improvement is important. As asserted by Adger (2006) and Ribot (2009), acknowledging the roles of processes, institutions and stakeholders that work in different scales might help in planning targeted and appropriate actions that are best implemented at each scale to effectively reduce the source of exposure. Therefore, the government, as it generates effective solutions to improve the water quality of the bay and reduce the exposure on the community, will require the collaboration and cooperation of all interested parties. This is necessary because the poor quality of water in Jakarta Bay is the consequence of many activities beyond the area of the bay. Figure 6.1 presents the multiple-scales of the water pollution problem in Jakarta Bay and helps to define appropriate key recommendations at each scale as identified by the findings of this research. The differentiation of these actions at scales for action, from the smallest unit of a household to larger inter-regional scale, provides useful insights for people such as the community, decision makers and managers as they aim to improve water quality and to reduce the adverse effects of water pollution on the community.

Regional collaboration is necessary to restore the water quality of Jakarta Bay (Figure 6.1). This is because the pollutants flow from the far upstream areas as well as from the more localised activities (such as from open sanitation channels in the villages, waste water treatment plants and from reclamation project) all contribute to the decline of water quality. For example, the watershed for the Ciliwung river, which is in the province of West Java and includes the urban areas of Puncak and Bogor, has had massive destruction of its natural landscape and deforestation that has contributed to the increase in river-borne sediments and pollutants (Astuti et al., 2008). In addition, domestic and industrial activities in this watershed have caused further deterioration in the quality of water that flows into Jakarta Bay. Land-use control, monitoring the quality of river water and the construction of an eco-village in the watershed are some examples of Jakarta and West Java inter-government cooperation to restore the quality of the Ciliwung watershed environment (Personal communication KKP, 2017). Considering the importance of the upstream areas in shaping the water quality of Jakarta Bay, such cooperation should be included as a continuous measure in the management of this watershed and of Jakarta Bay.

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Figure 6.1. Multi-scale key recommendations to improve water quality and reduce the communities' exposure to water pollution

At the regional scale (this includes the city of Jakarta and the Jakarta Bay area), further evaluation of the current sewage treatment systems and greater investment in infrastructure to improve the quality of the water that flows into the bay are required (Figure 6.1). This investment should include improved systems for dealing with domestic waste because this appears to be a major contributor of pollutants in the river systems (Apip et al., 2015). The improvement of waste-disposal infrastructure and the enforcement of regulations should go hand in hand with more effort to regulate and control the development activities in the Jakarta Bay area. As the findings show, the areas with the highest pollution are spatially associated with reclamation activities in the southwest of the bay (see Chapter 4; Figure 4.14). Therefore, an urgent reassessment of the ongoing development and future development, a new zonation plan for Jakarta Bay, along with ways to ensure adequate investment in infrastructure and strict enforcement

165 of regulations, are essential to avoid more damage to the environment and arrest or reverse the decline of water quality.

Concurrently, the government should support and ensure active participation at the micro-level (that includes the local communities and the households) in actions to reduce their exposure to water pollution and its effects. For instance, redesignation of mussel farming areas to suitable but less polluted areas or repurposing mussel farming for water purification rather than human consumption would help reduce exposure to the effects of pollution. The latter was suggested by Haryati et al. (2013) in their analysis of green mussel aquaculture in Jakarta Bay. This should be done concurrently with efforts to raise the awareness and improve the knowledge of water pollution and health-related issues (for example, pollutant contaminations, sanitation and hygiene issues) in the community. Dissemination of relevant and practical information is a way to increase the community's understanding of the risks of exposure to pollutant and to generate self-awareness to support them in the decision making to reduce exposure. These forms of micro-level interventions are appropriate considering the current existence of thousands of households that still rely on the coastal fisheries and aquaculture whose quality is deteriorating. This research adds to a consensus calling for urgent and substantial action to minimise the community's exposure to water pollution and to ensure the fishing communities stay adaptive and avoid collapse. Long-term solutions for preserving functioning ecosystems that support goods and services (such as traditional fisheries) and sustain the fishing communities' livelihoods on the edge of megacities seem to be plausible only through transformational changes that would include integrated multi-scale management to restore water quality, improve the capacity of households and communities and ongoing adaptation by the Jakarta Bay communities to these changes as identified in the previous section.

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Chapter 7

Conclusion

Water pollution is one of the most challenging environmental issues for the coastal areas of Jakarta megacity. As this research demonstrates, it threatens not only the sustainability of the natural ecosystems but also the livelihoods of the members of resource-dependent communities, such as the traditional fishers and green mussel farmers, whose livelihoods depend on the bays' fishery resources. Combining, for the first time, biophysical and livelihood assessments, this thesis adds to the knowledge and provides new insights about how water pollution has affected the livelihoods of these already marginalised groups. It also contributes insights on how the livelihoods and occupational characteristics play important roles in influencing the susceptibility of different groups in the community to the impacts of water pollution as well as their coping strategies. These key findings and the implications provide valuable insights to inform future research and decision making that seeks to improve management of traditional fishing communities and of water quality of Jakarta Bay.

7.1. Improved biophysical assessment of water quality

This research showed that monitoring water regularly for Jakarta Bay is indeed required and should be continued as a part of an integrated system of water quality management. This research also identified opportunities for improving the current monitoring program by, first, developing a composite or multi-parameters water quality index, second, by focusing the monitoring on particular areas (for example, the near-shore areas near the industrial and reclamation areas and near the mussel farming areas) and third, by engaging the communities, particularly the traditional fishing communities, in monitoring water quality.

A water pollution exposure index map, such as the one developed for this research and using available data, would be useful as a communication tool and it could be of further use in informing decision makers and the managers about the spatial aspects of water quality conditions in the bay. The composite (multi-parameter) index developed and applied for the purposes of this research provided quicker and more informative measures of water quality and dominant parameters whereas it had previously been

167 analysed with reference to individual parameters only (BPLHD, 2001-2013). The exposure map also identified those areas that might require priority in management intervention and it also illustrated the need for whole of bay and whole of catchment responses. For instance, the analysis of dominant parameters (DO, TSS, turbidity and phenols) suggests that influence of riverine inputs and coastal construction activities (including land reclamation) are likely to play an important role in further degrading water quality in the most polluted areas in the southwest and southeast parts of the bay (see Chapter 4, Table 4.1 and Chapter 6, Section 6.1.1). More detailed analysis and intensive monitoring in these most polluted areas is highly recommended because these areas are used intensively for various commercial activities, such as land reclamation, green mussel farming and tourism. More specific studies, such as temporal analysis of water quality and shorter term analysis were also identified as prospects considering the influence of the Bay's hydrodynamics on water quality conditions. In addition, the identification of the sources of pollutants is recommended to guide the evaluation of management responses and to improve measures to control the bay's environment (Chapter 6; Section 6.1.1).

Engaging the local communities, particularly the fishers and mussel farmers, in the management of water quality (such as monitoring and knowledge sharing) should be encouraged because, among other things, community cooperation has the potential to contribute new and enriching knowledge. As described in previous chapters (Chapter 4 and Chapter 6), the fishing community have demonstrated the value of their local knowledge of water pollution, of the dynamic changes in the environment, and of their fishing resources and their activities (for instance, the mussel and fish kills, changes in commodity and new fishing grounds). The discovery of the value and importance of involving local traditional fishers in assessing the environmental and fishery conditions has been described in other studies (Anticamara and Go, 2016 and Muallil et al., 2014). However, this research contributes first-hand experiences and knowledge of Jakarta Bay's fishers for the first time and shows these communities to be sources of credible information for researchers and managers who are working on halting the collapse of the aquatic system, on improving livelihoods and conserving the environmental values of Jakarta Bay. For example, fishers' knowledge add spatial coverage and temporal extent to the regular water quality data (see Chapter 4; Section 4.3). Their direct observations of organism kills and changes in mussel commodities (see Chapter 6;

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Section 6.1) also add meaning to the values they place on having functioning, uncontaminated ecosystems. This local knowledge is important and complements more formal biophysical scientific research because it enables abrupt environmental changes or events to be identified, events that are often not able to be assessed by researchers or managers at the time they happen.

7.2. Understanding the impacts of water pollution better by using an integrated approach

The successful integration of the biophysical and participatory methods used in this research provided a powerful approach that has enabled this research to deepen the knowledge of the impacts of environmental stressor on humans, which is not well researched compared to the biophysical aspects (Moser, 2010). This knowledge is most useful in informing decision makers who want to formulate appropriate management and support to help the fishing communities to cope better with the impacts of water pollution.

Water pollution is one of the environmental stressors that contribute to the degradation of the environment and ecosystem of Jakarta Bay. However, this research shows clearly that adverse consequences of water pollution affected individuals and their communities, especially the traditional fishing communities whose livelihoods depend on the quality of fishery resources. Water pollution threatens the traditional fisheries by causing changes in productivity and fishing locations. Fluctuations in productivity are common in most fisheries, yet water pollution contributes to sustained damage to marine organisms and natural habitats, which, in turn, affects productivity (see Chapter 4; Section 4.4). Decline in near-shore fisheries has also forced the fishers to spend more time and effort to find fishing locations further afield where the higher productivity was attributed to less pollution (see Chapter 4; Section 4.3). The consequences of declining productivity and of increased effort are that they further marginalise the members of traditional fisheries communities. Many fishing households have absorbed the increases in costs and uncertainty in size and quality of catches, of incomes and the additional resources needed to do fishing (for example, in terms of time and financial effort). This research shows that the traditional fishing households, whose primary occupations are already exposed to much uncertainty and risks (Finkbeiner et al., 2015), disproportionately suffer from these increased uncertainties caused by environmental

169 stressors, such as water pollution. This means additional pressure that in the end affects their capability to make a living and maintain a decent livelihood (see Chapter 6; Section 6.1.3).

