Global Cities and their Response to Climate Change

by

Lorraine Sugar

A thesis submitted in conformity with the requirements

for the degree of Master of Applied Science

Department of Civil Engineering

University of Toronto

© Copyright by Lorraine Sugar 2010

ii

Global Cities and their Response to Climate Change

Lorraine Sugar

Master of Applied Science

Department of Civil Engineering University of Toronto

2010

Abstract

Decision-makers in cities have realized their pivotal role in addressing climate change, and

they are responding accordingly. This thesis presents three papers that explore the process

of responding to climate change in cities, highlighting the situation in selected global cities

with varying economies and development priorities. The methodology for conducting an

urban greenhouse gas inventory in three Chinese cities is detailed in the first paper,

illustrating issues of economic development and climate change mitigation in a transitional

economy. Next, the greenhouse gas emissions savings of various strategic mitigation plans

are quantified for Toronto, demonstrating the aggressive actions needed in developed cities

to approach carbon neutrality. The third paper explores issues associated with climate

change in three developing cities, emphasizing the need for synergic development

incorporating strategies for both mitigation and adaptation. The thesis concludes with an

overview of the importance of innovation and further research to future responses to

climate change.

iii

Acknowledgments

It is hard to imagine that my past two years will be concluded with a single document, for

the experiences I have had cannot be fully expressed within the pages of this thesis. The

full value of my education was obtained beyond my courses and outside my graduate office:

the internship opportunities, the chances to travel, the seminars and presentations, and the

discussions and debates have shaped my perspective on sustainability in cities. It is for

these experiences that I am truly grateful.

My internship opportunities have been incredible learning experiences, and they have given

my research a unique, real-world advantage. Thank you to Zerofootprint, Edward Leman of

Chreod Inc., and the Urban Development Unit of the World Bank in Washington, DC for both

the research data support and the opportunities for professional development.

Thank you to my peers, the world-class Sustainable Infrastructure Group (SIG): Dave

Bristow, Sybil Derrible, Eugene Mohareb, Dave Rulff, Sheyda Saneinejad, Rob Stupka, and

Ryan Zizzo. It has been a pleasure to learn with you all, and I know we will remain

colleagues in the future.

Thank you to Dan Hoornweg and Murray Metcalfe, not only for their official roles as

internship supervisor and professor, but for their mentorship. Your wisdom, insight, and

encouragement will continue to inspire me as I enter the next phase of my career.

Most of all, thank you to Chris Kennedy for being an outstandingly supportive advisor. My

Masters experience was wonderful because of your encouragement and understanding.

Thank you for treating my compulsion to travel as an opportunity, not a complication; for

meeting my difficulty getting started with support and encouragement; and for humouring

me during the meetings when I just wanted to sit around and discuss ideas. You have

always maintained that my Masters could be whatever I wanted it to be, and it has

developed into an experience far greater than I could have ever imagined. Thank you.

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Table of Contents

Acknowledgments ..................................................................................................... iii

Table of Contents ...................................................................................................... iv

List of Tables ............................................................................................................ vi

List of Figures .......................................................................................................... vii

List of Boxes............................................................................................................viii

Status of Publications ................................................................................................ ix

Chapter 1 Introduction and Background ........................................................................1

The Response to Climate Change .................................................................................2

Understanding the Urban System.............................................................................3

Strategies for Mitigation..........................................................................................5

Strategies for Adaptation ........................................................................................6

The Mitigation-Adaptation Nexus..............................................................................7

Themes and Organization............................................................................................7

References for Chapter 1........................................................................................... 10

Chapter 2 Greenhouse Gas Emissions from Chinese Cities ............................................. 12

Introduction ............................................................................................................ 12

Calculating GHG Emissions from Chinese Cities ............................................................ 13

Emissions from Energy.......................................................................................... 15

Emissions from Industrial Processes ....................................................................... 17

Emissions from Waste........................................................................................... 18

Results of Chinese Cities ........................................................................................... 19

Comparison to Global Cities....................................................................................... 22

Discussion............................................................................................................... 26

References for Chapter 2........................................................................................... 29

Chapter 3 A Low-Carbon Infrastructure Plan for Toronto, Canada ................................... 31

Getting to Carbon Neutral: A Guide for Canadian Municipalities...................................... 31

Community Scale Low-Carbon Developments .............................................................. 33

v

Integration of Strategies: Toronto Case Study ............................................................. 33

Base-Case Scenario for 2004 ................................................................................. 36

Planned-Policy Scenario for 2031 ........................................................................... 40

Alternative-Aggressive Scenario for 2031 ................................................................ 45

Summary and Conclusions ........................................................................................ 50

References for Chapter 3........................................................................................... 55

Chapter 4 Synergies Between Adaptation and Mitigation in Development: Case Studies

from Amman, Jakarta, and Dar es Salaam............................................................... 57

Introduction ............................................................................................................ 57

The Vulnerabilities of Cities to Climate Change............................................................. 59

Determining Greenhouse Gas Emissions from Cities ..................................................... 61

Methodology and Data Sources .............................................................................. 62

Results ............................................................................................................... 63

Development Opportunities ....................................................................................... 66

Summary and Conclusions ........................................................................................ 68

References for Chapter 4........................................................................................... 71

Chapter 5 Moving Forward......................................................................................... 73

Comparison and Dissemination .................................................................................. 74

Scale of Strategic Response....................................................................................... 75

Financial Mechanisms ............................................................................................... 75

Conclusions ............................................................................................................. 76

References for Chapter 5........................................................................................... 78

Appendix A: Standard GHG Reporting Tables for Beijing................................................ 79

Appendix B: Standard GHG Reporting Tables for Shanghai ............................................ 85

Appendix C: Standard GHG Reporting Tables for Tianjin................................................ 91

Appendix D: Standard GHG Reporting Tables for Amman .............................................. 97

Appendix E: Standard GHG Reporting Tables for Jakarta ............................................. 103

Appendix F: Standard GHG Reporting Tables for Dar es Salaam ................................... 108

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List of Tables

Table 1: Emissions attribution by scope and spatial boundary. .........................................5

Table 2: Characteristics of Shanghai, Beijing, and Tianjin city-provinces. ........................14

Table 3: Sample energy balance table for select fuels, based on the 2006 energy balance

table for Shanghai.......................................................................................16

Table 4: Calorific values and GHG intensities for fuels consumed in Chinese cities. ...........17

Table 5: Per-capita GHG emissions by sector for Shanghai, Beijing, and Tianjin. ..............20

Table 6: Fuel supply and emission factors for thermal power production for Shanghai, Beijing, Tianjin, and China............................................................................21

Table 7: Sectors, strategies, and guidelines presented in Getting to Carbon Neutral .........32

Table 8: Estimated energy use and emissions for the Toronto building stock in 2004. .......37

Table 9: Energy, natural gas, and electricity breakdown by end-use for different building classifications. ............................................................................................37

Table 10: Vehicle-kilometres travelled (VKT), emissions, and mode share savings for

Toronto's transport infrastructure in 2004.. ...................................................39

Table 11: Comparison between 2004 Toronto GHG Inventory and 2004 Base-Case Scenario.................................................................................................................40

Table 12: Projected energy use, emissions, and policy-related emissions savings for the

Toronto building stock in 2031. ....................................................................42

Table 13: Vehicle kilometres-travelled (VKT), emissions, and planned mode share savings for Toronto's Metrolinx infrastructure in 2031. ...............................................44

Table 14: Energy use, emissions, and aggressive emissions savings for the Toronto building

stock in 2031.............................................................................................49

Table 15: Vehicle kilometres-travelled (VKT), emissions, and mode share savings for

Toronto's Metrolinx infrastructure with aggressive transport changes in 2031 ....52

Table 16: Comparison of final emissions values for all scenarios.....................................54

Table 17: Per-capita GHG emissions by sector for Amman, Jakarta, and Dar es Salaam. ...65

Table 18: Fuel supply and emission factors for electricity generation for Amman, Jakarta,

and Dar es Salaam. ....................................................................................65

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List of Figures

Figure 1: Cost-benefit analysis of mitigation and adaptation. ...........................................8

Figure 2: Shanghai's GHG emissions by sector; percentage breakdowns follow a similar

pattern for emissions from Beijing and Tianjin. ..............................................21

Figure 3: Per-capita GHG emissions from Chinese cities and 10 global cities. ...................23

Figure 4: Per-capita energy consumption as a function of heating degree-days for Chinese

cities and 10 global cities. ...........................................................................24

Figure 5: Per-capita transportation emissions as a function of population density for Chinese

cities and 10 global cities. ...........................................................................25

Figure 6: Per-capita electricity emissions and per-capita electricity consumption for Chinese

cities and 10 global cities.. ..........................................................................26

Figure 7: Amman's urban metabolism.........................................................................60

Figure 8: Total urban greenhouse gas emissions by sector for Jakarta, Dar es Salaam, and Amman.....................................................................................................64

viii

List of Boxes

Box 1: Dockside Green, Victoria, British Columbia. .......................................................34

Box 2: Beddington Zero-Energy Development (BedZED), London, UK. ............................34

Box 3: Vauban District of Freiberg, Germany. ..............................................................35

Box 4: Dongtan Eco-City, Chongming Dao, China.........................................................35

Box 5: Drake Landing Solar Community, Okotoks, Alberta.............................................47

Box 6: Canadair Facility Solarwall, Dorval, Quebec. ......................................................48

Box 7: Better Place Electric Vehicle Network, all cities in Israel. .....................................51

ix

Status of Publications

Chapter 2: Greenhouse Gas Emissions from Chinese Cities

Will be submitted for publication to the Journal of Industrial Ecology as Sugar, L., C.A.

Kennedy, E. Leman. 2010. “Greenhouse Gas Emissions from Chinese Cities.”

Chapter 3: A Low-Carbon Infrastructure Plan for Toronto, Canada

Adapted from Sugar, L. 2010. “Integration of Strategies: Toronto Case Study.” in Kennedy,

C.A. ed. 2010. Getting to Carbon Neutral: A Guide for Canadian Municipalities. Toronto and

Region Conservation Authority: Toronto.

