ACCESSING ASIA - Clean Air Asiacleanairasia.org/wp-content/uploads/portal/files/documents/... · Clean Air Asia was established as the premier air quality network for Asia by the

ACCESSING

Air Pollution and Greenhouse Gas Emissions Indicators for Road Transport and Electricity

ASIA

ACCESSING


ASIAAccessing Asia: Air Pollution and Greenhouse Gas Emissions Indicators for Road Transport and Electricity is one of the outputs in the Knowledge Partnership for Measuring Air Pollution and GHG Emissions in Asia.

This presents the first benchmark of air pollutant (as particulate matter, PM) and GHG (as CO2) emissions for 13 countries across Asia for road transport and electricity generation.

This biennial publication contains:

• Road transport CO2 and PM emissionsestimates for Asia and individualcountries from 2002 to 2010.

• Electricity CO2 emissions estimates forAsia and individual countries from2000 to 2009.

• Selected drivers of emissions fromroad transport and electricity.

This work is supported by the World Bank Development Grant Facility and the Clean Air Asia Knowledge Partnership.

ACCESSING


ASIA2012

ABOUT

www.cleanairasia.org | www.CitiesACT.org | www.baqconference.org

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Clean Air Asia (formerly Clean Air Initiative for Asian Cities) promotes better air quality and livable cities by translating knowledge to policies and actions that reduce air pollution and greenhouse gas emissions from transport, energy and other sectors.

Clean Air Asia was established as the premier air quality network for Asia by the Asian Development Bank, World Bank and USAID in 2001, and operates since 2007 as an independent non-profit organization. Clean Air Asia has offices in Manila, Beijing, and Delhi, networks in eight Asian countries (China, India, Indonesia, Nepal, Pakistan, Philippines, Sri Lanka, and Vietnam) and is a UN recognized partnership of 230 organizations in Asia and worldwide.

Clean Air Asia uses knowledge and partnerships to enable Asia’s 1000+ cities and national governments understand the problems and identify effective policies and measures. Our four programs are: Air Quality & Climate Change, Low Emissions Urban Development, Clean Fuels and Vehicles, and Green Freight and Logistics. The Better Air Quality (BAQ) conference is Asia’s leading event on air quality and climate change, bringing together more than 500 practitioners, policy makers, and experts.

The Knowledge Partnership improves access to air quality and climate change data. It aims to further enrich policy development interventions relevant to energy, transport and urban development in Asia.

The Partnership includes:

• World Bank Development Grant Facility (DGF)• Asian Development Bank (ADB)• China Sustainable Energy Program (Energy Foundation)• Cities Development Initiative for Asia (CDIA)• Clean Air Asia (formerly Clean Air Initiative for Asian Cities)• German International Development Cooperation (GIZ)• Institute for Global Environmental Strategies (IGES)• Institution for Transport Policy Studies (ITPS)• Institute for Transportation and Development Policy (ITDP)• Transport Research Laboratory (TRL)• United Nations Centre for Regional Development (UNCRD)• Veolia Environnement S.A.

Knowledge Partnership on Air Pollution and GHG Data and Indicators for Transport and Energy in Asia

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COPYRIGHT© 2012 Clean Air Initiative for Asian Cities Center Inc.

All rights reserved. Published 2012.

Clean Air Asia, 2012. “Accessing Asia: Air Pollution and Greenhouse Gas Emissions Indicators for Road Transport and Electricity." Pasig City, Philippines.

This publication may be reproduced in whole or in part in any form for educational or non-profit purposes without special permission from the copyright holder, provided that acknowledgment of the source is made. Clean Air Asia would appreciate receiving a copy of any publication that uses this Clean Air Asia publication as a source. No use of this publication may be made for resale or for any other commercial purpose whatsoever, without prior permission in writing from Clean Air Asia.

Disclaimer

The views expressed in this publication are those of Clean Air Asia staff, consultants, and management. These views do not necessarily reflect the views of the Board of Trustees of Clean Air Asia, the World Bank, and other Knowledge Partners. Clean Air Asia does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use.

The boundaries, colors, denominations, and other information shown on any map in this report do not imply any judgment or endorsement on the part of Clean Air Asia concerning the delimitation or the legal status of any territory or boundaries. In no event will Clean Air Asia be liable for any form of damage arising from the application or misapplication of this report.

Dinna Louise Dayao copy-edited the report.

Earl Paulo Diaz, Dana Raissa de Guzman, and Maria Katherina Patdu designed the cover page, infographics, and lay-out.

Photo credits: Clean Air Asia

Clean Air Asia Center Unit 3505 Robinsons-Equitable Tower ADB Avenue, Pasig City, 1605 Metro Manila, Philippines [email protected]

Clean Air Asia China Office901A, Reignwood Building No.8 YongAnDongLi Jianguomenwai Avenue Beijing 100022 China [email protected]

Clean Air Asia India OfficeRegus Elegance Elegance Tower, Jasola New Delhi – 110025, India [email protected]

Country NetworksChina / India / Indonesia / Nepal / Pakistan / Philippines / Sri Lanka / Vietnam

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FOREWORDOver the years, Clean Air Asia has become a trusted partner of cities and governments in managing air pollution and greenhouse gas emissions, aiming for blue skies and more livable cities. One reason is that we provide reliable analysis, data and tools to help them understand the problem and develop effective policies. A second reason is that we create platforms for them to work together with the right experts and organizations.

We were the first to show that air quality in Asian cities had improved since the 1990s. Starting with data for only 20 cities a decade ago, we now have air quality data for over 400 cities in 22 countries. The CitiesACT database is the main source for Asian cities data of the World Health Organization’s Global Outdoor Air Pollution in Cities database. More data were collected as our focus expanded to include greenhouse gases and as we went deeper into transport and energy sectors as major emission sources. Tools were developed to estimate emissions from transport projects and even entire cities, to then identify what urban planning decisions, policies and measures would be most effective in slowing emissions growth.

We came to realize that a major stumbling block is that data is often lacking or of low quality. Many development agencies and other international organizations shared this view and partnered to improve access to data and indicators for better policy and development decisions.

We thank the World Bank for providing a development grant for this partnership, our knowledge partners, our Country Networks, and other experts and organizations who supported data collection, development of guidelines and review of this report.

Through our Low Emissions Urban Development program, we will continue to improve knowledge and exchange on land use, transport and energy, and to mainstream low emissions transport strategies in policy and investment decisions.

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Sophie Punte Executive Director

Clean Air Asia

We trust that you will find the data and indicators contained in Accessing Asia and the CitiesACT database valuable and look forward to working with existing and new partners towards low emissions development across Asia.

The World Bank launched the Open Data Initiative in April 2010, providing free, open and easy access to development data, and challenged the global community to use the data to create new solutions to address poverty. Today, the World Bank’s Open Data Catalog includes over 8,000 development indicators going back over 50 years, in addition to searchable databases on projects, finances, and other operational activities. Data, and access to data, have become increasingly critical to climate science and as a source of knowledge regarding future changes more generally. However, limitations of access have frequently led to under-usage of data.

As part of its efforts to increase the accessibility of data, over the last three years the World Bank through its Development Grant Facility has supported the Knowledge Partnership on Air Quality and Greenhouse Gas Data and Indicators for Transport and Energy in Asia led by Clean Air Asia. I am pleased to note that this partnership has leveraged interest and resources from a number of other organizations, resulting in this report and the CitiesACT data portal. Given its focus of air pollution - which has mostly local impacts, and GHG emissions - whose impacts are global, this project helps to bridge the gap between two important inter-related topics.

With a focus on 13 countries and 23 cities, this report – Accessing Asia, and the data portal represent a small effort in making data that is necessary for assessing emissions from the transport and energy sectors, openly available. Given the fast growing economies of many Asian countries, easily accessible data on air pollution and GHG emissions for two high-emitting sectors, presents a valuable set of sector indicators for a range of stakeholders - from planners and policy-makers to development practitioners and the private sectors, as well as non-governmental institutions and individuals.

This report presents a collation of indicators from existing secondary sources. While these indicators are from standard sources, some of them are subject to considerable uncertainties, and the usual care must be taken in their interpretation. The World Bank is working to address challenges with data quality through improved metadata and the implementation of stronger data standards.

The World Bank remains deeply committed to addressing both air pollution and GHG emissions, working closely with our client countries. We are ramping up our efforts to address short-lived climate pollutants though partnerships such as the Climate and Clean Air Coalition to Reduce Short-lived Climate Pollutants, and looking at ways to capitalize on synergies between efforts to address both short- and long-lived climate pollutants within our portfolio of activities.

Mary A. Barton-Dock Director

Climate Policy and Finance Department

The World Bank

FOREWORD

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Clean Air Asia is deeply grateful to the World Bank Development Grant Facility and the members of the Knowledge Partnership for their support of this report.

This report was prepared by Clean Air Asia staff led by Maria Katherina Patdu and Eryn Gayle de Leon. Sophie Punte, May Ajero, Herbert Fabian, Sudhir Gota, and Alvin Mejia of Clean Air Asia substantially contributed to this report.

Sameer Akbar was the task leader for this report from the World Bank.

The following individuals played key roles in the development of this report:

• Peng Yan, Wan Wei, Song Su and Zhang Chu from Clean Air Asia China Office• Parthaa Bosu and Sameera Kumar Anthapur from Clean Air Asia India Office• Dollaris Suhadi and Mariana Sam from Swisscontact Indonesia• Anjila Manandhar, Amita Thapa Magar, and Suman Udas from Clean Air Network Nepal• Ahmad Saeed, Saadullah Ayaz, and Shahid Lutfi from the International Union for Conservation

of Nature Pakistan• Thusitha Sugathapala from Sri Lanka Sustainable Energy Authority• Phan Quynh Nhu from Vietnam Clean Air Partnership• Le Thi Ngoc Quynh from Electricity of Vietnam• Le Van Dat from Transport Development and Strategy Institute• Mongkut Piantanakulchai from Sirindhorn International Institute of Technology, Thammasat

University• Iris May Ellen Caluag from the Partnership for Clean Air

Clean Air Asia greatly appreciates the many experts who took the time and effort to review this report, including:

• Axel Friedrich formerly from the Environment and Transport, Noise - Umweltbundesamt(Federal Environment Agency Germany)

• Eric Zusman from the Institute for Global Environmental Strategies• Iwao Matsuoka from the Institution for Transport Policy Studies• Jenny Yamamoto from the United Nations Economic and Social Commission for Asia and the

Pacific• John Rogers from The World Bank• John Wells and Amornwan Resanond from Low Emissions Asian Development Program (LEAD)• Ko Sakamoto from the Asian Development Bank• Lewis Fulton from University of California Davis• Manfred Breithaupt from the German International Development Cooperation• Mylene Cayetano from Clean Air Asia• O.P. Agarwal and Natalia Kulichenko from The World Bank• Rajiv Garg from the United Nations Environment Programme• Stasys Rastonis from Chemonics International, Inc• Todd Litman from Victoria Transport Policy Institute

Clean Air Asia thanks all other organizations which helped make this report possible.

