KNOWLEDGE PAPERS WHAT A WASTE A Global Review of Solid Waste Management Cover photo on right and on this page: Conakry landfill, Guinea (Charles Peterson photographer). Cover photo on far left: separate containers for recyclables and non-recyclables, Barcelona, Spain (Perinaz Bhada-Tata photographer). KNOWLEDGE PAPERS WHAT A WASTE A Global Review of Solid Waste Management Daniel Hoornweg and Perinaz Bhada-Tata March 2012, No. 15 Urban Development Series Produced by the World Bank’s Urban Development and Local Government Unit of the Sustainable Development Network, the Urban Development Series discusses the challenge of urbanization and what it will mean for developing countries in the decades ahead. The Series aims to explore and delve more substantively into the core issues framed by the World Bank’s 2009 Urban Strategy Systems of Cities: Harnessing Urbanization for Growth and Poverty Alleviation. Across the five domains of the Urban Strategy, the Series provides a focal point for publications that seek to foster a better understanding of (i) the core elements of the city system, (ii) pro-poor policies, (iii) city economies, (iv) urban land and housing markets, (v) sustainable urban environment, and other urban issues germane to the urban development agenda for sustainable cities and communities. Copyright © World Bank, 2012 All rights reserved Urban Development & Local Government Unit World Bank 1818 H Street, NW Washington, DC 20433 USA www.worldbank.org/urban This publication is a product of the staff of the World Bank Group. It does not necessarily reflect the views of the Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. This note is provided for information only. The World Bank has no responsibility for the persistence or accuracy of URLs and citations for external or third-party sources referred to in this publication, and does not guarantee that any content is, or will remain, accurate or appropriate. TABLE OF CONTENTS Maxim Tupikov /Shutterstock.com Foreword vii Acknowledgements viii Executive Summary ix Abbreviations and Acronyms xi Country Classification According to Region xii Country Classification According to Income xiii 1. Introduction 1 2. Global Waste Management Practices 4 3. Waste Generation 8 4. Waste Collection 13 5. Waste Composition 16 6. Waste Disposal 22 7. Waste and the Environment 25 A Note on the Reliability of Solid Waste Data 32 iv URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS Annexes A. Map of Regions 36 B. Map of Income Distribution 38 C. Availability of MSW Data by Country 40 D. Countries Excluded for Lack of Data 45 E. Estimated Solid Waste Management Costs 46 F. MSW Generation Data for Cities Over 100,000 47 G. MSW Collection Data for Cities Over 100,000 63 H. MSW Disposal Methods for Cities Over 100,000 71 I. MSW Composition Data for Cities Over 100,000 78 J. MSW Generation by Country — Current Data and Projections for 2025 80 K. MSW Collection Rates by Country 84 L. MSW Disposal Methods by Country 87 M. MSW Composition by Country 90 N. IPCC Classification of MSW Composition 93 O. The Global City Indicators Program 94 References 95 WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT v List of Tables 1. Comparison of solid waste management practices by income level 5 2. Generators and types of solid waste 7 3. Current waste generation per capita by region 9 4. Waste generation projections for 2025 by region 10 5. Current waste generation per capita by income level 10 6. Waste generation projections for 2025 by income 11 7. Sources for 2025 projections of solid waste generation 12 8. Average MSW generation rates by income 12 9. Types of waste and their sources 16 10. Types of waste composition by income level 19 11. MSW disposal by income 23 12. MSW disposal in two contrasting regions 24 13. Landfill classifications 29 14. Landfill methane emissions and total GHG emissions for selected countries 30 15. Technical GHG mitigation opportunities by waste management component 31 List of Figures 1. Waste generation by region 9 2. Waste generation by income level 11 3. Urban waste generation by income level and year 12 4. Waste collection rates by income 15 5. Waste collection rates by region 15 6. Waste composition in China 17 7. Global solid waste composition 17 8. Waste composition by income 19 9. Solid waste composition by income and year 20 10. Waste composition by region 21 11. Total MSW disposed of worldwide 22 12. Low-income countries waste disposal 24 13. Upper middle-income countries waste disposal 24 14. Waste hierarchy 27 List of Boxes 1. What a Waste 1999: What’s changed (and what hasn’t) in the last decade 2 2. Definitions of Municipal Solid Waste 4 3. Components of an Integrated Solid Waste Management Plan 25 4. Integrated Sustainable Waste Management Framework 26 FOREWORD Photo: ©Simone D. McCourtie/World Bank Solid waste management is the one thing just than two million informal waste pickers, is now ITC landfill and about every city government provides for a global business with international markets and recycling center, its residents. While service levels, environ- extensive supply and transportation networks. Ankara, Turkey mental impacts and costs vary dramatically, Locally, uncollected solid waste contributes to solid waste management is arguably the most flooding, air pollution, and public health impacts important municipal service and serves as a such as respiratory ailments, diarrhea and dengue prerequisite for other municipal action. fever. In lower income country cities solid waste management is usually a city’s single largest Currently, world cities generate about 1.3 billion budgetary item. tonnes of solid waste per year. This volume is expected to increase to 2.2 billion tonnes by 2025. The report you have before you is an important Waste generation rates will more than double over one that provides a quick snapshot of the state of the next twenty years in lower income countries. today’s global solid waste management practices. Globally, solid waste management costs will A credible estimate is made for what the situation increase from today’s annual $205.4 billion to will look like in 2025. The findings are sobering. about $375.5 billion in 2025. Cost increases will Improving solid waste management, especially be most severe in low income countries (more in low income countries, is an urgent priority. than 5-fold increases) and lower-middle income Hopefully, this report will contribute to the countries (more than 4-fold increases). dialogue that leads to much-needed action. The global impacts of solid waste are growing Rachel Kyte fast. Solid waste is a large source of methane, a Vice President and Head of Network, powerful GHG that is particularly impactful in Sustainable Development the short-term. The recycling industry, with more The World Bank Ghabawi landfill, Amman, Jordan Photo: Perinaz Bhada-Tata Acknowledgements This report was written by Daniel Hoornweg and Perinaz Bhada-Tata; and managed by Abha Joshi- Ghani, Manager of the Urban Development and Local Government Unit and Zoubida Allaoua, Director of the Finance, Economics and Local Government Department. The ‘Waste and Climate Change’ section is from Charles Peterson. The authors would like to thank Christa Anderson, Julianne Baker Gallegos, Carl Bartone, Marcus Lee, Catalina Marulanda, John Norton, Charles Peterson, Paul Procee, and Sintana Vergara for their useful feedback and comments. The report was also discussed and reviewed by the World Bank’s Waste Management Thematic Group. Adelaide Barra, Xiaofeng Li, Jeffrey Lecksell and Claudia Lorena Trejos Gomez provided support and research assistance. EXECUTIVE SUMMARY Photo: Ron Perry/Oki Golf As the world hurtles toward its urban within the local government’s purview. A city that Golf course: future, the amount of municipal solid cannot effectively manage its waste is rarely able post closure use waste (MSW), one of the most important to manage more complex services such as health, of landfill site by-products of an urban lifestyle, is growing education, or transportation. even faster than the rate of urbanization. Ten years ago there were 2.9 billion urban Poorly managed waste has an enormous impact residents who generated about 0.64 kg on health, local and global environment, and of MSW per person per day (0.68 billion economy; improperly managed waste usually tonnes per year). This report estimates results in down-stream costs higher than what it that today these amounts have increased would have cost to manage the waste properly in the to about 3 billion residents generating 1.2 first place. The global nature of MSW includes its kg per person per day (1.3 billion tonnes contribution to GHG emissions, e.g. the methane per year). By 2025 this will likely increase from the organic fraction of the waste stream, and to 4.3 billion urban residents generating the increasingly global linkages of products, urban about 1.42 kg/capita/day of municipal solid practices, and the recycling industry. waste (2.2 billion tonnes per year). This report provides consolidated data on MSW Municipal solid waste management is the most generation, collection, composition, and disposal important service a city provides; in low-income by country and by region. Despite its importance, countries as well as many middle-income countries, reliable global MSW information is not typically MSW is the largest single budget item for cities available. Data is often inconsistent, incomparable and one of the largest employers. Solid waste and incomplete; however as suggested in this report is usually the one service that falls completely there is now enough MSW information to estimate Ghabawi landfill, Amman, Jordan Photo: Perinaz Bhada-Tata x URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS global amounts and trends. The report also makes Pollution such as solid waste, GHG emissions projections on MSW generation and composition and ozone-depleting substances are by-products of for 2025 in order for decision makers to prepare urbanization and increasing affluence. plans and budgets for solid waste management in the coming years. Detailed annexes provide Improving MSW is one of the most effective ways available MSW generation, collection, compo- to strengthen overall municipal management and sition, and disposal data by city and by country. is usually a prerequisite for other, more compli- cated, municipal services. Waste workers, both Globally, waste volumes are increasing quickly – formal and informal, have a significant impact on even faster than the rate of urbanization. Similar overall MSW programming. While in more affluent to rates of urbanization and increases in GDP, rates countries ageing workers are a growing challenge, of MSW growth are fastest in China, other parts the effective integration of waste pickers, particu- of East Asia, and parts of Eastern Europe and the larly in low-income countries, is critical. Middle East. Municipal planners should manage solid waste in as holistic a manner as possible. This report is a follow-up to What a Waste: Solid Waste There is a strong correlation between urban solid Management in Asia, a Working Paper Published by waste generation rates and GHG emissions. This the East Asia and the Pacific Region Urban and Men pick up used link is likely similar with other urban inputs/ Local Government Sector of the World Bank in cardboard boxes to outputs such as waste water and total energy use. 1999. The report has been expanded to include the sell for recycling in the San Joaquin Reviewing MSW in an integrated manner with a entire world, given data availability and increased open-air market in more holistic approach, focusing on urban form inter-dependence between nations and linkages in Salvador, Brazil and lifestyle choice may yield broader benefits. global trade, particularly that of secondary materials. Photo: Alejandro Lipszyc/World Bank WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT xi Abbreviations and Acronyms AFR Africa region C&D Construction and demolition CDM Clean Development Mechanism EAP East Asia and Pacific region ECA Europe and Central Asia region GDP Gross Domestic Product GHG Greenhouse gas HIC High-income country ICI Industrial, commercial, and institutional IPCC Intergovernmental Panel on Climate Change ISWM Integrated solid waste management Kg/capita/day kilograms per capita per day LCR Latin America and the Caribbean region LIC Low-income country LMIC Lower middle-income country MENA Middle East and North Africa region METAP Mediterranean Environmental Technical Assistance Program MRF Materials recovery facility MSW Municipal solid waste mtCO2e Million tonnes of carbon dioxide equivalent OECD Organisation for Economic Co-operation and Development PAHO Pan-American Health Organization RDF Refuse–derived fuel SAR South Asia region SWM Solid waste management tCO2e Tons of carbon dioxide equivalent UMIC Upper middle-income country xii URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS Country Classification According to Region East Asia Eastern Latin America Middle East Organisation for Africa South Asia & Pacific & Central Asia & the Caribbean & North Africa Economic Co-operation (AFR) (SAR) (EAP) (ECA) (LAC) (MENA) and Development (OECD) Angola Brunei Darussalam Albania Antigua and Barbuda Algeria Andorra Bangladesh Benin Cambodia Armenia Argentina Bahrain Australia Bhutan Botswana China Belarus Bahamas, The Egypt, Arab Rep. Austria India Burkina Faso Fiji Bulgaria Barbados Iran, Islamic Rep. Belgium Maldives Burundi Hong Kong Croatia Belize Iraq Canada Nepal Cameroon Indonesia Cyprus Bolivia Israel Czech Republic Pakistan Cape Verde Lao PDR Estonia Brazil Jordan Denmark Sri Lanka Central African Republic Macao, China Georgia Chile Kuwait Finland Chad Malaysia Latvia Colombia Lebanon France Comoros Marshall Islands Lithuania Costa Rica Malta Germany Congo, Dem. Rep. Mongolia Macedonia, FYR Cuba Morocco Greece Congo, Rep. Myanmar Poland Dominica Oman Hungary Cote d’Ivoire Philippines Romania Dominican Republic Qatar Iceland Eritrea Singapore Russian Federation Ecuador Saudi Arabia Ireland Ethiopia Solomon Islands Serbia El Salvador Syrian Arab Republic Italy Gabon Thailand Slovenia Grenada Tunisia Japan Gambia Tonga Tajikistan Guatemala United Arab Emirates Korea, South Ghana Vanuatu Turkey Guyana West Bank and Gaza Luxembourg Guinea Vietnam Turkmenistan Haiti Monaco Kenya Honduras Netherlands Lesotho Jamaica New Zealand Liberia Mexico Norway Madagascar Nicaragua Portugal Malawi Panama Slovak Republic Mali Paraguay Spain Mauritania Peru Sweden Mauritius St. Kitts and Nevis Switzerland Mozambique St. Lucia United Kingdom St. Vincent and the Namibia United States Grenadines Niger Suriname Nigeria Trinidad and Tobago Rwanda Uruguay Sao Tome and Principe Venezuela, RB Senegal Seychelles Sierra Leone South Africa Sudan Swaziland Tanzania Togo Uganda Zambia Zimbabwe WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT xiii Country Classification According to Income Lower Income (LI) Lower Middle Income (LMI) Upper Middle Income (UMI) High Income (HIC) Chad Bulgaria Colombia Barbados Comoros Cameroon Costa Rica Belgium Congo, Dem. Rep. Cape Verde Cuba Brunei Darussalam Eritrea China Dominica Canada Ethiopia Congo, Rep. Dominican Republic Croatia Gambia Cote d'Ivoire Fiji Cyprus Ghana Ecuador Gabon Czech Republic Guinea Egypt, Arab Rep. Georgia Denmark Haiti El Salvador Grenada Estonia Kenya Guatemala Jamaica Finland Lao PDR Guyana Latvia France Liberia Honduras Lebanon Germany Madagascar India Lithuania Greece Malawi Indonesia Malaysia Hong Kong, China Mali Iran, Islamic Rep. Mauritius Hungary Mauritania Iraq Mexico Iceland Mongolia Jordan Myanmar Ireland Mozambique Lesotho Namibia Israel Nepal Macedonia, FYR Panama Italy Niger Maldives Peru Japan Rwanda Marshall Islands Poland Korea, South Senegal Morocco Romania Kuwait Serbia Nicaragua Russian Federation Luxembourg Sierra Leone Nigeria Seychelles Macao, China Tanzania Pakistan South Africa Malta Togo Paraguay St. Kitts and Nevis Monaco Uganda Philippines St. Lucia Netherlands Vanuatu Sao Tome and Principe St. Vincent and the Grenadines New Zealand Vietnam Solomon Islands Suriname Norway Zambia Sri Lanka Tajikistan Oman Zimbabwe Sudan Uruguay Portugal Swaziland Venezuela, RB Qatar Syrian Arab Republic Saudi Arabia Thailand Singapore Tonga Slovak Republic Tunisia Slovenia Turkey Spain Turkmenistan Sweden West Bank and Gaza Switzerland Trinidad and Tobago United Arab Emirates United Kingdom United States WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 1 Introduction In 1999 the World Bank published What a Waste: Managing municipal solid waste is an intensive Solid Waste Management in Asia (Hoornweg and service. Municipalities need capacities in Thomas 1999), with an estimate of waste quantities procurement, contract management, profes- and composition for Asia. In the intervening sional and often unionized labor management, decade more accurate and comprehensive data and ongoing expertise in capital and operating became available for most regions of the world. budgeting and finance. MSW also requires a OECD-country estimates are typically reliable and strong social contract between the municipality consistent—added to these were comprehensive and community. All of these skills are prerequi- studies for China and India and the Pan-American sites for other municipal services. Health Organization’s study for Latin America. Therefore a global update of the 1999 report is The original What a Waste Report provided waste possible, and timely. estimates for South and East Asia. This waste stream represents about 33% of the world’s total Municipal solid waste managers are charged with quantities. Most growth predictions made in What an enormous task: get the waste out from underfoot a Waste: Solid Waste Management in Asia were and do so in the most economically, socially, and reasonably accurate and in most cases, even taking environmentally optimal manner possible. Solid into account the recent economic contraction, waste management is almost always the respon- waste growth estimates were conservative. This is sibility of local governments and is often their especially true in China. In 2004, China surpassed single largest budget item, particularly in devel- the US as the world’s largest waste generator. In oping countries. Solid waste management and 2030, China will likely produce twice as much street sweeping is also often the city’s single largest municipal solid waste as the United States. source of employment.1 Additionally, solid waste is one of the most pernicious local pollutants The main objective of this updated What a Waste — uncollected solid waste is usually the leading Report is to provide current municipal solid waste contributor to local flooding and air and water pollution. And if that task were not large enough, local waste management officials also need to deal with the integrated and international aspects of solid waste, and increasingly with demographic change in the work force, employment generation, and management of staff — both formal and informal. 1 Solid waste management — formal and informal – represents 1% to 5% of all urban employment. As formality increases so do issues of labor organi- zation, health and safety, ageing demographics (solid waste workers tend to be younger), the friction between ‘sanctioned’ and ‘unsanctioned’ recycling, and producer pay arguments and apportioning costs and responsibilities. Ferry men parking their boats on Buriganga River, Dhaka. Photo taken as part of Development 360 project. Photo: Scott Wallace Illustration: Brian Fray 2 BOX 1 What a Waste 1999: What’s Changed (and What Hasn’t) in the Last Decade ``What a Waste (1999) predicted that by 2025 the daily MSW generation rate in Asia would be 1.8 million tonnes per day. These estimates are still accurate. At present, the daily generation rate in South Asia and East Asia and the Pacific combined is approximately 1 million tonnes per day. ``Low-income countries continue to spend most of their SWM budgets on waste collection, with only a fraction going toward disposal. This is the opposite in high-income countries where the main expendi- ture is on disposal. ``Asia, like much of the world, continues to have a majority of organics and paper in its waste stream: The combined totals are 72% for EAP and 54% for SAR. Growth in waste quantities is fastest in Asia. ``There is a greater emphasis on labor issues: in high- income countries, demographics and immigration are critical factors; in low-income countries working conditions and integration of waste pickers has gained in importance. ``Rates of recycling are increasingly influenced by global markets, relative shipping costs, and commodity prices. Lisbon, Portugal, used aluminum cans are deposited into a container for recycling Bigstock Photo © generation, composition, collection, and disposal urbanize, their economic wealth increases. As data by country and by region. Both developing standards of living and disposable incomes and developed countries are included. This increase, consumption of goods and services report makes projections on MSW generation increases, which results in a corresponding and composition on a country and regional level increase in the amount of waste generated. This for 2025. This should provide decision makers report estimates that at present almost 1.3 billion with a sufficient foundation on which to base tonnes of MSW are generated globally every year, waste management policy decisions. In most cases or 1.2 kg/capita/day. The actual per capita rates, further local analysis will be needed, but this report however, are highly variable, as there are consid- is intended to provide a broad global review. For erable differences in waste generation rates across a summary on the main differences between the countries, between cities, and even within cities. data presented in the 1999 publication and this publication, please refer to Box 1. Solid waste is generally considered an ‘urban’ issue. Waste generation rates tend to be much Solid waste is inextricably linked to urban- lower in rural areas since, on average, residents ization and economic development. As countries are usually poorer, purchase fewer store-bought WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 3 items (which results in less packaging), and have generally an attractive option. Solid waste is higher levels of reuse and recycling. Today, more the most visible and pernicious by-product of a than 50 percent of the world’s population lives in resource-intensive, consumer-based economic cities, and the rate of urbanization is increasing lifestyle. Greenhouse gas emissions, water quickly. By 2050, as many people will live in cities pollution and endocrine disruptors are similar as the population of the whole world in 2000. This by-products to our urban lifestyles. The long term will add challenges to waste disposal. Citizens sustainability of today’s global economic structure and corporations will likely need to assume more is beyond the scope of this paper. However, solid responsibility for waste generation and disposal, waste managers need to appreciate the global specifically, product design and waste separation. context of solid waste and its interconnections to Also likely to emerge will be a greater emphasis on economies and local and global pollution. ‘urban mining’ as the largest source of materials like metal and paper may be found in cities. This report makes projections for MSW gener- ation in 2025, based on expected population and Waste is mainly a by-product of consumer-based economic growth rates. As countries, particularly lifestyles that drive much of the world’s economies. India and China, continue their rapid pace of In most cities, the quickest way to reduce waste urbanization and development, global solid waste volumes is to reduce economic activity—not quantities are projected to increase considerably. Illustration: Brian Fray 4 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS Global Waste Management Practices At a Glance: ` In solid waste management there is no throwing ‘away’. ` The organic fraction of waste, collection vehicles, and waste disposal methods contribute to GHG emissions. ` The last two decades have brought a new challenge for waste management: the growing vagaries of global secondary materials markets. In solid waste management there is no ‘away’. containerized to stay dry, and much of the waste When ‘throwing away’ waste, system complex- stream is not combustible. Landfills require land ities and the integrated nature of materials and availability, and siting is often opposed by potential pollution are quickly apparent. For example, waste neighboring residents. Solving one problem often incineration is expensive and poses challenges introduces a new one, and if not well executed, of air pollution and ash disposal. Incineration the new problem is often of greater cost and requires waste placed outside for collection to be complexity. BOX 2 Definitions of Municipal Solid Waste By OECD: Municipal waste is collected and treated by, or for municipalities. It covers waste from households, including bulky waste, similar waste from commerce and trade, office buildings, insti- tutions and small businesses, yard and garden, street sweepings, contents of litter containers, and market cleansing. Waste from municipal sewage networks and treatment, as well as municipal construction and demolition is excluded. By PAHO: Solid or semi-solid waste generated in population centers including domestic and, commercial wastes, as well as those originated by the small-scale industries and institutions (including hospital and clinics); market street sweeping, and from public cleansing. Photo: ©Simone D. McCourtie/World Bank By IPCC: The IPCC includes the following in MSW: food waste; garden (yard) and park waste; paper and cardboard; wood; textiles; nappies (disposable diapers); rubber and leather; plastics; metal; glass (and pottery and china); and other (e.g., ash, dirt, dust, soil, electronic waste). ITC landfill and recycling center, Ankara, Turkey WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 5 TABLE 1 Comparison of Solid Waste Management Practices by Income Level (adapted from What a Waste 1999) Activity Low Income Middle Income High Income Source Reduction No organized programs, but reuse and Some discussion of source reduction, but Organized education programs emphasize low per capita waste generation rates are rarely incorporated into an organized the three ‘R’s’ — reduce, reuse, and recycle. common. program. More producer responsibility & focus on product design. Collection Sporadic and inefficient. Service is limited Improved service and increased collection Collection rate greater than 90%. to high visibility areas, the wealthy, and from residential areas. Larger vehicle Compactor trucks and highly mechanized businesses willing to pay. High fraction fleet and more mechanization. Collection vehicles and transfer stations are common. of inerts and compostables impact rate varies between 50 to 80%. Transfer Waste volume a key consideration. Aging collection—overall collection below 50%. stations are slowly incorporated into the collection workers often a consideration in SWM system. system design. Recycling Although most recycling is through Informal sector still involved; some Recyclable material collection services and the informal sector and waste picking, high technology sorting and processing high technology sorting and processing recycling rates tend to be high both for facilities. Recycling rates are still facilities are common and regulated. local markets and for international markets relatively high. Materials are often Increasing attention towards long-term and imports of materials for recycling, imported for recycling. Recycling markets markets. including hazardous goods such as e-waste are somewhat more regulated. Material and ship-breaking. Recycling markets prices fluctuate considerably. Overall recycling rates higher than low are unregulated and include a number of and middle income. Informal recycling ‘middlemen’. Large price fluctuations. still exists (e.g. aluminum can collection.) Extended product responsibility common. Composting Rarely undertaken formally even though Large composting plants are often Becoming more popular at both backyard the waste stream has a high percentage unsuccessful due to contamination and and large-scale facilities. Waste stream of organic material. Markets for, and operating costs (little waste separation); has a smaller portion of compostables than awareness of, compost lacking. some small-scale composting projects at low- and middle-income countries. More the community/ neighborhood level are source segregation makes composting more sustainable. Composting eligible easier. Anaerobic digestion increasing in for CDM projects but is not widespread. popularity. Odor control critical. Increasing use of anaerobic digestion. Incineration Not common, and generally not successful Some incinerators are used, but Prevalent in areas with high land costs because of high capital, technical, and experiencing financial and operational and low availability of land (e.g., islands). operation costs, high moisture content in difficulties. Air pollution control equipment Most incinerators have some form of the waste, and high percentage of inerts. is not advanced and often by-passed. environmental controls and some type of Little or no stack emissions monitoring. energy recovery system. Governments Governments include incineration as a regulate and monitor emissions. About possible waste disposal option but costs three (or more) times the cost of landfilling prohibitive. Facilities often driven by per tonne. subsidies from OECD countries on behalf of equipment suppliers. Landfilling/ Low-technology sites usually open Some controlled and sanitary landfills Sanitary landfills with a combination of Dumping dumping of wastes. High polluting with some environmental controls. Open liners, leak detection, leachate collection to nearby aquifers, water bodies, dumping is still common. CDM projects for systems, and gas collection and treatment settlements. Often receive medical waste. landfill gas are more common. systems. Often problematic to open new Waste regularly burned. Significant health landfills due to concerns of neighboring impacts on local residents and workers. residents. Post closure use of sites increasingly important, e.g. golf courses and parks. Costs Collection costs represent 80 to 90% of Collection costs represent 50% to 80% Collection costs can represent less (see Annex E) the municipal solid waste management of the municipal solid waste management than 10% of the budget. Large budget budget. Waste fees are regulated by some budget. Waste fees are regulated by some allocations to intermediate waste local governments, but the fee collection local and national governments, more treatment facilities. Up front community system is inefficient. Only a small innovation in fee collection, e.g. included participation reduces costs and increases proportion of budget is allocated toward in electricity or water bills. Expenditures options available to waste planners (e.g., disposal. on more mechanized collection fleets and recycling and composting). disposal are higher than in low-income countries. 