Policy Implications for Disaster Risk Reduction

MEASURING SOCIAL VULNERABILITY OF CHINESE COASTAL

COUNTIES TO NATURAL HAZARDS

Chunjing Liu

A thesis submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Master of Science in Disaster Science and Management

Spring 2014

UMI Number: 1562400

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MEASURING SOCIAL VULNERABILITY OF CHINESE COASTAL

COUNTIES TO NATURAL HAZARDS

Chunjing Liu

Approved: ______James Corbett, Ph.D. Professor in charge of thesis on behalf of the Advisory Committee

Approved: ______Maria Aristigueta, D.P.A Director of the School of Public Policy and Administration

Approved: ______George Watson, Ph.D. Dean of the College of Arts and Sciences

Approved: ______James G. Richards, Ph.D. Vice Provost for Graduate and Professional Education ACKNOWLEDGMENTS

I would like to take a moment to express my appreciation for all those people who have made my research and this degree possible. I would like to express my deep appreciation to Dr. James Corbett, my advisor and the Chair of my thesis committee. In my two years here, he gave me complete freedom and strong support regarding my research of interest. He always encouraged me to think big on topics and provided me with a lot of creative ideas that allowed me to study my research area more deeply. Your advice regarding both my research as well as my career have been priceless. I also have great gratitude for Dr. Sue McNeil. She taught me how to conduct research more rigorously. She gave extremely helpful advice on writing both my thesis as well as other papers. She provided me not only with recommendations on my research, but also provided great support regarding future research opportunities and life more generally that I will remember. I want to thank Dr. Qinzheng Liu, my committee member in China, who gave me a number of valuable recommendations on how to address the Chinese circumstances that are important in this research.

I also wish to thank all of my professors, colleagues, and friends in the Disaster Research Center (DRC). In Dr. Joseph Trainor’s classes, I gained a broad knowledge on disaster management and identified my research interest. I appreciated his time and effort on the improvement of my research methods, particularly qualitative research methods. He also spent time reviewing my study on disaster risk reduction in China and gave me a lot of valuable comments and advice. In Dr. James Kendra and Dr.

Tricia Wachtendorf’s classes, I was able to deepen my understanding of the topics I’m

iii most interested in and complete the initial framework of literature review and methodologies for my thesis research in their classes. Thanks to Dr. Benigno Aguirre for encouraging me to be more critical in my literature review. Also thanks to Gail, Vicky, and Pat in DRC for always providing me convenience in DRC. Many thanks to Wenfang Chen. She was always willing to share her experiences and give me a lot of help during my research. Thanks to my classmate,

Edward Carr, for helping me on GIS mapping. And thanks to Chelsea Leiper for editing my thesis, which makes it more readable. My friends in DRC, College of Earth, Ocean, and Environment (CEOE), and School of Public Policy & Administration (SPPA), I will remember all of your encouragement and help. I want to give my special appreciation to my supervisors and colleagues in the State Oceanic Administration (SOA) of China, who I have a long list of in my heart.

They have always given me warmth, care, and encouragement, and more importantly, they have taken on the extra work that is my duty in SOA, providing convenience for my life during the period I study abroad. I could not complete my studies here without your great support. Furthermore I would also like to acknowledge with much appreciation the School of Public Policy & Administration and the Office of Graduate and Professional

Education at the University of Delaware, the China Scholar Council, and the U.S.

Institute of International Education for your support with the Fulbright Program. I’m very proud as a Fulbrighter. I must also thank my family, my husband, my parents and parents-in-law, my sister and brother-in-law, and my cute little nephew. I can always get strength and encouragement from you. Without your love, I could not make it.

TABLE OF CONTENTS

LIST OF TABLES ...... ix LIST OF FIGURES ...... x ABSTRACT ...... xii

Chapter

1 INTRODUCTION ...... 1

1.1 Motivation ...... 1 1.2 Problem Statement ...... 3 1.3 Objectives and Research Questions ...... 4 1.4 Outline of Thesis ...... 5

2 CONTEXT: CHINESE COASTAL REGIONS ...... 8

2.1 Physical Environment and Coastal Natural Hazards ...... 8

2.1.1 Storm Surges and Disruptive Waves ...... 8 2.1.2 Tsunamis ...... 10 2.1.3 Sea Ice ...... 11 2.1.4 Other Disasters in Coastal Areas ...... 11

2.2 Socioeconomic Conditions ...... 12

2.2.1 Administrative Division System ...... 12 2.2.2 Economic Status ...... 13 2.2.3 Population Density ...... 15 2.2.4 Spatial Variation ...... 17

3 LITERATURE REVIEW ...... 18

3.1 From Analyzing Hazards to Assessing Vulnerability ...... 18

3.1.1 Definition ...... 19 3.1.2 Conceptual Frameworks ...... 23 3.1.3 Social Vulnerability ...... 26

3.2 Vulnerability Assessment ...... 28

3.3 Vulnerability Research and Assessment in China ...... 31 3.4 Debates, Discussion and Conclusion ...... 33

4 METHODOLOGY ...... 36

4.1 Study Area ...... 37 4.2 Data ...... 38

4.2.1 Indicators and Variables ...... 38 4.2.2 Data Sources & Handling Missing Data ...... 41 4.2.3 Data Limitations ...... 44

4.3 Analysis ...... 47

5 RESULTS AND FINDINGS ...... 49

5.1 SoVI® 2010 ...... 49

5.1.1 Principal Components ...... 49 5.1.2 Spatial Variability ...... 52 5.1.3 Spatial Variability of Individual Principal Components ...... 58

5.2 Temporal and Spatial Changes in Social Vulnerability: SoVI® 2000 .... 60

5.2.1 Study Units, Data Sources and Missing Data ...... 61 5.2.2 Results and Findings ...... 61

5.2.2.1 Principal Components ...... 61 5.2.2.2 Spatial Variations ...... 62

5.2.3 Limitations ...... 64

6 MOVING FORWARD: POLICY IMPLICATIONS FOR DISASTER RISK REDUCTION ...... 65

6.1 Lessons Drawn from the Results ...... 65

6.1.1 National Level: Focus on Driving Factors and Gaps between Regions ...... 66

6.1.1.1 Driving Forces ...... 66 6.1.1.2 Reducing Gaps between the Two Geographic Ends and Southeastern Coast ...... 67 6.1.1.3 Reducing Gaps between City Districts and Counties/County-Level Cities ...... 73

6.1.2 Local Level: Place-Based Strategies ...... 76

6.2 Reducing Disaster Risk towards Resilient Communities ...... 79

6.2.1 Conceptual Framework: Hyogo Framework for Action (HFA) .. 80 6.2.2 Reviewing Disaster Risk Reduction in China under HFA ...... 81

6.2.2.1 Governance ...... 82

6.2.2.1.1 Institutions ...... 82 6.2.2.1.2 Legislation ...... 84 6.2.2.1.3 Discussion ...... 87

6.2.2.2 Risk Identification, Assessment, Monitoring and Early Warning ...... 88

6.2.2.2.1 Risk Identification, Investigation and Assessment ...... 88 6.2.2.2.2 Monitoring and Early Warning ...... 89 6.2.2.2.3 Discussion ...... 90

6.2.2.3 Building Understanding and Awareness ...... 91

6.2.2.3.1 Education ...... 91 6.2.2.3.2 Training Programs ...... 92 6.2.2.3.3 Public Awareness ...... 93 6.2.2.3.4 Discussion ...... 93

6.2.2.4 Reducing Underlying Risk Factors ...... 94

6.2.2.4.1 Disaster Control and Prevention Projects ... 94 6.2.2.4.2 Discussion ...... 95

6.2.2.5 Preparedness for Effective Response and Recovery .... 96

6.2.2.5.1 Preparing for Disaster Response ...... 96 6.2.2.5.2 Preparing for Disaster Recovery ...... 97 6.2.2.5.3 Discussion ...... 98

6.2.3 Achievements, Inadequacies, Opportunities, Challenges and Recommendations ...... 99

6.2.3.1 Achievements and Inadequacies ...... 99 6.2.3.2 Opportunities ...... 100 6.2.3.3 Challenges ...... 101

vii

6.2.3.4 Recommendations ...... 102

6.3 Discussion ...... 103

7 CONCLUSIONS ...... 106

7.1 Conclusions ...... 106 7.2 Contributions ...... 108 7.3 Limitations ...... 109 7.4 Future Research ...... 110

REFERENCES ...... 112

Appendix

A SOCIAL VULNERABILITY INDEX OF CHINESE COASTAL COUNTIES (2010) ...... 127 B TOTAL VARIANCE EXPLAINED SOVI®2010 ...... 134 C ROTATED COMPONENT MATRIX SOVI®2010 ...... 135 D PEARSON CORRELATION SOVI®2010 ...... 136 E SOCIAL VULNERABILITY INDEX OF CHINESE COASTAL COUNTIES (2000) ...... 138 F TOTAL VARIANCE EXPLAINED SOVI®2000 ...... 144 G ROTATED COMPONENT MATRIX SOVI®2000 ...... 145 H SOCIAL VULNERABILITY INDEX OF CHINESE COASTAL COUNTIES 2000-2010 ...... 146 I TOTAL VARIANCE EXPLAINED SOVI®2000-2010 ...... 159 J ROTATED COMPONENT MATRIX SOVI®2000-2010 ...... 160

viii

LIST OF TABLES

Table 1: An Overview of the Conceptual Frameworks of Vulnerability from the Five-Shift Perspective (Source: author) ...... 25

Table 2: Vulnerability Assessment Approaches (Source: author) ...... 30

Table 3: Study Units: 238 Chinese Coastal Counties ...... 38

Table 4: Indicators and Variables Used for SoVI of Chinese Soastal Counties (Adapted from Chen et al., 2013) ...... 40

Table 5: Incomplete Data and Processing ...... 43

Table 6: Populations and Sampling of Data for SoVI®2010 ...... 46

Table 7: Principal Components of SoVI® of Chinese Coastal Counties ...... 52

Table 8: The Most Socially Vulnerable Coastal Counties ...... 55

Table 9: The Least Socially Vulnerable Coastal Counties ...... 56

Table 10: Driving Factors of Social Vulnerability of Chinese Coastal Provinces in 2010 ...... 77

Table 11: Policy Implications Supported by SoVI® 2010 and Hyogo Framework for Action (2005-2015) ...... 105

LIST OF FIGURES

Figure 1: Statistics of Marine Disasters (2000-2012) (Data source: SOA China) .... 2

Figure 2. Death Toll (Person) and Direct Economic Loss (100M million Yuan) of Marine Disasters in China, 2000-2012 (Data Source: SOA, China) .... 9

Figure 3: Spatial Distribution of the Consequences By Storm Surges and Disruptive Waves, 2000-2012 (Data Source: SOA, China) ...... 10

Figure 4: The Five-Level Administrative Division System in China (Adapted from Chen et al., 2013) ...... 13

Figure 5: GDP and Per Capita GDP of Chinese Coastal Regions in 2010 (Data Source: NBS, China) ...... 15

Figure 6: Population Density (2010) and Growth Rate (2000-2010) of Chinese Coastal Regions (Data source: the 5th and the 6th census) ...... 16

Figure 7: Overall Spatial Distribution of Social Vulnerability of Chinese Coastal Counties ...... 54

Figure 8: SoVI® Distribution of Coastal City Districts, Counties and County- level Cities ...... 57

Figure 9: SoVI® Principal Components Distribution of Chinese Coastal Counties ...... 60

Figure 10: SoVI® 2000 and SoVI® 2010 ...... 63

Figure 11: Spatial Changes of Primary Factors from 2000 to 2010 in the Same Matrix ...... 72

Figure 12: riorities Identified by Hyogo Framework for Action (2005-2015) (Source: UNISDR, 2005) ...... 81

Figure 13: The Administrative Structure of Disaster Management in China (Source: author) ...... 83

Figure 14: The National 12th Five-Year Plan on Integrated Disaster Prevention and Reduction (2011-2015): Objectives, Tasks, Projects (Source: MCA, 2011) ...... 85

ABSTRACT

In China, more than 40% of the nation’s population lives in coastal areas, where they contribute up to 60% of national GDP. Dense population and great wealth in coastal zones make these areas prone to grave consequences from natural hazards. It is important to have knowledge of disaster risk and vulnerability in coastal areas in order to protect people’s lives and property. Social vulnerability is those characteristics that influence the capacity of the community to prepare for, respond to, and recover from hazards and disasters. This research explores the social vulnerability of 238 coastal counties in China to natural hazards, using the place-based Social

Vulnerability Index (SoVI®) methodology. The results indicate three patterns of the distribution of social vulnerability along the Chinese coast: (1) the north and south end areas are more socially vulnerable than southeastern areas; (2) counties and county- level cities are more vulnerable than highly developed city districts; and (3) the vulnerability of each county is driven by different factors even though they have similar SoVI scores. By comparing the change in spatial distribution of social vulnerability between 2000 and 2010, the following factors emerge as driving factors of social vulnerability in Chinese coastal counties: urbanization, education, social dependency, employment, poverty, age, gender, minority and poor housing quality. This study also explains how to draw lessons from the measurement results of social vulnerability from national and regional perspective in detail. Based on the review of Chinese practice under the Hyogo Framework for Action (HFA, 2005-2015), this thesis identifies the achievements, inadequacies, opportunities and challenges of

xii disaster risk reduction in China, and proposes five major recommendations to move forward for a resilient society: (1) to start formulating the law and regulations for disaster risk reduction and working mechanisms; (2) to start risk identification and assessment at all levels and enhance related researches; to enhance public awareness and training; (3) to promote building resiliency of physical, human, social, institutional, technical, economic, environmental and ecological systems; (4) to establish the systematic guidance of development of volunteers and NGOs; and (5) to enhance international and domestic cooperation, at both governmental and academic levels.

xiii

Chapter 1

INTRODUCTION

1.1 Motivation China has a long coastline, approximately 18,000 km, extending north from Yalu River of Liaoning Province and south to Beilun Estuary of Guangxi. Due to an abundance of resources and convenient transportation, coastal areas have attracted high population densities and a significant amount of the nation’s wealth. There are more than 570 million people living in coastal regions and up to 60% of the national GDP is generated in these areas (NBS, 2010). The long coastline is also a hotbed for various hydrographic, meteorological and biologic hazards. According to incomplete statistics, from 2000 to 2012, marine disasters such as storm surges, huge waves, red tides, sea ice and oil spills, hit Chinese coastal zones more than1800 times. These disasters left 2424 people dead or missing, damaged 1.2 million houses and 50,000 boats, affected 150 million people, and caused 168 billion Yuan ($27.8 billion) direct economic loss (SOA, 2012). The annual date is shown in Figure 1.

Statistics of Marine Disasters (2000-2012) 600 500 400 300 200 100 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Death Toll (person) Direct Economic Loss (100 Million Yuan)

Figure 1: Statistics of Marine Disasters (2000-2012) (Data source: SOA China, 2012)

While development contributes to greater resilience, it also leads to higher economic losses. In future years, Chinese coastal regions are expected to see even more development. The Twelfth Five-Year Plan for National Marine Economic Development (2011-2015) outlines the goal for the gross output value of marine economy to be up to 10% of the national GDP before 2015 (State Council, 2012). Accordingly, governments in coastal areas developed local Twelfth Five-Year Plan goals in succession. With the implementation of the Plans, investment and projects will be increasingly concentrated in coastal regions, which make these areas more sensitive to natural hazards. In this context, understanding and reducing disaster risk in Chinese coastal regions becomes extremely important for decision makers to protect people’s lives and properties. Measuring vulnerability is a core task of risk assessment to natural hazards. In recent decades, many counties of the world have conducted comprehensive vulnerability assessment like the United States (Clark et al., 1998; Cutter et al., 2000,

2003; Wu et al., 2002; Chakraborty et al., 2005; Rygel et al., 2006; Kleinosky et al., 2007), the United Kingdom (Tapsell et al., 2002), Norway (O’Brien et al., 2004), Australia (Dwyer et al., 2004), the Philippines (Acosta-Michlik, 2005) or generally for regions worldwide (Watson et al., 1998). The results of vulnerability assessment provide information and knowledge base for decision makers to allocate resources more efficiently and effectively. The need to promote strategic and systematic approaches to reduce vulnerability and disaster risk has been underlined by the United Nations in the final document of the World Conference on Disaster Reduction, “Hyogo Framework for Action 2005-2015”, as one of the five priorities for action:

“The starting point for reducing disaster risk and for promoting a culture of disaster resilience lies in the knowledge of the hazards and the physical, social, economic and environmental vulnerabilities to disasters that most societies face, and of the ways in which hazards and vulnerabilities are changing in the short and long term, followed by action taken on the basis of that knowledge.” (UNISDR, 2005)

1.2 Problem Statement Disasters are a major concern and reducing disaster risk is an urgent priority for sustainable development. Chinese researchers have done extensive work on vulnerability and risk, including the development of general theoretical frameworks, measurement methods and tools, techniques and criteria for operationalization, and current applications in coastal natural hazards. However, it has also been found that although Chinese coastal regions are one of the most vulnerable areas to natural hazards, there have been few attempts to either capture only a limited area or capture vulnerability by a single dimension or very few variables. Many of the underlying drivers of social vulnerability (e.g. age, ethnicity, occupation, employment) are absent

from this previous work (Chen et al., 2013). There is still no comprehensive profile of social vulnerability or a sub-regional index map for whole coastal areas in China. Existing assessments on social factors often exclude critical variables; therefore they cannot accurately interpret the whole picture of social vulnerability for a specific place. In addition, most approaches to vulnerability measurement still remain in the scientific arena; there is very little literature discussing how a model can be used for practical planning and risk management decision-making, or how to support disaster risk reduction strategies using the results of assessment.

1.3 Objectives and Research Questions To address the aforementioned gap in the literature, the objectives of this study are: (1) to conduct social vulnerability measurement in China at the county level, using an indicator-based Social Vulnerability Index (SoVI®); and (2) to provide a method oriented information base to support setting priorities for current management and future long-term objectives of disaster risk reduction according to the problems identified. To achieve the objectives, this study employs the following two major research questions and sub-questions:

1. What are the social vulnerabilities of 238 Chinese coastal counties to natural hazards? a. Is there regional variability in social vulnerability? b. What specific factors are the driving forces that determine the difference in social vulnerability among regions? Do these factors change over time?

2. What strategies can be proposed to reduce disaster risk?

a. How can the results of the social vulnerability assessment be used in practice? b. What strategies can be proposed to increase resiliency in coastal areas?

1.4 Outline of Thesis This thesis includes six primary chapters:  Chapter 2: Context: Chinese Coastal Regions  Chapter 3: Literature Review  Chapter 4: Methodology: Data and Analysis  Chapter 5: Results and Findings  Chapter 6: Moving Forward: Policy Implications for Disaster Risk

Reduction  Chapter 7: Contributions, Limitations, Conclusion and Future Research Chapter 2 introduces the physical environment and social conditions of Chinese coastal regions. This chapter presents the geographic position that makes coastal regions prone to natural hazards, particularly storm surges, ocean waves, and tsunamis. It highlights social and economic conditions including the administrative division system of China, economic status, population density, and spatial variation of social and economic aspects in Chinese coastal regions. Chapter 3 provides a broad overview of the literature on vulnerability research and practice. This chapter explains the development of disaster risk analysis, emphasizing the change from hazard analysis to vulnerability measurement. Then literature regarding the definition of and measurement approaches to vulnerability and

its social dimension is reviewed. It also identifies the gaps in social vulnerability assessment in China. Chapter 4 describes the methodology of this study. It introduces the units of analysis of this study, the indicator and variables of the index, and sources of data. It explains the way missing data is processed and the data limitations of the study. Finally, this chapter discusses how the data is analyzed.

Chapter 5 is comprised of two parts. The first part presents the results and findings of this research, including the results of SoVI®2010. The extracted principal components, meanings of principal components, and their impact to overall social vulnerability are described first. Additionally, the spatial distribution of social vulnerability and its driving forces among Chinese coastal counties is presented using maps generated with ArcGIS followed by a discussion of the distribution results. The second part of Chapter 5 discusses the temporal and spatial changes in social vulnerability in Chinese coastal counties by comparing the results of SoVI® 2000 and 2010. Chapter 6 aims to add to the literature on social vulernability by giving insight into how to use the results to implicate policies and decision-making. The first part of the chapter addresses what should be focused on in order to reduce social vulnerability with regard to the identified problems, both at the national and local levels. The second part of the chapter discusses how to reduce disaster risk following the guidance of Hyogo Framework for Action (2005-2015). Through the review of Chinese practice under Hyogo Framework, the opportunities and challenges are identified. Based on the results of the analysis, recommendations for reducing disaster risk in Chinese coastal areas are proposed.

The last chapter (Chapter 7) addresses the contributions, limitations, and conclusions of this study, as well as directions for future research. It summarizes the results, findings, and policy implications of this research and explains how it advances our knowledge on disaster risk reduction. The limitations of this study are also discussed. Lastly, this chapter provides insight regarding future steps toward improving the approach of the research to gain overall vulnerability profiles for disaster risk reduction.

Chapter 2

CONTEXT: CHINESE COASTAL REGIONS

The coastal area of the Chinese mainland contains 11 Provincial-level administrative regions. These administrative regions cover approximately 1,291,390 km² of land, which represents 13.4% of the territory of the nation (NBS, 2010). From north to south, they are Liaoning, Hebei, Tianjin, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi and Hainan. Among them, Tianjin and Shanghai are centrally administrated municipalities. Guangxi is an autonomous region with the full name of Guangxi Zhuang Autonomous Region, which is an ethnic autonomous area. These regions are the most wealthy, resourceful and densely- populated areas within China. Yet, they are also the most vulnerable regions to various natural hazards. This chapter introduces the physical environment and social conditions of Chinese coastal regions.

2.1 Physical Environment and Coastal Natural Hazards

2.1.1 Storm Surges and Disruptive Waves The Chinese coastal zone is situated on the east coast of the Pacific Ocean, where typhoon activity is the most vigorous in the world. On average, there are 27 typhoons generated in this region annually. Thus, the southeast coast of China is frequently attacked by typhoon-induced storm surges. Due to the fact that the mainland of China covers both tropical and extra tropical climate zones, in winter and spring the northern coasts of China are vulnerable to the extra tropical storm surges.

Typhoons and cyclones often lead to disruptive waves that cause water levels to rise suddenly and sharply, causing dams to be breached, farmland to be submerged, and buildings to collapse, resulting in both human and animal casualties. Storm surges and disruptive waves have been the most serious natural hazards in the Chinese coastal area. It is recorded that from 2000 through 2012, storm surges hit Chinese coastal areas 219 times, resulting in 806 deaths or disappearances with

15,000 people impacted in total, the flooding of 71,000 km² of farmland and agricultural land, and the destruction of 3.62 million houses and 51,062 boats. During this same time period, disruptive waves occurred 429 times, resulting in an additional 1,553 casualties (SOA, 2012). The death toll and economic loss caused by storm surges and disruptive waves accounts for more than 90% of all marine disasters, as shown in Figure 2. Additionally, it was observed that the percentage of landed typhoons, landing strength, and occurrence of super typhoons has increased in the past decade.

Figure 2. Death Toll (Person) and Direct Economic Loss (100M million Yuan) of Marine Disasters in China, 2000-2012 (Data Source: SOA, 2012)

In addition, storm surges and disruptive waves impact the Chinese coastal zone unequally. Among the 11 coastal municipalities and provinces, the Zhejiang, Fujian and Guangdong coasts were the worst-hit areas. Figure 3 shows the spatial distribution of death toll (missing included) and direct economic loss caused by storm surges and disruptive waves during 2000-2012 (SOA, 2012).

Figure 3: Spatial Distribution of the Consequences By Storm Surges and Disruptive Waves, 2000-2012 (Data Source: SOA, 2012)

2.1.2 Tsunamis China’s marginal seas lie on the circum-Pacific seismic belt, where tragic tsunamis occur frequently. The biggest threat of regional tsunamis to China is from the Manilla Trench and the Ryukyu Trench. Most notably, the Manilla Trench, a well- known potential tsunami source in the world, poses a great threat to South China,

including Guangdong and Hainan. According to computer simulations conducted by the National Marine Environmental Forecasting Center (NMEFC), if an earthquake with the magnitude of 9.0 occurred in this region, a huge tsunami would be triggered off the coast of Southern China very quickly. These devastating waves would crash into the seashores of Taiwan, Guangdong and Fujian provinces and result in death and destruction at a colossal scale. It is estimated that the maximum tsunami wave height could reach up to 8-9 meters when it arrives at the coast of Guangdong province (NMEFC, 2013). The preliminary tsunami hazard risk assessment shows that the coastal regions of Zhejiang, Fujian, Guangdong and Taiwan Island are the most vulnerable to tsunamis.

2.1.3 Sea Ice Sea ice disaster poses a great threat to the northern coasts of China in winter, including areas along the Bohai Sea and northern Yellow Sea. In the winter of 1969, a huge sea ice disaster occurred in this area. It is recorded that the whole Bohai Sea was covered by sea ice because of particularly cold weather. Floating ice pushed down the oil platforms and the vessels were locked offshore. Remarkable economic losses were recorded in that year. In 2009-2010, severe sea ice disaster hit the Bohai Sea again, which caused great damages in marine shipping, port operation, and aquaculture. It was reported that the direct economic loss was 6.4 billion Yuan (SOA, 2010).

2.1.4 Other Disasters in Coastal Areas In addition to the marine disasters mentioned above, sea level rise, coastal erosion, ecosystem deterioration, and pollution (agricultural diffuse, livestock wastes, domestic sources, industrial sources) are of great concern in Chinese coastal regions as

well (Cao et al., 2007). China's coastal sea level has been rising 2.6 mm every year for the past three decades, which increases the risk of storm tides, coastal erosion, seawater invasion and other disasters (SOA, 2010). As a result of inappropriate development of coastal resources, the inshore ecological environment deterioration has become more severe. During the past 50 years, 50% of coastal wetlands have disappeared, 80% of coral reefs and mangroves have been destroyed, and 29,000 square kilometers of seawater have been seriously polluted. These severely polluted water areas included East Liaoning, Bohai and Hangzhou bays, and the estuaries of the Yellow, Yangtze and Zhujiang rivers, as well as inshore areas of major coastal cities (Xinhua.Net, 2008). In 2003 alone, as much as 79.6 km of coastline has retreated in coastal areas where coastal erosion is severe and has been quantitatively monitored (Cao et al., 2007). These chronic disasters pose a great threat to local residents’ livelihoods and daily life.

2.2 Socioeconomic Conditions

2.2.1 Administrative Division System China's administrative units are based on a five-level system: Provincial level (including provinces, municipalities, and autonomous regions); Prefectural level

(including prefecture-level cities and autonomous prefectures); County level (including city districts, county-level cities and autonomous counties); Town level (including townships, ethnic townships and towns); and Villages (informal administration units), as shown in Figure 4. Taking into consideration the availability of data and the usefulness for practice, this study conducted research at the county level.

Figure 4: The Five-Level Administrative Division System in China (Adapted from Chen et al., 2013)

2.2.2 Economic Status Coastal areas have outstanding advantages in transportation and resources, making these regions the most developed in the nation, particularly in regard to economic aspects. In 2010, 48.71% of national fixed asset investment was completed by the 11 coastal provinces. With the large number of government-backed investment and policy support, the coastal provinces achieved 24192.5 billion Yuan ($3940.6 billion) gross domestic product (GDP), accounting for 61.16% of the national GDP. The per capita GDP in these areas is 1.5 times higher than the national average, at approximately 43642 Yuan per capita (NBS, 2010).

It is notable that the three most important coastal economic zones, Round-the- Bohai Economic Zone, Changjiang River Delta Economic Zone (as shown in Figure 5), and Zhujiang River Delta Economic Zone contribute almost half of the national GDP, according to the 2007 national statistic data (Chinese Central Government, 2008). Marine economy sees a great development that the gross ocean product (GOP) of national marine economy reached 3957.3 billion Yuan, which was 9.86% of national GDP. The economic achievement of GOP is a result of the aforementioned three important economic zones. The GOP of Round-the-Bohai Economic Zone, Changjiang River Delta Economic Zone, and Zhujiang River Delta Economic Zone reached 1386.9 billion Yuan, 1265.9 billion Yuan, and 825.4 billion Yuan respectively, which accounted to 35%, 32% and 20.9% respectively of the national GOP (SOA, 2012).

Figure 5: GDP and Per Capita GDP of Chinese Coastal Regions in 2010 (Data Source: NBS, 2010)

2.2.3 Population Density With the advantages in resources and economic development, coastal regions have become the most populated areas in China. According to census data from 2010, 42.05% of the national population, or about 576.33 million people, lived in coastal areas. The population density in the 11 coastal regions was as high as 5.5 times the national population density. Still, people are continuously moving to coastal regions. From 2000 to 2010, 53.9 million people moved to coastal areas, accounting to a 1.03%

growth rate of population each year. The population density and growth rate are shown in Figure 6.

Figure 6: Population Density (2010) and Growth Rate (2000-2010) of Chinese Coastal Regions (Data source: NBS, 2000; 2010)

Shanghai and Tianjin, two coastal municipalities, are the most crowded among the coastal regions, with population densities that reach 3630.78 and 1082.70 people per km² respectively (NBS, 2010). The destination of immigrants concentrates on Shanghai, Tianjin, Guangdong and Zhejiang, and has manifested in a very high population growth rate in these areas.

2.2.4 Spatial Variation The social and economic conditions are not distributed evenly throughout the 11 coastal municipalities and provinces. Guangdong, Jiangsu and Shandong receive the most fixed assets investment and contribute the highest GDP, while the highest per capita GDP is generated in the Shanghai and Tianjin municipalities. Shanghai and Tianjin have an advanced industry structure, where the Three Industrial Structures are

3:43:54 and 20:39:41 (NBS, 2010). In contrast, Guangxi, Hebei, Hainan and Shandong, where the proportions of the primary industry are more than 50%, still mainly rely on traditional agriculture, forestry, animal husbandry, fishery and mining, which heavily rely on natural envrionment. The proportions of minorities in Guangxi, Hainan and Liaoning are much higher than the other provinces, with minorities comprising 37.17%, 16.44% and

15.19% of the population respectively, compared to the national average of 8.49%. Guangxi Zhuang Autonomous Region is the place where the Zhuang nationality is most concentrated. About 14.45 million people of the Zhuang nationality live in Guangxi, accounting for 31.39% of the Region’s population. 14.73% of the population in Hainan province is Li nationality, and Liaoning is comprised of predominately Man, Menggu, Hui and Chaoxian nationalities (NBS, 2010).

The diversity of physical and social characteristics of the coastal regions contributes to the different degrees of vulnerability among these areas. Continuous population growth and environmental deterioration, inequitable treatment of immigrant workers, backward industry structure, and the unsustainable exploitation of natural resources, significantly increase the vulnerability of Chinese coastal regions to natural hazards.

Chapter 3

LITERATURE REVIEW

3.1 From Analyzing Hazards to Assessing Vulnerability Traditional hazard-oriented views suggest that the severity of consequences of disasters is determined by the frequency and magnitude of natural hazards. For instance, Hewitt (1983) argued that disasters may be seen as impacts that “derive from natural processes of events”. John Oliver (1980) defined disaster as a part of the environmental process that is of greater than expected frequency and magnitude, and causes major “human hardship with significant damage.” Therefore, many disaster research focuses on the analysis of frequency and magnitude of certain natural hazards such as earthquakes, typhoons, storm surges, floods, tsunamis, and so forth (e.g. Mogi, 1963; Utsu, 1965; Westerink et al., 1992; Huang, 1997; Bernier et al., 2006; Hyndman et al., 2010). Efforts to decrease the negative consequences of disasters, including damage to properties and loss of human lives, have focused primarily on technical solutions, such as building stronger dams as a preventative measure against flooding or storm surges. Instead of defining disasters primarily as the occurrence of “natural” events, others assert that disasters are not “natural” and that hazards do not equate to disasters. Instead, it is suggested that potentially damaging hazard events only become worthy of the classification of “disaster” when people’s lives and livelihoods are impacted (Annan, 2003). Disasters are the result of the complex interaction between the natural environment and the vulnerability of a society, including its infrastructure, economy,

and environment, all factors that are both determined and influenced by human behavior (Alexander, 1993; Dynes, 1998; Quarantelli, 1998; Rosenthal, 1998; Cutter, 2005b; Birkmann, 2006). It is not the hurricane’s wind or storm surge that makes the disaster; these forces are only the source of the damage. Vulnerabilities are the root causes of disasters, and disasters reflect individual coping patterns and the inputs and outputs of social systems (Blaikie et al., 1994; Quarantelli, 2005a). Bates and Peacock

(1993) characterize disasters as social events arising out of “a process that involves a socio-cultural system’s failure” to protect its population from external or internal vulnerability. The understanding of disasters as a result of interaction between hazard events and social systems requires disaster management initiatives to analyze risk from the perspective of both physical hazards and social systems dimensions. With this insight, disaster research has moved slightly away from the “pure hazard centered” approach, focusing on natural hazards and their quantification, and toward a greater focus on the identification, assessment, and ranking of various vulnerabilities which places people and social relationships at the core of disaster study (Bogardi and Birkmann, 2004; Quarantelli, 2005a).