ACKNOWLEDGEMENTS

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LIST OF ABBREVIATIONS

2W 3W

ARAI ASIF BAN

CNG CPCB CO2 DIESEL

EEA GDP GHG GIZ HCV IEA IEO

IND INO IPCC

ITF LAO LCV LPG MAL MEET

MOEF MUV NEP

two-wheelersthree-wheelersAsian Development BankAutomotive Research Association of India Activity-Structure-Intensity-Fuel ApproachBangladesh Cities Development Initiative for Asia compressed natural gas Central Pollution Control Board Carbon dioxideDeveloping Integrated Emission Strategies for Existing Land Transport ProjectDevelopment Grant FacilityEuropean Environment Agencygross domestic productgreenhouse gasesGerman International Development Cooperation heavy commercial vehiclesInternational Energy AssociationInternational Energy OutlookInstitute for Global Environmental StrategiesIndiaIndonesiaIntergovernmental Panel on Climate Change Institute for Transportation and Development PolicyInstitution for Transport Policy StudiesInternational Transport ForumLao People’s Democratic Republic light commercial vehiclesliquefied petroleum gasMalaysiaMinisterial Conference on Global Environment and Energy in Transport Ministry of Environment and Forests multi-utility vehicles NepalNon-government organization

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NGO

IGES

ADB

ITDPITPS

CDIA

DGF

NOx

PM PM10

PM2.5

PRC

SIN SO2 SRI TERI THA

ULW

US EIA US EPA VIE VKT WHO WRI

Nitrogen oxidesOrganisations for Economic Co-operation and DevelopmentPakistanpassenger carsPollution Control DepartmentPhilippinesparticulate matter particles less than 10 microns in diameter particles less than 2.5 microns in diameter People’s Republic of ChinaPhotovoltaicSingaporeSulfur dioxideSri LankaThe Energy Research Institute Thailand Transport Research Laboratoryunladen weight United Nations Centre for Regional DevelopmentUnited States Agency for International DevelopmentUnited States Energy Information Administration United States Environmental Protection Agency Vietnamvehicle kilometers travelledWorld Health OrganizationWorld Research Institute

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PHI

UNCRD

TRL

PCD PC

OECD

PAK

US AID

UNITS OF MEASUREMENT

% BTU BTU/kWh cc

gCO2/pkm gCO2/tkm GDP per capita kg

percentBritish thermal unitBritish thermal unit per kiloWatt-hour cylinder capacitytons Carbon dioxide per capitagrams of Carbon dioxide per passenger-kilometer grams of Carbon dioxideper ton-kilometer gross domestic product per capita kilograms

kgPM per capita kilograms of particulate matter per capitakm km/L ktoe kWh MW MWh pkm TJ/kt tkm tonsCO2/kWh TWh USD

kilometerskilometer per liter kilotonne of oil equivalent kiloWatt-hourmegaWattmegaWatt-hourpassenger-kilometersterajoule per kilotonneton-kilometerstons of Carbon dioxide per kiloWatt hour teraWatt-hourUS dollars

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tonsCO2/capita

TABLE OF CONTENTS

INTRODUCTION 1

ROAD TRANSPORT EMISSIONS IN ASIA 1112 131718202122232432

OverviewMethodologyDefinitionsCO2 Emissions and Growth CO2 Emissions by Vehicle Type CO2 Emissions by Fuel Type CO2 Emissions per Passenger/ton-kilometer Particulate Matter Emissions and Growth Drivers of EmissionsData Sources

ELECTRICITY EMISSIONS IN ASIA 394041454648495051

OverviewMethodologyDefinitionsCO2 Emissions from Generation CO2 Emissions from ConsumptionCO2 Emissions per Capita (Consumption)CO2 Emissions Intensity per kWhDrivers of Emissions Data Sources 57

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RECOMMENDATIONS AND NEXT STEPS

REFERENCES

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INTRODUCTION

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Asia is urbanizing fast. Over 50% of the population now lives in cities. Over the next 30 years, another 1.1 billion people are expected to be living in cities. In 2010, 12 megacities are in Asia and by 2025, it is expected that 21 of the 37 megacities in the world will be in this region (Asian Development Bank (ADB), 2010).

Asian economies are growing. Many emerging market economies in Asia are growing above pre-recession trends, and they are projected to continue their growth (International Monetary Fund (IMF), 2012). PR China, India, and Indonesia had the highest gross domestic product (GDP) in the region, ranging from at least 250 million to 3.2 billion USD in 2010. PR China (14.47%), Nepal (25.3%), and Singapore (10.4%) had the fastest GDP growth rates (World Bank, 2012). Growth for Asia and the Pacific region is projected to be at 6% in 2012 before rising to about 6.5% in 2013 (IMF, 2012).

Air pollution in Asia is worsening, and greenhouse gas (GHG) emissions is increasing. Air pollution in Asia is causing over 800,000 premature deaths each year, according to the World Health Organization (WHO, 2011). Carbon dioxide (CO2) emissions are also on the rise. In 2010, Asia emitted at least 30% of the world’s CO2 emissions (International Energy Agency (IEA), 2011). The business-as-usual scenario suggests that Asia will contribute around 45% of global energy-related CO2 emissions by 2030 and an estimated 60% by 2100 (United Nations Environment Programme (UNEP), 2012). Some Asian cities are also estimated to have higher CO2 emissions per capita compared with cities in the developed countries. For example, in 2010, the estimated CO2 emissions per capita in Shanghai (11.1 tons per capita) and Beijing (10.1 tons per capita) were higher when compared with London (6.8 tons per capita) and New York (7.5 tons per capita) (Want China Times, 2012; City of New York, 2010; The Guardian, 2010).

The growth of the region will boost energy demand in the transport and electricity (and heat) sectors. The annual average growth rate of Asia’s energy demand from 1980 to 2007 was 4.6%. This is more than twice the global average of 2% (Komiyama, n.d.). In 2010, Asia accounted for 30% of the world’s total energy demand and this share is expected to increase further in the near future (British Petroleum, 2011). PR China accounts for the largest share of the growth in global energy use, with demand projected to increase up to 60% by 2035 (IEA, 2012).

Transport is the fastest growing contributor to global CO2 emissions. The transport sector consumed 19% of total fuel use and contributed 22% of total (energy-related) CO2 emissions (IEA estimates, 2012). Of the total CO2 emissions, about 74% comes from road transport. Transport CO2 emissions are also expected to increase 57% worldwide in 2005-2030, with PR China and India accounting for more than half of this growth. Air pollution from transport is rising due to the sharp increase in vehicle use, which has offset efforts to make fuels and vehicles cleaner. Of particular concern are diesel emissions and small particulates (PM10 and PM2.5). Diesel fumes can cause lung cancer as confirmed by the WHO (International Agency for Research on Cancer (IARC), 2012). Small particulates worsen asthma and other respiratory and cardiovascular diseases. Black carbon, a component of soot, which comes from gasoline and diesel vehicles also contributes to global warming more than previously thought.

Electricity and heat production has the largest share of global CO2 emissions. Electricity and heat production worldwide contributes 41% of total CO2 emissions (IEA, 2012). Asia boosted its electricity generation to 6,290 teraWatt-hours (TWh) in 2010—a 139% increase from 2000 figures (IEA, 2012).

Introduction

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Need for Information to Manage Emissions

In 2009, 81% of electricity was generated from fossil fuels, specifically coal, which accounts for 70% of total electricity generation. Fossil fuels are a significant source of GHG and Sulfur dioxide (SO2). Although GHG emissions (as CO2 emissions) have yet to be abated, there have been significant advancements in reducing air pollution from power generation. The implementation of abatement technologies, such as flue-gas desulfurization devices in power plants, has reduced SO2 emissions from this sector.

Such aggregated data are of minimal use in developing targeted policies to reduce emissions.

Reliable data. Many international organizations echo the need to improve data accuracy, timeliness, and comparability. This includes the 2009 Ministerial Conference on Global Environment and Energy in Transport (MEET) and the IEA, among others. The need for better government data is expected to increase considerably as climate negotiations call for a more regular and updated national communications by developing countries and for a measurement, reporting and verification (MRV) mechanism to assess progress in emission pledges and/or obligations.

Standard methodologies and assumptions are important to ensure that data are reliable and comparable. Supporting data and assumptions used in emissions calculation by different organizations vary and are often not transparent. For example, the CO2 emissions estimates for India’s transport sector by the IEA, The Energy Research Institute (TERI), World Resource Institute (WRI), and other organizations in 2005 ranged from 98 million tons to 216 million tons—a difference of more than 100% (see figure below).

India transport CO2 emission estimates and forecasts by different institutions and publications

Gota, S., 2012

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Relevant data. Policy and decision makers need relevant data and emissions indicators of road transport and electricity sectors to track the progress of policies that aim to increase energy efficiency and to reduce emissions. This is relevant to low-emissions development strategies at the national and local levels and participation in international climate market mechanisms.

While there are initiatives on emissions indicators from transport and energy, few focus on Asia. Data and indicators that are available for Asia usually are aggregate values. For example, indicators are combined for a group of Asian countries (e.g. South East Asia), or they are presented as total transport emissions without a breakdown for different fuel and vehicle categories.

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Accessible data. Collected data are often not easily accessible, or are incomplete. For example, the Sri Lanka Department of Motor Traffic collects detailed data as part of vehicle registrations. However, the only data made publicly available through the Central Bank and the Department of Census and Statistics are the number of vehicles registered and fuel used aggregated by vehicle class. Another example is pilot projects and local programs that generate interesting data and emission factors but their use is limited, i.e. these factors cannot be extrapolated easily to an entire city, sector, or country.

Furthermore, various ministries—ranging from finance, customs, and trade to energy, environment and transport, collect relevant data, but coordination among them is often lacking. An added complication is that universities, development agencies, corporations, and other institutions collect data for their own research and programs but seldom share these with government authorities or the public.

Clean Air Asia, 2010

General Vehicle Registration Data Flow in Sri Lanka

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To address the challenges explained earlier, Clean Air Asia brought together various organizations in a knowledge partnership to improve access to air quality and climate change data. The partnership aims to further enrich policy development interventions relevant to energy, transport, and urban development. It was initiated with funding from the World Bank Development Grant Facility (DGF) and with co-financing from other partners.

Benchmarking Emissions in Asia

Knowledge Partnership for Measuring Air Pollution and GHG

Emissions in Asia

The World Bank Development Grant Facility, Asian Development Bank, the German International Development Cooperation (GIZ), Energy Foundation, Institute for Global Environmental Strategies (IGES), Institution for Transport Policy Studies (ITPS), Institute for Transportation and Development Policy (ITDP), Transport Research Laboratory (TRL), United Nations Centre for Regional Development (UNCRD), and Veolia Environnement S.A.

Air Pollution and GHG Emissions Indicators for Road Transport

Input Parameters

T1 Total CO2 emissions from road transport • Average vehicle-kilometers traveled(VKT) by vehicle and fuel type

• Vehicle population by vehicle and fueltype

•

• Average speed• Emission factor• Fuel characteristics• GDP• Total population• Average occupancy• Average load

T2 Road transport CO2 emissions per GDP T3 Road transport CO2 emissions per capitaT4 Road transport CO2 emissions per passenger kmT5 Road transport CO2 emissions per freight ton-kmT6 Road transport CO2 emissions per vehicle type T7 Road transport CO2 emissions per vehicle and fuel typeT8 Total PM emissions from road transport T9 Road transport PM emissions per GDPT10 Road transport PM emissions per capitaT11 Road transport PM emissions per passenger kmT12 Road transport PM emissions per freight ton-kmT13 Road transport PM emissions per vehicle typeT14 Road transport PM emissions per vehicle and fuel typeT15 Total NOx emissions from road transportT16 Road transport NOx emissions per GDPT17 Road transport NOx emissions per capitaT18 Road transport NOx emissions per passenger kmT19 Road transport NOx emissions per freight ton-kmT20 Road transport NOx emissions per vehicle typeT21 Road transport NOx emissions per vehicle and fuel typeT22 Road transport total fuel consumptionT23 Road transport fuel consumption per capitaT24 Road transport fuel consumption per GDP

Air Pollution and GHG Emissions Indicators and Input Parameters for Road Transport

Notes: Vehicle categories are: two-wheelers (2W), three-wheelers (3W), passenger cars (PC), multi-utility vehicles (MUV), bus, light commercial vehicles (LCV), heavy commercial vehicles (HCV). Fuel categories are diesel, gasoline, LPG, CNG, electric.