Will be submitted for publication as Sugar, L., and C.A. Kennedy. 2010. “A Low-Carbon

Infrastructure Plan for Toronto, Canada.”

Chapter 4: Synergies between Adaptation and Mitigation in Development: Case

Studies of Amman, Jakarta, and Dar es Salaam

Will be submitted for publication to Environment and Urbanization as Sugar, L., C.A.

Kennedy, and D. Hoornweg. 2010. “Synergies between Adaptation and Mitigation in

Development: Case Studies of Amman, Jakarta, and Dar es Salaam.”

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Chapter 1 Introduction and Background

We are in an era of rapid change that will result in significant consequences for future

generations. The emergence of a new and heavily connected global economy has resulted in

business models based on cheap labour and cheap resources located beyond the confines of

national borders. We have realized the environmental impacts of our actions, such as a

changing global climate system, and we are increasingly experiencing the consequences.

Cities are positioned at the epicentre of this new reality. They are hubs of business and

commerce, heavy consumers of resources, and home to innovation and technological

development. People all over the world are flocking to cities to be a part of this global

phenomenon, in search of quality of life and opportunities that are not available anywhere

else. As a result, urbanization is occurring at unprecedented rates, and the majority of the

world’s population now lives in cities. The relationship between cities and climate change is

somewhat circular: the activities in cities are a chief cause of climate change, and cities will

be severely impacted by climate change. This complex relationship has prompted authors to

refer to cities as a “battleground for sustainability” (Clarke 2003). Cities are where the fight

against global climate change will be won or lost.

The traditional response to global problems is a globally coordinated effort by nations and

international agencies, such as the United Nations Framework Convention on Climate

Change (UNFCCC) and its Kyoto Protocol. However, the importance of locally oriented

solutions and actions is becoming more recognized. City leaders, such as those in the C40

and the Covenant of Mayors, have acknowledged that their local decisions will have global

impacts. Their cities no longer operate in isolation, but instead have an inherent

responsibility to respond locally to global issues. While nations may be the entities agreeing

to treaties and setting targets, cities are where these changes will occur – simply because

cities are where the majority of people live. The lifestyle, consumption habits, and everyday

choices of the urban resident have profound impacts on the global economy and the level of

greenhouse gas production. City leaders are recognizing that sustainability-motivated

changes to policy and infrastructure will have a direct impact on those choices.

In keeping a global perspective, it is important to acknowledge and understand the

variations between cities. The differing economies and political priorities in cities will

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certainly play a role in a coordinated global effort against climate change. Challenges for a

city in a developing country will vary greatly from those a developed city. The true

opportunities will lie in the communication and dissemination between cities that is enabled

by our globalized networks.

This thesis presents three papers about global cities and their response to climate change.

Each paper highlights a different type of urban economy and a different developmental

priority. Through these papers, the thesis explores the response to climate change from the

perspective of a developed city, cities with transitional economies, and cities in developing

regions of the world. The variations also illustrate the progressive stages of local action on

global climate change. While responses undertaken by cities are diverse, when viewed

together they represent a holistic, global approach to one of our generation’s most pressing

issues.

The Response to Climate Change

Climate change is a complex problem. In many ways, it has the characteristics of what

Rittel and Webber (1973) coined a “wicked problem”; that is, climate change is a problem

that is not fully understood, there is not one correct solution, and attempts at solutions may

in fact cause further problems. The impacts of cities on global climate change, and, in turn,

the impacts of climate change on cities, are topics that have received significant attention in

the literature. The problems and the solutions are explored simultaneously: urban

infrastructure strategies for climate change mitigation and adaptation are developed and

piloted as the debate ensues about standardized methodologies for inventorying greenhouse

gas emissions. However, if the effectiveness of the action on climate change is to be

maximized, there is an increasing need for coordination and planning.

This thesis explores the response to climate change in cities as a process. The first step is

an understanding of the systems in a city; this includes, for example, knowledge of the level

of resource consumption, the amount of greenhouse gases produced by various sectors,

infrastructure vulnerabilities, and development priorities. Following a thorough assessment,

decision-makers are able to best target funds and resources into areas that will have the

largest impact. The mitigation and adaptation strategies presented are catered to the

circumstances of each city, but the most effective effort will require knowledge-sharing

between cities.

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Understanding the Urban System

The phenomenon of urbanization has occurred throughout history, placing cities at the focal

point of nearly all civilizations. People have always followed their social instincts to group

together and rely on each other for survival, creating systems and processes to ease the

consistent demands of daily life. Similar to biological ecosystems, the services found in

cities are diverse and support the wide range of human requirements. Urban services meet

physiological and social needs while providing economic and civic opportunities that serve

the population. Resources are brought into the city for consumption and waste products are

expelled.

The different types of infrastructure in cities support the diverse aspects of urban life. The

physical infrastructure in cities provides the foundation for physiological survival: shelter

and mobility, as well as access to energy, water, and goods, allow time traditionally devoted

to life-sustaining activities, such as firewood or water collection, to be used for social and

economic pursuits. The social and economic infrastructures provided by individual urban

citizens are vital. Each urban participant has a role and contributes to the function of the

city; for example, the grocer brings in food, the bus driver enables mobility, the factory

worker manufactures goods, and the business professional supports economic growth and

monetary flow. The economic and civic infrastructures in cities support localized decision-

making and, in democratic states, provide a feedback mechanism that allows urban citizens

to influence the form and function of their cities.

Viewing the city as an ecosystem allows for the application of the concept of metabolism: a

representation of the inflows and outflows of the urban system. Numerous authors are

exploring urban metabolism as a method to understand the global impacts of cities (for

example: Kennedy et al. 2007; Zhang, et al. 2009.), including the resource and material

demands of the city, as well as the waste and by-products. For example, Figure 7 in Chapter

4 shows an urban metabolism diagram for the city of Amman. Energy and water resources

enter various sectors of the urban system, and solid waste and greenhouse gas (GHG)

emissions are produced. In a discussion of the climate change impacts of cities, urban

metabolism is particularly relevant: in viewing the city as an open system interacting with

the outside world, the GHG emissions that are attributed to cities become increasingly

apparent (Kennedy et al. 2009b; Kennedy et al. 2010).

Local-scale GHG emissions inventories have been pursued for nearly 20 years. One of the

first organizations to undertake city-level GHG emissions reporting was the International

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Council for Local Environment Initiatives (ICLEI) – now known as ICLEI-Local Governments

for Sustainability. As part of the ‘Local Agenda 21’ efforts in 1992 (United Nations 1992),

ICLEI initiated a campaign to quantify and reduce GHG emissions in cities. At this time,

issues of boundary, emissions allocation, and methodological consistency across cities were

topics of discussion in the academic literature (see Harvey 1993, Kates et al. 1998). Over

the past ten years, the number of organizations producing GHG inventories for cities has

increased, and methodological issues continue to be discussed (see Bader and Bleischwitz

2009; Kennedy et al. 2009a).

Today, a prominent complication associated with conducting an urban greenhouse gas

inventory is still the issue of emissions attribution. The GHG emissions inventories for

nations assign any emissions that occur within the jurisdictional borders of a country as that

country’s responsibility (IPCC 2006). However, such an approach is not possible for cities

given their heavy reliance on resource and material flow from outside their boundaries. In

fact, the interconnected nature of the global economy makes this strategy debatable for

countries as well: calculations using IPCC methodology neglect cruising emissions from

international flights, and the majority of emissions produced to manufacture goods

consumed in rich countries do not take place within their borders (see Davis and Caldeira

2010).

The attribution of emissions may be classified as either “production” or “consumption” (see

Kennedy et al. 2009a): emissions are either directly produced within the jurisdictional

boundary or they are a result of consumption activities taking place within the boundary.

The IPCC (2006) inventory methodology for nations is strictly based on production. The

WRI/WBCSD GHG Protocol (WRI/WBCSD 2004) is designed for corporate and institutional

reporting and separates emissions attribution into “scopes” that cover production and

consumption (Table 1). Recently, a harmonized standard for urban emissions inventories

was proposed by UNEP, UN-HABITAT, and the World Bank: the International Standard for

Determining GHGs from Cities (UNEP et al. 2010; followed in Appendices A-F) is based on

the methodological discussion of Kennedy et al. (2009a).

The International Standard is an emissions reporting guideline, and it requires a hybrid of

production and consumption inventories. It includes emissions produced in the city, such as

those from fossil fuel combustion, industrial processes, and agriculture and land-use

(AFOLU). It also includes emissions that would not be produced if not for activity in the city;

for example, emissions from electricity generated outside the city boundaries but consumed

inside the city; emissions from the decomposition of waste produced in the city; and

5

Table 1: Emissions attribution by scope and spatial boundary.

WRI / WBCSD Definition

Spatial Boundary Components

• Embodied emissions from food and materials consumed in cities

• Emissions upstream of electric powerplants

• Upstream emissions from fossil fuel use • Combustion of aviation and marine fuels

Scope 3

Out of boundary energy use and

emissions not included in Scope 2

• Out of boundary waste (landfill) emissions

• Out of boundary district heating emissions Scope 2 In boundary

energy use • Out of boundary electricity emissions at powerplant

• In boundary fossil fuel combustion

• In boundary waste (landfill) emissions • In boundary industrial processes and product use

Scope 1 In boundary

emissions

• In boundary agriculture, forestry, and other land uses

Source: Adapted from Kennedy et al. 2010.

emissions from aviation and marine vessels resulting from the economic activity of the city.

Though the overall inventory philosophy varies from that of nations, the methodology of the

IPCC (2006) is followed for calculations in each specific sector.

A city’s greenhouse gas inventory is particularly valuable as the first step in a city’s

response to climate change. The inventory serves as an indicator of particularly emissions-

intensive sectors, as well as providing verifiable metrics upon which to facilitate targeted

project financing. As further actions on climate change are taken, methodologically

consistent greenhouse gas inventories can indicate if the actions are reducing emissions as

expected, or if their impacts are negated by unforeseen circumstances. An inventory that

reveals few emissions may also point to areas of greater concern to the city, such as the

need for low-carbon development of services or adaptation to climate change.