3.1.1 Definition The International Strategy for Disaster Reduction defines vulnerability as “the conditions determined by physical, social, economic and environmental factors or processes, which increase the susceptibility of a community to the impact of hazards (HFA, 2004). Vogel and O’Brien (2004) report the common characteristics of vulnerability as: multi-dimensional and differential, and scale dependent and dynamic. This notion stresses the fact that the understanding of vulnerability should

consider temporal and spatial differentiations, a single or multi-hazard approach, and related dimensions including physical and other factors. It needs to be defined as “vulnerability of what”, “vulnerability to what” and “what scale” to mention but the most important aspects (Villagrán, 2006). This theory is fundamental for the construction of vulnerability assessment methodologies. However, there is virtually no consensus on the definition of vulnerability. The current literature demonstrates that the term vulnerability has been defined in many different ways (Schneiderbauer and Ehrlich, 2004; Birkmann, 2006). The concept of vulnerability has been discussed and defined from many different perspectives, such as in the context of climate change (Cutter et al., 2009), or its relationship with resilience and adaption capability (Adger, 2006). The literature demonstrates that there have been five major shifts in the development of a definition for and understanding of vulnerability among academics and professionals: I. From the primary physical dimension to a multidimensional concept, which includes social, economic, political, cultural, environmental and institutional aspects, and requires interdisciplinary analysis; II. From a two-element structure (exposure and susceptibility) to a multi-

element structure, which incorporates resilience, coping capacity, adaptive

capacity, and other related elements; III. From a static precondition/outcome to a dynamic process; IV. From analyzing at the local scale to analysis at a larger or global scale, such as in the context of globalization and global environmental change;

V. From demonstrating the status quo about what, where, when, and how to exploring the root causes of the phenomenon, although “in multi-causal situations and a dynamic environment, it is hard to differentiate between the causal links of different dynamic pressures on unsafe conditions and the impact of root causes on dynamic pressures (Wisner, 2004)”. While there is no one universal definition of vulnerability, the different normative implications of vulnerability research stem from the formulation of the objectives of study (or the system) in each case (Adger, 2006), thus affecting the adoption of conceptual frameworks and methodology for measuring the risk and relative vulnerability (Birkmann, 2006). For example, UNDP BCPR (2004) defines vulnerability as “a human condition or process resulting from physical, social, economic and environmental factors, which determine the likelihood and scale of damage from the impact of a given hazard”. Based on this definition, UNDP measures the relative vulnerability of a country to a given hazard from a human-centered perspective, dividing the number of people killed by the number of people exposed in the Disaster Risk Index (Peduzzi, 2009). In contrast, UNESCO-IHE defines the term as “the extent of harm, which can be expected under certain conditions of exposure, susceptibility and resilience”, and calculate vulnerability by the equation

“Vulnerability = Exposure + Susceptibility – Resilience” in its Flood Vulnerability

Indices (FVI, UNESCO-IHE; Balica et al., 2010). Villagrán (2006) defines vulnerability in an interesting way, classifying the authors that propose the definitions into particular groups such as academia, disaster management agencies, the climate change community, and the development agencies. He argues that the different views on vulnerability arise as a consequence of the needs confronted by each particular

group to address particular issues of the potential impacts of disasters (Villagrán, 2006). The dimensions of vulnerability encompass physical, social, human, economic, environmental, political, institutional, cultural and historical aspects. However, due to the various definitions of the term, the complexity of the concept, and the purpose of research and applications, there are different understandings of the various dimensions: (i) regarding the concept of social vulnerability, two main definitions are adopted in the literature on theory and assessment approaches: narrow definition and broad definition. Narrow definition refers to the traditional demographic characteristics such as age, gender, ethnicity, knowledge of languages, disabilities, mobility, home ownership, employment status and so on. Broader definition demonstrates that social vulnerability comprehensively includes socio-economic, political (institutional), built environmental and cultural dimensions, normally comparing to physical vulnerability. This broader definition is used as human vulnerability in some studies (UNEP, 2003); (ii) physical vulnerability is understood as the potential physical damage caused by exposure and the physical susceptibility of the built-environment (Cardona, 1999; Cardona et al., 2000; Kappes et al., 2012), for instance, buildings, rivers, and forests, which are strongly linked to certain types of disasters; (iii) environmental vulnerability mostly refers to the natural resources such as soil, water, forest, vegetation and air (USAID, 2007). Environmental vulnerability is distinguished from built-environmental vulnerability, which mostly refers to infrastructures, facilities, historical assets, and residential and commercial buildings; (iv) there is no unified definition of cultural vulnerability. In some schools it is understood as the psychological factors such as public awareness and preparedness; it

can also be interpreted to include factors such as trust in government and neighbors, and social networks (Cashman et al., 2008; Bang, 2008; FVI, UNESCO-IHE). On the other hand, it can be defined as the exposure of cultural-related facilities such as monuments, temples, and churches (ADPC, 2002). While in some studies these facilities are regarded as historical heritages, in others they belong to the built- environment characterization.

3.1.2 Conceptual Frameworks Conceptual models are an essential step towards the development of methods for measuring vulnerability and for the systematic identification of relevant indicators (Downing, 2004). During the last few decades, several conceptual frameworks have been proposed by various schools. They include the pressure and release (PAR) model

(Blaikie et al. 1994; Wisner et al. 2004), the hazard-of-place (HOP) model (Cutter, 1996; 2003), vulnerability within the framework of hazard and risk (Davidson, 1997; Bollin et al., 2003; UN/ISDR framework for disaster risk reduction, 2004), the sustainability livelihood framework (Asley et al.,1999; Chambers and Conway, 1992), a holistic approach to risk and vulnerability assessment (Cardona and Barbat, 2000), the double structure of vulnerability (Bohle, 2001), vulnerability in the global environmental change community (Turner et al., 2003), the onion framework (Bogardi and Birkmann, UNU-EHS, 2004), the BBC model (Cordona, 1999; 2001; Bogardi and Birkmann, UNU-EHS, 2004). These theoretical models have been critically reviewed by Birkmann (2006), Villagrán (2006), Cutter et al. (2009) and Ciurean et al. (2013). A common view of vulnerability is expressed across the aforementioned conceptual frameworks, such as “vulnerability represents the inner conditions of a society or community that make it liable to experience harm and damage, as opposed

to the estimation of the physical event (hazard)” (Birkmann, 2006), in accordance with the five shifts discussed previously regarding the definition of vulnerability. The distinctions between the conceptual and analytical frameworks focus on five points:  Whether multiple dimensions are involved such as physical, social, economic, institutional, environmental and cultural aspects;  Whether capacities such as coping, adaptation, and response should be a

part of vulnerability or be viewed as a separate feature (PAR, 2004; UN/ISDR DRR, 2004; vs. Turner et al., 2003; Cardona et al., 2000; BBC, 2004). It leads to different calculation of vulnerability and disaster risk;  Whether vulnerability is viewed as a static precondition or dynamic process (PAR, 2004; Cardona et al., 2000; BBC, 2004);  Whether the larger context is considered (DFID, 1999; Turner et al., 2003);

 Whether the root causes of vulnerability and sustainable development is included (PAR, 2004). An overview of the conceptual frameworks of vulnerability from the Five-Shift perspective is shown in Table 1:

Table 1: An Overview of the Conceptual Frameworks of Vulnerability from the Five-Shift Perspective (Source: author)

The a DRR- double Vul. in Onion DFID holistic BBC PAR HOP UN/IS structu global frame model approa model DR re context work ch model Multi-dimensions (Physical, social, economic, x x x x x x x x x environmental, etc.) Capacities included x x x x x x x (Preparing, coping, etc.) Dynamic process x x x x x x x Larger context x x x x considered Root causes x x x x considered

The discussion and exploration of theoretical frameworks to address vulnerability is still ongoing. It should be noted that the integrity of a model is not based on how broad of a scope the model takes or the number of concepts included within it. In fact, engaging more concepts and a broader scope from multiple perspectives increases the complexity of the model, resulting in a more complicated analysis, with more knowledge and data input required. The drawbacks of interdisciplinary cooperation and lack of availability or accuracy of data increase the

difficulties of developing methods to measure vulnerability in real practice. Nevertheless, there is now a solid body of work on the applications or case studies of such conceptual models in understanding vulnerability to various hazards. Regarding the calculation of risk in terms of hazards, vulnerability and capacities, Downing et al. (2005) and Villagrán (2006) proposed reviews from the conceptual and methodological perspective. It is found that with different conceptual

frameworks of vulnerability, a number of mathematical expressions for risk in terms of hazards, vulnerability and capacities are conducted over time, such as: Risk = Hazard * Vulnerability (UNISDR, 2004), or Risk = Hazard * Exposure * Vulnerability (Dilley et al., 2005) The above expressions consider capacities as the inherent component of vulnerability. Some schools consider capacities or preparedness as the opposite of vulnerability, like:

Risk = (UNISDR, 2002).

3.1.3 Social Vulnerability As there are many definitions and dimensions of vulnerability, there are also multiple definitions of social vulnerability (Blaikie et al., 1994; Henninger, 1998;

Frankenberger et al., 2000; Alwang et al., 2001; Cannon et al., 2003; Cutter et al.,

2003; Thomalla et al., 2006), which means that different authors use the term differently. A broad definition describes social vulnerability as a comprehensive variable that includes socio-economic, political (institutional), built environmental and cultural dimensions. In some studies, this term is called human vulnerability (UNEP, 2003;

Vincent, 2004). This definition is predominately used as a term to differentiate from physical vulnerability. Another definition views social vulnerability as initial well- being, livelihood and resilience, self-protection, social protection, and social and political networks and institutions (Cannon et al., 2003). This definition views social vulnerability as the result of social fragilities and capacities of individuals or institutions, including variables that are addressed by some conceptual frameworks of vulnerability. However, this definition of social vulnerability neglects the determined

factors of social fragilities such as income, gender, urbanization, growth rates and economy vitality. Cutter et al. (2003) argue that “social vulnerability describes those characteristics of the population that influence the capacity of the community to prepare for, respond to, and recover from hazards and disasters” and “social vulnerability is present, independent of the hazard type or threat source.” This definition addresses the root causes of socially fragilities and reflects the interactions of social, economic and political factors. However, it excludes the practical capacities of individuals and community. Moreover, this notion neglects the dynamic nature of vulnerability, or that vulnerability is changing over time. A large amount of research has been conducted to identify the social factors that increase or decrease the impact of specific natural hazard events on the local population. Those characteristics include age, income, gender, race and ethnicity, employment, family structure, residence type, household type, tenure type, health insurance, house insurance, car ownership, disability, language skills and debts/savings (Wisner et al., 1993; Morduch, 1994; Peacock et al., 1997; Moser, 1998; Morrow, 1998; Ngo, 2001; Brooks, 2003; Cutter et al., 2003; Fothergill and Peek 2004; Dwyer et al., 2004; Tierney, 2006; Bates and Swan 2007; Masozera et al. 2007). With social and economic development, the characteristics of a community or society are changing over time, thus resulting in the social vulnerability of places to natural hazards to be dynamic. Cutter et al. (2003) explain how these factors increase or decrease overall social vulnerability, assigning each factor as positive or negative based on a broad review of the literature. Factors such as age, gender, income, race and housing conditions are for the most part data that is easy to obtain from census or related statistics books, and are

generally used as indicators in quantitative indices. However, the capacities of individuals or communities are excluded: such as community isolation/cohesion, the culture insensitivity of majority population, the awareness and preparedness of the population, the functionality of infrastructure and critical facilities, and the capacities of institutions such as government agencies or other stakeholders. In order to address these factors, primary data and qualitative research is required.

3.2 Vulnerability Assessment Although research regarding theoretical frameworks and the definition of vulnerability is still ongoing, there is a significant body of work using case studies to apply these conceptual models as a means of understanding vulnerability to various hazards. There are several ways to categorize these approaches based on different criteria, such as the covered dimensions, the focused scale, the capturing of changing, and the methodology of data collection: (i) the models cover different dimensions of vulnerability. While most early studies only considered the physical parameters (CVI, Gornitz et al., 1994; Theiler and Hammar-Klose, 1999, 2000), numerous models have moved beyond hazard assessment to incorporate the examination of contextual factors, including demographic, economic, environmental, cultural and institutional aspects

(e.g. CM, IPCC-CZMS, 1992; Harvey et al., 1999; SoVI, Cutter et al., 2003; CVI+SoVI, Boruff et al., 2005; Borden et al., 2007). Additionally, the impact of global change to local vulnerabilities, particularly in small geographic areas, has been examined (e.g. Pelling and Uitto, 2001; Turvey, 2007); (ii) scales of vulnerability assessment are distinguished at the global (Peduzzi et al., 2009), national (Cardona, 2006; Arakida, 2006), to local and community level (e.g. Flax et al., 2002; Parkins and

MacKendrick, 2007); (iii) most approaches only present the situation at a certain

moment in time, while there are other approaches using dynamic computer models to try to capture the change in vulnerability over time (e.g. DIVA, (UNFCCC, 2004)); (iv) quantitative indicators and indices are generally used to translate the concept of vulnerability into concrete terms that are easy for understanding, comparing, and identifying impacts and problems. Meanwhile, qualitative research methods are generally used to assess complex capacities and resilience, emphasizing the involvement and engagement of stakeholders for risk reduction activities (PCVA, (Honorio, 2002); Australian Government, 2012; VCA, (IFRC, 2006); Bjarnadottir et al., 2011; Cadag & Gaillard, 2012; Biddle et al. 2013); (v) due to the different definitions and understandings of vulnerability, historic losses and mortalities (e.g. DDI by Cardona, 2006; UNDP BCPR, 2004), or social characteristics (e.g. SoVI by Cutter et al., 2003) are used to reflect vulnerability of a given place in different approaches. Table 2 reports the typical approaches to vulnerability assessment.

Table 2: Vulnerability Assessment Approaches (Source: author)

Data Model Developer Application Collection Hazards Scales Key Input Strength Weakness Method Gornitz et al., 1994; Theiler and Hammar- Six physical variables: Klose, geomorphology, coastal slope, Only address the physical dimension and Coastal Quantitative, Simple historical data input 1999,2000; Sea level County, relative sea-level rise rate, neglect other dimensions of vulnerability Vulnerability USGS Indicator- and calculation required; easy Boruff et al., rise extendible shoreline erosion/accretion rate, such as social, economic, environmental, as Index (CVI) based to integrate with other indices 2005; mean tide range, and mean wave well institutional factors McLaughlin height and Cooper, 2010

Only address the present situation of social Captured complex social Social indicators such as median vulnerability that needs update over time; characteristics using indicators Social Quantitative, age, ratio of not always independent of hazards; the Cutter et al., All- County, and variables; easy to integrate Vulnerablity US NOAA Indicator- female/children/elderly/minorities definitions of negative/positive impact of 1996; 2003 hazard extendible with other indices; easy to get Index (SoVI) based , housing type, ratio of urban indicators/components to the overall data from census which is cost- residents, and so force vulnerabilities are sometimes too simple to effective and time-saving

30 reflect the real condition.

Vulnerability profile and the list It presents a list of analyses that should be IPCC, 1992; of future policy need to adapt both done, but does not explicitly instruct the Common Kay et al. UN-IPCC; Sea level physically and economically. A Comprehensive impact user on how to perform the analyses; Basic Methodology Qualitative National 1996; Harvey AU rise range of impacts of sea level rise, assessment approach knowledge of coastal elevations and of (CM) et al., 1999 including land loss and associated natural coastal processes and trends are value and uses, wetland loss, etc required

Relative risk or vulnerability Integrated hazards analysis of coastal communities to identification and vulnerability a series of existing threats, The realistic operation may be difficult Community Qualitative + assessment of critical facilities, including hazard identification because quite a lot of different interest Vulnerability Flax et al., US NOAA; Quantitative, All- Community social economic, and prioritization; hazard analysis; parties are involved in the process, which Assessment 2002 UNFCCC Indicator- hazard , extendible environmental and mitigation critical facilities analysis; social highly effective and efficient organization Tool (CVAT) based opportunities (capacities); the analysis; economic analysis; and cooperation are required design adequately engaged environmental analysis; and stakeholders mitigation opportunities analysis

Flexible modular design that allows individual choosing High requirement to the users’ Dynamic scenarios for secondary computational ability and deep Interactive UNFCCC, Quantitative, Sea level development within all scales, understanding and knowledge of the model; Hinkel et al., Vulnerability EU, 22 Indicator- rise, Multi-scale The user’s chosen scenarios as “sub-systems”, to integrate requires high performance of the local 2005; 2009 Assessment regions based extendible local knowledge and computer to run the model; High cost (DIVA) perspective, hence, realize the required to maintain the flexibility of the dynamic assessment of the iterative model development process coastal vulnerabilities

3.3 Vulnerability Research and Assessment in China Scholars in China have conducted notable research on vulnerability to disasters. The main focus has included disaster loss assessment, hazard grading, hazard zoning, risk and vulnerability assessment, and so on. Through comprehensive research and drawing lessons from practice and international experiences, many aspects have been studied such as vulnerability conceptual frameworks, assessment approaches at different levels, and weighting and aggregation of indices. Shi (2002, 2005, and 2009) discussed vulnerability in the context of risk management and argued that disaster was the result of the comprehensive effect of a hazard, hazard-formative environment, and the vulnerabilities of hazard-affected bodies, mainly depending on regional economy and security construction. Xu et al.

(2006) reviewed the international experiences of disaster management and suggested that disaster research in coastal cities in China should focus on vulnerability assessment indices and methods, risk assessment criteria and dynamic models, data management patterns and standards, and the application of GIS tools. Ren et al. (2010) proposed the integrated risk analysis framework of marine disasters for Chinese coastal areas. The framework addressed five steps of marine disaster risk analysis, including coastal zoning, hazard identification, hazard analysis, vulnerability assessment, and disaster loss risk evaluation. This framework comprehensively ties together hazard analysis and vulnerability assessment in risk analysis and presents a list of analyses that should be done, but does not explicitly instruct the user on how to perform the analyses. In addition to research on theories and frameworks, Chinese scholars have conducted a number of vulnerability assessments in regard to a variety of natural

hazards. Based on the discussion of the definition of vulnerability, Ali Sun et al. (2009) identified social factors of population density, per capital output, and GDP, as the main elements that have a significant correlation with vulnerability. In addition to research on vulnerability and risk theories, a number of quantitative indices have also been developed. Early in 1990’s, Du et al. (1997) conducted research regarding the impact of sea level rise to vulnerable Chinese coastal areas in the physical dimension.

Based on the Coastal Vulnerability Index (CVI), Tan et al. (2011) assessed physical vulnerability of eleven coastal provinces to storm surges using storm surge disaster loss data from 1990 to 2009. Sun (2007) conducted a vulnerability assessment index of coastal areas that included social, economic, cultural, infrastructure and management indicators, and applied the index to six counties of Shanghai City. However, Sun’s (2007) measurement of cultural factors and management capacities is still regarded as questionable at present. Bin et al. (2010) established an index system for regional vulnerability assessment of natural disasters. It is a relatively comprehensive model that took into consideration social, economic, environmental and infrastructural vulnerabilities assessments. However, only a few indicators are included in each dimension, for instance, population density and age structure are the only factors selected to represent social vulnerability of a certain place, resulting in a lack of representativeness.

In summary, the current research on vulnerability assessment in China either captures only a limited area, or only captures vulnerability by a single dimension or very few variables. Many of the underlying drivers of social vulnerability (e.g. age, ethnicity, occupation, employment) are absent from this previous work (Chen et al.,

2013). There is still no comprehensive profile of social vulnerability, or sub-regional index map, for whole coastal areas in China.

3.4 Debates, Discussion and Conclusion Compared with the deeper understanding of the concept of vulnerability (see 3.2.1 Five-Shift of vulnerability researches) and the development of conceptual frameworks, the measurement of vulnerability is still underway. The approaches that have been applied have proven to be effective tools that enable decision-makers to assess the elements at risk and the potential damage and impact of hazards on physical, social, economic and environmental conditions. However, there are several topics that require further discussion. First, there are several potential weaknesses in relation to the development of indices as a combination of indicators, including the subjective choice of variables, averaging and weighing variables and indicators, aggregation problems, and data availability (Villagrán, 2006). In addition, current formal models differ greatly among hazards, which makes comparison very challenging and perhaps misleading. The costs of obtaining data to use in the models may restrict the use of the models, as well as the maintenance of keeping the database up-to-date.

Second, the assessment of immeasurable factors is still in a stage of exploration. For example, risk perception has an important impact on people’s vulnerability and is related to local culture, tradition, belief, memory and experience; institutional capabilities and resilience significantly contribute to reduce the vulnerability. Current studies mainly use qualitative methods such as surveys, interviews, and focus groups to obtain primary data. The study and application of how to integrate this kind of data into indices, how measurement of factors such as cultural

vulnerability and institutional capacities contribute to the result of the whole vulnerability measurement, as well as the cost-benefit analysis of the measurement, are all still in their infancy. Lastly, one of the most important goals in developing tools for measuring vulnerability is to help bridge the gap between theoretical concepts of vulnerability and day-to-day decision-making (Birkmann, 2006). However, at present, most of the literature about vulnerability measurement remains at the methodology level. There is a lack of literature devoted to deeply interpreting how to support the disaster management strategies using the results of assessments. Moreover, there is a very poor understanding of how models are used in risk management decision-making. The ability of models to capture non-quantifiable dimensions of risk trade-offs is considered a problem since the risk management decisions must be negotiated, and models cannot fully incorporate values and other non-commensurable variables (OFCM, 2001). Given the inherent limitations of measurement, each methodology has its strengths and weaknesses. For instance, a single hazard assessment focusing on physical factors represents the most basic approach for understanding the specific risk, and requires the least data inputs and resources such as time, money, and human resources. However, the output only linearly reflects what, when, and where, in regard to the hazard, and commonly neglects the multi-hazard situation and human interactions within a given geographic place. Thomas (2010) did a comparison of the outcomes, data requirements, and considerations, of a variety of vulnerability assessment approaches in order to present the different concerns of each form of assessment. While there is no single approach that is ideal for all contexts, the

adoption of methodologies varies and depends on well-defined study objectives, areas, scopes, as well the availability of data and other resources. As a conclusion, vulnerability is a very complex concept that encompasses a great number of sub-concepts and contributing factors. Measurement approaches simplify the complexity of the concept, not perfectly, but more importantly, make it understandable and comparable for decision makers to move forward by addressing the gaps and proposing initiatives to reduce disaster risk. Practice could not wait till theoretical debates were settled. This sentlement is summarized nicely in the following quote from Birkmann (2006):

“We have to bear in mind the limitations of measuring and simplifying the complex interactions that provide a context for and also shape the various vulnerabilities. Regarding the use of indicators and indices……indicators are necessary tools, but that one has to handle them with care (Birkmann, 2006; Morse, 2004)”.

Chapter 4

METHODOLOGY

This study applies an indicator-based approach, which was adapted from the

Social Vulnerability Index (SoVI®), to measure the social vulnerability of Chinese coastal counties. The use of a quantitatively derived SoVI was employed for three main reasons. First, SoVI provides a useful tool for comparing the spatial variability in socioeconomic vulnerability by using a single value derived from multivariate characteristics. Secondly, SoVI can be linked (statistically and spatially) to physical, economic, and environmental based indices to calculate the overall vulnerability of a specific place. For instance, Boruff et al. (2005) integrated SoVI with Coastal Vulnerability Index (CVI) to assess erosion hazard vulnerability of U.S. coastal counties. Borden et al. (2007) explored the variability among the 132 urban areas of the U.S. to natural hazards by aggregating three indices: a social vulnerability index (SoVI), a built environment vulnerability index (BEVI), and a hazard vulnerability index (HazVI). Finally, SoVI requires secondary data that is available in census and national statistics reports to capture the social characteristics of vulnerability to natural hazards, thus it is more cost-effective and time-efficient. Consequently, SoVI is ideal for this study, which aims to get an overall picture of the geographic variation of Chinese coastal areas in the context of social vulnerability, highlighting where there is uneven capacity for preparedness and response and where resources might be used most effectively to reduce pre-existing vulnerability (HVRI).

4.1 Study Area According to the administrative division defined by the Chinese Marine Statistical Yearbook (2012), excluding the two Special Administrative Regions of Hongkong and Macau, China currently has 237 coastal districts, counties, and county- level cities (hereafter referred to as “counties”). They are affiliated with 53 coastal cities of 11 provincial level regions. Among them, Jinmen Xian, a county of Fujian

Province, is excluded from this study because no data is available. Since Dongguan and Zhongshan are two special prefectural cities of Guangdong Province that are not divided into city districts or counties, the two cities are included in this study as two counties. In total, 238 counties are selected as the units for this analysis. The 238 counties cover about 252,648 km² of land area, accounting for approximately 20% of the 11 coastal municipalities and provinces land area and

2.62% of national territory. Compared with the land area, these counties contribute 22.47% of the national GDP. Approximately 176.5 million people live in these counties. The population density reaches as high as 2230.8 people per sq. km, which is about 15.6 times the national average. The socioeconomic conditions are not even distributed throughout the study units. The 238 coastal counties contain 117 city districts, 65 counties, and 56 county level cities. City districts are mostly urban, developed areas with dense population, while counties and county-level cities consist of towns, villages, and farmland, where rural areas predominate rather than urban areas. Table 3 outlines the provincial regions and prefectural cities that the 238 coastal counties are affiliated with.

Table 3: Study Units: 238 Chinese Coastal Counties

Coastal Region Coastal City (number of counties/districts)

Tianjin (1) Tianjin (1) Hebei (11) Tangshan (4), Qinhuangdao (5), Cangzhou (2) Dalian (10), Dandong (1), Jinzhou (1), Yingkou (4), Liaoning (22) Panjin (2), Huludao (4) Shanghai (5) Shanghai (5) Jiangsu (15) Nantong (5), Lianyungang (5), Yancheng (5) Hangzhou (2), Ningbo (11), Wenzhou (7), Jiaxing (3), Zhejiang (35) Shaoxing (2) Zhoushan (4), Taizhou (6) Fuzhou (6), Xiamen (6), Putian (5), Quanzhou (7), Fujian (33) Zhangzhou (5), Ningde (4) Qingdao (10), Dongying (5), Yantai (11), Weifang Shandong (37) (3), Weihai (4), Rizhao (2), Binzhou (2) Guangzhou (9), Shenzhen (6), Zhuhai (3), Shantou (7), Jiangmen (5), Zhanjiang (9), Maoming (3), Guangdong (58) Huizhou (3), Shanwei (3), Yangjiang (3), Dongguan (1), Zhongshan (1), Chaozhou (2), Jieyang (3) Guangxi (8) Beihai (4), Fangchenggang (3), Qinzhou (1) Hainan (13) Haikou (3), Sanya (10)

4.2 Data

4.2.1 Indicators and Variables In SoVI® 2006-2010, created by Susan Cutter et al. (2003), seven significant components explained 72% of the variance in the data. These components included race and class; wealth; elderly residents; Hispanic ethnicity; special needs individuals;

Native American ethnicity; and service industry employment. The index synthesized 30 socioeconomic variables, which research literature suggests contribute to reduction in a community’s ability to prepare for, respond to, and recover from hazards. SoVI ® data sources include primarily those from the United States Census Bureau (HVRI). Since social conditions and vulnerablity factors differ between China and the United States, it is necessary to select the indicators and variables from the index to make it suitable for use in a Chinese setting. Consequently, this study adopts indicators and variables used by Chen et al. (2013) from their social vulnerability assessment of the Yangtze River Delta Region in China. The percentage of immigratory population, one variable used in Cutter et. al.’s research, was not included in the final index for this study because of high correlation with another variable, the percentage of households who are living in rented houses. In total, 29 variables were selected to be in the index. The relationships between these variables and vulnerability to natural hazards and the rationale for including theses variables in vulnerability assessments have been well studied and explained by many scholars for decades (e.g. Bolin et al., 1998; Cutter et al., 1998; 2003; Morrow, 1999; Dwyer et al., 2004; Yeletaysi et al., 2009; Chen et al., 2013). Table 4 outlines the indicators and variables of the index used in this study.

Table 4: Indicators and Variables Used for SoVI of Chinese Soastal Counties (Adapted from Chen et al., 2013)

Indicator No. Variables Description Abbreviation Socioeconomic Per capita disposable income of 1 Income UBINCM status urban residents Percentage of female resident Gender 2 Ratio of female QFEMALE population Race & Percentage of minority 3 Ratio of minorities QMINOR Ethnicity population(refers to non-Han) Age 4 Median age Median age MEDAGE Employment Unemployment rate of population 15 5 Unemployment rate QUNEMP loss years or older 6 Population density Resident population density POPDEN 7 Ratio of urban residents Percentage of urban residents QUBRESD Rural/Urban Ratio of non- Percentage of population with non- 8 QNONAGRI agricultural population agricultural hukou, Percentage of households who are Renters 9 Ratio of renters QRENT living in rented houses Ratio of primary Percentage of labors working in the 10 QAGREMP industry labors primary industry and mining Ratio of secondary Percentage of labors working in the Occupation 11 QMANFEMP industry labors secondary industry except mining Ratio of tertiary Percentage of labors working in the 12 QSEVEMP industry workers tertiary industry Family Average number of people per 13 Family size PPUNIT structure family household Ratio of college Percentage of population 25 years 14 QCOLLEGE educated population and older with college diploma Ratio of high school Percentage of population 20 years Education 15 QHISCH educated population and older with high school diploma Illiteracy rate of population 15 years 16 Ratio of illiteracy QILLIT or older Population Growth rate of resident population 17 Population growth rate POPCH change (2000-2010) Average number of occupied rooms 18 Household rooms PHROOM Housing per household conditions 19 Building area Per capita building area PPHAREA 20 Houses w/o Piped water Percentage of households without QNOPIPWT

piped water in the houses Percentage of households without 21 Houses w/o Kitchen QNOKITCH kitchen in the houses Percentage of households without 22 Houses w/o Toilet QNOTOILET toilet in the houses Percentage of households without 23 Houses w/o Bath QNOBATH bath in the houses Number of hospital beds of health 24 Hospital beds care institutions per one thousand HPBED Medical resident population service Number of medical technical Medical technical 25 personnel per one thousand resident MEDPROF personnel population Ratio of young 26 Percentage of population with age <5 QPOPUD5 population Percentage of population with 27 Ratio of old population QPOPAB65 age >65 Social Population dependency ratio (the dependency ratio of the total population above 65 Ratio of population 28 years old and below 15 years old to QDEPEND dependency the total population between 15 and 65 years old) Ratio of under the Percentage of urban and rural Special needs 29 minimum living residents covered by subsistence QSUBSIST population standard population allowances from the government

4.2.2 Data Sources & Handling Missing Data Most of the data was collected from the 2010 census, including gender, race,

age, family structure, population, education, unemployment rate, occupation, and housing conditions. The 2011 Ministry of Civil Affairs Statistics Yearbook was used to provide the number of urban and rural residents that are covered by subsistence allowances from the government. The population growth rate required comparing the populations of the years 2000 and 2010, which were collected from the 2000 and 2010 census. Among the 238

counties, 10 counties were not established in 2000; the average population growth

rates of their higher-level cities were adopted. In addition, with the administrative reformation, 15 counties changed name without changing the administrative area during the decade of 2000 to 2010, in which 10 counties and county-level cities became city districts. Regarding the per capita disposable income of urban residents, data for part of the counties were available in the 2011 China Statistics Yearbook and 2010 Economy and Social Development Statistic Bulletin for the individual districts. The number of hospital beds within healthcare institutions and number of medical technical personnel were obtained from the 2010 census and 2011 Health Statistics Yearbook of China. Among the 238 counties, income data for 73 city districts and medical data for 83 counties was not available. Therefore, average values of the prefecture-level cities were used to fill in this gap in the data. Table 5 lists the counties and the data processed for each county.