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Average fuel efficiency by vehicle andfuel type

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Air Pollution and GHG Emissions Indicators for Electricity

Input Parameters

E01 Total CO2 emissions (from electricity generation) • Electricity generation, total and by source type• Electricity consumption, total and by end-use

sector• Electricity imports and exports• Own-use consumption• Losses from transmission and distribution• Heat rate (fuel efficiency)• Emission factor• GDP• Total population• Population with access to electricity

E02 CO2 emissions by source type (generation)E03 CO2 emissions per kWh (generation)E04 CO2 emissions by end-use sector (consumption)E05 CO2 emissions per GDP (consumption)E05 CO2 emissions per capita (consumption)E07 Total PM emissions (from electricity generation)E08 PM emissions by source type (generation)E09 PM emissions per kWh (generation)E10 PM emissions by end-use sector (consumption)E11 PM emissions per GDP (consumption)E12 PM emissions per capita (consumption)E13 Total SO2 emissions (from electricity generation)E14 SO2 emissions by source type (generation)E15 SO2 emissions per kWh (generation)E16 SO2 emissions by end-use sector (consumption)E17 SO2 emissions per GDP (consumption)E18 SO2 emissions per capita (consumption)E19 Total electricity consumptionE20 Electricity consumption per GDPE21 Electricity consumption per capita

Air Pollution and GHG Emissions Indicators and Input Parameters for Electricity

Notes: Source type includes coal, oil and natural gas (not included for SO2). End-use sector includes residential, commercial, industrial, transport and other sector/s.

The development of road transport and electricity emissions indicators was supplemented by (a) guidelines for the development, measurement and use of these indicators and (b) an online database where the indicators along with supporting data and assumptions for its calculation are provided. This process followed the broad steps provided in the figure on the right.

Step 1. Select Indicators and Input Parameters (Long list)

Step 2. Data Mapping exercise for road transport & electricity data

Step 3. Select Indicators and Input Parameters (short list)

Step 4. Develop Guidelines for development,measurement and use

Step 5. Data collection

Step 6. Derive emissions indicators

Step 7. Review of results

Step 8. Upload Input Parameters and Pndicators in CitiesACT

Step 9. Benchmarking Report

Main Activities in the Knowledge Partnership for Measuring Air Pollution

and GHG Emissions in Asia

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The partnership first focused on 13 countries in Asia. These countries represent 95% of Asia’s total population and 89% of its total GDP (based on current exchange rates) (IEA, 2012). It includes two BRICS countries (India and PR China), representing some of the world’s leading emerging economies.

In most of these countries, Clean Air Asia has an established country network, which can facilitate the process of sustaining this initiative in the country.

In this report, “Asia” refers to the following 13 countries.

Pakistan

India

Sri Lanka

Indonesia

Singapore

Malaysia

Philippines

Vietnam

Thailand

Bangladesh

Lao PDR

Nepal

China

Notes: The boundaries, colors, denominations, and other information shown on any map in this report do not imply any judgment or endorsement on the part of Clean Air Asia concerning the delimitation or the legal status of any territory or boundaries.

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This knowledge partnership has four outputs:

1. Air Pollution and GHG Emissions Indicators for Road Transport and Electricity Sectorsin Asia: Guidelines for their Development, Measurement, and Use

The Guidelines documents the process involved in developing the air pollution and GHG emissions indicators for road transport and electricity and detailed methodology on how to measure and use the emissions indicators. The general outline of the methodology sheets for the emissions indicators and input parameters is provided in the table below.

The methodology was based on existing guidelines by the European Environment Agency (EEA), IEA, Intergovernmental Panel on Climate Change (IPCC), and the US Environmental Protection Agency (US EPA). The sources for the input parameters used to derive the indicators are also provided. This document was prepared to facilitate and encourage consistent data collection in the future.

Structure of the Guidelines

2. Accessing Asia: Air Pollution and Greenhouse Gas Emissions Indicators from RoadTransport and Electricity

Accessing Asia presents the first benchmark of air pollutant (as particulate matter, PM) and GHG (as CO2) emissions for 13 countries across Asia for road transport and electricity generation. To be released biennially, it compares selected emissions indicators and emissions drivers at the national level.

Future editions will feature city emissions indicators and drivers. Updates on national level indicators will also be included.

This publication is available at this URL:http://cleanairinitiative.org/portal/projects/MeasuringAPandGHGEmissions

Indicator Input Parameter

• Indicator name• Indicator code•• Unit of measurement• Policy relevance (purpose; international

conventions and agreements;international targets/recommendedstandards)

• Methodology (measurement m ethods;Related Indicators; limitations o f theindicator)

• Other organizations using this indicator• References

• Input parameter name•• Unit of measurement• Data sources (source, description a nd

limitations)• Periodicity of data collection• Disaggregation• For which indicators is this needed for

calculation• References

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The report features the following:

Road transport - Total road transport emissions for Asia and individual countries, and emissions intensities expressed by population, GDP, passenger and freight movement, vehicle types, and fuel types. Data are provided for underlying emission drivers, including growth in vehicle numbers, motorization index, fuel consumption, and travel activity.

Electricity - Total electricity generation and consumption emissions for Asia and individual countries, and consumption and emissions intensities expressed by population, GDP, end-use sector, and fuel type. Data are provided for underlying emission drivers, including electricity access, generation, consumption, trade, and fuel mix.

This publication is available in print and may also be accessed at this URL: http://cleanairinitiative.org/portal/projects/MeasuringAPandGHGEmissions

3. Country Profiles

Accompanying Accessing Asia, country profiles were developed using selected emissions indicators and emissions drivers on a per country and per city level

The profiles are available online at this URL:http://cleanairinitiative.org/portal/projects/MeasuringAPandGHGEmissions

CitiesACT database (www.CitiesACT.org) was developed by Clean Air Asia with support from the ADB, the Global Air Pollution Forum, and the World Bank together with Clean Air Asia Partnership members.

The revamped www.CitiesACT.org was launched at the Better Air Quality (BAQ) conference in Hong Kong in December 2012(www.baq2012.org).

4. www.CitiesACT.org - Clean Air Asia’s online database on air quality, climate change,energy, and transport

This online database contains the following:

● Air pollutant (PM, SO2, and NOx) andCO2 emissions indicators for roadtransport and electricity for 13 countriesand 23 cities in Asia.

● Input parameters used to derive theemissions indicators.

● Reported ambient air quality levelscompiled for over 400 Asian cities.

● Ambient air quality standards, fuelquality, and vehicle emission standards for22 Asian countries.

● Air quality monitoring informationin Asian cities.

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ROAD TRANSPORT EMISSIONS IN

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Transport accounts for 22% of overall CO2 emissions from fossil fuel combustion globally (IEA, 2012). Road dominates the demand for fuel in the transport sector, accounting for 74% of total transport CO2 emissions. Transport emissions will continue to grow as a result of rapid motorization and urbanization across Asia.

Overview

This chapter presents CO2 and PM emissions indicators for road transport in Asia and individual countries. The indicators include total emissions and emissions intensity expressed by population, GDP, passenger and freight movement, vehicle and fuel types. The drivers of emissions, including motorization, fuel consumption, passenger and ton-kilometers, and fuel efficiency of different modes, are described. The methodology and data availability for input parameters used to derive the emissions indicators are also discussed.

Overview

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A detailed methodology for each input parameter and emissions indicator is described in the “Air Pollution and GHG Emissions Indicators for Road Transport and Electricity Sectors: Guidelines for their Development, Measurement, and Use” (Guidelines for short). These guidelines are available at this URL: http://cleanair init iat ive.org/portal/projects/MeasuringAPandGHGEmissions

Air pollutant (PM and NOx) and CO2 emissions from the road transport sector were estimated using the Activity-Structure-Intensity-Fuel (ASIF) approach (adapted from Schipper and Marie 1999; Schipper, Gorham, and Marie, 2000). In this approach, road transport emissions are dependent on the level of travel activity (A); the road transport mode structure (S); the fuel intensity (I); and the carbon content of the fuel or emission factor (F). The impact of individual ASIF parameters on emissions from transport can be estimated, each of which is influenced by different policies.

G = A * S * I * FWhere:G = CO2 emissionsA = Activity (total transport activity in

passenger kilometers or ton freight kilometers)

S = Structure (travel shares of each road transport mode)

I = Intensity (fuel intensity of each road transport mode in liters)

F = Fuel (carbon content of fuels or emission factor in grams of carbon or pollutant per liter of fuel consumed)

A S I F

CO2

Total transport activity

Activity Structure Intensity Fuel

Travel shares by mode and vehicle type

Todal energy intensity

Carbon contentof fuels

Methodology

Components of the ASIF Approach

Subsequently, insights are gained on the effectiveness of transport policies on emissions (Schipper, Fabian and Leather, 2009).

The calculation of emissions using the ASIF approach is as follows:

Adapted from Schipper and Marie, 1999

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The figure above illustrates the calculation flowchart that was used to estimate emissions from the road transport sector using the ASIF approach. The input parameters used to derive emissions indicators were selected

based on a data mapping exercise. The table on the next page provides an overview of data availability of input parameters for each country.

Flowchart to Estimate Emissions from Road Transport

Total PKM and TKM

Total fuel consumption

Total CO2 Emissions

Total NOx Emissions

Total PM Emissions

Vehicle population by vehicle sub-types

Average VKT for each of the vehicle sub-types (VKT/year)

-types at 50 kmph (km/liter)

Average speed for each of the vehicle types

CO2 emission factors for the different fuel types (km/Liter)

NOx emission factors for each of the vehicle sub-types (g/km)

% blend of bio-fuels in the gasoline and diesel being used for road transportation

GDP per capita

Average occupancy/loading of the vehicle types

Population

PM emission factors for each of the vehicle sub-types (g/km)

Select how detailed your

vehicle population data

is

Can be broken down by vehicle-fuel type

Can be broken down to vehicle-fuel-emissions

standards

Surveys: Vehicle inventory and use, household travel and national travel surveys

Vehicle Registration

Vehicle mortality equation

Vehicle Production, Sales and Imports

Estimated from Average Trip Length and Average Number of Trips

Origin and Destination (O-D) surveys

Number Plate survey

Average Odometer Readings

Speed Surveys

Vehicle Occupancy Data Collection Methods

Axle Load survey

National bio-fuel mandates and targets

Studies deriving emission factors for different vehicle types

Dynamometer-based drive cycle tests to

speeds

Total VKT


Notes: VKT = Vehicle kilometers travelled; PM = Particulate matter; CO2 = Carbon dioxide;NOx = Nitrogen oxide; GDP = Gross domestic product; pkm = passenger kilometers; tkm= ton kilometers

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Overview of Data Availability of Input Parameters for Each Country


BAN IND INO LAO MAL NEP PAK PHI PRC SIN SRI THA VIE

Act

ivit

y Vehicle kilometer travelled

By vehicle type

2W3WPCMUVBusLCVHCV

Stru

ctur

e

Vehicle population

By vehicle type

2W3WPCMUVBusLCVHCV

By vehicle-fuel type

GasolineDieselLPGCNGElectric

By technology type

Pre-EuroEuro 1Euro 2Euro 3 or above

Inte

nsit

y

Average fuel

By vehicle type

2W3WPCMUVBusLCVHCV

By vehicle-fuel type

GasolineDieselLPGCNGElectric

Occupancy Loading

By vehicle type

2W3WPCMUVBusLCVHCV

Fuel Fuel

CharacteristicsEmission factor

Biofuel blend

Data Available Limited Data Data not available

Data Requirements for Estimating Emissions from Road Transport

Notes: 2W = two-wheelers; 3W = three-wheelers; PC = passenger cars; MUV = multi-utility vehicles; LCV = light commercial vehicles; HCV = heavy commercial vehicles

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The data mapping exercise highlighted the data challenges to estimating road transport emissions. The main challenges

and approaches taken to overcome these are summarized below.