By incorporating participative methods, new insights were gained about differential water pollution exposure experienced by the occupational groups. Mussel farmers were considered to be the group most exposed to water pollution because their occupation requires them to work in the most polluted areas. It is almost impossible for them to avoid direct immersion in polluted water because they dive to harvest the mussels. To minimise their exposure to pollutants they wear protective face masks and suits made from a fabric that they consider can lessen their health risks. In addition, it is clear that the risks of exposure to some pollutants might also be from eating contaminated green mussels. Therefore, relocating mussel farming to cleaner areas of the bay (accompanied by the effort to improve the condition of the bay's water quality) is highly recommended if this type of aquaculture is to continue to produce food for human consumption.

Different approaches are required to support the fishers group in their effort to minimise their exposure to water pollution. The traditional fishers are more mobile and the evidence shows they are adapting to their changed circumstance by shifting their fishing activities to the less polluted areas farther offshore. Therefore, support from NGOs or governments should be to help these fishers adjust to working in the offshore environments by making arrangements for them to have the use of bigger fishing boats and different types of fishing gear. Other supportive schemes, such as fuel subsidies, and health, life, and property insurance, will make for better working conditions and enable their productivity to continue as they move their operations to the deeper parts of the bay.

The communities were capable and keen to develop their own mechanisms to deal with the uncertainty and water pollution exposure. For mussel farmers, innovation in the use of protective suits and adjustments to their harvest times, and shifts in fishing areas by fishers were ways of coping that related to their fishing activities (see Chapter 4). In addition to these, the traditional fishing community used other strategies (that included income diversification from additional jobs or from additional employment by family members, reliance on social networks, and making the decision to leave fishing) that

170 helped them to cope in times of hardship (see Chapter 5). Increased uncertainties resulting from water pollution make these coping strategies more important than before.

However, some practiced strategies, such as diversification of sources of income are less likely to provide better options for the communities' livelihoods if the additional income sources are still related to fisheries. Shifting from the complete reliance on fisheries for their livelihoods is likely to be more effective in ensuring more sustainable livelihoods and at the same time reducing pressure on the environment and fishery resources. In the face of declining fisheries, equipping the fishing communities and their household members with non-fishery skills is probably a prudent alternative. Building these non-fishery skills, such as in agricultural or farming sectors, might be apt for those who already own land in their hometowns and villages, and improving the basic education standards (literacy) should be given priority in any empowerment program. Most important, programs to develop these skills should include the other household members as a way to reduce the households' dependency on fisheries, to increase their resilience and to provide more employment opportunities, particularly for those children who wish to pursue non-fishery jobs.

7.3. Reducing the livelihood vulnerability: a two-pronged approach

The research found that there were multiple elements that contributed to the vulnerability to water pollution of the different occupational groups in the community. The elements of exposure, adaptive capacity and sensitivity, were found to vary among the groups. Therefore, interventions to reduce the vulnerability of these different groups will require specific management action. Better solutions can be expected to come after considering all the key elements of vulnerability. Development programs to reduce vulnerability should consider a two-pronged approach, so that they can effectively reduce the households' exposure and empower them at the same time by building their capacities. This is because, the conditions required for the traditional fishing community to maintain their livelihoods from fishery in Jakarta Bay depend on reducing their exposure to external stressors such as water pollution, and also redesigning their livelihood capacity.

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7.3.1. The need to reduce exposure to water pollution

The research contributed new evidence to complement other studies (Allison et al., 2011) that demonstrate that the traditional fishing communities (that is, the traditional fishers and mussel farmers) are not always necessarily the poorest in financial or capacity terms. Rather, these particular groups are the most vulnerable because of their high exposure to water pollution. Although some coping strategies enabled the fishers and the mussel farmers to deal with the impacts of water pollution (described in Section 7.2), clean environments and ecosystems, and healthy fishery resources are important outcomes to achieve the sustainability of the traditional fisheries in Jakarta Bay. The implication of this finding is that it is now urgent and very important for decision makers to understand that improving water quality, the environments and ecosystems of Jakarta Bay, amongst other things (such as preventing overfishing and controlling coastal development) should be integral to their planned solutions. By reducing fishing communities' vulnerability and sustaining their livelihoods, the authorities will be saving Jakarta Bay and ensuring its long-term socio-economic and environmental value.

The analysis of the water pollution sources performed in this research helps to make specific multi-scale (inter-regional to household scale) recommendations to guide the efforts to restore water quality and minimise the exposure. The findings show that provision of health and sanitation education and facilities for the households is as important a consideration in management of water quality as inter-regional collaboration. As previously described (in Chapter 6; Section 6.3.2), acknowledging how the processes occuring at different spatial (and also temporal) scales influence the water quality in this specific area and for a particular community will contribute to the development of more effective management plans to solve the problem of water pollution in Jakarta Bay.

7.3.2. Capacity building to improve resilience

Capacity building, intended to help traditional fishing communities to be more resilient, is vital considering: (i) Firstly, the results from restoration and improvements in water quality and the ecosystems will take a long time to eventually take into effect because of the multi-scale nature of the problem as well as some other challenges that Breckwoldt et al., (2016) referred to as 'political priorities' and the need to incorporate large and expensive infrastructure projects (for example, sophisticated and integrated waste

172 treatment systems). Yet, there is an urgency to ameliorate the current vulnerability of the fishers and mussel farmers, especially with regard to the potential effects of contamination and banning the farming of green mussels. (ii) Secondly, some of the indicator-based recommendations identified in this research could help the fishing community to cope not only with the impacts of water pollution, but also with other stressors, and with the uncertainties that are already embedded in their occupations and lifestyle. The livelihood analysis (see Chapter 5) provided a multi-dimensional perspective of livelihood well-being and insights that can be used as a guide for the government or NGOs in developing their capacity building programs. The livelihood capital and its indicator indices, enriched by accounts of lived experience, enabled important and detailed aspects of livelihoods to be identified and to be given the most attention, so that the type of support given by stakeholders can be aligned with the needs of the groups in the community.

For the fishers and mussel farmers, investments and development programs to improve several aspects of human, physical and social capital were shown to be very important (see Chapter 5 and 6). These are particularly important for the mussel farmers, the most vulnerable group, whose livelihoods are threatened by the ongoing ban on mussel farming and by the effects of contamination. There are hundreds of households in Jakarta Bay that still rely on mussel farming as their source of livelihood with no (or temporary or ineffective) solutions to mitigate the problems caused by contamination of the water and the green mussels. As explained before, increasing non-fishery skills (in addition to basic education) might be one of the keys to shift their reliance on fisheries. This skill building is hoped to provide the households with wider livelihood options (either to complement or to substitute for work in fisheries) and thus it can help reduce their sensitivity to stressors, such as water pollution. Provision of basic and supporting amenities, that include proper dwellings with legal titles, accesses to health insurance, clean water, proper sanitation and to soft credit, were found to be necessary to lessen the adverse consequences of water pollution and other stressors and to help the households be more resilient.

The function of formal social organisations such as KUB, and the active involvement of the fishing communities should also be encouraged. It is important for governments to establish and maintain such occupational organisations and accompany this with regular monitoring and inclusive communication to achieve the effectiveness of the

173 organisations in performing its functions. It is necessary to expand the functions of KUBs to be an extension of governments' hands in providing professional support and also for KUBs to be a communication channel and interactive platform that enables and encourages active participation of the traditional fishing communities in giving voice to their knowledge, concerns and thoughts that can be integrated with the decision making and development processes.

7.4. Future research

Using an integrated approach that combined biophysical and participative methods, this research has achieved its objectives to understand better the societal impacts of water pollution on the traditional fishing community in Jakarta Bay and how they have responded to and coped with the adverse impacts. This integrated approach and the holistic perspective resulted in important contributions to the discipline of oceanography, which has been predominated by biophysical analysis. In the context of vulnerability, the insights gained from this research also contributed to demonstrating livelihood vulnerability to be a powerful concept that offers ways to understand better the interaction between resource-dependent communities and the environment (as an example of complex socio-environmental problem) and provides more holistic perspectives for natural resource management. Furthermore, the ideas and methods used in this research may inspire other coastal megacities that are dealing with similar socio- environmental issues and seeking ways to develop comprehensive solutions to support the sustainability of the livelihoods and the environment. Although, as this research has shown, the concept of livelihood vulnerability is contextual in nature, this research provides a foundation of ideas and methods that are transferrable and could be adjusted as necessary.

The development of weighting methods through community and other experts' discussions in the livelihood vulnerability assessment, however, is recommended in any replication of this research. Equal weighting was used in this research for all types of capital and their indicators because of restraints on time and resource. For example, natural capital and social capital were weighted equally, although it is possible that the communities put different values on particular type of capital that they considered more important in the context of coping with water pollution. The use of a weighting method in future research will improve the results of vulnerability assessment by ensuring that

174 the variability of the indicators that shapes the indices of vulnerability and its elements are not overlooked (Moret, 2014). Participatory-based approaches (that include the communities and experts) used in determining the indicators' weights are strongly advised to enable the identification of the most important and relevant indicators and to better capture and represent the reality of local conditions.