Table 5: Incomplete Data and Processing

Processing Variable Study Units method

Licheng Qu, Xiuyu Qu, Lanshan Qu, Nansha Qu, Luogang Qu, The average of POPCH (10) Jinwan Qu, Jinping Qu, Haojiang Qu, Chaonan Qu, Maogang prefecture-level Qu cities’ values Haigang Qu, Shanhaiguan Qu, Changli Xian, Haixing Xian, Huanghua Shi, Zhongshan Qu, Xigang Qu, Ganjingzi Qu, Jinzhou Qu, Changhai Xian, Pulandian Shi, Zhuanghe Shi, Linghai Shi, Xishi Qu, Panshan Xian, Lianshan Qu, Longgang Qu, Suizhong Xian, Xingcheng Shi, Binjiang Qu, Xiang’an Qu, Fengze Qu, Luojiang Qu, Quangang Qu, Hui’an Xian, Shishi Shi, Nan’an Shi, Zhangpu Xian, Zhao’an Xian, Dongshan Xian, Longhai Shi, Shibei Qu, Sifang Qu, Licang Qu, Changyi Shi, The average of UBINCM (73) Wendeng Shi, Rushan Shi, Lanshan Qu, Baiyun Qu, Nansha prefecture-level Qu, Luohu Qu, Nanshan Qu, Yantian Qu, Xiangzhou Qu, cities’ values Doumen Qu, Jinwan Qu, Longhu Qu, Jinping Qu, Haojiang Qu, Chaoyang Qu, Chaonan Qu, Chenghai Qu, Nan’ao Xian, Xinhui Qu, Taishan Shi, Enping Shi, Potou Qu, Mazhang Qu, Suixi Xian, Xuwen Xian, Leizhou Shi, Wuchuan Shi, Maonan Qu, Dianbai Xian, Huiyang Qu, Huidong Xian, Haifeng Xian, Lufeng Shi, Jiangcheng Qu, Yangxi Xian, Yangdong Xian, Raoping Xian, Jiedong Xian Fengnan Qu, Haigang Qu, Shanhaiguan Qu, Beidaihe Qu, Pulandian Shi, Donggang Shi, Linghai Shi, Xishi Qu, Bayuquan Qu, Laobian Qu, Gaizhou Shi, Dawa Xian, Panshan Xian, Lianshan Qu, Longgang Qu, Suizhong Xian, Xingcheng Shi, Lianyun Qu, Binjiang Qu, Siming Qu, Haicang Qu, Huli Qu, Jimei Qu, Tong’an Qu, Xiang’an Qu, Fengze Qu, Luojiang Qu, Quangang Qu, Jiaocheng Qu, Shibei Qu, Sifang Qu, Huangdao Qu, Dongying Qu, Guangrao Xian, Muping Qu, Longkou Shi, The average of HPBED ,MEDPROF Laiyang Shi, Laizhou Shi, Penglai Shi, Hanting Qu, Shouguang Shi, Huancui Qu, Wendeng Shi, Lanshan Qu, Zhanhua Xian, prefecture-level (83) Luohu Qu, Futian Qu, Nanshan Qu, Bao’an Qu, Longgang Qu, cities’ values Xiangzhou Qu, Doumen Qu, Jinwan Qu, Enping Shi, Chikan Qu, Xiashan Qu, Potou Qu, Mazhang Qu, Suixi Xian, Xuwen Xian, Lianjiang Shi, Leizhou Shi, Wuchuan Shi, Maonan Qu, Maogang Qu, Dianbai Xian, Huicheng Qu, Huiyang Qu, Huidong Xian, Chengqu, Haifeng Xian, Lufeng Shi, Xiangqiao Qu, Raoping Xian, Rongcheng Qu, Jiedong Xian, Huilai Xian, Haicheng Qu, Yinhai Qu, Tieshangang Qu, Meilan Qu, Longhua Qu, Xiuying Qu

4.2.3 Data Limitations This study has three major limitations in regard to the data used that could have a potential influence on the reliability of the results: i) the selection of indicators and variables; ii) the incompleteness of the data; iii) the data quality of the variables. First, the chosen indicators and variables may not cover all the aspects of social vulnerability. Although previous research has identified factors that influence the capacity of societies and communities in disaster mitigation, response and recovery (e.g. Cutter et al., 2001), social vulnerbility is such a complex and dynamic field that the selected indicators may not be able to capture all of the influencing factors within this concept. In addition, due to data availability, the selected variables may not ideally represent the meaning of indicators. For instance, the per capita disposable income of urban residents was used to represent the indicator of socialeconomic status, but ideally it would be better to collect data of the disposable income for all residents rather than only urban residents. The population numbers of 2000 and 2010 were adopted to calculate the population change rate, which neglected the fact that population change rate is dynamic and changing over time. Another example is in regard to the special needs population. Groups such as the infirm, transient, and homeless persons were not included in the variable, again, due to a lack of available data. Secondly, since the per capita disposable income of urban residents was not available for 73 of the city districts, and the number of hospital beds and medical technical personnel per one thousand residents was not available for 83 counties, data for these counties was borrowed from the related prefecture-level cities’ value. The incomplete data rate was 3.46%. The processing of these three variables may impact the ranking of each county in some way.

Lastly, data quality of the 29 variables varies. The different data qualities are a result of different populations and sample sizes. In China, people that live in a place constitute the residential population. Due to the household registration system, also known as hukou policy, residential can be divided into two groups: registered residents who own the hukou of the place, and unregistered residents who are known as the migrant population. The statistics collected from the 6th Census and related statistics yearbooks are different for the 29 variables. Moreover, data for some of the variables, for example, unemployment rate, percentage of households who are living in rented houses, percentage of labors working in different industries, and the percentage of households without piped water/kitchen/toilet/bath in the houses, were collected using a sampling technology. The sampling rate of each county was various. Table 6 lists the population size and sample range for each of the variables.

Table 6: Populations and Sampling of Data for SoVI®2010

Residential Population (registered + unregistered Registered Residents No. Variables residents) All All Sampling Rate Sampling Rate people people 1 UBINCM x

2 QFEMALE x

3 QMINOR x

4 MEDAGE x

5 QUNEMP x (5.1%-39.4%)

6 POPDEN x

7 QUBRESD x

8 QNONAGRI x

9 QRENT x (7.5%-11.5%)

10 QAGREMP x (5.1%-39.4%)

11 QMANFEMP x (5.1%-39.4%)

12 QSEVEMP x (5.1%-39.4%)

13 PPUNIT x

14 QCOLLEGE x

15 QHISCH x

16 QILLIT x

17 POPCH x

18 PHROOM x

19 PPHAREA x

20 QNOPIPWT x (7.5%-11.5%)

21 QNOKITCH x (7.5%-11.5%)

22 QNOTOILET x (7.5%-11.5%)

23 QNOBATH x (7.5%-11.5%)

24 HPBED x

25 MEDPROF x

26 QPOPUD5 x

27 QPOPAB65 x

28 QDEPEND x

29 QSUBSIST x

4.3 Analysis The goal of data analysis is to transform the raw data of variables into a unified value format that allows for comparison between the 238 counties, in order to present the social vulnerability of Chinese coastal counties to natural hazards and identify problems based on the results. To analyze the large multivariate datasets following the SoVI® approach, a principal component analysis (PCA) was applied. PCA is a technique for reducing the dimensionality of a data set consisting of a large number of interrelated variables, while retaining as much as possible of the variation present in the data set (Jolliffe, 2005). After PCA, the 29 original variables listed in Table 4 were transformed to a new set of variables, the principal components (PCs). The new PCs were uncorrelated and ordered, which the variation was presented by retaining components with eigenvalues (λ) > 1.. Typically, between 70% and 80% of total variability should be explained as shown in previous studies (e.g. Cutter et al. 2003; Borruf et al. 2005; Borden et al. 2007). Varimax rotation in PCA was chosen to maximize the sum of the variances of the squared correlations between variables and components. It enabled the loading matrix to have “simple structure” for greater separation and to be able to easily interpret the new created components. All the variables in each component were transformed to standard deviations (SDs) with the value between -1 to +1, displaced in the rotated component matrix. In this study, PCA was processed using SPSS, a software package for statistical analysis created by IBM. Once PCA was completed, each component was defined as a positive value that increases the vulnerability, or a negative value that decreases vulnerability. By ranking the loadings of the variables in each component, the variables whose absolute values were larger than 0.5 were identified to explain the certain component.

According to the practical meaning of the set of variables, the principal components were named and defined as positive or negative based on subjective examination. Finally, the values of all principal components were aggregated to produce an overall social vulnerability score for each study unit. Since there was no exisiting theory indicating which of the components or indicators are more significant than others in deciding social vulnerability, we applied equal weighting method which assumes that each component contributed equally to the overall index even though each major component is comprised of a different number of variables. The equal weighting method was validated by Emrich (2005) through his comparison with expert judgments.

Chapter 5

RESULTS AND FINDINGS

5.1 SoVI® 2010 The first part of this chapter presents the results and findings of social vulnerability index 2010 of Chinese coastal counties, including the principal components, spacial variation and the distribution of each driven factor.

5.1.1 Principal Components Six components were extracted and explained 75.15% of the variation among the 238 study units. According to the driving variables, the six components were identified as: Urbanization and Education (34.16%); Poverty and Livelihood (13.62%); Age, Social Dependency and Gender (11.88%); Poor Housing Quality (6.80%); Housing Size (4.90%); and Minorities (3.79%). Table 4 outlines the driving variables and their impact on social vulnerability. Urbanization and Education. This component included counties that have a high percentage of employment in the third industry, a higher percentage of non- agricultural and urban residents, a higher unemployment rate, high population density, and better medical service, all characteristics of urbanization. This component was also driven by the education variables that illustrated a higher percentage of residents completing a high school and college education. Urbanization provided more job opportunities in the third industries, which do not rely on natural resources, as well as better medical services to the residents. Meanwhile, existing research demonstrates that often people with a higher level of education allows for a greater capacity to

mitigate, prepare for, respond to, and recover from disasters (e.g. Rogers, 1997; Cutter et al., 2003). Overall, this component decreased the social vulnerability of Chinese coastal areas to natural hazards. Poverty and Employment. This compoent represents the counties with a lower level of income, more socially dependent populations, less renters, and more large-sized families. This component also showed that the counties were dominated by agriculture, since there were a larger percentage of people working in the first industry and less in the secondary industry. People living in poverty may lack the accessibility to resources and thus are more vulnerable (e.g. Morduch, 1994; Moser, 1998; Ouna et al., 2012); relying on an agriculture-based mode of production increases the dependence on natural resources and the environment, which is positively related to social vulnerability.

Age, Social Dependency, and Gender. It has been well studied that age and gender are important factors in determining vulnerability to natural hazards (e.g. Wisner et al., 1993; Morrow, 1998; Ngo, 2001; Roy et al. 2014). This component shows the places having a higher percentage of elderly people, children and women. Consequently, this component was assigned as having a positive relationship to overall vulnerability.

Poor Housing Quality. The quality of human settlements (housing type and construction, infrastructure, and lifelines) is an important determinant of a household’s vulnerability to disasters such as floods or windstorms, which influences potential economic losses, injuries, and fatalities (Brooks, 2003; Cutter et al., 2003). Houses without basic facilities such as a toilet, kitchen, bath, or pipe water, increase the social vulnerability of those households to natural hazards.

Housing Size. Contrary to the last component of poor housing quality, houses with more rooms and building area occupied by each household reflects a higher living quality of the household. This component was favorable in reducing social vulnerability. Minorities. Subsistence Security System is a social relief system under which the government grants allowances to urban and rural people living in poverty. Areas where a significant portion of the population is living under the minimum life standard tend to be more socially vulnerable. Most minorities in China live in a disadvantageous condition with low income, little education, and few employment opportunities (Chen et al., 2013) due to language, cultural and historical factors. Therefore, a higher percentage of poor and minority populations indicate a higher level of vulnerability.

The variance and driving variables of each component, as well as their impact on the overall social vulnerability are shown in Table 7.

Table 7: Principal Components of SoVI® of Chinese Coastal Counties

Component Variance Impact Driving Variables QSEVEMP(0.883), QHISCH(0.847), QNONAGRI(0.837), QUNEMP(0.774), Urbanization and Decrease 34.16% QCOLLEGE(0.763), POPDEN(0.721), Education (9) (-) QUBRESD(0.679), HPBED(0.527), MEDPROF(0.503) UBINCM(-0.842), QDEPEND(0.822), Poverty and Increase 13.62% QRENT(-0.806), PPUNIT(0.744), Employment (6) (+) QMANFEMP(-0.65), QAGREMP(0.606) MEDAGE(0.926), QPOPAB65(0.785), Age, Social Dependency Increase 11.88% QPOPUD5(-0.653), HPBED(0.585), and Gender (5) (+) QFEMALE(0.522) Increase QNOTOILET(0.737), QNOKITCH(0.734), Poor Housing Quality (4) 6.80% (+) QNOPIPWT(0.636), QNOBATH(0.614) Decrease PPHAREA(0.813), QILLIT(0.583), Housing Size (3) 4.90% (-) PHROOM(0.513) Increase Minorities (2) 3.79% QSUBSIST(0.741), QMINOR(0.635) (+)

5.1.2 Spatial Variability The SoVI® scores range from -5.21 (low social vulnerability) to 7.69 (high social vulnerability) with a mean vulnerability score of almost 0 and a standard deviation (SD) from a mean of 2.45 for the 238 Chinese coastal counties. To visually show the spatial distribution using GIS mapping software, we define counties with an SoVI score >1.5 SD as high vulnerability; an SoVI between 0.5～1.5 SD as high- medium vulnerability; an SoVI between 0.5～-0.5 SD as medium vulnerability; an

SoVI between -0.5～-1.5 SD as medium-low vulnerability; and SoVI >1.5 SD as low vulnerability. With some notable exceptions, in general, the social vulnerability of Chinese coastal counties shows a clear spatial pattern with highest values at the two geographic ends and lowest values in the middle coastal area. Counties of Liaoning, Hainan, Hebei, Guangxi and Shandong provinces have higher social vulnerability, while counties with lower social vulnerability are concentrated in Zhejiang, Fujian, Shanghai, Guangdong, Jiangsu and Tianjin provinces. Figure 7 shows the spatial distribution if social vulnerability of Chinese coastal counties in 2010.

Figure 7: Overall Spatial Distribution of Social Vulnerability of Chinese Coastal Counties

Eighteen counties are labeled as the most socially vulnerable, accounting for 7.6% of all the study units. Among the most vulnerable counties, 11 counties are affiliated with Liaoning Province, two from Hebei, two from Shandong, and three from Hainan Province. Their vulnerabilities are mainly determined by differences in

the factors regarding of minorities, poor housing quality, age, social dependency and gender, and housing size (See Table 8).

Table 8: The Most Socially Vulnerable Coastal Counties

Poor Age, Social Housin Housing Minori Dependency Coastal Coastal g Size ties SoVI and Gender Region County/District Quality Score + + - + Liaoning Xingcheng Shi 3.64 7.69

Liaoning Suizhong Xian 2.76 7.66

Liaoning Gaizhou Shi 1.43 6.91

Liaoning Linghai Shi -1.67 6.36

Liaoning Donggang Shi -2.12 5.49

Liaoning Wafangdian Shi 1.71 5.42

Lingshui Lizu Hainan 3.54 3.00 5.32 Zizhixian Liaoning Pulandian Shi 1.84 5.21

Ledong Lizu Hainan 3.40 5.03 Zizhixian Liaoning Zhuanghe Shi 1.66 5.03

Liaoning Panshan Xian 1.44 4.73

Liaoning Lianshan Qu 1.44 4.47

Hebei Changli Xian 1.13 4.34

Shandong Haiyang Shi 1.92 4.16

Liaoning Dawa Xian -1.04 4.15

Changjiang Lizu Hainan 2.51 4.06 Zizhixian Hebei Funing Xian 1.67 3.96

Shandong Penglai Shi 1.57 3.84

1.02 1.13 -0.96 1.17 Mean 5.21 (19.5%) (21.8%) (18.4%) (22.4%)

Four counties (1.7%) are identified as the least socially vulnerable counties. The least vulnerable counties are all located along the southeast coast. Nanshan Qu and Futian Qu are two districts of Shenzhen Shi of Guangdong Province; Siming Qu is affiliated with Xiamen Shi of Fujian Province and Binjiang Qu is under Hangzhou Shi of Zhejiang Province. The favorable condition of urbanization, education, and employment, and a lack of poverty lead to their low vulnerability scores. Table 9 lists the least vulnerable counties and the driving forces of each county.

Table 9: The Least Socially Vulnerable Coastal Counties

Urbanization Poverty and Coastal Coastal Coastal and Employment SoVI Region City County/District Education - + Guangdong Shenzhen Nanshan Qu 1.90 -2.25 -5.21 Fujian Xiamen Siming Qu 2.66 -0.72 -4.66 Zhejiang Hangzhou Binjiang Qu 1.85 -1.90 -4.45 Guangdong Shenzhen Futian Qu 2.07 -1.01 -4.38 2.12 -1.47 Mean -4.67 (45.4%) (31.5%)

The results also show that on the whole, coastal city districts are less socially vulnerable than coastal counties and county-level cities in terms of SoVI scores. The four least vulnerable places are all coastal districts (3.42%). Districts of medium and low vulnerable occupy 31.62% and 52.99% of all districts respectively. Only 1 district, Lianshan Qu of Huludao City in Liaoning Province, is labeled as the most socially vulnerable with a SoVI score of 4.47. The average SoVI score of the 117 coastal districts is -1.18, less than -0.5 SD. By contrast, 17 of the 18 most vulnerable

places are coastal counties or county-level cities. In total 100 counties (82.64%) are more vulnerable than the medium level with SoVI scores higher than -0.5 SD. The average SoVI score of the 121 coastal counties and county-level cities is 1.14, which is almost 0.5 SD the whole 238 study units. In addition, the distribution of social vulnerability demonstrates that areas in the north and south end of the Chinese coast are more vulnerable than the southeastern coastal areas. The distributions of city districts and counties and county-level cities are shown in Figure 8.

Figure 8: SoVI® Distribution of Coastal City Districts, Counties and County- level Cities

5.1.3 Spatial Variability of Individual Principal Components The distribution of each single component shows different patterns among the study units and reveals that the vulnerability of coastal counties is driven by different components and that the resources for reducing vulnerability factors and disaster risk need to be allocated accordingly. Urbanization and education are not adequate within all coastal regions to some extent, evidenced by the first component covering almost all of the regions. City districts are less vulnerable, with only 44 out of 117 city districts scored as negative values, which increases overall SoVI. Overall, 107 out of 121 counties and county- level cities received negative scores. In terms of poverty and employment, the counties of Guangdong, Guangxi and Hainan provinces rank higher than other regions. The most vulnerable counties (>1.5

SD) are all from Guangdong province and include Lufeng Shi, Huilai Xian, Chaonan Qu, Haojiang Qu, Chaoyang Qu, Wuchuan Shi, Maogang Qu and Dianbai Xian. It is notable that Guangdong province has five districts/counties. Dongguan Shi and four districts of Shenzhen Shi are the least vulnerable, which illustrates the inequality of income and industry structure of this province. In addition, half of the lowest ranking counties are from Zhejiang (11), with the rest from Shanghai (1), Fujian (3) and

Tianjin (1). Most of them are city districts rather than counties or county-level cities. The distribution of age, social dependency and gender reveals that the northern region and part of the southeastern coastal region has a higher level of medium age, social dependency, and ratio of females, mainly in Liaoning, Shandong, Jiangsu and Hebei provinces. City districts rank slightly lower than counties and county-level cities without any major differences.

Poor quality housing is concentrated in Hainan, Guangxi, Liaoning and Guangzhou provinces. Guangxi, Hainan and Liaoning are also the areas inhabited by a large number of minority nationality people, and where the material and structure of the houses are often not secure enough due to historical and cultural reasons. Regarding housing size factor, counties of Guangdong and Liaoning provinces rank high. Of the 10 most vulnerable counties that have relatively low levels of per capita building area and number of occupied rooms per household, half are from highly urbanized city districts, and half are from counties and county-level cities. High values of minorities appear in Guangxi, Liaoning and Hainan provinces, the three regions that have a high percentage of minority nationalities people. The two counties of Hebei and Shanghai obtain high scores because of the high percentage of residents covered by subsistence allowances from the government.

Figure 9 shows the distribution of each principal component.

Figure 9: SoVI® Principal Components Distribution of Chinese Coastal Counties

5.2 Temporal and Spatial Changes in Social Vulnerability: SoVI® 2000 Social vulnerability is complex and dynamic, changing over space and through time. The analysis of historical social vulnerability will enhance the understanding of the driving factors of social vulnerability and benefit strategy and policy making for reducing vulnerability and risk to natural hazards. This section compares the results of SoVI® in 2000 and 2010 of Chinese coastal counties.

5.2.1 Study Units, Data Sources and Missing Data The study units of SoVI® 2000 include 227 counties. Eleven counties included in SoVI®2010 were not established in 2000. The counties excluded in the analysis are all city districts in Fujian, Shandong and Guangdong provinces. They are Xiang’an Qu, Licheng Qu, Xiuyu Qu (Fujian), Lanshan Qu (Shandong), Nansha Qu, Luogang Qu, Jinwan Qu, Jinping Qu, Haojiang Qu, Chaonan Qu, Maogang Qu (Guangdong).

All data was available in the 5th Census except for five variables: income, population growth rate, ratio of population living under the minimum living standard, and the number of hospital beds and medical technical personnel. Therefore, the average values of the provinces are used for the variables of income, population growth rate, and the ratio of the population living under the minimum living standard. Since there are huge variances of number of hospital beds and medical technical personnel per 1000 people within each province, the values of the two variables for each county in 2000 are calculated based on the 2010 values, using the average change rate of the province they are affiliated with.

5.2.2 Results and Findings

5.2.2.1 Principal Components In SoVI 2000, six principal components were extracted that explained 76.43% of the variation among the 227 Chinese coastal counties. These components include education and urbanization, social dependency and employment, poverty and population change, age, minority, poor housing quality and gender. The number of components did not change within the decade, but the meaning of each component slightly changed and new driving factors were extracted along with population change.

Except the component education and urbanization, which is defined as decreasing

social vulnerability, the rest of the five components are assigned as contributing to overall vulnerability. Note that the component of poverty and population change is defined as increasing the overall social vulnerability, and the variable of population growth rate in the matrix is negative related (-0.784) to the component. Thus, population growth in this context can be understood as decreasing social vulnerability to natural hazards.

5.2.2.2 Spatial Variations The 12 most vulnerable counties (SoVI >1.5 SD) are located in Liaoning (3), Hainan (5), Guangxi (1) and Fujian (2) provinces. The driving forces are mainly social dependency and employment, minority population, poor housing quality, and gender. Five counties appeared among the most vulnerable counties both in SoVI 2000 and

2010: two county-level cities from Liaoning, Suizhong Xian and Xingchengshi, and three minority autonomous counties from Hainan, Lingshui Lizu Zizhixian, Changjiang Lizu Zizhixian and Ledong Lizu Zizhixian. High levels of social dependency, a large percentage of agriculture-based employment, large minority populations, and poor housing quality determined their high social vulnerability to natural hazards.

Fourteen counties are labeled as the least socially vulnerable (SoVI <-1.5 SD). Two of them are in Fujian and the remaining 12 counties are affiliated with Guangdong province. Among the fourteen least vulnerable counties, twelve are city districts and only two are county-level cities, Dongguan Shi and Zhongshan Shi (Guangdong). Their low vulnerabilitiy scores are predominately due to having highly educated populations, high levels of urbanization and wealth, high population growth rates, and a low percentage of elderly residents. Two city districts of Shenzhen City

(Guangdong), Nanshan Qu and Futian Qu, are the least vulnerable both in 2000 and 2010 indices. Figure 10 presents the changing of social vulnerability of Chinese coastal counties from 2000 to 2010.

Figure 10: SoVI® 2000 and SoVI® 2010

The distribution of social vulnerability in 2000 displays the same pattern with 2010, which is the two geographic ends are more vulnerable than southeastern coast. The comparison of SoVI 2000 and 2010 also uncovered the fact that If putting the data of 2000 and 2010 in the same matrix, the distribution of the

5.2.3 Limitations The biggest limitation of SoVI®2000 is the missing data. Data for five variables was not available and resulted in the use of the provinces’ average values, which resulted in an incomplete rate of 17.2%. Moreover, instead of using the average values of prefecture-level cities, which were also not available, the adoption of provinces’ average values further decreases the accuracy of the data. The incomplete data can be expected to have a certain influence on both the extract components and the final scores and ranking of SoVI®2000.

Chapter 6

MOVING FORWARD: POLICY IMPLICATIONS FOR DISASTER RISK REDUCTION

One of the significances of researches is implicate policies. The results and findings of SoVI® 2010 provide the undercover the patterns and characteristics of social vulnerability of Chinese coastal counties. They will help the nation, coastal communities and each region to identify focuses and priorities in terms of disaster risk reduction, in another word, to identify what need to do. However, social vulnerability is complex and dynamic and strongly linked to changing of social, economic, political, environmental conditions. Reducing social vulnerability and thus reducing disaster risk cannot be isolated from the planning process and implementation of programs, as well as in post-disaster situations (Blaikie et al., 1994; HFA, 2005). Disaster risk can’t be reduced by directly changing the driven factors of social vulnerability, but it can be reduced by building or enhancing the capacity or resiliency of the vulnerable groups. Therefore, it requires discussing the policy implications not only by directly drawing lessons from the results of SoVI ® 2010, but also looking at the full profile of disaster risk reduction. Hyogo Framework for Action (2005-2015), as a guidance which was conducted by the United Nations, comprehensively proposes the priority actions for disaster risk reduction towards building resilience.

6.1 Lessons Drawn from the Results The social vulnerability of Chinese coastal areas in 2010 has three characteristics: the north and south end area are more vulnerable than the southeastern

area, counties and county-level cities are more vulnerable than city districts, and the vulnerability of each county is driven by different factors even though they have approximate SoVI scores. The geographic variability of social vulnerability across Chinese coastal areas exhibits disparity in the abilities of coastal communities to mitigate, prepare for, respond to, and recovery from natural hazards. The results call for actions to reduce vulnerability and build resilience at all levels.

6.1.1 National Level: Focus on Driving Factors and Gaps between Regions

6.1.1.1 Driving Forces As a practical matter, it is important not only to know what regions are vulnerable to natural hazards, but also to understand the unique characteristics of what makes those regions vulnerable in order to adequately develop strategies for mitigation. The extracted components identified the driving factors that cause coastal communities to lack the capacity to deal with disasters. Mapping an individual factor helps illuminate which aspects need to be focused on in order to reduce social vulnerability and disaster risk, even in the least vulnerable places. During the past decade (2000-2010), Chinese coastal areas experienced major transformations in development patterns, economic conditions, population size, and social characteristics. This research presents the current social vulnerability of Chinese coastal areas and empirical evidence on the spatial and temporal patterns of social vulnerability from 2000 to 2010 at the county level. The results of this study suggest that those components that consistently increased social vulnerability across all time periods were: urbanization, education, social dependency, employment, poverty, age, gender, minority, and poor housing quality.

Although the above factors were extracted with PCA, which converts the original possibly correlated variables into a set of values of linearly uncorrelated variables, these factors are not isolated in a complex society. For instance, urbanization results not only in an increasing number of residents living in urban areas, but also improves opportunities for jobs, education, housing, and medical services. However, urbanization may also increase inequality and poverty among some groups, such as migrant workers in cities. Therefore, polices for disaster risk reduction need to be integrated with social and economic development strategies and plans in order to increase effectiveness and efficiency. Since social vulnerability changes with the transformation of social conditions, it is necessary to update the social vulnerability profile at regular intervals. Although there is no precedent for how often the profile should be updated, updates should occur in consonance with social and economic development. In China, the National Economic and Social Development Plan is formulated every five years. Accordingly, we recommend updating the social vulnerability profile at a similar time interval.

6.1.1.2 Reducing Gaps between the Two Geographic Ends and Southeastern Coast Mapping social vulnerability in 2000 and 2010, it was found that the spatial distributions of 2000 and 2010 exhibiting a similar geographic pattern. Both the SoVI® 2000 and 2010 illustrate that the two geographic ends of Chinese coastal regions, Liaoning, Hebei, Shandong, Guangxi and Hainan, are more socially vulnerable than the southeastern coast, which includes Shanghai, Zhejiang, Fujian, Guangdong and Jiangsu provinces. The principal factors that make the two end regions more vulnerable are age, social dependency, gender, poor housing quality, and

minorities. The major factors that determine low vulnerability in the southeastern coastal regions are urbanization, education, low level of poverty, and non-agriculture employment. The results reflect the pattern of economic development and population change seen in Chinese coastal regions. Two of the most important economic zones, Changjiang Delta Economic Zone and Zhujiang Delta Economic Zone, are located in

Southeastern China. The rapid urbanization of these areas promotes industrial reformation that provides more job opportunities in non-agriculture industries. In highly-urbanized areas, the availability of resources like education and medical services creates an ideal environment for settlement compared to more rural areas, and attracts a variety of people including a highly educated population as well as low- skilled, young workers. In traditional, agriculture-based regions, the average number of children per household is much higher than in developed areas, resulting in high social dependency, high average age, and a larger female population. Liaoning, Guangxi and Hainan are the regions inhabited by minorities. Due to historical and cultural reasons, many ethnic minorities live in mobile houses (e.g. yurts), or wooden or earthen houses. The structure and material of these houses are more vulnerable to natural hazards. Additionally, the ethnic policy of population allows minority nationalities to have more children, increasing their social dependency. In addition, language is an obstacle that restricts the social and economic development of minority regions (Hansen, 1999). The mapping also shows that the gap between the two geographic ends and southeastern coast is increasing from year 2000 to 2010. More highly vulnerable counties appear at the two geographic ends, especially along the north coast, while the

number of highly vulnerable counties has decreased along the east coast during this decade. The spatial patterning of social vulnerability, although initially dispersed in only certain geographic regions, has become more concentrated over time. The concentration of highly vulnerable areas at the two ends reflects the increasing inequality of development in Chinese coastal regions. To reduce the gaps, the nation should strengthen the support to Liaoning, Hebei, Shandong, Guangxi, and Hainan provinces, both with resources and polices. Although the comparison between social vulnerability in 2000 and 2010 shows that the gap between the two ends and southeastern coast is increasing, with more highly vulnerable counties appearing in the two ends region, it does not mean that social conditions are declining. In contrast, when comparing the social vulnerability of 2000 and 2010 in the same matrix, the trend shows a steady improvement in some factors. As seen previously, the changing of major factors is quite different across regions. Figure 11 present the spatial changes of primary factors from 2000 to 2010, processing data from 2000 and 2010 in the same matrix. Six components were extracted and explained 73.9% of the variation.

Figure 11: Spatial Changes of Primary Factors from 2000 to 2010 in the Same Matrix

Generally speaking, from 2000 to 2010 urbanization and education were enhanced, mainly in Shandong and Jiangsu; social dependency and poor housing quality improved in Hebei, Fujian, Guangdong, and Hainan; age and gender conditions declined in the two geographic ends areas and Jiangsu province; wealth and employment conditions improved in nationwide; and medical services were greatly improved in Tianjin, Jiangsu, Zhejiang and Guangdong, but did not improve in other provinces due to increasing populations. The factor of minorities here is not comparable because the minimum living standard lines are set by each province and increased over time.

6.1.1.3 Reducing Gaps between City Districts and Counties/County-Level Cities It is necessary to consider the differences between coastal city districts and counties/county-level cities when making strategies and plans for disaster risk reduction and resilience building. Referring to the scores of each principal component, generally coastal city districts have better conditions in regards to urbanization and education, poverty and employment, age and gender, housing quality, and minorities, compared with coastal counties and county-level cities. However, they have a less favorable score when it comes to housing size.

Such findings are not surprising. In China, city districts represent the center of cities, which are highly urbanized and industrialized due to preferential policies, while counties and county-level cities contain a substantial portion of agriculture-dominated rural areas. Industrialization in highly concentrated urban areas promotes social resources like education opportunities, medical services, social welfare, job opportunities and advanced infrastructures and facilities, attracting a large population from rural areas seeking fortune and social mobility in cities (Wei, 2002). This

migration to cities from rural areas is not just attributed to the lure of economic and social opportunities, but also to loss or degradation of farmland and pastureland due to development, pollution, land grabs, and conflict (Zhang et al., 2003). The large scale population migration from rural to urban areas results in high population density in the limited urban areas, with small per capita housing space. While many male workers are finding work in urban cities, their dependents, including children, aged parents and poorly educated female wives, remain in rural areas. As a result of this phenonmenon, many rural areas have a higher percentage of social dependency and larger female population. The root cause of this disparity in distribution of social vulnerability is the unequal allocation of resources between urban and rural areas. The unequal allocation of social resources not only exists among urban and rural areas, but also is a significant phenomenon within urban cities. In recent years,

China has been promoting urbanization at an unprecedented rate across the country. By 2011, more than 250 million migrant workers and their dependents had relocated to urban areas to find work (CIA, 2013). However, the transformation in China has been so fast that China is confronted with the challenge of changing from the pursuit of quantity to quality in regard to urbanization (Qiu et al., 2012). A major result is that the inflow of huge numbers of migrant workers creates enormous pressure on public resources and services in cities, including transportation, housing, and the environment. Some rural migrants cannot find work or afford to buy or even rent houses. the urban village, named chengzhongcun, appear in many cities, which have functioned as a favorable residential concentration of migrants who are low-income and are denied access to low-cost state housing (Zhang et al., 2003). To alleviate the pressures caused by large-scale immigration, the hukou policy, which is a record of

household registration, is adopted to control mass migration of workers from rural to urban areas. Workers without local hukou are restricted in their ability to acquire low- cost housing, apply for loans, access health care services, receive children’s education, receive job opportunities, and receive subsistence supporting, among other services (e.g. Chen et al., 1994; Zhang et al., 2003; Fleisher et al., 2006; Bao et al., 2011). Although they live in cities, their living environment, social security, health care, and education opportunities have not improved. The restrictions toward migrants’ involvement in city life cause a vicious cycle of vulnerability for this population. However, Hukou policy is not the predominant barrier for rural people’s enjoyment of advanced services and resources that serve to decrease their social vulnerability; it is a periodic tool for mitigating the conflict between the limited social resources and the rapid and enormous population growth in cities, especially metropolises. Nevertheless, migrants are a vulnerable group in China that lack the adequate ability to prepare for, respond to and recovery from natural hazards due to limited access to resources. To solve this problem, on the one hand, urbanized cities should enhance the capacities to provide adequate resources and services to all of their residents. On the other hand, more resources should be allocated in rural areas to support their industrialization and modernization.