Vehicle kilometers travelled (VKT). For most countries, data is only available for certain years and not in time-series. Most countries do not conduct regular national household surveys or record and analyze odometer readings as part of regular vehicle registration processes. Only Singapore and Thailand had annual average VKT data readily available for most vehicle types. For other countries, annual average VKT values were sourced from national transport planning documents and local studies. In the absence of data, average VKT values from countries with similar transport conditions were used.

Vehicle population. In most countries, in-use vehicle population data were not readily available, especially where vehicle registrations are not renewed annually. Vehicle sales, imports, and production data, and, in some cases, models, were used to refine vehicle population estimates further.

Disaggregation of vehicle population. Most countries provide vehicle population by vehicle type. However, official data for multi-utility vehicles (such as the jeepneys in the Philippines and angkots in Indonesia) are limited. Most countries do not provide comprehensive vehicle classifications by fuel type (especially for vehicles running on alternative fuels and on electricity) and by technology type (which is important for air pollutant emissions estimations). In the absence of data, the disaggregation by vehicle and fuel type was estimated using vehicle tax data, sales statistics from oil companies, market research data (e.g., Segment Y Ltd.), and local studies.

Average speed. Vehicle kilometers travelled at different speeds and resulting fuel efficiencies based on local drive cycles were not readily available. Therefore, average values were used.

Fuel efficiency. Where possible, local estimates for fuel efficiency were used. These data were collected from vehicle manufacturer’s statistics, transport company records, fleet operators surveys, national targets, transport planning documents, and local studies. When data were not available, average fuel efficiency from countries with similar transport conditions were used.

Emission factors. Where possible, locally developed emissions factors were used. In addition, several studies in Asia were collated to compile default values for the Asian fleet. Examples of these studies are the Developing Integrated Emissions Strategies for Existing Land (DIESEL) Transport project of the World Bank and the Emission Factor Development for Indian Vehicle by the Automotive Research Association of India for Central Pollution Control Board (CPCB) and Ministry of Environment and Forests (MOEF).

Activity

Structure

Intensity

Fuel

General Data Assumptions and Approach

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Two-wheeler (2W) A two-wheeled motor vehicle not exceeding 400 kg of unladen weight. This category includes vehicles with a cylinder capacity of 50 cc or above.

Three-wheeler (3W)A three-wheeled motor vehicle. This category includes vehicles designed by manufacturers and two-wheelers fitted with extended chassis to increase passenger capacity.

Passenger car (PC)A motor vehicle, other than a moped or a motorcycle, intended for the carriage of passengers and designed to seat no more than nine persons (including the driver). This category includes passenger cars, vans designed and used primarily for passenger transport, taxis, hire cars, ambulances, and motor homes.

Multi-utility vehicle (MUV)A vehicle designed to carry passengers or goods unladen weight (ULW) + 450 kg (six passengers x 75 kg).

Bus A motor vehicle designed to carry up to 60 passengers. Buses may be constructed with areas for standing passengers, to allow frequent passenger movement, or they may be designed to allow the carriage of standing passengers in the gangway.

Light commercial vehicle (LCV) A vehicle with a gross vehicle weight of not more than 3,500 kg that is designed, exclusively or primarily, to carry goods. This category includes vans designed for and used primarily for the transport of goods, pick-ups, and small trucks.

Heavy commercial vehicle (HCV) A vehicle with a gross vehicle weight above 3,500 kg that is designed, exclusively or primarily, to carry goods.

Definitions

Adapted from International Transport Forum (ITF), Eurostat andEconomic Commission for Europe, 2009. “Illustrated Glossary for Transport Statistics, 4th Edition” and Automotive Research

for Indian vehicles”

2W in Ho Chi Minh city Autorickshaws in Hyderabad

Taxi in Beijing Angkot in Padang

Jeepney in Manila Mini-bus

Bus in Colombo LCV

HCV

Association of India (ARAI), 2007. “Emission factor development

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The road transport sector in Asia emits more than a billion tons of CO2 per year (2010). The largest emissions from this sector come from PR China and India, which contribute over two-thirds of estimated emissions. However, the emissions from South East Asian countries, when taken as a bloc, are also significant and are similar to India.

Although PR China and India constitute the largest share of total emissions, Malaysia and Singapore have the highest per capita CO2 emissions. Nonetheless, the road transport per capita emissions from these countries are much lower compared to the Organisation for Economic

Co-operation and Development (OECD) per capita emissions of 2.408 tons per capita in 2010 (IEA, 2012). Nepal and Bangladesh have the lowest per capita road transport emissions, with only 0.04 and 0.11 tons per capita in 2010, respectively.

Road transport CO2 emissions grew by an average annual rate of 10% since 2002. Emissions from PR China and Vietnam increased the most at 13.4% and 12.5%, respectively. At current rates, emissions from these countries would double in six years. Philippines and Singapore had the lowest average growth rates for the same period.

CO2 EmissionsRoad Transport CO2 Emissions in Asia (2010)


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The carbon intensity (CO2 emissions per GDP) of the road transport sector in Asian countries increased by up to 4.8% annually since 2002. The highest average annual growth rates were observed in Vietnam and Indonesia.

There is a strong correlation between growth in GDP and CO2 emissions per capita. One exception is Singapore which has successfully decoupled road transport emissions and economic growth.

CO2 Emissions Growth

BAN INDI NO

MAL

LAO

PAK

PRC SIN

NEP

SRI

PHI

THA VIE

5.5%

13.4%

ASIA

LAOL

10.1%

9.7% 8.7% 10.0% 16.1%

8.7% 4.7% 2.5%

3.1% 5.9% 12.5%5.3%

0

1

10

0 100 1000 10000 100000

Tons

CO

2 p

er c

apit

a

GDP per capita (USD at constant 2000 prices) per capita (in log values)

BAN

IND

INO

LAO

MAL

NEP

PAK

PHI

PRC

SIN

SRI

THA

VIE

Clean Air Asia estimates, 2012


(in lo

g v

alue

s)Average Annual Growth Rate of Road Transport CO2 Emissions in Asia (2002-2010)

Road Transport CO2 Emissions per Capita versus GDP per Capita (2002-2010)

t

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For Asia as a whole, CO2 emissions are equally split between light and heavy duty vehicles versus passenger vehicles, but significant variations exist between countries. In most countries, light and heavy commercial vehicles used for freight movement have the largest share of emissions. The exceptions are emissions from multi-utility vehicles in Pakistan and two-wheelers in Vietnam.

Curbing emissions from road freight is essential to reduce total road transport sector emissions in Asia. Road freight makes up only 9% of the total vehicle population in Asia, according to 2010 estimates. However, freight vehicles contribute as much as 57% of total road transport emissions in PR China and 54% overall in Asia.

CO2 Emissions by Vehicle TypeRoad Transport CO2 Emissions by Vehicle Type (2010)

Notes: 2W = two-wheelers; 3W = three-wheelers; PC = passenger cars; MUV = multi-utility vehicles; LCV = light commercial vehicles; HCV = heavy commercial vehicles Clean Air Asia estimates, 2012

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Emissions from diesel are two-thirds of total CO2 emissions from road transport (2010). At about 60%, diesel is the primary fuel used in most countries. One factor of influence is the lower pump price for diesel compared to gasoline in many countries (GIZ, 2010). Malaysia and Vietnam are the only countries where the emissions from gasoline are higher because of the large numbers of gasoline-fed passenger cars and two-wheelers.

The data availability for vehicles running on alternative fuels and electricity in Asia needs to improve to assess their uptake. Few countries provide comprehensive vehicle population data by vehicle and fuel type, and data for alternative fuel vehicles are often lacking. Different data management systems are seldom consolidated. For example, in Bangladesh, oil companies provide statistics on the number of vehicles converted to compressed natural gas, but these figures are not reflected in national statistics.

CO2 Emissions by Fuel TypeRoad Transport CO2 Emissions by Fuel Type (2010)


million

Gasoline Diesel Electric

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Asia’s average CO2 emissions per passenger kilometer (pkm) is 36.8 grams and per freight-ton kilometer (tkm) is 99.1 grams (2010). Malaysia, PR China, and Singapore had the highest grams CO2 per pkm values. PR China, Singapore, and Bangladesh had the highest grams CO2 per tkm values. These values are lower compared with the average values for

Europe: 113.25 grams CO2 per pkm and 108.08 grams CO2 per tkm in 2009 (EEA, 2010). It is noted that the values of individual Asian countries fall within the range of those reported for European countries. Data limitations in Asia most likely affect the validity of a comparison with Europe, where data are more available.

CO2 Emissions per passenger/ton-kilometersRoad Transport CO2 Emissions per pkm, tkm (2010)

CO2CO2


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Particulate Matter (PM) Emissions and Growth

Data on vehicle population by technology type, which were needed to calculate PM emissions, were purchased from market research companies. These companies were the only source of reliable data for six Asian countries: India, Indonesia, Philippines, PR China, Thailand, and Vietnam. The remaining countries were excluded due to the absence of data.

The six Asian countries generated six million tons of PM emissions from road transport (2010). India and PR China had the highest PM emissions in 2010, accounting for two-thirds of the total emissions for the

six countries. The highest road transport PM emissions per capita are from Thailand (0.57 tons) and Philippines (0.64 tons).

Annual average growth rates for road transport PM emissions since 2005 range from -1.7% to 8.6%, and they are relatively lower than for CO2 emissions (2.5% to 16.1%). As for CO2 emissions, Vietnam and PR China have the highest average growth rate for PM emissions. The reduction of PM emissions from Thailand is largely due to the implementation of stricter vehicle emissions and fuel quality standards.

Road Transport PM Emissions in Asia (2010)


Average Annual

kil

(2005 - 2010)

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Asia is home to almost half a billion vehicles, with almost 90% two-wheelers and passenger cars (2010). PR China, India, and Indonesia have the largest fleets in the region, which is explained by their large populations and fast economic growth. Singapore, Lao PDR, Bangladesh, and Nepal have the smallest fleets.

Malaysia, Indonesia, Thailand, and Vietnam have the highest number of vehicles per 1,000 people, mostly two-wheelers and passenger cars. Nepal, Pakistan,

Bangladesh, and Singapore, on the other hand, have the lowest motorization rates.

At current annual average growth rates, the number of vehicles in Asia will double in less than seven years from 2010. Indonesia, Nepal, PR China, Sri Lanka, and India show the fastest annual average growth rates. Singapore, on the other hand, has the lowest growth rate combined with a low motorization index due to its policies restricting car ownership.

Drivers of Emissions: Motorization

Motorization in Asia (2010)

> 400

251– 400

151 – 250

50 – 150

<50

Motorization Index (vehicles per 1000

persons)

Total number of vehicles (millions)

ASIA 460.4

BAN 1.0

IND 120.3

INO 65.8

LAO 1.0

MAL 19.7

NEP 1.0

PAK 9.8

PHI 6.6

PRC 176.7

SIN 0.9

SRI 2.5

THA 22.7

VIE 32.4120

10

Vehicle numbers (millions)

Clean Air Asia from various sources, 2012

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Rising incomes have been associated with higher levels of vehicle ownership and usage (World Bank, 2009). Two patterns emerge: one with increasing GDP per capita and vehicle ownership (e.g., Malaysia), and

another where vehicle ownership is stabilized with rising incomes (e.g., Singapore). Most Asian countries appear to follow Malaysia’s pattern.

Drivers of Emissions: Motorization GrowthAverage Annual Growth Rate of Vehicles in Asia (2002-2010)

Motorization Index versus GDP Per Capita (2002-2010)


Clean Air Asia estimates, 2012(in log values)

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Drivers of Emissions: Motorization by Type

The Asian vehicle fleet is dominated by private passenger vehicle (88%) comprised of two-wheelers (67%) and passenger cars (21%). Their numbers are expected to double in the next five to seven years. In contrast, with current average growth rates, it will take about a decade for the number of buses to double.