6 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS Locally, waste collection vehicles are large and municipalities are often forced to subsidize sources of emissions and both incineration and the disposal costs of these items. landfilling contribute GHG emissions. Uncol- lected waste can provide breeding areas and food In the last ten to twenty years an additional to potentially disease carrying vectors such as challenge has emerged for the waste manager: the insects and rodents, with their associated health growing global vagaries of secondary materials and nuisance issues. Waste management cannot markets. Many municipal recycling programs in be effectively managed without due consider- Europe and North America were started with the ation for issues such as the city’s overall GHG recycling markets relatively close to source. More emissions, labor market, land use planning, and recently, marketing of secondary-materials has myriad related concerns. emerged as a global business. The price paid per tonne of waste paper in New York City is often Despite progress in solid waste management based on what the purchase price is in China. practices in the decade since the original What a The majority of waste recycled in Buenos Aires, Waste Report was published, fundamental insti- for example, is shipped to China. The volatility tutional, financial, social, and environmental of secondary materials prices has increased, problems still exist. Although each country and making planning more difficult. The price is often city has their own site-specific situations, general predictive of economic trends, dropping signifi- observations can be made across low-, middle-, cantly during economic downturns (when a city and high-income countries, as delineated in is least able to afford price drops). There are Table 1. some hedging opportunities for materials pricing, however secondary materials marketing does not The average city’s municipal waste stream is have the same degree of sophistication as other made up of millions of separate waste items. commodities (largely due to issues of reliability, For a compilation of the different definitions for quality, externalities, and the sheer number of Municipal Solid Waste, please refer to Box 2. In interested parties). many cases, items in a city’s waste stream origi- nated from other countries that have countless In the years that have passed since the original What factories and independent producers. Some of a Waste report was released, two comprehensive the larger waste fractions, such as organics (food World Bank studies on India and China have been and horticultural waste) and paper are easier prepared (Hanrahan et al 2006 and Hoornweg et to manage, but wastes such as multi-laminates, al 2005). Additionally, OECD and PAHO have hazardous (e.g. syringes), and e-waste, pose dispro- released MSW data for Latin America and the portionately large problems. Industry programs, Caribbean. This version of What a Waste includes such as voluntary plastic-type labeling, are largely the data presented by these reports. ineffective (no facilities exist to differentiate containers by numbers, either mechanically or by MSW, as defined in this report, encompasses waste-worker) and deposit-return systems often residential, industrial, commercial, institutional, meet industry and consumer resistance. Hybrid, municipal, and construction and demolition ad hoc, and voluntary take-back programs are (C&D) waste. Table 2 gives sources and types of emerging, however they are generally inefficient waste generated. WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 7 TABLE 2 Source Typical Waste Generators Types of Solid Wastes Generators and Residential Single and multifamily dwellings Food wastes, paper, cardboard, plastics, Types of Solid Waste textiles, leather, yard wastes, wood, (adapted from glass, metals, ashes, special wastes (e.g., What a Waste 1999) bulky items, consumer electronics, white goods, batteries, oil, tires), and household hazardous wastes (e.g., paints, aerosols, gas tanks, waste containing mercury, motor oil, cleaning agents), e-wastes (e.g., computers, phones, TVs) Industrial Light and heavy manufacturing, Housekeeping wastes, packaging, food fabrication, construction sites, power wastes, construction and demolition and chemical plants (excluding specific materials, hazardous wastes, ashes, process wastes if the municipality does special wastes not oversee their collection) Commercial Stores, hotels, restaurants, markets, office Paper, cardboard, plastics, wood, food buildings wastes, glass, metals, special wastes, hazardous wastes, e-wastes Institutional Schools, hospitals (non-medical waste), Same as commercial prisons, government buildings, airports Construction and Demolition New construction sites, road repair, Wood, steel, concrete, dirt, bricks, tiles renovation sites, demolition of buildings Municipal Services Street cleaning, landscaping, parks, Street sweepings; landscape and tree beaches, other recreational areas, water trimmings; general wastes from parks, and wastewater treatment plants beaches, and other recreational areas, sludge All of the above should be included as municipal solid waste. Industrial, commercial, and institutional (ICI) wastes are often grouped together and usually represent more than 50% of MSW. C&D waste is often treated separately: if well managed it can be disposed separately. The items below are usually considered MSW if the municipality oversees their collection and disposal. Process Heavy and light manufacturing, refineries, Industrial process wastes, scrap materials, chemical plants, power plants, mineral off-specification products, slag, tailings extraction and processing Medical waste Hospitals, nursing homes, clinics Infectious wastes (bandages, gloves, cultures, swabs, blood and body fluids), hazardous wastes (sharps, instruments, chemicals), radioactive waste from cancer therapies, pharmaceutical waste Agricultural Crops, orchards, vineyards, dairies, Spoiled food wastes, agricultural wastes feedlots, farms (e.g., rice husks, cotton stalks, coconut shells, coffee waste), hazardous wastes (e.g., pesticides) 8 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS Waste Generation At a Glance: ` MSW generation levels are expected to double by 2025. ` The higher the income level and rate of urbanization, the greater the amount of solid waste produced. ` OECD countries produce almost half of the world’s waste, while Africa and South Asia regions produce the least waste. Current global MSW generation levels are and as disposable incomes and living standards approximately 1.3 billion tonnes per year, and are increase, consumption of goods and services corre- expected to increase to approximately 2.2 billion spondingly increases, as does the amount of waste tonnes per year by 2025. This represents a signif- generated. Urban residents produce about twice as icant increase in per capita waste generation rates, much waste as their rural counterparts. from 1.2 to 1.42 kg per person per day in the next fifteen years. However, global averages are broad Waste Generation by Region estimates only as rates vary considerably by region, country, city, and even within cities. Waste generation varies as a function of affluence, however, regional and country variations can be MSW generation rates are influenced by economic significant, as can generation rates within the development, the degree of industrialization, public same city. Annex A. Map of Regions illustrates habits, and local climate. Generally, the higher the the regional classification used in this report. Throughout the report, when Africa is mentioned Collecting paper economic development and rate of urbanization, as a region, we refer to Sub-Saharan Africa. Data to be recycled, the greater the amount of solid waste produced. are particularly lacking for Sub-Saharan Africa. Mumbai, India Income level and urbanization are highly correlated Waste generation in sub-Saharan Africa is approxi- mately 62 million tonnes per year. Per capita waste generation is generally low in this region, but spans a wide range, from 0.09 to 3.0 kg per person per day, with an average of 0.65 kg/capita/day. The countries with the highest per capita rates are islands, likely due to waste generated by the tourism industry, and a more complete accounting of all wastes generated. The annual waste generation in East Asia and the Pacific Region is approximately 270 million tonnes per year. This quantity is mainly influenced by waste generation in China, which makes up 70% of the regional total. Per capita waste generation ranges from 0.44 to 4.3 kg per person per day for Photo: Jeroo Bhada WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 9 TABLE 3 Waste Generation Per Capita (kg/capita/day) Current Waste Region Lower Boundary Upper Boundary Average Generation Per AFR 0.09 3.0 0.65 Capita by Region EAP 0.44 4.3 0.95 (see Annex J) ECA 0.29 2.1 1.1 LAC 0.11 14 2 1.1 MENA 0.16 5.7 1.1 OECD 1.10 3.7 2.2 SAR 0.12 5.1 0.45 the region, with an average of 0.95 kg/capita/day In South Asia, approximately 70 million tonnes of (Hoornweg et al 2005). waste is generated per year, with per capita values ranging from 0.12 to 5.1 kg per person per day and In Eastern and Central Asia, the waste generated an average of 0.45 kg/capita/day. per year is at least 93 million tonnes. Eight countries in this region have no available data on waste gener- Table 3 shows current waste generation per capita ation in the literature. The per capita waste gener- by region, indicating the lower boundary and upper ation ranges from 0.29 to 2.1 kg per person per day, boundary for each region, as well as average kg per with an average of 1.1 kg/capita/day. capita per day of waste generated within each region.2 Latin America and the Caribbean has the most Figure 1 illustrates global waste generation per comprehensive and consistent data (e.g. PAHO’s region, where OECD countries make up almost half Regional Evaluation of Solid Waste Management, Figure 1. Current Waste Generation by Region 2005). The total amount of waste generated per year in this region is 160 million tonnes, with per capita values ranging from 0.1 to 14 kg/capita/ day, and an average of 1.1 kg/capita/day. Similar AFR to the high per capita waste generation rates SAR 5% on islands in Africa, the largest per capita solid 5% FIG. 1 waste generation rates are found in the islands of MENA Waste Generation 6% the Caribbean. by Region ECA In the Middle East and North Africa, solid waste 7% OECD generation is 63 million tonnes per year. Per capita 44% waste generation is 0.16 to 5.7 kg per person per day, and has an average of 1.1 kg/capita/day. LAC 12% The OECD countries generate 572 million tonnes of solid waste per year. The per capita values range from 1.1 to 3.7 kg per person per day with an average of 2.2 kg/capita/day. EAP 21% 10 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS TABLE 4 Current Available Data Projections for 2025 Waste Generation Projections for Urban Waste Generation Projected Population Projected Urban Waste Region Total Urban 2025 by Region Population Per Capita Total Total Popula- Urban Popula- Per Capita Total (millions) (kg/capita/day) (tons/day) tion (millions) tion (millions) (kg/capita/day) (tons/day) AFR 260 0.65 169,119 1,152 518 0.85 441,840 EAP 777 0.95 738,958 2,124 1,229 1.5 1,865,379 ECA 227 1.1 254,389 339 239 1.5 354.810 LCR 399 1.1 437,545 681 466 1.6 728,392 MENA 162 1.1 173,545 379 257 1.43 369,320 OECD 729 2.2 1,566,286 1,031 842 2.1 1,742,417 SAR 426 0.45 192,410 1,938 734 0.77 567,545 Total 2,980 1.2 3,532,252 7,644 4,285 1.4 6,069,703 TABLE 5 Waste Generation Per Capita (kg/capita/day) Current Waste Income Level Generation Lower Boundary Upper Boundary Average Per Capita High 0.70 14 2.1 by Income Level Upper Middle 0.11 5.5 1.2 Lower Middle 0.16 5.3 0.79 Lower 0.09 4.3 0.60 of the world’s waste, while Africa and South Asia group, the average per capita waste generation figure as the regions that produce the least waste. amounts for the various income groups reflect the income level of the countries (see Figure 2). Table 4 shows estimates of waste generation for the The high, upper-middle, lower-middle, and low year 2025 as expected according to current trends income designations are somewhat inaccurate in population growth in each region. as these classifications are country-wide, and in several countries average national affluence can be very different from average affluence of the Waste Generation urban populations. Only the affluence of urban by Country Income Level 3 residents is important in projecting MSW rates. High-income countries produce the most waste For example, India and especially China have per capita, while low income countries produce disproportionately high urban waste generation the least solid waste per capita. Although the rates per capita relative to overall economic status total waste generation for lower middle income as they have large relatively poor rural populations countries is higher than that of upper middle that tend to dilute national figures. Annex B. Map income countries, likely skewed as a result of of Income Distribution illustrates the global classi- China’s inclusion in the lower middle income fication for income used in this report. 3 Countries are classified into four income levels according to World Bank Table 5 shows current waste generation per estimates of 2005 GNI per capita. High: $10,726 or above; Upper middle: $3,466-10,725; Lower middle: $876-3,465; and Lower: $875 or less. capita by income level, indicating the lower WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 11 Figure 2. Waste Generation by Country Income boundary and upper boundary for each region, Lower FIG. 2 Income as well as average kg per capita per day of 6% Waste Generation waste generated within each group according to by Income country income level. Figure 2 presents global waste generation by country per income level, showing decreasing average rates of Lower Middle per capita waste generation according to income level. Income High 29% Income 46% Table 6 shows estimates of waste generation for the year 2025 as expected according to current trends in population growth as determined by country income level. Methodology for collecting current data: Upper Middle Income 19% MSW generation data by country were collected from official government publications, reports by international agencies, and articles in peer- Where only total MSW generation numbers were reviewed journals. Where possible, this report has available, total urban population for that year was used used the same source for a group of countries so to calculate per capita waste generation, assuming that that the data are relatively standardized by method- most of the waste generated is in urban areas and only ology and year. For example, MSW generation a small fraction comes from rural areas. data for high-income countries are from OECD publications; countries in Latin America and the For several African countries, data were not readily Caribbean from PAHO studies; and some Middle available. Hence, a per capita amount of 0.5 kg/ Eastern countries from METAP data. capita/day is assumed for urban areas for 2005. This estimate is based on the USAID 2009 publication In cases where only per capita waste generation rates on Environmental Guidelines for Small-Scale Activities in were available, the total urban population for that year Africa (EGSSAA), 2nd Ed. and World Bank studies. (World Bank, World Development Indicators) was For further information on MSW generation rates used to calculate the total urban MSW generation. by country, please see Annex J. When reviewing TABLE 6 Current Available Data Projections for 2025 (from Annex J) Waste Generation Urban Waste Generation Projected Population Projected Urban Waste Projections for 2025 Region Total Urban Population Per Capita Total Popula- Urban Per Capita by Income Total Total (millions) (kg/capita/ tion Population (kg/capita/ (tons/day) (tons/day) day) (millions) (millions) day) Lower Income 343 0.60 204,802 1,637 676 0.86 584,272 Lower Middle Income 1,293 0.78 1,012,321 4,010 2,080 1.3 2,618,804 Upper Middle Income 572 1.16 665,586 888 619 1.6 987,039 High Income 774 2.13 1,649,547 1,112 912 2.1 1,879,590 Total 2,982 1.19 3,532,256 7,647 4,287 1.4 6,069,705 12 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS TABLE 7 Variable Data Source Sources for 2025 Projections of Current GDP (current US$, 2005) World Development Indicators Solid Waste GDP Projections by Region IEA Annual Energy Outlook (2005) Generation Urban Population Projections United Nations World Urbanization Prospects (2007) TABLE 8 Income Level Average MSW Generation (kg/cap/day) Average MSW Generation Rates Low-Income 0.6 – 1.0 by Income Middle-Income 0.8 – 1.5 High-Income 1.1 – 4.5 the values presented in this report, it’s important GDP (high-, middle-, or low-income) and an average to keep in mind that values for waste generation at range of MSW generation based on that income a regional level can differ markedly because of the level. Modest adjustments for current experience influence from a single country, such as the US, and waste generation practices were made where China or India. appropriate. Similar to ‘energy intensity’ urban residents also exhibit ‘waste intensity’. Methodology for calculating 2025 projections: For further information on the sources used for Projections for urban municipal solid waste gener- the 2025 projections please refer to Table 7. ation in 2025 were made by factoring expected growth in population and GDP and estimated Table Figure 3. Urban Waste 8 illustrates the range of MSW based on Generation per capita waste generation. Projections for each country income level. These values are supported country were made based on the level of expected by Table 6. 1,200 Waste Generated (millions tons/year) 956 FIG. 3 1,000 Urban Waste Generation 800 686 by Income Level 602 and Year 600 369 360 400 213 243 200 75 0 Urban Population (millions) 343 676 1,293 2,080 573 619 774 912 Waste (kg/capita/year) 219 343 288 344 423 628 777 840 Country Income Group Lower Lower Middle Upper Middle High Income Income Income Income 2010 Projected 2025 WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 13 Waste Collection At a Glance: ` MSW collection is an important aspect in maintaining public health in cities around the world. ` The amount of MSW collected varies widely by region and income level; collection within cities can also differ greatly. ` Collection rates range from a low of 41% in low-income countries to a high of 98% in high-income countries. Waste collection is the collection of solid waste from customers. Municipalities often license private point of production (residential, industrial commercial, operators and may designate collection areas to institutional) to the point of treatment or disposal. encourage collection efficiencies. Municipal solid waste is collected in several ways: Collected MSW can be separated or mixed, 1. House-to-House: Waste collectors visit each depending on local regulations. Generators can individual house to collect garbage. The user be required to separate their waste at source, e.g., generally pays a fee for this service. into “wet” (food waste, organic matter) and “dry” (recyclables), and possibly a third stream of “waste,” 2. Community Bins: Users bring their garbage or residue. Waste that is un-segregated could be to community bins that are placed at fixed separated into organic and recycling streams at points in a neighborhood or locality. MSW is a sorting facility. The degree of separation can picked up by the municipality, or its designate, vary over time and by city. ‘Separation’ can be False Creek, according to a set schedule. a misnomer as waste is not actually separated Vancouver, Canada 3. Curbside Pick-Up: Users leave their garbage directly outside their homes according to a garbage pick-up schedule set with the local authorities (secondary house-to- house collectors not typical). 4. Self Delivered: Generators deliver the waste directly to disposal sites or transfer stations, or hire third-party operators (or the municipality). 5. Contracted or Delegated Service: Businesses hire firms (or municipality with municipal facilities) who arrange collection schedules and charges with © iStockphoto.com/brytta 14 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS Separate Photo: Cyrus Tata garbage containers, Singapore but rather is placed out for collection in separate waste is more dispersed. Annex G provides data for containers without first being ‘mixed’ together. MSW collection in cities over 100,000. Often, especially in developing countries, MSW is not separated or sorted before it is taken for The percent of MSW collected varies by national disposal, but recyclables are removed by waste income and by region. Higher income countries tend pickers prior to collection, during the collection to have higher collection efficiency although less of process, and at disposal sites. the solid waste management budget goes towards collection. In low-income countries, collection The degree of source separation impacts the total services make up the bulk of a municipality’s SWM amount of material recycled and the quality of budget (as high as 80 to 90% in many cases), yet secondary materials that can be supplied. Recyclables collection rates tend to be much lower, leading to recovered from mixed waste, for example, tend to lower collection frequency and efficiency. In high- be contaminated, reducing marketing possibilities. income countries, although collection costs can However, source separation and separate collection represent less than 10% of a municipality’s budget, can add costs to the waste collection process. collection rates are usually higher than 90% on average and collection methods tend to be mecha- Collection programs need to be differentiated by nized, efficient, and frequent. While total collection type of generator. Often more attention is devoted budgets are higher, they are proportionally lower to residential waste even though this is usually less as other budget items increase. For further infor- than 50% of the total waste stream. Waste generated mation on estimated solid waste management costs by the ICI sector tends to be collected better, because according to income level, please refer to Annex E. of more efficient containerization and purpose-built vehicles, and benefits from the collection of fees. The degree and sophistication of waste picking Residential waste collection, on the other hand, influences overall collection. In cities like Buenos tends to be more expensive to collect per tonne as Aires, waste pickers tend to remove recyclables WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 15 after the waste is placed curbside. The resulting MSW Collection by Region scattered waste is more costly to collect: in some cases the value of recyclables are less than the Figure 5 shows MSW collection efficiency by extra costs associated with collecting the disturbed region. Regions with low-income countries tend waste. In some cities informal waste pickers have to have low collection rates. South Asia and Africa strong links to the waste program and municipally are the lowest with 65% and 46% respectively. Not sanctioned crews can be prevented from accessing surprisingly, OECD countries tend to have the the waste as informal waste pickers process the highest collection efficiency at 98%. Figure 4. Waste Collection by Income waste. Waste pickers can be formally or informally organized into groups or unions with varying degrees of autonomy and political voice. FIG. 4 Waste Collection Rates by Income Containerization is an important aspect for waste 100% collection, particularly from residential generators. If 90% waste is not set out for collection in closed containers 80% it can be disturbed by vermin such as dogs and rats, 70% and it can become water-logged, or set afire. 