The experience of working with Jakarta Bay's fishing community also identified opportunities for future research. The analysis of water quality showed several areas, which were found to be the most polluted areas and that require urgent attention, are near the industrial area of Marunda and near reclamation areas in the southwest part of the bay. More detailed biophysical research in these areas is necessary to understand the source of pollutants and their ecotoxicology impacts on the ecosystems (for example, biophysical research to investigate the cause of green mussel kills and the rise of a new black mussel species). The information gained from such research will further inform managers and decision makers and enable more tailored action to manage and control pollutants and conserve the ecosystems. The need for detailed analysis is also related to the potential health risks of pollutants on humans because the sites for green mussel farming were in these highly polluted areas. Such detailed analysis will also help to identify potential health risks to different groups of community. The framework employed in this study, which assumed that the informal workers group had zero exposure to water pollution, was a simplification likely to underestimate their exposure. Actually, their exposure to pollutants is probably related to their seafood consumption, another important source of exposure which was not covered in this research. In addition, there is a wide market for seafood caught from the bay (including the green mussels) that could contribute to the risk of a health disaster. That risk, common to other coastal megacities, means there is an urgent need to give more attention to reducing and remedying the risks to health from contaminated seafood.

Finally, the research identified the need to understand better the transforming factors and multi-scale (spatial and temporal) interactions that shape the households' vulnerability and the key elements of vulnerability (adaptive capacity, sensitivity and exposure). Further research about transforming structures (institutional landscapes and policy instruments) could improve and complement the understanding of the issue of capital accessibility, which is not covered directly in this research. For example, identifying the roles of government and the private sectors (such as developers and

175 industries) in influencing the access of fishing communities to fishery resources in Jakarta Bay is important to obtain better understanding of the inter-sectoral interactions and how these can be improved to reduce conflict and the vulnerability of the fishing communities. More research into the institutional and socio-economic landscapes that shape the upstream areas of Jakarta Bay is also required to understand how much these factors contribute as the sources of exposure and vulnerability for the coastal communities. Such studies are important for formulating more integrated coastal management systems in Jakarta Bay that can bring significant improvement to the bay's environment and the communities' livelihoods.

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APPENDIX

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Appendix 1: Coordinates of the sampling sites in Jakarta Bay

Position Sites Longitude Latitude A1 106o42’20” 05o59’40” A2 106o44’50” 05o59’00” A3 106o47’20” 05o58’20” A4 106o50’00” 05o57’50” A5 106o52’40” 05o57’10” A6 106o55’20” 05o56’30” A7 106o58’00” 05o56’00” B1 106o42’50” 06o02’00” B2 106o45’30” 06o01’30” B3 106o48’00” 06o01’00” B4 106o50’40” 06o00’20” B5 106o53’20” 05o59’40” B6 106o56’00” 05o59’00” B7 106o58’40” 05o58’30” C2 106o46’10” 06o04’10” C3 106o48’50” 06o03’30” C4 106o51’20” 06o02’50” C5 106o54’00” 06o02’10” C6 106o56’40” 06o01’40” D3 106o49’30” 06o05’50” D4 106o52’00” 06o05’20” D5 106o54’40” 06o04’40” D6 106o57’20” 06o04’00”

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Appendix 2: Water quality datasets summary (continued) Parameter DO BOD TSS Sites Mean Min Max Sdev Mean Min Max Sdev Mean Min Max Sdev A1 4.4562 1.6400 7.0550 1.4065 19.9959 1.3100 42.1900 13.8928 9.2000 1.0000 62.1000 13.8022 A2 4.7019 1.9500 9.4550 1.7780 16.8739 0.6000 38.0000 11.9852 7.4800 1.0000 47.9000 10.3079 A3 4.6162 2.1100 7.8650 1.4675 21.2711 0.3000 93.1000 22.7392 5.8840 1.0000 40.2000 8.5945 A4 4.4230 2.0750 7.7600 1.4802 17.8066 0.7000 53.3500 14.2811 6.4560 1.0000 48.3000 9.9986 A5 4.5002 2.1550 7.3950 1.3369 18.5346 0.1200 42.9000 14.2682 9.4120 1.0000 92.3000 19.5657 A6 4.4860 1.9000 8.7600 1.5987 17.3089 2.5300 52.3500 13.3866 6.1800 1.0000 47.0000 9.5338 A7 4.4300 2.0500 9.1800 1.6436 19.7428 1.3200 50.0000 13.1976 7.2731 1.0000 35.0000 6.7894 B1 4.2562 1.6900 6.9400 1.6216 21.1177 1.7800 55.0500 14.0556 10.4038 2.0000 71.2000 13.8938 B2 4.6119 1.4700 9.6400 1.8202 21.5671 1.0900 62.8500 17.2407 7.9120 2.0000 42.1000 9.5829 B3 4.9068 2.2350 12.5000 2.1533 19.1868 0.4000 44.2000 12.4294 7.4520 1.0000 51.7000 9.9468 B4 4.5806 2.0250 8.1450 1.5919 18.8169 1.0100 40.5000 12.3011 7.4615 1.0000 55.0000 12.2613 B5 4.6668 2.0950 8.7000 1.6917 19.6500 0.4000 55.2000 13.8828 8.2600 1.0000 58.4000 13.2537 B6 4.6171 1.5000 8.7250 1.6583 18.2839 0.3000 44.8000 11.9876 8.9920 1.0000 59.3000 14.0164 B7 4.4412 1.6900 9.0000 1.7489 18.9790 0.4000 54.8000 12.7256 10.3919 1.0000 79.3000 18.8730 C2 4.5650 1.3050 10.2550 1.9730 20.5650 0.9100 55.0000 12.5184 11.6038 2.0000 64.8000 12.3809 C3 4.6694 1.9521 10.8000 1.9557 21.4331 1.3100 53.9000 14.8098 8.0800 1.0000 57.1000 10.9516 C4 4.7197 2.1400 8.4700 1.7125 21.2387 0.1200 67.2000 15.8536 9.4269 1.0000 51.2000 12.7574 C5 4.9466 1.8050 12.8000 2.1794 22.4311 1.0100 63.9600 16.5845 10.6280 1.0000 63.1000 15.6111 C6 4.9040 1.8050 15.1000 2.6216 20.8779 1.6100 64.0000 15.5771 8.5654 0.3000 46.3000 10.0237 D3 4.5342 1.3536 12.1000 2.3854 25.2807 3.0900 74.4000 16.5242 11.3115 1.0000 72.9000 16.7795 D4 4.9138 1.4000 13.0000 2.4853 21.0953 0.4000 67.7000 15.1615 10.2500 1.0000 60.9000 15.2882 D5 5.2971 1.8450 16.0000 2.9649 20.3740 3.5800 45.2500 12.2657 9.6833 2.0000 81.0000 15.8678 D6 4.5305 1.8000 13.6000 2.3664 21.4821 1.1100 48.9000 15.2291 12.9996 1.0000 91.0000 22.0333

213

Appendix 2: Water quality datasets summary (continue)

Parameter Turbidity Phosphate Nitrate Sites Mean Min Max Sdev Mean Min Max Sdev Mean Min Max Sdev A1 3.7763 0.0000 14.0000 3.7485 0.0368 0.0000 0.2200 0.0640 0.0296 0.0000 0.1800 0.0463 A2 2.6694 0.0000 8.5500 2.5505 0.0319 0.0000 0.2900 0.0756 0.0516 0.0000 0.2600 0.0833 A3 2.1089 0.0000 8.5600 2.5377 0.0329 0.0000 0.1800 0.0558 0.0436 0.0000 0.2050 0.0643 A4 2.4084 0.0000 12.6000 3.5203 0.0196 0.0000 0.1600 0.0406 0.0331 0.0000 0.2300 0.0573 A5 2.3072 0.0000 6.1500 2.1170 0.0249 0.0000 0.2300 0.0564 0.0749 0.0000 0.9990 0.2393 A6 3.0589 0.0000 9.5000 2.9831 0.0153 0.0000 0.0730 0.0226 0.0599 0.0000 0.2170 0.0669 A7 6.2932 0.0000 28.5000 6.8611 0.0598 0.0000 0.9100 0.2011 0.0740 0.0000 0.2700 0.0930 B1 6.0484 1.5000 25.5000 5.4599 0.0425 0.0000 0.3100 0.0748 0.0444 0.0000 0.1280 0.0440 B2 2.6889 0.0000 5.0000 1.8544 0.0537 0.0000 0.7410 0.1578 0.0974 0.0000 0.6500 0.1709 B3 3.0961 0.0000 8.1000 2.5938 0.0454 0.0000 0.3100 0.0930 0.0475 0.0000 0.2300 0.0604 B4 1.9768 0.0000 6.3800 2.2475 0.0616 0.0000 0.2700 0.1021 0.0337 0.0000 0.1950 0.0545 B5 2.8194 0.0000 12.0000 3.0604 0.0386 0.0000 0.2560 0.0710 0.0581 0.0000 0.2100 0.0740 B6 2.7861 0.0000 7.0000 2.1557 0.1007 0.0000 0.8050 0.2304 0.0491 0.0000 0.3700 0.0890 B7 3.8868 0.0000 12.0000 2.8069 0.0352 0.0000 0.4040 0.0869 0.0572 0.0000 0.3310 0.0823 C2 5.7474 1.0000 16.0000 3.8306 0.1055 0.0000 0.9000 0.1913 0.0668 0.0000 0.2480 0.0678 C3 2.6711 0.0000 8.3000 2.5202 0.0351 0.0000 0.1900 0.0511 0.0380 0.0000 0.1800 0.0502 C4 2.7147 0.0000 8.0000 2.6976 0.0278 0.0000 0.2300 0.0622 0.0131 0.0000 0.0700 0.0200 C5 2.9917 0.0000 14.0000 3.4137 0.0425 0.0000 0.3710 0.0878 0.0181 0.0000 0.1400 0.0373 C6 3.4842 0.0000 12.0000 2.9541 0.0352 0.0000 0.2020 0.0640 0.0926 0.0000 0.9070 0.2078 D3 2.8279 0.0000 10.0000 3.0885 0.0362 0.0000 0.2400 0.0593 0.0196 0.0000 0.1300 0.0349 D4 2.8989 0.0000 8.0000 2.5047 0.0413 0.0000 0.3500 0.0833 0.0414 0.0000 0.2270 0.0676 D5 3.0433 0.0000 8.0000 2.3441 0.0319 0.0000 0.1430 0.0443 0.0690 0.0000 0.5700 0.1466 D6 3.5895 0.0000 13.0000 3.1633 0.0560 0.0000 0.7400 0.1575 0.0504 0.0000 0.2270 0.0598

