DEGREE PROJECT IN THE BUILT ENVIRONMENT, SECOND CYCLE, 30 CREDITS , 2020

Analysis and visualization of deficiencies and challenges in the waste collection system of the

NIKOLAOS MANOLIS-GRIGOROPOULOS

KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT Analysis and visualization of deficiencies and challenges in the waste collection system of the Stockholm archipelago

NIKOLAOS MANOLIS-GRIGOROPOULOS [email protected]

Master in Logistics and Transport Science Date: May 27,2020 Supervisor: Behzad Kordnejad Examiner: Albania Nissan School of Architecture and the Built Environment Host agency: Länsstyrelsen Stockholm Swedish title: Analys och visualisering av brister och utmaningar i avfallsinsamlingssystem i Skärgård

Acknowledgements

I would like to thank my supervisor, Behzad Kordnejad, for his guidance throughout the thesis work and for inspiring my interest in the field of logistics, my professors, Albania Nissan, for providing guidance and introducing me to the topic of this thesis, and Gyözö Gidofalvi, for offering me consulting in the Spatial Analysis aspects of the thesis.

Furthermore, I would like to thank my two supervisors at Länsstyrelsen Stockholm, Sara Dreijer and Tomas Norberg, for introducing the Stockholm archipelago to me, sharing their previous work on the subject, and providing guidance throughout the thesis work.

It is important to acknowledge the people from the municipalities in the Stockholm archipelago, as well as the representatives of the various companies that agreed to take part in interviews and respond to the questionnaires to share their knowledge and experience with me.

Last but not least, I would like to thank my family and friends for providing emotional support during the difficult time of working on the degree project.

Abstract

The objective of this thesis is to document and visualize the unique characteristics of the waste collection and transportation system in the Stockholm archipelago of Sweden from a geopolitical and logistics perspective.

So far there has been no extensive study of the waste management system in the area and this report aims to document the work and act as a basis for further research and future development.

The archipelago is unique from a geolocation perspective as it consists of many islands of different sizes, with varying degrees of infrastructural development and residential housing, as well as varying distances between them. Sweden follows several unique policies and guidelines in the way they deal with waste collection, such as that the municipalities have to an equal level of service to every tax-paying household within their administrative borders. The waste management system is complex due to the large number of actors partaking in operations and the unique attributes of each of the municipalities involved.

To collect information on the subject interviews were conducted with different actors that agreed to share their knowledge and experience working on the field. The content on the interviews is presented and evaluated in the report against theoretical concepts of the logistics and supply chain management field. Moreover, geospatial data were collected from open sources to perform spatial and data analysis and highlight the unique spatial characteristics of the islands and the infrastructure in the archipelago and the spatial relationships between them. The data gathered after undergoing editing were compiled into a spatial database that can be used in future projects.

The spatial characteristics of the islands in the archipelago are presented through a set of maps and spatial statistics reports created in a GIS software. The main strengths and weaknesses of the waste management system in the archipelago are presented in the form of analytical content. They are used as a basis to propose future improvements to the waste management operations and planning and suggest future projects in regards to collecting more comprehensive data for the archipelago area, as well as to help decrease the cost, time and environmental impact of the operations.

Sammanfattning

Syftet med denna avhandling är att dokumentera och visualisera de unika egenskaperna hos avfallssamlings- och transportsystemet i Stockholms skärgård från ett geopolitiskt och logistiskt perspektiv.

Hittills har det inte gjorts någon omfattande studie av avfallshanteringssystemet i området och denna rapport syftar till att dokumentera arbetet och fungera som grund för vidare forskning och framtida utveckling.

Skärgården är unik ur ett geolokaliseringsperspektiv eftersom den består av många öar i olika storlekar, med varierande grad av infrastrukturutveckling och bostadshus, samt olika avstånd mellan dem. Sverige följer flera unika policyer och riktlinjer för hur de hanterar avfallshantering, till exempel att kommunerna måste ha en lika hög servicenivå för varje skattebetalande hushåll inom sina administrativa gränser. Avfallshanteringssystemet är komplext på grund av det stora antalet aktörer som deltar i verksamheten och de unika attributen för var och en av de berörda kommunerna.

För att samla in information om ämnet genomfördes intervjuer med olika aktörer som gick med på att dela sin kunskap och erfarenhet av att arbeta på fältet. Innehållet i intervjuerna presenteras och utvärderas i rapporten mot teoretiska begrepp inom logistik- och leveranskedjan. Dessutom samlades geospatial data från öppna källor för att utföra rumsliga och dataanalys och belysa de unika rumsliga egenskaperna på öarna och infrastrukturen i skärgården och de rumsliga förhållandena mellan dem. Uppgifterna som samlats in efter redigering har sammanställts till en rumslig databas som kan användas i framtida projekt.

De rumsliga egenskaperna hos öarna i skärgården presenteras genom en uppsättning kartor och rumsliga statistikrapporter som skapats i en GIS-programvara. De viktigaste styrkorna och svagheterna i avfallshanteringssystemet i skärgården presenteras i form av analytiskt innehåll. De används som grund för att föreslå framtida förbättringar av avfallshantering och planering och föreslå framtida projekt när det gäller att samla in mer omfattande data för skärgårdsområdet, samt för att minska kostnaderna, tiden och miljöpåverkan för operationerna.

Table of contents

1. Introduction ...... 3 Background ...... 3 The waste management problem and the Swedish solution ...... 3 The Stockholm Archipelago ...... 5 The current waste collection system ...... 7 Objective ...... 8 Method ...... 8 Scope ...... 9 2. Theoretical Reference frame ...... 10 Terminology and Fundamental Concepts ...... 10 Literature review ...... 12 3. Methodology ...... 16 Collection of qualitative data ...... 16 Collection of quantitative data ...... 16 Data processing ...... 17 Database creation ...... 18 Spatial and Data Analysis ...... 19 4. Results and Analysis ...... 22 Analysis of maps, graphs, and figures - Highlighting challenges ...... 22 Interview content ...... 46 Interview with Österåker municipality ...... 46 Interview with Norrtälje municipality ...... 47 Interview with Svensk Tanktransport AB ...... 48 Interview with Värmdö municipality ...... 48 Interview with Roslagsvatten AB ...... 49 Interview with an operator within the waste collection and transport system ...... 51 Analysis of interviews-references ...... 52 5. Discussion ...... 57 Deficiencies in data-Additional data needed ...... 57 Challenges encountered during the thesis work ...... 58 Proposals for improvement ...... 60 Proposals for future projects ...... 61

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6. Conclusion ...... 62 7. Appendices ...... 63 Appendix 1: Interview questions ...... 63 8. References ...... 65

Table of figures

Figure 1: The organisation of waste management in 2018 ...... 4 Figure 2: The Stockholm archipelago and names of the different islands ...... 5 Figure 3: Municipalities within the ...... 6 Figure 4: The waste collection system in the Stockholm archipelago by municipality and type of waste ...... 7 Figure 5: Workflow diagram of Master Thesis ...... 9 Figure 6:The major functions of the different planning time horizons ...... 11 Figure 7: Example of features from the dataset ...... 18 Figure 8: ER diagram of the proposed database ...... 19 Figure 9: Map of island polygons created from processing ...... 22 Figure 10: Map of island polygons and inhabited island polygons created from processing ...... 23 Figure 11: Average Nearest Neighbor Tool Results ...... 24 Figure 12: Average Nearest Neighbor Tool Results for Norrtälje kommun ...... 25 Figure 13: Average Nearest Neighbor Tool Results for Värmdö kommun ...... 26 Figure 14: Average Nearest Neighbor Tool Results for Österåker and kommuner...... 27 Figure 15: Average Nearest Neighbor Tool Results for Haninge and Nynäshamn kommuner ..... 28 Figure 16: Multi-Distance Spatial Cluster Analysis results ...... 29 Figure 17: Spatial Autocorrelation Tool Report for Number of Houses in Islands ...... 30 Figure 18: Map of Clustered Extreme values in number of houses on islands ...... 31 Figure 19: Close-up of clusters in Northern Norrtälje ...... 32 Figure 20: Close-up of cluster in southern Norrtälje ...... 33 Figure 21: Close-up of islands in the border between Värmdö and Haninge ...... 33 Figure 22: Map of Number of households per island ...... 34 Figure 23: Map of number of houses in Norrtälje islands ...... 35 Figure 24: Map of number of houses in Värmdö islands ...... 36 Figure 25: Map of number of houses in Österåker-Vaxholm islands ...... 37 Figure 26: Map of number of houses in Haninge-Nynäshamn islands ...... 38 Figure 27: Histogram of residential buildings on islands ...... 39 Figure 28: Map of islands based on distance from shore ...... 40 Figure 29: Histogram of distance of island centroids from shore (coastline) ...... 41 Figure 30: Map of islands with a single household ...... 42 Figure 31: Map of islands with no ports or marinas ...... 43 Figure 32: Map of islands with no roads ...... 44 Figure 33: Map of islands that are closer to another municipality’s main port ...... 45 Figure 34: Supply chain of Waste-To-Energy planning ...... 53 Figure 35: Logistics plan of waste collection in the archipelago ...... 54

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1. Introduction

The aim of this thesis is to analyse the unique spatial characteristics of the Stockholm archipelago, document the characteristics of the waste collection system at place and then propose improvements to the system.

Background

The waste management problem and the Swedish solution

Waste generation is drastically rising at an increasing rate and by 2050 it is estimated to outpace population growth by more than double. Cities and countries are rapidly developing without the necessary systems to manage the changing composition of citizens. In 2016, 1.6 billion tonnes of CO2 equivalent greenhouse gas emissions, 5 percent of global emissions were generated from solid waste management. The number is projected to rise to 2.6 billion tonnes by 2050. Solid waste management is a critical piece of planning for sustainable, inclusive, and healthy cities and communities. It can, however, often end up being the single highest budget item for many local administrations. (World Bank Group, 2018)

According to statistics from 2018, waste quantities in Sweden decreased by seven kilograms per person, the collected food waste increased by four percent and the bulk waste decreased by the same amount. Avfall Sverige, the national authority for waste management, has set the goal in cooperation with the municipalities to a 25/25 target. The total amount of food and residual waste will be reduced by 25 percent per person by 2025. The municipalities in cooperation with the state are funding measures to prevent household waste through the waste collection charge.

Waste management is dictated by the Swedish Environmental Code (1998:808). The definition of waste is any matter or object that the bearer disposes of, intends to dispose of, or is obligated to dispose of. According to the law each municipality is responsible for ensuring that household waste within the municipality is transported and recycled or disposed of. Every municipality is required to have its own waste and sanitation ordinance which consists of a waste plan and regulations for waste management.

In 64 percent of the municipalities, the collection of food and residual waste is primarily handled by private contractors. 33 percent handle the waste collection themselves, while the rest follow some mixed collection methods.

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Figure 1: The organisation of waste management in 2018

source: Avfall Sverige, 2018

The figure above (Figure 1) shows the type of waste management in the as of 2018, with the majority following a self-administrative model and many others investing in partially or wholly owned municipal agencies.