60% 50% Frequency of collection is an important aspect 40% 30% readily under a municipality’s control. From a 20% health perspective, no more than weekly collection 10% is needed. However in some cities, largely because 0% of culture and habituation, three-times per day High Income Upper Middle Lower Middle Lower Income Income Income residential collection is offered (e.g. Shanghai). Good waste collection programming requires an ongoing Figure 5. Waste Collection by Region iterative approach between collection crews and generators (usually households). Therefore, waste FIG. 5 generators should be aware of the true costs of Waste Collection Rates by Region collection, and ideally be charged for these directly. 100% 90% MSW Collection by Income 80% 70% The data show that the average waste collection 60% rates are directly related to income levels. 50% Low-income countries have low collection rates, 40% around 41%, while high-income countries have 30% higher collection rates averaging 98%. Figure 4 20% 10% shows the average collection percentage by income. 0% Annex K details MSW collection rates by country. OECD MENA LAC ECA EAP SAR AFR 16 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS Waste Composition At a Glance: ` Waste composition is influenced by factors such as culture, economic development, climate, and energy sources; composition impacts how often waste is collected and how it is disposed. ` Low-income countries have the highest proportion of organic waste. ` Paper, plastics, and other inorganic materials make up the highest proportion of MSW in high- income countries. ` By region, EAP has the highest proportion of organic waste at 62%, while OECD countries have the least at 27%, although total amount of organic waste is still highest in OECD countries. ` Although waste composition is usually provided by weight, as a country’s affluence increases, waste volumes tend to be more important, especially with regard to collection: organics and inerts generally decrease in relative terms, while increasing paper and plastic increases overall waste volumes. In the municipal solid waste stream, waste is as 40% of the total waste stream. However, in this broadly classified into organic and inorganic. In report, C&D waste is not included unless specifi- this study, waste composition is categorized as cally identified. A separate case-by-case review is organic, paper, plastic, glass, metals, and ‘other.’ recommended for specific cities. These categories can be further refined, however, these six categories are usually sufficient for general Industrial, Commercial and Institutional (ICI) solid waste planning purposes. Table 9 describes waste also needs further local refinement. Many the different types of waste and their sources. industrial processes have specific wastes and by-products. In most cities this material, with its An important component that needs to be relatively easier flow and quality control, is the first considered is ‘construction and demolition waste’ material to be recycled. Some industrial process (C&D), such as building rubble, concrete and waste requires specific treatment. For most MSW masonry. In some cities this can represent as much management plans industrial by-products are not TABLE 9 Type Sources Types of Waste and Their Sources Organic Food scraps, yard (leaves, grass, brush) waste, wood, process residues Paper scraps, cardboard, newspapers, magazines, bags, boxes, Paper wrapping paper, telephone books, shredded paper, paper beverage cups. Strictly speaking paper is organic but unless it is contaminated by food residue, paper is not classified as organic. Plastic Bottles, packaging, containers, bags, lids, cups Glass Bottles, broken glassware, light bulbs, colored glass Metal Cans, foil, tins, non-hazardous aerosol cans, appliances (white goods), railings, bicycles Other Textiles, leather, rubber, multi-laminates, e-waste, appliances, ash, other inert materials Figure 6. Waste Composition in China WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 17 Metal Others 2000: Population Using Coal 10% 2000: Population Using Gas 1% FIG. 6 Glass 2% Waste Composition Plastic in China 13% Organic Organic Others 65% 47% 41% Paper 9% Paper Metal 5% 1% Glass Plastic 2% 4% Municipal Waste Genereated from Population Using Coal for household heating = 49,500,000 tons Municipal Waste Genereated from Population Using Gas for household heating = 100,500,000 tons Source: Hoornweg 2005 Total Municipal Waste Generation in 2000 = 150,000,000 tons included in waste composition analyses, however fication of MSW composition based on region (See household and general waste should be included Annex N). In high-income countries, an integrated since it is usually disposed at common facilities, approach for organic waste is particularly important, Figure 7. Global Solid Waste Composition and in most cities waste from the ICI sector repre- as organic waste may be diverted to water-borne sents the largest fraction of the waste collected. sewers, which is usually a more expensive option. Waste composition is influenced by many factors, such Geography influences waste composition by as level of economic development, cultural norms, determining building materials (e.g. wood versus geographical location, energy sources, and climate. steel), ash content (often from household heating), As a country urbanizes and populations become amount of street sweepings (can be as much as wealthier, consumption of inorganic materials (such 10% of a city’s waste stream in dry locations), and as plastics, paper, and aluminum) increases, while the horticultural waste. The type of energy source relative organic fraction decreases. Generally, low- and middle-income countries have a high percentage of organic matter in the urban waste stream, ranging Other 18% FIG. 7 from 40 to 85% of the total. Paper, plastic, glass, Global and metal fractions increase in the waste stream Solid Waste of middle- and high-income countries. For data on Metal Composition 4% MSW composition in cities with a population of over Glass Organic 100,000, please refer to Annex I. 5% 46% Figure 8 illustrates the differences between low- and Plastic 10% high-income countries: organics make up 64% of the MSW stream for low-income countries and paper only 5%, whereas in high-income countries it is 28% Paper 17% and 31% respectively. The IPCC uses its own classi- 18 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS in a location can have an impact on the compo- countries. The total waste composition figures by sition of MSW generated. This is especially true income and by region were then aggregated. in low-income countries or regions where energy for cooking, heating, and lighting might not come Figure 7 shows the MSW composition for the entire from district heating systems or the electricity world in 2009. Organic waste comprises the majority grid. For example, Figure 6 shows the difference of MSW, followed by paper, metal, other wastes, in waste composition in China between a section plastic, and glass. These are only approximate values, of the population that uses coal and another that given that the data sets are from various years. uses natural gas for space heating. The ‘other’ category is clearly higher: 47% when coal is used, Waste Composition by Income and an ash residue is included, as opposed to 10% when natural gas is used for home heating. As Figures 8 a-d show, the organic fraction tends to be highest in low-income countries and lowest Climate can also influence waste generation in in high-income countries. Total amount of organic a city, country, or region. For example, in Ulan waste tends to increase steadily as affluence increases Bator, Mongolia, ash makes up 60% of the MSW at a slower rate than the non-organic fraction. generated in the winter, but only 20% in the summer Low-income countries have an organic fraction of (UNEP/GRID-Arendal 2004). Precipitation is also 64% compared to 28% in high-income countries. important in waste composition, particularly when The data presented in Figure 9 illustrates solid measured by mass, as un-containerized waste can waste composition by income as compared between absorb significant amounts of water from rain and current values and values projected for 2025. Annex snow. Humidity also influences waste composition J provides data for MSW projections for 2025 by by influencing moisture content. income level. Methodology Table 10 represents a compilation of composition values of current day data presented in Annex M, This report includes waste composition data that and specific reports for larger countries such as was available for 105 countries from various sources. China and India. Estimates for waste composition Please see Annex M for further information on in 2025 are based on trends observed in OECD MSW composition data by country. Waste compo- countries and authors’ projections. sition data is generally available as percentages of the various waste streams, commonly divided Waste Composition by Region into the categories shown in Table 10. In some cases, ‘other’ wastes are further disaggregated into MSW composition by region is shown in Figures 10 textiles, rubber, ash, etc. However, for the purposes a-g. The East Asia and the Pacific Region has the of standardization and simplification the ‘other’ highest fraction of organic waste (62%) compared to category in this report includes all of these wastes. OECD countries, which have the least (27%). The Although the definitions and methodologies for amount of paper, glass, and metals found in the determining composition are not always provided MSW stream are the highest in OECD countries or standardized in the waste studies referenced, the (32%, 7%, and 6%, respectively) and lowest in the compositions for MSW are assumed to be based South Asia Region (4% for paper and 1% for both on wet weight. Each waste category was calculated glass and metals). Annex J provides data for MSW using waste generation figures from individual projections for 2025 by region. WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 19 Figure 8. Waste Composition by Income a. Waste Composition in Low-Income Countries b. Waste Composition in Lower Middle-Income Countries Other FIG. 8 Other 17% 15% Waste Composition Metal by Income 2% Metal 3% Glass 3% Glass 3% Organic Organic Plastic 64% Plastic 59% 8% 12% Paper 5% Paper 9% c. Waste Composition in Upper Middle-Income Countries d. Waste Composition in High-Income Countries Other Other 13% 17% Metal Organic 3% 28% Glass 5% Metal Organic 6% 54% Plastic Glass 11% 7% Plastic Paper 11% 14% Paper 31% CURRENT ESTIMATES* TABLE 10 Source: Types of Waste Income Level Organic (%) Paper (%) Plastic (%) Glass (%) Metal (%) Other (%) Composition by Low Income 64 5 8 3 3 17 Income Level Lower Middle Income 59 9 12 3 2 15 Upper Middle Income 54 14 11 5 3 13 High Income 28 31 11 7 6 17 2025 ESTIMATES** Income Level Organic (%) Paper (%) Plastic (%) Glass (%) Metal (%) Other (%) Low Income 62 6 9 3 3 17 Lower Middle Income 55 10 13 4 3 15 Upper Middle Income 50 15 12 4 4 15 High Income 28 30 11 7 6 18 *Source year: varies, see Annex C on Data Availability. **Source: By author from global trends, and Annex J. 20 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS Figure 9. Solid Waste Composition FIG. 9 CURRENT 2025 Solid Waste Other Composition Other Metal 17% Metal Low Income by Income 17% 3% Glass 3% Glass and Year 3% Organic 3% Organic Plastic 64% Plastic 62% 8% Paper 9% 5% Paper 6% 75 MT* 201 MT Other 15% Other Metal 15% Metal 2% Lower Middle 3% Glass Income Glass 3% 4% Plastic Organic Organic 12% 59% 55% Plastic 13% Paper 9% Paper 10% 369 MT 956 MT Other Other 15% Metal 3% 13% Metal Upper Middle 4% Glass Income 5% Glass 4% Organic Organic Plastic 54% 50% 11% Plastic 12% Paper 14% Paper 15% 243 MT 426 MT High Income Others Other 17% 18% Organic Organic 28% 28% Metal 6% Metal 6% Glass 7% Glass 7% Plastic 11% Plastic Paper 11% Paper 31% 30% 602 MT 686 MT Source: Current data vary by country. *Total annual waste volume in millions of tonnes WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 21 Figure 10. Global Solid Waste Composition a. AFR Waste Composition b. EAP Waste Composition FIG. 10 Other Other Waste Composition Metal 10% 13% 2% by Region Metal Glass 4% 3% Glass 4% Plastic 13% Organic Organic Plastic 57% 62% 13% Paper 10% Paper 9% c. ECA Waste Composition d. SAR Composition Other 19% Other Metal 37% 5% Organic Organic Glass 47 50% 7% Plastic Metal 8% 1% Paper Glass Paper 14% 1% Plastic 4% 7% e. MENA Waste Composition g. LAC Waste Composition Metal Other Other 3% 10% Metal 12% 2% Glass 3% Glass 4% Plastic 9% Plastic Organic 12% 54% Organic Paper 61% 14% Paper 16% f. OECD Waste Composition Other 17% Organic Metal 27% 6% Glass 7% Plastic 11% Paper 32% 22 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS Waste Disposal At a Glance: ` Landfilling and thermal treatment of waste are the most common methods of MSW disposal in high-income countries. ` Although quantitative data is not readily available, most low- and lower middle-income countries dispose of their waste in open dumps. ` Several middle-income countries have poorly operated landfills; disposal should likely be classified as controlled dumping. Waste disposal data are the most difficult to collect. Methodology Many countries do not collect waste disposal data at the national level, making comparisons across Waste disposal data was available for 87 countries income levels and regions difficult. Furthermore, through various sources. Annex L presents MSW in cases where data is available, the methodology disposal methods data by country. Waste disposal of how disposal is calculated and the definitions data sets are generally available as percentages of the used for each of the categories is often either not various waste disposal options, commonly divided known or not consistent. For example, some into the categories shown in Table 10. Although countries only give the percentage of waste that is the definitions and methodologies for calculating dumped or sent to a landfill, the rest falls under waste disposal methods and quantities are not ‘other’ disposal. In other cases, compostable and always provided or standardized in waste studies, recyclable material is removed before the waste the disposal of MSW is assumed to be based on wet reaches the disposal site and is not included in weight. Each waste disposal category was calculated waste disposal statistics. Please refer to Annex H using waste generation figures for the individual for MSW disposal data for cities with populations country. The total waste disposal figures by income Figure 11. Total MSW Disposed Worldwide over 100,000. and by region were then aggregated. Figure 11 shows current annual global MSW FIG. 11 disposal for the entire world. These are only Total MSW Disposed of Worldwide approximate values, given that the data is from 400 various years. Amount Disposed (millions tons/year) 350 300 MSW Disposal by Income 250 200 Table 11 shows in further detail how MSW disposal varies according to country income level. 150 100 Figures 12 and 13 illustrate the differences in 50 MSW disposal methods according to country 0 Landfill Recycled WTE Dump Compost Other income level, in particular low-income and upper middle-income countries. Disposal Options Ghabawi landfill, Amman, Jordan Photo: Perinaz Bhada-Tata WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 23 TABLE 11 High Income Upper Middle Income MSW Disposal Dumps 0.05 Dumps 44 by Income Landfills 250 Landfills 80 (million tonnes) Compost 66 Compost 1.3 Recycled 129 Recycled 1.9 Incineration 122 Incineration 0.18 Other 21 Other 8.4 Low Income Lower Mid dle Income Dumps 0.47 Dumps 27* Landfills 2.2 Landfills 6.1 Compost 0.05 Compost 1.2 Recycled 0.02 Recycled 2.9 Incineration 0.05 Incineration 0.12 Other 0.97 Other 18 *This value is relatively high due to the inclusion of China. 24 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS Table 12 contrasts the world’s richest (OECD) and ences in GHG emissions). Africa’s collected waste poorest (Africa) regions. Populations in the two is almost exclusively dumped or sent to landfills, regions are roughly equal, yet the OECD region while more than 60% of OECD’s waste is diverted produces about 100 times the waste of Africa from landfill. (these disparities are parallel to regional differ- FIG. 12 FIG. 13 Figure 12. Low-Income Countries Waste Disposal Figure 13. Upper Middle-Income Countries Waste Disposal Low-Income Countries Waste Disposal Upper Middle-Income Countries Waste Disposal Income 0% Compost Dumps 1% Other Other 13% 6% Recycled 26% 1% Dumps 33% Income 1% Recycled 0% Compost 1% Landfills Landfills 59% 59% TABLE 12 AFR OECD MSW Disposal in two contrasting Dumps Source: Hoornweg 2005 2.3 Dumps Source: Hoornweg 2005 — regions (million Landfills 2.6 Landfills 242 tonnes) Compost 0.05 Compost 66 Recycled 0.14 Recycled 125 Incineration 0.05 Incineration 120 Other 0.11 Other 20 WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 25 Waste and the Environment Integrated Solid Waste Management waste managers; and on the appreciation of the critical role that the community, employees, and Integrated solid waste management (ISWM) local (and increasingly global) ecosystems have in reflects the need to approach solid waste in a effective SWM. ISWM should be driven by clear comprehensive manner with careful selection and objectives and is based on the hierarchy of waste sustained application of appropriate technology, management: reduce, reuse, recycle — often adding working conditions, and establishment of a ‘social a fourth ‘R’ for recovery. These waste diversion license’ between the community and designated options are then followed by incineration and waste management authorities (most commonly landfill, or other disposal options. Please refer to local government). ISWM is based on both a Box 3 for a detailed list describing the components high degree of professionalism on behalf of solid of an ISWM Plan. Components of an Integrated Solid Waste Management Plan An integrated Solid Waste Management plan should conjunction with facilities and practices and ways include the following sections: in which this information will be regularly reported; ``All municipal policies, aims, objectives, and initia- ``Associated institutional reforms and regulatory tives related to waste management; arrangements needed to support the plan; ``The character and scale of the city, natural condi- ``Financial assessment of the plan, including anal- tions, climate, development and distribution of ysis of both investment and recurrent costs associ- population; ated with the proposed facilities and services, over the lifetime of the plan (or facilities); ``Data on all waste generation, including data covering both recent years and projections over ``All the sources of finance and revenues associated the lifetime of the plan (usually 15-25 years). This with developing and operating the plan including should include data on MSW composition and estimated subsidy transfers and user fees; other characteristics, such as moisture content and density (dry weight), present and predicted; ``The requirements for managing all non-MSW arisings, what facilities are required, who will BOX 3 ``Identify all proposed options (and combination of provide them and the related services, and how options) for waste collection, transportation, treat- such facilities and services will be paid for; ment, and disposal of the defined types and quan- tities of solid wastes (this must address options for ``The proposed implementation plan covering a all types of solid waste arising); period of at least 5-10 years, with an immediate action plan detailing actions set out for the first ``Evaluation of the Best Practical Environmental 2-3 years; Option(s), integrating balanced assessments of all technical, environmental, social, and financial issues; ``Outline of public consultations carried out during preparation of the plan and proposed in future; ``The proposed plan, specifying the amount, scale, and distribution of collection, transportation, treat- ``Outline of the detailed program to be used to site ment and disposal systems to be developed, with key waste management facilities, e.g. landfills, proposed waste mass flows proposed through each; compost plants, and transfer stations. ``Specifications on the proposed on-going moni- ``An assessment of GHG emissions and the role of toring and controls that will be implemented in MSW in the city’s overall urban metabolism. 26 Integrated Sustainable Waste Management Framework BOX 4Stakeholders: include individuals or groups that have an interest or roles. All stakeholders should be identified and where practical involved in creating a y Local/Regulatory Authorities NGOs/CBOs Service Users Informal/Formal Sector ilit Donor Agencies SWM program. b na tai Elements (Process): include the technical aspects Sus of solid waste management. All stakeholders impact one or more of the elements. The elements need to Stakeholders be considered simultaneously when creating an SWM program in order to have an efficient and effective system. Elements Aspects Aspects (Policies and Impacts): encompass Generation and Separation Collection Environmental the regulatory, environmental and financial realities Transfer Political/Legal Institutional in which the waste management system operates. Treatment and Disposal Recovery Socio-Cultural Specific aspects can be changeable, e.g. a community 3 R’s Financial/Economic Technical increases influence or environmental regulations are and Performance tightened. Measures and priorities are created based on these various local, national and global aspects. Adapted Adapted from from van van de de Klundert Klundert and Anschütz and Anschütz 2001. 2001. As outlined by the Dutch NGO, WASTE, ISWM Public Health: In most jurisdictions, public is based on four principles: equity for all citizens health concerns have been the basis for solid to have access to waste management systems for waste management programs, as solid waste public health reasons; effectiveness of the waste management is essential to maintaining public management system to safely remove the waste; health. Solid waste that is not properly collected efficiency to maximize benefits, minimize costs, and and disposed can be a breeding ground for insects, optimize the use of resources; and sustainability vermin, and scavenging animals, and can thus of the system from a technical, environmental, pass on air- and water-borne diseases. Surveys social (cultural), economic, financial, institutional, conducted by UN-Habitat show that in areas where and political perspective (van de Klundert and waste is not collected frequently, the incidence of Anschütz 2001). diarrhea is twice as high and acute respiratory infections six times higher than in areas where There are three interdependent and intercon- collection is frequent (UN-Habitat 2009). nected dimensions of ISWM, which need to be addressed simultaneously when designing a Environmental Protection: Poorly collected solid waste management system: stakeholders, or improperly disposed of waste can have a detri- elements, and aspects. Please refer to Box 4 for mental impact on the environment. In low- and further details on the interconnected dimensions middle-income countries, MSW is often dumped in of ISWM. low-lying areas and land adjacent to slums. Lack of enforced regulations enables potentially infec- An alternative framework is provided by UN-HABITAT, tious medical and hazardous waste to be mixed which identifies three key system elements in ISWM: with MSW, which is harmful to waste pickers and public health, environmental protection, and resource the environment. Environmental threats include management (UN-Habitat 2009). contamination of groundwater and surface water WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 27 by leachate, as well as air pollution from burning cost of virgin materials and their environmental of waste that is not properly collected and disposed. impact increases, the relative value of secondary materials is expected to increase. Resource Management: MSW can represent a considerable potential resource. In recent years, the global market for recyclables has increased Waste Disposal Options significantly. The world market for post consumer The waste management sector follows a generally scrap metal is estimated at 400 million tonnes accepted hierarchy. The earliest known usage of annually and around 175 million tonnes annually the ‘waste management hierarchy’ appears to be for paper and cardboard (UN-Habitat 2009). This Ontario’s Pollution Probe in the early 1970s. The represents a global value of at least $30 billion per hierarchy started as the ‘three Rs’ — reduce, reuse, year. Recycling, particularly in low- and middle- recycle — but now a fourth R is frequently added income countries, occurs through an active, — recovery. The hierarchy responds to financial, although usually informal, sector. Producing new environmental, social and management consid- products with secondary materials can save signif- erations. The hierarchy also encourages minimi- icant energy. For example, producing aluminum zation of GHG emissions. See Figure 14 for the from recycled aluminum requires 95% less energy waste hierarchy. than producing it from virgin materials. As the FIG. 14 Most preferred option Waste Hierarchy Reduce Reuse Waste Diversion Recycle Recover (digestion, composting) Landfill Incineration (with energy recovery) Waste Disposal Controlled Dump* Least preferred option *As a minimum, waste should be disposed at a “controlled dump,” which includes site selection, controlled access, and where practical, compaction of waste. Incineration requires a complimentary sanitary landfill, as bottom ash, non-combustibles and by-passed waste needs to be landfilled. 