Vietnam has the highest share of two-wheelers, comprising 96% of the total vehicle fleet. Singapore’s fleet, on the other hand, is predominantly passenger cars (63%).

Vehicle Population in Asia by Type (2010)


million

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0

10000

20000

30000

40000

50000

60000

70000

80000

90000

23456 78

kilo

met

ers

BANINDINOLAOMALNEPPAK PHIPRCSINSRITHAVIE"#$!2W 3W PC MUV HCVLCVBus

Average VKT per vehicle type in

Asia (km)

2W 7,500

3W 24,300

PC 11,700

MUV 35,900

Bus4 2,800

LCV2 7,800

HCV 42,9002W PC LCV3W BUSMUV HCV

0 10,000 20,000 30,000 40,000 50,000

Drivers of Emissions: Travel Activity

Across Asia, the average kilometers travelled each year is significantly higher for public transport and commercial vehicles than for passenger cars and two-wheelers.

However, the distance travelled for each vehicle type highly varies between countries. It is noted that these data are influenced by the survey methods applied in each country.

Average Annual Vehicle Kilometers Travelled (VKT) Per Vehicle Type in Asia

Average Annual VKT Per Vehicle Type in Different Asian Countries (km)

Notes: 2W = two-wheelers; 3W = three-wheelers; PC = passenger cars; MUV = multi-utility vehicles; LCV = light commercial vehicles; HCV = heavy commercial vehicles Clean Air Asia from various sources, 2012

Notes: 2W = two-wheelers; 3W = three-wheelers; PC = passenger cars; MUV = multi-utility vehicles; LCV = light commercial vehicles; HCV = heavy commercial vehicles Clean Air Asia from various sources, 2012

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Public passenger transport (buses, multi-utility vehicles, and three-wheelers) constitute the majority of total passenger-kilometers in Asia at 58%, with buses taking up the largest share at 46% (2010).

The exceptions are Malaysia, PR China, and Vietnam, where private cars and two-wheelers constitute more than 70% of passenger-kilometers. Private passenger-kilometers are projected to have higher average annualgrowth rates primarily due to rapid economic growth across Asia.

Drivers of Emissions: Passenger-kilometers

0% 20% 40% 60% 80% 100%

VIE

THA

SRI

SIN

PRC

PHI

PAK

NEP

MAL

LAO

INO

IND

BAN

ASIA

2W 3W PC MUV Bus

Notes: 2W = two-wheelers; 3W = three-wheelers; PC = passenger cars; MUV = multi-utility vehicles

Percent Share of Passenger-kilometers by Type (2010)


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Heavy commercial vehicles account for the majority of total ton-kilometers travelled with a 79% share for Asia (2010). The exception is Lao PDR, where tkm by light commercial vehicles are higher.

Drivers of Emissions: Ton-kilometers

The split between light and heavy commercial vehicles for some countries is based on assumptions in the absence of reliable disaggregated data. In Sri Lanka, for instance, the Department of Motor Traffic’s vehicle registration data combines light and heavy duty trucks.

0% 20% 40% 60% 80% 100%

VIE

THA

SRI

SIN

PRC

PHI

PAK

NEP

MAL

LAO

INO

IND

BAN

ASIA

LCV HCV

Percent Share of Ton-kilometers by Type (2010)

LCV = light commercial vehicles; HCV = heavy commercial vehicles Clean Air Asia estimates, 2012

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Drivers of Emissions: Fuel Consumption

>0.44

0.34 – 0.44

0.23 – 0.34

0.12 – 0.23

<0.12

Total Fuel Consumption

(ktoe)

Asia 354,785

BAN 1,539

IND 80,285

INO 30,612

LAO 353

MAL 15,461

NEP 828

PAK 9,062

PHI 14,613

PRC 172,648

SIN 2,620

SRI 1,790

THA 15,876

VIE 9,097

Fuel consumption per capita

(ktoe/capita)

80,000

14,000

Fuel consumption (ktoe)

Fuel Consumption in Asia (2010)

consumption per capita is found in Malaysia and Singapore.

Fuel consumption in the road transport sector is expected to double in seven years from 2010. Substantial fuel subsidies in most countries encourage further growth in fuel consumption. Fuel prices in the Philippines, Malaysia, and Singapore are market-driven, thus creating an incentive to travel less when fuel prices rise. The International Energy Outlook (IEO) 2011 estimates that in the next 25 years, the demand for liquid fuels in the transportation sector will be mostly driven by the developing non-OECD countries (IEO, 2011).

Asia’s road transport sector consumed 354,785 ktoe of fuel in 2010. This is lower than diesel and gasoline consumption alone by road transport in the United States (465,674 ktoe in 2009) (World Bank Databank, 2012) despite a ten-fold difference in population. It is noted that fuel consumption was based on the ASIF parameters because fuel consumption data are usually provided only for the transport sector as a whole, without a split between road, rail, marine, and air transport.

Fuel consumption is highest in PR China and India. It is lowest in Lao PDR, Bangladesh, Sri Lanka, and Nepal. The highest fuel


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Two-wheelers, on average, are the most fuel-efficient vehicles in Asia, but their average fuel efficiencies vary widely.

Data on technology type, brands, and other factors that influence fuel efficiency are scarce.

Average Annual Growth Rate of Fuel Consumption (2002 to 2010)

Average Fuel Efficiency by Type in Asian Countries



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6. World Bank. (2010). World BankDevelopment Indicators. World BankOpen Data. Retrieved online. URL: http://data.worldbank.org/data-catalog/world-development-indicators

PR CHINA1. Automotive Research Association of

India. (2007). Emission factor development for Indian vehicles as part of the ambient air quality monitoring and emission sources apportionment studies, air quality monitoring project-Indian clean air programme. China National Bureau of Statistics. (2000-2010). China Statistic Yearbook. GEF Trust Fund. (2008). Request for CEO endorsement/approval - Project Type: Full-sized Project. Huo, H., & et al., .. (2011). Vehicle-use intensity in China: Current status and future trend.Kebin, H., & et al., .. (2005). Oil consumption and CO2 emissions in China's road transport: current statust, future trends, and policy implications.Kenworthy, J.; & Hu, G.. (2002). Transport and Urban Form in Chinese Cities. PCD Thailand. (2008). DIESEL – Developing Integrated Emission Strategies for Existing Land Transport Project. PR China Ministry of Environment Protection. (2000-2010). China Vehicle Emission Control Annual Report. Wang, C., & et al., .. (2007). CO2 mitigation scenarios in China’s road transport sector. Wang, C., Cai, W., Lu, X., & Chen, J. (2006). CO2 mitigation scenarios in China’s road transport sector. World Bank. (2010). World Bank Development Indicators. Retrieved November 2012, from World Bank Open Data: http://data.worldbank.org/data-catalog/world-development-indicators

2.

3.

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4. Mechanical Engineering Dept,Rajamangaly University of TechnologyThunyaburi for Dept of AlternativeEnergy and Development Efficiency(DEDE).


6. Ministry of Transport. (2000-2010).Department of Highways statistics.

7. Ministry of Transport. (2000-2010).Department of Land Transport statistics .

8. Ministry of Transport. (2009). TransportStatistics: Transtat 2009.


10. Pongthanaisawan et al.. (2006). Landtransport demand analysis and energysaving potentials in Thailand.

11. Satiennam. (2007). Ecology FriendlyTransports in Southeast Asia.

THAILAND1. Asian Transportation Research Society

(ATRANS). (2009). Possibility of EthanolUsage as Diesel Substitutes in ThaiTransportation Sector.”

2. ATRANS, 2009. An Analysis of VehicleKilometers of Travel of Major Cities inThailand.

3. Automotive Research Association ofIndia. (2007). Emission factordevelopment for Indian vehicles as partof the ambient air quality monitoring andemission sources apportionment studies,air quality monitoring project-Indianclean air programme.

SRI LANKA1. Automotive Research Association of

India. (2007). Emission factordevelopment for Indian vehicles as partof the ambient air quality monitoring andemission sources apportionment studies,air quality monitoring project-Indianclean air programme.

2. Chandrasiri, S. (2006). Demand for road-fuel in a small developing economy: Thecase of Sri Lanka.

3. Department of Census and Statistics.(2010). Statistical Abstract 2010. CentralBank Annual Report 2009 and 2010

4. Energy Sector Management AssistanceProgram (ESMAP). (2003). SustainableTransport Options for Sri Lanka.

5. Ministry of Environment and NaturalResources. (2002). Urban Air QualityManagement Project: Vehicle EmissionsReduction.



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Retrieved online. URL: http://www.nhtsa.gov/

6. Nguyen Thi Kim Oanh; & Mai Thi ThuyPhuong. (2008). Emission Inventory forMotorcycles in Hanoi using theInternational Vehicle Emission Model.


8. Transport Development and StrategyInstitute (TDSI), 2003. TDSI CordonTraffic Counts.

9. Vietnam Register. (n.d.). Retrievedonline. URL: http://www.nhtsa.gov/

10. World Bank. (2010). World BankDevelopment Indicators. World Bank Open Data. Retrieved online. URL: http://data.worldbank.org/data-catalog/world-development-indicators

VIETNAM1. Automotive Research Association of

India. (2007). Emission factor development for Indian vehicles as part of the ambient air quality monitoring and emission sources apportionment studies, air quality monitoring project-Indian clean air programme.

2. EMBARQ (WRI). (2008). Measuring theInvisible - Quantifying emissions reductions from transportation solution.

3. General Statistics Office. (n.d.). RetrievedNovember 2012 from: http://www.gso. gov.vn/default_en.aspx?tabid=491

4. Ho Minh Dzung; & Dinh Xuan Thang.(2008). Estimation of emission factors of air pollutants from the road traffic in Ho Chi Minh City.

5. National Traffic Safety. (n.d.).

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39

ELECTRICITY EMISSIONS INASIA

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Electricity and heat contributed the largest share (41%) of global energy-related CO2 emissions in 2009 (IEA, 2012). IEA cited the carbon-intensive nature of power generation as the main factor for high CO2 emissions. Electricity demand is still expected to increase with rising electricity access and urbanization.

Overview

This chapter presents the CO2 emissions indicators from electricity generation and use for Asia and individual countries. This includes total emissions and emissions intensity expressed as by per capita, per GDP, and per kilowatt-hour (kWh). It also provides information on the drivers of emissions, including electricity access, trade, generation, and consumption. The methodology and data availability for input parameters used to derive the emissions indicators are also discussed.

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A detailed methodology for each input parameter and emissions indicator is described in the “Air Pollution and GHG Emissions Indicators for Road Transport and Electricity Sectors: Guidelines for their Development, Measurement, and Use” (Guidelines for short). These guidelines are available on this URL: http://cleanairinitiative.org/portal/projects/MeasuringAPandGHGEmissions

Air pollutant (PM and Sulfur dioxide [SO2]) and CO2 emissions from the electricity sector were estimated with guidance from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories – Chapter 2 Stationary Combustion and the 2009 USEPA Factors & AP-42 compilation of Air Pollutant and Emission Factors (for the estimation of PM and SO2). In this approach, the emissions in the electricity sector were calculated for both electricity generation and electricity consumption. Emissions from electricity consumption is considered to be indirectly attributable to the selected end-use sector. Its purpose is to show which sector consumes more electricity relative to others and consequently account for more emissions.

Tier 2 or country-specific values were used whenever possible based on the availability of data per country. Data considerations, definitions, and assumptions per country are found in the accompanying Guidelines cited earlier.

In this approach, the emissions in the electricity generation sector are dependent on the level of electricity produced in kilowatt-hour, across the fossil fuel types (coal, oil, and natural gas). The fuel efficiency (as measured by heat rate) for each fuel type, in BTU per kWh; and the carbon content of the fuel or emission factor, in tons of carbon per kWh. The formula is the same for PM and SO2 estimation using particulate matter and sulfur content. The relationships between these parameters are represented mathematically by equations below.