The cost of waste management to municipalities is recouped through a waste collection charge, set by the municipal council. This waste management fee covers the total cost, but any deficits may be funded through taxation. The charge is often divided into a fixed and a variable fee, for example one fee for waste collection and one for waste treatment. According to the prime cost principle in the Local Government Act, the municipalities’ revenue from the waste collection charges may not exceed their costs for waste management. (Avfall Sverige, 2018)

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The Stockholm Archipelago

Figure 2: The Stockholm archipelago and names of the different islands

source: Hydrographica AB, Jesper Sannel

The Stockholm archipelago extends along the east coast of Sweden at the latitude of the capital Stockholm. (Figure 2)The archipelago stretches about 200km from Singö in the north to Nynäshamn in the south and is at the widest near Stockholm where it extends 100km eastward. It consists of about 30000 islands of various sizes over an area of more than 35000 km2. The waters consist of basins and inlets that are connected by passages. The environment includes open waters, rocky coasts, fjord-like bays, and sheltered inlets. The inner archipelago is characterised by shallow bays and fjards that are sheltered from the open sea. The islands are large and covered by pine and spruce forests and some agricultural land. The inner part of the archipelago is usually covered by ice during winter. The central archipelago is influenced by wind and waves and the fjords are larger. There are more deciduous trees, bushes, and bare rocky shores on the islands. The outer areas of the archipelago are seldom covered by ice. The monthly mean air temperature ranges from -3°C to +17.2°C and the water temperature from +0.5°C to +16.1°C. The average precipitation is recorded as 540 mm. The mean depth of the water is 14 meters, while the maximum depth recorded is 241 meters. (Hill, Wallström, 2008)

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Figure 3: Municipalities within the Stockholm county

source: Länstyrrelsen Stockholm, 2017

From an administrative perspective the archipelago is divided into six municipalities as can be seen in Figure 3. The municipalities from north to south are: 1. Norrtälje 2. Österåker 3. Värmdö 4. Vaxholm 5. Haninge 6. Nynäshamn

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The current waste collection system

Figure 4: The waste collection system in the Stockholm archipelago by municipality and type of waste

source: Förstudie Skärgårdsavfall, 2019

The waste in the Stockholm archipelago can be classified in the following categories according to their type and the different operator collecting them: ● Sludge, gray- and blackwater ● Hazardous waste ● Recyclable materials ● Household waste ● Bulk waste

A diagram of the waste collection and transport system can be seen in Figure 4. In the Norrtälje and Värmdö municipalities the waste collection planning is performed in-house, while Österåker and Vaxholm municipalities have the municipal company Roslagsvatten AB (with non-archipelago municipalities also partaking) perform the planning process with member of the municipality attending meetings and Haninge and Nynäshamn municipalities have SRV a municipal company (with other non-archipelago municipalities also partaking) plan the waste collection and SMOHF as the quality control company. In Norrtälje the waste collection is performed by boats belonging to the operator Stridsholmen AB. Värmdö municipality has 4 operators handling the household waste: AB Melskär, Storösund AB, Suez Sverige AB, and Båtåkeri Kempe AB. The bulky waste is collected from properties by Båtåkeri Kempe AB and transported by ferries belonging to Suez Sverige AB. The recyclable materials are handled by Suez Sverige AB in Möja, AB Melskär in Runmarö and Svartsö and transported by boats belonging to Suez Sverige AB. Roslagsvatten AB has contracted Liselotte Miljö AB to handle household waste and recyclable materials and they in turn subcontract Saxarens Brygg AB and Elektrofors for operating the boats transporting the waste. Bulky waste is transported by Stridsholmen AB.

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In Haninge and Nynäshamn the operators are Utö Sjötransport AB and Ötransporter AB. Finally, Svensk TankTransport AB is handling the collection of sludge waste, gray- and blackwater from private sewers throughout the archipelago and their transportation to the mainland. (Förstudie Skärgårdsavfall, 2019 and interviews)

Objective The objective of this case study is to research and present the main characteristics of the waste collection system in the Stockholm archipelago.

Moreover, it is important to highlight what makes the archipelago unique from a geolocation perspective.

An evaluation of the waste management system from a logistics perspective needs to be presented.

Finally, improvements to the system will be proposed taking the unique character into perspective.

Method

For this report, different methods will be used. First, a number of semi-structured interviews will be conducted with different actors within the waste management system in the Stockholm archipelago. Data will be requested from the corresponding agencies and actors regarding historical waste volumes collected, sea routes used, spatial data of the archipelago area and possibly scheduling methods or algorithms used. Data analysis will be performed on the waste volumes to identify correlation between the data in regards to the period of the year and the population of the islands. A spatial correlation of the data within the archipelago or by each municipality will also be explored. The next step will be to use the spatial data in conjunction with the waste volume and the sea routes to construct a spatial database. The main characteristics of the waste collection system will be evaluated against the corresponding theoretical concepts. The routes will be modelled to calculate cost and emissions and present the figures accordingly. Finally, conclusions will be drawn and improvements for the future will be discussed. The following figure (Figure 5) is a graphical representation of the workflow to complete the thesis work.

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Figure 5: Workflow diagram of Master Thesis

Scope

The Stockholm archipelago while encompassing many islands, attributes to a really small total area as well as a low percentage of the waste volume generated in Sweden. While the waste management system is quite complex and the deficiencies can be encountered in different parts, this report will focus on the waste collection process and more specifically the waste volumes generated, the data collected and the planning and procedure of collecting them and transporting them to shore, as well as all the different actors within. Moreover, while information about the collection of different types of waste are provided the focus of the thesis will be on household waste which is the highest in volume and demands the most complex planning schemes.

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2. Theoretical Reference frame

Terminology and Fundamental Concepts In this chapter important definitions of terms in the spatial analysis and logistics fields are presented, to provide context when used throughout the analysis sections, but also to make the report more accessible to non-experts.

Spatial analysis: a toolkit afforded to GIS software, allows one to investigate geographic patterns in spatial data and the relationships between features and, if needed, to apply inferential statistics to determine the relevance of spatial relationships, trends, and patterns; to see if “what is next to what” and “what is connected to what” have significance. (Nelson et al., 2016)

Spatial cluster: a geographically bounded group of occurrences of sufficient size and concentration to be unlikely to have occurred by chance (Knox, 1989)

Statistical classification: a set of discrete categories, which may be assigned to a specific variable registered in a statistical survey or in an administrative file, and used in the production and presentation of statistics (Hoffmann, 1997)

Logistics: the management of all activities which facilitate movement and the coordination of supply and demand in the creation of time and place utility (Heskett et al. 1973)

Supply chain: includes logistics but also the supply of raw materials and components as well as the delivery of products to the final customer (Rushton et al., 2017)

Supply chain management planning: Figure 6 shows the 3 levels of supply chain management planning and their main characteristics in terms of timeframe, decision- making and interaction within the internal and external environment of a supply chain.

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Figure 6:The major functions of the different planning time horizons

source: Rushton et al.2017

Third-party logistics or Outsourcing: contracting a third-party business to operate elements of a company’s distribution, warehousing, and fulfilments services

The following terms refer to characterization of the supply chains in terms of segmentation by demand and supply characteristics.

Lean supply chain: producing no surplus or bulk (Rushton et al, 2017)

Agile supply chain: quick in movement, responsive to changes in demand (Rushton et al, 2017)

‘Leagile’ supply chain: combining characteristics of the two, postponement of adding finishing touches to the final product

The following terms refer to manufacturing management and the supply to demand relationship of products in a supply chain.

‘Push’ manufacturing system: goods are produced against the demand forecasts (Rushton et al, 2017)

‘Pull’ manufacturing system: goods are produced against the actual customer orders (Rushton et al, 2017)

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‘Hybrid’ manufacturing system: when a push system moves into a pull system in the Supply Chain (postponement) (Rushton et al, 2017)

Literature review

The focus of this chapter is a literature review of waste management in island nations or archipelagos around the world.

Kowlesser (2020) in his work presents the main challenges in waste management of small islands developing states with a focus on Mauritius. The main hindrance in most cases are the limited funds and the lack of specialised technical knowledge. Another important drawback, present in the Stockholm archipelago as well, is the scarcity of public land. A proposed solution might be the introduction of sanitary landfills, however in the case of the Stockholm archipelago that would be impossible due to the large number of islands, the average size of the majority of them, as well as, the division into different municipalities. However, another proposal that might be applicable would be the introduction of home composting to limit the volume of waste to be transported to shore.

In their work Estay-Ossandon and Mena-Nieto (2018) examine the contributing factors of solid waste generation in touristic islands with a focus on the Balearic Islands of Spain. The islands show similar characteristics such as the seasonality of the waste generation due to a large sector. Waste collection is similarly categorised into recyclable materials, paper, glass, plastic, and mix household, organic that are forwarded to landfills. Incineration is also quite prevalent with two large plants available. Other treatment methods include composting and biomethanisation. The balearic islands are, however, significantly larger in size and allow such solutions. It is quite interesting that due to the high operational cost of the plants, waste is imported from Ireland and Italy to make them more profitable. Also, of note is the conclusion that the overall size of and the income levels of the households as well as the level of education are the most common attributes affecting the generation of Municipal Solid Waste. For better planning it is imperative to discern between the tourist population and the resident population. Overall policies that would limit the generation of waste would lead to lower costs of collection and management methods.

Damanhuri, Handoko and Padmi (2013) describe the Municipal Solid Waste management in Indonesia. Indonesia comprises 13000 islands of various sizes and a population of almost 225 million. MSW management is performed by the local governments with only a few of them subcontracting to private companies. Waste is first transferred from households to transfer-points by hand-drawn carts and then subsequently transported to landfills by using trucks. Other forms include communal waste collection where individuals are responsible to transfer their waste to the collection points and individual direct collection with trucks passing by all the houses on the way to the landfill. The chapter mentions that data for MSW volumes is limited in most cases and the only systematic gathering of data is performed for case studies. More than half of the MSW generation is coming from households with commercial land use also having a 26% share. A relevant problem is the

12 waste handling since it is generally gathered in small containers, left on the ground or open plastic bags and boxes, thus requiring being shovelled and picked by hand, which can lead to exposure to hazardous and toxic materials. Almost 70% of the MSW is reported to be transported to landfills. Sadly, there is no mention in the text on how waste is transported from smaller islands to bigger ones for disposal.

Richard and Haynes (2013) discuss the solid waste management in the Pacific Island Countries and Territories region. The region comprises 7500 islands and coral atolls with 500 of them being inhabited. About 8 million people are reported to live in this area. The waste collection in these developing regions is mostly limited to big urban areas with less populated islands having no system in place. The main challenges for the region are the shortage of equipment and trained personnel with financing being dependent on external donations from large agencies and partners. The vast majority of waste is organic in nature with an increasing share of packaging waste due to imported goods. Most of the urban areas have a scarce waste gathering schedule ranging from 1 to 3 times per week with many being limited to 1 time. No further details for transportation from smaller islands are provided.