28 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS Photo: Eric Miller/World Bank Maputo – Fapel, 1. Waste Reduction: Waste or source reduction GHG emissions come from the carbon dioxide paper mill and paper initiatives (including prevention, minimi- associated with electricity consumption for recycling factory zation, and reuse) seek to reduce the quantity the operation of material recovery facilities. of waste at generation points by redesigning Informal recycling by waste pickers will have products or changing patterns of production little GHG emissions, except for processing and consumption. A reduction in waste the materials for sale or reuse, which can be generation has a two-fold benefit in terms of relatively high if improperly burned, e.g. metal greenhouse gas emission reductions. First, recovery from e-waste. the emissions associated with material and product manufacture are avoided. The second 3. Aerobic Composting and Anaerobic benefit is eliminating the emissions associated Digestion: Composting with windrows with the avoided waste management activities. or enclosed vessels is intended to be an aerobic (with oxygen) operation that avoids 2. Recycling and Materials Recovery: The the formation of methane associated with key advantages of recycling and recovery are anaerobic conditions (without oxygen). reduced quantities of disposed waste and the When using an anaerobic digestion process, return of materials to the economy. In many organic waste is treated in an enclosed vessel. developing countries, informal waste pickers Often associated with wastewater treatment at collection points and disposal sites recover facilities, anaerobic digestion will generate a significant portion of discards. In China, for methane that can either be flared or used to example, about 20% of discards are recovered generate heat and/or electricity. Generally for recycling, largely attributable to informal speaking, composting is less complex, more waste picking (Hoornweg et al 2005). Related forgiving, and less costly than anaerobic WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 29 digestion. Methane is an intended by-product 5. Landfill: The waste or residue from other of anaerobic digestion and can be collected processes should be sent to a disposal site. Landfills and combusted. Experience from many are a common final disposal site for waste and jurisdictions shows that composting source should be engineered and operated to protect separated organics significantly reduces the environment and public health. Landfill gas contamination of the finished compost, rather (LFG), produced from the anaerobic decompo- than processing mixed MSW with front-end or sition of organic matter, can be recovered and back-end separation. the methane (about 50% of LFG) burned with or without energy recovery to reduce GHG 4. Incineration: Incineration of waste (with emissions. Proper landfilling is often lacking, energy recovery) can reduce the volume of especially in developing countries. Landfilling disposed waste by up to 90%. These high usually progresses from open-dumping, controlled volume reductions are seen only in waste dumping, controlled landfilling, to sanitary streams with very high amounts of packaging landfilling (see Table 13). materials, paper, cardboard, plastics and horticultural waste. Recovering the energy value embedded in waste prior to final disposal Waste and Climate Change is considered preferable to direct landfilling — GHG emissions from MSW have emerged as a assuming pollution control requirements and major concern as post-consumer waste is estimated costs are adequately addressed. Typically, to account for almost 5% (1,460 mtCO2e) of total incineration without energy recovery (or global greenhouse gas emissions. Solid waste also non-autogenic combustion, the need to includes significant embodied GHG emissions. For regularly add fuel) is not a preferred option example, most of the GHG emissions associated due to costs and pollution. Open-burning with paper occur before it becomes MSW. Encour- of waste is particularly discouraged due aging waste minimization through MSW programs to severe air pollution associated with low can therefore have significant up-stream GHG temperature combustion. minimization benefits. TABLE 13 Operation and Engineering Measures Leachate Management Landfill Gas Management Landfill Semi-controlled Dumps Few controls; some directed placement Unrestricted contaminant None Classifications of waste; informal waste picking; no release engineering measures Controlled Dump Registration and placement/compaction Unrestricted contaminant None of waste; surface water monitoring; no release engineering measures Engineered Landfill/ Registration and placement/compaction Containment and some level of Passive ventilation or flaring Controlled Landfill of waste; uses daily cover material; leachate treatment; reduced surface and ground water monitoring; leachate volume through infrastructure and liner in place waste cover Sanitary Landfill Registration and placement/compaction Containment and leachate Flaring with or without of waste; uses daily cover; measures treatment (often biological energy recovery for final top cover and closure; proper and physico-chemical siting, infrastructure; liner and leachate treatment) treatment in place and post-closure plan. 30 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS TABLE 14 Methane Emissions from % Methane from Disposal Landfill Methane Greenhouse Gas Emissions** Country Post-Consumer Municipal Sites Relative Emissions and Total (CO2, CH4, N2O) (MtCO2e) Waste Disposa* (MtCO2e) to Total GHG Emissions GHG Emissions for Brazil 16 659 2.4% Selected Countries China 45 3,650 1.2% India 14 1,210 1.1% Mexico 31 383 8.1% South Africa 16 380 4.3% *EPA 2006a. **UNFCCC 2005. Methane from landfills represents 12% of total has a Global Warming Potential 21 times greater global methane emissions (EPA 2006b). Landfills than carbon dioxide, is the second most common are responsible for almost half of the methane greenhouse gas after carbon dioxide. emissions attributed to the municipal waste sector in 2010 (IPCC 2007).4 The level of methane from Greenhouse gas emissions from waste management landfills varies by country, depending on waste can readily be reduced. Within the European composition, climatic conditions (ambient temper- Union, the rate of GHG emissions from waste has ature, precipitation) and waste disposal practices. declined from 69 mtCO2e per year to 32 million Table 14 highlights some examples. tCO2e per year from 1990 to 2007 (ISWA 2009). Organic biomass5 decomposes anaerobically in a sanitary landfill. Landfill gas, a by-product of the Greenhouse Gas Mitigation anaerobic decomposition is composed of methane Opportunities (typically about 50%) with the balance being Efforts to reduce emissions from the municipal carbon dioxide and other gases. Methane, which solid waste sector include generating less waste, improving the efficiency of waste collection, 4 Wastewater management adds an equal amount of methane to the atmosphere. expanding recycling, methane avoidance (aerobic 5 Organic biomass excludes organic waste such as plastics that are derived composting, anaerobic digestion with combustion from fossil energy sources. A transfer station in Amman, Jordan Photo: Perinaz Bhada-Tata WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 31 TABLE 15 Waste Management Component Technology Options Technical GHG Waste Reduction Design of longer-lasting and reusable products; reduced consumption. Mitigation Waste Collection Use of alternative, non-fossil fuels (bio-fuel, natural gas). Opportunities by Recycling/Materials Recovery Materials recovery facility (MRF) to process source separated materials or mixed waste, Waste Management although source separated is the preferred option as the materials would have less Component contamination from other discards. MRFs use a combination of manual and mechanical sorting options. Waste pickers could be used as a source of labor for manual sorting stages. Composting/Anaerobic Digestion Institute composting programs ideally with source separated organics. As with recyclables source separated materials reduce the contamination associated with recovery from mixed waste. Compost the organic material after digestion to produce a useful soil conditioner and avoid landfill disposal. Finished compost applied to soils is also an important method to reduce GHG emissions by reducing nitrogen requirements and associated GHG emissions. Incineration/Waste-to-energy/ Use the combustible fraction of waste as a fuel either in a dedicated combustion facility Refuse–Derived Fuel (RDF) (incineration) with or without energy recovery or as RDF in a solid fuel boiler. Landfill Capture the methane generated in disposal sites and flare or use as a renewable energy resource. of produced methane and capture, treatment when they buy the product. The fees collected and use of landfill gas). Energy generated from would be directed to municipalities relative to the methane combustion can displace other fossil fuels waste generated. An example of this economic either as a process energy resource or as electricity. mechanism is an excise tax on tires assessed Suitable technology options by waste management by most states in the US. Product charges are a component are provided in Table 15. policy mechanism often better implemented by regional or national governments. Policy Recommendations ` Another pricing mechanism well suited to for Reducing GHG Emissions urban areas is user charges tied to quantity Governments have a range of policy options to of waste disposed. Consumers who separate encourage waste management practices that will recyclables pay a lower fee for waste disposal. reduce greenhouse gas emissions. Practical approaches This pricing policy can work well in locations that could be applied in most cities include: where waste collection is from individual households so that waste quantities for disposal ` Public education to inform people about their can be readily monitored. However, it may options to reduce waste generation and increase not be practical in many areas in developing recycling and composting. countries, particularly in those where there are communal collection points associated with ` Pricing mechanisms, such as product charges multi-unit households (such as apartment user can stimulate consumer behavior to reduce charges tied to quantity or volume). waste generation and increase recycling. A product charge is a cost assessment added to the ` Preferential procurement policies and pricing price of a product and is tied to the cost of the to stimulate demand for products made with desired waste management system. Consumers recycled post-consumer waste. Use of compost in would pay for the waste management service public parks and other property owned by cities. 32 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS A Note on the Reliability of Solid Waste Data Solid waste data should be considered with a degree middle-income countries where the informal sector of caution due to global inconsistencies in definitions, removes a large fraction of recyclables. Additionally, data collection methodologies, and completeness. in most low- and middle-income countries, waste The reliability of the data is influenced by: collection rates are low and formal service does not extend to all communities, thereby reducing the `` Undefined words or phrases quantities of waste delivered to final disposal sites. Measuring waste quantities for final disposal is `` Inconsistent or omitted units practical for municipal purposes. Large variation in waste quantity and composition can be observed if `` Dates, methodologies, or sources of data not the economic situation changes, yet growing waste indicated quantities associated with increasing GNP are not necessarily a true reflection of increased waste; they `` Estimates made without basis might be changes in the relative recoverable value of the secondary materials and improvements in overall `` Incomplete or inconsistent data (please see collection efficiency. Annexes C and D for further information on available data) Waste composition specifies the components of the waste stream as a percentage of the total mass or `` Information collected at a non-representative volume. The component categories used within moment this report are: In most low- and middle-income countries, the `` organics (i.e. compostables such as food, yard, reliability of solid waste data is further compromised and wood wastes) by large seasonal variations (e.g. seasonal rains and un-containerized waste, horticultural variations), `` paper incomplete waste collection and disposal (e.g. a significant level of waste is disposed directly through `` plastic local burning or thrown in waterways and low lying areas), and a lack of weight scales at landfill sites to `` glass record waste quantities. `` metal Rarely is it disclosed at what stage the waste gener- ation rates and composition were determined, and `` others (includes ceramics, textiles, leather, whether they were estimated or physically measured. rubber, bones, inerts, ashes, coconut husks, The most accurate method measures the waste bulky wastes, household goods) generated at source before any recycling, composting, burning, or open dumping takes place. However, ‘Others’ wastes should be differentiated into two the generation rate and composition are commonly categories: other-residue and other-consumer calculated using waste quantities arriving at the final products. Other-residue is made up of ash, inerts, disposal site. This method of measurement does not dirt, and sweepings and is a significant component fully represent the waste stream because waste can be of the waste stream in low- and middle-income diverted prior to final disposal, especially in low- and countries. Other-consumer products consist of WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT 33 bulky wastes, household appliances, electronics, Another major inconsistency among the various and multi-material packaging (e.g., tetrapaks and waste studies is the use of imperial units versus blister packaging). This waste stream is much metric units. Frequently the imperial ton and the more significant in high-income countries and metric tonne are interchanged when reporting differs from other-residue in that the volumes waste quantities. Data are also denoted by the letter are much higher per kilogram of waste and are “t” to denote the unit, causing the true value to be generally combustible. unknown. Within this report, all of the units are metric, unless clearly noted. Waste densities and It is important to cite whether the percentages are moisture contents are needed to convert data to a given on a dry or wet basis, because component common frame of reference for comparison (e.g. percentages will differ markedly depending on from mass to volume and from wet to dry). Usually moisture content. Rarely is it indicated within a the higher the percentage of organic matter, the waste study whether the percentage is on a wet higher the moisture content and often the higher or dry basis, or based on volume or mass. It is the density of the waste stream. assumed that the composition was determined on a wet basis. Probably both mass and volume There are major efforts being done to correct measurements were used depending upon the data inconsistencies at the city level. So far, there country. Low- and middle-income countries would is no single standard or comprehensive system be more inclined to use volume since it does not to measure and monitor city performance and require sophisticated measuring equipment and urban quality of life. In response to this need, the can be estimated. High-income countries usually Global City Indicators Program (GCIP), based in use mass as a basis since they have greater funding Toronto, has been developed. The GCIP (please resources and support to complete a more accurate see Annex O) provides a practical means for cities waste characterization. to collect credible information on MSW. Bangalore, India Photo: Cyrus Tata A couple salvage old bricks from an area demolished for renovation in Saigon Photo: Tran Thi Hoa ANNEXES 36 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX A Map of Regions Greenland (Den) Iceland Faeroe Islands (Den) The Netherlands Isle of Man (UK) Canada Unite Ireland Kingdo Channel Islands (UK) Luxembourg Liechtenstein F Switzerland Andorra United States Spain Portugal Gibraltar (UK) Bermuda (UK) Morocco A The Bahamas Former Spanish Sahara Cayman Is.(UK) Mexico Cuba Mauritania Haiti 1 Cape Verde Belize Jamaica Mali Guatemala Honduras Senegal El Salvador Nicaragua The Gambia Burkina Guinea-Bissau Faso Guinea Costa Rica Panama R.B. de Guyana Sierra Leone Côte Ghana Venezuela Suriname d’Ivoire Liberia French Guiana (Fr) Colombia To Equatorial G São Tomé and Prí Ecuador Kiribati Samoa Peru Brazil French Polynesia (Fr) American Samoa (US) Bolivia Fiji Tonga Paraguay 1 Dominican Germany Republic Puerto St. Martin(Fr) Poland Rico (US) St. Maarten(Neth) Czech Republic Uruguay Slovak Repu Antigua and Barbuda Chile U.S. Virgin Argentina Islands (US) Guadeloupe (Fr) Austria St. Kitts Hungary and Nevis Dominica Slovenia Croatia Martinique (Fr) Italy Bosnia and St. Lucia Herzegovina Serb Aruba Bonaire Curaçao St. Vincent and San (Neth) (Neth) (Neth) the Grenadines Barbados Marino Montenegro Koso Grenada Mac Trinidad Vatican Albania R.B. de Venezuela and Tobago 2 City G ANNEX 37 IBRD 39177 MARCH 2012 Norway Sweden Finland Russian Federation Estonia Denmark Russian Latvia Fed. Lithuania ed om Germany Poland Belarus Belgium Ukraine Moldova Kazakhstan Mongolia France Italy Romania Bulgaria Georgia Uzbekistan Kyrgyz 2 Armenia Azer- baijan Turkmenistan Rep. Dem.People’s Rep.of Korea Turkey Tajikistan Monaco Greece Japan Cyprus Syrian Rep.of Malta Lebanon Arab Islamic Rep. China Korea Tunisia Rep. of Iran Afghanistan Israel Iraq Kuwait West Bank and Gaza Jordan Algeria Bahrain Pakistan Bhutan Libya Arab Rep. Qatar Nepal of Egypt Saudi Arabia Bangladesh United Arab Emirates India Myanmar Oman Lao P.D.R. Niger N. Mariana Islands (US) Chad Sudan Eritrea Rep. of Yemen Thailand Vietnam Guam (US) Cambodia Philippines Djibouti Federated States of Micronesia Benin Marshall Islands Nigeria Central Ethiopia Sri a Lanka African South Republic Sudan Brunei Darussalam Cameroon Malaysia Palau ogo Somalia Guinea Maldives Uganda íncipe Kenya Nauru Kiribati Congo Singapore Gabon Rwanda Dem.Rep.of Burundi Seychelles Congo Solomon Tanzania Papua New Guinea Islands Comoros Indonesia Tuvalu Timor-Leste Angola Malawi Zambia Mayotte (Fr) Vanuatu Fiji Mozambique Zimbabwe Madagascar Mauritius Namibia Botswana New Réunion (Fr) Caledonia Australia (Fr) Swaziland d South Lesotho Africa Ukraine ublic New Zealand The world by region Romania bia ovo Bulgaria Classified according to Low- and middle-income economies FYR cedonia World Bank analytical East Asia and Pacific grouping Greece Europe and Central Asia High-income economies Latin America and the Caribbean OECD Middle East and North Africa Other South Asia Antarctica Sub-Saharan Africa No data 38 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX B Map of Income Distribution Greenland (Den) Iceland Faeroe Islands (Den) The Netherlands Isle of Man (UK) Canada Unite Ireland Kingdo Channel Islands (UK) Luxembourg Liechtenstein F Switzerland Andorra United States Spain Portugal Gibraltar (UK) Bermuda (UK) Morocco A The Bahamas Former Spanish Sahara Cayman Is.(UK) Mexico Cuba Mauritania Haiti 1 Cape Verde Belize Jamaica Mali Guatemala Honduras Senegal El Salvador Nicaragua The Gambia Burkina Guinea-Bissau Faso Guinea Costa Rica Panama R.B. de Guyana Sierra Leone Côte Ghana Venezuela Suriname d’Ivoire Liberia French Guiana (Fr) Colombia To Equatorial G São Tomé and Prí Ecuador Kiribati Samoa Peru Brazil French Polynesia (Fr) American Samoa (US) Bolivia Fiji Tonga Paraguay 1 Dominican Germany Republic Puerto St. Martin(Fr) Polan Rico (US) St. Maarten(Neth) Czech Republic Uruguay Slovak Repu Antigua and Barbuda Chile U.S. Virgin Argentina Islands (US) Guadeloupe (Fr) Austria St. Kitts Hungary and Nevis Dominica Slovenia Croatia Martinique (Fr) Italy Bosnia and St. Lucia Herzegovina Ser Aruba Bonaire Curaçao St. Vincent and San (Neth) (Neth) (Neth) the Grenadines Barbados Marino Montenegro Koso Grenada Mac Trinidad Vatican Albania R.B. de Venezuela and Tobago 2 City ANNEX 39 IBRD 39176 MARCH 2012 Norway Sweden Finland Russian Federation Estonia Denmark Russian Latvia Fed. Lithuania ed om Germany Poland Belarus Belgium Ukraine Moldova Kazakhstan Mongolia France Italy Romania Bulgaria Georgia Uzbekistan Kyrgyz 2 Armenia Azer- baijan Turkmenistan Rep. Dem.People’s Rep.of Korea Turkey Tajikistan Monaco Greece Japan Cyprus Syrian Rep.of Malta Lebanon Arab Islamic Rep. China Korea Tunisia Rep. of Iran Afghanistan Israel Iraq Kuwait West Bank and Gaza Jordan Algeria Bahrain Pakistan Bhutan Libya Arab Rep. Qatar Nepal of Egypt Saudi Arabia Bangladesh United Arab Emirates India Myanmar Hong Kong SAR, China Oman Lao P.D.R. Niger N. Mariana Islands (US) Chad Sudan Eritrea Rep. of Yemen Thailand Vietnam Guam (US) a Cambodia Djibouti Philippines Federated States of Micronesia Benin Marshall Islands Nigeria Central Ethiopia Sri a Lanka African South Brunei Darussalam Republic Sudan Palau Cameroon Malaysia Togo Somalia Guinea Maldives Uganda íncipe Kenya Nauru Kiribati Congo Singapore Gabon Rwanda Dem.Rep.of Burundi Seychelles Congo Solomon Tanzania Papua New Guinea Islands Comoros Indonesia Tuvalu Timor-Leste Angola Malawi Zambia Mayotte (Fr) Vanuatu Fiji Mozambique Zimbabwe Madagascar Mauritius Namibia Botswana New Réunion (Fr) Caledonia Australia (Fr) Swaziland nd South Lesotho Africa Ukraine ublic New Zealand Romania rbia ovo Bulgaria FYR As of July 2011, South Sudan is shown The world by income cedonia independent from Sudan. However, the Classified according to Low ($975 or less) Greece income data shown on this map is dated 2008, and applies to the former united World Bank estimates of Sudan. Lower middle ($976–$3,855) 2008 GNI per capita Upper middle ($3,856–$11,905) High ($11,906 or more) Antarctica No data 40 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX C Availability of MSW Data by Country Urban Income Gen- Year of Collec- Year of Year of Composi- Year of Country Region Source or Source Disposal Source Source Level eration Data tion Data Data tion Data Total Denmark Albania1 LMI ECA x 2006 Ministry of x T 2005 UNSD (2009) x 2005 UNSD (2009) Foreign Affairs METAP Algeria UMI MENA x 2002 METAP (2004) x U 2002 x 2002 METAP (2004) x 2002 METAP (2004) (2004) Andorra HIC OECD x 2007 UNSD (2009) x T 2007 UNSD (2009) x 2005 UNSD (2009) Angola2 LMI AFR x 2005 USAID (2009) Antigua and HIC LCR x 2001 PAHO (2005) x T 2007 UNSD (2009) x 2007 UNSD (2009) Barbuda Argentina UMI LCR x 2001 PAHO (2005) x 2001 UNSD (2009) Armenia LMI ECA x 2007 UNSD (2009) x T 2007 UNSD (2009) x 2007 UNSD (2009) x 2007 UNSD (2009) Australia HIC OECD x 1999 OECD (2008) x 2003 OECD (2008) x 2005 OECD (2008) Austria HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2004 OECD (2008) x 2004 OECD (2008) Bahamas, The HIC LCR x 2001 PAHO (2005) Bahrain3 HIC MENA x 2000 UNESCWA (2007) Bangladesh Department of Bangladesh4 LI SAR x 2004 x 2004 UNSD (2009) Environment (2004) Barbados HIC LCR x 2001 PAHO (2005) Belarus Ministry Belarus Ministry of Natural of Natural Belarus UMI ECA x 2005 x T 2007 UNSD (2009) x 2005 x 2004 UNSD (2009) Resources Resources (2006) (2006) Belgium HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2003 OECD (2008) x 2003 OECD (2008) Belize LMI LCR x 2001 PAHO (2005) x T 2005 UNSD (2009) x 2005 UNSD (2009) x 1997 UNSD (2009) Benin2 LI AFR x 2005 USAID (2009) x T 2000 UNSD (2009) x 2002 UNSD (2009) Bhutan LMI SAR x 2007 Phuntsho (2008) x 2008 Phuntsho (2008) Business News Bolivia LMI LCR x 2003 x 1999 UNSD (2009) Americas (2004) Kgathi and Botswana UMI AFR x 1998 Bolaane (2001) Brazil UMI LCR x 2001 PAHO (2005) x T 2007 UNSD (2009) x 2006 UNSD (2009) Brunei Ngoc and Ngoc and Schnitzer HIC EAP x 2006 x 2006 Darussalam Schnitzer (2009) (2009) European Bulgaria LMI ECA x 2007 Environment x T 2002 UNSD (2009) x 2007 UNSD (2009) Agency (2008) Burkina Faso2 LI AFR x 2005 USAID (2009) Burundi2 LI AFR x 2005 USAID (2009) Kum et al. Kum et al. Ngoc and Schnitzer Cambodia5 LI EAP x U 2000 x 2004 x 2000 (2005) (2005) (2009) Parrot et al. Parrot et al. Cameroon LMI AFR x 2000 x 2001 x 2006 UNSD (2009) (2009) (2009) Canada HIC OECD x 1990 OECD (2008) x T 1996 UNSD (2009) x 2004 OECD (2008) x 2004 OECD (2008) Cape Verde2 LMI AFR x 2005 USAID (2009) Central African LI AFR x 2005 USAID (2009) Republic2 Chad2 LI AFR x 2005 USAID (2009) Chile UMI LCR x 2001 PAHO (2005) x 2006 UNSD (2009) x 1998 UNSD (2009) Hoornweg et al. China LMI EAP x 2004 (2005) Colombia UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2005 PAHO (2005) x 2005 UNSD (2009) Comoros LI AFR x 2003 Payet (2003) x T 2003 Payet (2003) Congo, Dem. LI AFR x 2005 USAID (2009) Rep.2 Congo, Rep. LMI AFR x 2005 USAID (2009) Costa Rica UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) x 2005 UNSD (2009) Cote d'Ivoire2 LMI AFR x 2005 USAID (2009) Croatia6 HIC ECA x 2008 Vego (2008) x T 2005 UNSD (2009) x 2006 UNSD (2009) x 2000 UNSD (2009) Cuba UMI LCR x 2001 PAHO (2005) x T 2005 UNSD (2009) x 2005 UNSD (2009) x 2005 UNSD (2009) ANNEX 41 ANNEX C (continued) Availability of MSW Data by Country Urban Income Gen- Year of Collec- Year of Year of Composi- Year of Country Region Source or Source Disposal Source Source Level eration Data tion Data Data tion Data Total European Cyprus HIC ECA x 2007 Environment x 2007 UNSD (2009) x 2001 UNSD (2009) Agency (2008) Czech Republic HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2004 OECD (2008) x 1996 UNSD (2009) Denmark HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2003 OECD (2008) x 2005 OECD (2008) Dominica UMI LCR x 2001 PAHO (2005) x T 2005 UNSD (2009) x 2005 UNSD (2009) Dominican UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) x 2000 UNSD (2009) Republic Ecuador3 LMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) METAP Egypt, Arab Rep. LMI MENA x 2000 METAP (2004) x U 2000 x 2000 METAP (2004) x 2000 METAP (2004) (2004) El Salvador LMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) Eritrea2 LI AFR x 2005 USAID (2009) European Estonia HIC ECA x 2007 Environment x T 2001 UNSD (2009) x 2007 UNSD (2009) Agency (2008) Tadesse et al. Ethiopia7 LI AFR x 2006 x 1995 (2008) Fiji UMI EAP x 1994 McIntyre (2005) x 1994 McIntyre (2005) Finland HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2004 OECD (2008) x 2000 UNSD (2009) France HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2005 OECD (2008) x 2005 OECD (2008) Gabon2 UMI AFR x 2005 USAID (2009) Gambia2 LI AFR x 2005 USAID (2009) x 2001 UNSD (2009) Georgia UMI ECA x 2007 UNSD (2009) x T 2007 UNSD (2009) x 2007 UNSD (2009) Germany HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2004 OECD (2008) x 2005 OECD (2008) Asase et al. Asase et al. Asase et al. Ghana LI AFR x 2008 x U 2008 x 2008 x 2008 Asase et al. (2009) (2009) (2009) (2009) Greece HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2003 OECD (2008) x 1997 UNSD (2009) Grenada UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) Guatemala LMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) x 2006 UNSD (2009) Guinea LI AFR x 2007 UNSD (2009) Guyana LMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) x 2000 UNSD (2009) Haiti LI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) Honduras LMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) Hong Kong, HIC EAP x 2008 Shekdar (2009) x T 2007 UNSD (2009) x 2007 UNSD (2009) x 2008 Shekdar (2009) China Hungary HIC OECD x 2006 OECD (2008) x T 2003 UNSD (2009) x 2003 OECD (2008) x 2005 OECD (2008) Iceland HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2004 OECD (2008) x 2003 OECD (2008) Hanrahan et al. India LMI SAR x 2006 x 2004 UNSD (2009) (2006) Pasang et al. Ngoc and Schnitzer Indonesia8 LMI EAP x 2008 Shekdar (2009) x U 2006 x 2000 (2007) (2009) Iran, Islamic Damghani et al. Damghani et al. LMI MENA x 2005 x 2005 Rep.9 (2008) (2008) Iraq10 LMI MENA x 2005 UNESCWA (2007) x T 2005 UNSD (2009) Ireland HIC OECD x 2006 OECD (2008) x T 2005 UNSD (2009) x 2005 OECD (2008) x 2005 OECD (2008) Israel Ministry of Israel Ministry of Israel HIC MENA x 1996 the Environment x 1996 the Environment x 2005 UNSD (2009) (2000) (2000) Italy HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2005 OECD (2008) x 2005 OECD (2008) Jamaica3 UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) x 2007 UNSD (2009) Japan HIC OECD x 2005 OECD (2008) x T 2003 UNSD (2009) x 2003 OECD (2008) x 2008 Shekdar (2009) METAP Jordan LMI MENA x 2001 METAP (2004) x U 2001 x 2001 METAP (2004) x 2001 METAP (2004) (2004) Kenya Ministry Kenya LI AFR x 2002 of Environment (2002) Korea, South HIC OECD x 2005 OECD (2008) x T 2002 UNSD (2009) x 2004 OECD (2008) x 2005 OECD (2008) Personal Kuwait HIC MENA x 2009 communication 42 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX C (continued) Availability of MSW Data by Country Urban Income Gen- Year of Collec- Year of Year of Composi- Year of Country Region Source or Source Disposal Source Source Level eration Data tion Data Data tion Data Total Ngoc and Schnitzer Lao PDR LI EAP x 2008 Shekdar (2009) x 2000 (2009) European Latvia Ministry Latvia Ministry Latvia UMI ECA x 2007 Environment x T 1999 UNSD (2009) x 2003 of Environment x 2003 of Environment Agency (2008) (2006) (2006) METAP Lebanon UMI MENA x 2000 METAP (2004) x U 2000 x 2000 METAP (2004) x 2000 METAP (2004) (2004) Lesotho 2 LMI AFR x 2005 USAID (2009) Liberia11 LI AFR x 2004 UNEP (2007) Lithuania European Ministry of Lithuania UMI ECA x 2007 Environment x 2003 Environment Agency (2008) (2005) Luxembourg HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2003 OECD (2008) x 2005 OECD (2008) Macao, China HIC EAP x 2003 Jin et al. (2006) x T 2007 UNSD (2009) x 2007 UNSD (2009) x 2007 UNSD (2009) Hristovski et al. Macedonia, FYR