The approach used allows the estimation of the impact of changing aspects of the electricity system that affect CO2 emissions, whether it be source type and fuel efficiency. It can provide insights on how implementation of different policy measures can affect emissions from electricity.

Equation 1: Emissions from electricity generation

Where:

Ggen = Total emissions from electricity generation j = fuel type (limited to fossil fuels – coal, oil, natural gas)EGj = Electricity generation by fuel type jHRj = Heat rate by fuel type (fuel efficiency)EFj = Emission factor by fossil fuel type

Abatement technologies should be taken into account whenever possible.

Equation 2: Emissions from electricity consumption

Methodology

Where:Gcon = Total emissions from electricity consumption (net consumption after losses) k = sector (residential, industrial, commercial, transport and other sector/s)Eck = Electricity consumption by end-use sector Ggen = Total CO2 emissions from electricity generation EGtotal = Total electricity generation (both fossil and non-fossil sources)

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The figure above illustrates the calculation flowchart that was used to estimate emissions from electricity. The input parameters used to derive emissions indicators were selected

based on a data mapping exercise. The table on the next page provides an overview of data availability of input parameters for each country.

Fossil-fuel based gross electricity generation*

Heat rate*

Net electricity generation*

Emission factor* Total Emissions*

Electricity consumption by sector Emissions by sector

Population Emissions per capita

GDP Emissions per GDP

Fuel characteristics

Total electricity generation Emissions per kWh

Own-use consumption

Losses

Electricity imports

Electricity exports

Flowchart to Estimate Emissions from Electricity

Notes: Emissions = CO2, PM, and SO2 emissions; CO2 = Carbon dioxide; PM = Particulate matter SO2 = Sulfur dioxide; GDP = Gross domestic product Clean Air Asia, 2012

*By fuel type (applicable to fossil fuel fired plants only)

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Overview of data availability of input parameters for each country

GDP GDP (Constant)

Total Population

Access to electricity

Total Total

Coal

Oil

Natural gas

Geothermal power

Hydropower

Biomass/Agrofuels

Wind power

Solar power

Other source/s

Coal

Oil

Natural gas

Total Total

Residential sector

Commercial sector

Industrial sector

Transport sector

Other sector/s

Coal

Oil

Natural gas

Coal

Oil

Natural gas

Coal

Oil

Natural gas

Coal

Oil

Natural gas

Coal

Oil

Coal

Oil

Coal

Oil

Coal

Oil

Sulfur Dioxide Emission

Factor

Sulfur content (fuel)

Sulfur retention (ash)

SO2

Data Requirements for Estimating Emissions from the Electricity Sector

Electricity Consumption Electricity consumption by

end-use sector

Carbon Dioxide Emission

Factor

Carbon emission factor

Particulate Matter

Emission Factor Particulate matter

combustion factor

SRI THA VIE

General Population

Electricity Supply

Electricity generation by source type

Heat rate

MAL NEP PAK PHI PRC SINBAN IND INO LAO

Data Available Limited Data Data not available


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The data mapping exercise highlighted the data challenges to estimating electricity emissions. The main challenges and

approaches taken to overcome these are summarized below.

Access to electricity. Only a few countries report annual electricity access data but complete 2000 and 2009 data are available through the IEA.

Electricity generation. Electricity generation data included here only refers to on-grid power generation and does not involve activities using generators or batteries. Disaggregated data for electricity generation at the national level is generally available for different source types. Sources are defined differently per country. Some countries lump various fuel sources into general categories such as “thermal energy” or “renewable energy”; this makes it difficult to estimate emissions by fuel type. The data for electricity trade is generally reported. Non-revenue electricity and heat rate data are available on a very limited basis. Countries rarely report average heat rate of fossil fuels. Electricity imports and exports were not included in the calculation.

Heat rate. Heat rate determines the efficiency of energy produced on a per kWh basis. Heat rate data is usually disaggregated on the basis of fuel type (coal, oil, and natural gas). This however is seldom reported on an annual basis. For ease of calculation and the lack of comparative annual data, an average heat rate value was used for the entire calculation period of 2000 to 2009.

Electricity consumption. Electricity consumption data was disaggregated on the end-use sector level. Total electricity consumption is also generally collected and available at the national level. Disaggregation by end-use sector is variable. Residential, commercial, and industrial sectors are fairly available, but transport data is very limited. Other sectors often differ per country. Countries have different ways of categorizing end-use sectors.

Emission factors. Data used to calculate emission factors i.e., carbon emission factor, combustion efficiency, etc relied on international default values. Very few data values for calorific values, sulfur content, and SO2 abatement efficiency and other pollution measures were found. This data gap was the reason for the non-reporting of PM and SO2 emissions as this could result in the over-estimation of air pollutant emissions.

General Data Assumptions and Approach

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DefinitionsELECTRICITY GENERATION BY SOURCE TYPE*

FOSSIL FUELS includes:• Coal: refers to a variety of solid,

combustible, sedimentary, organic rocksthat are composed mainly of carbon andvarying amounts of other componentssuch as hydrogen, oxygen, sulphur andmoisture.

• Oil: includes crude oil, condensates,natural gas liquids, refinery feedstocksand additives, other hydrocarbons, andpetroleum products.

• Natural Gas: comprises gases, occurring inunderground deposits, whether liquefiedor gaseous, consisting mainly of methane.

RENEWABLE ENERGY includes:• Geothermal Energy: Energy available as

heat emitted from within the earth’s crust,usually in the form of hot water or steam. Itis exploited at suitable sites for electricitygeneration after transformation, ordirectly as heat for district heating,agriculture, etc.

• Hydropower: Potential and kineticenergy of water converted into electricityin hydroelectric plants. It includes large aswell as small hydro, regardless of the sizeof theplants.

• Solar Energy: Solar radiation exploitedfor hot water production and electricitygeneration. Does not account for passivesolar energy for direct heating, coolingand lighting of dwellings or other.

• Wind Energy: Kinetic energy of windexploited for electricity generation inwind turbines.

• Combustible Renewables:• Solid Biomass: Covers organic, non-fossil

material of biological origin which maybe used as fuel for heat production orelectricity generation.

• Wood, Wood Waste, Other Solid Waste:Covers purpose-grown energy crops(poplar, willow etc.), a multitude ofwoody materials generated by anindustrial process (wood/ paper industryin particular) or provided directly by

forestry and agriculture (firewood, wood chips, bark, sawdust, shavings, chips, black liquor etc.) as well as wastes such as straw, rice husks, nut shells, poultry litter, crushed grape dregs etc.

• Charcoal: Covers the solid residue of thedestructive distillation and pyrolysis of wood and other vegetal material.

• Biogas: Gases composed principally ofmethane and carbon dioxide producedby anaerobic digestion of biomass andcombusted to produce heat and/orpower.

• Liquid Biofuels: Bio-based liquid fuelfrom biomass transformation, mainlyused in transportation applications.

OTHERSOther source types vary per country. This includes different sources of energy that may have not been clearly identified and/or disaggregated. This includes nuclear energy.

ELECTRICITY CONSUMPTION BY END-USE SECTOR**

• Residential sector: consists of livingquarters for private households.

• Commercial sector: consists of service-providing facilities and equipment ofbusinesses and governments.

• Industrial sector: consists of all facilitiesand equipment used for producing,processing, or assembling goods.

• Transport sector: consists of all vehicleswhose primary purpose is transportingpeople and/or goods from one physicallocation to another.

• Other sectors: Varies per country. Thiscommonly includes agriculture, publiclighting and water works, non-commercialuse, and religious use.

* All definitions are directly quoted fromthe IEA unless otherwise stated.

** All definitions are directly quoted from the US Energy Information Agency unless otherwise stated.

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Electricity generation in Asia resulted in 4,259 million tons CO2 in 2009. Based on the current average growth rate, this is expected to double in 15 years.

In 2009, the OECD produced 10,393 TWh of electricity with corresponding CO2 emissions of 4,938 million tons (IEA, 2012). This is almost twice as efficient than Asia’s production of 5,410 TWh of electricity with corresponding emissions of 4,259 million tons of CO2 emissions.

Vietnam, Bangladesh, and PR China have the highest annual average growth rates of CO2 emissions. Nepal has a negative average growth rate of -8%. South East Asia’s 6% growth rate is comparable to India’s 5% average growth rate. China and India are the largest contributors to CO2 emissions.

CO2 Emissions from Generation

Average Annual

Notes: 1. Lao PDR was considered to have zero emissions since reported generation was 100%

from hydroelectricity.2. Emissions were calculated from coal, oil, and natural gas only. Emissions from non-fossil

fuel generation were considered to be zero. Clean Air Asia estimates, 2012

(million tons)

CO2 Emissions from Electricity Generation in Asia (2009)

7.6%

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CO2 emissions from electricity generation in Asia is still driven by the use of coal and other fossil fuels. As much as 91% of CO2 emissions in Asia, 99% in PR China and 91% in India are due to coal generated electricity.

CO2 Emissions From Generation

PR China and India together account for 90% of the total CO2 emissions from electricity generation in the region. CO2 emissions in Nepal and Sri Lanka are 100% due to oil-based power generation.

CO2 Emissions from Electricity Generation by Source Type (2009)

Notes:1. Emissions was calculated from coal, oil, and natural gas only.2. Emissions from non-fossil fuel generation was considered to be zero. Clean Air Asia estimates, 2012

Coal Oil Natural Gas

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CO2 Emissions from Electricity Consumption by End-use Sector (2009)

The largest CO2 emission share is attributed to the industrial sector. Industrial sector caused 68% of CO2 emissions based on electricity consumption followed by the residential sector (13%) and the commercial sector (12%).

CO2 Emissions From Consumption

95% of emissions attributable to industial sector is contributed by PR China and India. In Bangladesh and Pakistan, emissions from the residential sector accounted for more than half of total CO2 emissions.

Notes: Other sectors vary per country. This commonly includes agriculture, public lighting and water works, non-commercial use, and religious use Clean Air Asia estimates, 2012

million

Residential Commercial Industrial Transport Others

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CO2 emissions per capita from electricity consumption have increased at average annual growth rate of 7% from 2000 to 2009. This is about the same with GDP per capita increase of 7% per year.

There is a strong correlation between economic growth and the increase of CO2 emissions from electricity consumption. As a country’s economy grows, its CO2 emissions are also bound to increase. CO2 emissions per capita and GDP per capita are highest for Singapore and lowest for Nepal.

CO2 Emissions Per Capita (Consumption)


TonsCO2 Per Capita from Electricity Consumption in Asia (2009)


TonsCO2 Emissions Per Capita from Electricity

Consumption 2009 (TonsCO2/capita)

Notes:1. Lao PDR was considered to have zero emissions since reported generation was 100% from hydroelectricity. 2. Emissions was calculated from coal, oil, and natural gas only.3. Emissions from non-fossil fuel electricity generation was considered to be zero.

CO2 Emissions per Capita From Electricity Consumption versus GDP Per Capita

(2000-2009)

(in lo

g v

alue

s)

GDP per capita (USD at constant 2000 prices) per capita

(in log values)

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The average CO2 emissions per kWh remains relatively unchanged since 2000. Improved efficiency of kgCO2/kWh in five of the 12 countries (Lao PDR excluded) was neutralized by increased kgCO2/kWh in the other seven countries. The reduction in kgCO2/kWh was greatest in Nepal caused by the increased share of hydroelectricity.

Improved efficiency could be attributed to a shift to cleaner sources of energy or to more efficient electricity generation processes.

Based on IEA estimates, in 2009, the OECD‘s CO2 emissions intensity was 0.43 kgCO2/kWh. This is more efficient than Asia’s CO2 emissions intensity of 0.50 kgCO2/kWh.