Tavares et al. (2008) discuss optimization methods in a 3D GIS environment to minimize fuel consumption. This goal is similar to the approach of this thesis, however the 3D component would not be necessary as for travel over water a 2D approach might be more appropriate. The optimization is performed in the ArcGIS suite, the 3D Analyst tool creates the network, then the fuel cost for each segment is calculated and finally the optimal route is selected to minimize the consumption through the Network Analyst extension. To calculate the Fuel Consumption Factor data about the vehicle type, speed and load were used together with the gradient of the road network. To recreate the 3D road network a Digital Terrain Model and a 2D road network were combined. Finally, for the optimization the network and the collection points were used as reference. Two case studies for the optimization were performed. The first resulted in an 8% fuel saving scenario and the second in a 12% fuel saving outcome. The general conclusion is that a 3D approach can be deemed appropriate for MSW collection over land.

Bogiatzidis and Komilis (2015) in their article compare different scenarios for MSW management in small islands. The case study is implemented for the island of Syros in the Aegean Sea and five different economic and life cycle-based scenarios are examined with the use of the LCA-IWM software. The work described can be directly related to this thesis since it is one of the few examining off-site transportation and also refers to islands with relatively small populations. The methods of waste treatment are valuated according to their cost and their environmental impact. The transportation to mainland for treatment had the least investment cost for facilities and equipment and also the least operating cost per ton. Moreover, the study shows that transportation to the mainland is beneficial up to a distance of 1020km for financial criteria only before a new landfill solution would become a better option, while for environmental criteria the distance threshold reaches up to 6720km. The alternative scenarios allow for a larger distance threshold cost wise but a lower environmental threshold environmentally wise.

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In their paper Zis et al. (2013) examine different options for MSW management in small Greek islands that currently rely on open dumps. The current situation is compared with the alternative of moving the waste to the mainland to allow recycling, revenue from metals and energy recovery. A statistical analysis of population and waste generation data based on the past years is performed to predict the number of trips needed for the examined period. It is important to note that tourism effects are also considered which can be useful in the current thesis. The need for large treatment facilities near the harbours is also highlighted to avoid additional transportation costs after unloading the vessels. In the particular paper there is no comparison performed between the transportation and open dumps/sanitary landfill scenarios environmentally wise. The off-site transportation scenario involves low upfront investments, which could make it attractive. The maritime transportation approach will result in a 29 euros per ton of waste lower treatment cost for a 9% interest rate. The paper also briefly examines having to separate streams of waste transportation, one for fermentable and one for recyclable waste. If treatment of recyclables is implemented in islands that have currently no such option up to 49% of total waste could be recovered.

Willmott L. and Graci S. (2012) examine the solid waste management system in a case study in the island of Gili Trawangan, Indonesia. It is interesting due to the semi-structured interviews performed and the data collection through observations. The system is quite complex with a large number of stakeholders involved, the main being a group of environmentally concerned citizens, FMPL and a group of tourism entrepreneurs, GET. The island is heavily reliant on tourism with the unique characteristics of no motorized vehicles and sewage treatment system. The island community has turned to outside institutions for expertise and resources. For the financial support of the waste management system part of the tourist eco-tax is employed. Through these partnerships an initiative for separation and storage of waste into three different bins has become available. Recyclable materials are transported to the mainland for selling to recycling depot, while other specialized waste types are exported to Bali. These changes have turned the island to a model of success for waste management in Indonesia.

Ghose et al. (2006) present a GIS transportation model for collection and transport of solid waste with a case study in the Asansol Municipality Corporation of West Bengal State (India). The model takes into consideration the population density, the waste generation capacity, the road network and the road classification, the location of storage bins, the characteristics of collection vehicles etc. with the goal of minimizing the cost and distance to the landfill. It is interesting that the bin classification has to do with the frequency of collection needed. The vehicle classification has to do with the road type limitations, the handling of bins and the capacity. The vehicles are presented with the cluster of bins that needs to be handled. Calculations for the GIS output the total travel time, total distance and number of bins cleared for each vehicle in a day. Finally, the total number of vehicles, the total distance and the total working hours per week can be summarized. These are later used to help calculate the fixed and operating costs of the waste management system proposed.

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Sonesson U. (2000) proposes in his work, a model for calculating fuel consumption and time during the waste collection process. It could be interesting for this thesis as despite old it uses simple input statistics and also refers to figures from Swedish municipalities. The dominant collection vehicles are compacting trucks. The fuel consumption and work time of the trucks is influenced by the total distance driven, the number of stops, but also the energy required for picking up, emptying the bins, and compacting the waste. The operational time is further influenced by the traffic conditions and the time of dragging and handling the bins by the crew. The distance driven and the number of stops are used as independent variables. The energy needed for handling the waste is assumed to be proportional to the number of stops. The risk of traffic congestion is not taken into account as it is quite unpredictable. The input parameter ‘km between stops during collection’ is used to indicate the stop frequency. The distance from unloading point to collection area is also used to calculate the total distance. The type of truck and fuel can be adjusted in the consumption per km and per stop variables. Fuel consumption is calculated by consumption relative to distance driven and relative to extra stops. Driving distance is calculated relative to moving to and from collection area and relative to collection work. Finally, the time is calculated relative to hauling, driving, and emptying the bins. The model is characterised as pseudo-mechanistic. The model works quite well but is rather susceptible to the individual characteristics of the collection areas. By adjusting these numbers accordingly, it can be used to give a good estimate of important figures when planning a waste collection process.

Tarantilis et al. (2004) discuss the application of GIS and routing algorithms to tackle real life distribution problems. A decision support system is employed that uses the metaheuristic algorithm BoneRoute in order to solve an Open Vehicle Routing Problem. In the OVRP the important aspect is that the vehicles do not need to return to the depot. The problem has very high computational complexity (NP-hard) so that in every-day problems with up to 50 customers, a large number of approaches focus on using heuristic approaches to minimize distance travelled by vehicles, satisfying constraints, rather than using exact algorithms. The GIS module holds all the spatial information in the background and more importantly the spatial database, the geocoding operation, and the spatial data adaptation tool. The BoneRoute solution is unique in the sense that it produces a new solution out of components, sequences of nodes, of previous best routes. The particular method used results in new best solutions in all the case studies where it was applied to. The proposed solution resulted in savings of 8–15% in medium scale problems and 15– 25% in large-scale problems, great accuracy and efficiency of dynamic routing, reduction of labour cost and quality of service to customers.

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3. Methodology

Collection of qualitative data

The first step in conducting this project was to gather information from the municipalities and entrepreneurs involved. For this reason, the format of semi-structured interviews was selected. The questions developed for this reason were used in both phone, email and person-to-person interviews and were improved upon and further clarified upon feedback. The questionnaire is included in Appendix 1. A semi-structured format was selected due to the provided advantages such as the open-ended questions with the opportunity for follow- up queries and clarifications. The open-ended questions allow the interviewees to elaborate on the subject as they deem fit. A semi-structured format was also advantageous since there was limited knowledge of the subject of waste management and its application in each particular municipality beforehand. The drawbacks, in this case, are the inability to provide real-time clarification for the questions when the interview is conducted through email exchange and the many hours of recordings, notes and transcripts to analyse afterward. (Adams, 2015) As mentioned all the in-person interviews were voice recorded with accompanying notes, the phone interviews were accompanied by handwritten notes and through the email exchange, the answers were analysed and interpreted.

The data gathered will be analysed from a logistics perspective. The main characteristics that are examined are:  The Supply Chain and its management  The structure of planning  The waste as a product  The cost analysis of logistics in the archipelago  The information flow within the waste collection system  The outsourcing of logistics processes to third parties The theory behind the evaluation of the logistics will be based on the book ‘The handbook of logistics and distribution management’ by Rushton et al.

Collection of quantitative data

The data used for analysis in this thesis come from the online geodatabase of SLU (Sveriges lantbruksuniversitet-Swedish University of Agricultural Sciences). The platform contains spatial and numerical datasets from various Swedish agencies available for free to use in research and academic purposes. The site zeus.slu.se allows access with the KTH student credentials. It provides a map of Sweden and the user can select an area by drawing a polygon. Then the user can select different map packs to download for the specified area. For this thesis, the data downloaded were the Fastighetskartan vector shapefiles and the Terrangkartan vector shapefiles, which will be described further. It is important to note that all shapefiles used the SWEREF 99 TM coordinate system which

16 was further used for all data and analysis purposes in the GIS software. The area chosen was specified by a polygon fitting the administrative border of Stockholm county. The downloaded files contained shapefiles of features of the related classes, as well as documentation in Swedish. The data can be categorised as operational and were provided for use by Lantmäteriet. They are all secondary data as they were not collected for the purpose of this thesis but rather beforehand for land registry operations and available for use in different context. The data utilised were: ● Fastighetsindelning vektor: administrative boundaries: an_get.shp contains county polygons and ak_get.shp contains municipality polygons ● Bebyggelse vektor: by_get.shp that contains building polygons amd bo_get.shp that contains among others piers, quays, and jetties ● Kommunikation vektor: vl_get.shp that contains road line segments and vo_get_shp that contains other road type line segments ● Markdata vektor: ml_get.shp that contains land data lines and my_get.shp that contains comprehensive land data polygons ● Terrangkartan vektor: mk_get.shp that contains the coastline line segments While other layers were downloaded and examined in a GIS environment these were the ones that were chosen as most appropriate for the analysis performed in this thesis work.

During the analysis there was a need to depict the main port locations used in the waste transport process namely: ● Räfsnäs for Norrtälje kommun ● Åsättra for Vaxholm and Österåker kommuner ● Bolvik for Värmdö ● Dalarö for Haninge and Nynäshamn kommuner Since descriptive attributes were not present in the downloaded shapefiles, the location of these ports was pinpointed from OpenStreetMap and Google Maps by performing search operations and they were manually created as a different point layer in the database of ArcMap.

Data processing

The processing of the data was performed using the ArcMap and ArcCatalog software by ESRI, the licence for which was acquired through KTH. The first step was to reduce the data size to significantly increase performance in ArcMap. From the attribute table of the shapefile containing county polygons the one with the attribute LANNAMN = STOCKHOLMS LÄN was selected and was exported into a new shapefile. Then the shapefile containing the municipality boundaries was imported and the polygons with attributes KOMMUNNAMN equal to NORRTÄLJE, ÖSTERÅKER, VAXHOLM, VÄRMDÖ, HANINGE, NYNÄSHAMN were selected and exported into a new shapefile. A copy of that shapefile was used to perform the MERGE operation from the likewise named tool to create a polygon that encompasses the archipelago area of interest. The next step was to add all the aforementioned downloaded shapefiles into ArcMap and use the Clip tool to clip them into the archipelago area shapefile to reduce the amount of data and make navigation easier. Furthermore, the island polygons needed to be created manually as such a

17 shapefile type did not exist. The shapefile containing land data polygons was imported and the polygons with the attribute DETALJTYP = VATTEN were selected and deleted. To delete the selected records an editing session was utilised. The remaining polygons were merged into a single polygon. This was done because records with attribute types ÖPMARK, SKOGBARR, ODLÅKER etc. were depicted as separate polygons and therefore spatial operations would be hard to perform. Then the shapefile containing land data lines was imported. The records with attributes DETALJTYP LIKE ‘%STRAND%’ were selected and exported into a new shapefile. Since the lines were all comprised of segments, they were combined with the tool UNSPLIT LINE and a new shapefile was created. The land shapefile was then clipped on the STRAND line shapefile to create separate island polygons. This process was performed because the coastline line data only contained the coastline of the mainland and not that of the separate islands in the archipelago. The coastline shapefile also contained separate segments so these two needed to be combined using the MERGE tool. A ‘mainland’ polygon was created from the area between the municipality borders and the coastline feature. All land polygons intersecting with that mainland polygon were selected using the SELECT LAYER BY LOCATION tool and deleted. The resulting shapefile contains all the island polygons not connected to the mainland through land. (Figure 10) Finally to reduce the data size of the remaining shapefiles select statements of the building, road network and piers shapefiles were performed and only those interesting with the island polygons were kept and exported into new shapefiles. The two first maps in the results section present the results. (Figure 9, Figure 10) An example of the features contained in the finalised shapefiles can be seen in Figure 7.