LMI ECA x 2006 x 1996 Macedonia (1996) (2007) Madagascar LI AFR x 2003 Payet (2003) x T 2007 UNSD (2009) x 2007 UNSD (2009) x 2007 UNSD (2009) Malawi2 LI AFR x 2005 USAID (2009) Saeed et al. Ngoc and Schnitzer Malaysia12 UMI EAP x 2002 x 2000 (2009) (2009) Maldives LMI SAR x 1998 UNEP (2002) Samake et al. Mali 13 LI AFR x 2007 x T x 1995 UNSD (2009) (2009) European Malta HIC MENA x 2007 Environment x T 2007 UNSD (2009) x 2007 UNSD (2009) Agency (2008) Marshall Islands LMI EAP x T 2007 UNSD (2009) x 2007 UNSD (2009) x 2007 UNSD (2009) Mauritania2 LI AFR x 2005 USAID (2009) Mauritius UMI AFR x 2003 Payet (2003) x T 2007 UNSD (2009) x 2007 UNSD (2009) x 2007 UNSD (2009) Mexico UMI LCR x 2006 OECD (2008) x T x 2006 OECD (2008) x 2005 OECD (2008) Monaco HIC OECD x T 2007 UNSD (2009) x 2007 UNSD (2009) Mongolia Mongolia LI EAP x 2001 Ministry of Nature (2001) METAP Morocco LMI MENA x 2002 METAP (2004) x T 2002 x 2002 METAP (2004) x 2000 UNSD (2009) (2004) Mozambique 14 LI AFR x 2007 Grest (2008) x 2007 Grest (2008) Ngoc and Schnitzer Myanmar UMI EAP x 2000 IPCC (2006) x 2000 (2009) Namibia2 UMI AFR x 2005 USAID (2009) Shekdar (2009) Alam et al. Nepal LI SAR x 2008 x U 2003 x 2008 Shekdar (2009) per cap (2008) Netherlands HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2004 OECD (2008) x 2004 OECD (2008) New Zealand HIC OECD x 2006 OECD (2008) x 1999 OECD (2008) x 1995 UNSD (2009) Nicaragua3 LMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) Niger2 LI AFR x 2005 USAID (2009) x 2005 UNSD (2009) x 2005 UNSD (2009) Nigeria LMI AFR x 2008 Solomon (2009) x 2008 Imam et al. (2008) Norway HIC OECD x 2006 OECD (2008) x T 2004 UNSD (2009) x 2004 OECD (2008) x 2005 OECD (2008) Oman HIC MENA x 1997 Al-Yousfi Batool and Batool and Nawaz Pakistan15 LMI SAR x 2009 x 2009 Nawaz (2009) (2009) Panama UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) x 2000 UNSD (2009) Paraguay LMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) Peru UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) x 2001 UNSD (2009) Ngoc and Schnitzer Philippines LMI EAP x 2008 Shekdar (2009) x 2000 (2009) European Poland UMI ECA x 2007 Environment x 2005 OECD (2008) x 1990 UNSD (2009) Agency (2008) Portugal HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2005 OECD (2008) x 2001 OECD (2008) Qatar HIC MENA x 2004 UNESCWA (2007) ANNEX 43 ANNEX C (continued) Availability of MSW Data by Country Urban Income Gen- Year of Collec- Year of Year of Composi- Year of Country Region Source or Source Disposal Source Source Level eration Data tion Data Data tion Data Total European Romania UMI ECA x 2007 Environment x T 2002 UNSD (2009) x 2007 UNSD (2009) x 2006 Atudorei Agency (2008) Russian UMI ECA x 2000 IPCC (2006) Federation Rwanda2 LI AFR x 2005 USAID (2009) Sao Tome and LMI AFR x 2005 USAID (2009) Principe2 Saudi Arabia HIC MENA x 1997 Al-Yousfi Senegal2 LI AFR x 2005 USAID (2009) x T 2005 UNSD (2009) x 2007 UNSD (2009) Denmark Denmark Ministry Serbia1 LI ECA x 2006 Ministry of x T 2006 x 1999 UNSD (2009) of Foreign Foreign Affairs Affairs Seychelles UMI AFR x 2003 Payet (2003) x T 2003 Payet (2003) Patriotic Patriotic Patriotic Vanguard Sierra Leone16 LI AFR x 2007 x U 2007 Vanguard x 2007 Vanguard (2007) (2007) (2007) Singapore Ngoc and Schnitzer Singapore HIC EAP x 2008 x T 2007 UNSD (2009) x 2007 UNSD (2009) x 2000 (2009) (2009) Slovak Republic HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2005 OECD (2008) x 2002 OECD (2008) Slovenia European Ministry of the Slovenia HIC ECA x 2007 Environment x T 2002 UNSD (2009) x 2003 Environment Agency (2008) (2006) Solomon Islands LMI EAP x 1994 McIntyre (2005) x 1994 McIntyre (2005) City of Cape South Africa17 UMI AFR x 2003 Town (2008) Spain HIC OECD x 2006 OECD (2008) x 2004 OECD (2008) x 2002 OECD (2008) Sri Lanka LMI SAR x 2003 Perera (2003) x 2008 Shekdar (2009) St. Kitts and UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) Nevis3 St. Lucia UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) St. Vincent and UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) x 2002 UNSD (2009) the Grenadines Sudan LMI AFR x 2000 IPCC (2006) Suriname UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) Swaziland2 LMI AFR x 2005 USAID (2009) Sweden HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2005 OECD (2008) x 2005 OECD (2008) Switzerland HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2005 OECD (2008) x 2005 OECD (2008) Syrian Arab METAP LMI MENA x 2002 METAP (2004) x U 2002 x 2002 METAP (2004) x 2002 METAP (2004) Republic (2004) Tajikistan UMI ECA x 2001 CEROI (2001) Kassim and Ali Kassim and Tanzania LI AFR x 2005 x U 2005 (2006) Ali (2006) Ngoc and Schnitzer Thailand LMI EAP x 2008 Shekdar (2009) x 2000 UNSD (2009) x 2000 (2009) Togo2 LI AFR x 2005 USAID (2009) x 2007 UNSD (2009) Tonga LMI EAP x 1994 McIntyre (2005) x 1994 McIntyre (2005) Trinidad and HIC LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) x 2003 UNSD (2009) Tobago METAP Tunisia LMI MENA x 2000 METAP (2004) x U 2000 x 2000 METAP (2004) x 2000 METAP (2004) (2004) Turan et al. Turan et al. Turkey LMI ECA x 2006 OECD (2008) x T 2004 x 2004 x 2008 Turan et al. (2009) (2009) (2009) Turkmenistan Ministry Turkmenistan LMI ECA x 2000 of Nature Protection (2000) Uganda18 LI AFR x 2004 Bingh (2004) x U 2002 Bingh (2004) x 2006 UNSD (2009) x 2002 Bingh (2004) United Arab HIC MENA x 2000 UNESCWA (2007) Emirates3 44 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX C (continued) Availability of MSW Data by Country Urban Income Gen- Year of Collec- Year of Year of Composi- Year of Country Region Source or Source Disposal Source Source Level eration Data tion Data Data tion Data Total United Kingdom HIC OECD x 2006 OECD (2008) x T 2007 UNSD (2009) x 2005 OECD (2008) United States HIC OECD x 2006 OECD (2008) x T 2005 UNSD (2009) x 2005 OECD (2008) x 2005 OECD (2008) Uruguay UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) x 2003 UNSD (2009) Vanuatu LI EAP x 1994 McIntyre (2005) x 1994 McIntyre (2005) Venezuela, RB UMI LCR x 2001 PAHO (2005) x T 2001 PAHO (2005) x 2001 PAHO (2005) World Bank Ngoc and Schnitzer Vietnam LI EAP x 2004 x 2000 (2004) (2009) West Bank and METAP LMI MENA x 2001 METAP (2004) x U 2001 x 2001 METAP (2004) x 2001 METAP (2004) Gaza (2004) Environmental Zambia19 LI AFR x Council of x T 2005 UNSD (2009) Zambia (2004) Zimbabwe2 LI AFR x 2005 USAID (2009) x 2007 UNSD (2009) NOTES: 1 Year for generation data is assumed to be 2006 2 Generation rates calculated using a per capita rate of 0.5kg/cap/day 3 Generation value refers to domestic waste (household) only 4 Generation rates are for urban areas only 5 Collection and disposal values are for Pnom Penh only 6 Generation rate is for Dalmatia 7 Genearation value for Mekelle City 8 Collection value is for Jakarta only 9 Generation and composition values are for Tehran 10 Population values are for 1999, the most recent year available 11 Composition values for Monrovia only 12 Generation values are for Kuala Lumpur 13 Generation and composition values are for Bamako 14 Generation and composition values are for Maputo 15 Generation and composition values are for Lahore 16 All values are for Freetown 17 Generation values are based on Cape Town per capita values 18 All values are for Kampala city only 19 Generation values are from 1996; composition values are for Lusaka only ANNEX 45 ANNEX D Countries Excluded for Lack of Data Country Income level Region Afghanistan LI SAR American Samoa UMI EAP Aruba HIC OECD Azerbaijan LMI ECA Bermuda HIC OECD Bosnia and Herzegovina UMI ECA Cayman Islands HIC OECD Channel Islands HIC OECD Djibouti LMI MENA Equatorial Guinea HIC OECD Faeroe Islands HIC OECD French Polynesia HIC OECD Greenland HIC OECD Guam HIC OECD Guinea-Bissau LI AFR Isle of Man HIC OECD Kazakhstan UMI ECA Kiribati LMI EAP Korea, Dem. People’s Rep. LI EAP Kosovo LMI ECA Kyrgyz Republic LI ECA Libya UMI MENA Liechtenstein HIC OECD Mayotte UMI AFR Micronesia, Federated States of LMI EAP Moldova LMI ECA Montenegro UMI ECA Netherlands Antilles HIC OECD New Caledonia HIC OECD Northern Mariana Islands HIC OECD Palau LMI EAP Papua New Guinea LMI EAP Puerto Rico HIC OECD Samoa LMI EAP San Marino HIC OECD Somalia LI AFR Taiwan, China HIC EAP Timor-Leste LMI EAP Ukraine LMI ECA Uzbekistan LI ECA Virgin Islands (US) HIC OECD Yemen, Republic of LI MENA 46 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX E Estimated Solid Waste Management Costs Estimated Solid Waste Management Costs by Disposal Method 1 Low Income Countries Lower Mid Inc Countries Upper Mid Inc Countries High Income Countries Income <$876 $876-3,465 $3,466-10,725 >$10,725 (GNI/capita) Waste Generation 0.22 0.29 0.42 0.78 (tonnes/capita/yr) Collection Efficiency 43% 68% 85% 98% (percent collected) Cost of Collection and Disposal (US$/tonne) Collection2 20-50 30-75 40-90 85-250 Sanitary Landfill 10-30 15-40 25-65 40-100 Open Dumping 2-8 3-10 NA NA Composting3 5-30 10-40 20-75 35-90 Waste -to-Energy NA 40-100 60-150 70-200 Incineration4 Anaerobic Digestion5 NA 20-80 50-100 65-150 NOTE: This is a compilation table from several World Bank documents, discussions with the World Bank’s Thematic Group on Solid Waste, Carl Bar- tone and other industry and organizational colleagues. Costs associated with uncollected waste—more than half of all waste generated in low-income countries—are not included. Estimated Solid Waste Management Costs 2010 and 2025 Country Income Group 2010 Cost6 2025 Cost Low Income Countries 7 $1.5 billion $7.7 billion Lower Middle Income Countries 8 $20.1 billion $84.1 billion Upper Middle Income Countries 9 $24.5 billion $63.5 billion High Income Countries10 $159.3 billion $220.2 billion Total Global Cost (US$) $205.4 billion $375 billion Source: Authors’ calculations with input from What a Waste report (Hoornweg and Thomas 1999) and the World Bank Solid Waste Thematic Group and Carl Bartone. 1 All values provided in the table are exclusive of any potential carbon finance, subsidies, or external incentives. Costs included are for purchase (including land), operation, maintenance, and debt service. 2 Collection includes pick up, transfer, and transport to final disposal site for residential and non-residential waste. 3 Composting excludes sale of finished compost (which ranges from $0 to $100/ton). 4 Includes sale of any net energy; excludes disposal costs of bottom and fly ash (non hazardous and hazardous). 5 Anaerobic digestion includes sale of energy from methane and excludes cost of residue sale and disposal. 6 Cost of SWM (US$) = waste generated (tonnes) X percent of waste collected (%) X [cost of collection ($/ton) + cost of disposal ($/ton)] 7 2010: $1.5bil = 75,000,000 tonnes X 43% X ($30/ton + $15/ton); 2025: $7.7bil = 201,000,000 tonnes X 55% X ($45/ton + $25/ton) 8 2010: $20.1bil = 369,000,000 tonnes X 68% X ($50/ton +$30/ton); 2025: $84.1bil = 956,000,000 tonnes X 80% X ($65/ton + $45/ton) 9 2010: $24.5bil = 243,000,000 tonnes X 84% X ((0.9Landfill ($65/ton + $50/ton)) + (0.1Incinerate ($65/ton + $100/ton))); 2025: $63.5bil = 426,000,000 X 92% X ((0.85Landfill ($85/ton + $65/ton)) + (0.15Incinerate($85/ton +$145/ton))) 10 2010: $159.3bil = 602,000,000 tonnes X 98% X ((0.8Landfill ($180/ton + $75/ton)) + (0.2 Incinerate ($180/ton + $150/ton))); 2025: $220.2bil = 686,000,000 tonnes X 98% X ((0.75Landfill ($210/ton + $95/ton)) + 0.25Incinerate($210/ton + $185/ton))) ANNEX 47 ANNEX F MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Africa Benin Parakou (UNSD 2009) 2002 148,450 0.59 87,671 88 Porto Novo (Achankeng 2003) 1993 0.50 — Burkina Faso (UNSD 2009) Ouagadougou 2002 876,200 0.79 692,635 693 Burundi (Achankeng 2003) Bujumbura 1993 1.40 — Cameroon (Achankeng 2003) Douala 1993 0.70 Yaounde 1993 0.80 Congo, Rep. (Achankeng 2003) Brazzaville 1993 0.60 Cote d’Ivoire (Achankeng 2003) Abidjan 1993 1.00 Egypt (Achankeng 2003) Cairo 1993 0.50 Gambia, The (Achankeng 2003) Banjul 1993 0.30 Ghana Accra (Achankeng 2003) 1993 0.40 Kumasi (Asase 2009) 2006 1,610,867 0.60 966,520 967 Guinea (UNSD 2009) Conakry 2007 3,000,000 0.24 725,274 725 Madagascar (Achankeng 2003) Antananarivo 1993 0.30 Mauritania (Achankeng 2003) Nouakchott 1993 0.90 Morocco (Achankeng 2003) Rabat 1993 0.60 Namibia (Achankeng 2003) Windhoek 1993 0.70 Niger Niamey (Achankeng 2003) 1993 1.00 Zinder (UNSD 2009) 2006 242,800a 0.29 69,430 69 Nigeria (Achankeng 2003) Ibadan 1993 1.10 Lagos 1993 0.30 Rwanda (Achankeng 2003) Kigali 1993 0.60 Senegal (Achankeng 2003) Dakar 1993 0.70 Tanzania (Achankeng 2003) Dar es Salaam 1993 1.00 Togo (Achankeng 2003) Lome 1993 1.90 Tunisia (Achankeng 2003) Tunis 1993 0.50 Uganda (Achankeng 2003) Kampala 1993 6.00 Zambia (UNSD 2009) Lusaka 2005 1,300,000 0.90 1,171,994 1,172 Zimbabwe (UNSD 2009) Harare 2005 2,500,000 0.08 207,500 208 48 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) East Asia & Pacific China** (Hoornweg et al. 2005) Anshan, Liaoning 2000 1,453,000 0.90 1,307,701 1,308 Baotou, Inner Mongolia 2000 1,319,000 0.90 1,187,101 1,187 Beijing, Beijing 2000 10,839,000 0.90 9,755,101 9,755 Benxi, Liaoning 2000 957,000 0.90 861,301 861 Changchun, Jilin 2000 3,093,000 0.90 2,783,701 2,784 Changde, Hunan 2000 1,374,000 0.90 1,236,600 1,237 Changsha, Hunan 2000 1,775,000 0.90 1,597,501 1,598 Changzhou, Jiangsu 2000 886,000 0.90 797,400 797 Chengdu, Sichuan 2000 3,294,000 0.90 2,964,600 2,965 Chifeng, Inner Mongolia 2000 1,087,000 0.90 978,301 978 Chongqing, Chongqing 2000 4,900,000 0.90 4,410,000 4,410 Dalian, Liaoning 2000 2,628,000 0.90 2,365,200 2,365 Daqing, Heilongjiang 2000 1,076,000 0.90 968,400 968 Datong, Shanxi 2000 1,165,000 0.90 1,048,501 1,049 Dongguan, Guangdong 2000 1,319,000 0.90 1,187,101 1,187 Fushun, Guangdong 2000 1,413,000 0.90 1,271,701 1,272 Fuxin, Liaoning 2000 785,000 0.90 706,501 707 Fuyu, Jilin 2000 1,025,000 0.90 922,501 923 Fuzhou, Fujian 2000 1,397,000 0.90 1,257,301 1,257 Guangzhou, Guangdong 2000 3,893,000 0.90 3,503,701 3,504 Guiyang, Guizhou 2000 2,533,000 0.90 2,279,701 2,280 Handan, Hebei 2000 1,996,000 0.90 1,796,400 1,796 Hangzhou, Zhejiang 2000 1,780,000 0.90 1,602,000 1,602 Harbin, Heilongjiang 2000 2,928,000 0.90 2,635,200 2,635 Hefei, Anhui 2000 1,242,000 0.90 1,117,800 1,118 Hengyang, Hunan 2000 799,000 0.90 719,101 719 Heze, Shandong 2000 1,600,000 0.90 1,440,000 1,440 Huaian, Jiangsu 2000 1,232,000 0.90 1,108,800 1,109 Huaibei, Anhui 2000 814,000 0.90 732,600 733 Huainan, Anhui 2000 1,354,000 0.90 1,218,600 1,219 Huhehaote, Inner Mongolia 2000 978,000 0.90 880,200 880 Hunjiang, Jilin 2000 772,000 0.90 694,800 695 Huzhou, Zhejiang 2000 1,077,000 0.90 969,301 969 Jiamusi, Heilongjiang 2000 874,000 0.90 786,600 787 Jiaxing, Zhejiang 2000 791,000 0.90 711,901 712 Jilin, Jilin 2000 1,435,000 0.90 1,291,501 1,292 Jinan, Shandong 2000 2,568,000 0.90 2,311,200 2,311 Jingmen, Hubei 2000 1,153,000 0.90 1,037,701 1,038 Jining, Inner Mongolia 2000 1,019,000 0.90 917,101 917 Jinzhou, Liaoning 2000 834,000 0.90 750,600 751 Jixi, Liaoning 2000 949,000 0.90 854,101 854 Kaifeng, Henan 2000 769,000 0.90 692,101 692 Kunming, Yunnan 2000 1,701,000 0.90 1,530,901 1,531 Lanzhou, Gansu 2000 1,730,000 0.90 1,557,000 1,557 Leshan, Sichuan 2000 1,137,000 0.90 1,023,301 1,023 Linqing, Shandong 2000 891,000 0.90 801,901 802 Linyi, Shandong 2000 2,498,000 0.90 2,248,200 2,248 Liuan, Anhui 2000 1,818,000 0.90 1,636,200 1,636 Liupanshui, Guizhou 2000 2,023,000 0.90 1,820,701 1,821 Luoyang, Henan 2000 1,451,000 0.90 1,305,901 1,306 Mianyang, Sichuan 2000 1,065,000 0.90 958,501 959 Mudanjiang, Heilongjiang 2000 801,000 0.90 720,901 721 ANNEX 49 ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Nanchang, Jiangxi 2000 1,722,000 0.90 1,549,800 1,550 Nanjing, Jiangsu 2000 2,740,000 0.90 2,466,000 2,466 Neijiang, Sichuan 2000 1,393,000 0.90 1,253,701 1,254 Ningbo, Zhejiang 2000 1,173,000 0.90 1,055,701 1,056 Pingxiang, Jiangxi 2000 1,502,000 0.90 1,351,800 1,352 Qingdao, Shandong 2000 2,316,000 0.90 2,084,400 2,084 Qiqihar, Heilongjiang 2000 1,435,000 0.90 1,291,501 1,292 Shanghai, Shanghai 2000 12,887,000 0.90 11,598,301 11,598 Shantou, Guangdong 2000 1,176,000 0.90 1,058,400 1,058 Shenyang, Liaoning 2000 4,828,000 0.90 4,345,200 4,345 Shenzhen, Guangdong 2000 1,131,000 0.90 1,017,901 1,018 Shijianzhuang, Hebei 2000 1,603,000 0.90 1,442,701 1,443 Suining, Sichuan 2000 1,428,000 0.90 1,285,200 1,285 Suqian, Jiangsu 2000 1,189,000 0.90 1,070,101 1,070 Suzhou, Jiangsu 2000 1,183,000 0.90 1,064,701 1,065 Taian, Shandong 2000 1,503,000 0.90 1,352,701 1,353 Taiyuan, Shanxi 2000 2,415,000 0.90 2,173,501 2,174 Tangshan, Hebei 2000 1,671,000 0.90 1,503,901 1,504 Tianjin, Tianjin 2000 9,156,000 0.90 8,240,400 8,240 Tianmen, Hubei 2000 1,779,000 0.90 1,601,101 1,601 Tianshui, Gansu 2000 1,187,000 0.90 1,068,301 1,068 Tongliao, Jilin 2000 785,000 0.90 706,501 707 Wanxian, Chongqing 2000 1,759,000 0.90 1,583,101 1,583 Weifang, Shandong 2000 1,287,000 0.90 1,158,301 1,158 Wenzhou, Zhejiang 2000 1,269,000 0.90 1,142,101 1,142 Wuhan, Hubei 2000 5,169,000 0.90 4,652,101 4,652 Wulumuqi, Xinjiang 2000 1,415,000 0.90 1,273,501 1,274 Wuxi, Jiangsu 2000 1,127,000 0.90 1,014,301 1,014 Xian, Shaanxi 2000 3,123,000 0.90 2,810,701 2,811 Xiangxiang, Hunan 2000 908,000 0.90 817,200 817 Xiantao, Hubei 2000 1,614,000 0.90 1,452,600 1,453 Xianyang, Shaanxi 2000 896,000 0.90 806,400 806 Xiaoshan, Zhejiang 2000 1,124,000 0.90 1,011,600 1,012 Xinghua, Jiangsu 2000 1,556,000 0.90 1,400,400 1,400 Xintai, Hebei 2000 1,325,000 0.90 1,192,501 1,193 Xinyi, Jiangsu 2000 973,000 0.90 875,701 876 Xinyu, Guangdong 2000 808,000 0.90 727,200 727 Xuanzhou, Anhui 2000 823,000 0.90 740,701 741 Xuzhou, Jiangsu 2000 1,636,000 0.90 1,472,400 1,472 Yancheng, Jiangsu 2000 1,562,000 0.90 1,405,800 1,406 Yichun, Jiangxi 2000 871,000 0.90 783,901 784 Yichun, Jilin 2000 904,000 0.90 813,600 814 Yixing, Jiangsu 2000 1,108,000 0.90 997,200 997 Yiyang, Hunan 2000 1,343,000 0.90 1,208,701 1,209 Yongzhou, Hunan 2000 1,097,000 0.90 987,301 987 Yueyang, Hunan 2000 1,213,000 0.90 1,091,701 1,092 Yulin, Guangxi 2000 1,558,000 0.90 1,402,200 1,402 Yuyao, Zhejiang 2000 848,000 0.90 763,200 763 Yuzhou, Henan 2000 1,173,000 0.90 1,055,701 1,056 Zaoyang, Hubei 2000 1,121,000 0.90 1,008,901 1,009 Zaozhuang, Shandong 2000 2,048,000 0.90 1,843,200 1,843 Zhangjiagang, Jiangsu 2000 886,000 0.90 797,400 797 Zhangjiakou, Hebei 2000 880,000 0.90 792,000 792 Zhanjiang, Guangdong 2000 1,368,000 0.90 1,231,200 1,231 Zhaodong, Heilongjiang 2000 851,000 0.90 765,901 766 50 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Zhengzhou, Henan 2000 2,070,000 0.90 1,863,000 1,863 Zibo, Shandong 2000 2,675,000 0.90 2,407,501 2,408 Zigong, Sichuan 2000 1,072,000 0.90 964,800 965 China, Hong Kong SAR (UNSD 2009) Hong Kong 2007 6,926,000 2.47 17,128,767 17,129 China, Macao SAR (UNSD 2009) Macao 2007 525,760 1.51 792,932 793 Indonesia (UNSD 2009) Jakarta 2005 8,962,000 0.88 7,896,024 7,896 Philippines (UNSD 2009) Manila 2007 1,660,714 3.00 4,974,766 4,975 Quezon City 2005 2,392,701 1.56 3,728,911 3,729 Eastern Europe & Central Asia (UNSD 2009) Albania Tirana 2007 1,532,000 1.01 1,549,467 1,549 Belarus Minsk 2007 1,806,200 1.21 2,181,918 2,182 Croatia Zagreb 2006 784,900 1.24 974,904 975 Georgia Batumi 2007 303,200 2.00 605,391 605 Kutaisi 2007 185,960 3.06 568,133 568 Tbilisi 2007 1,300,000 0.82 1,064,384 1,064 Latin America and the Caribbean (PAHO 2005) Argentina Area Metropolitana Buenos Aires 2001 12,544,018 1.16 14,551,061 14,551 Bahia Blanca 2001 285,000 0.88 249,660 250 Neuquen 2001 202,518 0.95 192,392 192 Salta Capital 2001 472,971 0.49 232,040 232 Bahamas Nassau, Bahamas 2001 200,000 2.67 534,000 534 Barbados* Barbados 2001 268,792 0.95 255,352 255 Bolivia* Cochabamba 2001 717,026 0.60 430,216 430 El Alto 2001 629,955 0.36 226,784 227 La Paz 2001 790,353 0.53 419,677 420 Oruro 2001 201,230 0.33 66,406 66 Potosi 2001 135,783 0.33 45,352 45 Santa Cruz de la Sierra 2001 1,113,000 0.54 599,907 600 Sucre 2001 193,876 0.40 77,357 77 Tarija 2001 135,783 0.46 62,868 63 Brazil Abaetetuba 2001 119,152 0.29 35,000 35 Aguas Lindas de Goias 2001 105,746 0.44 47,000 47 Alagoinhas 2001 130,095 0.58 76,000 76 Alvorada 2001 183,968 1.14 210,000 210 Americana 2001 182,593 0.95 173,900 174 Ananindeua 2001 393,569 1.27 500,000 500 Anapolis 2001 288,085 0.62 180,000 180 Angra dos Reis 2001 119,247 0.75 89,200 89 Aparaceida de Goiania 2001 336,392 0.30 102,000 102 Apucarana 2001 107,827 0.88 95,000 95 Aracaju 2001 461,534 0.89 410,000 410 Aracatuba 2001 169,254 0.74 125,000 125 ANNEX 51 ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Araguaina 2001 113,143 0.53 59,500 60 Araguari 2001 101,974 0.88 90,000 90 Arapiraca 2001 186,466 0.99 185,000 185 Araraquara 2001 182,471 0.87 158,400 158 Araras 2001 104,196 0.72 75,000 75 Atibaia 2001 111,300 1.49 165,700 166 Bage 2001 118,767 0.42 50,000 50 Barbacena 2001 114,126 0.83 95,200 95 Barra Mansa 2001 170,753 0.76 130,000 130 Barreiras 2001 131,849 1.76 232,200 232 Barretos 2001 103,913 0.76 79,200 79 Barueri 2001 208,281 1.87 390,000 390 Bauru 2001 316,064 1.39 440,000 440 Belem 2001 1,280,614 1.57 2,012,000 2,012 Belford Roxo 2001 434,474 0.81 350,000 350 Belo Horizonte 2001 2,238,526 1.43 3,201,800 3,202 Betim 2001 306,675 0.49 150,000 150 Blumenau 2001 261,808 0.84 220,000 220 Boa Vista 2001 200,568 0.57 115,000 115 Botucatu 2001 108,306 1.41 153,000 153 Braganca Paulista 2001 125,031 1.03 128,500 129 Brasilia 2001 2,051,146 0.76 1,556,700 1,557 Cabo de Santo Agostinho 2001 152,977 0.92 140,000 140 Cabo Frio 2001 126,828 1.58 200,000 200 Cachoeirinha 2001 107,564 1.17 125,400 125 Cachoeiro de Itapemirim 2001 174,879 1.03 180,000 180 Camacari 2001 161,727 0.99 160,000 160 Camaragibe 2001 128,702 1.01 130,000 130 Campina Grande 2001 355,331 1.35 480,000 480 Campinas 2001 969,396 1.69 1,641,000 1,641 Campo Grande 2001 663,621 0.75 496,400 496 Campos dos Goytacazes 2001 406,989 0.73 296,000 296 Canoas 2001 306,093 0.68 207,000 207 Carapicuiba 2001 344,596 0.73 250,000 250 Cariacica 2001 324,285 1.05 340,000 340 Caruaru 2001 253,634 0.79 200,000 200 Cascavel 2001 245,369 0.59 145,000 145 Castanhal 2001 134,496 0.40 54,000 54 Catanduva 2001 105,847 0.94 100,000 100 Caucaia 2001 250,479 0.73 183,000 183 Caxias 2001 139,756 0.76 106,600 107 Caxias do Sul 2001 360,419 0.92 330,000 330 Chapeco 2001 146,967 0.49 72,200 72 Colatina 2001 112,711 0.71 80,000 80 Colombo 2001 183,329 0.39 72,000 72 Contagem 2001 538,017 1.86 1,000,000 1,000 Cotia 2001 148,987 0.78 116,700 117 Crato 2001 104,646 0.33 35,000 35 Criciuma 2001 170,420 0.56 96,000 96 Cubatao 2001 108,309 0.85 92,000 92 Curitiba 2001 1,587,315 0.75 1,186,700 1,187 Diadema 2001 357,064 0.79 281,600 282 Dourados 2001 164,949 1.33 219,000 219 52 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Duque de Caxias 2001 775,456 0.94 730,000 730 Embu 2001 207,663 0.67 140,000 140 Feira de Santana 2001 480,949 1.56 750,800 751 Ferraz de Vasconcelos 2001 142,377 0.58 83,000 83 Florianopolis 2001 342,315 1.27 435,000 435 Fortaleza 2001 2,141,402 1.11 2,375,000 2,375 Foz do Iguacu 2001 258,543 0.75 195,000 195 Franca 2001 287,737 0.95 273,000 273 Francisco Morato 2001 133,738 0.82 109,100 109 Franco da Rocha 2001 108,122 0.59 64,000 64 Garanhuns 2001 117,749 1.66 195,000 195 Goiania 2001 1,093,007 1.17 1,279,700 1,280 Governador Valadares 2001 247,131 1.21 300,000 300 Gravatai 2001 232,629 0.55 127,100 127 Guarapuava 2001 155,161 0.53 83,000 83 Guaratingueta 2001 104,219 0.58 60,000 60 Guaruja 2001 264,812 0.98 260,600 261 Guarulhos 2001 1,072,717 0.79 850,000 850 Hortolandia 2001 152,523 0.62 95,000 95 Ibirite 2001 133,044 0.83 110,000 110 Ilheus 2001 222,127 0.36 80,000 80 Imperatriz 2001 230,566 0.98 227,000 227 Indaiatuba 2001 147,050 0.61 90,400 90 Ipatinga 2001 212,496 0.94 200,000 200 Itaborai 2001 187,479 0.62 116,000 116 Itabuna 2001 196,675 1.27 250,000 250 Itajai 2001 147,494 0.95 140,000 140 Itapecerica da Serra 2001 129,685 0.66 85,500 86 Itapetininga 2001 125,559 0.50 62,200 62 Itapevi 2001 162,433 0.60 98,000 98 Itaquaquecetuba 2001 272,942 0.70 190,000 190 Itu 2001 135,366 0.96 130,000 130 Jaboatao dos Guararapes 2001 581,556 0.77 450,000 450 Jacarei 2001 191,291 0.63 120,000 120 Jaragua do Sul 2001 108,489 0.72 78,000 78 Jau 2001 112,104 1.03 115,400 115 Jequie 2001 147,202 0.48 70,000 70 Ji-Parana 2001 106,800 0.66 70,000 70 Joao Pessoa 2001 597,934 1.72 1,027,900 1,028 Joinville 2001 429,604 1.15 493,200 493 Juazeiro 2001 174,567 1.18 206,000 206 Juazeiro do Norte 2001 212,133 1.08 230,000 230 Juiz de For a 2001 456,796 0.64 290,500 291 Jundiai 2001 323,397 1.02 330,200 330 Lages 2001 157,682 0.51 80,000 80 Lauro de Freitas 2001 113,543 0.79 90,000 90 Limeira 2001 249,046 0.64 159,500 160 Linhares 2001 112,617 0.57 64,000 64 Londrina 2001 447,065 1.61 720,000 720 Luziania 2001 141,082 0.71 100,000 100 Macae 2001 132,461 1.89 250,000 250 Macapa 2001 283,308 1.34 380,000 380 Maceio 2001 797,759 1.32 1,050,000 1,050 ANNEX 53 ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Mage 2001 205,830 1.04 215,000 215 Manaus 2001 1,405,835 1.55 2,180,000 2,180 Maraba 2001 168,020 0.31 52,000 52 Maracanau 2001 179,732 0.64 115,000 115 Marilia 2001 197,342 0.98 192,500 193 Maringa 2001 288,653 0.98 284,000 284 Maua 2001 363,392 0.64 232,700 233 Mogi Guacu 2001 124,228 0.67 83,000 83 Moji das Cruzes 2001 330,241 0.63 208,100 208 Montes Claros 2001 306,947 1.51 462,000 462 Mossoro 2001 213,841 0.71 151,500 152 Natal 2001 712,317 1.72 1,223,000 1,223 Nilopolis 2001 153,712 1.63 250,000 250 Niteroi 2001 459,451 1.47 675,300 675 Nossa Senhora do Socorro 2001 131,679 0.38 50,500 51 Nova Friburgo 2001 173,418 0.81 140,000 140 Nova Iguacu 2001 920,599 0.75 693,900 694 Novo Hamburgo 2001 236,193 0.66 155,000 155 Olinda 2001 367,902 1.05 385,600 386 Osasco 2001 652,593 0.87 570,000 570 Palhoca 2001 102,742 0.24 25,000 25 Palmas 2001 137,355 0.59 81,000 81 Paranagua 2001 127,339 1.10 140,000 140 Parnaiba 2001 132,282 0.94 125,000 125 Parnamirim 2001 124,690 0.40 50,000 50 Passo Fundo 2001 168,458 0.60 101,300 101 Patos de Minas 2001 123,881 0.66 82,000 82 Paulista 2001 262,237 0.76 200,000 200 Pelotas 2001 323,158 0.56 180,000 180 Petrolina 2001 218,538 0.64 140,000 140 Petropolis 2001 286,537 1.05 300,000 300 Pindamonhangaba 2001 126,026 0.99 125,000 125 Pinhais 2001 102,985 0.58 60,000 60 Piracicaba 2001 329,158 0.73 239,700 240 Pocos de Caldas 2001 135,627 0.66 90,000 90 Ponta Grossa 2001 273,616 1.03 280,900 281 Porto Algre 2001 1,360,590 0.98 1,340,000 1,340 Porto Velho 2001 334,661 0.58 193,400 193 Pouso Alegre 2001 106,776 0.84 90,000 90 Praia Grande 2001 193,582 0.93 180,900 181 Presidente Prudente 2001 189,186 0.53 100,000 100 Queimados 2001 121,993 0.53 64,500 65 Recife 2001 1,422,905 0.97 1,376,000 1,376 Resende 2001 104,549 0.97 101,000 101 Ribeirao das Neves 2001 246,846 0.97 240,000 240 Ribeirao Pires 2001 104,508 1.71 179,000 179 Ribeirao Preto 2001 504,923 0.89 450,000 450 Rio Branco 2001 253,059 0.56 141,200 141 Rio Claro 2001 168,218 0.74 125,100 125 Rio de Janeiro 2001 5,857,904 1.20 7,058,700 7,059 Rio Grande 2001 186,544 1.29 240,000 240 Rio Verde 2001 116,552 0.87 101,300 101 Rondonopolis 2001 150,227 0.55 82,000 82 Sabara 2001 115,352 0.52 60,200 60 Salvador 2001 2,443,107 1.08 2,636,500 2,637 Santa Barbara D Oeste 2001 170,078 0.83 141,000 141 54 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Santa Cruz do Sul 2001 107,632 0.51 55,000 55 Santa Luzia 2001 184,903 0.49 91,300 91 Santa Maria 2001 243,611 0.66 160,000 160 Santa Rita 2001 115,844 0.65 75,000 75 Santarem 2001 262,538 0.51 133,700 134 Santo Andre 2001 649,331 0.99 640,000 640 Santos 2001 417,983 1.10 460,000 460 Sao Bernardo do Campo 2001 703,177 0.81 566,700 567 Sao Caetano do Sul 2001 140,159 1.43 200,000 200 Sao Carlos 2001 192,998 0.69 133,300 133 Sao Goncalo 2001 891,119 0.70 620,000 620 Sao Joao de Meriti 2001 449,476 0.69 312,000 312 Sao Jose 2001 173,559 1.18 205,000 205 Sao Jose de Ribamar 2001 107,384 0.47 50,000 50 Sao Jose do Rio Preto 2001 358,523 1.03 367,900 368 