CO2 Emissions Intensity per kWh

CO2 Emissions from Electricity Consumption per kWh (2009)

Average Annual Growth Rate of kgCO2 /kWh (2000-2009)


kgkg

kg

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More people in Asia are gaining access to electricity and consuming more electricity per person.

Average access to electricity increased from six in ten persons in 2000 to eight in ten persons in 2009.

Singapore has 100% coverage while four of the 13 countries have almost 100% electricity coverage.

Drivers of Emissions: Access to Electricity

Population with Access to Electricity (out of 10 persons) in 2009

Notes: Data on percentages of people with access to electricity were converted to ratio in ten persons.


Countries in South Asia have the least access to electricity, especially Bangladesh and Nepal. However, they have also experienced the biggest improvement from 2000.

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Electricity consumption per capita increased across Asia at an average annual growth rate of 6% from 2000-2009.

The OECD‘s average electricity consumption per capita was 8,483 kWh/capita for 2009 (IEA, 2011). OECD countries on average consume significantly more than Asia’s average of 1,501 kWh/capita for the same year but are at comparable levels to Singapore.

Singapore’s average consumption per capita is more than five times higher than the average electricity consumption per capita in Asia.

With increasing population and electricity consumption per capita, the energy demand in the region is expected to increase significantly in coming years.

Drivers of Emissions: Consumption Per Capita

0 2000 4000 6000 8000

VIE

THA

SRI

SIN

PRC

PHI

PAK

NEP

MAL

LAO

INO

IND

BAN

ASIA

kWh/capita

20002009

Asia Average --- 2000: 1111 kWh/capita --- 2009: 1501 kWh/capita

kWh/capita

(Consumption)AAGR2000-2009

ASIA 6%

BAN 8%

IND 5%

INO 5%

LAO 14%

MAL 3%

NEP 5%

PAK 0%

PHI 2%

PRC 2%

SIN 1%

SRI 5%

THA 4%

VIE 18%


Average Annual Growth Rate of

Electricity Consumption Per Capita

(2000-2009)

Electricity consumption Per capita (2000 and 2009)

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SIAPower trade in the region will continue

to expand to meet growing energy requirements.

Although electricity trade often occurs between countries sharing land borders, archipelagic and island countries such as

Indonesia, Malaysia, and Singapore are beginning to trade electricity to meet future demand.

Lao PDR is a major electricity exporter, exporting on average 70% of its generated electricity.

Drivers of Emissions: Electricity TradeElectricity Trade in Asia

NEPAL

IRAN

CHINA

PAKISTAN* BHUTAN

MONGOLIA

THAILAND

SRI LANKA

CAMBODIA

INDIA

MALAYSIA*

SINGAPORE*

INDONESIA*

LAO PDR

VIETNAM

MYANMAR

PHILIPPINES

BANGLADESH*

RUSSIA

Clean Air Asia from various sources, 2012Notes: *Countries that will start importing or exporting electricity by 2012 to 2014.

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Notes: 1. Other source types vary per country. This includes different sources ofenergy that may have not been clearly identified and/or disaggregated. For India, Pakistan, and PR China, this includes nuclear energy. 2. Noted source types are the reported sources for electricity generation.

Asia: Generation Mix

Electricity generation in Asia more than doubled in ten years to 5,410 TWh in 2009. Vietnam, PR China, and Bangladesh doubled electricity generation since 2000.

Seventy percent of produced electricity in Asia is from coal. Coal is commonly used for electricity generation except for Lao PDR, Nepal, Singapore, and Sri Lanka. Oil-power generated has decreased by 3% since 2000. All countries except in Lao PDR use oil for power. Most countries also use natural gas except Lao PDR, Nepal, and Sri Lanka.

Drivers of Emissions: Electricity Generation

Non-fossil fuel generated electricity has doubled since 2000, but its share remains small. Hydropower is still the primary renewable source for electricity. Geothermal, combustible renewables, wind power, and solar power have lesser shares, but wind and solar power are growing fastest. Total fossil fuel share (range: 81% to 83%) and renewable energy share (range: 15% to 18%) remain relatively unchanged despite the increase of projects, programs, and investments related to renewable energy.

Average AnnualGrowth Rate of

Electricity Generation by Source Type

(2000-2009)

Source Types Used in Asia (2000-2009)

Clean Air Asia fr om various sources, 2012


Coal Oil Natural Gas Renewable energy

2425 5410

2%

17%

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100200300400500600700800900

Hydropower

05

101520253035404550

2000 2002 2004 2006 2008

Geothermal Combustible renewablesWind power Solar power

Ele

ctri

city

Gen

erat

ion

by

Ren

ewab

le e

nerg

y (T

Wh)

Renewable Energy Targets in Asia

BANEnergy mix: 5% (by 2015) and 10% (by 2020) of energy demand from renewable sources

IND

INO

LAO

MALEnergy supply by 2030: 1340 MW biomass, 410 MW biogas, 490 MW mini-hydro, 854 MW solar, 390 MW municipal solid waste

NEPEnergy mix by 2020: 10% of total energy supply from renewable sources

PAK Energy mix by 2015: 10% (2700 MW) of totalenergy supply from renewable sources

PHIEnergy supply by 2030: 1500 MW geothermal, 2100 MW hydro, 950 MW wind,71 MW solar PV, 102 MW biomass

PRC

-Reducing carbon intensity by 40% by 2020 from 2005 levels-Energy mix by 2015: 11.4% of energy from non-fossil fuels- Energy mix by 2020: 15% of energy demand from renewable energy

SIN Energy mix to include 5% solar PV

SRIEnergy mix: 4.1% (by 2007), 8.5% (by 2012), 10% (by 2016), and 20% (by 2020) from renewable sources

THAEnergy supply: 6329 MW of renewable energy

VIEEnergy supply by 2030: 2100 MW wind, 2400 MW small hydro, 400 MW biomass


Renewable energy is pivotal to securing an energy future that is less dependent on fossil fuels.

PR China and India have the largest existing capacity for renewable energy, especially hydropower, combustible renewables, wind power, and solar power. Geothermal-powered electricity generation is highest in the Philippines and Indonesia.

The uptake of renewable energy as an electricity source is expected to rise as most of Asian countries have adopted policies, programs, and/or targets to promote alternative energy.

Drivers of Emissions: Fuel Diversity

Notes: MW = Mega-watts; PV=photovoltaic

Energy mix by 2020: 20% of total energy supply will come from renewable sources Jawaharlal Nehru National Solar Mission - 20,000 MW of grid connected solar power by 2022Energy mix by 2025: 5% biofuels, 5%geothermal, 2.6% hydropower, 0.03% wind, 0.74% biomass

More hydropower projects

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Electricity consumption in Asia more than doubled in ten years and consumption by all end-use sectors increased. The industrial sector remained the biggest consumer of electricity with a share of 67%. The industrial sector is growing fastest with 11% average growth rate equivalent to a two-fold increase from 2000. Electricity consumption by the industrial and transport sectors doubled in 10 years. These differences could be related to the increase of built environment in cities, however varying data availability for different sectors could also play a role.

Drivers of Emissions: Electricity Consumption

Growth in electricity consumption has outpaced economic growth since 2000. Electricity consumption grew at an average growth rate of 10% compared to GDP average growth rate of 8%. PR China and Vietnam consume most electricity per GDP, whereas Nepal and Bangladesh consume the least.

Average AnnualGrowth Rate of

Electricity Consumption by End-use Sector

(2000-2009)

Electricity consumption per GDP

2009 (kWh/GDP)

Asia: End-use Sector Consumption

kWh Per GDP from Electricity Consumption in Asia (2009)


2103

0.17

Notes: Other sectors vary per country. This commonly includes agriculture, public lighting and water works, non-commercial use, and religious use


Residential Commercial Industrial Transport Others

10%

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BANGLADESH1. Bangladesh Power Development Board.

(2008-2010). Bangladesh PowerDevelopment Board Annual Report2008-2010. Retrieved online: BangladeshPower Development Board: http://www.bpdb.gov.bd/annual%20report.htm

2. Md. Alam Hossain Modal, Wulf Boie,Manfred Denich. (2010). Future DemandScenarios of Bangladesh Power Sector.

3. Power Cell. (2006). Bangladesh PowerSector Data Book. Retrieved online:http://www.solar-bangladesh.com/Bangladesh%20Power%20Data.pdf

4. World Bank. (2010). World BankDevelopment Indicators. Retrievedonline: World Bank Open Data: http://data.worldbank.org/indicator

INDIA1. Central Electricity Agency. (2011).

Central Electricity Agency ElectricityYearbook 2011.

2. Ministry of Finance. (2010). EconomicSurvey 2010-2011. Retrieved online:Economic Survey: http://indiabudget.nic.in/survey.asp

3. Ministry of Statistics and ProjectImplementation – National StatisticalOrganization – Central Statistics Office.(2006-2011). Energy StatisticalYearbooks 2006-2011. Retrieved online:http://mospi.nic.in/Mospi_New/upload/statistical_year_book_2011.htm

4. Sewa Bhawan; & R.K. Puram. Ministry ofPower. (2008). CO2 Baseline Databasefor the Indian Power Sector User GuideVersion 4.0 October 2008. Retrievedonline: http://www.cea.nic.in/reports/planning/cdm_co2/user_guide_ver4.pdf

5. Indian Network for Climate ChangeAssessment. 2010. India: GreenhouseEmission 2007. Retrieved online: http://moef.nic.in/downloads/public-information/Report_INCCA.pdf

6. The Energy Research Institute. (2010).The Energy Data Directory Yearbook2010.

7. World Bank. (2010). World Bank

Development Indicators. Retrieved online: World Bank Open Data: http://data.worldbank.org/indicator

INDONESIA1. Badan Pengkajian Dan Penerapan

Teknolog. (2006-2011). Badan PengkajianDan Penerapan Teknolog Annual Reports2006-2011. Retrieved online: http://www.bppt.go.id/

2. Badan Pusat Statistik. (2003-2010).Statistical Yearbook of Indonesia 2003-2010. Retrieved online: Badan PusatStatistik: http://www.bps.go.id/eng/index.php

3. Kementerian Energi Dan Sumber Daya.(n.d.). Retrieved online: http://www.esdm.go.id/publikasi.html Mineral


LAO PDR1. Ministry of Energy and Mines –

Department of Electricity. http://www.laoenergy.gov.la/


MALAYSIA1. Malaysia Energy Information Hub. Online

Statistical Database. http://meih.st.gov.my/statistics


NEPAL1. Nepal Electricity Authority2. Central Bureau of Statistics. (2009).

Central Bureau of Statistics StatisticalYearbook 2009. Retrieved online: http://cbs.gov.np/?page_id=1233

3. National Energy Agency. (2007-2010).National Energy Agency Annual Reports

Data Sources

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PAKISTAN1. Economic Adviser’s Wing, Finance

Division, Pakistan. (2010) PakistanEconomic Survey 2010-2011. Retrievedonline: http://www.infopak.gov.pk/economicsurvey/highlights.pdf

2. National Electric Power RegulatoryAuthority. (2003-2011). National ElectricPower Regulatory Authority State ofIndustry Reports 2003-2011. Retrievedonline: State of Industry Reports ofNational Electric Power RegulatoryAuthority: http://www.nepra.org.pk/industryreports.htm

3. Water and Power DevelopmentAuthority. (2010). Water and PowerDevelopment Authority Power Statistics2010. Retrieved online: http://www.wapda.gov.pk/


PHILIPPINES1. Department of Energy. (2004). National

Energy Plan Statistics 2004. Retrievedonline: http://www.imaginet.com.ph/ii-the-philippine-energy-plan-pep

2. Department of Energy. (1983-2011).Power Statistics 1983-2011. Retrievedonline: Department of Energy PowerStatistics: http://www.doe.gov.ph/ep/Powerstat.htm

3. Light Rail Transport Authority.(2012).LRTA Electricity consumption for2007-2011.


PR CHINA1. China Electric Council. (2005-2011).