Figure 7: Example of features from the dataset

Database creation

A decision was made to compile the final shapefiles into a database that can be used to perform spatial queries, but also be used to add more qualitative characteristics in the future if they become available. Another use for the database would be to be made available for future research. The spatial database was created in PostgreSQL and more specifically in pgAdmin 4. The tables were not created manually but rather imported from shapefiles using the application PostGIS 2.0 to Shapefile and DBF Loader Exporter. The application allows connection to the database and selection of a shapefile to be converted

18 into a table and imported keeping the attributes and also creating a geometry attribute. This requires the Postgis extension to be created in the database with the command CREATE EXTENSION postgis; An Entity-Relationship diagram of the database will be as follows (Figure 8):

Figure 8: ER diagram of the proposed database

The different tables-entities are within polygon shapes, their respective attributes are within circles and the relationships are depicted with rhombuses. The type of the geometry is depicted in the top-left corner of each table. The type of relationship is presented by the numbers and letters 1, N which means 1 island can contain many road segments, buildings, piers, and each municipality can contain many islands. The primary key of each table is noted with an underlined name.

Spatial and Data Analysis

Different tools present in the ArcMap toolboxes were utilised to spatially analyse the different features present in the shapefiles and their relationship with each other. Furthermore, Microsoft Excel was used to import the attribute tables and perform basic statistical analysis and create graphs out of the calculated data.

The Near (Analysis) tool was used to detect and calculate the distance between input features and the closest instance of another feature. The tool then automatically adds to the input feature table the id of the closest feature as well as the distance in map units, in this case the planar method was selected, and the units were meters. This tool was utilised in several instances: First to calculate the distance between inhabited islands and the

19 closest main harbour used for waste collection and second the distance between inhabited islands and the mainland, in this case the closest segment of the coastline.

The Spatial Join (Analysis) tool was used to join the island shapefile to the buildings, road segments and piers shapefiles based on their spatial relationship. This was done to detect the number of features joined to each of the islands. The target features were the island polygons and the join features were in sequence the buildings, the road segments, and the piers. For the buildings and the road segments the Match Option was CONTAINS and for the piers INTERSECT. This operation creates a Join_Count column in the attribute table of the input features that shows the number of joined features matched. This was helpful in determining the number of houses and piers connected to each island as well as if an island contained road features.

Multiple SELECT statements were utilised to produce maps, both by attribute and by location. From the buildings’ shapefile those with the attribute ANDAMAL_1T LIKE ‘%Bostad%’ were selected and exported in a new layer. Since all kinds of buildings were present in islands only the residential ones were selected to be taken into consideration for the purpose of this thesis. Then the islands containing residential buildings were selected and exported in a new layer. From those islands the islands connected to the mainland through the road network were selected and deleted. This was done by selecting island polygons who intersected with road segments and then visually examining each one to pinpoint whether it was connected to the mainland or not. An assumption was made that since picking up waste with a land vehicle is significantly less costly both from a financial and environmental perspective, for those islands collection by truck would be selected. The islands containing one house were pinpointed through a SELECT SQL statement and presented in a map. Furthermore, inhabited islands with no road segments or piers were pinpointed with SELECT statements and presented in maps.

The Average Nearest Neighbour tool was utilised to detect whether the islands can be considered clustered or dispersed. The tool takes into consideration the feature centroid when calculating distances to its nearest neighbour. If the average distance is less than the average for a hypothetical random distribution, the distribution of the features being analysed is considered clustered. If the average distance is greater than a hypothetical random distribution, the features are considered dispersed. The average nearest neighbour ratio is calculated as the observed average distance divided by the expected average distance. (Mitchel, 2005) The Distance Method used for running the tool was the EUCLIDEAN_DISTANCE. The operation was performed for the whole archipelago and for the islands in each municipality separately. (Figure 11, Figure 12, Figure 13, Figure 14, Figure 15)

The Multi-Distance Spatial Cluster Analysis (Ripley's K Function) tool of ArcMap was used to pinpoint whether the island polygons show statistically significant clustering or dispersion over a range of distances. The default values were kept for running the tool since determining appropriate configuration parameters for such a large area was difficult. A graph of the results was generated and presented. (Figure 16)

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The Cluster and Outlier Analysis (Anselin Local Moran's I) tool was used to identify spatial clusters of islands with a high or low number of houses in the archipelago. The tool can also identify spatial outliers that may need special treatment when designing and optimising the waste collection system. (Mitchel, 2005) A map was produced showing these clusters and outliers. (Figure 18)

The Spatial Autocorrelation (Global Moran’s I) tool can measure spatial autocorrelation based on location of features, as well as feature values simultaneously. Given a set of features and an associated attribute, it evaluates whether the pattern is clustered, dispersed, or random. (Mitchel, 2005) The tool was used for the number of houses value for each island the INVERSE_DISTANCE conceptualization of spatial relationships and the EUCLIDEAN_DISTANCE method with no standardization. (Figure 17)

The tool Table to Excel was used to export the attribute table of the inhabited islands shapefile into a .xls file. The data analysis toolpak was utilised to calculate descriptive statistics on the number of residential buildings, the distance from shore and the island area. Then two histograms were created for the number of residential buildings and distance from shore. The bins were manually selected to better showcase the unique characteristics of the inhabited islands in the archipelago. (Figure 27, Figure 29)

When creating the maps different classification methods were used. For the number of houses the Natural Breaks (Jenks) classification method was used and 10 classes. Additionally, to make the data easier to comprehend 4 additional maps were created, one for each municipality or municipality group, with a fewer number of classes and a larger scale. (Figure 23, Figure 24, Figure 25, Figure 26) This method creates groups of similar values with a focus on maximizing the differences between classes. The class boundaries are set where there are relatively big differences in the values. (de Smith et al., 2018) For the distance from shore map the class intervals were selected manually as multiplicatives of 10 km.

For the maps showcasing unique characteristics and groupings of islands in the archipelago a bright red colour was selected to make features easily identifiable. For the map showing the islands relative to the distance to the main harbours the colours chosen for the ports are the same or similar to the colour of the island polygons for the same municipality or municipality grouping.

The large area of the archipelago and the sheer number of islands, many of which rather small, made selecting an appropriate map scale hard. For the maps, a small scale of 1:750000 was selected so as to encompass the whole archipelago area. Individual islands are hard to pinpoint but the large number of features and their relative location are easily observable.

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4. Results and Analysis In this chapter, the original results of the analysis performed are presented in the form of maps and graphs. The graphs were created in MS Excel, while the maps were created in ESRI’s ArcMap GIS software. The interview information presented are summaries and not full transcripts. The methodology to achieve the results is presented in Chapter 3.

Analysis of maps, graphs, and figures - Highlighting challenges

Figure 9: Map of island polygons created from processing

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The first map (Figure 9) presented in the results section shows all island polygons generated by the processing work described earlier. From this map it is abundantly clear the large number of islands in the archipelago as well as their dispersion within the different municipalities.

Figure 10: Map of island polygons and inhabited island polygons created from processing On the next map (Figure 10) the islands containing residential buildings are highlighted and discerned from those that are connected to the mainland through the road network. We can see that most of the large islands close to the coastline have bridges connecting them to the road network of Stockholm city.

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Figure 11: Average Nearest Neighbor Tool Results

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Figure 12: Average Nearest Neighbor Tool Results for Norrtälje kommun

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Figure 13: Average Nearest Neighbor Tool Results for Värmdö kommun

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Figure 14: Average Nearest Neighbor Tool Results for Österåker and Vaxholm kommuner

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Figure 15: Average Nearest Neighbor Tool Results for Haninge and Nynäshamn kommuner

The spatial statistics calculated show some patterns in the island location. The islands can be characterised as clustered with an observed mean distance of about 750 meters. (Figure 11) This again might not be indicative considering the reference points are the centroids and the varying size of the island polygons. The clustered characterisation is beneficial however in the way of implementing integrated routing solutions between neighbouring municipalities. Similar patterns are derived from Average Nearest Neighbour analysis in all municipalities and municipal groups. (Figure 12, Figure 13, Figure 14, Figure 15)

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Figure 16: Multi-Distance Spatial Cluster Analysis results The K function graph (Figure 16) shows statistically significant clustering in all distances above 5 kilometers with a higher clustering between 15 and 30 km. This can be helpful also in planning routing between islands close to each other or moving waste to central points of island clusters.

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Figure 17: Spatial Autocorrelation Tool Report for Number of Houses in Islands A spatial autocorrelation report was generated to find if islands with a relatively similar number of houses are close to each other, but the results show that such an assumption is completely unbased. (Figure 17)

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Figure 18: Map of Clustered Extreme values in number of houses on islands

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From the Cluster and Outlier Analysis (Figure 18) we can pinpoint instances of large values surrounded by low values depicted with the bright red colour. This analysis can be useful in order to create programs where waste can be collected in these islands with a higher number of residents, so as the ferries have to visit less islands and the scheduling can be further optimized. In Figure 19 one can observe an island with a high number of houses surrounded by islands with a few houses only in the area on the northern part of Norrtälje kommun. In Figure 20 one can observe an island with high values surrounded by several islands with low values in the middle part and a cluster of many islands in the eastern part with many islands with only a few houses. Finally, in Figure 21 there are two clusters present. A cluster of islands with low values in the side of the border belonging to Värmdö kommun and a cluster of islands with high values in the side of the border belonging to Haninge kommun.

Figure 19: Close-up of clusters in Northern Norrtälje

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Figure 20: Close-up of cluster in southern Norrtälje

Figure 21: Close-up of islands in the border between Värmdö and Haninge

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Figure 22: Map of Number of households per island

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Figure 23: Map of number of houses in Norrtälje islands

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Figure 24: Map of number of houses in Värmdö islands

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Figure 25: Map of number of houses in Österåker-Vaxholm islands

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Figure 26: Map of number of houses in Haninge-Nynäshamn islands

In Figure 22 we can see a classification of the houses based on the number of residential buildings they contain. The larger islands have a higher number of houses as anticipated. They are generally located close to each other. The islands with a few houses are in the outer ring of the archipelago. Norrtälje is hard to manage with small islands with few houses on the northern part far aaway from the central part of the archipelago, some large islands in a moderate distance from shore and many small islands with only a few houses towards the easter border. (Figure 23) Österåker and Vaxholm are probably the easiest ones to create a waste system for with a couple large islands with many houses near the shore and some islands with fewer residences towards the eastern border. (Figure 25, Figure 14)

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Värmdo is also hard to manage because most of the islands are far away from shore and the majority of them are of medium size with a moderate number of residences present on them. (Figure 24) Finally, Haninge and Nynäshamn do not contain a lot of islands but some of above average size in the central part of Haninge archipelago and a few of below average number of houses in the Nynäshamn side of the border. (Figure 26) The larger islands with many houses are surrounded by smaller ones which could help with centrally collecting waste in containers on the larger islands for the ferries to pick up since they may not be able to approach islands with shallow waters or small piers. The problem of islands with a few houses throughout the archipelago can be further identified in Figure 27.