Strategies for Mitigation

The technology exists for carbon-neutral cities; however, the capital costs are high, and the

way of life for some wealthy urban residents will be fundamentally challenged – especially in

North America. The infrastructure options that reduce greenhouse gas emissions are diverse

and cover a variety of sectors, including buildings, transportation, energy, waste, and other

municipal services (see Kennedy 2010; Lebel et al. 2007). Policy and governance also plays

6

an important role: Bai and colleagues have shown that vertical linkages are essential for the

down-scaling of policy and up-scaling of pilot projects (Bai et al. 2009; Bai et al. 2010).

Perhaps most importantly, the fundamental strategy for climate change mitigation in cities

is support from governments and citizens.

The greenhouse gas emissions saved by mitigation strategies require verification. Various

climate change scenarios will be simulated during the development of a city’s strategic

mitigation plans, often with models or software (for examples, see Lin et al. 2010; Li et al.

2010; Kennedy 2010). One of the benefits of approaching climate change quantitatively by

conducting an inventory and testing a strategic GHG reduction plan is that high impact

projects are favoured. It is important to assess whether the adopted mitigation strategies

will meet emissions targets and result in emissions reductions on a global scale.

Strategies for Adaptation

Cities are particularly at risk when it comes to the management of extreme events. To give

context, some authors point to the aftermath of Hurricane Katrina as an example of the ill-

preparedness of cities in coping with weather disasters (Susskind 2010; Simon 2010). The

comparison gives a dire image as to the realities of how climate change will impact cities

that are not equipped to cope.

The best strategies for how to adapt to climate change vary with each city. Similar to a

greenhouse gas inventory, adaptation planning is best preceded by an analysis: specifically,

an urban climate change risk assessment. While quantitative climate change risk

assessments are beyond the scope of this thesis, two models available include the UCCRN

(Urban Climate Change Research Network) “Framework for City Climate Risk Assessment”

(Mehrotra et al., 2009) and the PIEVC (Public Infrastructure Engineering Vulnerability

Committee) “Engineering Protocol for Climate Change Infrastructure Vulnerability

Assessment” (PIEVC, 2008). However, a quick overview of the geographic and social

circumstances of a city can usually point to areas of greatest concern. For example: a

coastal city may be at risk for sea-level rise; a city lacking water drainage infrastructure

may be risk for extreme flooding; and a city in a desert may face water shortages.

The most important adaptive approach for cities is that of resilience; that is, ensuring

infrastructure and urban systems are able to cope with extreme events. Redundancy and

elasticity are key components of resiliency. Implementing systems that can cope with

change, creating backup systems that can replace failed ones, and targeting infrastructure

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and populations that are in harms way are characteristics of resilient urban planning.

Unfortunately, the lack of reliable infrastructure, limited access to resources, and saturated

social systems make cities in developing countries the least resilient in the world.

There are numerous adaptation strategies for cities that address vulnerabilities in a variety

of urban sectors. Strengthening sea-walls, rehabilitating mangroves, and improving storm

water drainage address vulnerabilities associated with sea-level rise and flooding. Enhancing

buildings, physical infrastructure, health services, and urban greenery help alleviate issues

with extreme precipitation and heat event. Diversification of energy and water resources

ensures availability during disruptions. The specific portfolio of adaptive strategies will

depend on the specific needs of the city.

The Mitigation-Adaptation Nexus

The key to an effective climate change response requires strategies that address both

mitigation and adaptation. Mitigation and adaptation are complementary: they address

climate change from complementary geographic and temporal scales (Figure 2). The

benefits of mitigation are global, while the benefits of adaptation are local; the impacts of

mitigation action are long-term, while impacts of adaptation are immediate. Therefore, if

properly executed, mitigation strategies will keep climate change at a level that is

manageable by adaptive strategies.

Themes and Organization

The three papers presented in this thesis each highlight a different type of economy, a

different development priority, and a different stage in the climate change response

process.

The first paper illustrates the perspective of urban China: one of the fastest urbanizing

countries in the world. As the economy transitions, top priorities are economic growth,

improved quality of life, and, more recently, environmental preservation. The paper

presents detailed greenhouse gas emission inventories for Shanghai, Beijing, and Tianjin

using the new International Standard, and it highlights actions local and national

governments are taking to reduce emissions. The paper represents the first stage in the

climate change response: thorough and standardized emissions inventorying. It points to

the importance of standardized calculation, global perspectives, communication, and

governance to climate change action.

8

Figure 1: Scale-benefit analysis of mitigation and adaptation.

Source: Adapted from Wilbanks et al. 2003.

The second paper focuses on Canada; specifically, it presents a strategic low-carbon

infrastructure plan for Toronto. In Toronto’s developed economy and mature urban context,

greenhouse gas mitigation is the top development priority. Three scenarios are quantified

using the Getting to Carbon Neutral model (Kennedy 2010): a 2004 baseline; a projection

to 2031 based on currently planned government policies; and a projection to 2031 based on

a more aggressive action plan. By quantifying an aggressive mitigation response to climate

change, the paper highlights that current policies will be ineffective at producing the

emissions reductions needed for global-scale impacts.

Finally, the third paper explores the climate change response in three diverse cities:

Amman, Jakarta, and Dar es Salaam. Though an exploration of the emissions inventories

and urban systems, it becomes apparent that adaptation and resiliency are the most

necessary responses to climate change in these cities. Their economies are still developing,

and the basic provision of services is the top development priority. The paper emphasizes

that the developing regions’ responses to climate change are complex: data is scarce, the

poor are most vulnerable to impacts of climate change (though they did not cause it), and

there are few resources for strategic planning. In these cities, the truly global nature of

climate change is revealed.

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Building up these three papers, the thesis concludes with an overall discussion of some of

the broad issues raised, including: how to facilitate dissemination and mutual learning

between globally diverse cities; the scales of strategic response; and the importance of

funding mechanisms. Concluding thoughts and suggestions for further work are also

presented.

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Forum 21(3): 235-48.

Susskind, L. 2010. “Responding to the risks posed by climate change: Cities have no choice

but to adapt.” Town Planning Review 81(3): 217-35.

UNEP, UN-HABITAT, and the World Bank. 2010. International Standard for Determining

Greenhouse Gas Emissions. World Urban Forum, Brazil: March 2010.

United Nations. 1992. Agenda 21: The United Nations Programme of Action from Rio. United

Nations Conference on Environment and Development, Brazil: June 1992.

Wilbanks, T.J., S.M. Kane, P.N. Leiby, R.D. Perlack, C. Settle, J.F. Shogren, and J.B. Smith.

2003. “Possible Reponses to Global Climate Change: Integrating Mitigation and Adaptation.” Environment 45(5): 28-38.

WRI/WBCSD. 2004. The Greenhouse Gas Protocol: A Corporate Accounting and Reporting

Standard. World Resources Institute and the World Business Council for Sustainable Development: Washington, DC.

Zhang, Y., Z. Yang, and X. Yu. 2009. “Evaluation of urban metabolism based on emergy

synthesis: A case study for Beijing (China).” Ecological Modelling 220(13-14): 1690-

6.

12

Chapter 2 Greenhouse Gas Emissions from Chinese Cities

Introduction

The world is becoming increasingly urbanized, and the trend of urban migration is most

pronounced in countries with transitional economies. China, in particular, has experienced

rapid urbanization, with its urban population increasing from 34% to 43% in just 10 years

(World Development Indicators 2009). The environmental consequences of China’s rapidly

growing urban economy are significant, particularly related to greenhouse gas (GHG)

emissions and climate change. Cities are at the epicenter of the country’s economic growth,

and, consequently, they are responsible for the majority of emissions.

The patterns of urban growth in China are unique and highly resource intensive. The shift

from a legacy of central planning to growth driven by the private sector and foreign

investment has resulted in multiple satellite cities and rapidly expanding urban centres that

envelop hinterlands (Seto and Fragkias 2005). Not only are farmlands disappearing, the

resource burden associated with the construction of buildings and infrastructure is high.

Annually for the past ten years, China has built nearly half of all new buildings in the world

by volume, the majority of which are residential and commercial buildings that require the

highest quantity of material resources (Fernandez 2007).

The energy intensity, and therefore carbon intensity, of economic growth varies from city to

city in China. Dhakal (2009) observed varying energy-economy pathways. Highly energy-

intensive cities are found mostly in the central and western parts of China. They are home

to energy-intensive industries and many of these inland cities require more space-heating

due to a colder climate. In contrast, lower energy-intensive cities are on the climatically

moderate east coast with a stronger service industry. While there is a difference in both

cases of energy-economy, it is important to note that the energy systems, urban activity

and infrastructure in both cases are conducive to high GHG emissions.

Research to date exploring GHG emissions from Chinese cities has focused primarily on

energy-related emissions. Dhakal (2009) has explored the energy intensity of urban areas

in China, particularly the policy implications of rapid economic growth and energy-related

GHG emissions. In addition to estimating annual energy-related GHG emissions in Shanghai,

13

Li et al. (2010) quantified future projections of GHG emissions to 2020 under business-as-

usual and basic-policy scenarios. At the household scale, the energy consumption and GHG

emissions of residential homes in Chinese cities have been investigated (Zheng et al. 2009).

The importance of this research is significant to policy development: given the energy-

intensity of the growing Chinese economy, as well as the reliance on fossil fuels, emissions

from energy consumption are highly relevant to development.

Intensive energy consumption is not however the only by-product of a rapidly developing

economy: emissions from waste and industrial processes are also important to consider.

The existing literature on GHG emissions from Chinese cities has not fully explored these

additional emissions sources. Accordingly, the objective of this work is to provide a more

comprehensive and explicitly detailed emissions inventory methodology for three Chinese

cities, including previously unconsidered sources of emissions, that is in line with new

international GHG inventory standards and practices.