Sao Jose dos Campos 2001 539,313 1.23 661,600 662 Sao Jose dos Pinhais 2001 204,316 0.69 140,000 140 Sao Leopoldo 2001 193,547 0.52 100,000 100 Sao Luis 2001 870,028 0.85 740,000 740 Sao Paulo 2001 10,434,252 2.00 20,855,700 20,856 Sao Vicente 2001 303,551 0.96 290,000 290 Sapucaia do Sul 2001 122,751 0.59 73,000 73 Serra 2001 321,181 1.12 358,700 359 Sete Lagoas 2001 184,871 0.78 145,000 145 Sobral 2001 155,276 0.89 138,000 138 Sorocaba 2001 493,468 0.92 455,000 455 Sumare 2001 196,723 0.91 180,000 180 Suzano 2001 228,690 0.58 133,000 133 Taboao da Serra 2001 197,644 0.84 167,000 167 Taubate 2001 244,165 0.67 162,500 163 Teixeira de Freitas 2001 107,486 0.88 95,000 95 Teofilo Otoni 2001 129,424 0.40 52,000 52 Teresina 2001 715,360 1.48 1,058,900 1,059 Teresopolis 2001 138,081 0.83 115,000 115 Timon 2001 129,692 0.33 42,200 42 Uberaba 2001 252,051 1.55 391,000 391 Uberlandia 2001 501,214 0.90 451,600 452 Uruguaiana 2001 126,936 0.79 100,000 100 Varginha 2001 108,998 1.03 112,000 112 Varzea Grande 2001 215,298 0.58 125,000 125 Viamao 2001 227,429 0.77 175,000 175 Vila Velha 2001 345,965 0.95 330,000 330 Vitoria 2001 292,304 1.08 315,000 315 Vitoria da Conquista 2001 262,494 1.32 346,000 346 Vitoria de Santo Antao 2001 117,609 1.36 160,000 160 Volta Redonda 2001 242,063 0.66 160,000 160 Chile Antofagasta, Antofagasta 2001 318,779 0.80 255,023 255 Antofagasta, Calama 2001 138,402 0.65 89,961 90 Araucanía, Temuco 2001 245,347 1.03 252,707 253 B.O’Higgins, Rancagua 2001 214,344 0.80 171,475 171 Biobío, Chillán 2001 161,953 1.00 161,953 162 Biobío, Concepción 2001 216,061 0.80 172,849 173 Biobío, Talcahuano 2001 250,348 0.94 235,327 235 ANNEX 55 ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Coquimbo, Coquimbo 2001 163,036 0.90 146,732 147 Coquimbo, La Serena 2001 160,148 0.95 152,141 152 Los Lagos, Osorno 2001 145,475 1.00 145,475 145 Los Lagos, Puerto Montt 2001 175,938 1.00 175,938 176 Los Lagos, Valdivia 2001 140,559 0.42 59,035 59 Magallanes, Punta Arenas 2001 120,874 0.80 96,699 97 Maule, Curicó 2001 120,299 1.00 120,299 120 Maule, Talca 2001 203,231 0.95 193,069 193 Santiago, Cerro Navia 2001 148,312 1.00 148,460 148 Santiago, La Florida 2001 365,674 1.00 365,674 366 Santiago, La Pintana 2001 190,085 0.68 129,258 129 Santiago, Maipú 2001 468,390 1.01 472,137 472 Santiago, Providencia 2001 120,874 1.40 169,224 169 Santiago, Recoleta 2001 148,220 1.21 179,346 179 Santiago, Santiago 2001 200,792 1.63 327,893 328 Tarapacá, Arica 2001 185,268 0.71 131,540 132 Valparaíso, Valparaíso 2001 275,982 1.00 275,982 276 Valparaíso, Viña del Mar 2001 286,931 0.96 275,454 275 Colombia Armenia 2001 293,000 0.58 169,940 170 Barrancabermeja 2001 183,000 0.60 109,800 110 Barranquilla 2001 1,276,000 0.80 1,020,800 1,021 Bello 2001 353,000 0.49 172,970 173 Bogotá 2001 6,558,000 0.72 4,721,760 4,722 Bucaramanga 2001 543,000 0.55 298,650 299 Buenaventura 2001 230,000 0.65 149,500 150 Buga 2001 113,000 0.61 68,930 69 Cali 2001 2,181,000 0.77 1,679,370 1,679 Cartagena 2001 854,000 0.87 742,980 743 Cartago 2001 129,000 0.44 56,760 57 Cúcuta 2001 644,000 0.46 296,240 296 Dosquebradas 2001 166,000 0.40 66,400 66 Envigado 2001 145,000 0.31 44,950 45 Florencia 2001 116,000 1.04 120,640 121 Floridablanca 2001 232,000 0.50 116,000 116 Girardot 2001 117,000 1.02 119,340 119 Ibagué 2001 403,000 0.63 253,890 254 Itagüí 2001 246,000 0.62 152,520 153 Maicao 2001 115,000 0.60 69,000 69 Manizales 2001 345,000 0.72 248,400 248 Medellín 2001 1,909,000 0.81 1,546,290 1,546 Montería 2001 256,000 0.60 153,600 154 Neiva 2001 317,000 0.80 253,600 254 Palmira 2001 234,000 0.66 154,440 154 Pasto 2001 349,000 0.61 212,890 213 Pereira 2001 401,000 0.58 232,580 233 Popayán 2001 206,000 0.67 138,020 138 Santa Marta 2001 382,000 0.72 275,040 275 Sincelejo 2001 234,000 0.51 119,340 119 Soacha 2001 285,000 0.88 250,800 251 Sogamoso 2001 114,000 0.38 43,320 43 Soledad 2001 310,000 0.60 186,000 186 56 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Tuluá 2001 157,000 0.75 117,750 118 Tunja 2001 112,000 0.79 88,480 88 Valledupar 2001 278,000 0.85 236,300 236 Villavicencio 2001 289,000 0.51 147,390 147 Costa Rica Alajuela 2001 234,737 0.85 199,526 200 Desamparados 2001 203,770 1.38 281,203 281 San José 2001 326,384 1.02 332,585 333 Cuba Bayamo 2001 154,832 0.44 67,662 68 Camagüey 2001 308,288 0.50 154,144 154 Ciego de Ávila 2001 118,935 0.41 48,763 49 Cienfuegos 2001 154,897 0.75 116,173 116 Ciudad de La Habana 2001 2,186,632 0.75 1,639,974 1,640 Guantánamo 2001 222,217 0.56 124,442 124 Holguín 2001 268,843 0.50 134,422 134 Manzanillo 2001 110,846 0.44 48,440 48 Matanzas 2001 133,177 0.60 79,906 80 Pinar del Río 2001 162,078 0.60 97,247 97 Sancti Spíritus 2001 109,220 0.58 63,348 63 Santa Clara 2001 220,345 0.58 127,800 128 Santiago de Cuba 2001 452,307 0.50 226,154 226 Tunas 2001 144,381 0.47 67,859 68 Ecuador* Quito 2001 1,841,200 0.72 1,325,664 1,326 Santo Domingo de los Colorados 2001 200,421 0.65 130,274 130 El Salvador La Libertad - Nueva San Salvador 2001 136,909 0.70 95,836 96 San Miguel, San Miguel 2001 172,203 0.82 141,206 141 San Salvador - Apopa 2001 139,802 0.54 75,493 75 San Salvador - Ilopango, 2001 115,358 0.51 58,833 59 San Salvador - Mejicanos 2001 172,548 0.61 105,254 105 San Salvador - Soyapango 2001 285,286 0.57 162,613 163 San Salvador, San Salvador 2001 479,605 0.81 388,480 388 Santa Ana, Santa Ana 2001 167,975 0.63 105,824 106 Grenada Grenada 2001 95,551 0.85 81,218 81 Guatemala Antigua Guatemala 2001 248,019 1.20 297,623 298 Guatemala 2001 2,541,581 0.95 2,414,502 2,415 Jutiapa 2001 130,000 0.90 117,000 117 Quetzaltenango 2001 122,157 0.90 109,941 110 San Benito 2001 366,735 0.80 293,388 293 San Pedro Carchá 2001 130,118 0.85 110,600 111 Guyana Georgetown 2001 180,000 1.53 275,400 275 Haiti Cap-Haïtien 2001 141,061 0.60 84,637 85 Carrefour 2001 416,301 0.60 249,781 250 Croix des Bouquets 2001 143,803 0.30 43,141 43 Delmas 2001 335,866 0.60 201,520 202 Dessalines 2001 167,599 0.30 50,280 50 ANNEX 57 ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Gonaïves 2001 138,480 0.30 41,544 42 Jacmel 2001 138,504 0.60 83,102 83 Jean Rabel 2001 121,221 0.30 36,366 36 Léogâne 2001 105,806 0.25 26,452 26 Les Cayes 2001 152,845 0.30 45,854 46 Pétion Ville 2001 143,452 0.60 86,071 86 Petit Goâve 2001 125,433 0.25 31,358 31 Petite Rivière de l’Artibonite 2001 126,474 0.35 44,266 44 Port de Paix 2001 113,191 0.40 45,276 45 Port-au-Prince 2001 1,100,085 0.60 660,051 660 Saint Marc 2001 164,868 0.30 49,460 49 Saint Michel 2001 124,603 0.30 37,381 37 Honduras Choloma 2001 126,402 0.70 88,481 88 Distrito Central 2001 819,867 0.67 549,311 549 La Ceiba 2001 126,721 0.63 79,834 80 San Pedro Sula 2001 483,384 0.69 333,535 334 Jamaica* North Eastern Wasteshed( 2001 357,265 1.00 357,265 357 Portland, St.Mary and St.Ann) Portmore 2001 159,974 0.89 142,377 142 Retirement(Westmoreland, 2001 452,724 1.00 452,724 453 Hanover,Trelawny & St.James) Riverton ( Kgn, St.And, St.Cath. 2001 1,458,155 1.00 1,458,155 1,458 Clarendon and St.Thomas) Southern(Manchester, St.Elizabeth) 2001 331,190 1.00 331,190 331 Mexico Acapulco, Guerrero 2001 728,010 0.94 685,785 686 Acuña, Coahuila 2001 117,271 0.89 104,019 104 Aguascalientes, Aguascalientes 2001 656,245 0.80 522,371 522 Altamira, Tamaulipas 2001 130,425 0.85 110,340 110 Apatzingan, Michoacán 2001 108,466 0.53 57,704 58 Apodaca, Nuevo León 2001 297,776 1.17 348,398 348 Atizapan de Zaragoza, México 2001 475,683 0.80 380,546 381 Atlixco, Puebla 2001 117,929 0.53 62,974 63 Boca del Río, Veracruz 2001 135,875 0.92 124,733 125 Campeche, Campeche 2001 219,281 0.94 207,001 207 Cancún, Benito Juárez, Quintana Roo 2001 444,870 0.94 418,178 418 Cárdenas, Tabasco 2001 219,414 0.53 116,948 117 Carmen, Campeche 2001 169,784 0.94 159,937 160 Celaya, Guanajuato 2001 388,012 0.94 364,731 365 Chalco, México 2001 232,956 1.20 279,547 280 Chetumal, Othon P. Blanco, Quintana Roo 2001 209,241 0.94 196,896 197 Chihuahua, Chihuahua 2001 676,160 0.97 658,580 659 Chilpancingo, Guerrero 2001 197,275 0.94 186,030 186 Coatzacoalcos, Veracruz 2001 268,673 0.94 252,015 252 Colima, Colima 2001 131,268 0.95 124,048 124 Comitán de Domínguez, Chiapas 2001 107,065 0.52 55,995 56 Córdoba, Veracruz 2001 178,672 0.60 107,739 108 Cuauhtemoc, Chihuahua 2001 125,105 0.54 67,056 67 Cuautla, Morelos 2001 155,363 1.27 197,311 197 Cuernavaca, Morelos 2001 342,374 0.92 316,354 316 Culiacán, Sinaloa 2001 755,017 0.90 677,250 677 Delicias, Chihuahua 2001 117,215 0.92 107,838 108 Dolores Hidalgo, Guanajuato 2001 130,748 0.53 69,035 69 58 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Durango, Durango 2001 495,962 0.93 461,245 461 Ecatepec, México 2001 1,655,225 1.28 2,118,688 2,119 Ensenada, Baja California 2001 381,747 0.93 355,025 355 Fresnillo, Zacatecas 2001 183,941 0.53 98,041 98 General Escobedo, Nuevo León 2001 246,166 1.18 289,245 289 Gómez Palacio, Durango 2001 276,085 0.94 258,139 258 Guadalajara, Jalisco 2001 1,650,776 1.20 1,980,931 1,981 Guadalupe, Nuevo León 2001 679,230 1.18 801,491 801 Guadalupe, Zacatecas 2001 109,179 0.95 103,174 103 Guanajuato, Guanajuato 2001 144,166 0.92 132,921 133 Guasave, Sinaloa 2001 279,878 0.94 263,925 264 Guaymas, Sonora 2001 129,236 1.05 135,698 136 Hermosillo, Sonora 2001 619,185 0.99 615,470 615 Hidalgo del Parral, Chihuahua 2001 101,390 0.76 76,752 77 Hidalgo, Michoacán 2001 106,922 0.54 57,310 57 Huixquilucan, México 2001 198,564 1.13 224,377 224 Iguala, Guerrero 2001 125,395 0.93 116,994 117 Irapuato, Guanajuato 2001 445,778 0.95 423,489 423 Juárez, Chihuahua 2001 1,264,121 1.22 1,543,492 1,543 La Paz, Baja California Sur 2001 199,712 1.42 283,591 284 Lagos de Moreno, Jalisco 2001 128,407 0.54 68,955 69 Lázaro Cárdenas, Michoacán 2001 174,205 0.92 160,965 161 León, Guanajuato 2001 1,153,998 1.10 1,269,398 1,269 Lerdo, Durango 2001 113,705 0.85 96,649 97 Lerma, México 2001 103,909 1.13 117,417 117 Los Cabos, Baja California Sur 2001 113,727 0.50 56,864 57 Los Mochis-Topolobampo, Ahome, Sinaloa 2001 362,442 1.00 362,442 362 Madero, Tamaulipas 2001 184,289 0.85 155,908 156 Mante, Tamaulipas 2001 111,671 0.54 59,967 60 Manzanillo, Colima 2001 127,443 0.95 121,071 121 Matamoros, Tamaulipas 2001 427,966 0.98 419,407 419 Mazatlán, Sinaloa 2001 385,047 0.94 361,944 362 Mérida, Yucatán 2001 714,689 0.99 705,398 705 Metepec, México 2001 197,699 1.13 223,400 223 Mexicali, Baja California 2001 779,523 0.94 733,531 734 México, Federal District 2001 8,615,955 1.38 11,890,018 11,890 Minatitlán, Veracruz 2001 144,574 0.54 78,070 78 Monclova, Coahuila 2001 194,458 0.98 190,569 191 Monterrey, Nuevo León 2001 1,112,636 1.19 1,324,037 1,324 Morelia, Michoacán 2001 628,801 0.89 556,489 556 Naucalpan, México 2001 861,173 1.20 1,033,408 1,033 Navojoa, Sonora 2001 141,412 0.94 132,927 133 Nezahualcoyotl, México 2001 1,223,180 1.28 1,565,670 1,566 Nogales, Sonora 2001 164,819 0.94 154,930 155 Nuevo Laredo, Tamaulipas 2001 317,877 1.47 467,279 467 Oaxaca, Oaxaca 2001 259,343 0.92 237,818 238 Obregón, Cajeme, Sonora 2001 357,857 0.94 336,386 336 Orizaba, Veracruz 2001 119,405 0.98 117,256 117 Pachuca, Hidalgo 2001 249,838 0.80 198,621 199 Piedras Negras, Coahuila 2001 130,398 0.94 122,574 123 Poza Rica, Veracruz 2001 152,318 1.05 159,934 160 Puebla, Puebla 2001 1,372,446 1.38 1,893,975 1,894 Puerto Vallarta, Jalisco 2001 191,424 0.71 135,528 136 Querétaro, Querétaro 2001 657,447 0.83 542,394 542 Reynosa, Tamaulipas 2001 438,696 0.76 333,409 333 ANNEX 59 ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Río Bravo, Tamaulipas 2001 104,620 0.76 79,511 80 Salamanca, Guanajuato 2001 228,239 0.62 141,508 142 Saltillo, Coahuila 2001 587,730 0.86 502,509 503 San Andrés Tuxtla, Veracruz 2001 143,235 0.54 77,060 77 San Cristobal de las Casas, Chiapas 2001 135,731 0.92 125,008 125 San Francisco del Rincón, Guanajuato 2001 100,805 0.54 54,031 54 San Juan Bautista de Tuxtepec, Oaxaca 2001 134,895 0.53 71,899 72 San Juan del Río, Querétaro 2001 184,679 0.50 92,340 92 San Luis Potosi, San Luis Potosi 2001 678,645 0.97 658,286 658 San Luis Río Colorado, Sonora 2001 147,912 0.94 139,037 139 San Martín Texmelucan, Puebla 2001 123,072 0.80 98,458 98 San Miguel de Allende, Guanajuato 2001 138,393 0.52 71,964 72 San Nicolas de los Garza, Nuevo León 2001 497,078 1.20 596,494 596 San Pedro Garza García , Nuevo León 2001 127,254 1.10 139,979 140 Santa Catarina, Nuevo León 2001 231,809 1.20 277,012 277 Silao, Guanajuato 2001 134,539 0.53 71,306 71 Soledad de Graciano, San Luis Potosi 2001 185,063 0.53 97,528 98 Tampico, Tamaulipas 2001 298,063 0.85 252,161 252 Tapachula, Chiapas 2001 276,743 0.94 259,862 260 Taxco, Guerrero 2001 100,889 0.94 94,836 95 Tecoman, Colima 2001 101,049 0.53 53,556 54 Tehuacán, Puebla 2001 233,807 0.91 212,998 213 Tepatitlán, Jalisco 2001 121,076 0.53 64,049 64 Tepic, Nayarit 2001 307,550 0.84 256,804 257 Tijuana, Baja California 2001 1,262,520 1.22 1,537,749 1,538 Tlajomulco, Jalisco 2001 128,339 1.05 134,756 135 Tlalnepantla, México 2001 722,279 1.04 749,726 750 Tlaquepaque, Jalisco 2001 480,844 1.17 562,587 563 Toluca, México 2001 687,969 1.16 798,044 798 Tonalá, Jalisco 2001 350,648 1.18 413,765 414 Torreón, Coahuila 2001 533,457 0.94 502,516 503 Tulancingo, Hidalgo 2001 124,461 0.92 115,002 115 Tuxpan, Veracruz 2001 126,257 0.54 67,926 68 Tuxtla Gutiérrez, Chiapas 2001 443,782 1.05 463,752 464 Uruapan, Michoacán 2001 268,208 0.94 252,920 253 Valle de Chalco Solidaridad, México 2001 330,885 1.20 397,062 397 Valle de Santiago, Guanajuato 2001 130,553 0.54 70,107 70 Valles, San Luis Potosi 2001 147,086 0.94 137,967 138 Veracruz, Veracruz 2001 463,812 0.92 425,779 426 Victoria, Tamaulipas 2001 266,612 0.94 251,415 251 Villahermosa, Centro, Tabasco 2001 531,511 0.87 462,415 462 Xalapa, Veracruz 2001 404,788 0.80 323,830 324 Zacatecas, Zacatecas 2001 124,722 0.95 117,862 118 Zamora, Michoacán 2001 161,425 0.71 113,966 114 Zapopan, Jalisco 2001 1,018,447 1.20 1,222,136 1,222 Zitacuaro, Michoacán 2001 139,514 0.53 73,942 74 Nicaragua Chinandega 2001 124,107 0.61 75,085 75 Leon 2001 147,845 0.62 90,925 91 Managua 2001 952,068 0.71 676,920 677 Masaya 2001 115,369 0.61 70,029 70 Tipitapa 2001 108,861 0.43 46,266 46 Panama 60 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Arraiján 2001 149,918 0.66 98,946 99 Ciudad de Panamá 2001 708,438 0.94 665,932 666 Colón 2001 174,059 0.94 163,615 164 La Chorrera 2001 124,656 0.70 87,259 87 San Miguelito 2001 293,745 0.61 179,184 179 Paraguay Asunción 2001 513,399 1.31 673,579 674 Ciudad del Este 2001 223,350 1.04 232,507 233 Luque 2001 170,433 1.08 184,068 184 San Lorenzo 2001 202,745 1.07 217,748 218 Peru Callao, Callao Cercado 2001 449,282 0.81 365,716 366 Callao, Ventanilla 2001 148,767 0.68 101,162 101 Junín, El Tambo 2001 165,357 0.73 121,207 121 Junín, Huancayo 2001 112,203 0.64 72,147 72 Lima, Ate 2001 410,734 0.56 228,368 228 Lima, Carabayllo 2001 153,112 0.57 87,733 88 Lima, Chorrillos 2001 264,645 0.58 154,023 154 Lima, Comas 2001 469,747 0.52 244,268 244 Lima, El Agustino 2001 166,902 0.62 103,479 103 Lima, Independencia 2001 200,365 0.70 139,454 139 Lima, La Molina 2001 125,034 1.20 150,541 151 Lima, La Victoria 2001 205,554 1.08 222,409 222 Lima, Lima Cercado 2001 286,202 1.13 324,267 324 Lima, Los Olivos 2001 344,164 0.59 203,745 204 Lima, Lurigancho 2001 123,142 0.52 64,034 64 Lima, Puente Piedra 2001 183,861 0.50 91,379 91 Lima, Rímac 2001 192,449 0.59 112,968 113 Lima, San Borja 2001 122,270 1.05 128,261 128 Lima, San Juan de Lurigancho 2001 751,155 0.60 452,195 452 Lima, San Juan de Miraflores 2001 387,641 0.71 274,837 275 Lima, San Martín de Porres 2001 448,345 0.79 352,399 352 Lima, San Miguel 2001 134,908 0.78 105,363 105 Lima, Santa Anita 2001 148,752 0.54 80,177 80 Lima, Santiago de Surco 2001 251,567 0.87 219,618 220 Lima, Villa El Salvador 2001 364,476 0.56 202,649 203 Lima, Villa María del Triunfo 2001 341,971 0.55 186,716 187 Piura, Castilla 2001 106,926 0.61 64,690 65 Ucayali, Callería 2001 246,856 0.70 173,787 174 Saint Lucia St. Lucia 2001 162,157 1.18 191,345 191 Saint Vincent and the Grenadines* St. Vincent 2001 106,916 0.34 36,351 36 Suriname Greater Paramaribo 2001 287,131 1.00 287,131 287 Trinidad and Tobago Couva/Tabaquite/Talparo 2001 162,779 0.70 113,945 114 Diego Martin 2001 105,720 0.70 74,004 74 San Juan/Laventille 2001 157,295 3.20 503,344 503 Tunapuna/Piarco 2001 203,975 2.20 448,745 449 Uruguay ANNEX 61 ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Canelones 2001 539,130 0.90 485,217 485 Maldonado 2001 137,390 0.90 123,651 124 Montevideo 2001 1,303,182 1.23 1,602,914 1,603 Venezuela Distrito Capital 2001 1,836,286 1.10 2,019,915 2,020 Municipio Barinas Edo Barinas 2001 283,273 0.69 194,325 194 Municipio Caroni Edo Bolivar 2001 704,168 0.74 521,084 521 Municipio German Roscio Edo Guarico 2001 103,706 0.85 88,150 88 Municipio Girardot Edo Aragua 2001 396,125 2.93 1,160,646 1,161 Municipio Iribarren Edo Lara 2001 895,989 0.52 468,602 469 Municipio Lagunillas Edo Zulia 2001 144,345 1.50 216,518 217 Municipio Maracaibo Edo Zulia 2001 1,405,933 1.08 1,518,408 1,518 Municipio Pedraza Edo Apure 2001 283,273 0.28 80,166 80 Municipio Simon Rodriguez Edo Anzoategui 2001 147,800 1.15 169,970 170 Middle East & North Africa Egypt (UNSD 2009) Cairo 2007 7,765,000 1.77 13,766,234 13,766 Iran (Damghani et al. 2008) Tehran 2005 8,203,666 0.88 7,044,000 7,044 Iraq (UNSD 2009) Baghdad 2005 6,784,000 1.71 11,621,432 11,621 South Asia India (CPCB 2005) Agartala 2005 189,998 0.40 75,999 76 Agra 2005 1,275,135 0.51 650,319 650 Ahmedabad 2005 3,520,085 0.37 1,302,431 1,302 Aizwal 2005 228,280 0.25 57,070 57 Allahabad 2005 975,393 0.52 507,204 507 Amritsar 2005 966,862 0.45 435,088 435 Asansol 2005 475,439 0.44 209,193 209 Banglore 2005 4,301,326 0.39 1,677,517 1,678 Bhopal 2005 1,437,354 0.40 574,942 575 Bhubaneswar 2005 648,032 0.36 233,292 233 Chandigarh 2005 808,515 0.40 323,406 323 Chennai 2005 4,343,645 0.62 2,693,060 2,693 Coimbatore 2005 930,882 0.57 530,603 531 Dehradun 2005 426,674 0.31 132,269 132 Delhi 2005 10,306,452 0.57 5,874,678 5,875 Dhanbad 2005 199,258 0.39 77,711 78 Faridabad 2005 1,055,938 0.42 443,494 443 Gandhinagar 2005 195,985 0.22 43,117 43 Greater Mumbai 2005 11,978,450 0.45 5,390,303 5,390 Guwahati 2005 809,895 0.20 161,979 162 Hyderabad 2005 3,843,585 0.57 2,190,843 2,191 Imphal 2005 221,492 0.19 42,083 42 Indore 2005 1,474,968 0.38 560,488 560 Jabalpur 2005 932,484 0.23 214,471 214 Jaipur 2005 2,322,575 0.39 905,804 906 Jammu 2005 369,959 0.58 214,576 215 Jamshedpur 2005 1,104,713 0.31 342,461 342 Kanpur 2005 2,551,337 0.43 1,097,075 1,097 Kochi 2005 595,575 0.67 399,035 399 62 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX F (continued) MSW Generation Data for Cities Over 100,000 Urban Generation Rate Total MSW Total Waste City Year Population (kg/capita/day) Generated (kg/day) (tons/day) Kolkata 2005 4,572,876 0.58 2,652,268 2,652 Lucknow 2005 2,185,927 0.22 480,904 481 Ludhiana 2005 1,398,467 0.53 741,188 741 Madurai 2005 928,868 0.30 278,660 279 Meerut 2005 1,068,772 0.46 491,635 492 Nagpur 2005 2,052,066 0.25 513,017 513 Nashik 2005 1,077,236 0.19 204,675 205 Patna 2005 1,366,444 0.37 505,584 506 Pondicherry 2005 220,865 0.59 130,310 130 Pune 2005 2,538,473 0.46 1,167,698 1,168 Raipur 2005 605,747 0.30 181,724 182 Rajkot 2005 967,476 0.21 203,170 203 Ranchi 2005 847,093 0.25 211,773 212 Shillong 2005 132,867 0.34 45,175 45 Simla 2005 142,555 0.27 38,490 38 Srinagar 2005 898,440 0.48 431,251 431 Surat 2005 2,433,835 0.41 997,872 998 Tiruvanantapuram 2005 744,983 0.23 171,346 171 Vadodara 2005 1,306,227 0.27 352,681 353 Varanasi 2005 1,091,918 0.39 425,848 426 Vijaywada 2005 851,282 0.44 374,564 375 Vishakhapatnam 2005 982,904 0.59 579,913 580 Nepal (Alam 2008) Kathmandu 2003 738,173 0.31 226,800 227 Sri Lanka (UNSD 2009) Dehiwala-Mount Lavinia 2007 209,787 0.73 154,110 154 Moratuwa 2007 189,790 0.67 127,854 128 NOTES: * Denotes domestic waste data as MSW figures are unknown. PAHO defines municipal waste and domestic waste as follows: PAHO definitions: Municipal waste Solid or semi-solid waste generated in of population centers including domestic and commercial wastes, as well as those originated by the, small-scale industries and institutions (including hospital and clinics); markets street sweeping, and from public cleansing. Domestic waste Domestic solid or semi-solid waste generated by human activities a the household level. ** China cities have populations over 750,000 inhabitants ANNEX 63 ANNEX G MSW Collection Data for Cities Over 100,000 MSW Collection Coverage City Year Urban Population (%) Africa Benin (UNSD 2009) Parakou 2002 148,450 10 Burkina Faso (UNSD 2009) Ouagadougou 1995 876,200 51 Cameroon (Parrot et al. 2009) Yaounde 2005 1,720,000 43 Chad (Parrot et al. 2009) Ndjamena 2003 800,000 15 - 20 Côte d’Ivoire (Parrot et al. 2009) Abidjan 2002 2,777,000 30 - 40 Guinea (UNSD 2009) Conakry 2007 3,000,000 76 Kenya (Parrot et al. 2009) Nairobi 2006 2,312,000 30 - 45 Mauritania (Parrot et al. 2009) Nouakchott N/A 611,883 20 - 30 Niger (UNSD 2009) Zinder** 2007 242,800 77 Senegal (Parrot et al. 2009) Dakar 2003 1,708,000 30 - 40 Tanzania (Parrot et al. 2009) Dar es Salaam N/A 2,500,000 48 Togo (Parrot et al. 2009) Lome 2002 1,000,000 42 Zambia (UNSD 2009) Lusaka 2005 1,300,000 18 Zimbabwe (UNSD 2009) Harare 2007 2,500,000 99 East Asia & Pacific (UNSD 2009) China, Hong Kong SAR Hong Kong 2007 6,926,000 100 China, Macao SAR Macao 2007 525,760 100 Indonesia Jakarta 2004 8,962,000 83 Philippines Manila 2007 1,660,714 95 Eastern Europe & Central Asia (UNSD 2009) Albania Tirana 2007 1,532,000 90 Belarus Minsk 2007 1,806,200 100 Croatia Zagreb 2006 784,900 100 Georgia Tbilisi 2007 1,300,000 100 Batumi 2007 303,200 62 Kutaisi 2007 185,960 95 64 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX G (continued) MSW Collection Data for Cities Over 100,000 MSW Collection Coverage City Year Urban Population (%) Latin America and the Caribbean (PAHO, 2005) Argentina Area Metropolitana Buenos Aires 2001 12,544,018 100 Bahia Blanca 2001 285,000 100 Cordoba 2001 1,283,396 100 Neuquen 2001 202,518 100 Parana 2001 245,677 100 Rafaela 2001 100,000 100 Rio Cuarto 2001 154,127 100 Rosario 2001 906,004 100 Salta Capital 2001 472,971 100 Bahamas Nassau, Bahamas 2001 200,000 100 Barbados* Barbados 2001 268,792 100 Bolivia* Cochabamba 2001 717,026 90 El Alto 2001 629,955 76 La Paz 2001 790,353 87 Oruro 2001 201,230 92 Potosi 2001 135,783 88 Santa Cruz de la Sierra 2001 1,113,000 88 Sucre 2001 193,876 85 Tarija 2001 135,783 93 Chile Antofagasta, Antofagasta 2001 318,779 99 Antofagasta, Calama 2001 138,402 100 Araucanía, Temuco 2001 245,347 100 B.O’Higgins, Rancagua 2001 214,344 100 Biobío, Chillán 2001 161,953 100 Biobío, Concepción 2001 216,061 100 Biobío, Talcahuano 2001 250,348 100 Coquimbo, Coquimbo 2001 163,036 100 Coquimbo, La Serena 2001 160,148 100 Los Lagos, Osorno 2001 145,475 100 Los Lagos, Puerto Montt 2001 175,938 100 Los Lagos, Valdivia 2001 140,559 100 Magallanes, Punta Arenas 2001 120,874 100 Maule, Curicó 2001 120,299 100 Maule, Talca 2001 203,231 100 Santiago, Cerro Navia 2001 148,312 100 Santiago, La Florida 2001 365,674 100 Santiago, La Pintana 2001 190,085 100 Santiago, Maipú 2001 468,390 100 Santiago, Providencia 2001 120,874 100 Santiago, Recoleta 2001 148,220 100 Santiago, Santiago 2001 200,792 100 Tarapacá, Arica 2001 185,268 100 Valparaíso, Valparaíso 2001 275,982 100 Valparaíso, Viña del Mar 2001 286,931 100 ANNEX 65 ANNEX G (continued) MSW Collection Data for Cities Over 100,000 MSW Collection Coverage City Year Urban Population (%) Colombia Armenia 2001 293,000 100 Barrancabermeja 2001 183,000 100 Barranquilla 2001 1,276,000 100 Bello 2001 353,000 97 Bogotá 2001 6,558,000 100 Bucaramanga 2001 543,000 100 Buenaventura 2001 230,000 80 Buga 2001 113,000 100 Cali 2001 2,181,000 97 Cartagena 2001 854,000 97 Cartago 2001 129,000 98 Cúcuta 2001 644,000 100 Dosquebradas 2001 166,000 84 Envigado 2001 145,000 99 Florencia 2001 116,000 80 Floridablanca 2001 232,000 95 Girardot 2001 117,000 95 Ibagué 2001 403,000 97 Itagüí 2001 246,000 98 Maicao 2001 115,000 100 Manizales 2001 345,000 100 Medellín 2001 1,909,000 100 Montería 2001 256,000 100 Neiva 2001 317,000 98 Palmira 2001 234,000 100 Pasto 2001 349,000 100 Pereira 2001 401,000 94 Popayán 2001 206,000 98 Santa Marta 2001 382,000 97 Sincelejo 2001 234,000 100 Soacha 2001 285,000 95 Sogamoso 2001 114,000 81 Soledad 2001 310,000 100 Tuluá 2001 157,000 100 Tunja 2001 112,000 100 Valledupar 2001 278,000 98 Villavicencio 2001 289,000 98 Costa Rica Alajuela 2001 234,737 82 Desamparados 2001 203,770 40 San José 2001 326,384 100 Cuba Bayamo 2001 154,832 100 Camagüey 2001 308,288 100 Ciego de Ávila 2001 118,935 100 Cienfuegos 2001 154,897 97 Ciudad de La Habana 2001 2,186,632 100 Guantánamo 2001 222,217 100 Holguín 2001 268,843 100 Manzanillo 2001 110,846 100 66 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX G (continued) MSW Collection Data for Cities Over 100,000 MSW Collection Coverage City Year Urban Population (%) Matanzas 2001 133,177 100 Pinar del Río 2001 162,078 100 Sancti Spíritus 2001 109,220 91 Santa Clara 2001 220,345 98 Santiago de Cuba 2001 452,307 100 Tunas 2001 144,381 100 Dominican Republic La Romana 2001 201,700 100 Quito 2001 2,774,926 60 Santo Domingo de los Colorados 2001 244,039 90 Ecuador* Quito 2001 1,841,200 80 Santo Domingo de los Colorados 2001 200,421 83 El Salvador La Libertad - Nueva San Salvador 2001 136,909 94 San Miguel, San Miguel 2001 172,203 82 San Salvador - Apopa 2001 139,802 73 San Salvador - Ilopango, 2001 115,358 50 San Salvador - Mejicanos 2001 172,548 85 San Salvador - Soyapango 2001 285,286 95 San Salvador, San Salvador 2001 479,605 81 Santa Ana, Santa Ana 2001 167,975 83 Grenada Grenada 2001 95,551 100 Guatemala Antigua Guatemala 2001 248,019 80 Guatemala 2001 2,541,581 70 Quetzaltenango 2001 122,157 90 San Benito 2001 366,735 80 Guyana Georgetown 2001 180,000 100 Haiti Cap-Haïtien 2001 141,061 45 Carrefour 2001 416,301 16 Croix des Bouquets 2001 143,803 40 Delmas 2001 335,866 16 Gonaïves 2001 138,480 45 Jacmel 2001 138,504 80 Les Cayes 2001 152,845 45 Pétion Ville 2001 143,452 22 Port-au-Prince 2001 1,100,085 22 Saint Marc 2001 164,868 45 Honduras San Pedro Sula 2001 483,384 85 Jamaica* North Eastern Wasteshed( Portland, St.Mary and St.Ann) 2001 357,265 56 Retirement(Westmoreland,Hanover,Trelawny & St.James) 2001 452,724 68 Riverton ( Kgn, St.And, St.Cath. Clarendon and St.Thomas) 2001 1,458,155 66 Southern(Manchester, St. Elizabeth) 2001 331,190 48 Mexico Acapulco, Guerrero 2001 728,010 85 Acuña, Coahuila 2001 117,271 85 Aguascalientes, Aguascalientes 2001 656,245 90 Altamira, Tamaulipas 2001 130,425 85 Apatzingan, Michoacán 2001 108,466 85 Apodaca, Nuevo León 2001 297,776 100 ANNEX 67 ANNEX G (continued) MSW Collection Data for Cities Over 100,000 MSW Collection Coverage City Year Urban Population (%) Atizapan de Zaragoza, México 2001 475,683 90 Atlixco, Puebla 2001 117,929 85 Boca del Río, Veracruz 2001 135,875 85 Campeche, Campeche 2001 219,281 80 Cancún, Benito Juárez, Quintana Roo 2001 444,870 90 Cárdenas, Tabasco 2001 219,414 80 Carmen, Campeche 2001 169,784 85 Celaya, Guanajuato 2001 388,012 95 Chalco, México 2001 232,956 85 Chetumal, Othon P. Blanco, Quintana Roo 2001 209,241 80 Chihuahua, Chihuahua 2001 676,160 95 Chilpancingo, Guerrero 2001 197,275 85 Coatzacoalcos, Veracruz 2001 268,673 80 Colima, Colima 2001 131,268 85 Comitán de Domínguez, Chiapas 2001 107,065 85 Córdoba, Veracruz 2001 178,672 90 Cuauhtemoc, Chihuahua 2001 125,105 85 Cuautla, Morelos 2001 155,363 90 Cuernavaca, Morelos 2001 342,374 85 Culiacán, Sinaloa 2001 755,017 90 Delicias, Chihuahua 2001 117,215 85 Dolores Hidalgo, Guanajuato 2001 130,748 85 Durango, Durango 2001 495,962 90 Ecatepec, México 2001 1,655,225 90 Ensenada, Baja California 2001 381,747 95 Fresnillo, Zacatecas 2001 183,941 85 General Escobedo, Nuevo León 2001 246,166 100 Gómez Palacio, Durango 2001 276,085 85 Guadalajara, Jalisco 2001 1,650,776 90 Guadalupe, Nuevo León 2001 679,230 100 