214

Appendix 2: Water quality datasets summary (continue)

Parameter Total Ammonia Detergent Phenol Sites Mean Min Max Sdev Mean Min Max Sdev Mean Min Max Sdev A1 0.1032 0.0000 0.6100 0.1430 0.1048 0.0000 0.3300 0.1064 0.0519 0.0000 0.4400 0.0965 A2 0.1002 0.0000 0.4600 0.1069 0.0759 0.0000 0.1500 0.0528 0.0552 0.0000 0.6900 0.1396 A3 0.0967 0.0000 0.4700 0.1141 0.0674 0.0100 0.1600 0.0518 0.1024 0.0000 1.3300 0.2889 A4 0.1063 0.0000 0.5900 0.1564 0.0698 0.0000 0.2500 0.0883 0.0925 0.0000 1.5000 0.2978 A5 0.0832 0.0000 0.4500 0.1000 0.0787 0.0000 0.1800 0.0649 0.0468 0.0000 0.4100 0.0943 A6 0.1222 0.0000 0.8800 0.1840 0.0747 0.0000 0.1500 0.0502 0.0331 0.0000 0.2800 0.0610 A7 0.1131 0.0000 0.5100 0.1212 0.1314 0.0000 0.4000 0.1265 0.0409 0.0000 0.3000 0.0769 B1 0.1249 0.0000 0.4700 0.1400 0.1019 0.0000 0.4000 0.1305 0.0403 0.0000 0.2700 0.0754 B2 0.1401 0.0000 0.5800 0.1635 0.1379 0.0100 0.2600 0.0958 0.0361 0.0000 0.2600 0.0633 B3 0.1020 0.0000 0.3500 0.0978 0.0910 0.0400 0.1600 0.0387 0.0424 0.0000 0.2800 0.0785 B4 0.0660 0.0000 0.2900 0.0595 0.1321 0.0000 0.5700 0.1819 0.0560 0.0000 0.8100 0.1608 B5 0.0852 0.0000 0.4100 0.0938 0.1520 0.0000 0.4100 0.1474 0.0424 0.0000 0.2900 0.0759 B6 0.1222 0.0000 0.8000 0.1872 0.1311 0.0000 0.4400 0.1467 0.0365 0.0000 0.2300 0.0641 B7 0.1068 0.0000 0.3400 0.0951 0.1875 0.0400 0.6500 0.2156 0.0345 0.0000 0.2400 0.0600 C2 0.3498 0.0000 1.7900 0.3811 0.2181 0.0200 0.5400 0.1996 0.0412 0.0000 0.2400 0.0647 C3 0.1540 0.0000 0.4900 0.1620 0.1240 0.0200 0.2700 0.0751 0.0384 0.0000 0.2300 0.0635 C4 0.1103 0.0000 0.6900 0.1549 0.1161 0.0000 0.2900 0.0840 0.0442 0.0000 0.2330 0.0714 C5 0.1084 0.0000 0.4400 0.1052 0.1749 0.0700 0.5500 0.1689 0.0390 0.0000 0.2600 0.0690 C6 0.1980 0.0000 1.2600 0.2677 0.1480 0.0000 0.4900 0.1466 0.0732 0.0000 0.5800 0.1415 D3 0.1121 0.0000 0.3700 0.1032 0.1618 0.0400 0.5500 0.1682 0.0607 0.0000 0.7000 0.1438 D4 0.1360 0.0000 0.4100 0.1256 0.1381 0.0400 0.3800 0.1055 0.0674 0.0000 0.7300 0.1490 D5 0.2671 0.0000 2.4200 0.4674 0.1850 0.0600 0.5600 0.1755 0.0714 0.0000 0.8300 0.1690 D6 0.1830 0.0000 0.8400 0.2108 0.1640 0.0000 0.4800 0.1718 0.0638 0.0000 0.4700 0.1178

215

Appendix 2: Water quality datasets summary

Parameter H2S Pb Zn Sites Mean Min Max Sdev Mean Min Max Sdev Mean Min Max Sdev A1 0.0339 0.0000 0.1800 0.0435 0.1319 0.0000 0.6500 0.2474 0.0538 0.0000 0.1600 0.0474 A2 0.0234 0.0000 0.2000 0.0496 0.1356 0.0000 0.7500 0.2647 0.0505 0.0000 0.3300 0.0672 A3 0.0403 0.0000 0.4400 0.1082 0.1506 0.0000 0.7100 0.2678 0.0437 0.0000 0.1500 0.0409 A4 0.0423 0.0000 0.3000 0.0805 0.1393 0.0000 0.7400 0.2663 0.0534 0.0000 0.1800 0.0535 A5 0.0499 0.0000 0.5000 0.1220 0.1341 0.0000 0.5800 0.2099 0.0544 0.0000 0.2300 0.0627 A6 0.0202 0.0000 0.1300 0.0335 0.0995 0.0000 0.4100 0.1563 0.0638 0.0000 0.3100 0.0783 A7 0.0577 0.0000 0.3200 0.0920 0.0614 0.0000 0.2460 0.0964 0.1038 0.0000 1.4600 0.2931 B1 0.0399 0.0000 0.1480 0.0441 0.0910 0.0000 0.4000 0.1573 0.0705 0.0000 0.5200 0.1184 B2 0.0263 0.0000 0.1200 0.0367 0.0794 0.0000 0.3200 0.1291 0.0898 0.0000 0.6000 0.1531 B3 0.0316 0.0000 0.2200 0.0549 0.0819 0.0000 0.3800 0.1483 0.0672 0.0000 0.5600 0.1167 B4 0.0371 0.0000 0.3000 0.0741 0.0906 0.0000 0.3900 0.1519 0.0465 0.0000 0.1700 0.0502 B5 0.0528 0.0000 0.4400 0.1126 0.0654 0.0000 0.3100 0.1182 0.0477 0.0000 0.1600 0.0449 B6 0.0798 0.0000 1.0300 0.2542 0.0579 0.0000 0.2400 0.1005 0.0526 0.0000 0.2800 0.0651 B7 0.0958 0.0000 1.2900 0.3190 0.0658 0.0000 0.2210 0.0977 0.0458 0.0000 0.1500 0.0432 C2 0.0451 0.0000 0.3500 0.0828 0.0754 0.0000 0.2780 0.1116 0.0526 0.0000 0.2300 0.0607 C3 0.0503 0.0000 0.5500 0.1307 0.0519 0.0000 0.2400 0.0920 0.0460 0.0000 0.1700 0.0466 C4 0.0398 0.0000 0.3100 0.0746 0.0746 0.0000 0.2600 0.1103 0.0799 0.0000 0.8400 0.1688 C5 0.0254 0.0000 0.0760 0.0245 0.0580 0.0000 0.3190 0.1133 0.0970 0.0000 0.7400 0.1613 C6 0.0181 0.0000 0.0400 0.0194 0.0634 0.0000 0.2520 0.0978 0.0562 0.0000 0.3200 0.0746 D3 0.0458 0.0000 0.2500 0.0663 0.0730 0.0000 0.2500 0.1106 0.0534 0.0000 0.2600 0.0617 D4 0.0442 0.0000 0.3600 0.0833 0.0713 0.0000 0.2900 0.1157 0.0396 0.0000 0.1500 0.0425 D5 0.0266 0.0000 0.0800 0.0237 0.1121 0.0000 0.5400 0.2052 0.0432 0.0000 0.1800 0.0512 D6 0.0356 0.0000 0.2700 0.0650 0.1428 0.0000 0.5000 0.2096 0.0476 0.0000 0.2800 0.0578