Figure 27: Histogram of residential buildings on islands

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Figure 28: Map of islands based on distance from shore

In the previous map (Figure 28) the islands are classified based on their distance (perpendicular) to shore. The same results can be observed in the histogram of Figure 29.

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Figure 29: Histogram of distance of island centroids from shore (coastline)

The biggest problem is in Värmdö kommun where there is a large cluster of islands of above average area and with a medium size number of houses that need to be serviced. Another problem is the large dispersion between islands in Norrtälje as well as the low density of islands away from shore towards the eastern border both in Värmdö and Norrtälje.

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Figure 30: Map of islands with a single household

The previous map (Figure 30) shows all the islands with 1 household. Their number can be considered large and servicing them is especially ineffective considering the ratio between travel time and distance compared to waste volume for pickup. These islands also tend to be further away from shore. From the data analysis there are 438 islands with only one residence. Another challenge in logistics of waste collection in the archipelago is that furthermore 499 islands contain between 2 and 5 houses. So about 80% of the islands will be extremely inefficient to provide service for.

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Figure 31: Map of islands with no ports or marinas

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Figure 32: Map of islands with no roads The previous maps showcase the problems in the current dataset. From the publicly available data it becomes clear that there are many islands that do not have an access point or road network. Especially the lack of piers or marinas in 640 islands cannot be explained as boats are the only way to access these islands. (Figure 31) Any form of planning routes between islands in the dataset would therefore be unrealistic. The lack of roads is easier to explain. (Figure 32) In many of the smaller islands there is no need for vehicles and the residences are relatively close to access from the ports by walking. The use of 4-wheel vehicles for pickup of trash bags can also be attributed to the lack of a good road system. This however makes manual pickup even more challenging as the vehicle has to move at a low speed. In islands with rocky terrain where the house is not close to the port and the waste bin might not be at the pier, it will also be inefficient for the boat crew to pick up the bags. Concerning these two maps and to further show the challenge of

44 servicing the archipelago some calculations are presented. From the references and interviews the staff need about 2 minutes to pickup the trash bags from each household on average. The total number of residential buildings in the dataset are 24382. Only considering the islands with 1 house this makes about 438 minutes per round of collecting or 7 hours. Moreover, the driving of the vehicle door-to-door is also a considerable loss of time. In total there are 1743 km of roads in the inhabited islands of the dataset. Further calculations are unrealistic as there is no clear picture from the interviews so as to determine the speed of the vehicle and the length of road driven through. Some collection containers in the larger islands further help reduce the amount of time for on-island servicing.

Figure 33: Map of islands that are closer to another municipality’s main port

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The final map (Figure 33) helps highlight another deficiency in the waste collection system of the archipelago. The decision-making power of each municipality and the lack of communication between them leads to inefficient solutions from a planning perspective. Haninge and Nynäshamn are the only municipalities that have the main port in a location to service all the inhabited islands within the kommun boundaries. The average distance of the port in Dalarö from the islands however is 4,7 kilometres. This number however might be unrealistic since it is straight distance calculated from the centroids and not the closest island edge. Moreover, the distances referred are perpendicular so the minimum possible and not actual travel length by boat. In the other municipalities there is a significant number of islands closer to the main port of a neighbouring municipality. Utilising these ports to transport the waste would significantly reduce cost and emissions.

Interview content

Interview with Österåker municipality

The representatives were two planners employed by the community. One of them was the project leader from the municipality side for waste management. And the other one was the project leader specifically for the archipelago. Long-term planning is shared between the municipality and Roslagsvatten. There is a clear distinction between waste from municipal bins handled by the municipality and household waste handled by Roslagsvatten. There are regular project meetings between the actors in waste management. However, the municipality is only focused on strategic planning. The number of times the household waste is picked up from homes is based on the subscription level picked and paid for by the inhabitants. Nevertheless, there are many problems with this type of service for example, people saying they do not visit their cottage during winter and only paying for the summer months which can be often misleading.

The municipal bins are handled directly by the waste department in the municipality and outsourced to companies. The interviewees agreed that better cooperation in the collection of municipal and household waste could be achieved in the future to better save time and unnecessary boat trips at small capacity. A plan discussed but not yet implemented was to collect municipal waste at pickup stations on the islands and then transfer them to shore together with household waste. The interviewees were not sure if any of the bins have sensors to live-report volume, but the bins are visited and emptied at regular intervals regardless of load. A policy to reduce the number of bins and have people carry the litter to more centralized locations is being discussed. A new national policy will be implemented in the coming years forcing municipalities to collect recyclable materials within 50 meters of a household. These will lead to many changes in planning and operating waste collection throughout the archipelago. The introduction of centralized bins faces obstacles like the limited available space, the harsh weather conditions during winter and the power of habit of people having waste picked up from their homes.

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Concerning the recyclable materials, Roslagsvatten has picked up many of the responsibilities of FTI AB (a private company responsible for the collection and recycling of packaging and newspapers) in the archipelago part of the municipalities of Österåker and Vaxholm to collect and transport loads to shore. Unfortunately, due to the limited number of bins in some smaller islands, many of the recyclable materials may end up disposed of as regular waste.

Currently, there is no cooperation between neighboring municipalities in regards to waste collection management. It is quite interesting since taxing creates an equity model for waste collection since the tax amount for waste collection is the same throughout the municipality despite the higher cost of picking up and transporting waste in the archipelago part.

Interview with Norrtälje municipality

The municipality’s representative was the associate sanitation manager. The waste collection in the municipality is outsourced to Stridsholmen AB under a multiyear contract. The boats used for the collection are made from aluminum with an open cargo front deck. These accommodate waste directly or 4- to 6-wheel vehicles that can disembark to collect the trash bags. Some larger boats with cranes can also be employed. Larger islands are regularly visited by floating recycling centers. The boats often belong to the entrepreneur or can be rented from smaller businesses. The contracts are based on historical data about waste volumes. For the waste collection, a special software by EDP Consult AB is deployed. This software coordinates customer lists, subscriptions etc. It is used by both the municipality and the operator, in the office and in the field.

There is no definitive waste volume data collected for the archipelago unless requested by some organization. The waste is weighed in the plant, however, there is no distinction made for the point of origin, but rather the total volume collected from the municipality for that load. The driving schedules are planned by the contractors but approved by the municipality. All boats have GPS and can be observed in real-time through the application, however, there was uncertainty on whether route data are stored long term. During planning a different schedule is followed depending on the period of the year (summer/winter). There are no waste data saved long term, however, that will change next year with time and date data being added and stored. There was no info about the boat waste capacity, but different types of waste can be accommodated simultaneously like bags, bulk, and containers. The entrepreneur is choosing the types of boats used based on previous estimations.

The main difference between waste collection on land is the manual pickup of waste. Household waste are collected in bags, latrine in 25-liter containers and bulky waste in 15 kg packages. Injuries might occur due to sharp objects in the trash bags or stress from lifting weight. In some islands, the households have agreed to introduce a common pickup point which is rewarded with a discount fee if it covers at least 5 households. There are plans to increase the number of said stations in the future. There is an option for a

47 homeowner to request an additional pickup of waste through the application aside from the regular trips.

The main goals of the waste collection process are to minimize travel costs while maintaining a good level of service, to have a service that is not weather dependent and to finally incentivize people to carry the waste back to shore themselves instead of relying upon the municipality service. Strategic planning is also focused on minimizing environmental impact.

Interview with Svensk Tanktransport AB

The company is responsible for the collection of greywater and blackwater and a mix of the two in the vast majority of the archipelago. Oftentimes chemicals, latrine and sludge can also be transported by the company. Most of the households are visited two times per year to empty their tanks. The two routes are seasonal, one from April to Midsummer and the other from September to December. The archipelago is divided into 3 zones, south, west, and east, regardless of municipalities. It takes about 3 weeks to visit the islands in each zone. The average volume of graywater collected can reach up to 3 cubic meters. The boats used have large tanks below deck and a vehicle with a tank is transported on deck that visits the different households individually to empty the installations. The main problem encountered is that people avoid emptying the tanks to reduce the taxation cost. This together with the lack of inspection mechanisms by the municipalities may lead to leaks of the contents in the groundwater that will eventually reach the sea causing pollution. There is a need to enforce stricter regulations and control over the sanitation tanks in the islands by the municipalities.

Interview with Värmdö municipality

The representative was a waste engineer within the waste and cleaning department of the municipality. The Värmdö municipality procures entrepreneurs who collect household waste within the municipality and transport it to a treatment plant. Currently five different entrepreneurs are employed. While there is no cooperation concerning the waste collection the neighboring municipalities work together in the procurement of food waste. Large ferries are used to transport bulky waste and containers, while smaller boats are employed for household waste. The boats are owned by the entrepreneurs. Sadly, no specific technical characteristics were known.

According to the interviewee, the routes used are planned long-term. No waste data are reported from the islands. Waste are weighed when they reach the treatment plant, however this concerns the total load from the whole municipality and not island waste specifically. For the collection of household waste operational planning is left to the captain of the boat, the bulky waste routes are decided in cooperation with the municipality. There is special permission needed for the entrepreneur to accommodate mixed types of waste

48 on the boats. There are centrally located collection points for waste in some of the islands, however for the majority a door to door approach is followed. In the strategic planning of the municipality the focus is put on procurements, cost analysis, managing invoices, budget, and tax planning.

The long-term goals of waste collection are to extend the potential of separation of waste types collected and to keep a good and friendly work environment for all actors.

Interview with Roslagsvatten AB

Roslagsvatten is a municipal company responsible for collecting and treating the residual waste throughout the municipalities of Vaxholm and Österåker. The interviewee was the head of the waste department in the company. He is responsible for the strategic planning. While the company is responsible for the collection, they are not responsible for quality control over the work done. That is the obligation of the municipality. For example, the municipality is responsible for controlling the latrine containers used and its contents. is in a collaboration with neighboring municipalities while Österåker is solely focused on its territory. Roslagsvatten does not differentiate when planning the waste collection between the two archipelago sections, however.

The new regulation for the collection of packaging materials and food waste was also highlighted during the interview. The waste departments of different municipalities within Stockholm County would have a meeting in the near future to discuss how to better implement the stated changes. For the bulky waste, a recycling center ferry is employed to transport. The household waste is collected manually in plastic bags in the archipelago, something different than the rest of Stockholm, where the bags are automatically loaded to the trucks. Manual pickup is way more dangerous from a health safety perspective due to risk of injuries from metal or glass objects. While the dominant regulation requires the bags to be up to 15kg weight in the archipelago area they can reach up to 30 kilograms. A small vehicle with a large cage trailer is deployed to visit the households and transport the plastic bags. Subsequently, the vehicle is transferred to shore on a small open deck ferry. Another collection method is to put the plastic bags in large nets which can be picked up and stored in a larger ferry through a crane usage. In some islands, there are centrally located bins or bins near the port that can accommodate waste for the large ferry to pick up. Moreover, special bins can be found for the storing of glass materials and newspapers/magazines that are not supposed to be stored in plastic bags due to safety reasons and weight. A problem however is that the vehicle would still have to visit all the islands regardless. Due to the newly introduced regulations, more centrally located bins are planned for the future. The problem is once again the available land. Many spots are not owned by the municipalities, but by the ferry companies or the road department etc.