Guadalupe, Zacatecas 2001 109,179 85 Guanajuato, Guanajuato 2001 144,166 90 Guasave, Sinaloa 2001 279,878 85 Guaymas, Sonora 2001 129,236 85 Hermosillo, Sonora 2001 619,185 100 Hidalgo del Parral, Chihuahua 2001 101,390 85 Hidalgo, Michoacán 2001 106,922 85 Huixquilucan, México 2001 198,564 85 Iguala, Guerrero 2001 125,395 85 Irapuato, Guanajuato 2001 445,778 90 Juárez, Chihuahua 2001 1,264,121 90 La Paz, Baja California Sur 2001 199,712 85 Lagos de Moreno, Jalisco 2001 128,407 85 Lázaro Cárdenas, Michoacán 2001 174,205 85 León, Guanajuato 2001 1,153,998 90 Lerdo, Durango 2001 113,705 85 Lerma, México 2001 103,909 85 Los Cabos, Baja California Sur 2001 113,727 85 Los Mochis-Topolobampo, Ahome, Sinaloa 2001 362,442 85 Madero, Tamaulipas 2001 184,289 85 Mante, Tamaulipas 2001 111,671 85 Manzanillo, Colima 2001 127,443 85 Matamoros, Tamaulipas 2001 427,966 85 Mazatlán, Sinaloa 2001 385,047 85 Mérida, Yucatán 2001 714,689 95 Metepec, México 2001 197,699 85 68 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX G (continued) MSW Collection Data for Cities Over 100,000 MSW Collection Coverage City Year Urban Population (%) Mexicali, Baja California 2001 779,523 80 México, Federal District 2001 8,615,955 100 Minatitlán, Veracruz 2001 144,574 85 Monclova, Coahuila 2001 194,458 85 Monterrey, Nuevo León 2001 1,112,636 100 Morelia, Michoacán 2001 628,801 85 Naucalpan, México 2001 861,173 90 Navojoa, Sonora 2001 141,412 85 Nezahualcoyotl, México 2001 1,223,180 80 Nogales, Sonora 2001 164,819 85 Nuevo Laredo, Tamaulipas 2001 317,877 100 Oaxaca, Oaxaca 2001 259,343 80 Obregón, Cajeme, Sonora 2001 357,857 85 Orizaba, Veracruz 2001 119,405 90 Pachuca, Hidalgo 2001 249,838 95 Piedras Negras, Coahuila 2001 130,398 100 Poza Rica, Veracruz 2001 152,318 85 Puebla, Puebla 2001 1,372,446 95 Puerto Vallarta, Jalisco 2001 191,424 85 Querétaro, Querétaro 2001 657,447 100 Reynosa, Tamaulipas 2001 438,696 85 Río Bravo, Tamaulipas 2001 104,620 85 Salamanca, Guanajuato 2001 228,239 90 Saltillo, Coahuila 2001 587,730 90 San Andrés Tuxtla, Veracruz 2001 143,235 85 San Cristobal de las Casas, Chiapas 2001 135,731 85 San Francisco del Rincón, Guanajuato 2001 100,805 90 San Juan Bautista de Tuxtepec, Oaxaca 2001 134,895 85 San Juan del Río, Querétaro 2001 184,679 90 San Luis Potosi, San Luis Potosi 2001 678,645 85 San Luis Río Colorado, Sonora 2001 147,912 90 San Martín Texmelucan, Puebla 2001 123,072 85 San Miguel de Allende, Guanajuato 2001 138,393 90 San Nicolas de los Garza, Nuevo León 2001 497,078 100 San Pedro Garza García , Nuevo León 2001 127,254 100 Santa Catarina, Nuevo León 2001 231,809 100 Silao, Guanajuato 2001 134,539 90 Soledad de Graciano, San Luis Potosi 2001 185,063 85 Tampico, Tamaulipas 2001 298,063 85 Tapachula, Chiapas 2001 276,743 85 Taxco, Guerrero 2001 100,889 85 Tecoman, Colima 2001 101,049 85 Tehuacán, Puebla 2001 233,807 90 Tepatitlán, Jalisco 2001 121,076 85 Tepic, Nayarit 2001 307,550 80 Tijuana, Baja California 2001 1,262,520 95 Tlajomulco, Jalisco 2001 128,339 85 Tlalnepantla, México 2001 722,279 95 Tlaquepaque, Jalisco 2001 480,844 95 Toluca, México 2001 687,969 85 Tonalá, Jalisco 2001 350,648 95 Torreón, Coahuila 2001 533,457 100 Tulancingo, Hidalgo 2001 124,461 85 Tuxpan, Veracruz 2001 126,257 85 ANNEX 69 ANNEX G (continued) MSW Collection Data for Cities Over 100,000 MSW Collection Coverage City Year Urban Population (%) Tuxtla Gutiérrez, Chiapas 2001 443,782 85 Uruapan, Michoacán 2001 268,208 85 Valle de Chalco Solidaridad, México 2001 330,885 80 Valle de Santiago, Guanajuato 2001 130,553 85 Valles, San Luis Potosi 2001 147,086 85 Veracruz, Veracruz 2001 463,812 90 Victoria, Tamaulipas 2001 266,612 90 Villahermosa, Centro, Tabasco 2001 531,511 80 Xalapa, Veracruz 2001 404,788 90 Zacatecas, Zacatecas 2001 124,722 85 Zamora, Michoacán 2001 161,425 90 Zapopan, Jalisco 2001 1,018,447 90 Zitacuaro, Michoacán 2001 139,514 85 Nicaragua Chinandega 2001 124,107 80 Leon 2001 147,845 70 Managua 2001 952,068 80 Panama Arraiján 2001 149,918 63 Ciudad de Panamá 2001 708,438 80 Colón 2001 174,059 66 La Chorrera 2001 124,656 64 San Miguelito 2001 293,745 95 Paraguay Asunción 2001 513,399 99 Capiatá 2001 154,469 35 Ciudad del Este 2001 223,350 60 Fernando de la Mora 2001 114,332 97 Lambare 2001 119,984 42 Luque 2001 170,433 54 San Lorenzo 2001 202,745 26 Peru Callao, Callao Cercado 2001 449,282 75 Callao, Ventanilla 2001 148,767 57 Junín, El Tambo 2001 165,357 66 Junín, Huancayo 2001 112,203 70 Lima, Ate 2001 410,734 89 Lima, Carabayllo 2001 153,112 78 Lima, Chorrillos 2001 264,645 89 Lima, Comas 2001 469,747 90 Lima, El Agustino 2001 166,902 80 Lima, Independencia 2001 200,365 66 Lima, La Molina 2001 125,034 75 Lima, La Victoria 2001 205,554 75 Lima, Lima Cercado 2001 286,202 85 Lima, Los Olivos 2001 344,164 87 Lima, Lurigancho 2001 123,142 65 Lima, Puente Piedra 2001 183,861 73 Lima, Rímac 2001 192,449 89 Lima, San Borja 2001 122,270 63 Lima, San Juan de Lurigancho 2001 751,155 47 Lima, San Juan de Miraflores 2001 387,641 65 Lima, San Martín de Porres 2001 448,345 74 Lima, San Miguel 2001 134,908 80 70 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX G (continued) MSW Collection Data for Cities Over 100,000 MSW Collection Coverage City Year Urban Population (%) Lima, Santa Anita 2001 148,752 71 Lima, Santiago de Surco 2001 251,567 79 Lima, Villa El Salvador 2001 364,476 77 Lima, Villa María del Triunfo 2001 341,971 80 Piura, Castilla 2001 106,926 77 Ucayali, Callería 2001 246,856 70 Saint Lucia St. Lucia 2001 162,157 100 Saint Vincent and the Grenadines* St. Vincent 2001 106,916 90 Suriname Greater Paramaribo 2001 287,131 82 Trinidad and Tobago Couva/Tabaquite/Talparo 2001 162,779 100 Diego Martin 2001 105,720 100 San Juan/Laventille 2001 157,295 100 Tunapuna/Piarco 2001 203,975 100 Uruguay Canelones 2001 539,130 75 Maldonado 2001 137,390 95 Montevideo 2001 1,303,182 90 Venezuela Distrito Capital 2001 1,836,286 80 Municipio Barinas Edo Barinas 2001 283,273 100 Municipio Caroni Edo Bolivar 2001 704,168 68 Municipio Girardot Edo Aragua 2001 396,125 88 Municipio Iribarren Edo Lara 2001 895,989 80 Municipio Lagunillas Edo Zulia 2001 144,345 90 Municipio Maracaibo Edo Zulia 2001 1,405,933 87 Municipio Pedraza Edo Apure 2001 283,273 100 Municipio Simon Bolivar Edo Anzoategui 2001 344,593 80 Municipio Simon Rodriguez Edo Anzoategui 2001 147,800 100 Middle East & North Africa (UNSD 2009) Egypt Cairo 2007 7,765,000 77 Iraq Baghdad 2005 6,784,000 86 South Asia Nepal (Alam 2008) Kathmandu 2003 738,173 94 Sri Lanka (UNSD 2009) Dehiwala-Mount Lavinia 2007 209,787 96 Moratuwa 2007 189,790 90 NOTES: * Domestic waste data used as MSW figures not available; hence it is assumed that waste collection coverage is for domestic waste and not MSW ** Urban population data from 2007; Waste collection coverage data from 2006 PAHO definitions: Municipal waste Solid or semi-solid waste generated in of population centers including domestic and commercial wastes, as well as those originated by the, small-scale industries and institutions (including hospital and clinics); markets street sweeping, and from public cleansing. Domestic waste Domestic solid or semi-solid waste generated by human activities a the household level. ANNEX 71 ANNEX H MSW Disposal Methods for Cities Over 100,000 Sanitary Controlled Open Water- Urban Other City Landfill Landfill Dump courses Population (%) (%) (%) (%) (%) Latin America & Caribbean (PAHO 2005) Argentina Area Metropolitana Buenos Aires 12,544,018 100 0 0 0 0 Bahia Blanca 285,000 80 0 0 0 0 Neuquen 202,518 100 0 0 0 0 Parana 245,677 0 0 100 0 0 Salta Capital 472,971 100 0 0 0 0 Bolivia Cochabamba 717,026 87 0 0 0 13 El Alto 629,955 0 74 16 N.A. 11 La Paz 790,353 87 0 0 N.A. 13 Oruro 201,230 89 0 5 0 7 Potosi 135,783 85 0 0 0 15 Santa Cruz de la Sierra 1,113,000 85 0 0 9 6 Sucre 193,876 83 0 9 0 9 Tarija 135,783 90 0 0 0 10 Barbados Antofagasta, Antofagasta 318,779 0 100 0 0 0 Antofagasta, Calama 138,402 0 75 0 0 25 Araucanía, Temuco 245,347 98 0 0 0 2 B.O'Higgins, Rancagua 214,344 100 0 0 0 0 Barbados 268,792 35 48 0 N.A. 17 Biobío, Chillán 161,953 0 0 100 0 0 Biobío, Concepción 216,061 0 100 0 0 0 Biobío, Talcahuano 250,348 0 75 0 0 25 Coquimbo, Coquimbo 163,036 0 100 0 0 0 Coquimbo, La Serena 160,148 0 100 0 0 0 Los Lagos, Osorno 145,475 100 0 0 0 0 Los Lagos, Puerto Montt 175,938 0 96 0 0 4 Los Lagos, Valdivia 140,559 83 0 0 0 17 Magallanes, Punta Arenas 120,874 0 85 0 0 15 Maule, Curicó 120,299 100 0 0 0 0 Maule, Talca 203,231 100 0 0 0 0 Santiago, Cerro Navia 148,312 100 0 0 0 0 Santiago, La Florida 365,674 100 0 0 0 0 Santiago, Maipú 468,390 99 0 0 0 2 Santiago, Providencia 120,874 100 0 0 0 0 Santiago, Recoleta 148,220 100 0 0 0 0 Santiago, Santiago 200,792 86 0 0 0 14 Tarapacá, Arica 185,268 0 95 0 0 5 Valparaíso, Valparaíso 275,982 100 0 0 0 0 Valparaíso, Viña del Mar 286,931 0 99 0 0 1 Cuba Bayamo 154,832 0 9 90 0 1 Camagüey 308,288 0 100 0 0 0 Ciego de Ávila 118,935 0 100 0 0 0 Cienfuegos 154,897 14 0 85 0 1 Ciudad de La Habana 2,186,632 0 90 11 0 0 Guantánamo 222,217 0 100 0 0 0 Holguín 268,843 20 80 0 0 0 Manzanillo 110,846 20 0 80 0 0 72 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX H (continued) MSW Disposal Methods for Cities Over 100,000 Sanitary Controlled Open Water- Urban Other City Landfill Landfill Dump courses Population (%) (%) (%) (%) (%) Matanzas 133,177 0 100 0 0 0 Pinar del Río 162,078 20 80 0 0 0 Sancti Spíritus 109,220 0 88 13 0 0 Santa Clara 220,345 93 0 5 0 2 Santiago de Cuba 452,307 100 0 0 0 0 Tunas 144,381 81 0 19 0 0 Colombia Armenia 293,000 100 0 0 0 0 Barrancabermeja 183,000 0 0 100 0 0 Barranquilla 1,276,000 100 0 0 0 0 Bello 353,000 97 0 0 0 3 Bogotá 6,558,000 100 0 0 0 0 Bucaramanga 543,000 0 98 0 0 2 Buenaventura 230,000 0 0 0 100 0 Buga 113,000 100 0 0 0 0 Cali 2,181,000 0 0 100 0 0 Cartagena 854,000 100 0 0 0 0 Cartago 129,000 82 0 0 0 18 Dosquebradas 166,000 100 0 0 0 0 Envigado 145,000 99 0 0 0 1 Florencia 116,000 0 0 100 0 0 Floridablanca 232,000 0 90 0 0 10 Ibagué 403,000 99 0 0 0 1 Itagüí 246,000 98 0 0 0 2 Maicao 115,000 0 0 0 100 0 Manizales 345,000 100 0 0 0 0 Medellín 1,909,000 100 0 0 0 0 Montería 256,000 0 0 100 0 0 Palmira 234,000 100 0 0 0 0 Pasto 349,000 99 0 0 0 1 Popayán 206,000 0 98 0 0 2 Santa Marta 382,000 0 86 0 0 14 Sincelejo 234,000 100 0 0 0 0 Soacha 285,000 0 0 100 0 0 Sogamoso 114,000 100 0 0 0 0 Soledad 310,000 0 0 100 0 0 Tuluá 157,000 100 0 0 0 0 Valledupar 278,000 95 0 0 0 5 Costa Rica Alajuela 234,737 100 0 0 0 0 Cartago 138,940 100 0 0 0 0 Desamparados 203,770 90 0 0 0 10 Goicoechea 123,375 100 0 0 0 0 Heredia 109,398 100 0 0 0 0 Pérez Zeledón 129,219 0 30 0 0 70 Pococí 109,367 0 100 0 0 0 Puntarenas 108,214 0 0 100 0 0 San Carlos 135,133 0 0 97 0 3 San José 326,384 98 0 0 0 2 Dominican Republic San Francisco de Macorís 210,580 0 0 100 0 0 Santiago de los Caballeros 594,424 0 0 100 0 0 Santo Domingo 2,774,926 83 10 0 3 4 ANNEX 73 ANNEX H (continued) MSW Disposal Methods for Cities Over 100,000 Sanitary Controlled Open Water- Urban Other City Landfill Landfill Dump courses Population (%) (%) (%) (%) (%) Ecuador Quito 1,841,200 84 0 0 0 16 Santo Domingo de los Colrados 200,421 0 91 0 0 9 El Salvador San Salvador, San Salvador 479,605 81 0 0 0 19 San Salvador - Soyapango 285,286 95 0 0 0 5 Grenada Grenada 95,551 90 0 0 0 10 Guatemala Guatemala 2,541,581 0 40 0 0 60 Guyana Georgetown 180,000 0 90 0 10 0 Haiti Cap-Haïtien 141,061 0 0 65 25 10 Carrefour 416,301 0 0 38 0 62 Croix des Bouquets 143,803 0 0 80 0 20 Delmas 335,866 0 0 44 0 56 Gonaïves 138,480 0 0 60 0 40 Jacmel 138,504 0 0 35 0 65 Les Cayes 152,845 0 0 54 23 23 Pétion Ville 143,452 0 0 38 26 36 Port-au-Prince 1,100,085 0 0 30 0 70 Saint Marc 164,868 0 0 54 23 23 Honduras Distrito Central 819,867 0 100 0 0 0 Jamaica North Eastern Wasteshed( Portland, St.Mary and St.Ann) 357,265 0 100 0 0 0 Portmore 159,974 0 100 0 0 0 Retirement(Westmoreland,Hanover,Trelawny & St.James) 452,724 0 100 0 0 0 Riverton ( Kgn, St.And, St.Cath. Clarendon and St.Thomas) 1,458,155 0 100 0 0 0 Southern(Manchester, St.Elizabeth) 331,190 0 100 0 0 0 Southern(Manchester, St.Elizabeth) 331,190 0 100 0 0 0 Mexico Acapulco, Guerrero 728,010 94 0 0 0 6 Acuña, Coahuila 117,271 0 0 94 0 6 Aguascalientes, Aguascalientes 656,245 94 0 0 0 6 Altamira, Tamaulipas 130,425 0 94 0 0 6 Apatzingan, Michoacán 108,466 0 0 94 0 6 Apodaca, Nuevo León 297,776 93 0 0 0 7 Atizapan de Zaragoza, México 475,683 94 0 0 0 6 Atlixco, Puebla 117,929 0 0 94 0 6 Boca del Río, Veracruz 135,875 0 94 0 0 6 Campeche, Campeche 219,281 0 0 94 0 6 Cancún, Benito Juárez, Quintana Roo 444,870 94 0 0 0 6 Cárdenas, Tabasco 219,414 0 0 94 0 6 Carmen, Campeche 169,784 0 0 94 0 6 Celaya, Guanajuato 388,012 94 0 0 0 6 Chalco, México 232,956 0 0 94 0 6 Chetumal, Othon P. Blanco, Quintana Roo 209,241 0 0 94 0 6 Chihuahua, Chihuahua 676,160 93 0 0 0 7 Chilpancingo, Guerrero 197,275 0 0 94 0 6 Coatzacoalcos, Veracruz 268,673 0 0 94 0 6 Colima, Colima 131,268 94 0 0 0 6 74 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX H (continued) MSW Disposal Methods for Cities Over 100,000 Sanitary Controlled Open Water- Urban Other City Landfill Landfill Dump courses Population (%) (%) (%) (%) (%) Comitán de Domínguez, Chiapas 107,065 0 0 94 0 6 Córdoba, Veracruz 178,672 0 0 94 0 6 Cuauhtemoc, Chihuahua 125,105 0 0 94 0 6 Cuautla, Morelos 155,363 94 0 0 0 6 Cuernavaca, Morelos 342,374 0 0 94 0 6 Culiacán, Sinaloa 755,017 94 0 0 0 6 Delicias, Chihuahua 117,215 0 0 94 0 6 Dolores Hidalgo, Guanajuato 130,748 0 0 94 0 6 Durango, Durango 495,962 92 0 0 0 8 Ecatepec, México 1,655,225 94 0 0 0 6 Ensenada, Baja California 381,747 0 0 94 0 6 Fresnillo, Zacatecas 183,941 0 94 0 0 6 General Escobedo, Nuevo León 246,166 93 0 0 0 7 Gómez Palacio, Durango 276,085 0 92 0 0 8 Guadalajara, Jalisco 1,650,776 0 94 0 0 6 Guadalupe, Nuevo León 679,230 93 0 0 0 7 Guadalupe, Zacatecas 109,179 0 0 94 0 6 Guanajuato, Guanajuato 144,166 94 0 0 0 6 Guasave, Sinaloa 279,878 94 0 0 0 6 Guaymas, Sonora 129,236 0 0 94 0 6 Hermosillo, Sonora 619,185 94 0 0 0 6 Hidalgo del Parral, Chihuahua 101,390 0 0 94 0 6 Hidalgo, Michoacán 106,922 0 0 94 0 6 Huixquilucan, México 198,564 0 94 0 0 6 Iguala, Guerrero 125,395 0 0 92 0 8 Irapuato, Guanajuato 445,778 0 94 0 0 6 Juárez, Chihuahua 1,264,121 92 0 0 0 8 La Paz, Baja California Sur 199,712 0 0 92 0 8 Lagos de Moreno, Jalisco 128,407 0 0 94 0 6 Lázaro Cárdenas, Michoacán 174,205 0 94 0 0 6 León, Guanajuato 1,153,998 92 0 0 0 8 Lerdo, Durango 113,705 0 0 94 0 6 Lerma, México 103,909 0 0 94 0 6 Los Cabos, Baja California Sur 113,727 94 0 0 0 6 Los Mochis-Topolobampo, Ahome, Sinaloa 362,442 94 0 0 0 6 Madero, Tamaulipas 184,289 0 0 94 0 6 Mante, Tamaulipas 111,671 0 0 94 0 6 Manzanillo, Colima 127,443 0 0 94 0 6 Matamoros, Tamaulipas 427,966 94 0 0 0 6 Mazatlán, Sinaloa 385,047 0 94 0 0 6 Mérida, Yucatán 714,689 93 0 0 0 7 Metepec, México 197,699 0 94 0 0 6 Mexicali, Baja California 779,523 0 94 0 0 6 México, Distrito Federal 8,615,955 92 0 0 0 8 Minatitlán, Veracruz 144,574 0 0 94 0 6 Monclova, Coahuila 194,458 0 0 94 0 6 Monterrey, Nuevo León 1,112,636 93 0 0 0 7 Morelia, Michoacán 628,801 0 0 94 0 6 Naucalpan, México 861,173 0 94 0 0 6 Navojoa, Sonora 141,412 0 0 94 0 6 Nezahualcoyotl, México 1,223,180 0 70 23 0 7 Nogales, Sonora 164,819 94 0 0 0 6 ANNEX 75 ANNEX H (continued) MSW Disposal Methods for Cities Over 100,000 Sanitary Controlled Open Water- Urban Other City Landfill Landfill Dump courses Population (%) (%) (%) (%) (%) Nuevo Laredo, Tamaulipas 317,877 96 0 0 0 4 Oaxaca, Oaxaca 259,343 0 0 94 0 6 Obregón, Cajeme, Sonora 357,857 0 0 94 0 6 Orizaba, Veracruz 119,405 94 0 0 0 6 Pachuca, Hidalgo 249,838 94 0 0 0 6 Piedras Negras, Coahuila 130,398 94 0 0 0 6 Poza Rica, Veracruz 152,318 94 0 0 0 6 Puebla, Puebla 1,372,446 93 0 0 0 7 Puerto Vallarta, Jalisco 191,424 94 0 0 0 6 Querétaro, Querétaro 657,447 94 0 0 0 6 Reynosa, Tamaulipas 438,696 0 0 94 0 6 Río Bravo, Tamaulipas 104,620 94 0 0 0 6 Salamanca, Guanajuato 228,239 0 0 94 0 6 Saltillo, Coahuila 587,730 94 0 0 0 6 San Andrés Tuxtla, Veracruz 143,235 0 0 94 0 6 San Cristobal de las Casas, Chiapas 135,731 0 0 94 0 6 San Francisco del Rincón, Guanajuato 100,805 0 0 92 0 8 San Juan Bautista de Tuxtepec, Oaxaca 134,895 0 0 94 0 6 San Juan del Río, Querétaro 184,679 94 0 0 0 6 San Luis Potosi, San Luis Potosi 678,645 94 0 0 0 6 San Luis Río Colorado, Sonora 147,912 0 0 94 0 6 San Martín Texmelucan, Puebla 123,072 0 0 94 0 6 San Miguel de Allende, Guanajuato 138,393 94 0 0 0 6 San Nicolas de los Garza, Nuevo León 497,078 93 0 0 0 7 San Pedro Garza García , Nuevo León 127,254 93 0 0 0 7 Santa Catarina, Nuevo León 231,809 93 0 0 0 7 Silao, Guanajuato 134,539 94 0 0 0 6 Soledad de Graciano, San Luis Potosi 185,063 0 0 94 0 6 Tampico, Tamaulipas 298,063 0 0 94 0 6 Tapachula, Chiapas 276,743 94 0 0 0 6 Taxco, Guerrero 100,889 0 0 94 0 6 Tecoman, Colima 101,049 0 0 94 0 6 Tehuacán, Puebla 233,807 0 94 0 0 6 Tepatitlán, Jalisco 121,076 0 94 0 0 6 Tepic, Nayarit 307,550 94 0 0 0 6 Tijuana, Baja California 1,262,520 94 0 0 0 6 Tlajomulco, Jalisco 128,339 92 0 0 0 8 Tlalnepantla, México 722,279 94 0 0 0 6 Tlaquepaque, Jalisco 480,844 0 94 0 0 6 Toluca, México 687,969 0 94 0 0 6 Tonalá, Jalisco 350,648 0 94 0 0 6 Torreón, Coahuila 533,457 94 0 0 0 6 Tulancingo, Hidalgo 124,461 0 0 94 0 6 Tuxpan, Veracruz 126,257 94 0 0 0 6 Tuxtla Gutiérrez, Chiapas 443,782 0 0 94 0 6 Uruapan, Michoacán 268,208 0 0 94 0 6 Valle de Chalco Solidaridad, México 330,885 0 0 94 0 6 Valle de Santiago, Guanajuato 130,553 0 0 94 0 6 Valles, San Luis Potosi 147,086 0 0 94 0 6 Veracruz, Veracruz 463,812 0 94 0 0 6 Victoria, Tamaulipas 266,612 94 0 0 0 6 Villahermosa, Centro, Tabasco 531,511 0 0 94 0 6 76 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX H (continued) MSW Disposal Methods for Cities Over 100,000 Sanitary Controlled Open Water- Urban Other City Landfill Landfill Dump courses Population (%) (%) (%) (%) (%) Xalapa, Veracruz 404,788 0 0 94 0 6 Zacatecas, Zacatecas 124,722 0 0 94 0 6 Zamora, Michoacán 161,425 0 0 94 0 6 Zapopan, Jalisco 1,018,447 92 0 0 0 8 Zitacuaro, Michoacán 139,514 0 0 94 0 6 Nicaragua Chinandega 124,107 0 0 58 0 42 Managua 952,068 0 49 0 0 51 Masaya 115,369 0 0 71 0 29 Tipitapa 108,861 0 0 61 0 39 Panama Arraiján 149,918 0 0 63 N.D. 37 Ciudad de Panamá 708,438 80 0 N.D. N.D. 20 Colón 174,059 0 0 66 N.D. 34 La Chorrera 124,656 0 0 64 N.D. 36 San Miguelito 293,745 95 0 N.D. N.D. 5 Paraguay Asunción 513,399 37 61 0 0 2 Luque 170,433 0 100 0 0 0 Peru Callao, Callao Cercado 449,282 0 67 18 0 15 Callao, Ventanilla 148,767 0 51 35 0 14 Junín, El Tambo 165,357 0 59 26 0 15 Junín, Huancayo 112,203 0 63 21 0 16 Lima, Ate 410,734 0 79 3 0 18 Lima, Carabayllo 153,112 70 0 14 0 16 Lima, Chorrillos 264,645 0 79 3 0 18 Lima, Comas 469,747 80 0 2 0 18 Lima, El Agustino 166,902 0 71 12 0 17 Lima, Independencia 200,365 0 59 28 0 13 Lima, La Molina 125,034 0 67 20 0 13 Lima, La Victoria 205,554 0 66 21 0 13 Lima, Lima Cercado 286,202 76 0 11 0 13 Lima, Los Olivos 344,164 78 0 5 0 17 Lima, Lurigancho 123,142 0 58 27 0 15 Lima, Puente Piedra 183,861 0 65 19 0 16 Lima, Rímac 192,449 0 79 3 0 18 Lima, San Borja 122,270 0 56 32 0 12 Lima, San Juan de Lurigancho 751,155 0 42 46 0 12 Lima, San Juan de Miraflores 387,641 0 58 29 0 13 Lima, San Martín de Porres 448,345 66 0 20 0 14 Lima, San Miguel 134,908 0 71 13 0 16 Lima, Santa Anita 148,752 0 63 21 0 16 Lima, Santiago de Surco 251,567 70 0 15 0 15 Lima, Villa El Salvador 364,476 0 68 16 0 16 Lima, Villa María del Triunfo 341,971 0 71 12 0 17 Piura, Castilla 106,926 0 69 16 0 15 Ucayali, Callería 246,856 0 62 23 0 15 ANNEX 77 ANNEX H (continued) MSW Disposal Methods for Cities Over 100,000 Sanitary Controlled Open Water- Urban Other City Landfill Landfill Dump courses Population (%) (%) (%) (%) (%) St. Lucia St. Lucia 162,157 70 18 0 0 13 St. Vincent and the Grenadines St. Vincent 106,916 80 0 0 0 20 Suriname Greater Paramaribo 287,131 0 0 100 0 0 Trinidad and Tobago Couva/Tabaquite/Talparo 162,779 0 100 0 0 0 Diego Martin 105,720 0 100 0 0 0 San Juan/Laventille 157,295 0 100 0 0 0 Tunapuna/Piarco 203,975 0 100 0 0 0 Uruguay Canelones 539,130 0 0 100 0 0 Maldonado 137,390 100 0 0 0 0 Montevideo 1,303,182 0 100 0 0 0 Venezuela Municipio Guacara Carabobo 142,227 0 0 100 0 0 Municipio Valencia Edo Carabobo 742,145 0 100 0 0 0 78 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX I MSW Composition Data for Cities Over 100,000 Total Region/Country/ Urban Organic Plastic Year Recyclables Paper (%) Glass (%) Metal (%) Other (%) City Population (%) (%) (%) Africa Ghana (Asase 2009) Kumasi 2008 1,610,867 64 — 3 4 — 1 28 East Asia & Pacific Cambodia (Kum et al. 2005) Phnom Penh 2002 65 — 4 13 5 1 12 Middle East & North Africa (Al-Yousfi) Egypt Cairo 2002 67 — 18 3 3 2 7 Jordan Amman 2002 55 — 14 13 3 2 13 Saudi Arabia Riyadh 2002 34 — 31 2 3 16 14 Syria Aleppo 2002 59 — 13 12 8 1 8 Tunisia Tunis 2002 68 — 10 11 3 2 6 Yemen Aden 2002 57 — 11 11 3 5 14 South Asia India (CPCB 2005) Agartala 2005 1,89,998 59 14 — — — — 28 Agra 2005 12,75,135 46 16 — — — — 38 Ahmedabad 2005 35,20,085 41 12 — — — — 48 Aizwal 2005 2,28,280 54 21 — — — — 25 Allahabad 2005 9,75,393 35 19 — — — — 45 Amritsar 2005 9,66,862 65 14 — — — — 21 Asansol 2005 4,75,439 50 14 — — — — 35 Bangalore 2005 43,01,326 52 22 — — — — 26 Bhopal 2005 14,37,354 52 22 — — — — 25 Bhubaneswar 2005 6,48,032 50 13 — — — — 38 Chandigarh 2005 8,08,515 57 11 — — — — 32 Chennai 2005 43,43,645 41 16 — — — — 42 Coimbatore 2005 9,30,882 50 16 — — — — 34 Daman 2005 35,770 30 22 — — — — 48 Dehradun 2005 4,26,674 51 20 — — — — 29 Delhi 2005 1,03,06,452 54 16 — — — — 30 Dhanbad 2005 1,99,258 47 16 — — — — 37 Faridabad 2005 10,55,938 42 23 — — — — 35 Gandhinagar 2005 1,95,985 34 13 — — — — 53 Gangtok 2005 29,354 47 16 — — — — 37 Greater Mumbai 2005 1,19,78,450 62 17 — — — — 21 Guwahati 2005 8,09,895 54 23 — — — — 23 Hyderabad 2005 38,43,585 54 22 — — — — 24 Imphal 2005 2,21,492 60 19 — — — — 21 Indore 2005 14,74,968 49 13 — — — — 38 Itanagar 2005 35,022 52 21 — — — — 27 Jabalpur 2005 9,32,484 58 17 — — — — 25 Jaipur 2005 23,22,575 46 12 — — — — 42 Jammu 2005 3,69,959 52 21 — — — — 27 Jamshedpur 2005 11,04,713 43 16 — — — — 41 ANNEX 79 ANNEX I (continued) MSW Composition Data for Cities Over 100,000 Total Region/Country/ Urban Organic Plastic Year Recyclables Paper (%) Glass (%) Metal (%) Other (%) City Population (%) (%) (%) Kanpur 2005 25,51,337 48 12 — — — — 41 Kavarati 2005 10,119 46 27 — — — — 27 Kochi 2005 5,95,575 57 19 — — — — 23 Kohima 2005 77,030 57 23 — — — — 20 Kolkata 2005 45,72,876 51 11 — — — — 38 Lucknow 2005 21,85,927 47 16 — — — — 37 Ludhiana 2005 13,98,467 50 19 — — — — 31 Madurai 2005 9,28,868 55 17 — — — — 27 Meerut 2005 10,68,772 55 11 — — — — 35 Nagpur 2005 20,52,066 47 16 — — — — 37 Nasik 2005 10,77,236 40 25 — — — — 35 Panjim 2005 59,066 62 17 — — — — 21 Patna 2005 13,66,444 52 13 — — — — 35 Pondicherry 2005 2,20,865 50 24 — — — — 26 Port Blair 2005 99,984 48 28 — — — — 24 Pune 2005 25,38,473 62 17 — — — — 21 Raipur 2005 6,05,747 51 16 — — — — 32 Rajkot 2005 9,67,476 42 11 — — — — 47 Ranchi 2005 8,47,093 51 10 — — — — 39 Shillong 2005 1,32,867 63 17 — — — — 20 Silvassa 2005 50,463 72 14 — — — — 14 Simla 2005 1,42,555 43 37 — — — — 20 Srinagar 2005 8,98,440 62 18 — — — — 20 Surat 2005 24,33,835 57 11 — — — — 32 Tiruvananthapuram 2005 7,44,983 73 14 — — — — 13 Vadodara 2005 13,06,227 47 15 — — — — 38 Varanasi 2005 10,91,918 45 17 — — — — 38 Vijaywada 2005 8,51,282 59 17 — — — — 23 Visakhapatnam 2005 9.82,904 46 24 — — — — 30 Nepal (calculated from Alam 2008) Kathmandu 738,173 68 — 8 — 2 11 11 80 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX J MSW Generation by Country — Current Data and Projections for 2025 Current Available Data 2025 MSW Gen- Total MSW MSW Gen- Total MSW Income Country Region Total Urban eration Per Generation Total Urban eration Per Generation Level Population Capita (kg/ (tonnes/ Population Population Capita (kg/ (tonnes/day) capita/day) day) capita/day) Albania LMI ECA 1,418,524 0.77 1,088 3,488,000 2,006,000 1.2 2,407 Algeria LMI MENA 19,225,335 1.21 23,288 42,882,000 31,778,000 1.45 46,078 Angola LMI AFR 8,973,498 0.48 4,329 27,324,000 18,862,000 0.7 13,203 Antigua and Barbuda HIC LCR 24,907 5.50 137 101,000 35,000 4.3 151 Argentina UMI LCR 33,681,145 1.22 41,096 46,115,000 43,470,000 1.85 80,420 Armenia LMI ECA 1,964,525 0.68 1,342 2,908,000 1,947,000 1.2 2,336 Australia HIC OECD 16,233,664 2.23 36,164 24,393,000 22,266,000 2.1 46,759 Austria HIC OECD 5,526,033 2.40 13,288 8,622,000 6,204,000 2.15 13,339 Bahamas, The HIC LCR 252,689 3.25 822 397,000 346,000 2.9 1,003 Bahrain HIC MENA 574,671 1.10 630 972,000 875,000 1.6 1,400 Bangladesh LI SAR 38,103,596 0.43 16,384 206,024,000 76,957,000 0.75 57,718 Barbados HIC LCR 92,289 4.75 438 303,000 152,000 4 608 Belarus UMI ECA 7,057,977 0.78 5,479 8,668,000 6,903,000 1.2 8,284 Belgium HIC OECD 10,265,273 1.33 13,690 10,742,000 10,511,000 1.8 18,920 Belize UMI LCR 124,224 2.87 356 389,000 237,000 2.3 545 Benin LI AFR 3,147,050 0.54 1,699 14,460,000 7,286,000 0.75 5,465 Bhutan LMI SAR 225,257 1.46 329 819,000 428,000 1.7 728 Bolivia LMI LCR 5,587,410 0.33 1,863 12,368,000 9,047,000 0.7 6,333 Botswana UMI AFR 860,779 1.03 890 2,265,000 1,591,000 1.4 2,227 Brazil UMI LCR 144,507,175 1.03 149,096 228,833,000 206,850,000 1.6 330,960 Brunei Darussalam HIC EAP 282,415 0.87 247 526,000 426,000 1.3 554 Bulgaria UMI ECA 5,423,113 1.28 6,959 6,551,000 5,011,000 1.6 8,018 Burkina Faso LI AFR 2,549,805 0.51 1,288 23,729,000 6,899,000 0.75 5,174 Burundi LI AFR 700,922 0.55 384 15,040,000 2,577,000 0.8 2,062 Cameroon LMI AFR 7,914,528 0.77 6,082 25,136,000 17,194,000 1 17,194 Canada HIC OECD 21,287,906 2.33 49,616 37,912,000 31,445,000 2.2 69,179 Cape Verde LMI AFR 274,049 0.50 137 750,000 526,000 0.7 368 Central African Republic LI AFR 1,596,934 0.50 795 5,831,000 2,634,000 0.7 1,844 Chad LI AFR 2,566,839 0.50 1,288 17,504,000 6,566,000 0.7 4,596 Chile UMI LCR 13,450,282 1.08 14,493 19,266,000 17,662,000 1.5 26,493 China LMI EAP 511,722,970 1.02 520,548 1,445,782,000 822,209,000 1.7 1,397,755 Colombia LMI LCR 29,283,628 0.95 27,918 55,563,000 44,179,000 1.5 66,269 Comoros LI AFR 161,070 2.23 359 1,217,000 405,000 2.1 851 Congo, Dem. Rep. LI AFR 18,855,716 0.50 9,425 107,481,000 48,980,000 0.75 36,735 Congo, Rep. LMI AFR 2,056,826 0.53 1,096 5,362,000 3,678,000 0.75 2,759 Costa Rica UMI LCR 2,390,195 1.36 3,260 5,549,000 3,973,000 1.8 7,151 Cote d'Ivoire LI AFR 9,006,597 0.48 4,356 26,233,000 15,677,000 0.7 10,974 Croatia UMI ECA 2,539,903 0.29 740 4,274,000 2,735,000 0.8 2,188 Cuba UMI LCR 8,447,447 0.81 6,822 11,231,000 8,763,000 1.3 11,392 Cyprus HIC ECA 595,707 2.07 1,230 1,018,000 760,000 2.1 1,596 Czech Republic HIC OECD 7,547,813 1.10 8,326 9,910,000 7,575,000 1.65 12,499 Denmark HIC OECD 4,684,754 2.34 10,959 5,578,000 5,027,000 2.15 10,808 Dominica UMI LCR 50,793 1.24 63 69,000 55,000 1.6 88 Dominican Republic LMI LCR 5,625,356 1.18 6,658 12,172,000 9,523,000 1.5 14,285 Ecuador LMI LCR 7,599,288 1.13 8,603 16,074,000 12,027,000 1.5 18,041 Egypt, Arab Rep. LMI MENA 29,894,036 1.37 40,822 98,513,000 46,435,000 1.8 83,583 El Salvador LMI LCR 3,504,687 1.13 3,945 8,525,000 5,726,000 1.6 9,162 Eritrea LI AFR 878,184 0.50 438 7,684,000 2,368,000 0.7 1,658 Estonia HIC ECA 931,657 1.47 1,367 1,252,000 903,000 1.7 1,535 Ethiopia LI AFR 12,566,942 0.30 3,781 124,996,000 30,293,000 0.65 19,690 Fiji UMI EAP 339,328 2.10 712 905,000 557,000 2.1 1,170 ANNEX 81 ANNEX J (continued) MSW Generation by Country — Current Data and Projections for 2025 Current Available Data 2025 MSW Gen- Total MSW MSW Gen- Total MSW Income Country Region Total Urban eration Per Generation Total Urban eration Per Generation Level Population Capita (kg/ (tonnes/ Population Population Capita (kg/ (tonnes/day) capita/day) day) capita/day) Finland HIC OECD 3,301,950 2.13 7,030 5,464,000 3,805,000 2.1 7,991 France HIC OECD 47,192,398 1.92 90,493 65,769,000 53,659,000 2 107,318 Gabon UMI AFR 1,144,675 