This paper explores multiple aspects of the urban GHG inventory for the Chinese cities of

Shanghai, Beijing, and Tianjin (city characteristics shown in Table 2). An inventory

methodology for Chinese cities is presented that is consistent with both the format of

Chinese statistical data and the recently released International Standard for Determining

Greenhouse Gas Emissions from Cities (UNEP et al. 2010). The GHG inventory results for

the three Chinese cities are then compared with other global cities and their significance on

a global scale is discussed.

Calculating GHG Emissions from Chinese Cities

The first step towards reduction of GHG emissions is transparent and robust quantification.

GHG inventories should be conducted regularly, so as to track progress and indicate areas

for improvement. Regular reporting of emissions requires two important components: a

standard inventory methodology and up-to-date activity data. In the case of Chinese cities,

the latter is readily available and robust due to China’s thorough statistical collection.

However, as is the case with many global cities, the methodology for estimating and

attributing emissions has varied in past research.

The International Standard for Determining Greenhouse Gas Emissions from Cities (UNEP et

al. 2010) was released jointly by UNEP, UN-HABITAT, and the World Bank at the 2010

World Urban Forum in Rio de Janeiro in response to the increasing need for a harmonized

GHG standard for cities. Harmonization of GHG inventory methodologies have been

14

Table 2: Characteristics of Shanghai, Beijing, and Tianjin city-provinces.

Population (2006)

Total Land Area (km2)

Urbanized Population Density

(persons/km2)

Heating Degree-Days (ref. 18˚C)

Shanghai 18,150,000 6,200 21,691 1566 Beijing 15,810,000 16,800 12,817 2865 Tianjin 10,750,000 11,300 12,085 2693

Source: Data from CSY 2007; BizEE Degree Days 2010.

successfully adopted at the national, institutional, and project levels. For city-level

emissions, the number of organizations producing GHG inventories has increased in recent

years, and methodological issues are continually discussed in the literature (summarized by

Kennedy et al. 2009a). A recent study comparing six local-scale inventory tools (Bader and

Bleischwitz 2009) concluded that interoperability requires the adoption of a reporting

standard that rectifies issues such as emissions sources, sector definitions, and

measurement scope.

The GHG Standard is characterized by standard reporting tables, displaying emissions by

both sector and fuel or activity type. In-boundary emissions are reported from energy

(stationary and mobile combustion), industrial processes and product use, waste, and

agriculture, forestry, and other land use (AFOLU; however, they are not included in this

study). Out-of-boundary emissions are also reported from the generation of electricity and

district heating consumed in the city (including transmission and distribution losses),

aviation and marine vessels carrying passengers and freight away from the city, and waste

generated in the city. Emissions embodied in food, water, fuels, and building materials are

encouraged to be reported as additional information items. Consistency with sector-level

calculation methodology developed by the Intergovernmental Panel on Climate Change

(IPCC) is required.

There are several benefits of standardized reporting of emissions from cities. The GHG

Standard supports urban policy and improves access to finances for city projects.

Standardized reporting allows cities to create a GHG baseline from which to evaluate

subsequent emissions inventories. The opportunity for cities to learn from one another and

disseminate best practices is enhanced by a harmonized reporting standard.

Greenhouse gas emissions are generally calculated as follows:

15

FactorEmissionDataActivityEmissionsGHG ×= (1)

Activity data varies with each of the inventory components; for example, it may refer to the

consumption of energy or production of waste. In calculations for cities, activity data is

specific to the city while emission factors are often based on national values or IPCC

defaults. For the Chinese cities presented, the data was available to determine city-specific

emission factors for electricity and district heating, while IPCC defaults were used for other

emission factors, similar to Dhakal (2009).

Emissions from Energy

Every year, the Chinese Energy Statistical Yearbook (CESY) reports the energy

consumption, production, import, and export for all the provinces, including energy balance

tables reported in the style of the International Energy Agency (IEA). Shanghai, Beijing, and

Tianjin are provincial-level municipalities in China, whereby they are governed by a local

municipal authority reporting directly to the State Council. Energy statistics, and IEA-style

energy balance tables, are available annually for all three municipalities, providing essential

data for GHG emissions calculations.

Interesting patterns emerge when the energy balance tables are viewed through the lens of

urban metabolism (Kennedy et al. 2007; Zhang et al. 2009). Fuels can be tracked from

their entry into the provincial energy system through to their consumption. For example,

Table 3 shows part of the 2006 energy balance table for Shanghai (CESY 2007), highlighting

three fuels: “coal cleaned”, “coke”, and “coke oven gas”. The entry of the fuel into the

province, its transformation into other fuels or electricity, and its consumption by sector can

be tracked in the table. In the case of “coal cleaned”, the fuel enters the province via

interprovincial import, a portion is consumed by industry for energy and the majority is

transformed into “coke” and “coke oven gas”. The “coke oven gas” is used primarily for

industrial energy, with a portion transformed into electricity in thermal power processes.

Following on in the energy balance table (beyond what is shown in Table 3), the

consumption of the electricity can be tracked further by sector.

The energy balance tables explicitly provide the quantities of fuels entering the

transformation processes “thermal power” and “heating supply”, which were used to

determine the GHG intensities of electricity and steam heating respectively. For additional

emissions from stationary combustion, the total consumption of each fuel type was

16

Table 3: Sample energy balance table for select fuels, based on the 2006 energy balance table for Shanghai.

Coal Cleaned 109 kg

Coke 109 kg

Coke Oven Gas

109 cu.m Total Primary Energy Supply 11.0723 -0.9278 Indigenous Production Recovery of Energy

Interprovincial Imports 12.7400 0.7436 Import Chinese Vessels Refueling Abroad

Interprovincial Exports -1.6400 -1.6124

Export Foreign Vessels Refueling in China Stock Change -0.0277 -0.0590

Total Transformation -10.9219 7.1052 2.1610 Thermal Power -0.1710 Heating Supply

Coal Washing

Coking -10.9213 7.4806 2.3320 Petroleum Refineries Gas Works -0.0006 -0.3754

Briquettes Losses in Transformation Total Consumption 0.1473 6.1792 2.1590 Material Producing Sectors

Agriculture Industry 0.1473 6.1792 2.1340 Construction

Transportation, Telecommunications, Postal Wholesale, Retail, and Catering Service Non-Material Sectors

Residential consumption 0.0250 Other

Statistical Difference 0.0031 -0.0018 0.0020

Source: Adapted from CESY 2007.

multiplied by the corresponding China-specific calorific value and IPCC (2006) default value

for GHG intensity, shown in Table 4.

Emissions from mobile combustion were taken to consist of all emissions from gasoline

consumption, diesel consumption, and kerosene and fuel oil consumed by the transportation

sector (with jet kerosene and marine fuel oil emission factors). To capture jet fuel and

marine fuel oil loaded onto vessels carrying passengers and freight away from the city, as

indicated in the GHG standard, fuel imported from “Chinese vessels refueling abroad” was

17

Table 4: Calorific values and GHG intensities for fuels consumed in Chinese cities.

Calorific Value (KJ/kg or KJ/cu.m)

Emission Factor (tCO2e / TJ)

Coal Raw 20,934 98.3 Coal Cleaned 26,377 98.3

Coal Washed 8,374 98.3 Coal Briquettes 20,934 98.3 Coke 28,470 107.0

Coke Oven Gas 17,375 44.4 Coal Gas not Coke Source 5,234 44.4 Coke Other Products 28,470 107.0

Crude Oil 41,868 73.3

Gasoline 43,124 72.3 Kerosene 43,124 71.9 Jet Kerosene 43,124 72.1

Diesel Oil 42,705 75.4

Fuel Oil 41,868 77.4 Marine Fuel Oil 41,868 78.2

Liquid Petroleum Gas 50,241 64.5

Refinery Gas 46,055 57.6 Natural Gas 38,979 56.1

Petroleum Other Products 41,868 73.3

Source: Data from CESY 2007; IPCC 2006.

subtracted from consumption totals and fuel exported from “foreign vessels refueling in

China” was re-added.

Emissions from Industrial Processes

Industrial process emissions are non-combustion emissions released as a consequence of a

chemical process. Therefore, the amount of GHG emissions released can be determined by

multiplying the quantity of product by the production-based emission factor, as follows:

processproductindustry EFMGHG ×= (2)

where Mproduct is the mass the product and EFprocess is the emission factor of the process.

Chinese cities have heavy industrial activity, with emissions resulting from the mineral,

metal, chemical, and electronics industries. Emissions from the cement and steel industries

were quantified in this study, as China is the leading producer of cement and crude steel

globally (Fernandez 2007). The cement and steel industries located in the three cities were

found to produce significantly high amounts of GHG emissions, which were on par with

18

emissions from other sectors in the cities. The masses of cement and steel produced

annually are recorded as industry statistics (CSY 2007). These values were multiplied by

IPCC (2006) default production-based emission factors for cement (based on 60% clinker

content) and steel.

Emissions from Waste

The IPCC (2006) recommends the use of the First Order Decay methodology for calculating

waste emissions; however, it requires time-series data spanning 20 or more years, which is

usually not available for cities. As an alternative, Kennedy et al. (2010) used a pragmatic

adaptation of the IPCC (1996) Total Yield Gas approach. The Total Yield Gas method

estimates the GHG emissions that will be released over many years from the waste

produced in the inventory year, as opposed to the emissions released from waste

decomposition during the inventory year alone.

The mass of waste produced is combined with the emission factor specific to the disposal

method (landfill, incineration, etc.). The emission factor depends on the composition, and

degradable organic carbon content, of the waste. For example, the calculations for GHG

emissions from landfills are made as follows:

)1(21 0 reclandfilllandfill fLMGHG −⋅⋅= (3)

where Mlandfill is the mass of waste sent to landfill in the inventory year, L0 is the methane

generation potential, 21 represents the 100-year global warming potential for methane, and

frec is the fraction of methane recovered. The methane generation potential, L0, is calculated

as follows:

FDOCDOCMCFL F ⋅⋅⋅⋅=12

160 (4)

where MCF is the methane correction factor (1.0 for managed landfills), DOC is the

degradable organic carbon (calculated as ∑ ⋅i

ii fW , with Wi representing IPCC default waste

weightings associated with each waste category fraction, fi), DOCF is the fraction DOC

dissimilated (assumed to be 0.6), F is the fraction of methane in landfill gas (assumed to be

0.5), and 16/12 is the stoichiometric ratio between methane and carbon.