216

Appendix 3: Boolean operator for calculation of exposure index Boolean DO TSS Phophate Turbidity Site DO TSS Phosphate Turbidity value BValue BValue BValue BValue (Total) A1 1.6400 1 10.0000 1 0.0200 1 5.0000 1 4 A2 1.9500 1 9.5000 0 0.0125 0 4.8750 0 1 A3 2.1100 0 5.0000 0 0.0315 1 4.4250 0 1 A4 2.0750 0 5.0000 0 0.0153 1 4.7950 0 1 A5 2.1550 0 5.0000 0 0.0100 0 4.2850 0 0 A6 1.9000 1 6.0000 0 0.0140 0 5.0000 1 2 A7 2.0500 0 8.9500 0 0.0308 1 7.0000 1 2 B1 1.6900 1 11.5000 1 0.0375 1 7.2500 1 4 B2 1.4700 1 7.0000 0 0.0193 1 4.6250 0 2 B3 2.2350 0 7.0000 0 0.0270 1 5.0000 1 2 B4 2.0250 0 5.9750 0 0.0500 1 4.5900 0 1 B5 2.0950 0 5.1000 0 0.0350 1 4.7500 0 1 B6 1.5000 1 6.7000 0 0.0600 1 4.7875 0 2 B7 1.6900 1 9.2500 0 0.0300 1 5.2500 1 3 C2 1.3050 1 14.3750 1 0.1250 1 7.5000 1 4 C3 1.9521 1 9.3000 0 0.0575 1 5.0000 1 3 C4 2.1400 0 10.0000 1 0.0160 1 5.0000 1 3 C5 1.8050 1 10.0000 1 0.0500 1 5.0000 1 4 C6 1.8050 1 8.0750 0 0.0343 1 5.0000 1 3 D3 1.3536 1 10.2500 1 0.0475 1 5.0000 1 4 D4 1.4000 1 8.7500 0 0.0320 1 5.0000 1 3 D5 1.8450 1 9.4750 0 0.0500 1 5.0000 1 3 D6 1.8000 1 10.2000 1 0.0450 1 5.0000 1 4 Notes: Threshold values DO: 2 mg/l; TSS: 10 mg/l; Turbidity: 5 NTU; Phosphate: 0.015 mg/l

Exposure Cluster Sites Index 1 A3, A4, A5, A6, B3 0.30 2 A1, A2, B2, B4, B5, B6, B7, C3, C4, C5, D3 0.64 3 A7, B1 0.75 4 C6, D4, D5, D6 0.81 5 C2 1.00 Exposure index calculation for cluster 3 (illustration): BValue(Total)A7 + BValue(Total)B1 6 = = 3 2 2

Standardised value of Exposure Index:

= = 0.75

Value cluster 3 3 Max value−Min value 4−0 217

Appendix 4: Questionnaire Form Indonesian version

Survey kuesioner untuk komunitas pesisir Jakarta Utara, Indonesia

ID/Nama : Jenis Kelamin :

Lokasi : Tanggal :

Pekerjaan : Waktu :

A. Informasi Pekerjaan 1. Apakah pekerjaan utama Anda? 1. Nelayan tradisional 2. Pembudidaya kerang tradisional 3. Pekerja lainnya/buruh 2. Pada tahun berapa Anda memulai pekerjaan utama Anda tersebut? ______3. Sejak kapan Anda tinggal di kampung nelayan ini? ______

B. Perubahan Lingkungan 1. Menurut pendapat Anda, apakah tiga permasalahan lingkungan yang paling penting di Teluk Jakarta dan pesisir Jakarta saat ini? (Lingkari jawaban yang sesuai) 1. Suplai air bersih______2. Polusi udara ______3. Polusi di perairan teluk______1 4. Sampah dan limbah______5. Pembangunan di pesisir Jakarta______6. Hilangnya sumber daya alam laut dan pesisir______7. Banjir ______8. Lainnya______

Urutkan jawaban Anda di Pertanyaan B.1 sesuai dengan urutan kepentingannya, dimana 1 sebagai masalah paling penting sampai 3 sebagai masalah yang kurang penting. (Mohon gunakan kotak di sebelah kanan untuk mengisi ranking/urutan)

2. Berdasarkan pengalaman dan pengamatan Anda, apakah ada masalah polusi di perairanTeluk Jakarta? 1. Ya 2.Tidak 3.Tidak tahu (Silahkan lanjut ke pertanyaan B.7 jika anda menjawab selain ”Ya")

3. Apa yang pernah Anda amati di Teluk Jakarta, yang menurut Anda mengindikasikan masalah polusi di perairan tersebut? ______1 ______2

218

______3

4. Menurut Anda, bagaimanakah tingkat pencemaran di perairan Teluk Jakarta saat ini dibandingkan saat pertama kali Anda tinggal di kampung nelayan ini?

1. Tidak tahu 2. Memburuk 3.Tidak ada perubahan 4.Membaik

5. Menurut pendapat Anda, apakah tiga sumber terbesar dari polusi di perairanTeluk Jakarta? 1. Limbah rumah tangga______2. Limbah industri______1 3. Aktivitas pelabuhan dan kapal______4. Aktifitas pariwisata ______5. Aktivitas perikanan dan budidaya______6. Lainnya______

Urutkan jawaban Anda di Pertanyaan B.5 sesuai dengan urutan kepentingannya, dimana 1 sebagai sumber terbesar sampai 3 sebagaisumber terkecil. (Mohon gunakan kotak di sebelah kanan untuk mengisi ranking/urutan)

6. Secara umum, bagaimanakah pendapat Andamengenai beberapa pernyataan berikut

1. Tidak tahu 2. Sangat tidak setuju 3. Tidak setuju 4.Setuju 5. Sangat setuju

6.1. Polusi di Teluk Jakarta tidak memiliki dampak apapun pada pekerjaan saya 6.2. Polusi di Teluk Jakarta menimbulkan resiko kesehatan bagi masyarakat 6.3. Polusi di Teluk Jakarta tidak memiliki dampak apapun bagi keindahan lingkungan laut dan pesisir Jakarta 6.4. Polusi di Teluk Jakarta menyebabkan kerusakan bagi lingkungan laut 6.5. Polusi di Teluk Jakarta tidak memiliki dampak apapunbagi biota laut

7. Apakah Anda pernah menerima informasi atau peringatan mengenai polusi di Teluk dan pesisir Jakarta? (Jika jawaban Anda "Tidak", silahkan lanjut ke Bagian C) 1. Ya 2. Tidak

8. Apakah isi dari informasi atau peringatan tersebut? ______

9. Darimanakah Anda menerima informasi atau peringatan tersebut? ______

219

C. Kegiatan Organisasi dan Bantuan Sosial

1. Apakah Anda merupakan salah satu anggota aktif dari organisasi atau jaringan sosial seperti koperasi, komunitas budaya, parpol, LSM, keagamaan, atau organisasi pekerjaan? (Jika jawaban Anda "Tidak", silahkan lanjut ke Bagian D) 1. Ya 2.Tidak

2. Berapa banyak organisasi atau jaringan sosial yang aktif Anda ikuti? ______

3. Organisasi atau jaringan sosial apa yang paling aktif Anda ikuti (pilih salah satu)? 1. Koperasi 5. Keagamaan 2. Komunitas budaya 6. Organisasi terkait bidang pekerjaan 3. Politik 7. Lainnya______4. LSM

4. Apa manfaat dan dukungan terpenting yang telah Anda peroleh dari keanggotaan di organisasi atau jaringan sosial tesebut (pilih satu)? 1. Dukungan yang bersifat teknik/profesional terkait dengan pekerjaan 2. Dukungan finansial 3. Akses ke pelayanan publik seperti air bersih dan pendidikan 4. Ajang bersosialisasi 5. Partisipasi untuk menjadi perwakilan komunitas lokal di tingkat pemerintahan 6. Lainnya______

D. Informasi Umum

1. Berapakah umurAnda?

2. Berapa jumlah anggota rumah tangga Anda?

3. Berapa jumlah anggota rumah tangga Anda yang mempunyai penghasilan (termasuk Anda)?

4. Apakah saat ini Anda melakukan pekerjaan tambahan di luar pekerjaan utama Anda? (Jika jawaban Anda "Tidak", silahkan lanjut ke pertanyaan D.8) 1. Ya 2. Tidak

5. Mohon jelaskan jenis pekerjaan tambahan yang Anda lakukan saat ini 1. Pekerjaan terkait perikanan______2. Pekerjaan lainnya______

6. Mohon berikan penjelasan mengenai alasan Anda melakukan pekerjaan tambahan tersebut ______

220

7. Berapakah penghasilan yang Anda dapatkan per bulan dari pekerjaan tambahan? 1. minimum 2. maksimum

8. Berapakah penghasilan yang Anda dapatkan per bulan dari pekerjaan utama? 1. minimum 2. maksimum

E. Kepemilikan Lahan dan Bangunan

1. Apakah status dari lahan dan bangunan yang Anda tempati saat ini? 1. Hak milik sendiri 2. Sewa atau kontrak 3. Lahan dan bangunan pemerintah 4. Lainnya______

2. Apakah Anda memiliki kepemilikan atas suatu lahan/tanah di lingkungan tempat tinggal atau daerah lain? 1. Ya 2. Tidak

F. Akses Layanan Publik dan Finansial

1. Berapa waktu yang dibutuhkan untuk menuju ke rumah sakit atau klinik kesehatan dalam keadaan darurat atau saat sakit? 1. < 30 menit 2. 30 – 60 menit 3. > 1 jam