0.45 521 1,698,000 1,524,000 0.7 1,067 Gambia LI AFR 822,588 0.53 438 2,534,000 1,726,000 0.75 1,295 Georgia LMI ECA 2,316,296 1.69 3,904 3,945,000 2,272,000 1.85 4,203 Germany HIC OECD 60,530,216 2.11 127,816 80,341,000 61,772,000 2.05 126,633 Ghana LI AFR 11,680,134 0.09 1,000 31,993,000 19,713,000 0.5 9,857 Greece HIC OECD 6,755,967 2.00 13,499 11,236,000 7,527,000 2 15,054 Grenada UMI LCR 31,324 2.71 85 108,000 40,000 2.3 92 Guatemala LMI LCR 5,237,139 2.00 10,466 19,926,000 11,478,000 2 22,956 Guyana LMI LCR 215,946 5.33 1,151 683,000 230,000 3.5 805 Haiti LI LCR 3,227,249 1.00 3,233 12,305,000 7,966,000 1.4 11,152 Honduras LMI LCR 2,832,769 1.45 4,110 9,682,000 5,544,000 1.8 9,979 Hong Kong, China HIC EAP 6,977,700 1.99 13,890 8,305,000 8,305,000 2 16,610 Hungary HIC OECD 6,717,604 1.92 12,904 9,448,000 7,011,000 2 14,022 Iceland HIC OECD 280,148 1.56 438 337,000 314,000 1.7 534 India LMI SAR 321,623,271 0.34 109,589 1,447,499,000 538,055,000 0.7 376,639 Indonesia LMI EAP 117,456,698 0.52 61,644 271,227,000 178,731,000 0.85 151,921 Iran, Islamic Rep. LMI MENA 46,219,250 0.16 7,197 88,027,000 66,930,000 0.6 40,158 Ireland HIC OECD 2,589,698 3.58 9,260 5,275,000 3,564,000 3 10,692 Israel HIC MENA 5,179,120 2.12 10,959 8,722,000 8,077,000 2.1 16,962 Italy HIC OECD 39,938,760 2.23 89,096 58,079,000 42,205,000 2.05 86,520 Jamaica UMI LCR 1,353,969 0.18 247 2,908,000 1,733,000 0.9 1,560 Japan HIC OECD 84,330,180 1.71 144,466 121,614,000 86,460,000 1.7 146,982 Jordan LMI MENA 3,850,403 1.04 4,000 8,029,000 6,486,000 1.3 8,432 Kenya LI AFR 6,615,510 0.30 2,000 57,176,000 16,952,000 0.6 10,171 Korea, South HIC OECD 38,895,504 1.24 48,397 49,019,000 41,783,000 1.4 58,496 Kuwait HIC MENA 2,683,301 5.72 15,342 3,988,000 3,934,000 4 15,736 Lao PDR LI EAP 1,916,209 0.70 1,342 7,713,000 3,776,000 1.1 4,154 Latvia UMI ECA 1,549,569 1.03 1,600 2,072,000 1,476,000 1.45 2,140 Lebanon UMI MENA 3,244,163 1.18 3,836 4,784,000 4,275,000 1.7 7,268 Lesotho LMI AFR 461,534 0.50 230 2,211,000 850,000 0.8 680 Lithuania UMI ECA 2,256,263 1.10 2,474 3,102,000 2,193,000 1.5 3,290 Luxembourg HIC OECD 390,776 2.31 904 569,000 473,000 2.2 1,041 Macao, China HIC EAP 466,162 1.47 685 535,000 535,000 1.75 936 Macedonia, FYR LMI ECA 1,341,972 1.06 1,425 2,001,000 1,493,000 1.6 2,389 Madagascar LI AFR 4,653,890 0.80 3,734 29,954,000 11,350,000 1.1 12,485 Malawi LI AFR 2,288,114 0.50 1,151 21,353,000 6,158,000 0.8 4,926 Malaysia UMI EAP 14,429,641 1.52 21,918 33,769,000 27,187,000 1.9 51,655 Maldives LMI SAR 70,816 2.48 175 411,000 233,000 2.2 513 Mali LI AFR 3,900,064 0.65 2,534 20,589,000 8,987,000 0.95 8,538 Malta HIC MENA 384,809 1.78 685 431,000 416,000 2 832 Mauritania LI AFR 1,197,094 0.50 603 4,548,000 2,203,000 0.8 1,762 Mauritius UMI AFR 519,206 2.30 1,195 1,406,000 674,000 2.2 1,483 Mexico UMI LCR 79,833,562 1.24 99,014 124,695,000 102,258 1.75 179 Mongolia LMI EAP 1,370,974 0.66 904 3,112,000 1,965,000 0.95 1,867 Morocco LMI MENA 15,753,989 1.46 23,014 37,865,000 23,994,000 1.85 44,389 Mozambique LI AFR 7,706,816 0.14 1,052 28,954,000 14,493,000 0.5 7,247 Myanmar LI EAP 12,847,522 0.44 5,616 55,374,000 24,720,000 0.85 21,012 Namibia LMI AFR 708,907 0.50 356 2,560,000 1,226,000 0.9 1,103 Nepal LI SAR 3,464,234 0.12 427 38,855,000 10,550,000 0.7 7,385 82 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX J (continued) MSW Generation by Country — Current Data and Projections for 2025 Current Available Data 2025 MSW Gen- Total MSW MSW Gen- Total MSW Income Country Region Total Urban eration Per Generation Total Urban eration Per Generation Level Population Capita (kg/ (tonnes/ Population Population Capita (kg/ (tonnes/day) capita/day) day) capita/day) Netherlands HIC OECD 13,197,842 2.12 27,945 16,960,000 14,860,000 2.1 31,206 New Zealand HIC OECD 3,612,147 3.68 13,293 4,764,000 4,229,000 3 12,687 Nicaragua LMI LCR 2,848,165 1.10 3,123 7,075,000 4,478,000 1.5 6,717 Niger LI AFR 2,162,063 0.49 1,068 26,250,000 5,503,000 0.75 4,127 Nigeria LI AFR 73,178,110 0.56 40,959 210,129,000 126,634,000 0.8 101,307 Norway HIC OECD 3,605,500 2.80 10,082 5,228,000 4,187,000 2.3 9,630 Oman HIC MENA 1,629,404 0.70 1,142 3,614,000 2,700,000 1.15 3,105 Pakistan LI SAR 60,038,941 0.84 50,438 224,956,000 104,042,000 1.05 109,244 Panama UMI LCR 2,008,299 1.21 2,438 4,267,000 3,501,000 1.65 5,777 Paraguay LMI LCR 3,052,320 0.21 630 8,026,000 5,584,000 0.6 3,350 Peru LMI LCR 18,678,510 1.00 18,740 34,148,000 25,593,000 1.4 35,830 Philippines LMI EAP 58,654,205 0.50 29,315 115,878,000 86,418,000 0.9 77,776 Poland UMI ECA 23,398,400 0.88 20,630 36,337,000 23,236,000 1.2 27,883 Portugal HIC OECD 6,162,205 2.21 13,616 10,712,000 7,389,000 2.15 15,886 Qatar HIC MENA 759,577 1.33 1,014 1,102,000 1,066,000 1.7 1,812 Romania UMI ECA 11,648,240 1.04 12,082 19,494,000 11,783,000 1.45 17,085 Russian Federation UMI ECA 107,386,402 0.93 100,027 128,193,000 96,061,000 1.25 120,076 Rwanda LI AFR 1,573,625 0.52 822 15,220,000 3,831,000 0.85 3,256 Sao Tome and Principe LI AFR 88,673 0.49 44 216,000 155,000 0.9 140 Saudi Arabia HIC MENA 15,388,239 1.30 20,000 34,797,000 29,661,000 1.7 50,424 Senegal LI AFR 4,693,019 0.52 2,438 17,999,000 8,992,000 0.85 7,643 Serbia UMI ECA 3,830,299 0.79 3,041 9,959,000 5,814,000 1.05 6,105 Seychelles UMI AFR 43,172 2.98 129 94,000 60,000 2.5 150 Sierra Leone LI AFR 2,029,398 0.45 904 8,639,000 3,949,000 0.85 3,357 Singapore HIC EAP 4,839,400 1.49 7,205 5,104,000 5,104,000 1.8 9,187 Slovak Republic HIC OECD 3,036,442 1.37 4,164 5,308,000 3,300,000 1.6 5,280 Slovenia HIC ECA 986,862 1.21 1,192 1,941,000 958,000 1.7 1,629 Solomon Islands LI EAP 50,992 4.30 219 705,000 183,000 4 732 South Africa UMI AFR 26,720,493 2.00 53,425 52,300,000 36,073,000 2 72,146 Spain HIC OECD 33,899,073 2.13 72,137 46,623,000 37,584,000 2.1 78,926 Sri Lanka LMI SAR 2,953,410 5.10 15,068 20,328,000 3,830,000 4 15,320 St. Kitts and Nevis UMI LCR 15,069 5.45 82 61,000 23,000 4 92 St. Lucia UMI LCR 44,119 4.35 192 195,000 64,000 4 256 St. Vincent and the UMI LCR 48,255 1.70 82 125,000 69,000 1.85 128 Grenadines Sudan LMI AFR 12,600,333 0.79 10,000 54,267,000 30,921,000 1.05 32,467 Suriname UMI LCR 343,331 1.36 466 482,000 389,000 1.6 622 Swaziland LMI AFR 270,983 0.51 137 1,242,000 417,000 0.85 354 Sweden HIC OECD 7,662,130 1.61 12,329 9,854,000 8,525,000 1.85 15,771 Switzerland HIC OECD 5,490,214 2.61 14,329 7,978,000 6,096,000 2.3 14,021 Syrian Arab Republic LMI MENA 9,109,737 1.37 12,493 27,519,000 16,890,000 1.7 28,713 Tajikistan LI ECA 1,653,091 0.89 1,479 8,929,000 2,774,000 1.2 3,329 Tanzania LI AFR 9,439,781 0.26 2,425 59,989,000 21,029,000 0.55 11,566 Thailand LMI EAP 22,453,143 1.76 39,452 68,803,000 29,063,000 1.95 56,673 Togo LI AFR 2,390,840 0.52 1,233 9,925,000 5,352,000 0.85 4,549 Tonga LMI EAP 22,162 3.71 82 112,000 37,000 3.5 130 Trinidad and Tobago HIC LCR 144,645 14.40 2,082 1,401,000 291000 10 2,910 Tunisia LMI MENA 6,063,259 0.81 4,932 12,170,000 8,909,000 1.15 10,245 Turkey UMI ECA 48,846,780 1.77 86,301 89,557,000 67,981,000 2 135,962 Turkmenistan LMI ECA 2,061,980 0.98 2,027 6,068,000 3,485,000 1.25 4,356 ANNEX 83 ANNEX J (continued) MSW Generation by Country — Current Data and Projections for 2025 Current Available Data 2025 MSW Gen- Total MSW MSW Gen- Total MSW Income Country Region Total Urban eration Per Generation Total Urban eration Per Generation Level Population Capita (kg/ (tonnes/ Population Population Capita (kg/ (tonnes/day) capita/day) day) capita/day) Uganda LI AFR 3,450,140 0.34 1,179 54,011,000 9,713,000 0.65 6,313 United Arab Emirates HIC MENA 2,526,336 1.66 4,192 6,268,000 5,092,000 2 10,184 United Kingdom HIC OECD 54,411,080 1.79 97,342 65,190,000 59,738,000 1.85 110,515 United States HIC OECD 241,972,393 2.58 624,700 354,930,000 305,091,000 2.3 701,709 Uruguay UMI LCR 3,025,161 0.11 329 3,548,000 3,333,000 0.6 2,000 Vanuatu LMI EAP 33,430 3.28 110 328,000 113,000 3 339 Venezuela, RB UMI LCR 22,342,983 1.14 25,507 35,373,000 34,059,000 1.5 51,089 Vietnam LI EAP 24,001,081 1.46 35,068 106,357,000 40,505,000 1.8 72,909 Zambia LI AFR 4,010,708 0.21 842 16,539,000 6,862,000 0.55 3,774 Zimbabwe LI AFR 4,478,555 0.53 2,356 15,969,000 7,539,000 0.7 5,277 Summary by Income Level Current Available Data Projections for 2025 Number of Total Urban Urban MSW Generation Projected Population Projected Urban MSW Generation Income Level Countries Population Per Capita Total Total Population Urban Population Per Capita Total Included (millions) (kg/capita/day) (tonnes/day) (millions) (millions) (kg/capita/day) (tonnes/day) Lower Income 38 343 0.60 204,802 1,637 676 0.86 584,272 Lower Middle Income 42 1,293 0.78 1,012,321 4,011 2,080 1.26 2,618,804 Upper Middle Income 35 572 1.16 665,586 888 619 1.59 987,039 High Income 46 774 2.13 1,649,546 1,112 912 2.06 1,879,590 Total 161 2,982 1.19 3,532,255 7,648 4,287 1.42 6,069,705 Summary by Region Current Available Data Projections for 2025 Number of Urban MSW Generation Projected Population Projected Urban MSW Generation Region Countries Total Urban Included Population Per Capita (kg/ Total Per Capita (kg/ Total (tonnes/ (millions) Total (millions) Urban (millions) capita/day) (tonnes/day) capita/day) day) AFR 42 261 0.65 169,120 1,153 518 0.85 441,840 EAP 17 777 0.95 738,959 2,124 1,230 1.52 1,865,380 ECA 19 227 1.12 254,389 339 240 1.48 354,811 LCR 33 400 1.09 437,545 682 466 1.56 728,392 MENA 16 162 1.07 173,545 379 257 1.43 369,320 OECD 27 729 2.15 1,566,286 1,032 842 2.07 1,742,417 SAR 7 426 0.45 192,411 1,939 734 0.77 567,545 Total 161 2,982 1.19 3,532,255 7,648 4,287 1.42 6,069,705 84 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX K MSW Collection Rates by Country Country Income Region Collection (%) Urban/Total Albania LMI ECA 77 T Algeria UMI MENA 92 U Andorra HIC OECD 100 T Antigua and Barbuda HIC LCR 95 T Armenia LMI ECA 80 T Austria HIC OECD 100 T Belarus UMI ECA 100 T Belgium HIC OECD 100 T Belize LMI LCR 50 T Benin LI AFR 23 T Brazil UMI LCR 83 T Bulgaria UMI ECA 81 T Cambodia LI EAP 75 U Canada HIC OECD 99 T Colombia UMI LCR 98 T Comoros LI AFR 20 T Costa Rica UMI LCR 74 T Croatia HIC ECA 92 T Cuba UMI LCR 76 T Czech Republic HIC OECD 100 T Denmark HIC OECD 100 T Dominica UMI LCR 94 T Dominican Republic UMI LCR 69 T Ecuador LMI LCR 81 T Egypt, Arab Rep. LMI MENA 30-95 U El Salvador LMI LCR 71 T Estonia HIC ECA 79 T Finland HIC OECD 100 T France HIC OECD 100 T Georgia LMI ECA 60 T Germany HIC OECD 100 T Ghana LI AFR 85 U Greece HIC OECD 100 T Grenada UMI LCR 100 T Guatemala LMI LCR 72 T Guyana LMI LCR 89 T Haiti LI LCR 11 T Honduras LMI LCR 68 T Hong Kong, China HIC EAP 100 T Hungary HIC OECD 90 T Iceland HIC OECD 100 T Indonesia LMI EAP 80 U Iraq LMI MENA 56 T Ireland HIC OECD 76 T Italy HIC OECD 100 T Jamaica UMI LCR 62 T Japan HIC OECD 100 T Jordan LMI MENA 95+ U Korea, South HIC OECD 99 T Latvia UMI ECA 50 T Lebanon UMI MENA 100 U Luxembourg HIC OECD 100 T ANNEX 85 ANNEX K (continued) MSW Collection Rates by Country Country Income Region Collection (%) Urban/Total Macao, China HIC EAP 100 T Madagascar LI AFR 18 T Mali LI AFR 40 T Malta HIC MENA 100 T Marshall Islands LMI EAP 60 T Mauritius UMI AFR 98 T Mexico UMI LCR 91 T Monaco HIC OECD 100 T Morocco LMI MENA 72-100 T Nepal LI SAR 94 U Netherlands HIC OECD 100 T Nicaragua LMI LCR 73 T Norway HIC OECD 99 T Panama UMI LCR 77 T Paraguay LMI LCR 51 T Peru UMI LCR 74 T Portugal HIC OECD 100 T Romania UMI ECA 90 T Senegal LI AFR 21 T Serbia UMI ECA 65 T Seychelles UMI AFR 95 T Sierra Leone LI AFR 33-55 U Singapore HIC EAP 100 T Slovak Republic HIC OECD 100 T Slovenia HIC ECA 93 T St. Kitts and Nevis UMI LCR 98 T St. Lucia UMI LCR 100 T St. Vincent and the Grenadines UMI LCR 91 T Suriname UMI LCR 80 T Sweden HIC OECD 100 T Switzerland HIC OECD 99 T Syrian Arab Republic LMI MENA 80 U Tanzania LI AFR 48 U Trinidad and Tobago HIC LCR 100 T Tunisia LMI MENA 95 U Turkey UMI ECA 77 T Uganda LI AFR 39 U United Kingdom HIC OECD 100 T United States HIC OECD 100 T Uruguay UMI LCR 86 T Venezuela, RB UMI LCR 86 T West Bank and Gaza LMI MENA 85 U Zambia LI AFR 20 T 86 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX K (continued) MSW Collection Rates by Country Summary by Income Level Number MSW Collection (%) Income Level of Countries Included Lower Limit Upper Limit Lower Income 13 10.62 55.00 Lower Middle Income 20 50.20 95+ Upper Middle Income 27 50.00 100.00 High Income 35 76.00 100.00 Total 95 Summary by Region Number MSW Collection (%) Region of Countries Included Lower Limit Upper Limit AFR 12 17.70 55.00 EAP 6 60.00 100.00 ECA 12 50.00 100.00 LCR 28 10.62 100.00 MENA 10 55.60 95+ OECD 26 76.00 100.00 SAR 1 94.00 Total 95 ANNEX 87 ANNEX L MSW Disposal Methods by Country Landfills Compost Recycled Country Income Region Dumps (%) WTE (%) Other (%) (%) (%) (%) Algeria UMI MNA 96.80 0.20 1.00 2.00 — — Antigua and HIC LCR 99.00 1.00 — — Barbuda Armenia LMI ECA — 100.00 — — — — Australia HIC OECD — 69.66 — 30.34 — — Austria HIC OECD — 6.75 44.72 26.54 21.10 0.90 Belarus UMI ECA — 96.00 4.00 — — — Belgium HIC OECD — 11.57 22.77 31.10 34.32 — Belize LMI LCR — 100.00 — — — — Bulgaria UMI ECA — 82.90 — — — 17.10 Cambodia LI EAP 100.00 — — — — — Cameroon LMI AFR 95.00 — — 5.00 — — Canada HIC OECD — — 12.48 26.78 — 60.74 Chile UMI LCR — 100.00 — — — — Colombia UMI LCR 54.00 46.00 — — — — Costa Rica UMI LCR 22.37 71.95 — 0.29 — 5.39 Croatia HIC ECA — 69.50 0.90 2.40 — 27.20 Cuba UMI LCR — 100.00 11.10 4.80 — — Cyprus HIC ECA — 87.20 — — — 12.80 Czech Republic HIC OECD — 79.78 3.24 1.27 13.97 1.74 Denmark HIC OECD — 5.09 15.28 25.57 54.04 0.03 Dominica UMI LCR — 100.00 — — — — Greece HIC OECD — 92 — 8 — — Grenada UMI LCR — 90 — — — 10 Guatemala LMI LCR — 22 — — — 78 Guyana LMI LCR 37 59 — — — 4 Haiti LI LCR 24 — — — — 76 Hong Kong, China HIC EAP — 55 — 45 — — Hungary HIC OECD — 90 1 3 6 0 Iceland2 HIC OECD — 72 9 16 9 — Ireland HIC OECD — 66 — 34 — — Israel HIC MENA — 90 — 10 — — Italy HIC OECD — 54 33 — 12 — Jamaica UMI LCR — 100 — — — — Japan HIC OECD — 3 — 17 74 6 Jordan3 LMI MENA — 85 — — — 15 Korea, South HIC OECD — 36 — 49 14 — Kyrgyz Republic LI ECA — 100 — — — — Latvia UMI ECA 60 40 — — — — Lebanon UMI MENA 37 46 8 8 — 1 Lithuania UMI ECA — 44 — 4 2 50 Luxembourg HIC OECD — 19 19 23 39 — Macao, China2 HIC EAP — 21 — — — 100 Madagascar2 LI AFR — 97 4 — — — Malta HIC MENA — 88 — — — 13 Marshall Islands LMI EAP — — 6 31 — 63 Mauritius UMI AFR — 91 — 2 — — Mexico UMI LCR — 97 — 3 — — Monaco4 HIC OECD — 27 — 4 — 132 Morocco LMI MENA 95 1 — 4 — — Netherlands HIC OECD — 2 23 25 32 17 New Zealand HIC OECD — 85 — 15 — — Nicaragua LMI LCR 34 28 — — — 38 88 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX L (continued) MSW Disposal Methods by Country Landfills Compost Recycled Country Income Region Dumps (%) WTE (%) Other (%) (%) (%) (%) Niger LI AFR — 64 — 4 — 32 Norway HIC OECD — 26 15 34 25 0 Panama UMI LCR 20 56 — — — 24 Paraguay LMI LCR 42 44 — — — 14 Peru UMI LCR 19 66 — — — 15 Poland UMI ECA — 92 3 4 0 — Portugal5 HIC OECD — 64 6 9 21 — Romania UMI ECA — 75 — — — 25 Singapore6 HIC EAP — 15 — 47 — 49 Slovak Republic HIC OECD — 78 1 1 12 7 Slovenia HIC ECA — 86 — — — 14 Spain HIC OECD — 52 33 9 7 — St. Kitts and UMI LCR — 100 — — — — Nevis St. Lucia UMI LCR — 70 — — — 30 St. Vincent and UMI LCR — 78 — — — 22 the Grenadines Suriname UMI LCR 100 — — — — 0 Sweden HIC OECD — 5 10 34 50 1 Switzerland HIC OECD — 1 16 34 50 — Syrian Arab LMI MENA >60 <25 <5 <15 — — Republic Thailand LMI EAP — — — 14 — 85 Trinidad and HIC LCR 6 — — — 94 Tobago Tunisia LMI MENA 45 50 0 5 — — Turkey UMI ECA 66 30 1 — 0 3 Uganda LI AFR — 100 — — — — United Kingdom HIC OECD — 64 9 17 8 1 United States HIC OECD — 54 8 24 14 — Uruguay UMI LCR 32 3 — — — 66 Venezuela, RB UMI LCR 59 — — — — 41 West Bank and LMI MENA 69 30 — 1 — — Gaza NOTES: For sources and year of data, see Annex C. 1. All waste is taken to landfills, where the waste is classfied and then sent to different destinations, such as recycling and composting plants. 2. Percentages may not add up to 100 because residues of some treatments, such as incineration and composting, are landfilled. 3. Landfilling refers to all waste disposed on land. 4. Recycled amount refers to both recycled and composted waste; other includes wastes imported from France for incineration with energy recovery. 5. Landfill includes non-controlled dumping sites. 6. MSW includes industrial waste from manufacturing industries; landfill includes ash from incineration. ANNEX 89 ANNEX L (continued) MSW Disposal Methods by Country Summary by Income Level Income Level Number of Countries Included Lower Income 7 Lower Middle Income 17 Upper Middle Income 27 High Income 39 Total 90 Summary by Region Region Number of Countries Included AFR 6 EAP 6 ECA 13 LCR 27 MENA 10 OECD 0 SAR 28 Total 90 90 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX M MSW Composition by Country Income Country Region Organic (%) Paper (%) Plastic (%) Glass (%) Metal (%) Other (%) Level Albania LMI ECA 38 10 8 5 5 34 Algeria UMI MENA 70 10 5 1 2 12 Andorra HIC OECD 19 26 14 11 3 27 Argentina UMI LCR 40 24 14 5 2 15 Armenia LMI ECA 51 12 10 9 5 14 Australia HIC OECD 47 23 4 7 5 13 Austria HIC OECD 35 22 11 8 5 19 Bangladesh LI SAR 71 5 7 — — 16 Belarus UMI ECA 29 28 10 13 7 13 Belgium HIC OECD 39 17 5 7 3 29 Belize LMI LCR 60 20 5 5 5 5 Benin LI AFR 52 3 7 2 2 1 Bhutan LMI SAR 58 17 13 4 1 7 Bolivia LMI LCR 24 6 8 2 1 59 Brazil UMI LCR 61 15 15 3 2 5 Brunei Darussalam HIC EAP 44 22 2 4 5 13 Cambodia LI EAP 55 3 10 8 7 17 Cameroon LMI AFR 48 4 5 4 5 35 Canada HIC OECD 24 47 3 6 13 8 Chile UMI LCR 50 19 10 2 2 4 Colombia UMI LCR 54 11 10 5 2 18 Costa Rica UMI LCR 50 21 18 2 2 7 Croatia HIC ECA 46 20 12 7 4 11 Cuba UMI LCR 69 12 10 5 2 3 Cyprus HIC ECA 38 27 11 1 9 13 Czech Republic HIC OECD 18 8 4 4 2 63 Denmark HIC OECD 29 27 1 5 6 32 Dominican Republic UMI LCR 39 14 36 1 1 10 Egypt, Arab Rep. LMI MENA 60 10 12 3 2 13 Ethiopia LI AFR 88 4 2 1 1 4 Fiji UMI EAP 68 15 8 3 3 4 Finland HIC OECD 33 40 10 5 5 7 France HIC OECD 32 20 9 10 3 26 Gambia LI AFR 35 10 — 2 2 51 Georgia LMI ECA 39 34 3 3 5 16 Germany HIC OECD 14 34 22 12 5 12 Ghana LI AFR 64 3 4 — 1 28 Greece HIC OECD 47 20 9 5 5 16 Guatemala LMI LCR 44 18 13 5 4 16 Guinea LI AFR 58 9 4 1 1 27 Guyana LMI LCR 49 24 10 2 2 12 Hong Kong, China HIC EAP 38 26 19 3 2 12 Hungary HIC OECD 29 15 17 2 2 35 Iceland HIC OECD 26 26 17 4 3 24 India LMI SAR 35 3 2 1 — 59 Indonesia LMI EAP 62 6 10 9 8 4 Iran, Islamic Rep. LMI MENA 43 22 11 2 9 13 Ireland HIC OECD 25 31 11 5 4 23 Israel HIC MENA 40 25 13 3 3 16 Italy HIC OECD 29 28 5 13 2 22 Jamaica UMI LCR 57 13 18 5 4 3 Japan HIC OECD 26 46 9 7 8 12 ANNEX 91 ANNEX M (continued) MSW Composition by Country Income Country Region Organic (%) Paper (%) Plastic (%) Glass (%) Metal (%) Other (%) Level Jordan LMI MENA 62 11 16 2 2 6 Korea, South HIC OECD 28 24 8 5 7 28 Lao PDR LI EAP 46 6 10 8 12 21 Latvia UMI ECA 57 — — — — 43 Lebanon UMI MENA 63 18 7 5 3 4 Liberia LI AFR 43 10 13 1 2 31 Luxembourg HIC OECD 45 22 1 12 4 16 Macao, China HIC EAP 4 4 24 4 1 63 Macedonia, FYR UMI ECA 20 24 11 5 3 37 Madagascar LI AFR 52 4 1 1 1 41 Malaysia UMI EAP 62 7 12 3 6 10 Mali LI AFR 18 4 2 1 4 1 Marshall Islands LMI EAP 20 15 15 5 20 22 Mauritius UMI AFR 70 12 9 2 3 4 Mexico UMI LCR 51 15 6 6 3 18 Morocco LMI MENA 69 19 4 4 3 2 Mozambique LI AFR 69 12 10 3 2 4 Myanmar LI EAP 54 8 16 7 8 7 Nepal LI SAR 80 7 3 3 1 7 Netherlands HIC OECD 35 26 19 4 4 12 New Zealand HIC OECD 56 21 8 3 7 5 Niger LI AFR 38 2 2 — 1 57 Nigeria LMI AFR 57 11 18 5 5 4 Norway HIC OECD 30 33 9 4 4 20 Pakistan LMI SAR 67 5 18 2 — 7 Panama UMI LCR 44 25 11 8 5 7 Peru UMI LCR 55 7 4 3 2 28 Philippines LMI EAP 41 19 14 3 5 18 Poland UMI ECA 38 10 10 12 8 23 Portugal HIC OECD 34 21 11 7 4 23 Romania UMI ECA 46 11 3 11 5 24 Senegal LI AFR 44 10 3 1 3 39 Serbia UMI ECA 5 37 12 10 5 31 Sierra Leone LI AFR 85 — — — — 15 Singapore HIC EAP 44 28 12 4 5 7 Slovak Republic HIC OECD 38 13 7 8 3 31 Solomon Islands LMI EAP 65 6 17 5 6 2 Spain HIC OECD 49 21 12 8 4 7 Sri Lanka LMI SAR 76 11 6 1 1 5 St. Vincent and the Grenadines UMI LCR 34 32 12 8 6 8 Sweden HIC OECD — 68 2 11 2 17 Switzerland HIC OECD 29 20 15 4 3 29 Syrian Arab Republic LMI MENA 65 10 12 4 2 7 Thailand LMI EAP 48 15 14 5 4 14 Togo LI AFR 46 4 10 2 2 35 Tonga LMI EAP 47 31 5 3 8 5 Trinidad and Tobago HIC LCR 14 32 24 3 16 12 Tunisia LMI MENA 68 9 11 2 4 6 Turkey UMI ECA 40-65 7-18 5-14 2-6 1-6 7-24 Uganda LI AFR 78 3 1 1 2 16 United States HIC OECD 25 34 12 5 8 16 Uruguay UMI LCR 54 20 11 3 5 8 92 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX M (continued) MSW Composition by Country Income Country Region Organic (%) Paper (%) Plastic (%) Glass (%) Metal (%) Other (%) Level Vanuatu LMI EAP 71 11 8 3 4 3 Vietnam LI EAP 60 2 16 7 6 9 West Bank and Gaza LMI MENA 61 14 7 3 2 13 Zambia LI AFR 50 5 5 2 2 37 Zimbabwe LI AFR 40 21 20 4 4 11 NOTE: For sources and year of data, see Annex C. Summary by Income Level Number of Organic (%) Paper (%) Plastic (%) Glass (%) Metal (%) Other (%) Income Level Countries Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Included Limit Limit Limit Limit Limit Limit Limit Limit Limit Limit Limit Limit Lower Income 22 18 88 2 21 1 20 1 8 1 12 1 57 Lower Middle 27 20 76 3 34 2 18 1 9 1 20 2 59 Income Upper Middle 25 5 70 7 37 3 36 1 13 1 8 3 43 Income High Income 35 4 56 4 68 1 24 1 13 1 16 5 63 Total 109 Summary by Region Number of Organic (%) Paper (%) Plastic (%) Glass (%) Metal (%) Other (%) Region Countries Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower Upper Included Limit Limit Limit Limit Limit Limit Limit Limit Limit Limit Limit Limit AFR 19 18 88 2 21 1 20 1 5 1 5 1 57 EAP 17 4 71 2 31 2 24 3 9 1 20 2 63 ECA 12 5 65 10 37 3 12 1 13 3 9 11 43 LCR 18 14 69 6 32 4 36 1 8 1 16 3 59 MENA 10 40 70 9 25 4 16 1 5 2 9 2 16 OECD 6 14 56 8 68 1 22 2 13 2 13 5 63 SAR 27 35 80 3 17 2 18 1 4 1 1 5 59 Total 109 ANNEX 93 ANNEX N IPCC Classification of MSW Composition Paper/ Food Rubber/ Region Card- Wood Textiles Plastic Metal Glass Other Waste Leather board Asia Eastern Asia 26.2 18.8 3.5 3.5 1 14.3 2.7 3.1 7.4 South-Central Asia 40.3 11.3 7.9 2.5 0.8 6.4 3.8 3.5 21.9 South-Eastern Asia 43.5 12.9 9.9 2.7 0.9 7.2 3.3 4 16.3 Western Asia & Middle 41.1 18 9.8 2.9 0.6 6.3 1.3 2.2 5.4 East Africa Eastern Africa 53.9 7.7 7 1.7 1.1 5.5 1.8 2.3 11.6 Middle Africa 43.4 16.8 6.5 2.5 4.5 3.5 2 1.5 Northern Africa 51.1 16.5 2 2.5 4.5 3.5 2 1.5 Southern Africa 23 25 15 Western Africa 40.4 9.8 4.4 1 3 1 Europe Eastern Europe 30.1 21.8 7.5 4.7 1.4 6.2 3.6 10 14.6 Northern Europe 23.8 30.6 10 2 13 7 8 Southern Europe 36.9 17 10.6 Western Europe 24.2 27.5 11 Oceania Australia & New Zealand 36 30 24 Rest of Oceania 67.5 6 2.5 America North America 33.9 23.2 6.2 3.9 1.4 8.5 4.6 6.5 9.8 Central America 43.8 13.7 13.5 2.6 1.8 6.7 2.6 3.7 12.3 South America 44.9 17.1 4.7 2.6 0.7 10.8 2.9 3.3 13 Caribbean 46.9 17 2.4 5.1 1.9 9.9 5 5.7 3.5 NOTES: 1. Data are based on weight of wet waste of MSW without industrial waste at generation around year 2000. 2. The region-specific values are calculated from national, partly incomplete composition data. The percentages given may therefore not add up to 100%. Some regions may not have data for some waste types - blanks in the table represent missing data. 94 URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS ANNEX O The Global City Indicators Program No single standard or comprehensive system to measure and monitor city performance and urban quality of life exists today. The Global City Indicators Program, driven by cities themselves, fills this important gap. Through the collection and analysis of city data in a comparative format and data domain, elected officials, city managers and the public will be able to monitor the performance of their cities over time based on a core set of indicators. The Global City Indicators Program (GCIP) is a decentralized, city-led initiative that enables cities to measure, report, and improve their performance and quality of life, facilitate capacity building, and share best practices through an easy-to-use web portal. GCIP assists cities in providing support to decision makers in making informed policy decisions, in addition to enhancing government accountability to the public. Managing cities effectively and efficiently is critical and becoming more complex as population growth and economic development are taking place in urban areas. Today’s big challenges, such as poverty reduction, economic development, climate change, and the creation and maintenance of an inclusive and peaceful society, will all need to be met through the responses of cities. So too will the day-to-day challenges of garbage collection, responding to the house on fire and larger disasters, and facilitating the provision of water, electricity, education, health care, and the myriad of other services that make life more productive and enjoyable. The pace of change within and among cities is increasing. Indicators need to be anchored on baseline data and need to be sufficiently broad to capture social and economic aspects of urban development. Standardized indica- tors are essential in order to measure the performance of cities, capture trends and developments, and support cities in becoming global partners. The Global City Indicators Program is organized into two broad categories: city services (which includes services typically provided by city governments and other entities) and quality of life (which includes critical contributors to overall quality of life, though the city government may have little direct control on these activities). The two categories are structured around 18 themes. The Global City Indicators Program process encompasses monitoring, reporting, verifying, and amending the indicators. Similar to a Wikipedia approach, the Global City Indicators Program is a dynamic web-based resource (www.cityindicators.org) that allows participating cities across the world to standardize the collection of their indicators and analyze and share the results and best practices on service delivery and quality of life. The Global City Indicators Program is run by the Global City Indicators Facility based at the University of Toronto, which manages the development of indicators and assists cities in joining the Program. A Board of Directors and an Advisory Board oversee the Global City Indicators Facility and provide technical and advisory support to the Facility. The Boards are made up of representatives from cities, international organizations, and academia. The Global City Indicators Program was initiated by the World Bank through funding from the Government of Japan. For more information, please contact the Global City Indicators Facility at: 170 Bloor Street West, Suite 1100 Toronto, Ontario M5S 1T9 Canada Tel + 416-966-2368 Fax +416-966-0478 www.cityindicators.org cityindicators@daniels.utoronto.ca REFERENCES 95 References Achankeng, Eric. 2003. Globalization, Urbanization and CEROI. 2001. State of the Environment in Dushanbe. Municipal Solid Waste Management in Africa. African Cities Environment Reports on the Internet. UNEP Studies Association of Australasia and the Pacific and GIRD-Adrenal. 2003 Conference. Chalam, Philippe and Catherine Gaillochet. 2009. From Alam, R., M. Chowdhury, G. Hasan, B. Karanjit, L. Waste to Resources: World Waste Survey, Economica: Paris. Shrestha. 2008. “Generation, storage, collection and transportation of municipal solid waste — A case City of Cape Town. 2008. 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Experiences from the Urban 2004 — Solid Waste. Jointly prepared by World Bank, Waste Expertise Programme (1995-2001). WASTE. Vietnam Ministry of Environment and Natural Re- sources, and CIDA. For more information about the Urban Development Series, contact: Urban Development and Local Government Unit Sustainable Development Network The World Bank 1818 H Street, NW Washington, DC, 20433 USA Email: urbanhelp@worldbank.org Website: www.worldbank.org/urban March 2012, No. 15