In conclusion, China needs to continually promote urbanization nationwide, allowing for more resources to be allocated to rural areas. Meanwhile, increasing the quality of urbanization is significant for reducing social vulnerability to natural hazards. To quote Li Keqiang, the Premier-elect of China, “Urbanization is not about simply increasing the number of urban residents or expanding the area of cities. More importantly, it’s about a complete change from

rural to urban style in terms of industry structure, employment, living environment and social security.”

6.1.2 Local Level: Place-Based Strategies It is important to recognize the variability in the driving forces of each region and to develop place-based strategies accordingly. Policies and resources should aim to reduce the factors that increase the social vulnerability in their particular region of interst. Although this study focuses on counties, social vulnerability of counties within the same provinces presents similar patterns. The major factors that increase the social vulnerability of the counties, which should be the focus when proposing strategies, are concentrated in the same province.

Table 10: Driving Factors of Social Vulnerability of Chinese Coastal Provinces in 2010

Province /Municipality Driving Factors Mapping (SoVI® 2010) Urbanization Age, Social Poor Poverty and Housing and Dependency housing Minorities Employment size Education and Gender quality

Liaoning x x x x

Tianjin x

Hebei x x x x

Shandong x x x

Jiangsu x x

Shanghai x x

Zhejiang x x x

Fujian Province

Fujian x x x

Guangdong x x x

Guangxi x x

Hainan x x x

6.2 Reducing Disaster Risk towards Resilient Communities Vulnerability assessment is the starting point for promoting a culture of disaster resilience. The next step is to propose strategies and programs to reduce vulnerability and risk towards a resilient society. This section follows the Hyogo Framework conducted by the united community UNISDR for action guidance.

6.2.1 Conceptual Framework: Hyogo Framework for Action (HFA) The Hyogo Framework for Action is a 10-year plan to make the world less threatened by and more resilient to natural hazards. The Hyogo Framework for Action was endorsed by the UN General Assembly in the Resolution A/RES/60/195 following the 2005 World Disaster Reduction Conference. The framework was negotiated and adopted by 168 countries, shifting the paradigm for disaster risk management from post-disaster response to a more comprehensive approach that would also include prevention and preparedness measures. The Hyogo Framework for Action is the first plan to explain, describe, and detail the work that is required from all different sectors and actors in order to reduce disaster losses. Its goal is to substantially reduce disaster losses by bolstering the resilience of nations and communities to disasters (UNISDR, 2005). It proposes how risks can be reduced using five priorities for action (PAs), as shown in Figure 12:

•Make Disaster Risk Reduction a Priority Priority Action 1 • Ensure that disaster risk reduction is a national and a local priority with a strong institutional basis for implementation.

•Know the Risks and Take Action Priority Action 2 • Identify, assess and monitor disaster risks and enhance early warning.

•Build Understanding and Awareness Priority Action 3 • Use knowledge, innovation and education to build a culture of safety and resilience at all levels.

•Reduce Risk Priority Action 4 •Reduce the underlying risk factors.

•Be Prepared and Ready to Act Priority Action 5 •Strengthen disaster preparedness for effective response at all levels.

Figure 12: riorities Identified by Hyogo Framework for Action (2005-2015) (Source: UNISDR, 2005)

6.2.2 Reviewing Disaster Risk Reduction in China under HFA Following the approaches proposed by the HFA, this section discusses the disaster risk reduction in China from five major aspects: (i) governance, including institutions and legislation; (ii) risk identification, assessment, monitoring and early warning; (iii) building understanding and awareness; (iv) reducing underlying risk factors; and (v) preparedness for effective response and recovery.

6.2.2.1 Governance

6.2.2.1.1 Institutions In China, the Central Government (State Council) provides overall leadership and guidance through the Office of Emergency Management. Government agencies such as the Station Oceanic Administration and China Earthquake Administration are responsible for managing specific disasters including marine disasters, meteorological disasters, and geological disasters. Local governments should the responsibility for disaster response and recovery, as well as for implementing mitigation and preparedness at the local level. China does not have a unified emergency management coordination agency like the Eedral Emergency Management Agency (FEMA) in the United States. Consequently, the organizational structure of China’s disaster management system features central leadership, departmental responsibility, and graded disaster administration with major responsibilities allocated to local authorities (G20 Report, 2012). Chinese governments have established several disaster reduction centers at both the national and local level. The National Disaster Reduction Center of China (NDRCC), International Centre for Drought Risk Reduction (ICDRR) and Satellite Application Center for Disaster Reduction (SACDR) were established in 2003, 2007 and 2009 respectively. The government agencies created disaster reduction centers for implementation of specific affairs, and the regional governments established corresponding centers at the local level. These disaster reduction centers are in charge of researching and implementating disaster risk reduction activities, and work closely with cooperating universities and research institutes. These centers work as the bridge between academia and government, applying their research and knowledge on disaster

risk modeling, assessment technologies and methodologies, and helping to transfer this knowledge into practice. Figure 13 shows the administrative structure of disaster management in China.

Figure 13: The Administrative Structure of Disaster Management in China (Source: author)

Volunteers and Non-Governmental Organizations (NGOs) are relatively new in China, but the influence and scale of these organizations are rapidly increasing. During the Wenchuan Earthquake alone, approximately 300 million volunteers provided support in the disaster area. Despite the governmental foundations, the private foundations and NGOs such as One Foundation have gained a favorable

reputation because of their effective and efficient response and transparent financial system.

6.2.2.1.2 Legislation It has only been a few years since China made disaster risk reduction a national priority. The first time that China put forward the guidelines, goals, main tasks, and major measures of disaster reduction work in 1998 with the release of The Disaster Reduction Plan of the People's Republic of China 1998-2010. Now planning for disaster risk reduction has been a long-term mechanism and the National Plans are released every five years. In 2011, the National 12th Five-Year Plan on Integrated Disaster Prevention and Reduction (2011-2015) was released. The 2011 Plan explicitly defined the development guidelines, main tasks, and major projects of

China’s disaster reduction work during the 12th Five-Year period (2011-2015). This plan impacts Chinese disaster risk reduction in a profound way. It set 8 objectives, identified 10 tasks, and emphasized 8 projects. The objectives, tasks, and projects addressed in the Plan reflect the application of the concept of Integrated Natural Disaster Risk Management (INDRM) in China, which emphasizes a comprehensive and integrated approach of management for all types of natural disasters during all phases of the disaster management cycle. Figure 14 presents the eight objectives, ten tasks and eight major projects of the the National 12th Five-Year Plan on Integrated Disaster Prevention and Reduction (2011-2015).

Eight Objectives Ten Tasks Eight Projects

•Identify and managing •Enhance capacity building •Nationwide integrated risk natural hazards risk on: investigation project •Control death toll and •Natural hazard (towards natural hazards) economic loss monitoring and early •National integrated disaster •Integrate disaster mitigation warning mitigation and risk into sustainable •Information management management development and service informationization project •Ensure12-hour relief •Natural hazard risk •National emergency •Enhance public awareness management response and relief and education •Prevention project to operation system natural hazards construction project •Increase human resources (towards natural disasters) of disaster mitigation to •Disaster prevention and 2.75 million mitigation in local area •National disaster relief goods reservation project •Construct National and the county level Comprehensive Disaster •Natural hazard response, •Environmental disaster Reduction Demonstration recovery and mitigation satellites system Communities reconstruction construction project •Improve operational •Disaster prevention and •Construction project of mechanism and cooperation mitigation science and national simulation system at all levels technology on major natural disasters prevention •Social mobilization of disaster prevention and •Construction project of mitigation pilot integrated disaster mitigation community and •Enhance the construction of shelter human resources and professional team •Disaster prevention and mitigation promotion, •Enhance cultural building education and training of disaster prevention and project mitigation

Figure 14: The National 12th Five-Year Plan on Integrated Disaster Prevention and Reduction (2011-2015): Objectives, Tasks, Projects (Source: The State Council, 2011)

China has released and updated concrete laws and regulations for specific disaster management scenarios such as flood, fire, geological disasters, earthquakes, drought, meteorological disasters, and oceanic disasters (e.g. Flood Control Regulations (1991; 2005), Fire Control Law (1998; 2008), Regulation on the Prevention and Control of Geological Disasters (2003), Drought Control Regulations

(2009), the National Plan for Meteorological Disaster Prevention (2009-

2020),Marine Observation and Forecast Regulation (2012)). The only regulation about disaster recovery is the Regulations on post-Wenchuan Earthquake Restoration and Reconstruction, which was released after the devastating Sichuan earthquake in 2008. The regulation addressed how to reduce earthquake risk and consequences through identifying and reducing risk and vulnerablity factors (Article 45-50). In order to build a culture of resilience across the entireity of society and promote community engagement, the State Commission of Risk Reduction released the Criteria for the National Comprehensive Disaster Reduction Demonstration Communities (2010), the Guideline for Enhancing Comprehensive Disaster Reduction of Urban-Rural Community (2011), and Tentative Regulation on the Construction of National Comprehensive Disaster Reduction Demonstration Communities (2012). At the end of the year 2012, 1,273 National Comprehensive Disaster Reduction

Demonstration Communities were developed in order to push forward the establishment of a working mechanism for disaster reduction in communities, focusing predominately on urban areas. Disaster risk reduction strategy has been integrated into some laws and regulations, although arguably its inclusion has not been adequate. In the 12th Five- Year Plan for National Economic and Social Development of China (2011-2015), disaster prevention and mitigation were mentioned in different chapters and sections, focusing on the responsibilities of government agencies. There is no special chapter or section focusing exclusively on disaster mitigation and risk reduction. Another example is reducing disaster risk by poverty reduction. The Rural Poverty Alleviation and Development Plan (2011-2020), the major poverty reduction policy in China, proposed enhancing the prevention and treatment of geological disasters in two

sections of the report, including events such as landslides, mud-rock flows, and collapsing, through focusing on monitoring and early warning systems, relocation, and engineering treatment. It also addressed providing support to vulnerable groups like females, the elderly, children, the poor, and minorities.

6.2.2.1.3 Discussion The organizational structure of disaster management, the systematic, cross- cutting and in-depth coverage of disaster risk reduction objectives, projects, and mechanisms proposed by the 12th Five-Year Plan at the national level, the release of related plans at all levels of the nation, and the laws and regulations on natural hazards released by government agencies are all illustrative examples of China’s initial framework toward natural disaster risk reduction. However, there is still room for improvement: (i) the strategies are only proposed in the form of Plans, but there is no singular law or regulation on comprehensive risk reduction which would ensure legal support at all levels; the laws and regulations are mainly focused on individual hazards and are dependent on specific departments; (ii) the current laws and regulations mostly emphasize emergency response rather than disaster risk reduction, which are still post- event oriented; (iii) there is inadequate integration of disaster risk reduction into economic and social development strategies, poverty reduction, and recovery efforts at all levels; (iv) reviewing the plans, laws, and regulations, the terminologies of disaster management are not clearly defined; terms such as mitigation, prevention, control, treatment, disaster reduction, and disaster risk reduction are used interchangeably in different laws and regulations despite their different meanings; (v) it has been acknowledged that volunteers and NGOs are important resources throughout the cycle of disaster management, but the framework on the management of volunteers and

NGOs, including how to mobilize, manage, and cooperate with these resources, has been not formulated.

6.2.2.2 Risk Identification, Assessment, Monitoring and Early Warning

6.2.2.2.1 Risk Identification, Investigation and Assessment Disaster risk programs in China mainly concentrate on hazard analysis and exposure investigation, such as flood mapping, earthquake zoning, geological disaster surveying and zoning, agricultural and rural meteorological disaster investigation, marine disasters risk investigation for large coastal projects (G20 Report, 2012), sea level variation investigation and evaluation, and determination of tidal warnings using tidal gauges. These programs are implemented by different government agencies such as State Oceanic Administration (marine disasters), China Earthquake Administration

(earthquakes) and State Forestry Administration (wildfires). In short, vulnerability assessment has not been uniformly adopted and wideley implemented by the government. China has enhanced its knowledge and information base for the management of disasters, particularly regarding natural hazards. National Disaster Reduction Center of China (NDRCC) established National Natural Disaster Database and National

Disaster Management Information System to support decision-making. In 2010, The State Bureau of Surveying and Mapping (SBSM) launched the National Platform for Common GeoSpatial Information Services, named “Tianditu”. Data of disruptive events, hazards statistics, and damage statistics from July 2008 are included in the platform, yet it is only at the province level. In 2011, the first Atlas of Chinese Natural Hazards Risk was released. This Atlas included the regional distribution and frequent

patterns of fourteen natural hazards, including earthquake, typhoon, flood, drought, landslide and mud-rock flow, sandstorm, storm surge, snowstorm, hail, tornado, frost, forest fire, and grassland fire. Economic loss of each region is predicted according to historical data and patterns of natural hazards. The Atlas is the first product where a variety of forms of natural hazards are comprehensively summarized and mapped at the county level. The Atlas focuses on analyzing the geographic characteristics of natural hazards rather than social vulnerabilities. China has also participated in many international programs on disaster data sharing, for instance, the International Charter Space and Major Disasters (CHARTER) and the United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER) (G20 Report, 2012). Meanwhile, China has started to actively engage in international cooperation on disaster risk reduction such as proposing and leading the Integrated Risk Governce (IRG) Project under the framework of International Human Dimensions Programme on Global Environmental Change (IHDP).

6.2.2.2.2 Monitoring and Early Warning The Chinese government has exhibited a great effort toward improving natural disaster monitoring and early warning systems. Since the 1990s, China has increased investment toward building an advanced natural disaster monitoring and early warning system, including the adoption of modern earth-observation technology, satellite communication technology, and network technology. A number of meteorological, oceanic, and resource observation satellites have come into service. Currently, a three- dimensional natural disaster monitoring system, covering land, marine and seabed, and space-air-ground monitoring, has been established. Early warning and forecasting

systems for meteorological, hydrologic, seismic, geological, environmental, oceanic, forestry, and grassland disasters have all been developed and implemented. Cooperation and sharing mechanisms have been put in place between governments as well. Consequently, an integrated monitoring, early warning, and forecasting system for natural hazards is unfolding in China. In future years, more monitoring stations will be established, and the quality and resolution of early warning and forecast systems will be further enhanced (the 12th Five-Year Plan, 2011-2015).

6.2.2.2.3 Discussion Overall, China has developed risk identification and assessment mechanisms for a variety of natural hazards and has established primary monitoring and early warning systems for these hazards. These related disaster risk reduction efforts effectively reduce the potential consequences of death and economic loss, and provide strong support for emergency response, relief, and recovery, as well as decision- making regarding the allocation of limited disaster prevention and recovery resources. In order to match the requirements of disaster management within a dynamic social, economic, and environmental context, two key considertations must be taken into account: (i) currently risk identification has not been implemented in a majority of rural counties and villages; (ii) there is no unified definition, standard, and criteria for risk assessment. This situation produces substantial differentiation among hazard , which makes integration and comparison very challenging. For example, The State Earthquake Administration defined “risk” as the product of hazards and vulnerabilities, in which vulnerability is considered as differentiations among population density, GDP, and building area in each study area (Xu et al., 2004). SOA defined marine disaster risk as “the likelihood of marine disasters caused by marine phenomena such

as storm surge, wave, sea ice and tsunami” (Guidelins of the Marine Disaster Risks Investigation for Large-scale Coastal Projects). which excluded examining vulnerabilities; (iii) current approaches of risk assessment mainly focus on analyzing hazards rather than vulnerable elements, and exclude examination of capacities, which may cause inaccurate assessment; (iv) the capacity to precisely disseminate early warning messages in a timely and understable fashion to those at risk requires enhancement. The contents, formats, and method of delivery of the early warning messages should take into account the age, gender, livelihood and economic factors of the target audiences.

6.2.2.3 Building Understanding and Awareness

6.2.2.3.1 Education The Ministry of Education approved an emergency management major in 2003, which means that China has started to include professional emergency management into its higher education plan (Yi, 2011). Currently, there are three colleges of emergency management in three different universities: the Joint MCA-MOE Academy of Disaster Reduction and Emergency Management (ADREM) hosted by the Beijing Normal University (BNU), Management School & Emergency Management School at

Jinan University (JNU) and Emergency Management School at Henan Polytechnic University (HPU). Two universities offer an emergency management major: The University of Electronic Science and Technology of China and Zhongshan Institute. Three universities offer an emergency management focus within their public management major: the Renmin University of China, Northwest University and South China Agricultural University. Additionally, several emergency management research

centers have been established within universities, including Beijing Emergency Management Base at Tsinghua University, Center for Socially Risk and Public Crisis Management at Nanjing University, Emergency Management Research Center at Chinese Academy of Science, Emergency Management Research Center at Inner Mongolia University, and Emergency Management and Disaster Relief Base of Nankai University which is located in Sichuan Province. Another important research institution is located at the China Agricultural University within the research program of Disaster Management, focusing on the social science of disaster and emergency management. To enhance the understanding and knowledge of young people on disaster risk, one of China’s strategic plans is to integrate disaster prevention and mitigation courses into the national education system. Following the strategy, Anhui province has integrated metrological disaster prevention and reduction into the national education system as of 2008 as one of the courses for grade-five students. Yet, in other regions, this strategy has not been implemented as well.

6.2.2.3.2 Training Programs The Chinese government has started to train emergency management personnel both at the national and local level. The Chinese Academy of Governance (CAG) - National Emergency Management Institute has conducted more than 100 workshops on disaster risk management, and has involved thousands of participants from various levels of government, public sector departments, and institutions (UNISDR, 2013). In 2012, the National Emergency Management Training Base came into service, which is able to train 2,000 people per year (CAG, 2012). Local academies of governance are

required to offer emergency management training for local government officials as well.

6.2.2.3.3 Public Awareness In 2009, the Chinese government designated May 12, the day of the Wenchuan Earthquake, as the National Disaster Prevention and Reduction Day in order to increase public awareness of disaster risk and promote disaster risk reduction. Government has taken the lead in promoting public awareness, including disaster management departments disseminating leaflets or books about disaster knowledge, arranging visits to disaster forecasting agencies, and organizing disaster response drills. Websites about events information and knowledge of disaster mitigation, preparation, response, and recovery are developed especially for the National Disaster Prevention and Reduction Day. These examples show that the nation has started to value the importance of raising public awareness of citizens regarding disaster risk. Yet, China still lacks a formulated, long-term mechanism, and has not engaged public participation adequately.

6.2.2.3.4 Discussion The understanding and awareness regarding disaster risk reduction among communities in China has greatly improved in recent years. Major challenges to promoting education and awareness include: (i) while the effectiveness of disaster management depends greatly on the effective acquisition and utilization of disaster- related knowledge and information, the limited choices of universities and colleges that offer the major of emergency management restrains the development of human resources on emergency management; (ii) most of the courses in the universities and

the research being conducted on risk reduction are within the discipline of geography, physics, environmental science, and engineering; the involvement of social sciences, economics, political science, and other disciplines are only in the beginning stage; (iii) the training mechanism for government officials is well developed, but it has not been adequately developed for students and citizens, especially for vulnerable population groups such as the elderly, women, minorities, the disabled, and poor people, particularly in remote rural communities; (iv) to build a culture of disaster risk reduction amongst the entireity of society, communities should be more engaged and mobilized. Using modern communication platforms and tools to enhance the interaction between government and citizens is necessary for achieving this.

6.2.2.4 Reducing Underlying Risk Factors

6.2.2.4.1 Disaster Control and Prevention Projects In recent years, China has engaged in a series of important disaster-reduction projects to reduce physical risk factors, including flood control on major rivers and small and medium-sized rivers, mountain torrent disaster prevention, seepage prevention and reinforcement for unsafe reservoirs, geological disaster and soil erosion prevention and control, highway disaster prevention, housing renovation for impoverished rural residents, drinking water safety in rural areas, and ecological construction and environmental improvement (State Council, 2009). Drawing lessons from the destructive Wenchuan Earthquake, the nation released the Law on Protection Against and Mitigation of Earthquake Disasters (2008), which requires that construction and engineering projects operate in accordance with the standards of earthquake-resistance and corresponding building

codes. China also pushed forward several projects, including the rural housing earthquake-resistance program, renovation projects for high-risk buildings in primary and middle schools, and the safe school building project to reinforce that schools meet the earthquake-resistance standards. State Oceanic Administration (SOA) of China has carried out in-situ investigations in the aftermath of major marine disasters, and has made efforts to investigate the disaster-prevention capability of coastal infrastructure. Currently, SOA is evaluating the marine disaster risk along the Chinese coasts based on the in-situ investigation and historic data. The marine disaster risk assessment and zoning for some important coastal cities has been finished in the past few years, including coastal cities such as Tianjin, Wenzhou and Taizhou, where storm surges are most severe. Large-scale evacuation maps have been made for these cities. The ongoing risk investigation of large projects to natural hazards will provide solid information for revising existing or developing new building codes, standards, rehabilitation and reconstruction practices at the national and local levels. Risk zoning, combined with land use plans, will strongly reduce the disaster risk factors to natural hazards.

6.2.2.4.2 Discussion Disaster risks related to changing social, economic, environmental, and land use conditions, and the impact of hazards associated with geological events, weather, water, climate variability, and climate change, are addressed in sector development planning and programs as well as in post-disaster situations (Hyogo Framework, 2005). Further efforts should be focused on: (i) the natural hazard risk investigation of critical facilities and infrastructures, including hospitals, schools, electric power plants, oil and gas plants, nuclear plants, communication towers, dams, airports, railways, roads,

bridges, ports, and historical heritage sites; (ii) integrating risk reduction into land use planning, food security, poverty alleviation efforts, environmental protection, adaptation to climate change, and so on; (iii) the driving factors that impact the abilitity to mitigate, prepare for, respond to, and recovery from natural hazards of a society, include urbanization, education, poverty, age, gender, employment, social dependency, housing quality and minorities. Long-term strategies need to promote urbanization in rural areas, enhance education, reduce poverty, optimize industry structure, promote diverse livelihoods, and improve settlement conditions and medical services of residents.

6.2.2.5 Preparedness for Effective Response and Recovery

6.2.2.5.1 Preparing for Disaster Response The Law of the People's Republic of China on Emergency Responses, released in 2007, is a milestone of systemic emergency management in China. With the master state plan, the emergency management system established its own framework of emergency planning, resulting in the development of a variety of emergency plans based on this framework (Bai, 2012). The Law explicitly defined that the Emergency Plan Framework consists of the national comprehensive plan (issued by the central government), the national special plan (issued by the central government and government agencies), department plan for single hazard (issued by the government agencies), local plan (issued by local government at all levels), plan of corporations, and plan for large-scale exhibition and events. The Law also classified the emergencies into four levels according to the characteristics, the degree of severity,

the level of control, and the extent of influence of the areas: extremely serious (Level I), serious (Level II), major incident (Level III) and normal cases (Level IV). Currently, the Emergency Planning Framework has been established including one national comprehensive plan, 18 national special plans, 57 single hazard plans, and a large number of plans at the local level. Based on the scoring mechanism defined by the national plan, all the other plans have adopted the same criteria in regard to identifying the level of severity of disasters. For example, the coal mine production safety incident reporting and investigation by SAWS set the criteria as: Level I, over 30 fatalities, need to be communicated promptlyto the state council; level II, between 10-30 fatalities, escalate to province level; level III , 3-10 fatalities, escalate to city level; level IV, less than 3 fatalities, escalate to local level. The completed framework on emergency planning has effectively enhanced the preparedness, response, and recovery. Cities such as Beijing, Shanghai, Guangzhou, and Chongqing have dozens of shelters all across the city that can provide tenting areas, fresh water and medical attention during disaster recovery. These shelters are designed for the public in the event of earthquakes, disease epidemics, flooding, fire and other emergency situations, and are built mostly in municipalities.

6.2.2.5.2 Preparing for Disaster Recovery China has established 10 central-level disaster relief goods storage warehouses and is investing in building and improving these central relief goods storage facilities. Provinces, cities, and counties have all constructed local disaster relief goods storage warehouses.

As a traditional agricultural nation, the national disaster risk transfer mechanism currently only focuses on rural areas through agricultural insurance and rural housing insurance, in order to ensure farmers’ livelihoods and help them rebuild housing in the event of disaster. Although the insurance regulation against destructive earthquake, flood, storm and other natural hazards has not been released, the central government has approved Yunnan and Shenzhen to serve as pilots for catastrophic insurance.

6.2.2.5.3 Discussion Current disaster preparedness in China is mainly following a top-down model, dominated by governments. The vast majority of disasters are local, and the skills and capabilities to fight them must be available and deployed in the local community. The systems and factors determining the resiliency of a community include:  Physical system (e.g. critical infrastructure, communication systems, etc.)  Human system (e.g. skills, knowledge, health, education, etc.)  Social system (e.g. community networks, community participation and engagement, trust, civic engagement, norms, etc.)  Institutional system (e.g. first responders, response systems, etc.)

 Technical system (e.g. warning systems, emergency plans, etc.)  Economic system (e.g. income, productivity, etc.)  Environmental system (e.g. fresh water, arable land, etc.)  Ecological system (e.g. pollination, carbon sinks, etc.) (Sherrieb et al., 2010; Cutter et al., 2010; Constanza, 2012; Gall, 2013) Therefore, to enhance disaster preparedness for effective response and recovery, the local capacities of above stated systems are in need of examination and

enhancement. Comprehensive short-term and long-term strategies to build resilieny should include resiliency in terms of physical, human, social, institutional, technical, economic, environmental, and ecological elements.

6.2.3 Achievements, Inadequacies, Opportunities, Challenges and Recommendations

6.2.3.1 Achievements and Inadequacies Based on the review and examination of disaster risk reduction work in China under the Hyogo Framework, it can be concluded that China has achieved great progress in disaster risk reduction. These achievements include:  Establishment of institution and legislation frameworks;  Starting disaster risk identification and assessment for various natural

hazards and obtaining certain achievements, including developing a disaster information database and platform;  Establishing multi-dimensional national monitoring and early warning systems;  Incoporating disaster risk reduction in higher education and training programs especially for government officials and beginning to promote

public awareness on natural hazards;  Prposing a number of disaster control and prevention projects to reduce underlying risk factors  Formulating a complete top-down emergency plan framework for disaster response and starting to practice preparedness for recovery such as implementing catastrophe insurance for natural hazards.

We have to acknowledge that disaster risk reduction in China is still in its infancy and a number of inadequacies are present, including:  The absence of one comprehensive law for risk reduction and the lack of systematic management of volunteers and NGOs,  The absence of unified standards and criteria for risk assessment, as well as incomplete criteria for disaster risk assessment;

 The inadequate capacity to precisely disseminate early warning messages to vulnerable groups;  The limited higher education resources for long-term professional development and the lack of long-term programs for increasing public awareness to natural hazards;  Insufficient programs for building community resiliency;

6.2.3.2 Opportunities China has many national and international opportunities for building upon their achievements and bridging the identified gaps of disaster risk reduction. Internal opportunities include enhancing existing institutional capacities, building upon past experiences from disasters and developing ongoing programs, and a rapidly developing economy. These established and gradually improving institutional capacities will provide a strong support for promoting disaster risk reduction from the national level to the local level. The implementation of sustainable development strategies provides opportunities to integrate disaster risk reduction into the national economic and social development policies, strategies, and plans. Past experiences, in particular, large-scale disasters such as SARS, the Wenchuan

Earthquake and Ya’an Earthquake, have deepened the understanding of disaster

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management and significantly increased public awareness across Chinese society. Ongoing risk investigation, assessment, and zoning projects of several types of natural hazards will favor the development and application of related theories, methodologies, techniques, and tools, and will better serve future programs. The continuous economic development of the nation will continue to make it possible to provide financial support for disaster mitigation, preparedness, response, and recovery.

Additionally, the development of science and technologies such as Geographic Information Systems (GIS), data mining and integration, Emergency Operations Center (EOC) software and Communication Technology, and new media, along with the development of theories and research within related disciplines will accelerate the application of this research into practice. Moreover, the international community, the UNISDR, has started to formulate a post-2015 HFA, which will provide global guidance for disaster risk reduction based on the lessons drawn from the past decade. The international experiences of disaster management and risk reduction from both developed and developing countries are valuable for reference. Increasing international, inter-governmental, inter-regional, and academic cooperation will promote experience sharing and coordination within certain fields and programs (e.g. EU-China Disaster Risk Management Project, 2012; Joint research of China Japan, and Korea on disaster data and terminology, 2014).

6.2.3.3 Challenges The greatest challenge to disaster risk management in China is the complexity of its large population. As the most populous country in the world, this massive population poses a major challenge for disaster risk mitigation, response, and recovery.

13.26% of the population are 60 years and older, and this ratio is growing rapidly due

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to birth control policy; 50.32% of the population live in rural areas; 19.5% are migrants; 8.49% are minorities; 4.08% are illiterate (the 6th Census, 2010), and 11.8% of the population live under the poverty line (the World Bank, 2009). The disparity in population density and urbanization patterns and the income inequality seen throughout the country increase the complexity of disaster risk reduction in China. The scale of urbanization in China is without precedent in human history, yet, the transformation is complex and occurring at such a quick pace China is facing serious challenges regarding the environment, public resources, and culture (Karen Seto, 2014). China still relies on coal as their predominant energy source, which has resulted in heavy air pollution nationwide. The low cost of industrial pollution leads to severe environmental and ecosystem deterioration. Public services in municipalities fall behind in the wake of a rapidly increasing population. In most of cities, public resources are still limited in their ability to serve the huge number of rural migrants who expect to find work and obtain advanced services like medical care and education in cities. These challenges are also reflected in cultural and moral aspects of society such as the way economic values impact social cohesion.

6.2.3.4 Recommendations Given the achievements, inadequacies, opportunities and challenges, five recommendations for disaster risk reduction in China are proposed: (1) To start formulating the law and regulations for disaster risk reduction and working mechanisms to ensure a long-term sustained approach; to promote the integration of disaster risk reduction strategies into social, economic and environmental plans.

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(2) To start risk identification and assessment for variaty natural hazards at all levels; adopting new technology and methods to enhance disaster monitoring and early warning systems, particularly focus on vulnerable places and groups; to enhance public awareness and training; to enhance researches on disaster risk reduction and promote the application in practice;

(3) To promote building resiliency of physical, human, social, institutional, technical, economic, environmental and ecological systems, particularly at the local and community level and in less developed areas; (4) To enlarge human resources in theemergency management field by increasing higher education opportunities and establishing the systematic guidance of development of volunteers and NGOs.

(5) To enhance international and domestic coorperation in experience sharing and joint programs of disaster risk reduction, at both governmental and academic levels.

6.3 Discussion The first part of this chapter suggested the focuses of national and regional level which is directly drawn lessons from the results and findings of SoVI® 2010. The second part discusses policy implications encompassing the results of this study to include a comprehensive review of disaster risk reduction in China. The suggested ntional and regional level focuses and five major recommendations are supported by both of the key results and findings of SoVI® 2010 and the Hyogo Framework for Action (2005-2015) mutually. The analyses are not spacifically focus on coastal areas because the root causes and driven factors of social vulnerability in this research are

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national level situations rather than only existing in coastal areas. While “top-down” is still the dominate model for disaster risk reduction policies and initiatives in China, coastal communities will follow national strategies in most cases. Yet, government agencies who are responsible for coastal zone management are also be able to drawn lessons from the results and have different priorities. Table 11 presents the suggested focus of the nation, region, and coastal zone management agencies; and how they are supported by SoVI® 2010 and Hyogo Framework for Action (2005-2015).