19

Chinese waste statistics are available for all provinces, and therefore they are available for

Shanghai, Beijing, and Tianjin (CSY 2007). The mass of waste is reported by disposal

method, including landfill, compost, and incineration. The IPCC (2006) default waste

composition values for East Asia were used to estimate the emission factor, and it was

assumed that equipment for methane capture was not installed at landfill sites.

Results of Chinese Cities

A summary of the GHG emissions for Shanghai, Beijing, and Tianjin are shown in Table 5

(full standard reporting tables are shown in Appendices A-C). All three cities are all heavy

GHG emitters: in total, Shanghai, Beijing, and Tianjin emitted 12.9 tCO2e per capita, 10.8

tCO2e per capita, and 12.2 tCO2e per capita respectively in 2006. The distribution of

emissions by sector is similar for all cities; as an example, the emissions distribution for

Shanghai is shown in Figure 2. Emissions were primarily due to electricity consumption and

heating and industrial energy use, followed by the transportation sector. The GHG

inventories reveal some important physical and socioeconomic characteristics of the three

cities studied.

Like many Chinese cities, Shanghai, Beijing, and Tianjin rely primarily on coal for power

production. The share of coal in thermal power generation is exceptionally high (87% in

Shanghai, 86% in Beijing, and 98% in Tianjin), making the GHG intensity of power

production higher than other global cities (see Figure 6). As illustrated by the local

electricity production mixes and emission factors shown in Table 6, a switch from coal-

power would have a significant impact on reducing GHG emissions.

A closer look at emissions produced from residential heating and industrial activity reveal

some interesting differences between the three cities. In general, coal combustion is

prevalent: a portion of stationary combustion in the cities is from steam produced in district

heating facilities. The largest production share of the steam heat is from coal, and the

majority of it is used by industry. However, the partitioning of other energy by stationary

combustion varies. Industrial energy emissions per capita are highest in Shanghai and

Tianjin, while residential energy emissions per capita are highest in Beijing. Differences in

climate, quality of life, and type and scale of industrial activities can explain these patterns.

Non-energy emissions from industrial processes also provide insight into the variation in

industrial activity. Shanghai is the largest producer of steel (~19 Mt) and Beijing is the

largest producer of cement (~12 Mt); accordingly, Shanghai has the highest industrial

20

Table 5: Per-capita GHG emissions (kgCO2e/capita) by sector for Shanghai, Beijing, and Tianjin.

Scope SHANGHAI BEIJING TIANJIN ENERGY a) Stationary Combustion

Electricity 1,2 4,900 3,840 3,936 Steam (Heating Supply) 1,2 362 781 1,279 Heating & Industrial

Agriculture 1 1 65 37 Industry 1 3,982 2,343 3,936 Construction 1 52 22 37

Transportation, Telecommunications, Postal 1 52 54 37

Wholesale, Retail, and Catering Service 1 78 141 122 Residential consumption 1 233 510 232 Other 1 91 618 110

b) Mobile Combustion

Road Transportation (all gasoline & diesel) 1 1,112 911 1,109 Aviation 1 465 445 37

Marine 1 272 - 110

INDUSTRIAL PROCESSES Mineral Industry (Cement) 1 129 239 146 Metal Industry (Steel) 1 1,125 553 938

WASTE Landfill 1,3 52 304 97 Compost 1,3 5 2 -

Incineration 1,3 26 2 12

TOTAL (kgCO2e/capita) 12,929 10,847 12,185

21

Figure 2: Shanghai's GHG emissions by sector; percentage breakdowns follow a similar pattern for emissions from Beijing and Tianjin.

Table 6: Fuel supply and emission factors for thermal power production for Shanghai, Beijing, Tianjin, and China.

Shanghai Beijing Tianjin China Fuel Supply Coal Products 86.8% 86.0% 97.9% 96.6%

Coke Oven Gas 0.4% 0.3% - 0.4% Coal Gas not Coke Source 6.7% 5.5% 1% 0.3% Diesel Oil 0.1% - - 0.5%

Fuel Oil 2.8% 1.4% - 1.5% Refinery Gas - - - 0.1% Natural Gas 1.9% 6.8% 1% 0.6%

Petroleum Other Products 1.2% - - 0.1%

Emission Factor (tCO2e/GWh) 845 862 930 1,042

Source: Adapted from CESY 2007.

37%

37%

9%

6%

10%

Electricity

Heating & Industrial

Ground Transport

Aviation & Marine

Industrial Processes

Waste

1%

22

emissions per capita from the metal industry and Beijing from the mineral industry. Tianjin

has significant steel production as well, putting its industrial emissions per capita from the

metal industry close behind those of Shanghai.

Emissions from the transportation sector are high for all cities, particularly the amount of

emissions from road transportation. In this sector, the cities are distinguished by the level

of emissions from aviation and marine activity. Shanghai’s per capita emissions from

aviation and marine are highest of the three cities, indicating that it is the most active hub

of international economic activity and trade. Beijing’s busy international airport is reflected

in its high level of aviation emissions, while Tianjin’s marine activity is more GHG intensive

than its aviation activity.

Lastly, the waste management practices in the three cities impact their GHG emissions. The

method of waste treatment varies by city: the majority of Shanghai’s waste is incinerated,

while waste in Beijing and Tianjin is primarily sent to landfill. Beijing’s waste emissions are

significantly higher than the other cities, reflecting a higher amount of waste disposed.

Waste that is incinerated or composted produces fewer emissions, contributing to the low

waste emissions per capita for Shanghai.

Comparison to Global Cities

GHG baselines are interesting indicators of physical and economic urban structure.

Greenhouse gases are, in essence, waste products. The level of economic and social

activity, as well as the systems and structures that enable the activities, inform the amount

of greenhouse gases produced. For example, a city with heavy industry, high car usage, and

coal-generated electricity will have higher per-capita emissions than a city with a

knowledge-based industry, expansive public transit, and nuclear power production. These

differences between cities, as well as changes through time as a city develops, are captured

by comparing GHG baselines.

Compared to the ten global cities studied by Kennedy et al. (2009b), Shanghai, Beijing, and

Tianjin are among the highest per-capita emitters (Figure 3). The bulk of the emissions are

produced by the electricity, heating and industrial sectors, largely due to the predominately

coal-based energy structure. The level of ground transportation emissions is comparable to

developed cities with extensive public transit networks, such as London or Barcelona. The

heavy manufacturing and industrial base of the economy in Chinese cities is reflected in the

large portion of industrial process emissions.

23

Figure 3: Per-capita GHG emissions from Chinese cities and 10 global cities.

0.0 5.0 10.0 15.0 20.0 25.0

Bangkok

Barcelona

Cape Town

Denver

Geneva

London

Los Angeles

New York City

Prague

Toronto

Tianjin

Shanghai

Beijing

Electricity

Heating & Industrial Fuels

Industrial Processes

Ground Transportation

Waste

Aviation

Marine

tCO2e / capita

Source: Adapted from Kennedy et al. 2009b.

As shown in Figure 4, heating and industrial energy consumption for all global cities is

closely related to climate; however, some cities have energy consumption exceeding that

expected from the correlation to heating degree-days. This is likely representative of an

active industrial sector. Shanghai, in particular, falls well above the correlation trendline due

to its extensive industrial energy consumption.

The emissions from transportation are indicative of urban form: Figure 5 shows an inversely

proportional relationship between transportation emissions and population density. As

described in Kennedy et al. (2009b), North American cities tend to be spread out, resulting

in heavy reliance on the automobile and, hence, high transport-related emissions. In

contrast, the three Chinese cities are densely populated with low transport emissions. The

Chinese cities have transportation emissions comparable to European cities with extensive

public transport networks. This is potentially good news for Chinese cities: if they can

maintain density as they grow, per-capita transport emissions will likely remain constant.

24

Figure 4: Per-capita energy consumption as a function of heating degree-days for Chinese cities and 10 global cities.

Barcelona

Cape Town

Los Angeles

Bangkok

London

Prague

Geneva

New York City

Toronto

Denver

TianjinShanghai

Beijing

0

10

20

30

40

50

60

70

80

0 500 1000 1500 2000 2500 3000 3500 4000

Heating Degree-Days (˚C-day)

En

erg

y C

on

sum

pti

on

(G

J/ca

pit

a)

Source: Adapted from Kennedy et al. 2009b.

25

Figure 5: Per-capita transportation emissions as a function of population density for Chinese cities and 10 global cities.

Barcelona

London

Cape TownNew York City

Prague

Geneva

Bangkok

Toronto

Los Angeles

Denver

Tianjin Beijing

Shanghai

0

1

2

3

4

5

6

7

0 5,000 10,000 15,000 20,000 25,000

Population Density (persons per km2)

Tra

nsp

ort

ati

on

Em

issi

on

s (t

CO

2e

/ca

pit

a)

Source: Adapted from Kennedy et al. 2009b.

One of the biggest areas of variability among the cities is the GHG intensity of electricity

production. Figure 6 shows the electricity consumption, electricity emissions, and electricity

emissions factor for the Chinese cities and 10 global cities. While they may not have the

most electricity emissions or the highest level of electricity consumption, the Chinese cities,

along with Cape Town, have the most GHG-intensive methods of electricity production. The

high emission factors reflect the heavy reliance on coal for electricity production, and it

raises concerns about how Chinese cities will mitigate the GHG impacts of future heightened

electricity demands.

In addition to a comparison to other global cities, the total amount of GHG emissions

attributed to Chinese cities is so high that it is comparable to nations. When compared to

the GHG emissions (excluding forestry and land use changes) reported to the United

26

Figure 6: Per-capita electricity emissions and per-capita electricity consumption for Chinese cities and 10 global cities. The emission factors of electricity production are

indicated by the radial axis.