2. Apakah Anda memiliki Asuransi Kesehatan? (BPJS atau sejenisnya) 1. Ya 2. Tidak

3. Apakah Anda memiliki akses langsung ke air bersih di rumahAnda? 1. Ya 2. Tidak

4. Jika jawaban Anda "Tidak" pada pertanyaan F.3, mohon jelaskan dari mana Anda mendapat akses air bersih? ______

5. Kemanakah biasanya Anda pergi bila Anda membutuhkan bantuan finansial? 1. Tidak pernah butuh bantuan 4. Bank 2. Teman 5. Keluarga 3. Koperasi 6. Lainnya______

(Bila jawaban sama dengan no. 1, lanjutkan ke Bagian G)

6. Untuk tujuan apa biasanya bantuan finansial tersebut Anda gunakan (pilih salah satu)? 1. Kebutuhan sehari-hari (makanan, rumah tangga) 2. Kebutuhan pendidikan 221

3. Kebutuhan terkait dengan pekerjaan 4. Kebutuhan kesehatan 5. Lainnya______

7. Seberapa sering Anda membutuhkan bantuan finansial? 1. Setiap bulan 2. Sering (>6 kali/tahun) 3. Kadang-kadang (3-6 kali/tahun) 4. Jarang (<3 kali/tahun)

G. Latar Belakang Pendidikan

1. Apakah pendidikan terakhir Anda? 1. Tidak pernah bersekolah 2. SD 3. SMP 4. SMA 5. Pendidikan tinggi

H. Pekerjaan Spesifik: NelayanTradisional

1. Mohon tuliskan dalam persen, berapa bagiankah hasil tangkap yang Anda gunakan untuk: 1.1. Konsumsi pribadi______1.2. Dijual segar ______1.3. Pengolahan kembali______

2. Mohon lingkari perubahan yang terjadi pada aktivitas dan hasil perikanan Anda saat ini dibandingkan dengan saat pertama kali Anda memulai pekerjaan Anda (mohon jelaskan perubahan) 2.1. HasilTangkapan 1. Ukuran______2. Kuantitas______3. Kualitas ______4. Jumlah spesies______5. Jenis spesies______2.2. Aktivitas Penangkapan 1. Durasi per perjalanan______2. Biaya per perjalanan______3. Jarak dari pantai______

3. Faktor apa saja menurut Anda yang dapat menurunkan jumlah ikan di laut? 1. ______2. ______4. Menurut Anda, apa saja yang dapat dilakukan untuk bisa meningkatkan jumlah ikan di laut? 1. ______2. ______222

5. Apakah Anda pernah berpindah lokasi tangkapan untuk menghindari polusi di perairan Teluk Jakarta? 1. Ya 2.Tidak

6. Mohon jelaskan spesifikasi kapal yang digunakan dalam kegiatan penangkapan Anda 6.1. GT______6.2. Mesin 1.Motor tempel 2. Motor mesin 3. Tanpa mesin 6.3. Kepemilikan kapal 1. Ya 2. Tidak 6.4. Jarak tempuh maksimal______6.5. Jumlah awak______

7. Apakah orang tua Anda memilliki pekerjaan yang sama seperti Anda? 1. Ya 2. Tidak

8. Apakah Anda berharap anak Anda untuk memiliki pekerjaan yang sama dengan Anda? 1. Ya 2. Tidak

9. Dari daerah mana Anda berasal? ______------Ini adalah akhir dari kuesioner. Terimakasih atas partisipasi Anda------

I. Pekerjaan Spesifik: Pembudidaya Kerang

1. Mohon tuliskan dalam persen, berapa bagiankah hasil budidaya yang Anda gunakan untuk: 1.1. Konsumsi pribadi ______1.2. Dijual segar ______1.3. Pengolahan kembali ______

2. Mohon lingkari perubahan yang terjadi pada aktivitas dan hasil budidaya Anda saat ini dibandingkan dengan saat pertama kali Anda memulai pekerjaan Anda (mohon jelaskan perubahan) 2.1. Komoditas 1. Ukuran______2. Kuantitas ______3. Kualitas ______4. Jumlah spesies______5. Jenis spesies______2.2. Aktivitas Budidaya 1. Waktu panen ______2. Biaya budidaya______3. Faktor apa saja menurut Anda yang dapat menurunkan jumlah produksi Anda? 1. ______2. ______

4. Menurut Anda, apa saja yang dapat dilakukan untuk bisa meningkatkan produktivitas Anda? 1.______223

2.______

5. Apakah Anda pernah berpindah lokasi budidaya untuk menghindari polusi di perairanTeluk Jakarta? 1. Ya 2. Tidak

6. Mohon jelaskan beberapa hal di bawah ini yang terkait dengan aktivitas budidaya Anda 6.1. Kepemilikan bagan 1. Ya 2.Tidak 6.2. Jarak bagan dari pantai______6.3. Jumlah pembudidaya per bagan______

7. Apakah orang tua Anda memilliki pekerjaan yang sama seperti Anda? 1. Ya 2. Tidak

8. Apakah Anda berharap anak Anda untuk memiliki pekerjaan yang sama dengan Anda? 1. Ya 2. Tidak

9. Dari daerah mana Anda berasal? ______

------Ini adalah akhir dari kuesioner. Terimakasih atas partisipasi Anda------

I. Pekerjaan Spesifik: Buruh

1. Apakah orang tua Anda memilliki pekerjaan yang sama seperti Anda? 1. Ya 2. Tidak

2. Apakah Anda berharap anak Anda untuk memiliki pekerjaan yang sama dengan Anda? 1. Ya 2. Tidak

3. Dari daerah mana Anda berasal? ______

------Ini adalah akhir dari kuesioner. Terimakasih atas partisipasi Anda------

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Appendix 4: Questionnaire Form

English version

Questionnaire survey for coastal communities in north Jakarta, Indonesia

ID/Name : Gender :

Village : Date :

Occupation : Starting Time :

A. Occupational Information 1. What is your main occupation? 4. Traditional fisherman 5. Mariculture farmer 6. Labour 2. When did you start this main occupation? Please specify the year ______3. Since when did you live in the village?

______

B. Environmental Awareness 1. What would you say are the three most important environmental issues in Jakarta Bay and coastal area today? (Please circle that apply) 1. Water supply ______2. Air pollution ______3. Water pollution/water quality ______1 4. Waste ______5. Coastal development and planning ______6. Loss of natural environment ______7. Flood ______8. Other ______

Please rank your answers in Question B.1 based on the importance where 1 as the most important issue to 3 as less important (Please use the boxes in the right side of Question B.1 to fill the ranks)

2. Based on your knowledge and observation, is there any water pollution problem in the Jakarta Bay? 1. Yes 2. No 3. I don’t know (Please continue to Question B.7 if you answer “No” or “I don’t know”)

3. What do you observe in the Jakarta Bay that, in your opinion indicates the water pollution problem? ______1 ______2 ______3 225

4. How would you rate the water pollution that occurs in the Jakarta Bay now compared to the first time you lived here?

1. I don’t know 2. Worse 3.No changes 4. Better

5. What would you say are the three most significant sources of pollution in Jakarta Bay and coastal area? 1. Household waste ______2. Industrial waste ______3. Port and shipping activities ______1 4. Tourism activities ______5. Fishing and mariculture activities ______6. Others, please specify ______Please rank your answer in Question B.5 based on the significance where 1 as the most significant source to 3 as less significant (Please use the boxes in the right side of Question B.5 to fill the ranks)

6. In general, do you agree or disagree with the following statements

1. I don’t know 2. Strongly disagree 3. Disagree 4. Agree 5. Strongly agree

6.1. Water pollution in Jakarta Bay has no impact on my occupation 6.2. Water pollution in Jakarta Bay brings health risk to the people 6.3. Water pollution in Jakarta Bay has no aesthetic impact 6.4. Water pollution in Jakarta Bay causes damage to the marine environment 6.5. Water pollution in Jakarta Bay has no impact on marine organisms 6.6. Water pollution in Jakarta Bay has impact on ______

7. Have you ever received any information or alerts about water pollution in the Jakarta Bay and coastal area? (If the answer is “No”, please continue to Part C) 1. Yes 2. No

8. What is the content of the information or alert? Please explain ______

9. Where did you receive the information or alert from? ______

C. Social Networks and Supports

1. Are you an active member of these organisations or social networks (cooperative, cultural association, political, NGO, religious, occupational organization)? (If your answer is “No”, please continue to Part D) 1. Yes 2. No

2. How many organization that you involved in? ______226

3. What is the type of the organization or the social networks that you involved in mostly (choose one)? 1. Cooperative 5. Religious 2. Village community 6. Occupational organization 3. Political 7. Other, please specify ______4. NGO

4. What is the most important support or advantage that you have obtained from the membership of the organisation and social networks (choose one)? 1. Technical and professional aspect supports related to occupation 2. Financial supports 3. Supports and accesses to service such as clean waters and education 4. Social networking 5. Participation to be a representative of local communities in government level 6. Other, please specified ______

D. General and Household Information

1. What is your age? 2. How many people are there in your household? 3. How many people in your household that earn income (including yourself)? 4. Are you currently conducting an extra job? (If your answer is “No”, please continue to question D.8) 1. Yes 2. No 5. Please specify the extra job that you are conducting currently 3. Fishery-related jobs, please specify ______4. Other jobs, please specify ______6. Please explain the reason on why you are conducting extra job(s) ______7. How much do you earn (minimum and maximum) per month from your extra jobs? 1. minimum 2. maximum 8. How much do you earn (minimum and maximum) per month from your main occupation? 1. minimum 2. maximum