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Table 11: Policy Implications Supported by SoVI® 2010 and Hyogo Framework for Action (2005-2015)

Suggested Focus Supported Supported Key Results/Insights Coastal Zone by SoVI by HFA Nation Region Management 2010 Agencies The north and south end coastal areas are more vulnerable than 1.1 x x √ southeastern coastal areas. Counties and county-level cities are more vulnerable than city 1.1 x x x √ districts. 1.1 The vulnerability of each county is driven by different factors. x √

Driven factors of social vulnerability in Chinese coastal counties: 105 1.1 Urbanization; Education; Social dependency; Employment; x x √

Poverty; Age; Gender; Minority; Poor housing quality. To enhance legislation construction and integration of disaster risk 2.1 x x x √ reduction. To promote disaster risk identification and assessment, training 2.1 and research programs; to enhance monitoring and early warning x x x √ √ systems; To enhance disaster resilience building, particularly for vulnerable 2.1 groups, and those at the local and community level and in less x x x √ √ developed areas; To enlarge human resources of disaster management and 2.1 x x √ systematically develop volunteers and NGOs. 2.1 To enhance international and domestic cooperation. x x x √

Chapter 7

CONCLUSIONS

7.1 Conclusions This thesis conducted an assessment of social vulnerability in Chinese coastal areas at the county level. It highlighted regional variability and the factors that had the greatest impact on the social vulnerability of the study areas. Drawing lessons from the results, this research emphasized the strategies and directions for decision makers to allocate resources in the study areas and proposed recommendations for action and effort to reduce disaster risk and build resilience.

I have argued that social vulnerability in Chinese coastal areas presents three characteristics: I. The north and south end coastal areas are more vulnerable than southeastern coastal areas; II. Counties and county-level cities are more vulnerable than city districts; III. The vulnerability of each county is driven by different factors even though they have approximate scores in the index.

Derived from the analysis of social vulnerability index (SoVI®) 2010 and its spatial and temporal changes from 2000 to 2010, I have argued that the components which consistently determine social vulnerability for all time periods include:  Urbanization  Education  Social dependency

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 Employment  Poverty  Age  Gender  Minority  Poor housing quality.

According to the presented results and analysis, I have suggested that at the national level, strategies and efforts should focus on improving the nine driving factors and reducing the gap between the coasts of the two geographic ends and southeastern coast and gap between counties/county-level cities and city districts. At the local level, place-based disaster mitigation policies and initiatives should be proposed to address the variability in the driving forces for each coastal province and county. The research revealed that social vulnerability reduction is not an isolated task, but requires being integrated with relevant social and economic development plans. It also demonstrates that reducing social resource inequality among urban and rural areas will significantly decrease social vulnerability to natural hazards. Through comprehensively considering the achievements, inadequacies, opportunities, and challenges of disaster risk reduction in China, I have proposed five major recommendations towards building disaster resilient communities:

I. To enhance legislation construction and integration of disaster risk reduction; II. To promote disaster risk identification and assessment, training and research programs; to enhance monitoring and early warning systems;

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III. To enhance disaster resilience building, particularly at the local and community level and in less developed areas; IV. To enlarge human resources of disaster management and systematically develop volunteers and NGOs. V. To enhance international and domestic cooperation.

7.2 Contributions This research is the first time that the overall social vulnerability assessment to natural hazards of Chinese coastal areas has been addressed. It presents the variations of social vulnerability in the study areas, identifies the determining factors, and addresses the ways in which social vulnerability is changing. The knowledge acquired from this research bridges the gaps between the theoretical concepts and day-to-day decision-making, and broadens and deepens the understanding of disaster risk along the Chinese coast, which is a core step toward integrated risk analysis. Moreover, this research is one of the first efforts to include detailed interpretations of the lessons drawn from the results, and recommendations on setting priorities for current management and further long-term objectives of risk reduction, planning, and mitigation according to the problems identified. The knowledge provided by this study is valuable to millions of people who are living in Chinese coastal areas by preventing or reducing loss of life, injury, and environmental consequences before a hazard occurs, particularly for vulnerable groups such as women, elderly, poor and minorities. This research contributes to two major theoretical literatures. It will add to the growing body of work on the understanding of vulnerability by providing evidence of the dynamic nature of vulnerability, and by addressing the fact that social vulnerability encompasses various vulnerability features that are driven by multiple stressors and

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differential exposure and are often rooted in multiple attributes of human actors and social networks (Downing et al., 2006). This project will also contribute to the application of indices in measuring social vulnerability. It assessed social vulnerability in coastal areas in a developing country using Social Vulnerability Index (SoVI®), which was initially created to examine social vulnerability in the United States. This research presents the same distribution pattern between urban city districts and rural counties and county-level cities, compared with Chen et al. (2013)’s work. The driving factors differ from previous works using this same methodology, reflecting the place-based nature of social vulnerability. The comparison supports the utility of SoVI® in measuring social vulnerability in China and pinpoints the concordance and discordance in their identification of barriers, enhancing validity.

7.3 Limitations The limitations of this research can be summarized in respect to three aspects: data, methodology and application. The major data limitations of this study include the selection of indicators and variables, the incompleteness of data, and the data quality of the variables. This part was explained in detail in the methodology introduction section (see Chapter 4: 4.2.3

Data Limitation). This methodology of social vulnerability index has its own limitations. First, the selection of variables is dependent on data availability, which may influence the final ranking of the study units in social vulnerability. Secondly, since SoVI is developed for assessing present situations, it failed to incorporate a time dimension to identify a possible future pattern of social vulnerability based on demographic, economic, and social changes expected to take place. Therefore, social vulnerability

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assessment requires updating over time to keep up with changes in society. In addition, when integrating with other indices, data from different time periods may need to be adopted. For instance, the measurement of vulnerability to sea level rise of a place may require a longer time frame of sea level change than demographic change. Thirdly, although social vulnerability is hazard-independent, certain properties of a system will make it more vulnerable to certain types of hazard than to others (Brooks,

2003). For instance, housing quality is a factor of great importance in determining earthquake risk, but not as important in sea level rise or sea ice risk measurement. Lastly, the meanings of extracted principal components are often not as clear as the original variables, requiring interpretation based on the researcher’s own understanding. Also, the assignment of negative or positive impact regarding overall social vulnerability is always subjective.

Note that the final scores of the social vulnerability index were presented as deviations from the mean value, representing only relative variance of social vulnerability across the units of analysis and cannot be used as the absolute measure for a particular place. It is important to consider how to use the results of the tool for decision-making in practice. It is important to understand that social indicators represent only a portion of the human-environment interaction that amplifies or attenuates the vulnerability of populations to natural hazards (Borruf et al., 2005).

Therefore, it requires decision makers to comprehensively consider the hazard risk, including the physical, economic, and environmental vulnerabilities, when proposing disaster mitigation strategies and policies.

7.4 Future Research Future research on this topic can be approached from three different directions:

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First, the methodology of this research can be further improved according to the identified limitations, for instance, addressing incomplete data or optimizing the weighting strategies of the factors/indicators. Since social vulnerability changes over time, but SoVI® only captures a present situation, future studies may take this uncertainty into account to calculate a range of probable outcomes. Second, it would be useful to integrate the social vulnerability assessment with other components such as physical factors, in order to assess the overall vulnerability of a place. Additionally, it would be helpful to integrate capacity measurement with hazard and vulnerability assessment to work out the whole picture of disaster risk in order to increase resilience to natural hazards. Lastly, the methodology enables one to generalize the research to other areas in China, or in other countries. Vertically, understanding how to use the results of this research to implicate decision-making and disaster risk reduction initiatives is worth studying further.

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APPENDIX A

SOCIAL VULNERABILITY INDEX OF CHINESE COASTAL COUNTIES (2010)

FAC1_ FAC2_ FAC3_ FAC4_ FAC5_ FAC6_ Province ID County SoVI 1 1 1 1 1 1 Tianjin 1 Binhai Xinqu 0.05 -1.57 -0.48 -0.08 -1.34 0.78 -0.06 Hebei 2 Fengnan Qu -0.61 0.38 0.53 -0.21 -0.98 -0.60 1.70 Hebei 3 Luannan Xian -0.92 0.62 0.25 0.11 -0.59 -0.15 2.34 Hebei 4 Leting Xian -1.25 0.56 1.00 -0.71 -1.16 0.28 3.54 Hebei 5 Tanghai Xian -0.55 -0.71 0.26 0.00 -1.10 1.18 2.38 Hebei 6 Haigang Qu 1.86 0.40 0.24 -1.04 -0.10 0.89 -1.27 Hebei 7 Shanhaiguan Qu 0.86 0.06 0.58 -1.08 -0.44 3.07 2.22 Hebei 8 Beidaihe Qu 1.34 0.38 0.72 -1.12 0.81 0.82 -1.35 Hebei 9 Changli Xian -0.91 0.45 1.03 1.13 -0.71 0.10 4.34 Hebei 10 Funing Xian -0.80 0.19 0.42 1.67 -0.12 0.76 3.96 Hebei 11 Haixing Xian -1.07 1.03 0.01 0.42 -0.82 -0.12 3.23 Hebei 12 Huanghua Shi -0.40 0.61 -0.02 0.28 -0.65 0.46 2.38 Liaoning 13 Zhongshan Qu 2.74 0.63 1.75 -0.73 0.05 0.17 -0.97 Liaoning 14 Xigang Qu 2.48 0.23 1.78 -0.30 -0.62 0.03 -0.11 Liaoning 15 Shahekou Qu 3.06 0.49 1.38 -0.47 -0.03 -0.19 -1.82 Liaoning 16 Ganjingzi Qu 1.42 -0.30 0.25 -0.81 -0.57 0.99 -0.71 Liaoning 17 Lvshunkou Qu 0.58 -0.32 0.70 -0.76 -0.53 1.20 0.77 Liaoning 18 Jinzhou Qu 0.22 -0.45 0.44 -0.76 -0.63 0.99 0.64 Liaoning 19 Changhai Xian -0.49 0.16 1.16 0.12 -1.07 0.67 3.67 Liaoning 20 Wafangdian Shi -0.89 -0.02 1.71 1.03 -1.06 0.75 5.42 Liaoning 21 Pulandian Shi -0.96 -0.03 1.84 1.36 -0.84 0.23 5.21 Liaoning 22 Zhuanghe Shi -1.35 0.05 1.66 0.64 -0.99 0.33 5.03 Liaoning 23 Donggang Shi -1.40 -0.06 1.49 -0.12 -2.12 0.65 5.49 Liaoning 24 Linghai Shi -1.28 0.28 1.50 0.20 -1.67 1.43 6.36 Liaoning 25 Xishi Qu 1.62 0.42 1.32 -1.78 -0.36 3.49 2.19 Liaoning 26 Bayuquan Qu 0.35 -0.16 0.42 -0.25 -1.10 1.45 2.21 Liaoning 27 Laobian Qu -0.67 -0.16 1.02 -0.60 -1.45 1.06 3.44

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Liaoning 28 Gaizhou Shi -1.29 0.58 1.40 0.37 -1.83 1.43 6.91 Liaoning 29 Dawa Xian -0.07 0.24 1.02 0.78 -1.04 0.99 4.15 Liaoning 30 Panshan Xian -1.20 0.36 1.44 -0.03 -1.36 0.40 4.73 Liaoning 31 Lianshan Qu -0.09 0.41 1.44 0.31 -1.35 0.87 4.47 Liaoning 32 Longgang Qu 0.84 -0.30 0.56 -0.52 -0.60 0.99 0.50 Liaoning 33 Suizhong Xian -0.97 -0.15 1.22 1.84 -1.02 2.76 7.66 Liaoning 34 Xingcheng Shi -0.61 -0.20 1.37 0.93 -1.34 3.64 7.69 Shanghai 35 Baoshan Qu 1.92 -2.01 -0.67 0.90 1.10 1.68 -3.12 Shanghai 36 Pudong Xinqu 1.04 -1.19 0.04 0.05 0.35 0.60 -1.88 Shanghai 37 Jinshan Qu 0.18 -0.72 0.83 -0.40 0.94 -0.25 -1.67 Shanghai 38 Fengxian Qu -0.14 -1.30 0.01 0.20 0.19 0.29 -0.85 Chongming Shanghai 39 0.08 -0.46 2.08 -0.19 2.47 1.73 0.60 Xian Jiangsu 40 Tongzhou Qu -0.77 0.24 1.77 -1.34 2.00 -0.49 -1.03 Jiangsu 41 Hai'an Xian -0.50 0.42 1.80 -1.22 1.88 -0.44 -0.81 Jiangsu 42 Rudong Xian -0.73 0.12 1.96 -0.83 1.88 -0.11 -0.02 Jiangsu 43 Qidong Shi -0.72 -0.04 2.06 -0.92 1.78 -0.01 0.02 Jiangsu 44 Haimen Shi -0.39 0.19 1.97 -0.53 1.48 -0.33 0.21 Jiangsu 45 Lianyun Qu 1.34 0.20 -0.43 -0.73 1.30 0.77 -2.83 Jiangsu 46 Xinpu Qu 1.73 0.31 -0.24 0.07 0.43 0.07 -1.96 Jiangsu 47 Ganyu Xian -0.12 1.07 -0.42 -0.05 1.41 -0.34 -1.04 Jiangsu 48 Guanyun Xian -0.35 1.25 -0.69 0.75 0.70 -0.29 0.66 Jiangsu 49 Guannan Xian -0.39 1.25 -0.68 -0.18 0.91 -0.29 -0.42 Jiangsu 50 Xiangshui Xian -0.11 0.91 -0.75 0.69 1.13 -0.06 -0.23 Jiangsu 51 Binhai Xian -0.42 0.94 -0.44 0.27 0.15 0.06 1.10 Jiangsu 52 Sheyang Xian -0.74 0.61 0.62 -0.92 -0.21 0.16 1.42 Jiangsu 53 Dongtai Shi -0.42 0.41 1.65 -0.86 1.79 0.12 -0.05 Jiangsu 54 Dafeng Shi -0.60 0.08 1.31 -0.85 0.80 0.19 0.53 Zhejiang 55 Binjiang Qu 1.85 -1.90 -0.83 1.42 0.86 -0.43 -4.45 Zhejiang 56 Xiaoshan Qu -0.09 -1.20 0.08 -0.07 1.98 -0.19 -3.27 Zhejiang 57 Haishu Qu 2.33 -0.44 2.00 1.46 -1.03 -2.44 -0.71 Zhejiang 58 Jiangdong Qu 1.89 -0.50 0.87 0.58 -0.19 -1.41 -2.17 Zhejiang 59 Jiangbei Qu 0.67 -1.51 0.55 1.69 -0.34 -1.25 -0.87 Zhejiang 60 Beilun Qu -0.67 -1.80 -0.14 0.17 -0.43 -0.40 -1.08 Zhejiang 61 Zhenhai Qu -0.44 -1.87 0.11 1.19 -0.69 -0.64 -0.08 Zhejiang 62 Yinzhou Qu -0.60 -1.92 -0.16 0.71 -0.12 -0.41 -1.07 Zhejiang 63 Xiangshan Xian -0.50 -0.90 0.55 0.08 1.62 -0.33 -1.72 Zhejiang 64 Ninghai Xian -0.72 -1.11 0.11 0.45 0.76 -0.39 -0.98 Zhejiang 65 Yuyao Shi -0.85 -1.40 0.49 -0.03 0.76 -0.16 -1.01 Zhejiang 66 Cixi Shi -0.94 -2.01 0.19 0.65 0.64 -0.18 -1.06

128

Zhejiang 67 Fenghua Shi -0.90 -1.03 0.93 -0.02 0.19 -0.26 0.34 Zhejiang 68 Longwan Qu -0.83 -2.17 -1.74 0.39 -0.10 0.30 -2.30 Zhejiang 69 Ouhai Qu -1.01 -1.96 -1.45 -0.09 -0.61 0.12 -1.76 Zhejiang 70 Dongtou Xian 0.07 -0.19 0.31 0.17 2.82 0.30 -2.30 Zhejiang 71 Pingyang Xian -0.38 -0.21 -0.24 -0.51 1.41 -0.11 -2.09 Zhejiang 72 Cangnan Xian -0.28 -0.18 -0.75 -0.41 1.42 -0.04 -2.51 Zhejiang 73 Rui'an Xian -0.64 -1.29 -0.67 0.08 0.95 -0.30 -2.50 Zhejiang 74 Yueqing Shi -0.48 -1.20 -0.87 0.60 1.02 -0.31 -2.32 Zhejiang 75 Haiyan Xian -0.76 -0.90 0.53 -0.49 1.64 -0.61 -2.35 Zhejiang 76 Haining Shi -0.62 -0.93 0.42 -0.39 1.52 -0.75 -2.54 Zhejiang 77 Pinghu Shi -0.81 -1.26 0.35 -0.23 1.71 -0.58 -2.63 Zhejiang 78 Shaoxing Xian -0.78 -1.70 0.05 0.64 0.44 -0.35 -1.02 Zhejiang 79 Shangyu Shi -0.68 -0.85 0.92 -0.27 1.47 -0.26 -1.26 Zhejiang 80 Dinghai Qu 0.11 -1.14 0.57 0.28 0.57 -0.22 -1.19 Zhejiang 81 Putuo Qu -0.37 -1.02 0.66 0.09 0.32 0.14 -0.07 Zhejiang 82 Daishan Xian -0.94 -1.25 0.84 0.00 0.45 0.50 0.57 Zhejiang 83 Shengsi Xian 0.00 -0.77 1.36 0.75 1.18 0.31 0.46 Zhejiang 84 Jiaojiang Qu -0.09 -1.07 -0.14 0.36 1.03 -0.88 -2.68 Zhejiang 85 Luqiao Qu -0.75 -1.65 -0.56 0.53 0.90 -0.64 -2.46 Zhejiang 86 Yuhuan Xian -0.53 -2.01 -0.87 1.38 1.36 -0.33 -2.65 Zhejiang 87 Sanmen Xian -0.43 -0.37 0.10 0.66 2.48 -0.45 -2.11 Zhejiang 88 Wenling Shi -0.83 -1.39 -0.05 0.56 1.03 -0.56 -1.65 Zhejiang 89 Linhai Shi -0.39 -0.35 0.20 0.38 2.16 -0.71 -2.26 Fujian 90 Mawei Qu 0.58 -0.38 -0.52 -0.86 0.09 -0.40 -2.85 Fujian 91 Lianjiang Xian -0.44 0.72 -0.39 -1.19 0.24 0.04 -0.61 Fujian 92 Luoyuan Xian -0.33 0.35 0.20 -0.42 -0.30 0.80 1.57 Fujian 93 Pingtan Xian 0.19 1.07 -0.60 -0.65 0.96 0.30 -1.03 Fujian 94 Fuqing Shi 0.07 0.44 -0.67 -0.40 2.16 -0.32 -3.20 Fujian 95 Changle Shi -0.29 0.03 -0.35 -0.97 0.04 0.01 -1.05 Fujian 96 Siming Qu 2.66 -0.72 -0.55 0.25 0.82 -0.16 -4.66 Fujian 97 Haicang Qu -0.21 -1.56 -1.20 -0.54 -0.67 -0.11 -2.53 Fujian 98 Huli Qu 0.37 -1.89 -1.19 0.33 -1.54 -0.88 -2.47 Fujian 99 Jimei Qu 0.23 -2.03 -1.41 0.10 -0.75 -0.31 -3.12 Fujian 100 Tong'an Qu -0.54 -0.82 -0.70 -0.34 0.12 -0.45 -1.88 Fujian 101 Xiang'an Qu -0.33 -0.60 -0.23 -0.14 1.58 -0.15 -2.36 Fujian 102 Chengxiang Qu 0.44 0.23 -0.44 -0.47 1.78 -0.48 -3.37 Fujian 103 Hanjiang Qu -0.25 0.01 -0.46 -1.03 1.25 -0.09 -2.57 Fujian 104 Licheng Qu -0.32 0.35 -0.50 -0.92 1.44 -0.91 -3.11 Fujian 105 Xiuyu Qu -0.46 1.02 -0.44 -0.39 3.46 -0.62 -3.45

129

Fujian 106 Xianyou Xian -0.39 0.94 -0.57 -0.46 0.91 0.15 -0.46 Fujian 107 Fengze Qu 0.64 -1.11 -1.17 0.00 0.03 -0.34 -3.28 Fujian 108 Luojiang Qu -0.44 -0.85 -0.71 0.34 1.39 -0.35 -2.52 Fujian 109 Quangang Qu -0.16 0.08 -0.36 0.14 2.26 -0.37 -2.62 Fujian 110 Hui'an Xian -0.50 -0.16 -0.64 -0.25 2.37 -0.51 -3.43 Fujian 111 Shishi Shi -0.64 -1.31 -1.14 -0.14 -0.24 -0.29 -2.00 Fujian 112 Jinjiang Shi -0.86 -1.15 -1.36 -0.06 0.29 -0.04 -2.04 Fujian 113 Nan'an Shi -0.80 0.05 -0.81 -0.71 1.02 -0.63 -2.32 Fujian 114 Yunxiao Xian -0.63 1.11 -0.54 -0.37 -0.54 0.06 1.42 Fujian 115 Zhangpu Xian -0.85 0.73 -0.49 0.34 -0.20 -0.49 1.13 Fujian 116 Zhao'an Xian -1.26 0.91 -0.66 -0.46 -1.27 -0.31 2.01 Fujian 117 Dongshan Xian -0.53 0.65 -0.06 -0.99 0.09 -0.01 0.03 Fujian 118 Longhai Shi -0.63 0.12 -0.54 -0.47 0.39 -0.03 -0.68 Fujian 119 Jiaocheng Qu 0.28 0.54 -0.28 -0.54 0.22 0.28 -0.50 Fujian 120 Xiapu Xian -0.45 0.69 -0.19 -0.41 -0.16 0.56 1.26 Fujian 121 Fu'an Shi -0.18 0.48 -0.33 -0.48 0.10 0.90 0.65 Fujian 122 Fuding Shi -0.26 0.14 -0.24 -0.06 0.66 0.25 -0.31 Shandong 123 Shinan Qu 3.35 0.37 1.64 0.84 -0.37 -1.67 -1.80 Shandong 124 Shibei Qu 2.76 0.57 0.75 -0.56 0.26 0.00 -2.25 Shandong 125 Sifang Qu 2.42 0.27 0.86 -0.19 -0.37 -0.15 -1.25 Shandong 126 Huangdao Qu 0.75 -1.44 -0.79 0.53 -0.38 -0.24 -2.33 Shandong 127 Laoshan Qu 0.98 -0.72 0.01 -0.40 0.73 0.03 -2.78 Shandong 128 Licang Qu 1.53 -0.11 0.24 -0.47 -0.38 -0.15 -1.63 Shandong 129 Chengyang Qu -0.22 -0.68 -0.02 -0.75 -0.13 -0.60 -1.69 Shandong 130 Jiaozhou Shi -0.64 0.32 0.39 -0.60 0.41 -0.63 -0.29 Shandong 131 Jimo Shi -0.83 0.26 0.49 -0.66 0.14 -0.71 0.07 Shandong 132 Jiaonan Shi -0.59 0.05 0.59 -0.40 0.80 -0.35 -0.33 Shandong 133 Dongying Qu 0.98 -0.04 0.36 0.24 0.03 -0.38 -0.83 Shandong 134 Hekou Qu 0.17 -0.03 0.78 0.73 -0.36 -0.45 1.22 Shandong 135 Kenli Xian -0.92 0.32 0.56 -0.65 -0.29 0.56 2.01 Shandong 136 Lijin Xian -1.00 0.47 1.09 0.41 0.36 0.07 2.68 Shandong 137 Guangrao Xian -0.72 0.29 0.86 0.24 0.36 -0.92 0.85 Shandong 138 Zhifu Qu 1.34 0.19 -0.26 -1.66 0.13 1.27 -1.93 Shandong 139 Fushan Qu -0.33 -0.48 -0.26 -1.18 -0.52 0.40 -0.66 Shandong 140 Muping Qu -0.84 0.34 1.72 -0.67 -0.63 -0.26 2.60 Shandong 141 Laishan Qu 0.85 -0.35 0.22 -0.64 0.29 0.04 -1.88 Shandong 142 Changdao Xian -0.25 0.20 1.72 0.02 -0.59 -0.27 2.51 Shandong 143 Longkou Shi -0.67 0.08 1.16 -0.22 -0.77 -0.27 2.19 Shandong 144 Laiyang Shi -1.06 0.68 1.35 0.05 -0.79 -0.95 2.99

130

Shandong 145 Laizhou Shi -0.90 0.49 1.67 -0.42 -0.55 -0.23 2.95 Shandong 146 Penglai Shi -0.69 0.12 1.57 1.45 -0.49 -0.47 3.84 Shandong 147 Zhaoyuan Shi -0.79 0.27 1.63 0.05 -0.34 -0.11 2.97 Shandong 148 Haiyang Shi -1.25 0.22 1.92 0.28 -0.81 -0.33 4.16 Shandong 149 Hanting Qu -0.61 0.46 0.94 0.36 -0.08 -0.58 1.87 Shandong 150 Shouguang Shi -0.89 0.41 0.80 -0.13 -0.50 -0.94 1.53 Shandong 151 Changyi Shi -0.85 0.62 0.89 0.49 0.09 -0.40 2.36 Shandong 152 Huancui Qu 0.49 -0.18 0.19 -0.75 -0.50 -0.43 -1.18 Shandong 153 Wendeng Shi -0.53 0.20 1.96 -0.13 -0.22 -0.65 2.14 Shandong 154 Rongcheng Shi -0.45 0.13 2.24 0.21 -0.97 -1.04 2.96 Shandong 155 Rushan Shi -0.81 0.22 1.99 -0.42 -0.60 -0.22 2.99 Shandong 156 Donggang Qu -0.12 0.44 -0.06 -0.90 0.38 0.44 -0.35 Shandong 157 Lanshan Qu -0.81 0.34 0.89 0.30 0.88 -0.13 1.32 Shandong 158 Wudi Xian -1.00 0.59 0.50 -0.66 -0.29 -0.28 1.44 Shandong 159 Zhanhua Xian -0.80 0.52 1.28 0.29 0.31 0.11 2.69 Guangdong 160 Liwan Qu 2.02 -0.07 0.65 -0.36 -0.71 -0.42 -1.51 Guangdong 161 Yuexiu Qu 4.47 0.23 2.92 3.05 -1.86 -4.47 -0.90 Guangdong 162 Haizhu Qu 1.81 -0.34 0.44 -0.40 -0.91 -0.91 -2.10 Guangdong 163 Tianhe Qu 2.38 -0.83 -0.30 0.11 -0.82 -1.07 -3.65 Guangdong 164 Baiyun Qu 0.19 -1.29 -0.53 -0.56 -1.12 -0.30 -1.74 Guangdong 165 Huangpu Qu 0.29 -1.32 -0.81 -0.76 -1.49 -0.25 -1.94 Guangdong 166 Panyu Qu 0.20 -1.00 -0.54 -0.64 -0.39 -0.57 -2.54 Guangdong 167 Nansha Qu -0.69 -1.40 -0.66 -0.34 -0.84 -0.03 -0.91 Guangdong 168 Luogang Qu 0.03 -1.40 -1.09 -0.90 -0.65 -0.01 -2.77 Guangdong 169 Luohu Qu 1.58 -0.69 -0.76 -0.69 -0.81 -0.61 -3.52 Guangdong 170 Futian Qu 2.07 -1.01 -0.72 -0.15 -0.54 -0.96 -4.38 Guangdong 171 Nanshan Qu 1.90 -2.25 -1.44 0.38 0.06 0.06 -5.21 Guangdong 172 Bao'an Qu -1.03 -2.98 -1.28 -0.14 -2.42 -0.61 -1.56 Guangdong 173 Longgang Qu -0.60 -2.03 -1.38 -0.60 -1.88 -0.46 -1.98 Guangdong 174 Yantian Qu 0.07 -1.58 -1.27 -0.79 -1.65 -0.15 -2.22 Guangdong 175 Xiangzhou Qu 1.35 -0.51 -0.45 -0.57 -0.67 -0.54 -2.76 Guangdong 176 Doumen Qu -0.21 -0.52 -0.25 -0.41 -0.84 -0.66 -0.79 Guangdong 177 Jinwan Qu 0.03 -1.38 -0.59 -0.24 -1.39 -0.55 -1.40 Guangdong 178 Longhu Qu 0.46 0.78 -1.23 -1.79 -0.04 -0.14 -2.79 Guangdong 179 Jinping Qu 2.13 1.38 0.53 -0.88 -0.72 -0.65 -1.02 Guangdong 180 Haojiang Qu 0.28 1.91 -1.52 -1.80 0.33 -0.62 -2.64 Guangdong 181 Chaoyang Qu -0.87 1.85 -1.67 -1.89 -1.31 -1.28 -0.81 Guangdong 182 Chaonan Qu -0.90 2.10 -2.20 -1.80 -0.90 -1.51 -1.60 Guangdong 183 Chenghai Qu -0.67 1.20 -0.78 -1.98 -0.72 -0.60 -0.76

131

Guangdong 184 Nan'ao Xian 0.11 1.12 0.28 -1.13 -0.06 0.03 0.24 Guangdong 185 Pengjiang Qu 0.26 -0.17 -0.98 -1.67 -0.16 0.39 -2.53 Guangdong 186 Jianghai Qu -0.26 -0.05 -1.00 -1.79 -0.47 0.31 -1.81 Guangdong 187 Xinhui Qu -0.30 0.20 0.09 -0.92 -0.47 -0.31 -0.15 Guangdong 188 Taishan Shi -0.59 0.74 0.37 -0.48 -0.52 -0.04 1.71 Guangdong 189 Enping Shi -0.40 0.50 0.16 -0.26 -0.03 0.12 0.95 Guangdong 190 Chikan Qu 2.12 1.08 -0.54 -1.37 0.21 0.60 -2.56 Guangdong 191 Xiashan Qu 1.79 1.06 -0.53 -1.28 -0.13 0.59 -1.81 Guangdong 192 Potou Qu -0.29 1.23 -0.53 0.78 0.06 -0.51 1.21 Guangdong 193 Mazhang Qu 0.03 1.11 -1.05 2.24 -0.15 -0.93 1.51 Guangdong 194 Suixi Xian -0.34 1.40 -0.67 1.90 -0.04 -0.96 2.04 Guangdong 195 Xuwen Xian -0.51 1.40 -0.66 1.62 -1.03 -0.75 3.15 Guangdong 196 Lianjiang Shi -0.14 1.39 -0.96 2.35 0.37 -0.75 1.80 Guangdong 197 Leizhou Shi -0.27 1.34 -1.01 2.71 -0.15 -0.88 2.58 Guangdong 198 Wuchuan Shi 0.16 1.66 -1.03 1.11 0.94 -0.88 -0.25 Guangdong 199 Maonan Qu 0.81 1.32 -0.92 -0.34 0.42 -0.24 -1.41 Guangdong 200 Maogang Qu -0.24 1.60 -1.21 2.17 1.06 -1.35 0.39 Guangdong 201 Dianbai Xian 0.12 1.55 -1.39 1.83 1.03 -0.73 0.10 Guangdong 202 Huicheng Qu 0.06 -0.64 -1.26 -0.63 -0.73 -0.32 -2.17 Guangdong 203 Huiyang Qu -0.66 -0.77 -1.29 -0.86 -1.04 -0.22 -1.44 Guangdong 204 Huidong Xian -0.60 0.59 -1.32 -0.54 -0.56 -0.75 -0.85 Guangdong 205 Chengqu -0.16 1.16 -1.15 -1.69 -0.98 -0.13 -0.67 Guangdong 206 Haifeng Xian -0.42 1.17 -1.10 -1.23 -0.82 0.32 0.40 Guangdong 207 Lufeng Shi -0.05 2.51 -1.75 -0.30 -0.56 -0.81 0.27 Guangdong 208 Jiangcheng Qu 0.45 0.96 -0.02 -0.66 -0.06 -0.10 -0.22 Guangdong 209 Yangxi Xian -0.71 1.03 -0.60 0.77 -0.19 -0.25 1.85 Guangdong 210 Yangdong Xian -0.74 0.59 -0.49 0.68 -0.47 0.10 2.10 Guangdong 211 Dongguan Shi -1.21 -2.74 -1.02 -0.24 -1.60 -0.49 -1.68 Guangdong 212 Zhongshan Shi -0.71 -1.42 -0.75 -0.73 -1.19 -0.44 -1.43 Guangdong 213 Xiangqiao Qu 0.65 0.73 -0.48 -1.95 -0.19 0.36 -1.80 Guangdong 214 Raoping Xian -0.75 1.50 -0.78 -0.66 -0.84 -0.67 0.98 Guangdong 215 Rongcheng Qu -0.05 1.18 -1.15 -2.01 -1.49 -0.62 -1.06 Guangdong 216 Jiedong Xian -1.11 1.46 -0.97 -1.21 -1.89 -1.07 1.23 Guangdong 217 Huilai Xian -0.53 2.44 -1.95 0.06 -0.69 -1.61 0.15 Guangxi 218 Haicheng Qu 1.78 0.79 -0.70 -1.10 0.97 0.94 -2.81 Guangxi 219 Yinhai Qu -0.07 0.44 -0.85 1.01 -0.10 0.14 0.91 Guangxi 220 Tieshangang Qu -0.39 1.32 -0.38 1.74 0.12 0.05 3.00 Guangxi 221 Hepu Xian -0.20 1.32 -0.79 1.85 0.18 -0.08 2.32 Guangxi 222 Gangkou Qu 1.11 -0.28 -1.34 -0.70 0.64 6.74 2.67

132

Guangxi 223 Fangcheng Qu 0.62 0.63 -0.96 2.09 0.37 2.76 3.53 Guangxi 224 Dongxing Shi 0.74 0.46 -1.27 0.31 1.02 2.92 0.67 Guangxi 225 Qinnan Qu 0.78 0.78 -0.99 1.68 0.40 0.79 1.09 Hainan 226 Meilan Qu 0.20 0.54 -0.60 0.87 -1.26 -0.59 1.28 Hainan 227 Longhua Qu 1.76 0.23 -0.54 0.18 -0.37 -0.18 -1.70 Hainan 228 Xiuying Qu 1.35 0.74 -0.60 0.78 -0.59 -0.01 0.16 Hainan 229 Qionghai Shi 0.02 0.93 -0.28 1.29 -0.25 0.09 2.25 Hainan 230 Danzhou Shi 0.33 1.04 -1.12 1.26 0.03 1.49 2.30 Hainan 231 Wenchang Shi -0.15 1.06 0.28 1.28 -0.14 0.09 2.99 Hainan 232 Wanning Shi 0.02 0.59 -0.71 1.94 -0.41 1.15 3.36 Hainan 233 Dongfang Shi 0.03 0.67 -1.12 2.38 0.00 0.69 2.59 Hainan 234 Chengmai Xian -0.03 0.78 -0.80 2.18 -0.53 0.53 3.24 Hainan 235 Lingao Xian -0.06 1.35 -1.17 1.98 -0.02 0.19 2.43 Changjiang Lizu Hainan 236 0.13 0.37 -0.61 1.63 -0.28 2.51 4.06 Zizhixian Ledong Lizu Hainan 237 -0.14 0.29 -0.87 3.40 -0.15 1.91 5.03 Zizhixian Lingshui Lizu Hainan 238 0.46 0.47 -1.29 3.54 -0.05 3.00 5.32 Zizhixian

133

APPENDIX B

TOTAL VARIANCE EXPLAINED SOVI®2010

Total Variance Explained Extraction Sums of Squared Rotation Sums of Squared Initial Eigenvalues Compon Loadings Loadings ent % of Cumulati % of Cumulat % of Cumulat Total Total Total Variance ve % Variance ive % Variance ive % 1 9.907 34.162 34.162 9.907 34.162 34.162 5.981 20.623 20.623 2 3.951 13.626 47.788 3.951 13.626 47.788 5.038 17.373 37.997 3 3.445 11.88 59.667 3.445 11.88 59.667 3.666 12.642 50.638 4 1.971 6.797 66.465 1.971 6.797 66.465 3.256 11.228 61.867 5 1.418 4.89 71.355 1.418 4.89 71.355 2.058 7.098 68.965 6 1.1 3.794 75.149 1.1 3.794 75.149 1.793 6.184 75.149 7 0.956 3.298 78.446

8 0.954 3.289 81.735

9 0.749 2.581 84.317

10 0.688 2.371 86.688

11 0.578 1.994 88.682

12 0.479 1.651 90.333

13 0.373 1.287 91.619

14 0.361 1.246 92.865

15 0.343 1.184 94.05

16 0.243 0.839 94.888

17 0.231 0.796 95.684

18 0.224 0.771 96.455

19 0.192 0.662 97.117

20 0.167 0.577 97.694

21 0.146 0.504 98.198

22 0.142 0.491 98.689

23 0.097 0.334 99.024

24 0.095 0.326 99.35

25 0.085 0.292 99.641

26 0.055 0.188 99.83

27 0.04 0.137 99.967

28 0.01 0.033 100

0.00000 0.00000 29 100 1735 5983 Extraction Method: Principal Component Analysis.