1000

800

600

400

200

100

Geneva

Toronto

Barcelona

London Los Angeles

New York CityBangkok

BarcelonaCape Town

Denver

Tianjin

Beijing

Shanghai

0

2

4

6

8

10

12

0 2 4 6 8 10 12

Consumption (MWh/capita)

Ele

ctri

city

Em

issi

on

s (t

CO

2e

/ca

pit

a)

Ele

ctricity Em

ission

Facto

r (tCO

2e

/GW

h)

Source: Data from Kennedy et al. 2009b.

Nations Framework Convention on Climate Change (UNFCCC) by countries, Shanghai ranks

25th (Hoornweg et al. 2010). Shanghai is producing more emissions than many countries,

including Thailand, the Netherlands, Venezuela, and Saudi Arabia. This reality makes it

important to consider that action to reduce GHG emissions is just as necessary at a city-

level as it is at a national level.

Discussion

The results of this study, as well as recent research in the area, point to some important

issues arising in China. There is great potential in Chinese cities for sustainable change and

27

progressive policy, and cities are already showing leadership in this area. This study

indicates that the primary sector to target for reducing emissions in Chinese cities is energy,

which is supported by resource potential and government support. However, this is not

without certain barriers to take into consideration.

The GHG emissions inventories for Shanghai, Beijing, and Tianjin highlight specific areas to

be addressed in mitigation plans; the most significant of which is the reliance on coal for

energy. The GHG intensities of electricity production in Chinese cities are among the highest

in the world. Therefore, emissions will increase with increasing electricity consumption at a

steeper rate than other cities. A shift away from coal to renewable technologies will be a

powerful step in GHG reduction.

The opportunities for renewable energy in China are strong. There exists a tremendous

potential capacity for hydro, wind, and solar power, as well as strong policies that will

increase the share of renewables and drive technological development (Cherni and Kentish

2007; Feller 2006). Li et al. (2010) describe how Shanghai, in particular, has concrete plans

to spur the development of wind and solar energy sources and reduce the share of coal

used for electricity generation. Aggressive policy actions, such as adoption of solar

integrated building construction, biogas power generation, and improved urban waste

disposal, are on the agenda for many cities in China.

A significant factor contributing to the move towards sustainability in China is buy-in by the

national and local governments. China has recognized the need for reducing the GHG

intensity of their economic growth, and they have set specific goals to do so in the 11th 5-

year plan. The target is to reduce the energy consumption per unit GDP by 20%, which will

have significant impacts on emissions reduction (Li et al. 2010). In an analysis of 30 best

sustainable urban practices in Asia, Bai et al. (2010) have demonstrated that political will is

essential for the success and scaling of pilot projects. The political will demonstrated in

China will prove to be a driving factor towards action on climate change.

However, there are important barriers to consider that may jeopardize the adoption of

sustainable practices in China. Issues of governance and emissions relocation are

particularly relevant. At the local level, issues with down-scaled governance and the central

government’s control over the enforcement of local environmental policy are issues in

Chinese cities (Bai et al. 2009). From the perspective of city officials, unprecedented

environmental actions, such as aggressive reduction targets, are also undesirable as they

may undermine national level policy (Bai 2007). Given the demonstrated governance

28

barriers, it becomes increasingly necessary for communication and dissemination to take

place between local and national governments.

If urban emissions are considered in isolation, the issue of emissions relocation becomes

prevalent. Frequently, urban air quality is preserved by moving emissions-producing

factories and enterprises out of the city: to other cities, suburbs, or rural areas. This has

been observed in China, as some environmental improvements in Chinese cities are due to

the relocation of pollutant sources (Bai et al. 2009). While this may improve local air quality

and emissions inventories, it results in consistent, or worsened, levels of emissions on larger

scales.

The issue of emissions relocation may also be considered on a global level. When comparing

emissions from the global cities in Figure 3, it is interesting to reflect on the nature of the

global economy. Many of the products consumed in the low-emitting European cities are

produced in the high-emitting Chinese cities. It becomes relevant to question who should be

allocated responsibility for the emissions released during the production of exported goods

(see Dodman 2009). A recent study by Davis and Caldeira (2010) found that 22.5% of

emissions produced in China were “exported” to consumers abroad. Developed countries

have, in essence, relocated the emissions associated with the production of their goods to

China. While many of the emissions resulting from manufacturing and industrial activity are

“exported” abroad, China receives the economic benefit. Chinese cities have the highest

GHG emissions per unit GDP of all cities in the world (Hoornweg et al. 2010). There is an

enormous need, and potential economic opportunity, for Chinese cities to mitigate their

emissions while continuing to maintain their GDP growth.

The level of China’s urban greenhouse gas emissions is changing rapidly over time. The

urban population is increasing at unprecedented rates, and the economy is flourishing. As

China grows, urbanizes, and secures its position as a powerful player on the global stage, it

is important for the country to adopt the global standard for reporting GHG emissions from

its cities. Chinese cities are global participants in the urban drive for emissions reduction,

which requires accurate, up-to-date GHG inventories. With the increasing collaboration

between city mayors and officials, new leadership has emerged in the arena of climate

change mitigation, and China will undoubtedly play an important role.

29

References for Chapter 2

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Bai, X. 2007. “Integrating global environmental concerns into urban management.” Journal

of Industrial Ecology 11(2): 15-29.

Bai, X., A.J. Wieczorek, S. Kaneko, S. Lisson, and A. Contreras. 2009. “Enabling

sustainability transitions in Asia: The importance of vertical and horizontal linkages.” Technological Forecasting & Social Change 76(2): 255-66.

Bai, X., B. Roberts, and J. Chen. 2010. “Urban sustainability experiments in Asia: patterns and pathways.” Environmental Science & Policy 13(4): 312-25.

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Cherni, J.A. and J. Kentish. 2007. “Renewable energy policy and electricity market reforms

in China.” Energy Policy 35(7): 3616-29.

CSY. 2007. China Statistical Yearbook 2006. Beijing: China Statistical Press.

Davis, S. J. and K. Caldeira. 2010. “Consumption-based accounting of CO2 emissions.” PNAS Early Edition.

Dhakal, S. 2009. “Urban energy use and carbon emissions from cities in China and policy

implications.” Energy Policy 37(11): 4208-19.

Dodman, D. 2009. “Blaming cities for climate change? An analysis of urban greenhouse gas emissions inventories.” Environment & Urbanization 21(1): 185-201.

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Fernandez, J.E. 2007. “Resource consumption of new urban construction in China.” Journal

of Industrial Ecology 11(2): 99-115.

Hoornweg, D., P. Bhada, M. Freire, C.L. Trejos, and L. Sugar. 2010. “Cities and Climate

Change: An Urgent Agenda.” Washington, DC: The World Bank.

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Kennedy, C., J. Cuddihy, and J. Engel-Yan. 2007. “The Changes Metabolism of Cities.”

Journal of Industrial Ecology 11(2): 35-54.

Kennedy C., A. Ramaswami, S. Carney, and S. Dhakal. 2009a. “Greenhouse Gas Emission Baselines for Global Cities and Metropolitan Regions.” Proceedings of the 5th Urban

Research Symposium Marseille, France: June 28-30, 2009.

Kennedy, C., J. Steinberger, B. Gasson, Y. Hansen, T. Hillman, M. Havranek, D. Pataki, A.

Phdungsilp, A. Ramaswami, and G.V. Mendez. 2009b. “Greenhouse Gas Emissions

from Global Cities”. Env. Sci. & Tech. 43(19): 7297-309.

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Kennedy, C., J. Steinberger, B. Gasson, Y. Hansen, T. Hillman, M. Havranek, D. Pataki, A. Phdungsilp, A. Ramaswami, and G.V. Mendez. 2010. “Methodology for Inventorying

Greenhouse Gas Emissions from Global Cities.” Energy Policy: 38(9): 4828-37.

Li, L., C. Changhong, S. Xie, C. Huang, Z. Cheng, H. Wang, Y. Wang, H. Huang, J. Lu, and

S. Dhakal. 2010. “Energy demand and carbon emissions under different development scenarios for Shanghai, China.” Energy Policy 38(9): 4797-807.

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Zhang, Y., Z. Yang, and X. Yu. 2009. “Evaluation of urban metabolism based on emergy synthesis: A case study for Beijing (China).” Ecological Modelling 220(13-14): 1690-

6.

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Carbon Dioxide Emissions and Urban Development.” National Bureau of Economic

Research Working Paper 15621.

31

Chapter 3 A Low-Carbon Infrastructure Plan for Toronto, Canada

Getting to Carbon Neutral: A Guide for Canadian

Municipalities

Together with the Toronto and Region Conservation Authority, the Sustainable

Infrastructure Group at the University of Toronto has developed a strategic planning tool for

Canadian municipalities. Getting to Carbon Neutral (Kennedy 2010) outlines best practices

in sustainable infrastructure throughout the world, and it offers policy and technology

strategies to apply in Canadian municipalities. To assist with the decision-making process,

the guide gives an overview of how to conduct a greenhouse gas (GHG) emissions inventory

for a municipality, as well as quick ‘Estimation Guidelines’ of resultant GHG emissions

savings for each strategy. The strategies focus on four sectors: buildings, transportation,

energy supply, and municipal services (Table 7).

The potential for carbon savings in the building sector is very large, given that the operation

of buildings account for up to 40% of GHG emissions in cities (UNEP and UNEP SBCI 2009).

Efficient electricity use and advanced building envelopes can be combined with renewable

energy technologies to make a building operationally carbon neutral, even in Canada.

Strong incentives for building retrofits and sustainable building design are needed to make

this possible on a city scale.

In the transportation sector, a focus on public and active transportation is needed. Use of

private automobiles needs to be reduced and replaced with alternative fuels and vehicle

technologies. Behaviour change will require strong financial incentives, as well as

appropriate infrastructure to make the change conductive to a high quality of life.

Reducing the carbon intensity of the energy sector requires collaboration at all levels of

government. Renewable electricity generation lowers the greenhouse gas intensity of the

regional electricity grid. For heating and cooling of buildings, community-based energy

technologies, such as district heating and underground thermal energy storage, reduce or

replace the need for fossil fuel combustion. While some technologies are capital intensive,

energy savings are significant and have proven effective in cities throughout the world.