E. Land Ownership

1. Could you tell me the status of your dwelling? 1. Privately owned 2. Rent 3. Government’s property 4. Other, please specify ______

2. Do you have any land ownership here or someplace else? 2. Yes 2. No

227

F. Public Service and Financial Accesses

1. How much time do you need to travel to the hospital or health clinic in case of emergency or illness? 1. < 30 minutes 2. 30 – 60 minutes 3. > 1 hour 2. Do you have a health insurance? (BPJS or any other insurance) 1. Yes 2. No 3. Do you have direct access to clean water inside your house? 2. Yes 2. No 4. If your answer is “No” on question F.3, please specify HOW could you get the access to clean water?______5. Where do you go mostly if you need additional financial support? 1. Never need any 4. Bank 2. Friends 5. Family 3. Cooperative 6. Other, specify ______

(If your answer is no. 1, please continue to Part G)

6. For what purpose do you usually need the financial support(choose one)? 1. Basic needs (food, housing, utilities) 2. Education needs 3. Occupational needs 4. Health needs 5. Other, specify ______

7. How often do you need the financial support? 1. Every month 2. Often (>6 times/year) 3. Sometimes (3-6 times/year) 4. Seldom (<3 times/year)

G. Education Background

1. What was the last level of education that you have attained? 1. Never attend school 2. Elementary school 3. Junior high school 4. Senior high school 5. Tertiary education H. Specific Occupation: Traditional Fisherman

2. Please describe how much catch that you use for (Total catch is equal to 100%): 1.1. Own consumption ______1.2. Sell in market ______1.3. Post-processing ______228

2. Please circle all that apply the changes that you notice from your fishing activities and results, now compared to the first time you started the occupation as a fisherman (Please specify) 2.1. Fish catch 1. Size ______2. Quantity______3. Quality ______4. Number of species ______5. Type of species ______2.2. Fishing effort 1. Time spent per trip ______2. Fishing expenses ______3. Distance from coast______

3. What are the factors that you think can decrease the number of fish in the sea? 1. ______2. ______4. What do you think could be done to increase the number of fish in the sea? 1. ______2. ______5. Do you ever have to move from your usual fishing area to another (new) area to avoid water pollution? 1. Yes 2. No

6. Please describe the vessel that you use for your fishing activity 6.1. Tonnage ______6.2. Machine 1. Outboard 2. Inboard 3. No machine 6.3. Own the vessel 1. Yes 2. No 6.4. Maximum travel range ______6.5. Number of crews ______

7. Did your parents have the same occupation as yours? 2. Yes 2. No

8. Do you wish your children to have the same occupation as yours? 2. Yes 2. No

9. Where do you come from originally? ______I. Specific Occupation: Mariculture Farmers

1. Please describe how much catch that you use for (Total catch is equal to 100%): 1.1. Own consumption ______1.2. Sell in market ______1.3. Post-processing ______

229

2. Please circle all that apply the changes that you notice from your mariculture activities and results, now compared to the first time you started the occupation as a farmer (Please specify) 2.1. Commodity 1. Size ______2. Quantity ______3. Quality ______4. Number of species ______5. Type of species ______2.2. Farming effort 1. Harvest time ______2. Fishing expenses ______3. What are the factors that you think can decrease the mussel productivity? 1. ______2. ______

4. What do you think could be done to increase the mussel productivity? 1.______2.______

5. Do you ever have to move from your usual fishing area to another (new) area to avoid water pollution? 1. Yes 2. No

6. Please describe these components related to your mussel farming activities 6.1. Platform ownership 1. Yes 2. No 6.2. Distance of platform from coastline ______6.3. Number of farmers ______

7. Did your parents have the same occupation as yours? 1. Yes 2. No

8. Do you wish your children to have the same occupation as yours? 1. Yes 2. No

9. Where do you come from originally? ______

------This is the end of the questionnaire. Thank you very much for your cooperation----

J. Specific Occupation: Another Occupation

1. Did your parents have the same occupation as yours? 1. Yes 2. No

230

2. Do you wish your children to have the same occupation as yours? 1. Yes 2. No

3. Where do you come from originally? ______

------This is the end of the questionnaire. Thank you very much for your cooperation----

231

Appendix 4: Group discussion guideline

Indonesian version

Topik dan panduan untuk diskusi grup

Lokasi : Kelompok okupasi:

Waktu :

Informasi Peserta :

1. Kelompok Nelayan

Topik: Aktivitas perikanan tangkap, kerentanan nelayan, dan adaptasi yang dilakukan terhadap pencemaran

Sub topik 1:

Kalendar musiman kegiatan perikanan tangkap (bulan dilakukannya kegiatan menangkap ikan, jumlah tangkapan maksimal dan minimum musiman, jenis spesies, dll)

Sub topik 2:

1. Nilai pentingnya menjaga lingkungan dan kualitas perairan Teluk Jakarta (apakah penting? Mengapa?) 2. Perubahan lingkungan yang terjadi di Teluk Jakarta (termasuk ekosistem bakau, terumbu karang, kualitas perairan, dll) dengan menggunakan pemetaan dan timeline (partisipasi aktif peserta) 3. Pemetaan polusi perairan (partisipasi aktif peserta) 4. Adaptasi dan hal-hal yang dilakukan untuk mengatasi dampak negatif dari polusi di perairan 5. Rencana atau visi masa depan bagi Teluk Jakarta dan komunitas

Partisipan:

• Perwakilan dari komunitas nelayan tradisional • Tokoh di komunitas

2. Kelompok Nelayan Budidaya Kerang Hijau

Topik:Aktivitas budidaya kerang hijau, kerentanan nelayan, dan adaptasi yang dilakukan terhadap pencemaran

Sub topik 1:

1. Kalendar musiman kegiatan budidaya kerang hijau (bulan dilakukannya kegiatan panen, jumlah produksi maksimal dan minimum musiman, jenis spesies, dll)

232

Sub topik 2:

Partisipan:

• Perwakilan dari komunitas nelayan budidaya kerang hijau • Tokoh di komunitas

233

Appendix 4: Group discussion guideline

English version

Topics and outline for group discussion

Village : Occupational Group:

Date and Starting Time :

Participants information:

1. Fishermen Group

Topic: Fishery activities, fisher’s vulnerability and adaptation practices to water pollution

Sub topic 1:

1. Seasonal calendar of fishery activities (months when they performing fishing, maximum and minimum catch seasonally) 2. Changes in capture and fishing efforts (including size, quantity, quality, number and type of species, time spent on the sea, fishing expenses, travel distance)

Sub topic 2:

1. The importance to support the water quality improvement of Jakarta Bay (is it important for this group? Why?) 2. Environmental changes in Jakarta Bay (including mangrove, coral reef, changes in water quality) with mapping and timeline 3. Water pollution mapping 4. Current adaptation or practices to cope with adverse impacts of water pollution 5. Future plans for Jakarta Bay and the communities

Participants:

• Representatives from the traditional fishery-based communities • Community leaders

2. Mussel Farmers Group

Topic:Mussel farming activities, farmer’s vulnerability and adaptation practices to water pollution

Sub topic 1:

1. Seasonal calendar of farming activities (months when they performing harvesting, maximum and minimum productivity seasonally) 2. Changes in commodity and efforts (including size, quantity, quality, number and type of species, harvest time, farming expenses)

234

Sub topic 2:

Participants:

• Representatives from the traditional fishery-based communities • Community leaders

235

Appendix 4: Interview guideline

Indonesian version

K. Topik interview untuk narasumber

Nama :

Institusi & Jabatan :

Tanggal & Waktu :

1. Perspektif mengenai nilai dan fungsi dari lingkungan pesisir Jakarta dan Teluk Jakarta. 2. Kondisi perairan Teluk Jakarta dan daerah pesisir Jakarta. 3. Dampak dari polusi di Teluk Jakarta terhadap ekosistem Teluk Jakarta. 4. Dampak dari polusi di Teluk Jakarta terhadap komunitas perikanan tradisional. 5. Fasilitasi dan dukungan dalam ranka memperkuat kapasitas adaptasi komunitas perikanan tradisional terhadap polusi di perairan Teluk Jakarta. 6. Faktor kunci yang memengaruhi kondisi Teluk Jakarta. 7. Faktor kunci yang memengaruhi kondisi komunitas perikanan tradisional di pesisir Jakarta. 8. Rencana di masa depan dan visi untuk Teluk Jakarta, pesisir Jakarta, dan komunitas perikanan tradisional.