134

APPENDIX C

ROTATED COMPONENT MATRIX SOVI®2010

Rotated Component Matrixa Component 1 2 3 4 5 6 UBINCM 0.17 -0.842 0.038 -0.074 0.074 -0.283 QFEMALE 0.074 0.324 0.522 -0.299 0.348 -0.263 QMINOR 0.001 -0.022 -0.057 0.352 -0.129 0.635 MEDAGE -0.085 0.008 0.926 -0.1 0.201 0.099 QUNEMP 0.774 0.144 -0.168 -0.122 -0.104 0.253 POPDEN 0.721 -0.13 0.112 -0.013 -0.182 -0.316 QUBRESD 0.679 -0.457 -0.127 -0.388 -0.204 -0.034 QNONAGRI 0.837 -0.066 0.193 -0.153 -0.225 0.095 QRENT 0.296 -0.806 -0.306 -0.092 -0.166 -0.152 QAGREMP -0.474 0.606 0.166 0.49 -0.092 0.17 QMANFEMP -0.152 -0.65 -0.261 -0.451 0.162 -0.22 QSEVEMP 0.883 -0.196 0.038 -0.241 -0.04 -0.014 PPUNIT -0.173 0.744 -0.496 0.107 -0.039 -0.028 QCOLLEGE 0.763 -0.329 0.029 -0.063 -0.095 -0.119 QHISCH 0.847 -0.251 -0.028 -0.197 -0.177 -0.096 QILLIT -0.318 0.077 0.013 0.231 0.583 -0.024 POPCH 0.333 -0.329 -0.177 0.069 0.019 0.042 PHROOM -0.332 0.431 0.054 0.103 0.513 -0.08 PPHAREA -0.188 -0.148 0.249 -0.138 0.813 -0.061 QNOPIPWT -0.263 0.422 -0.134 0.636 -0.038 0.149 QNOKITCH -0.063 0.032 -0.477 0.734 0.154 0.029 QNOTOILET -0.208 0.304 -0.117 0.737 0.121 0.204 QNOBATH -0.361 0.111 0.266 0.614 -0.095 0.346 HPBED 0.527 -0.044 0.585 0.197 -0.188 -0.27 MEDPROF 0.503 -0.218 0.424 0.26 -0.271 -0.332 QPOPUD5 -0.102 0.484 -0.653 0.34 0.174 0.035 QPOPAB65 -0.13 0.37 0.785 0.026 0.334 0.043 QDEPEND -0.226 0.822 -0.088 0.295 0.266 -0.023 QSUBSIST -0.033 0.262 0.03 0.113 -0.007 0.741 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 17 iterations.

135

APPENDIX D

PEARSON CORRELATION SOVI®2010

QFE QUB QNO QAG QMA QSE QCO PPH QNO QNO QNO QNO MED QPO QPO QDE QSU UBI QMI MED QUN POP QRE PPU QHI QIL POP PHR HPB QIM MAL RES NAG REM NFE VEM LLE ARE PIP KIT TOI BAT PRO PUD PAB PEN BSIS NCM NOR AGE EMP DEN NT NIT SCH LIT CH OOM ED MIG E D RI P MP P GE A WT CH LET H F 5 65 D T

UBI 1 -0 - 0 -0 .362** .476** .154* .763** - .583** .347** - .427** .373** -0 .245** - .178** - -.128* - - .174** .349** - - - - .726** NCM .171** .657** .639** .355** .480** .426** .334** .469** .295** .703** .368**

QFE -0 1 - .525** -0 0 -0 0 - 0 -0 0 -0 0 -0 0 -.162* .236** .376** -.144* - - -0 .190** 0 - .586** .214** - - MAL .215** .359** .359** .208** .255** .202** .356** E QMI - - 1 -0 0 -0 -0 -0 -0 .240** - -0 .134* -0 -.130* -0 -0 -0 -.160* .326** .251** .259** .363** -0 -0 .169** -0 0 .401** -0 NOR .171** .215** .232**

MED 0 .525** -0 1 - -0 - 0 - .128* - -0 - -0 -.144* .155* -.136* 0 .358** -.165* - -0 .212** .376** .178** - .876** -0 0 - ** ** ** ** ** ** ** ** 136 AGE .186 .172 .360 .167 .455 .423 .599 .312

QUN -0 -0 0 - 1 .423** .513** .675** .173** - -0 .654** 0 .414** .585** - .136* - - -.151* -0 -0 - .268** .210** 0 - -0 0 .265** EMP .186** .343** .289** .324** .306** .281** .186**

POP .362** 0 -0 -0 .423** 1 .554** .572** .389** - 0 .686** - .585** .615** - .244** - - - -.155* - - .511** .558** - -0 - - .375** DEN .460** .221** .328** .381** .224** .284** .336** .345** .249** .306** .216**

QUB .476** -0 -0 - .513** .554** 1 .703** .674** - .409** .770** - .689** .823** - .343** ------.276** .348** - - - - .783** RES .172** .810** .422** .439** .567** .221** .597** .280** .544** .536** .394** .424** .679** .233** D QNO .154* 0 -0 0 .675** .572** .703** 1 .273** - -0 .789** - .669** .778** - .252** ------.481** .471** - -0 - -0 .386** NAG .449** .295** .408** .466** .274** .336** .276** .285** .214** .356** .365** RI QRE .763** - -0 - .173** .389** .674** .273** 1 - .588** .443** - .484** .512** - .347** - -.140* - 0 - - 0 .262** - - - - .928** NT .359** .360** .724** .521** .262** .546** .455** .407** .379** .265** .625** .740** .319**

QAG - 0 .240** .128* - - - - - 1 - - .456** - - .233** - .473** -0 .688** .287** .626** .622** -.151* - .388** .380** .678** .353** - REM .657** .343** .460** .810** .449** .724** .749** .681** .537** .594** .282** .236** .782** P QMA .583** -0 - - -0 0 .409** -0 .588** - 1 0 - 0 0 -0 .130* - .241** - -.144* - - - -0 - - - - .577** NFE .232** .167** .749** .306** .222** .504** .465** .444** .178** .278** .347** .521** .355** MP QSE .347** 0 -0 -0 .654** .686** .770** .789** .443** - 0 1 - .729** .818** - .282** - -.142* - - - - .424** .438** - - - -.141* .543** VEM .681** .350** .328** .469** .480** .274** .431** .449** .278** .189** .447** P PPU - -0 .134* - 0 - - - - .456** - - 1 - - .133* - .325** - .485** .360** .362** 0 - - .665** -0 .740** .205** - NIT .639** .455** .221** .422** .295** .521** .306** .350** .416** .380** .172** .244** .355** .353** .581**

QCO .427** 0 -0 -0 .414** .585** .689** .669** .484** - 0 .729** - 1 .866** - .355** - -.128* - -.166* - - .425** .424** - - - - .579** LLE .537** .416** .386** .379** .350** .379** .318** .312** .271** .524** .226** GE QHI .373** -0 -.130* -.144* .585** .615** .823** .778** .512** - 0 .818** - .866** 1 - .327** ------.432** .454** - - - - .616** SCH .594** .380** .481** .429** .215** .429** .227** .427** .485** .306** .305** .520** .206**

QIL -0 0 -0 .155* - - - - - .233** -0 - .133* - - 1 - .274** .295** 0 .214** .252** .241** - - .257** .295** .427** 0 - LIT .289** .328** .439** .408** .262** .328** .386** .481** .180** .216** .224** .362**

POP .245** -.162* -0 -.136* .136* .244** .343** .252** .347** - .130* .282** - .355** .327** - 1 - -0 -.143* 0 -.128* -.134* 0 0 -0 - - -0 .358** CH .282** .172** .180** .238** .250** .281**

PHR - .236** -0 0 - - - - - .473** - - .325** - - .274** - 1 .517** .367** 0 .244** .161* -.138* - .306** .305** .493** .152* - OO .355** .324** .381** .567** .466** .546** .222** .469** .379** .429** .238** .252** .554** M PPH .178** .376** -.160* .358** - - - - -.140* -0 .241** -.142* - -.128* - .295** -0 .517** 1 -0 -0 -0 -0 -0 -.137* -0 .364** 0 -0 -.145* ARE .306** .224** .221** .274** .244** .215** A

QNO - -.144* .326** -.165* -.151* - - - - .688** - - .485** - - 0 -.143* .367** -0 1 .545** .637** .545** - - .454** 0 .541** .266** - PIP .480** .284** .597** .336** .455** .504** .480** .350** .429** .182** .227** .506** WT QNO -.128* - .251** - -0 -.155* - - 0 .287** -.144* - .360** -.166* - .214** 0 0 -0 .545** 1 .681** .307** - -.165* .538** - .349** 0 -0 KIT .359** .423** .280** .276** .274** .227** .213** .216** CH QNO - - .259** -0 -0 - - - - .626** - - .362** - - .252** -.128* .244** -0 .637** .681** 1 .586** - - .484** .152* .536** .248** - TOI .426** .208** .336** .544** .285** .407** .465** .431** .379** .427** .178** .192** .469** LET QNO - -0 .363** .212** - - - - - .622** - - 0 - - .241** -.134* .161* -0 .545** .307** .586** 1 -0 -0 .134* .239** .287** .271** - BAT .334** .281** .345** .536** .214** .379** .444** .449** .318** .485** .393** H HPB .174** .190** -0 .376** .268** .511** .276** .481** 0 -.151* - .424** - .425** .432** - 0 -.138* -0 - - - -0 1 .816** - .290** - -0 0 ED .178** .355** .216** .182** .213** .178** .386** .185**

MED .349** 0 -0 .178** .210** .558** .348** .471** .262** - -0 .438** - .424** .454** - 0 - -.137* - -.165* - -0 .816** 1 - 0 - -.137* .265** PRO .236** .353** .224** .252** .227** .192** .343** .298** F QPO - - .169** - 0 - - - - .388** - - .665** - - .257** -0 .306** -0 .454** .538** .484** .134* - - 1 - .645** .223** - PUD .469** .255** .599** .249** .394** .356** .265** .278** .278** .312** .306** .386** .343** .264** .355** 5 QPO - .586** -0 .876** - -0 - -0 - .380** - - -0 - - .295** - .305** .364** 0 - .152* .239** .290** 0 - 1 .422** .150* - PAB .295** .186** .424** .625** .347** .189** .271** .305** .250** .216** .264** .621** 65 QDE - .214** 0 -0 -0 - - - - .678** - - .740** - - .427** - .493** 0 .541** .349** .536** .287** - - .645** .422** 1 .267** - PEN .703** .306** .679** .365** .740** .521** .447** .524** .520** .281** .185** .298** .830** D QSU - - .401** 0 0 - - -0 - .353** - -.141* .205** - - 0 -0 .152* -0 .266** 0 .248** .271** -0 -.137* .223** .150* .267** 1 - BSIS .368** .202** .216** .233** .319** .355** .226** .206** .279** T QIM .726** - -0 - .265** .375** .783** .386** .928** - .577** .543** - .579** .616** - .358** - -.145* - -0 - - 0 .265** - - - - 1 MIG .356** .312** .782** .581** .362** .554** .506** .469** .393** .355** .621** .830** .279** **. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

137

APPENDIX E

SOCIAL VULNERABILITY INDEX OF CHINESE COASTAL COUNTIES (2000)

Province ID County FAC1 FAC2 FAC3 FAC4 FAC5 FAC6 SoVI- 2000 Tianjin 1 Binhai Xinqu 1.11 -0.43 0.15 0.13 0.36 0.51 -0.40 Hebei 2 Fengnan Qu -0.59 0.10 0.70 0.18 0.62 -0.77 1.42 Hebei 3 Luannan Xian -0.96 0.15 0.63 0.15 0.34 -0.74 1.47 Hebei 4 Leting Xian -0.99 -0.02 0.38 0.40 0.32 -0.97 1.10 Hebei 5 Tanghai Xian -0.52 -0.18 0.37 0.51 0.49 -0.04 1.67 Hebei 6 Haigang Qu 1.55 -0.50 0.79 -0.27 -0.13 -0.01 -1.67 Hebei 7 Shanhaiguan Qu 0.47 -0.41 0.60 0.41 0.61 0.24 0.97 Hebei 8 Beidaihe Qu 0.73 -0.64 0.37 0.45 0.06 -0.06 -0.54 Hebei 9 Changli Xian -0.71 0.31 0.37 0.36 0.06 -1.46 0.35 Hebei 10 Funing Xian -1.00 0.18 0.67 0.06 0.37 -0.76 1.52 Hebei 11 Haixing Xian -1.04 0.50 0.35 0.23 0.33 -0.52 1.93 Hebei 12 Huanghua Shi -0.56 0.46 0.47 0.37 0.49 0.19 2.53 Liaoning 13 Zhongshan Qu 2.95 0.31 1.42 1.51 1.15 -0.92 0.52 Liaoning 14 Xigang Qu 3.31 0.64 1.61 1.74 1.07 -1.26 0.49 Liaoning 15 Shahekou Qu 3.38 0.36 1.41 1.18 0.68 -1.09 -0.85 Liaoning 16 Ganjingzi Qu 1.22 -0.78 1.01 -0.25 1.83 0.11 0.71 Liaoning 17 Lvshunkou Qu 0.02 -0.89 0.60 0.06 1.95 -0.53 1.18 Liaoning 18 Jinzhou Qu -0.24 -0.92 0.82 -0.46 2.01 -0.86 0.84 Liaoning 19 Changhai Xian -0.67 0.08 1.18 -0.11 1.54 -0.65 2.71 Liaoning 20 Wafangdian Shi -0.57 -0.08 1.45 0.48 1.87 -0.82 3.47 Liaoning 21 Pulandian Shi -0.79 0.16 1.41 0.48 1.59 -1.46 2.98 Liaoning 22 Zhuanghe Shi -1.10 0.04 1.31 0.52 1.47 -0.99 3.46 Liaoning 23 Donggang Shi -0.92 -0.04 1.13 -0.10 2.37 -1.07 3.20 Liaoning 24 Linghai Shi -1.17 -0.14 1.32 -0.22 2.23 -0.76 3.60 Liaoning 25 Xishi Qu 1.65 -0.46 0.68 1.23 2.37 1.58 3.75 Liaoning 26 Bayuquan Qu 0.28 -0.57 1.13 -1.25 2.43 -0.33 1.14 Liaoning 27 Laobian Qu -0.30 -0.31 0.89 -0.08 2.04 -0.62 2.21 Liaoning 28 Gaizhou Shi -1.06 0.11 1.32 -0.33 2.31 -0.86 3.61 Liaoning 29 Dawa Xian -0.82 -0.08 0.80 -0.09 2.29 -0.61 3.13 Liaoning 30 Panshan Xian -1.12 0.05 0.91 0.13 2.27 -0.93 3.54 Liaoning 31 Lianshan Qu -0.12 0.14 1.36 0.36 2.41 -0.73 3.67 Liaoning 32 Longgang Qu 0.89 -0.65 1.06 -0.48 2.08 -0.55 0.56 Liaoning 33 Suizhong Xian -1.48 -0.08 1.84 -0.68 3.71 0.32 6.59 Liaoning 34 Xingcheng Shi -1.11 -0.14 2.19 -0.67 3.66 0.36 6.52 Shanghai 35 Baoshan Qu 1.07 -0.74 -1.16 0.83 1.53 1.16 0.55 Shanghai 36 Pudong Xinqu 1.02 -0.74 -1.21 0.89 1.45 0.50 -0.13

138

Shanghai 37 Jinshan Qu -0.31 -0.80 -1.24 1.46 0.99 -0.89 -0.18 Shanghai 38 Fengxian Qu -0.54 -1.07 -1.24 1.30 0.90 -0.24 0.19 Shanghai 39 Chongming Xian -0.76 -0.52 -1.16 2.96 0.90 -0.88 2.06 Jiangsu 40 Tongzhou Qu -0.98 -0.87 -0.40 1.88 -0.47 -1.14 -0.03 Jiangsu 41 Hai'an Xian -0.72 -0.47 -0.22 1.77 -0.33 -1.36 0.11 Jiangsu 42 Rudong Xian -1.04 -0.71 0.16 2.17 -0.36 -0.37 1.93 Jiangsu 43 Qidong Shi -0.98 -0.92 -0.42 1.95 0.31 -1.02 0.88 Jiangsu 44 Haimen Shi -0.93 -0.74 -0.39 2.27 -0.17 -1.19 0.71 Jiangsu 45 Lianyun Qu 1.09 -0.73 0.20 0.26 -0.39 1.56 -0.20 Jiangsu 46 Xinpu Qu 2.30 -0.13 0.64 0.28 -0.49 0.88 -1.11 Jiangsu 47 Ganyu Xian -0.80 0.66 0.74 -0.11 -0.74 -0.35 0.99 Jiangsu 48 Guanyun Xian -0.75 0.77 0.69 -0.41 -0.27 -0.02 1.51 Jiangsu 49 Guannan Xian -0.75 0.69 0.78 -0.61 -0.32 -0.05 1.23 Jiangsu 50 Xiangshui Xian -0.37 0.52 0.89 -0.29 -0.81 0.31 0.99 Jiangsu 51 Binhai Xian -0.79 0.22 0.61 -0.44 0.11 -0.26 1.04 Jiangsu 52 Sheyang Xian -0.79 -0.43 0.20 0.13 0.42 -0.34 0.78 Jiangsu 53 Dongtai Shi -0.74 -0.72 0.03 1.57 -0.01 0.09 1.71 Jiangsu 54 Dafeng Shi -0.73 -0.91 0.07 1.08 0.23 0.11 1.31 Zhejiang 55 Binjiang Qu -0.07 -1.47 -0.54 0.66 -1.22 0.89 -1.61 Zhejiang 56 Xiaoshan Qu -0.74 -1.29 -0.77 0.73 -0.68 0.27 -1.01 Zhejiang 57 Haishu Qu 2.40 0.10 0.40 0.67 -0.31 -0.91 -2.45 Zhejiang 58 Jiangdong Qu 1.74 -0.47 -0.10 0.20 -0.23 -0.23 -2.57 Zhejiang 59 Jiangbei Qu 0.97 -0.85 0.08 0.20 -0.08 0.07 -1.56 Zhejiang 60 Beilun Qu -0.31 -1.52 -0.83 0.54 0.28 0.48 -0.73 Zhejiang 61 Zhenhai Qu 0.15 -1.22 -0.50 0.20 0.28 0.50 -0.89 Zhejiang 62 Yinzhou Qu -0.47 -1.26 -0.66 0.96 -0.13 1.18 0.56 Zhejiang 63 Xiangshan Xian -0.47 -0.76 -0.68 1.20 0.24 0.76 1.24 Zhejiang 64 Ninghai Xian -0.83 -1.16 -0.39 0.38 0.06 0.43 0.15 Zhejiang 65 Yuyao Shi -0.74 -1.16 -0.89 0.91 0.04 0.15 -0.22 Zhejiang 66 Cixi Shi -0.75 -1.58 -0.87 0.22 -0.10 0.46 -1.11 Zhejiang 67 Fenghua Shi -0.52 -0.95 -0.60 0.80 0.11 0.70 0.59 Zhejiang 68 Longwan Qu -0.35 -1.92 -0.52 -2.21 -0.30 1.22 -3.37 Zhejiang 69 Ouhai Qu -0.53 -1.51 -0.78 -0.88 -0.28 1.22 -1.70 Zhejiang 70 Dongtou Xian -0.34 -0.42 -0.46 1.18 -0.41 1.84 2.07 Zhejiang 71 Pingyang Xian -0.39 -0.69 -0.50 0.64 -0.18 1.60 1.26 Zhejiang 72 Cangnan Xian -0.42 -0.52 -0.73 -0.02 0.14 1.21 0.49 Zhejiang 73 Rui'an Xian -0.50 -0.99 -0.89 0.23 -0.29 1.07 -0.36 Zhejiang 74 Yueqing Shi -0.46 -0.91 -0.62 0.13 -0.54 1.46 -0.02 Zhejiang 75 Haiyan Xian -0.80 -1.19 -0.86 0.98 -0.78 0.18 -0.86 Zhejiang 76 Haining Shi -0.83 -1.33 -1.02 1.39 -0.35 0.33 -0.14 Zhejiang 77 Pinghu Shi -0.94 -1.26 -1.02 1.28 -0.64 0.32 -0.38 Zhejiang 78 Shaoxing Xian -0.83 -1.17 -0.78 0.80 -0.55 0.71 -0.16 Zhejiang 79 Shangyu Shi -0.83 -0.70 -0.60 1.26 -0.52 0.37 0.64 Zhejiang 80 Dinghai Qu 0.02 -1.12 -0.45 1.10 -0.33 0.39 -0.43 Zhejiang 81 Putuo Qu -0.03 -1.00 -0.34 0.84 -0.04 0.85 0.33 Zhejiang 82 Daishan Xian -0.49 -1.00 -0.55 1.59 -0.20 0.99 1.31 Zhejiang 83 Shengsi Xian 0.02 -0.54 0.03 1.30 -0.49 1.79 2.06 Zhejiang 84 Jiaojiang Qu -0.14 -1.11 -0.80 0.97 -0.20 0.98 -0.04

139

Zhejiang 85 Luqiao Qu -0.30 -1.18 -0.58 0.18 -0.67 1.26 -0.70 Zhejiang 86 Yuhuan Xian -0.23 -1.35 -0.28 0.06 -0.54 1.93 0.05 Zhejiang 87 Sanmen Xian -0.81 -0.66 -0.23 0.92 -0.49 0.90 1.25 Zhejiang 88 Wenling Shi -0.71 -1.40 -0.73 0.49 0.06 0.63 -0.25 Zhejiang 89 Linhai Shi -0.82 -0.79 -0.42 0.94 -0.87 -0.07 -0.40 Fujian 90 Mawei Qu 0.73 -0.64 -0.68 -0.25 -0.13 0.36 -2.07 Fujian 91 Lianjiang Xian -0.35 -0.03 -0.42 0.48 0.31 1.70 2.39 Fujian 92 Luoyuan Xian -0.27 0.04 0.30 0.43 0.66 1.91 3.61 Fujian 93 Pingtan Xian -0.41 0.94 -0.04 -0.09 -0.68 0.62 1.17 Fujian 94 Fuqing Shi -0.58 0.14 0.09 -0.01 -1.41 0.35 -0.26 Fujian 95 Changle Shi -0.04 -0.39 -0.22 -0.06 0.20 1.53 1.10 Fujian 96 Siming Qu 2.58 -0.60 0.59 -0.30 -1.02 0.48 -3.43 Fujian 97 Haicang Qu 0.18 -1.32 0.21 -1.29 -0.72 0.28 -3.02 Fujian 98 Huli Qu 1.09 -1.34 0.43 -2.93 -0.43 -0.17 -5.51 Fujian 99 Jimei Qu 0.52 -0.98 0.77 -1.23 -1.96 -0.23 -4.13 Fujian 100 Tong'an Qu -0.60 -0.22 0.35 -0.12 -1.09 0.20 -0.29 Fujian 102 Chengxiang Qu 0.62 -0.69 -0.20 -0.26 -0.99 -0.30 -3.05 Fujian 103 Hanjiang Qu -0.07 -0.63 -0.58 -0.10 -0.59 0.17 -1.66 Fujian 106 Xianyou Xian -0.62 0.66 0.17 -0.09 -1.24 0.67 0.80 Fujian 107 Fengze Qu 0.45 -1.66 0.40 -1.92 -0.90 1.02 -3.50 Fujian 108 Luojiang Qu -0.56 -0.37 0.56 -1.02 -1.51 0.51 -1.27 Fujian 109 Quangang Qu -0.55 0.24 0.49 -0.67 -1.11 0.48 -0.01 Fujian 110 Hui'an Xian -0.91 -0.45 0.03 -0.47 -1.51 0.04 -1.44 Fujian 111 Shishi Shi -0.18 -1.39 -0.04 -1.23 -0.48 1.41 -1.55 Fujian 112 Jinjiang Shi -0.72 -1.24 0.03 -1.37 -0.98 0.98 -1.86 Fujian 113 Nan'an Shi -0.75 -0.15 0.06 -0.45 -1.22 0.52 -0.48 Fujian 114 Yunxiao Xian -0.48 0.68 -0.03 -0.21 0.12 0.98 2.03 Fujian 115 Zhangpu Xian -0.83 0.61 0.36 -0.54 -0.28 0.62 1.59 Fujian 116 Zhao'an Xian -0.67 1.01 0.21 -0.69 -0.47 0.31 1.03 Fujian 117 Dongshan Xian -0.38 0.31 -0.28 0.24 -0.14 0.29 0.80 Fujian 118 Longhai Shi -0.65 0.06 -0.04 -0.31 -0.21 0.63 0.78 Fujian 119 Jiaocheng Qu 0.02 0.14 0.28 0.33 0.11 1.43 2.27 Fujian 120 Xiapu Xian -0.29 0.13 0.39 0.45 0.44 2.36 4.08 Fujian 121 Fu'an Shi -0.33 0.14 0.22 0.35 0.32 1.60 2.96 Fujian 122 Fuding Shi -0.22 0.01 0.15 0.66 0.27 2.45 3.76 Shandong 123 Shinan Qu 3.28 0.56 1.23 1.19 -1.08 -1.41 -2.78 Shandong 124 Shibei Qu 2.68 0.06 0.43 1.34 -0.90 0.09 -1.66 Shandong 125 Sifang Qu 2.43 -0.08 0.74 0.86 -0.74 -0.19 -1.84 Shandong 126 Huangdao Qu 0.73 -0.60 1.25 -1.19 -1.77 -0.37 -3.40 Shandong 127 Laoshan Qu 0.49 -1.02 0.62 -0.66 -0.86 -0.34 -2.75 Shandong 128 Licang Qu 1.13 -0.63 0.41 0.15 -0.05 -0.39 -1.64 Shandong 129 Chengyang Qu -0.45 -0.83 0.35 -0.21 -1.11 -1.45 -2.80 Shandong 130 Jiaozhou Shi -0.67 -0.04 0.80 0.29 -1.27 -1.07 -0.63 Shandong 131 Jimo Shi -0.66 0.02 1.01 -0.09 -1.16 -1.14 -0.70 Shandong 132 Jiaonan Shi -0.30 0.45 1.73 -0.16 -2.99 -0.76 -1.43 Shandong 133 Dongying Qu 1.01 -0.26 0.84 -0.46 -0.56 -0.37 -1.81 Shandong 134 Hekou Qu 0.43 0.04 0.93 -0.56 -0.88 -0.57 -1.47 Shandong 135 Kenli Xian -0.63 -0.11 0.76 0.22 -1.11 0.03 0.42