Three boats are used in the waste transport in the archipelago. Technical specifications were not known at the time of the interview. The contract for collection is held by one company Liselotte Lööf Miljö AB and they in turn subcontract Saxaren Bygg AB and AB Elektrofors that own the boats. The company has discussed future plans to buy a boat that runs with electricity if the solution is competing on cost with the current plan. This is

49 something that will need to be discussed with the other archipelago municipalities to move to collaborative planning.

Waste from both municipalities is moved to the same treatment plant where it is weighted. There is no distinction between waste from islands or land during the weighting. The number of bags collected from the islands is also not documented. The operators try to minimize the length of the trips to save money. Roslagsvatten does not require a specific operational planning of the entrepreneurs. Smaller islands with one inhabitant do not have a way of reporting waste so the operators have to visit them on regular intervals regardless. There are future plans to add volume sensors to waste bins to only visit islands that have waste to transport. Another method is to add a button that will send a signal when a particular bin needs emptying. Every day the small and large boats operate together. The small boat is faster so it can visit more islands. The large boat cannot visit all the islands due to the depth near the shoreline. The staff of the small boat can collect plastic bags on the net for the larger boat to pick up. There are plans however to move away from the net solution in the near future. The vehicles are only transported by the small boat. The travel plans are decided by the captains of the boats. The staff is using tablets with special software where they can see the household bins that need emptying and also report on the households they visit on each trip. The data is shared with Roslagsvatten in near real-time.

There are different subscription plans for the regularity of emptying the household bins. The fees are adjusted accordingly. The plans require the operators to visit the islands on a specific week. This might lead to waste remaining in a bin for a moderate time period. Since the cost is not large for most households (ex.1800SEK/year), Roslagsvatten is discussing moving to fewer subscription options. The subscription options are the same for all households throughout the archipelago. The planning is done only according to the level of service and data about waste volume or weight are not collected and not taken into consideration. In the future, if sensors are installed in centrally located bins more dynamic planning solutions can be implemented. The plan is to install such bins by 2025 at the latest for the whole archipelago.

Waste from all over the municipalities is collected in large bins. When those bins become full, they are sent to the treatment plan for weighting, a process that can take several weeks. The only daily information stored is the number of households visited. The large boat is obligated to have specific travel plans on board. However, the small boat can operate freely, even also do work for other contractors. Note that mixing waste from different municipalities is however forbidden. The boats have GNSS receivers, but the data are probably not stored long-term. Further information could not be provided at the time. Since only paper and glass is separated all other recyclable materials end up as household waste. These waste types can all be transported on the same boats but in different compartments. The operators are paid every time they empty the recycling bins, however the instructions are to avoid emptying the bins when they are near empty. Since there is no form of control, this can be confusing for both parties. As such more advanced solutions are required. Transportation of latrine containers is quite rare in the archipelago nowadays, since most houses have septic tanks. Waste in Sweden is not included in general taxation, but a

50 separate fee is required to be paid. There are serious safety concerns with the materials that may be thrown away in the plastic bags.

Worth highlighting is that operators are not allowed to treat any households differently, only to separate the municipality into smaller areas. All households within an area receive the same level of service. For the two municipalities in question, a distinction between land and islands is in effect. One of the long-term goals set is to lower the amount of household waste produced despite the population increase. There are campaigns run every year towards that goal. Any statistics collected and used at the household level are disaggregated from the total loads weighted in the treatment plan. Boats are allowed to operate from 6 am to 10 pm, however they rarely operate for long periods. The recycling center ferry requires several stops before it fills up. All operational planning is done according to rough estimations of waste produced by the number of households on the islands. Usually, the planning is capped not by the capacity of the boats but rather the time they operate within a day. Since the staff is paid an hourly wage and boats are operationally costly to run, the entrepreneurs have to balance the associated work and costs. Long term goals for waste planning in the two municipalities is to have more satisfied customers, increase safety in handling the waste collection and turn to fossil-free alternatives for transporting the waste to shore.

Interview with an operator within the waste collection and transport system

The company a contractor for waste collection in the archipelago They offer services in transport of goods on both land and sea. The interviewee is the operational manager of the company and also acts as a contact link between the municipality and the employees working on waste collection. There is a contractual agreement between the company and the municipality with clear terms and guidelines concerning waste collection and transport. The company is subcontracting other entrepreneurs to assist with the waste collection. There is currently no contact with other municipalities or operators in other municipalities for integrated solutions.

Two small boats and a ferry are used for the waste collection in the archipelago. The boats are owned by the subcontractors. The schedule for pickup is determined at the start of each year and it is evaluated at the end of the year. Pickup times are transmitted to the municipality, however waste volumes are only weighed at the treatment plant. The weekly schedule is usually pre-determined for the week, but it can be changed by the captain for example in case of bad weather conditions or if other obstacles are encountered like another boat docked at the island. The routes of the trips are not documented but the routes and location of houses and islands are visible in the tablet application EDP Mobile Fordon. Nowadays the routes rarely change due to an unprecedented volume of waste encountered. However, in the past, changes were implemented to improve logistics. The company is currently happy with the way the logistics and the collection planning work. The only waste volumes kept are the aggregated results from weighting in the treatment plants.

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The boats can accommodate household waste, glass, and newspapers/magazines. The floating recycling centre ÅVC can also collect metal, bulky waste wood and hazardous materials. The ÅVC vessel can collect about 100 cubic meters of waste. During the collection of plastic bags for household waste it has been noted that the average volume on a given week can exceed 1000 bags. One of the goals of planning is to maximize the waste collected per trip to reduce the overall number of trips needed. The municipality has strict guidelines to ensure that the same rules apply to waste collection in the archipelago as on land. Currently only household waste is being picked up from each household and the recyclable materials are only collected from bins near the port. Bags are collected by a 4-wheel vehicle or in rare cases directly at the dock when bins or containers are available. The bags are transported directly to the ferry where a compactor is located. If large containers are present, they are emptied on the ferry by crane. Bulky waste is only received on the Åvc ferry. Accidents mainly occur during handling of glass or sharp objects in trash bags. Even in islands where large containers are located for household waste, transporting the bags by individuals for each household is optional. The ferries visit these islands only when a container is near full.

On a daily basis the waste collection starts at 6:00. The small boats which are faster move ahead to unload the vehicles for manual bag collection. The large boat is visiting the islands with large containers and loading by crane. When the boats and the ferry meet up in a port, they can directly unload waste bags there. At the end of the shift the ferry transports all waste to the mainland where the bags are placed in large containers for dumping. For planning EDP Mobile and EDP Future are used. This is software by a private company largely used for waste collection purposes across many Swedish municipalities. There is a high level of integration between the mobile and desktop applications. Municipalities also have access to data from the applications.

Even though specific information about the subscription plans could not be shared, an estimation is that about 75% of them are seasonal. The interviewee highlighted the need for collection containers in all harbours where waste can be transported by individuals or by the vehicles for the large ferry to pick up. The main challenge for such a solution is the limited availability of property as most of it is leased to private parties. The long-term goal of the company is to achieve cost-effective logistics and green solutions as well as a good working environment to benefit both residents in the archipelago and company staff.

Analysis of interviews-references

The interviews help further present and highlight the unique challenges of having to service islands in a large archipelago. This problem is almost unique to Sweden. Most of the developed countries with GDP to invest in waste collection do not have archipelagos with so many small islands or have many islands with a bigger area where treatment solutions can be implemented on site. Countries with archipelagos are mainly located in SouthEast and the Pacific Ocean. However, most of these countries do not provide adequate financing and waste is disposed of locally through burning or local dumps, which are bad for the environment. The obligation of municipalities in Sweden to service each residential

52 building door-to-door is also something unique that is not encountered in other parts of the world. This has led to Sweden being a global innovator in collection and treatment of waste but subsequently may lead to challenges in areas like the archipelago with low population and house density and rough geomorphological characteristics.

From the interviews some things became apparent. First of all, while the planning departments and agencies of the municipalities cooperate in setting long-term goals and participate in meetings with the operators, they don’t take part or know specifics about how the operational planning is done. The municipalities also have an observational role since they cannot directly enforce policies on the citizens or influence the way the operators handle the waste collection. While they get regular data updates on collection goals, they do not have any forms of quality control or run opinion polls on island residents concerning their view of the waste collection system. The municipalities were hesitant in providing contact details of people working in the entrepreneur’s agencies or contact them to request some data or clarification of information. This might suggest that the municipalities have chosen to not participate in the operational planning or interfere with the contractors’ work. The fact that the contracts for waste collection usually last for several years and the terms are predetermined makes them inflexible to changes. Furthermore, the fact that the collected waste statistics are aggregated to the municipal level makes identifying and solving unique problems in the archipelago hard. Due to the costly operations of running such a service on the sea, it is also hard for the municipalities to enforce and monitor optimisation of the operational planning concerning scheduling and routing of pickup rounds, as well as environmentally friendly transportation. On the bright side, this is something that would benefit the operators to implement themselves. The outsourcing and subcontracting of the operations leads to many gaps in the information and statistics department. While it is considerably hard for one company to own and operate the whole fleet on the archipelago the subdivision of the area and the separate routes planned may lead to lengthier and more costly trips.

Figure 34: Supply chain of Waste-To-Energy planning

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Figure 35: Logistics plan of waste collection in the archipelago

Table 1: SWOT Analysis Matrix

Helpful Harmful

Weaknesses Strengths Bad communication/cooperation Experience Separation of waste handling Internal Equity in taxation Environmentally costly vessels origin Planning software Inflexible scheduling Lack of quality control Inflexible contracts

Threats Opportunities Weather conditions Additional financing from state External Location of islands Cooperation between municipalities origin National Policy Solutions already present on land Lack of available space

Lack of quantitative data

In Figure 34 a diagram shows the Waste-to Energy supply chain that is primarily followed in the Swedish model of waste handling. In this thesis, the logistics explored only focus on the supplier to manufacturer part. This supply chain does not include the typical actors since it describes partly a service and on the other hand an end-product repurposed for other use. Moreover, Figure 35 shows the roles of the main actors in the waste collection service and finally Table 1 briefly summarizes data collected from the interviews in a SWOT analysis.