236

Appendix 4: Interview guideline

English version

K. A guide for interview with experts and academics

Name :

Institution :

Date and Starting Time :

1. Perspectives about the values of Jakarta Bay and coastal environment 2. The states of Jakarta Bay waters and Jakarta coastal areas 3. What kind of impacts is the water pollution having on Jakarta Bay ecosystems? 4. What kind of impacts is the water pollution having on traditional fishery-based resource communities? 5. Facilitations and supports to strength household’s adaptive capacity to water pollution 6. Key drivers that influence the states of Jakarta Bay 7. Key drivers that influence the states of the traditional fishery-based resource communities 8. Future plan and vision for Jakarta Bay and the traditional fishery-based resource communities

237

Appendix 5: Count of responses and standardised values for indicators of vulnerability % of responses* Standardised value Vulnerability element Indicators TF MF IF TF MF IF Social capital Organisational membership 42.71 24.44 12.00 0.43 0.24 0.12 Information access 33.33 22.22 23.00 0.33 0.22 0.23 Physical capital Dwelling ownership 7.29 12.22 6.00 0.07 0.12 0.06 Access to health service 96.00 97.78 94.00 0.96 0.98 0.94 Health insurance 37.50 41.11 27.00 0.38 0.41 0.27 Clean water access 25.00 38.89 23.00 0.25 0.39 0.23 Financial capital Working extension (additional working family) 56.25 46.67 59.00 0.56 0.47 0.59 Access to credit other than middle man 59.38 66.67 74.00 0.59 0.67 0.74 Working expansion (additional job) 44.00 59.00 22.00 0.44 0.59 0.22 Human capital Education level 9.38 3.33 9.00 0.09 0.03 0.09 Non-fishery based skills 18.75 17.78 11.00 0.19 0.18 0.11 Awareness on water pollution 95.83 94.00 61.00 0.96 0.94 0.61 Natural capital Land ownership 44.79 44.44 40.00 0.45 0.44 0.40 Fisheries decline (Inverse) 34.00 34.00 34.00 0.34 0.34 0.34 Sensitivity Dependency on fisheries-based occupation 56.25 42.22 33.00 0.56 0.42 0.33 Note: *percentage of households who responded positive to indicators (e.g. percentage of households involved in organisation); TF: traditional fishers; MF: mussel farmers; IF: informal workers

Illustration for calculating capital index (social capital for traditional fishers): 0.43 + 0.33 = = 0.38 ( ) 2 𝑆𝑆𝑆𝑆 𝑇𝑇𝑇𝑇

238

Appendix 6: Proximity matrix and agglomeration table of cluster analysis Proximity Matrix

Squared Euclidean Distance

Case 1:A1 2:A2 3:A3 4:A4 5:A5 6:A6 7:A7 8:B1 9:B2 10:B3 11:B4 12:B5 13:B6 14:B7 15:C2 16:C3 17:C4 18:C5 19:C6 20:D3 21:D4 22:D5 23:D6

1:A1 .000 11.951 15.573 19.714 13.277 20.463 35.570 17.377 11.760 13.640 7.507 18.940 8.226 6.043 63.927 7.521 5.852 14.337 10.063 8.256 11.735 17.232 19.240 2:A2 11.951 .000 12.561 19.053 21.163 12.360 41.629 33.111 22.367 11.506 13.488 19.478 15.945 10.466 79.755 12.437 13.058 29.623 16.455 34.160 18.636 27.423 29.454 3:A3 15.573 12.561 .000 8.795 9.977 8.435 29.558 27.761 21.402 5.130 6.138 12.934 12.458 10.843 88.327 12.532 14.735 32.539 16.639 29.310 20.692 28.256 26.792 4:A4 19.714 19.053 8.795 .000 16.702 5.585 36.756 30.098 17.699 4.251 10.277 13.930 16.209 13.779 96.813 16.995 14.030 21.543 25.795 39.311 22.518 31.980 47.230 5:A5 13.277 21.163 9.977 16.702 .000 19.631 50.808 39.708 23.793 15.978 9.492 23.469 22.589 20.080 102.251 22.546 13.003 30.321 23.131 28.669 31.244 30.474 23.109 6:A6 20.463 12.360 8.435 5.585 19.631 .000 32.423 32.218 14.794 7.498 15.042 15.774 18.727 11.565 82.866 18.952 19.059 26.983 21.207 43.802 22.471 31.571 38.644 7:A7 35.570 41.629 29.558 36.756 50.808 32.423 .000 13.204 31.928 25.414 34.750 23.470 33.634 19.926 62.154 27.101 37.288 47.778 28.007 38.215 37.425 42.795 45.960 8:B1 17.377 33.111 27.761 30.098 39.708 32.218 13.204 .000 28.852 20.936 24.051 29.434 19.710 12.961 52.428 16.514 21.594 30.567 23.603 16.675 21.460 31.351 35.882 9:B2 11.760 22.367 21.402 17.699 23.793 14.794 31.928 28.852 .000 17.406 15.332 11.331 12.810 7.898 59.239 15.453 18.546 13.222 13.561 22.791 17.241 21.512 32.933 10:B3 13.640 11.506 5.130 4.251 15.978 7.498 25.414 20.936 17.406 .000 5.884 9.998 11.803 8.010 77.040 6.933 8.817 19.176 14.464 29.544 17.785 24.114 35.426 11:B4 7.507 13.488 6.138 10.277 9.492 15.042 34.750 24.051 15.332 5.884 .000 10.959 5.494 9.001 76.729 5.504 8.267 14.991 12.813 17.226 17.748 21.923 28.154 12:B5 18.940 19.478 12.934 13.930 23.469 15.774 23.470 29.434 11.331 9.998 10.959 .000 12.326 8.150 72.629 12.307 17.759 25.273 18.729 30.949 20.999 23.558 35.643 13:B6 8.226 15.945 12.458 16.209 22.589 18.727 33.634 19.710 12.810 11.803 5.494 12.326 .000 5.693 61.522 5.808 15.435 18.107 14.385 12.117 10.078 21.997 30.656 14:B7 6.043 10.466 10.843 13.779 20.080 11.565 19.926 12.961 7.898 8.010 9.001 8.150 5.693 .000 52.294 5.925 10.649 18.365 10.959 15.117 9.405 17.336 21.926 15:C2 63.927 79.755 88.327 96.813 102.251 82.866 62.154 52.428 59.239 77.040 76.729 72.629 61.522 52.294 .000 51.297 75.277 62.504 49.424 59.827 56.684 47.323 53.945 16:C3 7.521 12.437 12.532 16.995 22.546 18.952 27.101 16.514 15.453 6.933 5.504 12.307 5.808 5.925 51.297 .000 7.436 14.076 6.438 15.361 9.754 12.713 24.946 17:C4 5.852 13.058 14.735 14.030 13.003 19.059 37.288 21.594 18.546 8.817 8.267 17.759 15.435 10.649 75.277 7.436 .000 16.045 12.569 21.339 12.963 12.005 23.233 18:C5 14.337 29.623 32.539 21.543 30.321 26.983 47.778 30.567 13.222 19.176 14.991 25.273 18.107 18.365 62.504 14.076 16.045 .000 21.126 25.309 28.462 28.486 49.485 19:C6 10.063 16.455 16.639 25.795 23.131 21.207 28.007 23.603 13.561 14.464 12.813 18.729 14.385 10.959 49.424 6.438 12.569 21.126 .000 19.248 11.974 11.352 19.135 20:D3 8.256 34.160 29.310 39.311 28.669 43.802 38.215 16.675 22.791 29.544 17.226 30.949 12.117 15.117 59.827 15.361 21.339 25.309 19.248 .000 20.402 28.434 26.267 21:D4 11.735 18.636 20.692 22.518 31.244 22.471 37.425 21.460 17.241 17.785 17.748 20.999 10.078 9.405 56.684 9.754 12.963 28.462 11.974 20.402 .000 8.750 23.869 22:D5 17.232 27.423 28.256 31.980 30.474 31.571 42.795 31.351 21.512 24.114 21.923 23.558 21.997 17.336 47.323 12.713 12.005 28.486 11.352 28.434 8.750 .000 15.327 23:D6 19.240 29.454 26.792 47.230 23.109 38.644 45.960 35.882 32.933 35.426 28.154 35.643 30.656 21.926 53.945 24.946 23.233 49.485 19.135 26.267 23.869 15.327 .000

This is a dissimilarity matrix

239

Agglomeration Schedule

Cluster Combined Stage Cluster First Appears Stage Cluster 1 Cluster 2 Coefficients Cluster 1 Cluster 2 Next Stage 1 4 10 2.125 0 0 5 2 11 13 4.872 0 0 3 3 11 16 7.727 2 0 6 4 1 17 10.653 0 0 12 5 4 6 14.306 1 0 7 6 11 14 18.060 3 0 12 7 3 4 22.206 0 5 14 8 21 22 26.580 0 0 10 9 9 12 32.246 0 0 15 10 19 21 38.563 0 8 16 11 7 8 45.165 0 0 21 12 1 11 51.942 4 6 13 13 1 2 60.269 12 0 17 14 3 5 70.742 7 0 20 15 9 18 81.685 9 0 18 16 19 23 93.595 10 0 19 17 1 20 105.619 13 0 18 18 1 9 119.944 17 15 19 19 1 19 143.833 18 16 20 20 1 3 174.614 19 14 21 21 1 7 208.534 20 11 22 22 1 15 264.000 21 0 0

240

Appendix 7: Group discussions with the traditional fishers (left) and the green mussel farmers (right)

Brainstorming result that identified issues of concern

Top, from left to right Issues of concern: Litter on the ocean; reclamation project in the coastal area obstructed the fishing area

Bottom, from left to right: Issues of concern: The waste dumping activities from the industries that reduce the number of fish; illegal fishing using prohibited gear such as bottom trawl; increased fuel price that affects fishers’ income

241

Seasonal calendar exercise Timeline exercise

Participatory mapping

Map of fishing area (1980-2000)

242

Map of green mussel platforms locations (before 2008)

243