140

Shandong 136 Lijin Xian -0.77 0.45 1.22 0.19 -1.84 -0.59 0.20 Shandong 137 Guangrao Xian -0.69 0.44 1.31 0.31 -1.35 -1.17 0.23 Shandong 138 Zhifu Qu 1.36 -0.98 0.13 -0.54 -0.54 0.31 -2.98 Shandong 139 Fushan Qu -0.32 -0.72 0.38 -0.21 -0.43 -0.58 -1.23 Shandong 140 Muping Qu -0.51 -0.40 0.65 0.57 -0.49 -1.27 -0.43 Shandong 141 Laishan Qu 0.43 -0.64 0.88 -0.42 -1.24 -0.92 -2.77 Shandong 142 Changdao Xian 0.14 -0.11 0.86 -0.02 -0.15 -0.25 0.19 Shandong 143 Longkou Shi -0.51 -0.31 0.53 0.06 -0.13 -1.35 -0.69 Shandong 144 Laiyang Shi -0.66 0.00 0.93 0.30 -0.35 -1.35 0.19 Shandong 145 Laizhou Shi -0.54 0.02 0.95 0.85 -0.79 -1.25 0.32 Shandong 146 Penglai Shi -0.59 -0.09 0.79 0.24 -0.09 -1.53 -0.08 Shandong 147 Zhaoyuan Shi -0.62 -0.15 0.89 0.64 -0.62 -1.14 0.24 Shandong 148 Haiyang Shi -0.87 -0.18 0.82 0.89 -0.19 -1.20 1.01 Shandong 149 Hanting Qu -0.44 0.31 1.13 0.45 -1.48 -1.48 -0.62 Shandong 150 Shouguang Shi -0.60 0.43 1.14 0.13 -1.25 -1.50 -0.45 Shandong 151 Changyi Shi -0.89 0.32 0.78 0.57 -1.24 -1.77 -0.46 Shandong 152 Huancui Qu 0.46 -0.82 0.52 -0.44 -0.34 -0.73 -2.27 Shandong 153 Wendeng Shi -0.16 -0.09 1.19 0.91 -1.46 -1.50 -0.80 Shandong 154 Rongcheng Shi -0.15 -0.08 0.86 0.95 -0.47 -1.93 -0.52 Shandong 155 Rushan Shi -0.61 -0.45 0.66 1.20 -0.70 -1.16 0.15 Shandong 156 Donggang Qu -0.70 -0.29 0.46 0.32 -0.73 -0.50 -0.04 Shandong 158 Wudi Xian -0.53 0.57 1.76 -0.25 -2.46 -0.65 -0.50 Shandong 159 Zhanhua Xian -0.56 0.46 1.62 -0.02 -2.44 -1.23 -1.05 Guangdong 160 Liwan Qu 2.55 0.69 -1.36 1.93 0.82 0.32 -0.14 Guangdong 161 Yuexiu Qu 3.83 1.95 -0.05 2.50 0.71 -2.18 -0.89 Guangdong 162 Haizhu Qu 2.31 0.29 -1.02 0.20 0.24 -0.31 -2.91 Guangdong 163 Tianhe Qu 2.73 0.12 -0.32 -1.18 -0.67 -0.26 -5.02 Guangdong 164 Baiyun Qu 0.51 -0.32 -1.13 -1.24 0.45 -0.48 -3.23 Guangdong 165 Huangpu Qu 0.98 -0.78 -0.99 -1.63 0.47 0.36 -3.55 Guangdong 166 Panyu Qu -0.28 -0.51 -1.59 -1.35 0.43 -1.41 -4.15 Guangdong 169 Luohu Qu 1.79 -0.58 -0.94 -1.76 -0.04 -0.70 -5.81 Guangdong 170 Futian Qu 2.15 -0.60 -0.65 -2.00 -0.61 -0.78 -6.79 Guangdong 171 Nanshan Qu 1.20 -1.16 -0.66 -2.79 -0.26 -0.74 -6.81 Guangdong 172 Bao'an Qu -0.18 -1.62 -1.06 -3.66 0.33 -2.00 -7.83 Guangdong 173 Longgang Qu -0.48 -1.62 -1.41 -3.22 0.55 -2.21 -7.43 Guangdong 174 Yantian Qu 0.41 -1.44 -1.07 -3.12 0.20 -1.34 -7.19 Guangdong 175 Xiangzhou Qu 0.94 -0.43 -1.06 -1.63 0.23 -1.25 -5.09 Guangdong 176 Doumen Qu 0.26 0.74 -1.37 0.10 0.72 -0.38 -0.45 Guangdong 178 Longhu Qu 0.83 0.18 -2.02 -0.60 0.42 -0.25 -3.10 Guangdong 181 Chaoyang Qu -0.49 2.43 -2.09 -0.96 0.39 -1.54 -1.27 Guangdong 183 Chenghai Qu -0.53 0.90 -2.33 -0.08 0.74 -1.27 -1.51 Guangdong 184 Nan'ao Xian 0.29 1.00 -1.60 0.82 0.47 0.74 1.15 Guangdong 185 Pengjiang Qu 1.03 -0.51 -1.79 -0.08 0.40 0.35 -2.66 Guangdong 186 Jianghai Qu 0.00 -0.21 -2.03 -0.36 0.66 -0.27 -2.20 Guangdong 187 Xinhui Qu -0.35 0.36 -1.68 0.36 0.58 -0.71 -0.74 Guangdong 188 Taishan Shi -0.54 0.89 -1.55 0.75 0.48 -0.42 0.70 Guangdong 189 Enping Shi -0.43 1.03 -1.52 0.46 0.46 0.22 1.08 Guangdong 190 Chikan Qu 1.90 0.58 -1.40 0.43 -0.06 0.44 -1.91

141

Guangdong 191 Xiashan Qu 1.78 0.60 -1.42 0.34 0.29 0.56 -1.41 Guangdong 192 Potou Qu -0.22 2.10 -0.72 -0.14 -0.82 0.20 0.84 Guangdong 193 Mazhang Qu 0.04 2.52 -0.14 -0.76 -1.35 0.08 0.30 Guangdong 194 Suixi Xian -0.31 2.28 -0.72 -0.13 -0.90 0.00 0.84 Guangdong 195 Xuwen Xian -0.48 2.15 -0.80 -0.25 0.04 -0.12 1.50 Guangdong 196 Lianjiang Shi -0.53 2.36 -0.92 0.36 -0.36 -0.22 1.74 Guangdong 197 Leizhou Shi -0.33 2.86 -0.62 -0.46 -1.04 0.15 1.23 Guangdong 198 Wuchuan Shi -0.25 2.47 -0.90 -0.10 -1.33 -0.14 0.25 Guangdong 199 Maonan Qu 0.63 1.13 -1.04 -0.12 -0.88 0.01 -1.53 Guangdong 201 Dianbai Xian -0.39 2.30 -0.61 -0.14 -1.36 0.42 1.00 Guangdong 202 Huicheng Qu 0.50 -0.38 -1.53 -1.47 0.28 -1.03 -4.63 Guangdong 203 Huiyang Qu -0.46 -0.04 -1.40 -1.26 0.08 -1.00 -3.17 Guangdong 204 Huidong Xian -0.50 1.44 -1.85 -0.31 0.37 -0.58 -0.42 Guangdong 205 Chengqu 0.19 1.53 -2.06 -0.02 0.91 0.70 0.87 Guangdong 206 Haifeng Xian -0.25 1.70 -1.79 -0.15 0.61 0.34 0.97 Guangdong 207 Lufeng Shi -0.13 3.47 -1.73 -0.19 0.36 0.49 2.53 Guangdong 208 Jiangcheng Qu 0.36 0.87 -1.32 0.50 0.24 0.14 0.07 Guangdong 209 Yangxi Xian -0.47 1.75 -0.95 0.45 -0.49 0.63 1.85 Guangdong 210 Yangdong Xian -0.67 1.30 -0.88 0.13 -0.08 0.53 1.67 Guangdong 211 Dongguan Shi -0.72 -1.46 -1.46 -2.80 0.28 -2.09 -6.81 Guangdong 212 Zhongshan Shi -0.46 -0.60 -1.70 -1.36 0.54 -1.38 -4.06 Guangdong 213 Xiangqiao Qu 0.68 0.16 -2.00 0.37 0.47 -0.13 -1.81 Guangdong 214 Raoping Xian -0.64 1.70 -1.59 -0.27 0.59 -0.77 0.29 Guangdong 215 Rongcheng Qu 0.30 1.15 -2.36 -0.54 0.90 -0.68 -1.82 Guangdong 216 Jiedong Xian -0.77 1.71 -1.82 -0.77 0.77 -1.34 -0.68 Guangdong 217 Huilai Xian -0.65 2.70 -1.53 -0.80 0.18 -0.88 0.33 Guangxi 218 Haicheng Qu 1.33 -0.63 0.27 0.15 -0.26 1.53 -0.27 Guangxi 219 Yinhai Qu -0.30 0.41 0.73 -0.29 -0.31 1.42 2.27 Guangxi 220 Tieshangang Qu -0.44 1.31 0.90 -0.42 -0.84 0.29 1.68 Guangxi 221 Hepu Xian -0.41 1.17 0.74 -0.20 -0.54 0.87 2.46 Guangxi 222 Gangkou Qu 0.67 0.01 1.08 -1.12 0.12 2.34 1.75 Guangxi 223 Fangcheng Qu -0.16 0.66 1.76 -1.12 0.70 2.14 4.31 Guangxi 224 Dongxing Shi -0.13 0.17 1.34 -1.12 0.36 2.39 3.28 Guangxi 225 Qinnan Qu 0.19 0.86 1.17 -0.38 0.03 1.50 3.00 Hainan 226 Meilan Qu 2.45 0.42 0.62 -0.17 0.18 0.45 -0.93 Hainan 227 Longhua Qu 2.42 0.21 0.92 -0.84 -0.05 0.30 -1.89 Hainan 228 Xiuying Qu 0.27 1.68 0.78 0.08 0.50 -0.14 2.63 Hainan 229 Qionghai Shi 0.05 1.05 0.70 0.22 -0.10 0.59 2.42 Hainan 230 Danzhou Shi 0.02 1.66 0.53 -0.27 0.28 1.64 3.82 Hainan 231 Wenchang Shi -0.16 1.23 0.56 0.98 -0.39 0.25 2.78 Hainan 232 Wanning Shi -0.10 1.41 0.98 -0.59 0.50 1.40 3.80 Hainan 233 Dongfang Shi -0.31 1.52 1.33 -1.21 -0.08 1.74 3.61 Hainan 234 Chengmai Xian -0.08 1.59 0.63 0.02 0.59 1.81 4.72 Hainan 235 Lingao Xian -0.17 1.78 0.89 -0.74 -0.81 1.02 2.31 Hainan 236 Changjiang Lizu -0.22 0.98 1.21 -1.39 1.53 1.46 4.01 Zizhixian Hainan 237 Ledong Lizu -0.45 1.63 1.80 -2.23 0.57 1.50 3.72 Zizhixian

142

Hainan 238 Lingshui Lizu -0.92 1.14 1.63 -1.86 1.97 1.79 5.58 Zizhixian

143

APPENDIX F

TOTAL VARIANCE EXPLAINED SOVI®2000

Total Variance Explained Extraction Sums of Squared Rotation Sums of Squared Initial Eigenvalues Loadings Loadings % of % of % of Component Total Variance Cumulative % Total Variance Cumulative % Total Variance Cumulative % 1 9.877 34.06 34.06 9.877 34.06 34.06 7.899 27.236 27.236 2 4.804 16.566 50.626 4.804 16.566 50.626 4.418 15.236 42.472 3 3.465 11.947 62.574 3.465 11.947 62.574 3.403 11.735 54.207 4 1.701 5.865 68.439 1.701 5.865 68.439 2.763 9.528 63.735 5 1.304 4.498 72.936 1.304 4.498 72.936 1.867 6.436 70.171 6 1.014 3.496 76.433 1.014 3.496 76.433 1.816 6.261 76.433 7 0.866 2.985 79.418 8 0.76 2.62 82.038 9 0.655 2.26 84.298 10 0.612 2.11 86.408 11 0.541 1.866 88.274 12 0.502 1.732 90.006 13 0.421 1.451 91.457 14 0.399 1.377 92.833 15 0.378 1.305 94.139 16 0.297 1.025 95.164 17 0.234 0.806 95.969 18 0.222 0.766 96.736 19 0.207 0.715 97.451 20 0.153 0.528 97.979 21 0.133 0.459 98.438 22 0.114 0.394 98.832 23 0.101 0.349 99.181 24 0.087 0.3 99.482 25 0.058 0.201 99.683 26 0.051 0.175 99.858 27 0.03 0.105 99.962 28 0.011 0.038 100 29 2.66E- 9.18E- 16 16 100 Extraction Method: Principal Component Analysis.

144

APPENDIX G

ROTATED COMPONENT MATRIX SOVI®2000

Rotated Component Matrixa Component 1 2 3 4 5 6 QFEMALE -0.119 -0.326 -0.156 0.284 -0.055 -0.615 QMINOR -0.115 0.108 0.41 -0.284 0.49 0.286 MEDAGE 0.029 -0.384 0.258 0.803 0.092 -0.244 QUNEMP 0.757 0.128 -0.112 0.113 0.304 0.332 POPDEN 0.731 -0.03 -0.056 0.047 0.001 -0.176 QUBRESD 0.783 -0.31 -0.236 -0.175 0.064 -0.036 QNONAGRI 0.915 -0.066 0.011 0.087 0.192 -0.029 QRENT 0.751 -0.277 -0.083 -0.338 0.126 -0.069 QAGREMP -0.675 0.565 0.356 0.128 -0.035 -0.036 QMANFEMP 0.24 -0.703 -0.445 -0.217 -0.044 -0.063 QSEVEMP 0.899 -0.177 -0.108 0.026 0.111 0.135 PPUNIT -0.275 0.77 -0.197 -0.236 0.023 0.292 QCOLLEGE 0.814 -0.163 0.087 -0.122 -0.088 -0.119 QHISCH 0.921 -0.149 0.014 -0.105 -0.011 -0.128 QILLIT -0.502 0.196 0.143 0.352 -0.277 0.396 PHROOM -0.388 -0.043 0.128 0.116 -0.573 -0.074 PPHAREA -0.292 -0.623 -0.177 0.405 -0.223 0.045 QNOPIPWT -0.535 0.426 0.395 -0.104 -0.2 0.071 QNOKITCH -0.06 0.404 0.152 -0.413 -0.473 0.201 QNOTOILET -0.195 0.471 -0.077 -0.159 -0.2 0.63 QNOBATH -0.532 0.166 0.67 0.175 0.019 0.079 QPOPUD5 -0.245 0.781 -0.192 -0.169 -0.062 0.18 QPOPAB65 -0.171 0.054 0.146 0.902 -0.03 -0.104 QDEPEND -0.397 0.828 -0.071 0.172 -0.083 0.218 UBINCM 0.051 -0.048 -0.878 0.009 -0.13 0.035 POPCH 0.165 0.383 -0.784 -0.273 0.06 -0.125 HPBED 0.49 0.017 0.504 0.334 0.223 -0.357 MEDPROF 0.47 -0.03 0.418 0.182 0.337 -0.342 QSUBSIST 0.056 -0.041 0.318 0.111 0.699 -0.209 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 7 iterations.

145

APPENDIX H

SOCIAL VULNERABILITY INDEX OF CHINESE COASTAL COUNTIES 2000-2010

SoVI- ID County FAC1 FAC2 FAC3 FAC4 FAC5 FAC6 2000- 2010

1 Binhai Xinqu 1.08 -0.47 -0.50 -0.56 -0.61 -0.20 -1.08 2 Fengnan Qu -0.61 -0.62 0.07 -1.47 -0.33 -0.15 1.72 3 Luannan Xian -0.92 -0.47 0.10 -1.60 -0.38 -0.39 2.15 4 Leting Xian -0.93 -0.71 0.34 -1.45 -0.53 -0.31 2.22 5 Tanghai Xian -0.38 -0.41 0.15 -1.17 -0.65 -0.47 1.47 6 Haigang Qu 1.47 -0.71 -0.42 -0.78 -0.70 -0.37 -1.50

7 Shanhaiguan Qu 0.61 -0.80 -0.05 -1.17 -0.89 -0.09 0.52

8 Beidaihe Qu 0.78 -0.64 0.19 -0.52 -0.76 -0.66 -0.61 9 Changli Xian -0.74 -0.20 0.53 -0.95 -0.23 -0.33 1.93 10 Funing Xian -0.93 -0.53 0.02 -1.81 -0.42 -0.44 2.20 11 Haixing Xian -0.82 0.11 0.09 -1.26 -0.66 -0.61 2.32 12 Huanghua Shi -0.41 0.36 0.00 -1.09 -0.47 -0.31 2.02 13 Zhongshan Qu 2.70 -0.86 0.81 -1.37 -0.03 -0.18 -1.53 14 Xigang Qu 2.87 -0.75 0.97 -1.55 0.45 -0.24 -1.79 15 Shahekou Qu 3.03 -0.71 0.60 -1.32 0.15 -0.54 -2.51 16 Ganjingzi Qu 1.30 -1.31 -0.68 -0.98 -0.90 0.39 -1.03 17 Lvshunkou Qu 0.36 -1.41 -0.17 -0.90 -1.13 0.15 0.24 18 Jinzhou Qu 0.23 -1.52 -0.26 -1.27 -1.18 0.29 0.73 19 Changhai Xian -0.49 -0.51 -0.09 -1.74 -0.31 0.07 2.01 20 Wafangdian Shi -0.34 -0.90 0.48 -1.96 -0.37 0.62 2.88 21 Pulandian Shi -0.76 -0.82 0.54 -2.04 0.07 0.11 2.56 22 Zhuanghe Shi -0.95 -0.75 0.55 -2.06 -0.24 0.11 3.15 23 Donggang Shi -0.57 -1.01 0.04 -1.98 -0.48 0.53 2.59 24 Linghai Shi -0.88 -0.98 -0.01 -2.15 -0.38 0.90 3.31 25 Xishi Qu 2.70 -0.60 0.17 -1.25 -2.09 0.37 0.59

146

26 Bayuquan Qu 0.88 -0.95 -0.94 -1.28 -1.03 0.75 0.29 27 Laobian Qu 0.20 -0.72 0.00 -1.10 -0.95 0.33 1.46 28 Gaizhou Shi -0.67 -0.76 -0.03 -2.14 -0.46 0.87 3.35 29 Dawa Xian -0.29 -0.58 -0.01 -1.38 -0.79 0.42 2.30 30 Panshan Xian -0.79 -0.69 0.18 -1.66 -0.41 0.40 2.76 31 Lianshan Qu 0.26 -0.86 0.27 -2.15 -0.46 0.71 2.47 32 Longgang Qu 1.22 -1.13 -0.45 -0.92 -0.88 0.56 -0.43 33 Suizhong Xian -0.94 -0.89 -0.35 -2.78 -0.41 2.76 5.65 34 Xingcheng Shi -0.66 -0.96 -0.28 -2.84 -0.32 3.09 5.68 35 Baoshan Qu 1.17 -0.76 -0.61 -0.01 -0.99 0.13 -1.40 36 Pudong Xinqu 1.10 -0.90 -0.35 0.07 -0.95 -0.08 -1.54 37 Jinshan Qu -0.35 -0.72 0.76 0.56 -0.47 -0.58 -0.28 38 Fengxian Qu -0.52 -0.83 0.44 0.27 -0.65 -0.84 -0.32 39 Chongming Xian -0.77 -0.78 1.84 -0.30 -0.30 -0.97 1.47 40 Tongzhou Qu -0.97 -0.68 1.54 0.00 -0.69 -1.25 1.28 41 Hai'an Xian -0.68 -0.63 1.50 -0.42 -0.64 -1.18 1.42 42 Rudong Xian -1.26 -0.89 1.40 -1.17 -0.24 -1.20 1.98 43 Qidong Shi -0.83 -1.39 1.30 -0.92 -0.86 -1.32 1.21 44 Haimen Shi -0.83 -0.96 1.67 -0.65 -0.74 -1.75 1.18 45 Lianyun Qu 1.25 -0.07 -0.44 -0.03 -1.27 -0.35 -0.81 46 Xinpu Qu 2.33 0.13 -0.26 -0.38 -0.84 -0.42 -1.66 47 Ganyu Xian -0.96 0.48 -0.12 -1.48 -0.01 -0.81 2.00 48 Guanyun Xian -0.78 0.56 -0.45 -1.68 -0.06 -0.55 2.08 49 Guannan Xian -0.91 0.54 -0.57 -1.65 0.18 -0.47 1.88 50 Xiangshui Xian -0.70 0.46 -0.52 -1.60 0.21 -0.78 1.25 51 Binhai Xian -0.86 -0.35 -0.52 -1.90 -0.18 -0.42 1.64 52 Sheyang Xian -0.73 -0.99 -0.16 -1.43 -0.68 -0.52 1.16 53 Dongtai Shi -0.82 -0.91 0.77 -1.02 -0.62 -0.98 1.35 54 Dafeng Shi -0.89 -1.13 0.36 -1.06 -0.47 -0.80 0.83 55 Binjiang Qu -0.50 0.03 0.02 1.31 -0.43 -1.05 -1.37 56 Xiaoshan Qu -0.91 -0.19 0.24 0.73 -0.54 -1.11 -0.34 57 Haishu Qu 1.71 -0.63 0.04 -0.85 0.71 -0.72 -2.87 58 Jiangdong Qu 1.45 -0.76 -0.42 -0.49 -0.22 -0.72 -2.63 59 Jiangbei Qu 0.42 -1.08 -0.48 -0.59 -0.04 -0.41 -1.76 60 Beilun Qu -0.38 -1.24 -0.30 0.13 -1.00 -0.57 -0.86 61 Zhenhai Qu -0.07 -1.10 -0.61 -0.22 -0.61 -0.50 -1.30 62 Yinzhou Qu -0.84 -0.89 -0.34 -0.42 -0.45 -1.40 -0.92 63 Xiangshan Xian -0.37 -0.57 0.16 -0.48 -0.92 -0.85 0.49 64 Ninghai Xian -0.97 -0.81 -0.18 -0.44 -0.50 -0.49 0.43

147

65 Yuyao Shi -0.76 -0.75 0.18 0.18 -0.86 -0.82 0.05 66 Cixi Shi -0.93 -0.78 -0.38 0.52 -0.69 -0.82 -0.87 67 Fenghua Shi -0.65 -0.50 -0.02 -0.26 -0.49 -0.65 0.22 68 Longwan Qu -0.65 -0.83 -2.38 0.77 -0.89 -0.14 -2.57 69 Ouhai Qu -0.65 -0.40 -1.33 0.90 -1.07 -0.27 -1.18 70 Dongtou Xian -0.41 0.34 -0.03 -0.54 -0.53 -1.06 0.73 71 Pingyang Xian -0.38 0.03 -0.33 -0.25 -0.83 -0.70 0.46 72 Cangnan Xian -0.18 0.17 -0.67 0.08 -1.13 -0.41 0.32 73 Rui'an Xian -0.41 0.09 -0.39 0.73 -1.11 -0.71 -0.23 74 Yueqing Shi -0.54 0.23 -0.57 0.34 -0.71 -0.87 -0.29 75 Haiyan Xian -0.99 -0.11 0.47 0.81 -0.53 -1.17 -0.10 76 Haining Shi -0.93 -0.38 0.61 0.72 -0.71 -1.21 -0.07 77 Pinghu Shi -0.99 -0.15 0.66 0.81 -0.78 -1.31 0.16 78 Shaoxing Xian -1.01 -0.03 0.16 0.48 -0.42 -1.17 -0.09 79 Shangyu Shi -0.91 0.00 0.60 -0.20 -0.36 -1.15 0.91 80 Dinghai Qu -0.16 -0.93 0.22 -0.29 -0.67 -1.02 -0.61 81 Putuo Qu -0.10 -0.90 -0.11 -0.67 -0.80 -0.63 -0.08 82 Daishan Xian -0.51 -0.77 0.41 -0.72 -0.84 -1.19 0.53 83 Shengsi Xian -0.27 -0.15 0.00 -1.14 -0.16 -0.92 0.50 84 Jiaojiang Qu -0.26 -0.59 -0.22 0.15 -0.82 -1.27 -1.15 85 Luqiao Qu -0.58 -0.16 -0.67 0.47 -0.53 -0.84 -1.03 86 Yuhuan Xian -0.70 -0.51 -1.05 -0.22 -0.37 -0.92 -1.19 87 Sanmen Xian -1.07 -0.14 0.07 -0.59 -0.22 -0.88 0.95 88 Wenling Shi -0.87 -0.89 -0.30 0.09 -0.68 -0.69 -0.41 89 Linhai Shi -1.04 -0.05 0.52 0.28 -0.36 -0.90 0.69 90 Mawei Qu 0.99 -0.22 -0.70 0.10 -1.17 -1.08 -1.93 91 Lianjiang Xian 0.11 0.56 -0.34 -0.51 -1.32 -0.52 1.41 92 Luoyuan Xian -0.24 0.15 -0.49 -1.51 -0.46 -0.02 1.85 93 Pingtan Xian -0.18 1.81 -0.22 -0.50 -0.27 -1.07 1.46 94 Fuqing Shi -0.65 1.30 0.08 0.14 -0.25 -0.88 1.26 95 Changle Shi 0.20 0.23 -0.70 -0.45 -0.97 -0.50 0.25 96 Siming Qu 1.86 -0.74 -0.76 -0.48 -0.22 -0.54 -3.20 97 Haicang Qu -0.32 -0.59 -1.27 0.15 -0.13 -0.64 -2.19 98 Huli Qu 0.20 -1.22 -2.75 -0.21 0.35 -0.44 -4.74 99 Jimei Qu -0.43 -0.07 -0.81 0.07 0.63 -1.05 -2.20 100 Tong'an Qu -1.00 0.60 -0.12 -0.33 0.31 -0.87 0.63 102 Chengxiang Qu 0.69 0.04 -0.14 0.55 -0.91 -1.05 -1.47 103 Hanjiang Qu 0.30 0.33 -0.19 0.69 -1.34 -1.04 -0.55 106 Xianyou Xian -0.76 1.58 -0.28 -0.65 0.15 -1.40 1.16

148

107 Fengze Qu -0.37 -0.78 -1.97 0.18 0.09 -0.34 -2.99 108 Luojiang Qu -1.13 0.79 -0.78 -0.34 0.52 -1.02 -0.06 109 Quangang Qu -0.75 1.06 -0.52 -0.89 0.24 -1.07 0.86 110 Hui'an Xian -1.08 0.89 -0.14 0.11 -0.05 -1.52 0.24 111 Shishi Shi -0.34 -0.21 -1.44 0.28 -0.65 -0.56 -1.50 112 Jinjiang Shi -1.02 0.11 -1.27 0.34 -0.37 -0.81 -0.91 113 Nan'an Shi -0.86 0.95 -0.37 -0.15 -0.22 -1.19 0.62 114 Yunxiao Xian -0.34 0.89 -0.70 -1.17 -0.32 -0.49 1.54 115 Zhangpu Xian -0.92 0.84 -0.70 -1.54 0.20 -0.70 1.70 116 Zhao'an Xian -0.82 1.11 -0.88 -1.30 0.24 -0.57 1.53 117 Dongshan Xian -0.14 0.55 -0.13 -0.69 -0.69 -1.01 0.92 118 Longhai Shi -0.63 0.49 -0.56 -0.83 -0.31 -0.71 0.99 119 Jiaocheng Qu 0.11 0.34 -0.31 -1.03 -0.70 -0.11 1.54 120 Xiapu Xian -0.26 0.29 -0.60 -1.68 -0.55 0.00 2.19 121 Fu'an Shi -0.24 0.38 -0.37 -1.07 -0.68 0.04 2.03 122 Fuding Shi -0.13 0.58 -0.47 -1.18 -0.57 -0.42 1.59 123 Shinan Qu 2.53 -0.30 0.77 -1.01 0.74 -0.98 -2.77 124 Shibei Qu 2.46 -0.34 0.36 -0.88 -0.45 -1.21 -2.33 125 Sifang Qu 2.02 -0.62 0.09 -0.92 -0.24 -0.89 -2.29 126 Huangdao Qu -0.05 -0.18 -0.99 -0.40 0.39 -0.85 -1.96 127 Laoshan Qu 0.09 -0.86 -0.67 -0.34 -0.48 -0.89 -1.69 128 Licang Qu 1.06 -0.84 -0.24 -0.47 -0.72 -0.64 -1.59 129 Chengyang Qu -0.50 -0.60 0.10 -0.09 -0.75 -1.47 -0.63 130 Jiaozhou Shi -0.96 -0.03 0.43 -0.82 -0.13 -1.02 1.28 131 Jimo Shi -1.13 -0.14 0.05 -1.23 0.36 -0.90 1.00 132 Jiaonan Shi -1.53 0.85 -0.03 -1.04 1.51 -1.20 0.68 133 Dongying Qu 0.76 -0.31 -0.41 -0.56 -0.27 -0.35 -1.00 134 Hekou Qu 0.10 0.11 -0.41 -0.69 0.09 -0.63 -0.44 135 Kenli Xian -0.81 -0.04 0.04 -1.17 -0.41 -1.04 1.34 136 Lijin Xian -1.30 0.75 0.40 -1.15 0.54 -1.03 2.03 137 Guangrao Xian -1.28 0.28 0.46 -1.37 0.71 -1.00 1.69 138 Zhifu Qu 1.53 -0.78 -0.76 -0.16 -1.46 -0.67 -2.12 139 Fushan Qu -0.26 -0.82 -0.13 -0.76 -0.92 -0.52 0.47 140 Muping Qu -0.83 -0.81 0.59 -0.91 -0.16 -0.53 1.14 141 Laishan Qu -0.08 -0.66 -0.11 -0.42 -0.22 -0.45 -0.51 142 Changdao Xian -0.10 -0.31 -0.15 -1.14 -0.05 -0.29 0.53 143 Longkou Shi -0.67 -0.70 0.20 -0.93 -0.19 -0.55 0.74 144 Laiyang Shi -0.95 -0.73 0.40 -1.63 0.05 -0.62 1.59 145 Laizhou Shi -0.96 -0.51 0.77 -1.34 0.12 -0.65 1.79

149

146 Penglai Shi -0.84 -0.87 0.36 -1.48 -0.03 -0.36 1.47 147 Zhaoyuan Shi -0.96 -0.63 0.57 -1.40 0.06 -0.83 1.42 148 Haiyang Shi -1.20 -0.99 0.67 -1.76 0.04 -0.69 1.90 149 Hanting Qu -0.80 0.19 0.75 -0.82 0.11 -0.58 1.87 150 Shouguang Shi -0.96 0.32 0.55 -0.97 0.26 -0.58 1.95 151 Changyi Shi -1.06 0.18 0.99 -0.68 -0.21 -0.62 2.51 152 Huancui Qu 0.33 -0.86 -0.32 -0.37 -0.63 -0.50 -1.00 153 Wendeng Shi -0.90 -0.41 0.90 -1.05 0.61 -0.87 0.95 154 Rongcheng Shi -0.59 -0.73 1.03 -0.84 0.14 -0.46 1.13 155 Rushan Shi -0.98 -0.88 0.99 -0.99 -0.19 -0.77 1.50 156 Donggang Qu -0.65 -0.29 0.25 -0.89 -0.73 -0.87 1.36 158 Wudi Xian -1.63 0.80 -0.06 -1.43 1.52 -1.00 1.28 159 Zhanhua Xian -1.58 0.66 0.39 -1.04 1.29 -1.02 1.35 160 Liwan Qu 2.98 -0.25 0.42 -0.60 -1.37 -0.42 -1.26 161 Yuexiu Qu 3.42 0.00 1.51 -1.25 0.79 -0.39 -1.85 162 Haizhu Qu 2.18 -0.42 -0.63 -0.29 -0.50 -0.43 -2.88 163 Tianhe Qu 1.96 -0.22 -1.42 -0.03 0.35 -0.17 -4.09 164 Baiyun Qu 0.45 -0.46 -1.36 0.24 -0.60 -0.11 -2.02 165 Huangpu Qu 0.58 -0.84 -2.10 0.31 -0.40 0.20 -3.24 166 Panyu Qu -0.07 -0.48 -1.05 0.70 -0.93 -0.62 -1.85 169 Luohu Qu 1.52 -0.85 -1.71 0.14 -0.34 -0.51 -4.39 170 Futian Qu 1.46 -0.81 -1.87 0.22 0.18 -0.53 -5.08 171 Nanshan Qu 0.43 -1.26 -2.50 0.35 0.08 -0.24 -4.87 172 Bao'an Qu -0.63 -1.91 -2.81 0.10 -0.10 -0.86 -4.96 173 Longgang Qu -0.64 -1.82 -2.31 0.42 -0.54 -0.94 -4.31 174 Yantian Qu 0.05 -1.69 -2.58 0.24 -0.45 -0.63 -4.74 175 Xiangzhou Qu 0.95 -0.62 -1.36 0.35 -0.65 -0.50 -3.14 176 Doumen Qu 0.67 0.54 -0.35 -0.06 -0.88 -0.44 0.02 178 Longhu Qu 1.69 0.49 -0.83 0.98 -1.95 -0.73 -1.79 181 Chaoyang Qu 0.40 2.52 -0.68 0.65 -1.02 -0.76 1.06 183 Chenghai Qu 0.29 0.78 -0.25 0.74 -1.62 -0.91 0.22 184 Nan'ao Xian 0.89 0.90 -0.26 -0.44 -1.21 -1.13 0.27 185 Pengjiang Qu 1.61 -0.32 -0.69 0.71 -1.94 -0.41 -1.81 186 Jianghai Qu 0.69 -0.04 -0.72 0.80 -1.85 -0.51 -0.91 187 Xinhui Qu -0.01 0.24 -0.03 0.17 -0.90 -0.63 0.32 188 Taishan Shi -0.23 0.69 0.24 -0.36 -0.58 -0.47 1.63 189 Enping Shi 0.01 1.32 -0.06 -0.04 -0.70 -0.41 1.58 190 Chikan Qu 2.48 0.75 -0.25 0.45 -1.57 -0.48 -1.34 191 Xiashan Qu 2.42 0.63 -0.41 0.26 -1.60 -0.32 -1.17