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While no specific figures could be collected it is still important to evaluate the logistics from a qualitative perspective. Figure 34 shows a typical supply chain of the system in Sweden. The citizens act as both suppliers of waste and as customers in term of the energy produced from the waste treatment facilities throughout the region. The operators and more specifically the subcontractors running the boats act as collectors picking up the waste from the different households, transporting them to the mainland and then finally to the treatment facilities. The treatment facilities then act as a manufacturer treating the waste like raw materials to produce the final product that is energy. The energy is then distributed back to the citizens. The supply chain has thus a circular character. (Rushton et al., 2017)

Categorizing the Supply Chain Management levels in the waste collection to the different actors is quite complicated. The comparison is made to the theory and the graph (Figure 6) presented in the second chapter of the thesis. The strategic planning is mostly done at the municipality-level with the planning department assigning the taxation rate for waste collection and therefore the level of service, as in for example number of pickups per month, the general guidelines for waste collection as well as the particular terms of the contracts and payment rates. It is unclear if there is really a tactical management level present in the Supply Chain, but if one exists it is the responsibility of the main contracted companies. They are responsible for the planning of the routes, however as changes are hardly made during the year it is hard to consider yearly planning meetings as tactical management. The operational planning is mainly attributed to the subcontractors running the fleets, who take the schedule and realise it, but often have to make short-term adjustments to combat unprecedented hurdles. (Rushton et al., 2017)

The waste as a ‘product’ is difficult to characterize. Bulky waste and recyclable materials are not high-risk products thus the collection is planned long-term with 2-3 collection periods per year. On the other hand, leftover food is a time-constrained or perishable product as it cannot remain long in a bin to decompose. Moreover, the gray-, blackwater and latrine are also considered dangerous good and need special treatment. The fact that latrine boxes nowadays are rare in the archipelago and most residences have sewage tanks makes planning easier in seasonal periods as well. The different hazardous materials are also tricky to plan for as they need special handling aside from regular waste and special containers and vessels. Recyclable materials like glass and metal are also high risk from a safety perspective as they can cause damage to the waste collection employees. (Rushton et al., 2017)

The cost analysis of the archipelago waste collection and transport is definitely a complex problem. While specific financial figures were not available, certain conclusions can be made. The operators are required to service houses at a certain rate, despite the presence or not of bags at the premises. This leads to many problems. The operating cost of running and maintaining the fleet is quite high and the location of the islands and houses make the routing inefficient. From the interviews it became known that the operators are paid by the bag they collect. Therefore, sending the boats to islands with no bags to collect sets them at a clear financial loss. On the opposite hand, if the municipalities decide to pay more

55 standard rates to the operators, that additional cost will be transferred to the citizens, who will then have to pay for a level of service they don’t need.

These losses could be offset by investing in campaigns and marketing from the municipality’s perspective. During the interviews municipal workers shared that campaigns are already run to encourage citizens to reduce the amount of produced waste which has already showed results in the last couple of years. Further campaigns to encourage citizens to move waste to the mainland themselves or to move them to bigger islands during their trips to resupply or return home, would help further reduce service demand. Another option is a campaign to convince residents to send information on their actual needs, so each time a bag is placed in the bin. This would move the service system from a ‘push’ character to a hybrid with set daily routes in the archipelago planned long-term but the ability to be changed dynamically in short notice to accommodate actual demand.

Information in the currently running system appeared inflexible in between planning levels. As it became apparent in the interviews the lower the planning level, the better understanding of the unique characteristics of the problem did the participants have. The operators should help express the common hurdles that the staff collecting the waste encounters to the municipalities were the decision-making takes place. This could help alter the terms of the contracts and the overall guidelines to improve performance in the future. More flexible approaches in decision-making of all planning levels is also urgent. For example, this year the corona virus outbreak with the amount of people working from home increasing, as well as the hoarding of food supplies has led to an increase in waste production. It was apparent that the operators were facing higher demand than usual for the spring period. Readjustments in scheduling would therefore need to be made against the previous forecasts. Keeping the schedule planned at the beginning of the year would probably lead to citizen dissatisfaction.

Finally, it is important to refer to the outsourcing of logistics in the waste management system of the archipelago as it is widely used in all planning levels. The strategic planning is outsourced into established municipal companies in the cases of Haninge and Nynäshamn, SRV återvinning AB and in Vaxholm and Österåker, Roslagsvatten AB. These companies do not offer dedicated service since they plan waste collection for both municipalities. The rest of the municipalities do the strategic planning through dedicated departments inhouse. Most of the companies contracted at the operational planning level provide transport services for multiple-users and purposes, even private persons. The same can be said for the subcontractors that operate the fleets used for visiting the islands and transporting waste. The choice for outsourcing is mainly based on the sector expertise that the operators already held in the transport goods sector, as well as the geographical expertise of providing services in the archipelago waters. The price of leasing the services and not buying the fleet also helps to significantly lower the costs. The main corporate social responsibilities for all people operating in the waste collection and transport system is mainly based in providing eco-friendly options, have a good work environment and finally ensuring that the level of service for all citizens is the same. (Rushton et al., 2017)

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5. Discussion

Deficiencies in data-Additional data needed

The first thing to discuss is how realistic are the data collected for the purpose of the master thesis. Since the municipalities did not have free distributable data to provide, there is no way of knowing if the dataset corresponds to the ones used for planning purposes. Since the boat operators are using paid software from a private company for planning and collection it is also hard to export and share data on spatial features or routes for analysis. The lack of piers in many islands is especially contributing to that idea unless those islands are not serviced at all. The municipalities did not have spatial data on the inhabited islands to provide when they were contacted. An overall lack of data for the archipelago in general is a certain deduction of the thesis work.

Another lack of data has to do with specific volumes of waste collected from the islands. Since all waste statistics provided by Avfall Sverige are aggregated to the municipal level it makes it hard to pinpoint if the trends and figures of produced waste from households on land correspond to those of households on islands. This makes planning routes to optimise vehicle load hard.

Another concern is the lack of descriptive attributes for the spatial features collected. To be more precise, it is apparent that not all houses characterised as residential buildings correspond to actual living quarters for people the whole year round. The island polygons were exported and added to Google Earth and from visual inspection there were some islands with rocky terrain and no piers or access points with a building characterised as house. It is highly improbable that this building would need to be serviced regularly for waste collection, however due to lack of clarification in our database it would have to be included. This leads to two other problems.

The large number of spatial features in the database makes it impossible to ask operators or municipalities for clarification about whether they should be included in analysis work. The lack of qualitative attributes further contributes to that problem.

The number of piers is probably the most urgent one to address. In order to perform any kind of network analysis the first part is to pinpoint the exact access point of each island. There are islands with multiple piers or quays. The operators would need to pinpoint the exact one they use to dock the boats. In other island piers would need to be manually added to the database. In case the operators have found a better landing spot to use that might have to be added as a new feature type.

Seasonality of residence is also a matter of concern. While many buildings are characterised as residence not all of them have inhabitants the whole year round. Patterns of seasonal visits that are repeated over periods of time need to be documented and added to the database. Currently houses must be visited for pickup regardless of whether there

57 are people living there. A division of permanent residence buildings from others may significantly improve the planning process.

The lack of provided routes used in the planning process further made modelling for cost and emissions impossible. The only information on routes in the archipelago found online were from the SL ferries. This could hardly be used as the ferries have different sizes, engines, speed and can navigate different water depths. As mentioned throughout the thesis all distances calculated are perpendicular. That is obviously not realistic for planning as routes between islands may pass over land and depth and rock formations will not be considered. A hydrography map was available from the SLU data warehouse in raster format. However, using that dataset for cost path purposes would only lead to possible routes and not actual ones used. This would be helpful to find some relative differences between cost paths and perpendicular distances, but all conclusions would still differ significantly from reality.

To sum up, the created database needs to be supplemented or changed with additional data concerning: ● Classification of houses into permanent residence and seasonal, if seasonal add dates when the residents visit ● Validation of the already present road network and addition of other types of roads (ex. Dirt roads), connect roads to buildings or bin locations ● Pruning of the piers shapefile to only include actual access points used for the docking of boats and ferries during waste collection or create a new feature class of access points ● Implement a boat network connecting said access points between islands derived from data of all waste collection operators ● Create a shapefile where the location of all waste bins for household, recyclable or other types of waste are documented, add a capacity attribute to the table ● Work closely with operators to validate the islands that are serviced during the waste collection rounds ● Add a time/date attribute to each bin where the last pickup time will be constantly updated automatically with the help of installed sensors ● Add a waste volume attribute to each island, which can be updated by the operator with the number of bags collected after each pickup round

Challenges encountered during the thesis work

The main problem encountered concerns the communication with the operators. Many of them were not comfortable in having interviews in English and as my level of Swedish was not adequate to interpret their given answers in real time and have a full conversation, the questionnaire had to be translated and sent to them by mail. The answers were then translated to English and analysed at a later time. This led to a need for clarification from both sides and made exchange of information difficult and lengthy. Most of the information provided in the thesis comes from a rough translation of answers originally written in

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Swedish, so misunderstandings might have been present. The measures taken during the outbreak of Covid-19 and the move of many employees to remote work made it impossible to have face-to-face conversations. It was also hard to visit the offices of operators and see the software they use for planning on action. The measures made it difficult for conducting field work, as in to personally visit and assess islands of the archipelago to validate whether the data found correspond to the real world. Offers to follow the crew of a boat on a waste collection trip and document the process also had to be declined. The remote work also made it hard to contact operators through the kundservice phones or emails.

The reluctance of people at the operational level to share information about their work in contrast to the people at the planning level of the municipalities can be attributed to many factors. For example, I would assume that the contracts for waste collection is a big portion of their earnings every year. So they probably wouldn’t want to jeopardize their company’s survival and their chances to apply and win the contract for the next term by allowing the way they handle the logistics and planning exposed to the public in a research project, especially since from the interviews it became apparent that the operational planning is partially based on experience from previous years and empirical thinking rather than mathematical models, data analysis or routing algorithms. On the other hand, this also contributed to the municipalities being more open to share information, since they would benefit from the planning process becoming more transparent, the logistics analysed, and improvements being proposed. These led to the decision to keep all the names and roles of the people interviewed hidden from the final report to be published after several of them requested so. Towards the end of the thesis it became apparent that the tactic followed during the conversations to inquire about the levels of analysis, modelling and routing algorithms software, in order to validate my role as a student and researcher, might have the negative effect on people at the operational level, and make them less reluctant to share the figures of their work based mostly on experience.

The data needed for performing modelling or quantitative assessment of the waste collection system and its efficiency were not available by Länsstyrelsen Stockholm, any of the GIS departments in the municipalities contacted as well as from the operators. As a result, only free open data for research purposes could be used. Therefore, evaluation presented mainly relies on qualitative data. The operators and municipalities contacted for interviews all used the integrated systems provided by EDP so proposing alternate routing or scheduling with limited and unrealistic data would be unnecessary. As the operators seemed satisfied with the level of optimising present in the current routing and scheduling that came from years of experience on the field and under the unique archipelago conditions, proposals for changes in that regard should not be regarded as a secondary objective of the thesis.

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Proposals for improvement

Proposals concerning collection of quantitative data are analysed in a previous section. Therefore, this section will focus on qualitative solutions.

● A path towards further integration would be to combine the collection of household waste with municipal waste, as well as implement vessels and guidelines for integrating transport of both household and recyclable materials.

● The archipelago needs to be treated as a different entity planning and decision- making wise. Routing and scheduling needs to transcend municipality borders.

● Sensors need to be installed in waste bins across the archipelago. These would send a signal when the bin has enough waste to be collected or it can be manually activated by the residents when they leave a bag of waste for pickup. The signals will then be used for a dynamic planning process of collection and transport routes. This solution will significantly reduce cost and time in operations throughout the archipelago. The technology would help turn the demand-supply of waste collection services towards a more leagile path. (Rushton et al., 2017) Moreover, these sensors could lead into collecting and transmitting data on waste volume in each bin to create historical records for forecasting.