32

Table 7: Sectors, strategies, and guidelines presented in Getting to Carbon Neutral

SECTOR STRATEGY ESTIMATION GUIDELINES

Reduce Energy Demand

• Building Retrofits

• New Energy Efficient Buildings • Energy Efficient Appliances

• Vegetation

Utilize Solar Energy

• Photovoltaics • Solar Water Heating

• Solar Air Heating • Passive Solar Design

Buildings

Ground Source Heat Pumps • Ground Source Heat Pumps

Appropriate Land Use • Motorized Kilometres Travelled

Public Transportation

• Bus

• Bus Rapid Transit • Light Rail Transit • Subway

• Commuter Rail

Active Transportation • Bicycle Mode Share

Financial Policies

• Pedestrianization • Parking Price Increase

• Tolls, Taxes, Area Pricing

Transportation

Changing Vehicle Technology

• Biomass Fuel

• Fuel Cell Vehicles

• Plug-in Hybrid and Hybrid Electric Vehicles

Electricity from Renewable Sources

• Wind Power

• Hydro Power

• Wave and Tidal Power

Underground Thermal Energy Storage • Aquifer Thermal Energy Storage

• Borehole Thermal Energy Storage

District Heating and Cooling • Industrial Process Energy Sharing • District Space and Water Heating

Combined Heat and Power • Combined Heat and Power

Energy Supply

Integrated Community Energy Systems • Community Energy Systems

Increased Sorting and Recycling • Landfill Gas Emissions

Organic Waste Diversion

• Waste Separation

• Anaerobic Digestion • Composting

Waste Incineration and Gasification

Methane Capture

Water Demand Management • Urban Water Systems

Urban Greenery • Urban Forest

Urban Agriculture

Geological Sequestration

Municipal Services

Purchasing Carbon Offsets

Source: Adapted from Kennedy (2010).

33

Cities have direct control over municipal sector operations, including waste treatment and

urban forestry. Advanced waste sorting is cost effective and easy to implement, and

methane capture at landfill sites can reduce GHG emissions and improve air quality.

Enhancing the urban canopy not only sequesters carbon dioxide, but also helps moderate

the urban climate and reduces air conditioning requirements.

Community Scale Low-Carbon Developments

Best practices in sustainable infrastructure design have reduced GHG emissions for cities in

Canada and abroad. While specific projects have demonstrated success, achieving carbon

neutrality, or near-neutrality, requires a synergetic approach where a variety of GHG

reduction strategies are employed. This is currently demonstrated at the community scale in

three notable projects: Dockside Green in Victoria, B.C. (Box 1); Beddington Zero-Energy

Development (BedZED) in London, U.K. (Box 2); and Vauban District in Freiberg, Germany

(Box 3). All three have advanced energy efficient homes and combined heat and power

facilities. Dockside Green has online smart-metering and energy controls, BedZED homes

are heated and ventilated using passive solar techniques, and Vauban is parking-free and

pedestrian focused. The combination of numerous GHG reduction strategies has brought

these developments close to carbon neutral.

Moreover, the potential for carbon neutrality extends beyond the community scale to the

city scale, demonstrated by the planned development of Dongtan Eco-City near Shanghai,

China (Box 4). The proposed city is designed to be carbon neutral (at least with respect to

direct emissions) with advanced energy efficient homes, emission-free transportation

technology, renewable electrical generation, and combined heat and power facilities.

The GHG reduction strategies described in Getting to Carbon Neutral have already proved to

be effective in new community and city developments throughout the world. The strategies

will also make significant impacts when applied to existing cities in Canada. In this paper,

example calculations are conducted for the City of Toronto, highlighting the potential of

various combinations of reduction strategies.

Integration of Strategies: Toronto Case Study

The Toronto Case Study is divided into three scenarios: a 2004 Base-Case Scenario; a 2031

Planned-Policies Scenario; and a 2031 Aggressive-Alternatives Scenario. The 2004 Base-

Case Scenario based on current municipal infrastructure and demographics, and it is verified

with respect to values presented in the City of Toronto’s 2004 GHG Inventory (ICF

34

Box 1: Dockside Green, Victoria, British Columbia

Dockside Green is a 15-acre mixed-use community developed on a brownfield site near

downtown Victoria, BC, featuring a range of low carbon strategies. Buildings are designed to

be LEED® Platinum certified and 45-55% more efficient than buildings designed to current

national codes. This is achieved by heavy insulation, double-glazed low-e windows, heat

recovery ventilators, and external shading on south and west windows to minimize heat

gains. Electricity demands are reduced through use of Energy-Star appliances, low-energy

lighting with occupancy sensors, and day-lighting techniques. Energy use will be smart-

metered with digital controls accessible over the internet. Heat and some electricity will be

provided by a biomass energy cogeneration facility with peak heating needs met with

backup natural gas boilers. The community is designed to be pedestrian friendly with

abundant green spaces and walkways, as well as reduce transport emissions with a mini-

transit system and car share program. Compared to a traditional development, 5,215 tCO2e

per year are avoided through biomass use, space heating efficiency, and electricity

efficiency.

Source: Adapted from Kennedy 2010.

Box 2: Beddington Zero-Energy Development (BedZED), London, UK

BedZED, short for Beddington Zero-Energy Development, is a high density, mixed use,

carbon-neutral community developed on an urban brownfield site in Southwest London

completed in 2002. The community is designed to maximize social amenity and

environmental sustainability while maintaining financial effectiveness. All buildings have

advanced envelope insulation and air-tightness, and all heating, cooling, and ventilation is

achieved using passive techniques (including terraced blocks, building orientation that best

utilizes solar gains, and heat recovery wind cowls). The absence of mechanical systems

reduces electricity demand, as do smart meters, low energy appliances and light bulbs.

Electricity and additional heat is supplied from a bio-fuelled combined heat and power plant

(CHP), which runs on wood chips from local urban tree trimmings. Photovoltaic cells on

southern facades also generate electricity.

Source: Adapted from Kennedy 2010.

35

Box 3: Vauban District of Freiberg, Germany

Vauban is a city district of Freiberg, Germany developed using co-operative and

participatory planning strategies. All buildings comply with low energy standards, some of

which are passive houses or plus energy houses. A co-generation facility and over 450m2 of

solar collectors provide 45% of the community’s electricity requirements as well as district

heating. The community is “parking-free”, and close to half of the households are car-free.

Doorstep parking is replaced by a peripheral community car park, which also stores

community car sharing vehicles. Cars are permitted on residential streets for pick-up and

delivery purposes only, where they must travel at “walking speed” (5 km/h). Businesses,

schools, shopping and recreation facilities are all located within walking or cycling distance.

Public buses and tram lines connect Vauban to the Freiberg city centre. The community

design is estimated to save 28 GJ of energy and 2100 tCO2e per year.

Source: Adapted from Kennedy 2010.

Box 4: Dongtan Eco-City, Chongming Dao, China

Dongtan Eco-City is planned for development of the south-east end of Chongming Island

near Shanghai. Forty percent of the land area will be urbanized, leaving space for organic

agriculture and existing wetlands. All housing will be within walking distance to social

infrastructure, reducing demands for transportation. Public transportation will use emission-

free technologies, and visitors will leave vehicles that expel tail pipe emissions outside the

city. Green roofs and advanced building technologies, including natural ventilation, will

reduce building energy demands. Electricity will be produced from renewable sources,

including photovoltaic cells, wind turbines, and biogas from municipal waste and sewage. In

addition, a combined heat and power plant running on biomass waste from agriculture will

provide district heating. Energy will also be managed through resident education, smart

metering, and financial incentives. The development is planned to have near-zero carbon

emissions.

Source: Adapted from Kennedy 2010.

36

International 2007). The 2031 scenarios are future projections of GHG emissions for the

City. These scenarios consider reductions due to currently planned municipal and provincial

policies (Planned-Policies) and alternative actions that could be considered aggressive

(Aggressive-Alternatives). The GHG emissions and potential savings in all scenarios focus

on the sectors responsible for the largest amounts of emissions: Buildings (including

building relevant Energy Supply) and Transport.

Base-Case Scenario for 2004

In 2004, the population of the City of Toronto was about 2.65 million, and the city’s gross

domestic product was about 101.7 billion dollars (based on provincial GDP weighted by

employment data). The total land area for the City, kept constant in all scenarios, is about

63,000 hectares.

Buildings

The gross-floor-area (GFA) of Toronto’s building stock in 2004 was estimated using roof

areas and building heights provided by the City for the following building categories: Low-

Rise Residential; Apartments; and Commercial-Institutional. The GFA was estimated based

on assumptions of floor height. For Low-Rise Residential buildings and Apartments, the

estimated GFA was taken to be the average of the two total GFA values calculated assuming

both 10ft and 12ft ceilings. For Commercial buildings, 12 ft and 14 ft ceilings were assumed.

The total GFA of the Toronto building stock in square kilometres for each building type is

shown in Table 8.

The total energy consumption (in GJ) of each building type was calculated as the product of

building stock (in m2) and energy intensity (in GJ/m2), using the energy intensity values for

Ontario, shown in Table 9. The total energy consumption was further divided according to

end use: a portion of the total energy represents heat, and a portion represents electricity.

In Toronto, heat is generally fuelled by natural gas. Heating energy end uses are space

heating and water heating, and electrical energy end uses are lighting, appliances, and

space cooling. Toronto’s split between natural gas and electricity, as well as the Canadian

average split in end use consumption of natural gas and electricity, is also shown in Table 9.

The total energy consumption of the 2004 Toronto building stock, as well as the energy

associated with each end use, is shown in Table 8. GHG emissions (tCO2e) from heating

37

Table 8: Estimated energy use and emissions for the Toronto building stock in 2004.