150

192 Potou Qu -0.24 2.68 -0.34 -0.25 0.36 -0.59 1.88 193 Mazhang Qu -0.44 2.85 -0.89 -0.87 1.22 -0.63 1.41 194 Suixi Xian -0.45 2.72 -0.37 -0.37 0.61 -0.65 1.91 195 Xuwen Xian -0.29 2.12 -0.45 -0.88 0.11 -0.39 2.34 196 Lianjiang Shi -0.35 2.60 0.08 -0.37 0.11 -0.48 2.81 197 Leizhou Shi -0.44 3.45 -0.67 -0.43 0.80 -0.82 2.04 198 Wuchuan Shi -0.21 3.52 -0.10 0.45 0.43 -0.82 1.93 199 Maonan Qu 0.85 1.75 -0.31 0.55 -0.59 -0.53 0.10 201 Dianbai Xian -0.50 3.17 -0.35 -0.19 0.55 -0.88 2.07 202 Huicheng Qu 0.83 -0.38 -1.29 0.63 -1.24 -0.63 -2.50 203 Huiyang Qu -0.29 0.17 -0.99 0.40 -0.68 -0.70 -0.95 204 Huidong Xian 0.11 1.72 -0.43 0.47 -0.96 -0.82 0.85 205 Chengqu 1.21 1.79 -0.80 0.27 -1.72 -0.79 0.44 206 Haifeng Xian 0.44 1.90 -0.72 0.03 -1.06 -0.70 1.09 207 Lufeng Shi 0.92 3.95 -0.71 -0.16 -0.85 -0.99 2.34 208 Jiangcheng Qu 0.81 1.05 -0.06 0.05 -0.89 -0.49 0.52 209 Yangxi Xian -0.45 2.17 -0.17 -0.50 0.11 -0.61 2.22 210 Yangdong Xian -0.76 1.42 -0.45 -0.84 0.23 -0.38 1.97 211 Dongguan Shi -0.79 -1.40 -1.89 0.56 -0.56 -1.07 -3.57 212 Zhongshan Shi -0.23 -0.50 -1.06 0.87 -1.02 -0.60 -1.78 213 Xiangqiao Qu 1.38 0.15 -0.32 0.54 -1.83 -0.87 -1.13 214 Raoping Xian -0.18 1.36 -0.56 -0.50 -0.51 -0.72 1.27 215 Rongcheng Qu 1.24 1.12 -0.91 0.77 -1.70 -0.79 -0.89 216 Jiedong Xian -0.27 1.40 -0.80 0.02 -0.64 -0.50 0.99 217 Huilai Xian 0.04 2.74 -0.68 -0.28 -0.48 -0.84 1.94 218 Haicheng Qu 1.70 0.27 -0.33 0.18 -1.44 -0.24 -0.73 219 Yinhai Qu -0.36 1.12 -0.68 -0.97 -0.10 -0.43 1.42 220 Tieshangang Qu -0.50 1.95 -0.26 -1.00 0.35 -0.43 2.41 221 Hepu Xian -0.39 1.81 -0.34 -0.91 -0.02 -0.28 2.52 222 Gangkou Qu 0.65 0.77 -1.47 -0.82 -0.48 1.01 0.97 223 Fangcheng Qu -0.34 1.14 -1.20 -1.67 0.28 2.08 3.74 224 Dongxing Shi -0.18 1.08 -1.27 -1.07 -0.12 1.60 2.79 225 Qinnan Qu 0.07 1.37 -0.71 -1.10 0.10 0.59 2.18 226 Meilan Qu 2.68 0.54 -0.43 -0.18 -0.86 0.03 -1.51 227 Longhua Qu 2.29 0.30 -0.92 -0.29 -0.32 0.10 -2.19 228 Xiuying Qu 0.21 1.32 -0.19 -1.47 0.42 -0.25 1.73 229 Qionghai Shi -0.12 1.14 -0.19 -1.20 0.23 -0.12 1.92 230 Danzhou Shi 0.31 2.06 -0.80 -1.34 -0.30 -0.43 2.16 231 Wenchang Shi -0.26 1.15 0.44 -1.33 0.07 -0.68 2.44

151

232 Wanning Shi 0.07 1.61 -0.79 -1.77 0.02 0.54 3.04 233 Dongfang Shi -0.49 1.98 -1.37 -1.90 0.59 0.08 2.48 234 Chengmai Xian 0.03 1.67 -0.79 -1.77 -0.01 0.00 2.63 235 Lingao Xian -0.50 2.35 -0.97 -1.25 0.79 -0.59 1.73 Changjiang Lizu 236 0.03 0.91 -1.34 -1.90 -0.25 1.58 3.28 Zizhixian Ledong Lizu 237 -0.64 2.00 -1.81 -2.15 0.92 1.59 3.65 Zizhixian Lingshui Lizu 238 -0.59 1.13 -1.46 -2.50 -0.03 2.61 5.40 Zizhixian 1000 Binhai Xinqu 0.16 -0.99 -1.50 0.41 1.14 1.49 -2.71 2000 Fengnan Qu -0.23 -0.31 0.63 0.04 0.41 0.33 0.42 3000 Luannan Xian -0.60 0.06 0.64 0.10 -0.10 0.60 1.89 4000 Leting Xian -0.62 -0.68 0.94 -0.05 -0.47 0.77 2.16 5000 Tanghai Xian -0.25 -0.85 -0.42 0.01 0.24 1.65 0.38 6000 Haigang Qu 2.03 -0.37 0.64 0.19 -0.05 0.28 -1.61

7000 Shanhaiguan Qu 1.27 -0.74 0.65 0.45 -1.33 3.10 2.62

8000 Beidaihe Qu 1.36 -0.43 1.39 0.83 -0.55 0.12 -0.56 9000 Changli Xian -0.84 0.03 1.11 -0.40 0.57 0.85 2.65 10000 Funing Xian -1.10 0.45 0.67 -0.01 0.64 1.44 3.03 11000 Haixing Xian -0.64 0.45 0.52 -0.26 -0.27 0.56 2.70 12000 Huanghua Shi -0.11 0.29 0.35 0.06 -0.14 1.24 2.07 13000 Zhongshan Qu 2.90 -0.47 2.28 -0.44 0.72 -0.24 -1.60 14000 Xigang Qu 2.69 -0.67 1.90 -0.73 1.21 0.27 -1.67 15000 Shahekou Qu 3.07 -0.24 1.89 -0.44 1.48 -0.69 -3.15 16000 Ganjingzi Qu 1.66 -0.78 0.06 0.01 0.31 0.49 -2.20 17000 Lvshunkou Qu 0.92 -1.12 0.34 -0.09 -0.21 0.47 -0.93 18000 Jinzhou Qu 0.56 -1.31 -0.05 -0.18 -0.30 0.33 -1.12 19000 Changhai Xian -0.05 -0.90 0.82 -0.91 -0.11 0.79 1.78 20000 Wafangdian Shi -0.68 -0.87 1.12 -1.10 0.41 1.18 2.80 21000 Pulandian Shi -0.86 -0.62 1.44 -0.96 0.77 0.81 2.68 22000 Zhuanghe Shi -1.04 -1.11 1.13 -1.31 0.24 0.57 2.70 23000 Donggang Shi -0.67 -1.67 0.49 -1.41 -0.13 1.25 2.28 24000 Linghai Shi -0.76 -1.23 0.89 -1.31 -0.46 2.01 4.19 25000 Xishi Qu 2.31 -1.07 1.53 0.09 -1.96 2.74 2.75 26000 Bayuquan Qu 0.50 -0.83 -0.11 -0.53 0.40 1.24 -0.09 27000 Laobian Qu 0.01 -1.38 0.38 -0.37 -0.60 1.32 1.28 28000 Gaizhou Shi -0.74 -0.85 0.96 -1.09 -0.49 2.12 4.54 29000 Dawa Xian 0.41 -0.37 0.67 -1.00 -0.27 0.76 1.93

152

30000 Panshan Xian -0.62 -0.82 1.15 -0.70 -0.17 1.06 2.88 31000 Lianshan Qu 0.37 -0.82 1.14 -1.17 -0.13 1.24 2.48 32000 Longgang Qu 1.03 -1.03 0.21 -0.18 0.14 0.86 -0.95 33000 Suizhong Xian -1.15 -0.77 0.41 -1.72 0.09 3.74 6.17 34000 Xingcheng Shi -0.63 -1.35 0.43 -1.82 -0.35 4.75 6.63 35000 Baoshan Qu 0.11 0.42 -1.49 0.56 5.36 -1.18 -8.28 36000 Pudong Xinqu 0.78 -0.51 -0.19 0.66 1.30 0.27 -3.17 37000 Jinshan Qu -0.01 -0.78 0.96 1.06 0.69 0.03 -1.54 38000 Fengxian Qu -0.29 -1.02 -0.56 0.41 0.90 0.19 -2.41 39000 Chongming Xian -0.53 -0.56 2.71 1.37 0.14 1.63 2.79 40000 Tongzhou Qu -0.88 -0.72 2.68 1.88 -0.45 -0.74 0.67 41000 Hai'an Xian -0.57 -0.63 2.72 1.52 -0.39 -0.75 0.79 42000 Rudong Xian -0.80 -0.83 2.56 1.08 -0.34 -0.69 1.09 43000 Qidong Shi -0.73 -1.23 2.42 0.76 -0.48 -0.98 0.66 44000 Haimen Shi -0.45 -0.75 2.51 0.83 -0.14 -0.86 0.66 45000 Lianyun Qu 1.20 0.32 0.42 1.18 -0.24 -0.07 -1.48 46000 Xinpu Qu 1.43 0.60 0.47 0.63 1.04 0.07 -1.96 47000 Ganyu Xian -0.22 0.92 1.00 1.18 -0.48 -0.31 1.12 48000 Guanyun Xian -0.42 1.40 0.63 0.93 -0.25 0.25 2.01 49000 Guannan Xian -0.38 1.24 0.83 1.61 -0.54 0.48 1.86 50000 Xiangshui Xian -0.17 1.26 0.47 0.83 -0.32 0.16 1.56 51000 Binhai Xian -0.12 0.82 0.45 0.55 -0.57 0.34 1.75 52000 Sheyang Xian -0.20 -0.51 0.96 0.35 -0.82 0.18 1.30 53000 Dongtai Shi -0.46 -0.55 2.43 0.99 -0.52 -0.42 1.46 54000 Dafeng Shi -0.44 -0.99 1.52 0.44 -0.40 -0.15 0.78 55000 Binjiang Qu 0.72 -0.03 -1.20 0.61 3.40 -0.53 -6.50 56000 Xiaoshan Qu -0.65 -0.45 0.38 1.87 0.70 -0.14 -2.13 57000 Haishu Qu 1.97 -0.25 1.89 -0.73 3.83 -0.27 -3.70 58000 Jiangdong Qu 1.64 -0.23 0.93 0.02 2.39 -0.70 -4.05 59000 Jiangbei Qu 0.16 -0.54 -0.12 -0.10 2.95 -0.08 -3.76 60000 Beilun Qu -0.72 -1.30 -1.09 0.63 1.29 0.12 -3.47 61000 Zhenhai Qu -0.58 -1.09 -1.01 0.02 1.95 0.17 -3.31 62000 Yinzhou Qu -0.82 -1.11 -1.11 0.57 1.52 -0.07 -3.56 63000 Xiangshan Xian -0.81 -0.69 0.70 0.91 0.51 -0.33 -0.93 64000 Ninghai Xian -0.94 -0.61 -0.08 0.83 0.81 -0.08 -1.48 65000 Yuyao Shi -1.03 -1.14 0.07 1.08 0.62 -0.06 -1.81 66000 Cixi Shi -1.28 -1.28 -0.64 0.85 1.12 -0.08 -2.69 67000 Fenghua Shi -0.86 -1.12 0.51 0.71 0.55 0.23 -0.77 68000 Longwan Qu -1.19 -0.79 -2.76 1.01 1.52 0.09 -4.79

153

69000 Ouhai Qu -0.90 -1.14 -2.41 1.00 0.55 0.53 -3.67 70000 Dongtou Xian -0.40 -0.21 1.02 0.52 -0.20 -0.83 0.05 71000 Pingyang Xian -0.47 -0.16 0.37 1.32 -0.26 -0.11 -0.50 72000 Cangnan Xian -0.28 -0.03 -0.14 1.02 -0.44 -0.22 -0.68 73000 Rui'an Xian -0.78 -0.61 -0.81 1.05 0.46 -0.10 -2.25 74000 Yueqing Shi -0.80 -0.17 -0.85 1.21 0.70 -0.07 -2.21 75000 Haiyan Xian -1.05 -0.80 0.75 1.58 0.43 -0.54 -1.56 76000 Haining Shi -0.96 -0.69 0.66 1.68 0.66 -0.42 -1.84 77000 Pinghu Shi -1.22 -0.93 0.35 1.53 0.65 -0.72 -2.26 78000 Shaoxing Xian -1.08 -0.84 -0.53 1.16 1.15 0.13 -2.47 79000 Shangyu Shi -0.92 -0.89 1.02 1.24 0.27 -0.42 -0.88 80000 Dinghai Qu -0.12 -0.85 0.27 0.38 1.16 -0.05 -2.05 81000 Putuo Qu -0.29 -1.10 0.25 0.25 0.25 0.28 -0.77 82000 Daishan Xian -0.90 -1.37 0.17 0.20 0.28 0.31 -0.47 83000 Shengsi Xian -0.26 -0.89 1.12 -0.35 0.63 -0.19 0.02 84000 Jiaojiang Qu -0.48 -0.47 -0.15 0.88 1.31 -0.50 -2.83 85000 Luqiao Qu -1.10 -0.61 -0.87 1.17 1.15 -0.12 -2.83 86000 Yuhuan Xian -1.11 -0.49 -1.33 0.73 1.39 -0.21 -3.04 87000 Sanmen Xian -1.01 0.16 0.79 1.02 0.50 -0.71 -0.27 88000 Wenling Shi -1.20 -0.69 -0.29 1.02 1.01 -0.17 -1.98 89000 Linhai Shi -0.97 0.18 0.97 1.49 0.70 -0.48 -0.56 90000 Mawei Qu 0.76 -0.40 -0.28 1.05 0.32 -0.52 -3.33 91000 Lianjiang Xian -0.02 0.08 0.45 1.22 -1.05 0.34 0.72 92000 Luoyuan Xian -0.06 -0.16 0.56 0.60 -0.46 1.72 2.05 93000 Pingtan Xian 0.21 0.81 0.79 1.52 -0.90 0.62 1.40 94000 Fuqing Shi -0.46 0.88 0.82 2.38 -0.13 -0.16 -0.24 95000 Changle Shi 0.04 -0.40 0.02 1.06 -0.64 0.30 -0.54 96000 Siming Qu 1.61 0.53 -0.40 0.48 3.69 -1.68 -7.35 97000 Haicang Qu -0.15 -1.07 -1.86 1.15 0.79 0.83 -3.90 98000 Huli Qu 0.42 -1.04 -2.35 0.09 2.24 -0.14 -6.28 99000 Jimei Qu -0.22 -0.93 -2.48 0.69 2.66 -0.12 -6.67 100000 Tong'an Qu -0.59 -0.41 -0.61 1.57 0.40 0.69 -1.71 101000 Xiang'an Qu -0.84 0.09 0.47 2.03 0.47 0.63 -0.48 102000 Chengxiang Qu -0.10 0.58 0.71 1.98 0.54 -0.45 -1.58 103000 Hanjiang Qu -0.47 0.10 0.35 2.14 -0.06 -0.10 -1.27 104000 Licheng Qu -0.57 0.50 0.75 2.63 -0.04 -0.15 -0.89 105000 Xiuyu Qu -1.16 1.38 1.73 2.83 -0.51 -0.87 1.08 106000 Xianyou Xian -0.38 0.64 0.59 1.48 -0.81 0.34 1.28 107000 Fengze Qu 0.37 -0.35 -1.35 1.01 1.25 -0.16 -4.49

154

108000 Luojiang Qu -1.12 -0.11 -0.34 1.70 0.90 0.00 -1.93 109000 Quangang Qu -0.85 0.73 0.95 2.30 0.19 0.01 0.05 110000 Hui'an Xian -1.13 0.39 0.55 2.53 -0.05 -0.39 -0.80 111000 Shishi Shi -0.70 -0.66 -1.52 1.35 0.60 0.42 -3.02 112000 Jinjiang Shi -1.08 -0.46 -1.52 1.61 0.37 0.47 -2.40 113000 Nan'an Shi -0.96 0.38 0.17 2.48 -0.20 0.17 -0.59 114000 Yunxiao Xian -0.13 0.51 0.19 0.52 -0.81 0.63 1.74 115000 Zhangpu Xian -0.69 0.62 0.22 0.66 -0.08 0.34 1.29 116000 Zhao'an Xian -0.50 0.30 -0.20 0.41 -0.74 0.71 1.63 117000 Dongshan Xian -0.14 0.03 0.66 1.15 -0.76 0.45 0.89 118000 Longhai Shi -0.58 0.07 0.00 1.33 -0.20 0.37 -0.11 119000 Jiaocheng Qu 0.39 0.26 0.47 0.95 -0.30 0.78 0.49 120000 Xiapu Xian -0.18 0.26 0.44 0.76 -0.63 1.24 2.00 121000 Fu'an Shi -0.04 0.19 0.30 0.94 -0.63 1.64 1.85 122000 Fuding Shi -0.33 0.13 0.31 0.98 -0.16 0.66 0.61 123000 Shinan Qu 3.04 0.35 2.21 -0.63 3.10 -0.73 -3.69 124000 Shibei Qu 2.91 0.11 1.52 -0.01 0.65 -0.66 -2.58 125000 Sifang Qu 2.60 -0.10 1.28 -0.26 1.02 -0.12 -2.30 126000 Huangdao Qu 0.29 -0.37 -1.31 0.58 2.39 0.19 -4.75 127000 Laoshan Qu 0.58 -0.35 0.20 1.04 1.32 -0.19 -3.28 128000 Licang Qu 1.66 -0.33 0.47 0.39 0.74 0.23 -2.41 129000 Chengyang Qu -0.13 -0.88 -0.11 0.98 0.53 -0.24 -2.60 130000 Jiaozhou Shi -0.45 -0.22 0.96 0.96 -0.15 -0.30 0.09 131000 Jimo Shi -0.57 -0.30 0.96 1.02 -0.09 -0.10 0.21 132000 Jiaonan Shi -0.53 -0.40 1.00 0.64 0.04 -0.36 0.08 133000 Dongying Qu 0.78 0.05 0.68 0.35 1.25 0.10 -1.57 134000 Hekou Qu 0.12 -0.18 0.78 -0.28 1.14 0.22 -0.16 135000 Kenli Xian -0.54 -0.44 0.78 0.44 -0.52 0.75 1.71 136000 Lijin Xian -0.99 -0.06 1.49 0.12 0.14 0.33 2.49 137000 Guangrao Xian -0.83 0.00 1.40 0.58 0.82 0.21 1.05 138000 Zhifu Qu 1.77 -0.42 0.19 0.83 -0.96 0.13 -1.74 139000 Fushan Qu -0.02 -0.85 -0.41 1.01 -0.17 0.26 -1.81 140000 Muping Qu -0.49 -0.77 1.86 0.40 0.22 0.77 1.74 141000 Laishan Qu 0.52 -0.26 0.54 1.13 1.33 0.00 -2.70 142000 Changdao Xian -0.02 -0.70 1.72 -0.26 0.60 0.61 1.31 143000 Longkou Shi -0.50 -0.69 1.19 0.34 0.52 0.88 1.01 144000 Laiyang Shi -0.83 -0.27 1.65 0.06 0.63 0.30 1.82 145000 Laizhou Shi -0.61 -0.53 1.94 0.35 0.27 0.93 2.32 146000 Penglai Shi -0.85 -0.03 1.63 -0.20 1.30 0.66 2.01

155

147000 Zhaoyuan Shi -0.66 -0.57 1.77 0.05 0.46 0.79 2.14 148000 Haiyang Shi -1.02 -0.78 1.79 -0.30 0.61 0.67 2.40 149000 Hanting Qu -0.63 0.16 1.42 0.51 0.70 0.35 1.35 150000 Shouguang Shi -0.71 -0.06 1.20 0.61 0.65 0.49 1.08 151000 Changyi Shi -0.75 0.42 1.48 0.73 0.21 0.31 2.03 152000 Huancui Qu 0.69 -0.66 0.20 0.53 0.54 -0.14 -2.35 153000 Wendeng Shi -0.42 -0.66 2.15 0.26 0.74 0.44 1.35 154000 Rongcheng Shi -0.25 -0.79 2.15 -0.17 1.25 0.65 1.18 155000 Rushan Shi -0.44 -0.92 1.97 -0.06 0.26 0.53 1.81 156000 Donggang Qu 0.09 -0.17 0.52 0.83 -0.67 0.10 0.20 157000 Lanshan Qu -1.00 0.07 1.49 0.67 0.30 0.27 1.85 158000 Wudi Xian -0.73 -0.01 1.11 1.11 -0.10 1.08 1.91 159000 Zhanhua Xian -0.86 -0.20 1.70 0.11 0.25 0.61 2.61 160000 Liwan Qu 2.27 -0.30 0.77 -0.12 1.26 -0.18 -3.12 161000 Yuexiu Qu 3.63 0.88 3.14 -1.93 7.16 -1.07 -5.90 162000 Haizhu Qu 2.04 -0.49 0.38 0.04 1.60 -0.40 -4.19 163000 Tianhe Qu 2.15 -0.14 -0.45 0.21 2.83 -0.42 -6.20 164000 Baiyun Qu 0.40 -1.06 -1.21 0.72 1.02 0.73 -3.67 165000 Huangpu Qu 0.72 -1.18 -1.64 0.54 0.82 0.62 -4.29 166000 Panyu Qu 0.23 -0.80 -0.80 1.10 0.93 0.08 -3.77 167000 Nansha Qu -0.55 -0.92 -1.27 1.14 0.75 1.28 -2.25 168000 Luogang Qu 0.14 -0.92 -1.53 1.49 0.63 0.90 -3.81 169000 Luohu Qu 1.78 -0.52 -0.90 0.50 1.03 -0.33 -5.06 170000 Futian Qu 1.94 -0.12 -0.81 0.64 2.24 -0.56 -6.30 171000 Nanshan Qu 0.58 -0.09 -2.37 0.88 4.92 -1.07 -9.92 172000 Bao'an Qu -0.74 -2.27 -3.43 0.37 1.85 0.79 -6.38 173000 Longgang Qu -0.24 -1.67 -2.76 0.69 1.04 0.72 -5.20 174000 Yantian Qu 0.46 -1.16 -2.23 0.78 0.88 0.78 -4.73 175000 Xiangzhou Qu 1.41 -0.49 -0.50 0.63 1.11 0.12 -4.02 176000 Doumen Qu 0.06 -0.62 -0.38 0.83 0.58 0.80 -1.66 177000 Jinwan Qu 0.18 -1.14 -1.45 0.52 1.36 0.68 -3.97 178000 Longhu Qu 0.92 0.18 -0.41 1.37 -0.75 -0.42 -2.19 179000 Jinping Qu 2.62 0.23 1.48 -0.06 0.10 0.04 -0.91 180000 Haojiang Qu 1.04 0.92 0.16 1.27 -1.90 -0.66 0.01 181000 Chaoyang Qu 0.35 0.61 -0.52 1.19 -1.84 -0.17 0.22 182000 Chaonan Qu 0.15 1.32 -0.59 1.85 -1.72 -0.24 0.21 183000 Chenghai Qu 0.25 0.06 0.12 1.35 -1.50 0.02 0.09 184000 Nan'ao Xian 0.69 -0.09 1.05 0.23 -1.05 0.04 1.13 185000 Pengjiang Qu 0.65 -0.49 -0.79 1.31 -0.56 -0.04 -2.72

156

186000 Jianghai Qu 0.34 -0.68 -0.86 1.29 -1.06 0.25 -1.85 187000 Xinhui Qu 0.17 -0.47 0.35 0.77 -0.38 0.33 -0.34 188000 Taishan Shi -0.09 0.05 0.87 0.55 -0.58 0.52 1.56 189000 Enping Shi -0.20 0.18 0.74 0.88 -0.32 0.69 1.24 190000 Chikan Qu 2.39 0.41 0.52 0.70 -0.52 -0.07 -1.71 191000 Xiashan Qu 2.21 0.34 0.39 0.53 -0.70 0.19 -1.12 192000 Potou Qu -0.36 1.30 0.63 0.68 0.21 0.53 1.93 193000 Mazhang Qu -0.34 1.96 0.03 0.03 1.31 0.19 1.18 194000 Suixi Xian -0.56 1.86 0.53 0.14 0.84 0.20 2.16 195000 Xuwen Xian -0.41 1.50 0.22 -0.30 0.64 0.72 2.51 196000 Lianjiang Shi -0.65 2.22 0.40 0.36 1.08 0.47 2.30 197000 Leizhou Shi -0.60 2.15 0.11 -0.27 1.11 0.43 2.46 198000 Wuchuan Shi -0.20 2.23 0.80 1.25 0.35 0.17 1.79 199000 Maonan Qu 0.80 1.29 0.50 1.24 -0.08 0.24 0.07 200000 Maogang Qu -0.91 2.58 0.60 1.06 1.04 -0.12 1.88 201000 Dianbai Xian -0.39 2.41 0.39 0.85 0.49 0.23 2.07 202000 Huicheng Qu 0.15 -0.36 -1.44 1.00 0.84 0.14 -3.65 203000 Huiyang Qu -0.25 -0.67 -1.56 1.30 -0.05 1.00 -2.23 204000 Huidong Xian -0.14 0.63 -0.56 1.32 -0.35 0.53 -0.24 205000 Chengqu 0.83 0.13 -0.41 0.80 -1.63 0.30 0.02 206000 Haifeng Xian 0.35 0.40 -0.29 0.91 -1.39 0.95 1.18 207000 Lufeng Shi 0.66 1.79 -0.05 0.24 -1.28 -0.02 2.09 208000 Jiangcheng Qu 0.70 0.39 0.88 0.88 -0.31 0.50 0.50 209000 Yangxi Xian -0.63 1.11 0.28 0.57 0.03 0.59 2.01 210000 Yangdong Xian -0.58 0.64 -0.01 0.41 -0.01 0.90 1.70 211000 Dongguan Shi -1.05 -1.99 -2.68 0.99 1.39 0.93 -5.06 212000 Zhongshan Shi -0.43 -1.38 -1.61 0.93 0.65 0.59 -3.57 213000 Xiangqiao Qu 1.28 -0.40 0.12 0.91 -1.27 -0.26 -1.46 214000 Raoping Xian -0.02 0.61 0.16 0.49 -0.96 0.15 1.41 215000 Rongcheng Qu 1.09 -0.05 -0.54 0.75 -1.38 -0.27 -1.32 216000 Jiedong Xian -0.03 0.27 -0.33 0.50 -1.02 0.23 0.72 217000 Huilai Xian 0.09 2.05 -0.20 0.61 -0.78 -0.31 1.61 218000 Haicheng Qu 1.80 0.56 0.49 1.28 -0.78 0.38 -0.87 219000 Yinhai Qu -0.23 0.93 -0.26 0.37 0.60 1.03 0.96 220000 Tieshangang Qu -0.67 1.93 0.96 0.73 0.52 1.52 3.83 221000 Hepu Xian -0.46 2.03 0.53 0.51 0.50 1.09 3.09 222000 Gangkou Qu 0.81 0.82 -0.52 2.48 -1.82 8.00 6.83 223000 Fangcheng Qu 0.02 1.80 0.02 0.44 0.13 4.57 5.80 224000 Dongxing Shi 0.34 1.29 -0.15 1.39 -0.77 3.86 4.04

157

225000 Qinnan Qu 0.29 1.81 0.15 0.65 0.74 2.22 2.51 226000 Meilan Qu 0.41 0.69 -0.21 -0.02 0.80 1.23 0.51 227000 Longhua Qu 1.68 0.55 0.00 0.35 1.12 0.50 -2.10 228000 Xiuying Qu 1.32 0.91 0.12 0.07 0.76 1.17 0.05 229000 Qionghai Shi 0.00 1.11 0.48 -0.09 0.40 0.95 2.23 230000 Danzhou Shi 0.33 1.40 -0.21 -0.13 -0.40 2.18 3.57 231000 Wenchang Shi -0.11 0.89 1.01 -0.40 0.26 0.53 2.68 232000 Wanning Shi -0.13 1.04 -0.30 -0.60 0.25 1.95 3.18 233000 Dongfang Shi -0.26 1.30 -0.62 -0.92 0.46 1.37 2.77 234000 Chengmai Xian -0.07 1.40 -0.25 -0.64 0.48 1.32 2.69 235000 Lingao Xian -0.11 1.95 -0.06 -0.20 0.16 0.98 3.02 Changjiang Lizu 236000 -0.06 0.61 -0.44 -0.85 -0.18 3.66 4.92 Zizhixian Ledong Lizu 237000 -0.86 1.24 -0.75 -1.19 0.85 3.08 4.79 Zizhixian Lingshui Lizu 238000 -0.32 1.61 -0.96 -1.29 0.34 4.12 6.03 Zizhixian County in 2000: ID: 1-238 County in 2010: ID: 1000-238000

158

APPENDIX I

TOTAL VARIANCE EXPLAINED SOVI®2000-2010

Total Variance Explained Extraction Sums of Squared Rotation Sums of Squared Initial Eigenvalues Loadings Loadings Compon % of Cumulat % of Cumulat % of Cumulat ent Total Variance ive % Total Variance ive % Total Variance ive % 1 10.003 34.493 34.493 10.003 34.493 34.493 6.084 20.978 20.978 2 4.115 14.191 48.684 4.115 14.191 48.684 4.704 16.22 37.198 3 2.69 9.274 57.959 2.69 9.274 57.959 3.43 11.828 49.026 4 1.936 6.675 64.633 1.936 6.675 64.633 3.037 10.473 59.499 5 1.463 5.045 69.678 1.463 5.045 69.678 2.43 8.38 67.879 6 1.22 4.209 73.887 1.22 4.209 73.887 1.742 6.007 73.887 7 0.904 3.118 77.005 8 0.874 3.014 80.019 9 0.793 2.734 82.753 10 0.674 2.324 85.077 11 0.567 1.956 87.033 12 0.506 1.744 88.777 13 0.455 1.571 90.347 14 0.433 1.493 91.84 15 0.332 1.143 92.984 16 0.292 1.008 93.992 17 0.27 0.93 94.922 18 0.26 0.898 95.819 19 0.217 0.749 96.568 20 0.201 0.693 97.261 21 0.185 0.637 97.898 22 0.15 0.518 98.416 23 0.122 0.421 98.836 24 0.107 0.37 99.207 25 0.096 0.331 99.537 26 0.067 0.23 99.767 27 0.054 0.188 99.955 28 0.013 0.045 100 29 9.53E- 3.29E- 07 06 100 Extraction Method: Principal Component Analysis.

159

APPENDIX J

ROTATED COMPONENT MATRIX SOVI®2000-2010

Rotated Component Matrixa Component

1 2 3 4 5 6

UBINCM 0.043 -0.241 0.079 0.614 0.555 0.239 QFEMALE 0.018 -0.221 0.563 0.051 -0.196 -0.451 QMINOR -0.056 0.043 -0.168 -0.331 -0.01 0.668 MEDAGE -0.034 -0.468 0.79 0.076 0.138 0.135 QUNEMP 0.837 0.108 -0.083 -0.021 -0.125 0.119 POPDEN 0.665 -0.118 0.026 -0.016 0.384 -0.184 QUBRESD 0.73 -0.358 -0.274 0.301 0.102 -0.055 QNONAGRI 0.894 -0.201 0.043 -0.063 0.111 0.012 QRENT 0.374 -0.416 -0.524 0.256 0.427 0.002 QAGREMP -0.541 0.481 0.263 -0.518 -0.099 0.164 QMANFEMP 0.013 -0.523 -0.365 0.586 -0.027 -0.229 QSEVEMP 0.864 -0.2 -0.021 0.19 0.191 -0.012 PPUNIT -0.086 0.805 -0.172 -0.134 -0.351 0.001 QCOLLEGE 0.609 -0.204 -0.04 0.165 0.532 -0.027 QHISCH 0.799 -0.23 -0.073 0.192 0.351 0.009 QILLIT -0.433 0.285 0.04 -0.357 -0.19 -0.356 POPCH 0.126 0.013 -0.238 0.107 0.486 -0.108 PHROOM -0.444 0.308 0.423 0.35 -0.03 0.012 PPHAREA -0.333 -0.233 0.438 0.596 0.069 -0.073 QNOPIPWT -0.447 0.498 -0.029 -0.491 -0.004 0.048 QNOKITCH -0.229 0.571 -0.348 -0.089 0.266 0.043 QNOTOILET -0.227 0.669 -0.167 -0.232 -0.016 0.027 QNOBATH -0.443 0.129 0.045 -0.75 -0.089 -0.013 HPBED 0.407 -0.218 0.495 -0.086 0.543 0.178 MEDPROF 0.375 -0.243 0.306 -0.036 0.627 0.212 QPOPUD5 -0.128 0.836 -0.189 0.049 -0.124 0.113 QPOPAB65 -0.137 -0.057 0.899 -0.049 -0.013 0.02 QDEPEND -0.227 0.8 0.148 -0.308 -0.237 -0.126 QSUBSIST 0.016 0.043 0.269 0.249 -0.021 0.793 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 23 iterations.

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