● Campaigns should be run by municipalities to encourage residents to carry the waste back to shore when they leave an island or move the waste into a larger island when they go to resupply. Alternatively, seasonal residents could notify the municipality when they visit their island cottage. This would also significantly reduce time and cost in the operational planning.

● Centralised bins need to be added to islands to reduce time needed to drive to each doorstep. If these large containers can be near the quay that would be even better from a time efficiency perspective

● Since the number of boats operating for waste collection in each municipality is not significantly high, an investment could be made to buy electric boats or boats that operate with more eco-friendly fuel. Taking into consideration the large distance travelled on a daily basis that choice could lead to significant reductions in yearly cost and reduce the amount of emissions generated. These boats could operate throughout the whole archipelago or leased to multiple operators for use

● Operating vehicles on land is significantly cheaper than running a fleet of boats. The current approach is to transport vehicles on boats that are used for collection. This ranges from small buggies and 4-wheelers to full trucks. The added weight on the boats impacts their speed, as well as the amount of fuel consumed and therefore the produced emissions. Having land vehicles on the islands could help reduce that cost even if such an approach would be sensible only for islands with a larger area.

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Providing jobs for people to collect the waste bags on site with said vehicles and putting them in large containers near the quays would further help reduce the time- cost of operations and allow the boats to move on to the next island faster.

● While information about all the vessels utilised throughout the archipelago was not available, another proposal for further reduction of operational time would be to use standardised containers on the trailers behind the 4-wheelers which can be closed, secured and loaded to the ship by crane and unloaded when the vessels return to land. Standardized containers of a larger size that can accommodate the smaller ones can also be on board the ship. This significantly reduces loadding/unloading time instead of handling separate bags or bulky loads, especially considering the number of houses in many islands as well as the number of islands in the archipelago in need of service.

Proposals for future projects

The projects regarding the waste collection system in the Stockholm archipelago are intriguing due to the complexity of the situation from a geolocation and planning perspective. While this thesis might not have been able to properly analyse all the aspects of the logistics, in particular the planning and scheduling of the waste collection and transport, it offers a basis in the form of providing information for the Supply Chain management levels, as well as a database to be used by researchers and professionals in future work. Ideas for project to be undertaken in a future time could be:

● Finishing the database: A project can be run to visit different islands and document their main features. These could be used to supplement the proposed database. Such a system could be made available to public for research purposes and to the municipalities and operators for planning

● Application to calculate paths: An application could be made that would take requests for emptying waste bins. It would then calculate the shortest path from the current location to the bin over sea and through the road network of the island

● Multicriteria evaluation for the placement of collection containers: Constraints could be applied for analysis like the available land parcels and locations could be narrowed down by buffer zones of 50m from households. These and other characteristics could be used to determine the appropriate location for introducing containers for collecting large amounts of waste

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6. Conclusion

The original goal of the master thesis as discussed with the representatives of Länsstyrelsen Stockholm and decided upon was to document and visualize the deficiencies and challenges in the waste collection system of the archipelago. This is achieved through a mixture of qualitative and quantitative data and analysis performed on them.

This thesis helps present the main characteristics that make the Stockholm archipelago unique from a geopolitical perspective. The large number of islands, the varying number of residences on them, as well as the limited infrastructure present hurdles in designing an effective waste collection and transport system in the area.

Furthermore, through the valuable information collected by contacting interviews, the current choices in overcoming the hardships were documented and evaluated from a logistics and supply chain management perspective. While the lack of adequate and realistic quantitative data made it hard to valuate the cost and emissions of the current logistics system, the qualitative research was used to propose several improvements to the system and ideas for future projects.

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7. Appendices

Appendix 1: Interview questions

1. Briefly describe the company and its role in the waste collection in the Stockholm archipelago 2. What is your role within the company? Are you directly working in the waste collection department? 3. What is the relationship between the company and the municipality in regards to the waste collection? 4. What is the relationship between the company and the other operators in the waste collection system within the same or neighbouring municipalities? 5. What types of boats are used in the waste collection and what are their main characteristics? 6. Are the boats owned by the company or rented according to the daily needs? 7. Is the company getting data about the waste volumes from a different authority? 8. Are the routes and the schedule planned short-term or long-term according to historical data? A mix of both? (more details) 9. Are (close to) real-time data waste volume reported from individuals in the archipelago or local authorities? 10. Is the company recording the waste volume collected and if yes in which ways? 11. Are the routes planned in detail in advance or are up to the captain and crew to decide? 12. Are any navigating devices (ex. GNSS receiver) used to record the trips? If so in which form? Can they be shared for data and network analysis? 13. Are the routes changing dynamically according to the waste volume encountered? 14. Are historical data about waste volumes and trips recorded? If so in which form? Can they be shared for data and network analysis? 15. What kind of waste is accommodated on the boats? What is the maximum capacity? Have any other arrangements been tested in the past? Are multiple types of waste allowed to be collected on the same boat? 16. Is the planning focused on maximizing the capacity of waste accommodated in the boats? 17. What are the guidelines for waste collection? Are they the same as for the waste collection on land used throughout Sweden? Are they specified further within the municipality? 18. What are the guidelines for the door-to-door collection provided to the company employees? 19. What are the differences in handling different types of waste? 20. Any health or safety concerns for the handling of specific waste types? 21. Is there some specific classification of the islands used in regards to for example population, historical waste volumes, access to public transport to shore?

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22. Do the islands have ways of centrally collecting waste or do they have to manually be collected from each household? 23. Describe a typical day of an employee at the strategical and operational level? 24. Are any advanced technologies used in the planning process? (software/hardware) 25. What are the main challenges encountered in the strategic and operational levels? 26. What are the short- and long-term goals of the company in regards to the waste collection in the Stockholm archipelago?

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8. References

Adams W. C. (2015). Conducting Semi-Structured Interviews. In Handbook of Practical Program Evaluation: Fourth Edition (pp. 492-505). Wiley Blackwell.

Bogiatzidis C. & Komilis D. (2016). Economic and life cycle analysis of municipal solid waste management for small Islands: On-site management scenarios versus off-site transportation. GlobalNEST International Journal. 18. 197-213.

Tarantilis C.D., Diakoulaki D., Kiranoudis C.T., Combination of geographical information system and efficient routing algorithms for real life distribution operations, European Journal of Operational Research, 152 (2004), pp. 437-453

Estay-Ossandon C., Mena-Nieto A., Modelling the driving forces of the municipal solid waste generation in touristic islands. A case study of the Balearic Islands (2000–2030), Waste Management, Volume 75, 2018, Pages 70-81, ISSN 0956-053X, https://doi.org/10.1016/j.wasman.2017.12.029

Damanhuri E., Handoko W., Padmi T. (2014) Municipal Solid Waste Management in Indonesia. In: Pariatamby A., Tanaka M. (eds) Municipal Solid Waste Management in Asia and the Pacific Islands. Environmental Science and Engineering. Springer, Singapore de Smith M. J., Goodchild M. F., Longley P. A., Univariate classification schemes in Geospatial Analysis—A Comprehensive Guide, 6th edition, 2007–2018

Willmott L. & Graci S. R. (2012), Solid Waste Management in Small Island Destinations: A Case Study of Gili Trawangan, Indonesia, Téoros, 71–76, https://doi.org/10.7202/1036566ar

Dreijer S., (2019) Förstudie Skärgårdsavfall, Länsstyrelsen Stockholm

Nelson E. L., Greenough P. G., Ciottone's Disaster Medicine (Second Edition), 2016

Heskett J. L., Glaskowsky N. A., & Ivie R. M. (1973), Business logistics: Physical distribution and materials management, New York: Ronald Press Co

Hill C., Wallström K., “Chapter 14 The Stockholm Archipelago.” Ecology of Baltic Coastal Waters, by Ulrich Schiewer, Springer, 2008.

Hoffmann E. (1997), Methodological issues in the development, use, maintenance and revision of statistical classifications, Third Meeting of the Expert Group on International Economic and Social Classifications, New York, 1997

Knox E. G. (1989), Detection of clusters, In Elliott P (ed) Methodology of enquiries into disease clustering, Small Area Health Statistics Unit, London, pp. 17–20

65

Kowlesser P. (2020) Solid Waste Management in Small Island Developing States, Specifically in Mauritius. In: Ghosh S. (eds) Solid Waste Policies and Strategies: Issues, Challenges and Case Studies. Springer, Singapore

Länsstyrelsen Stockholm, Kommuner i Stockholms län, Länsstyrelsen Stockholm webpage, https://www.lansstyrelsen.se/stockholm/om-oss/om-lansstyrelsen-stockholm/om- lanet/kommuner-i-stockholms-lan.html, Accessed on 04/02/2020

Ghose M.K., Dikshit A.K., Sharma S.K., A GIS based transportation model for solid waste disposal – a case study on Asansol municipality, Waste Management, 26 (2006), pp. 1287- 1293

Mitchell A., The ESRI Guide to GIS Analysis, Volume 2, ESRI Press, 2005

Richards E., Haynes D. (2014) Solid Waste Management in Pacific Island Countries and Territories. In: Pariatamby A., Tanaka M. (eds) Municipal Solid Waste Management in Asia and the Pacific Islands. Environmental Science and Engineering. Springer, Singapore

Rushton, A., Croucher, P. and Baker, P. (2017), The handbook of logistics & distribution management - Understanding the Supply Chain (6th edition), Kogan Page, London

Sannel J. (2009), Stockholm archipelago, covering the islands between Arholma(north)- Landsort(south)-Stockholm(west)-Svenska Högarna(east), Hydrographica AB, April 2009, https://commons.wikimedia.org/wiki/File:Karta_Sthlm_skg_Wikipedia.gif, Accessed on 04/02/2020

Sonesson, U. (2000). Modelling of waste collection - a general approach to calculate fuel consumption and time. Waste Management & Research, 18(2), 115–123. https://doi.org/10.1177/0734242X0001800203

Swedish Waste Management (2019), Avfall Sverige, www.avfallsverige.se/Publikationer, Accessed on 04/02/2020

Tavares G., et al. “Optimisation of MSW Collection Routes for Minimum Fuel Consumption Using 3D GIS Modelling.” Waste management (New York, N.Y.), ISSN: 0956-053X, Vol: 29, Issue: 3, Page: 1176-85, Pergamon, 1 Oct. 2008, www.sciencedirect.com/science/article/pii/S0956053X08002717.

Kaza S., Yao L. C., Perinaz B., and Van Woerden F., 2018, What a Waste 2,0: A Global Snapshot of Solid Waste Management to 2050, Urban Development Series, Washington, DC: World Bank, https://doi:10,1596/978-1-4648-1329-0, License: Creative Commons Attribution CC BY 3,0 IGO

Zis T., Bell M. G. H., Tolis A. I. & Aravossis K. G. (2013). Economic Evaluation of Alternative Options for Municipal Solid Waste Management in Remote Locations. Waste and Biomass Valorization. 4. 287-296, https://doi.org/10.1007/s12649-012-9151-5

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