National Electric Power IndustryStatistics Express 2005-2011.

2. China Energy Group. (n.d.). China EnergyGroup Databook 7.0. Retrieved online:China Energy Databook of China EnergyGroup: http://china.lbl.gov/databook

3. National Bureau of Statistics. (2003-2010).China Statistic Yearbooks.Retrieved online: CNKI KnowledgeNetwork Service Platform: http://tongji.cnki.net/overseas/engnavi/result.aspx?id=n2011100024&file=n2011100024000230&floor=1


SINGAPORE1. Department of Statistics. (1986-2010).

Historical Electricity Consumption 1986-2010. Retrieved online: SingStat: http://www.singstat.gov.sg/

2. Department of Statistics. (2001-2010).Fuel mix for electricity generation 2001-2010. Retrieved online: SingStat: http://www.singstat.gov.sg/

3. Department of Statistics. (n.d.) ElectricityGeneration and Sales. Retrieved online:Publications – Miscellaneous of SingStat:http://www.singstat.gov.sg/pubn/reference/yos12/statsT-miscellaneous.pdf

4. Energy Market Authority. (2004-2010).Energy Market Authority Annual Report2004-2010. Retrieved online: http://www.ema.gov.sg/page/45/id:101/

5. KEMA Limited Singapore and EnergyMarket Authority. (2008). Review of theLRMC costs of CCGT electricitygeneration in Singapore to establish thetechnical parameters for setting theVesting Price for the period 1 January2009 to 31 December 201. 100106665Document 7 – Version 1.2.

6. World Bank. (2010). World BankDevelopment Indicators. Retrievedonline: World Bank Open Data: http://

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SRI LANKA1. Ceylon Electricity Board. (2001-2010).

Statistical Digests 2001-2010. Retrievedonline: http://www.ceb.lk/sub/publications/statistical.aspx

2. Sri Lanka Sustainable Energy Authority.www.energy.gov.lk/

3. Sri Lanka Sustainable Energy Authority.(2007). Sri Lanka Energy Balance 2007.Retrieved online: http://www.energy.gov.lk/pdf/Sri%20Lanka%20Energy%20Balance%202007.pdf


THAILAND1. Department of Alternative Energy

Development and Efficiency ElectricPower. (2004-2010). Annual Report2004-2010. Retrieved online: http://w ww . d e d e . g o . t h / d e d e / i n d e x .php?option=com_content&view=article&id=1841&Itemid=318&lang=en

2. Energy Policy and Planning Office. 2010.Energy Statistics of Thailand 2010.Retrieved online: http://www.eppo.go.th/info/yearbook/energystatisticsofthailand2010.pdf

3. Energy Policy and Planning Office.Online Statistical Database. Retrievedonline: http://www.eppo.go.th/info/cd-2012/index.html


VIETNAM1. General Statistics Office. (2010).

Statistical Yearbook of Vietnam 2010.Retrieved online: http://www.gso.gov.vn/default_en.aspx?tabid=515&idmid=5&ItemID=11974

2. Tran Minh Tuyen; & Axel Michealowa.Hamburg Institute of InternationalEconomics. (2004). HWWA DiscussionPaper Construction for Vietnam NationalElectricity Grid

3. Vietnam Electricity Corporate Profile2009-2010.

4. UNFCCC. Project 6919 Song Bung 4hydropower project. Retrieved online:http://cdm.unfccc.int/Projects/DB/RWTUV1343910005.49/view


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RECOMMENDATIONS AND NEXT STEPS

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The Knowledge Partnership on Air Pollution and GHG Data and Indicators for Transport and Energy in Asia has resulted in benchmark emissions indicators for road transport and electricity in 13 countries and 23 cities in Asia, supported by guidelines on their measurement and reporting and an online database – www.CitiesACT.org.

This is only the beginning. Continued efforts are needed to ensure that policy and development interventions integral to energy, transport, and urban development are based on relevant, reliable and accessible data. Three next steps are recommended.

1. Increase the Scope of Data Collected

Building on the existing data and indicators for road transport and electricity, it is recommended that:

• Data be collected for more Asiancountries and cities.

• Indicators be developed for emissionsfrom other transport modes, such asmarine and air transport, and other energytypes and uses, such as renewable energy.

• Land use indicators be developed forcities, such as green space availability,sidewalk area, bike lane length, and mixedland use percentage. Land use is adetermining factor in future emissionsfrom transport and energy.

• Policy summaries be prepared fortransport and energy sectors relevant toair pollution and greenhouse gasemissions to strengthen the link betweencollected data, derived indicators, and thepolicies these are meant to improve.

Collaboration between different air quality and climate change stakeholders in the transport and energy sectors is critical to achieve a consistent data set that meets collective requirements and to mainstream their use in decisions on policies and development interventions.

Coordination is needed between government agencies at the local and national levels and across sectoral agencies, such as transport, energy, industry, health, and environment ministries.

Furthermore, given the wealth of data collected by private sector and academic and research institutes, collaboration mechanisms between government and these organizations is also important.

Non-governmental organizations (NGOs) and development agencies can play an important role in setting up the partnerships that support such coordination.

2. Collaboration to HarmonizeMainstream Data

Recommendations and Next Steps

Coordination at international level is needed between governments across Asia, and with international networks and NGOs. Development agencies are increasingly seeing the benefit of partnering with each other and with NGOs, universities, associations and others.

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Examples of current initiatives are• Open Data Initiative of the World Bank• Sustainable Transport Initiative of the ADB• Climate and Clean Air Coalition hosted by

UNEP• Low Emissions Development Strategies

(LEDS) Global Partnership, with an AsiaLEDS partnership coordinated byUSAID

• Environmentally Sustainable TransportForum of UNCRD

3. Support to Countries and Cities

Much work is needed to help cities and countries to• Improve systems for data measurement,

reporting and verification that are used to track and steer progress in reducing emission intensities and pollution levels.

• Institutionalize systems within the relevantgovernment institutions, such as ministries and central statistical agencies. Considering government’s limited resources, data collection should thus reflect their data needs.

• Maximize the use of data for thedevelopment, implementation and evaluation of policies, programs and international reporting under the UNFCCC, including Nationally Appropriate Mitigation Actions (NAMAs).

NGOs and research institutes are well-positioned to give concrete support to cities and national governments, such as Clean Air Asia, ICLEI and IGES. The type of support provided includes toolkits, guidelines, assessment, pilot projects, capacity building, and providing access to experts.

REFERENCES

Asian Development Bank (ADB). (2010). Key Indicators for Asia and the Pacific.

Automated Research Association of India (ARIA). (2007). Emission factor development for Indian vehicles as part of the ambient air quality monitoring and emission sources apportionment studies, air quality monitoring project - Indian Clean Air Programme (ICAP).

British Petroleum. (2011). Statistical Review of World Energy.

Climate & Development Knowledge Network (CDKN). (2012). Parliamentarians bringing renewable energy to India.

European Environment Agency (EEA). (2011). Energy efficiency and specific CO2 emissions (TERM 027). Retrieved online. URL: http://www.eea.europa.eu/data-and-maps/indicators/energy-efficiency-and-specific-co2-emissions/energy-efficiency-and-specific-co2-3

German International Development Cooperation (GIZ). (2010). International Fuel Prices 2010/2011.

International Energy Agency (IEA). (2012). CO2 Emissions from Fuel Combustion 2012. Retrieved online. URL: http://www.iea.org/co2highlights/

IEA. (2012). Energy Technology Perspectives 2012: Pathways to a Clean Energy System. Retrieved online. URL: http://www.iea.org/Textbase/npsum/ETP2012SUM.pdf

IEA. (2012). World Energy Outlook. Retrieved online. URL: http://www.worldenergyoutlook.org/publications/weo-2012/

International Monetary Fund (IMF). (2012). World Economic Outlook. Retrieved online. URL: http://www.imf.org/external/pubs/ft/weo/2012/02/index.htm

International Panel on Climate Change (IPCC). (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Retrieved online. URL: http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol2.html

International Transport Forum (ITF), Eurostat and Economic Commission for Europe. (2009). Illustrated Glossary for Transport Statistics, 4th Edition. Retrieved online. URL: http://www.oecd-ilibrary.org/transport/illustrated-glossary-for-transport-statistics-4th-edition_9789282102947-en

Johnson, I., & Bradsher, K. (2010). China’s rise complicates goal of using less energy. New York Times. Retrieved online. URL: http://www.nytimes.com/2010/09/17/business/energy-environment/17energy.html?_r=0

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Gota, S. (2012). Crunching Numbers on Transport CO2 Emissions in Developing Countries.Retrieved online: http://transport-solutions.blogspot.in/2012/09/crunching-numbers-on-transport-co2.html

Komiyama, R. (n.d.). Energy Outlook to 2035 in Asia and its Pathways Towards a Low Carbon Energy System. Retrieved online. URL: http://www.worldenergy.org/documents/congresspapers/174.pdf

Pakistan Today. (2012). Alternate energy goals met, nearly. Pakistan Today. Retrieved online. URL: http://www.pakistantoday.com.pk/2012/09/03/city/islamabad/alternate-energy-goals-met-nearly/

Reve. (2011). Bangladesh to produce 500 MW from Solar Power. Retrieved online. URL: http://www.evwind.es/2011/03/16/bangladesh-to-produce-500-mw-from-solar-power/10796/

Schipper, L., & Marie-Lilliu, C. (1999). Carbon Emissions from Transport in IEA Countries: Recent Lessons and Long-Term Challenges. Stockholm: Swedish Board for Communications and Transportation Research.

Schipper, L., Fabian, H., & Leather, J. (2009). Transport and Carbon Dioxide Emissions: Forecasts, Options Analysis, and Evaluation. Retrieved online. URL: http://www.adb.org/sites/default/files/pub/2009/ADB-WP09-Transport-CO2-Emissions.pdf

Schipper, L., Marie-Lilliu, C., & Gorham, R. (2000). Flexing the link between transport and greenhouse gases: a path for the World Bank. Paris: International Energy Agency.

United Nations Environment Programme (UNEP). (2012). Global Environment Outlook 5: Summary for Asia and the Pacific Region.

United States Environment Protection Agency (US EPA). (n.d.). Emission Factors & AP 42, Compilation of Air Pollutant Emission Factors. Retrieved online. URL: http://www.epa.gov/ttnchie1/ap42/

World Health Organization (WHO). (2011). Air Quality and Health. Retrieved online. URL: http://www.who.int/mediacentre/factsheets/fs313/en/index.html

World Bank. (2009). Drivers of Changing Production and Consumption Pattern. Retrieved online. URL: http://www.un.org/esa/dsd/resources/res_pdfs/publications/trends/trends_sustainable_consumption_production/ch3_drivers_of_changing_scp.pdf

World Bank. (Various years). GDP (constant 2000 US$). Retrieved Online. World Bank Open Data. URL: http://data.worldbank.org/indicator/NY.GDP.MKTP.KD

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Kahn Ribeiro, S., Kobayashi, S., Beuthe, M., Greene, D., Lee, D., Muromachi, Y., et al. (2007). 2007: Transport and its infrastructure. In Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth.

For the data used in this publication, please visit CitiesACT.

www.CitiesACT.org

CitiesACT is Clean Air Asia’s online database on air quality, climate change, energy and transport. It was developed by

Clean Air Asia with support from the Asian Development Bank, Global Air Pollution Forum and the World Bank together with

Clean Air Asia Partnership members.

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www.cleanairasia.org

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ACCESSING ASIA - Clean Air Asiacleanairasia.org/wp-content/uploads/portal/files/documents/... · Clean Air Asia was established as the premier air quality network for Asia by the