Low-Rise Residential

Apartments Commercial TOTAL

Building Stock (km2) 93.04 53.37 72.12 218.5

Total Energy (TJ) 77,219 36,292 118,994 232,505

Heating (Natural Gas) (TJ) 63,648 29,914 60,796 154,357

Space Heating (TJ) 46,145 21,687 51,676 119,508

Water Heating (TJ) 17,503 8,226 9,119 34,849

Electricity (TJ) 13,572 6,378 58,198 78,149

Lighting (TJ) 2,918 1,371 14,666 18,955

Appliances (TJ) 8,143 3,827 33,755 45,725

Space Cooling (TJ) 2,511 1,180 9,836 13,526

Total Emissions (ktCO2e) 4,498 2,114 7,386 13,997

Table 9: Energy, natural gas, and electricity breakdown by end use for different building classifications.

Low-Rise Residential

Apartments Commercial

Energy Intensity (GJ/m2)1 0.83 0.68 1.65

Total Energy Breakdown

Non-electrical Energy 62% 52% 56% Canadian Average1

Electricity (incl. heating) 38% 48% 44%

Natural Gas 82% 82% 51% Toronto2

Electricity 18% 18% 49%

Total Natural Gas Breakdown

Space Heating 72.5% 72.5% 85% Canadian Average1

Water Heating 27.5% 27.5% 15%

Total Electricity Breakdown

Lighting 21.5% 21.5% 25%

Appliances 60% 60% 58% Canadian Average1

Space Cooling 18.5% 18.5% 17%

Source: 1 National Resources Canada 2007; 2 Data from ICF International 2007.

fuels were calculated as follows:

fuelfuelfuel ICGHG ⋅= (5)

where Cfuel (TJ) is the amount of fuel consumed, and Ifuel (tCO2e/TJ) is the GHG emissions

intensity. For natural gas, the principle heating fuel in Toronto, the GHG emissions intensity

38

is 56.1 tCO2e/TJ (IPCC 2006). GHG emissions (tCO2e) attributable to total electricity

consumption were determined by:

yelectricityelectricityelectricit ICGHG ⋅= (6)

where Celectricty (GWh) is the total electricity consumption. The GHG emissions intensity,

Ielectricity (tCO2e/TJ), for the Province of Ontario including line losses is 246 gCO2e/kWh (68.3

tCO2e/TJ). Overall, the building sector was responsible for about 14 megatonnes of GHG

emissions in Toronto in 2004.

Transport

GHG emissions associated with transport were calculated using the MUNTAG model (Derrible

et al. 2010) with inputs such as Toronto’s 2004 population, land area, GDP, and information

about the transit and bicycle infrastructure. The 2007 transit infrastructure for Toronto was

available from the Toronto Transit Commission (Toronto Transit Commission 2008), and

given there were no major infrastructure changes, it was assumed to be similar to the 2004

infrastructure:

• 1545 Buses

• 248 Streetcars, 69.2 km of Streetcar tracks

• 706 Subway cars, 68.3 km of Subway tracks

The resultant vehicle-kilometres traveled, VKT (km), of each motorized mode – private

automobiles, bus, streetcar, and subway – is shown in Table 10. The total length of

Toronto’s bicycle facilities was 403 km (City of Toronto 2009a), resulting in an estimated

bicycle mode share of 0.88%. This mode share fraction was subsequently subtracted from

the GHG emissions of each motorized mode.

GHG emissions (gCO2e) from transport were determined by:

fueleee INVKTGHG ⋅⋅= modmodmod (7)

The total vehicle-kilometres traveled, VKTtransport mode (km), for each mode was provided by

the MUNTAG model. The emissions factor for each mode (gCO2e/km), shown in Table 10, is

the product of the mode’s energy intensity, Ntransport mode (MJ/km), and the fuel GHG

emissions intensity, Ifuel (gCO2e/MJ). The energy intensity for each mode was taken as the

39

Table 10: Vehicle-kilometres travelled (VKT), emissions, and mode share savings for Toronto's transport infrastructure in 2004. Values estimated using the MUNTAG

model (Derrible et al. 2010).

Private

Automobiles Bus Streetcar LRT Subway TOTAL

VKT per capita (km) 4,077 29 12 - 39 4,157

Emissions Factor (kgCO2e/km) 0.271 2.01 1.07 1.15 0.852 -

Per Capita Emissions Before Savings (kgCO2e)

1,103 59 13 - 33 1,208

Emissions Before Savings (ktCO2e)

2,920 155 33.8 - 87.5 3,197

MODE SHARE SAVINGS

Active Transport

Biking (ktCO2e) 26 1.4 0.3 - 0.8 28

Total Savings (ktCO2e) 26 1.4 0.3 - 0.8 28

Total Emissions (ktCO2e) 2,895 154 33.5 - 86.7 3,169

North American average from the Millennium Cities Database (UITP 2001). IPCC defaults

were used for the GHG emissions intensity of diesel and gasoline (75.2 tCO2e/TJ and 72.2

tCO2e/TJ respectively; IPCC 2006). For electric modes, including streetcars, LRTs, and

subways, the GHG emissions intensity was taken to be that of the provincial electricity

supply (68.3 tCO2e/TJ).

The final results of the MUNTAG model, including the GHG savings due to the bicycle mode

share, are shown in Table 10. The results indicate that in 2004 passenger transport

contributed about 3.2 megatonnes to Toronto’s GHG footprint.

Comparison to City of Toronto Inventory

Emissions calculated using the Getting to Carbon Neutral guide were comparable to values

presented in the 2004 Toronto GHG Inventory (ICF International 2007), as shown in Table

11. For the sources of emissions that were common to both methods – including low-rise

residential homes, apartments, and commercial buildings, as well as private automobiles

and transit buses – there was only a 2% to 17% difference in numerical values. The current

version of Getting to Carbon Neutral excludes emissions from industrial buildings, trucks,

and waste; however, the calculations for other sources were shown to be verifiable.

40

Table 11: Comparison between 2004 Toronto GHG Inventory and 2004 Base-Case Scenario.

2004 Toronto GHG Inventory

2004 Base-Case Percent

Difference

Population (millions) 2.65 2.65 -

BUILDINGS (ktCO2e) 14,884 13,997 -6%

Low-Rise Residential (ktCO2e) 5,997 4,498 10%

Apartments (ktCO2e) 2,114

Commercial1 (ktCO2e) 8,887 7,386 -17%

PASSENGER TRANSPORT (ktCO2e) 8,559 3,169 -

Private Automobiles (ktCO2e) 2,839 2,895 2%

Bus (ktCO2e) 172 154 -11%

Streetcar (ktCO2e) - 34 -

LRT (ktCO2e) - - -

Subway (ktCO2e) - 87 -

Other Vehicles (incl. trucks) (ktCO2) 5,549 - -

WASTE (ktCO2e) 978 - -

1 From ICF International 2007, includes industrial emissions ("Commercial and small

industrial" and "Large commerical and industrial")

Planned-Policy Scenario for 2031

The Province of Ontario and the City of Toronto are implementing numerous plans and

initiatives to reduce GHG emissions. Using the Estimation Guidelines provided in the Getting

to Carbon Neutral guide, the GHG impacts of a few of the policies are quantified in this

scenario for the year 2031.

Following the linear population growth trend described in Ontario’s Growth Plan for the

Greater Golden Horseshoe (Ministry of Public Infrastructure and Renewal 2006), the

population of Toronto in the year 2031 will be about 3.08 million. The city’s GDP is projected

to be 178 billion dollars, and the land area will remain the same at 63,000 hectares.

Buildings

To extrapolate the building stock into the year 2031, the Growth Plan’s population trends,

employment trends, and residential housing construction trends were followed. The total

residential GFA (the sum of Low-Rise Residential and Apartments) was assumed to grow at

the same rate as population, which will increase 16% by 2031. Ten percent of the increase

in residential GFA was assigned to Low-Rise Residential housing and the remaining 90% was

41

assigned to Apartments. Commercial GFA was assumed to grow at the same rate as

employment, which is projected to increase 10% by 2031. The extrapolated building stock

values are shown in Table 12.

Following the same method used in the 2004 Base-Case Scenario, the total energy used by

each building type was calculated according to the intensity and energy breakdown schemes

in Table 9. The total emissions were also calculated according to Equations (5) and (6). The

emissions intensity of natural gas remained at 56.1 tCO2e/TJ, and the projected Integrated

Systems Plan electrical emissions intensity (including line losses) was taken to be 37.8

gCO2e/kWh (10.5 tCO2e/TJ) (Ontario Power Authority 2006). The total energy and

emissions before savings is shown in Table 12.

In addition to using the electricity emissions intensity of the Province’s Integrated Systems

Plan, which will promote small-scale urban renewable generation, five initiatives were

quantified in this scenario: banning incandescent bulbs; requiring ENERGY STAR appliances;

implementing the 2012 Ontario Building Code; completing the Mayor’s Tower Renewal

project; and promoting commercial green roofs.

Banning incandescent bulbs

The ‘Energy Efficient Appliances’ Estimation Guideline indicates a 75% energy savings for

CFL bulbs over incandescent bulbs. Assuming 70% of residential lighting energy and 10% of

commercial lighting energy is currently from incandescent bulbs, the energy savings to

lighting electricity is 52.5% and 7.5% respectively. This will result in a total GHG savings of

about 40.1 kilotonnes (Table 12).

Requiring ENERGY STAR Appliances

The ‘Energy Efficient Appliances’ Estimation Guideline offers a range of potential savings

based on different ENERGY STAR appliances. The average savings were taken as 30% for

these calculations, and it was assumed 60% of residential appliances and 30% of

commercial appliances were not already ENERGY STAR rated. The energy savings to

appliance electricity was therefore 18% for residential buildings and 9% for commercial

buildings, which will result in a total GHG savings of about 61.5 kilotonnes.

Implementing the 2012 Ontario Building Code

The planned 2012 Ontario Building Code will require that all new homes are built to a higher

standard of efficiency, similar to R2000 standards (Love 2009). The ‘New Energy Efficient

42

Table 12: Projected energy use, emissions, and policy-related emissions savings for the Toronto building stock in 2031.

Low-Rise Residential

Apartments Commercial TOTAL

Building Stock (km2) 95.55 76.00 80.13 251.7

Total Energy Before Savings (TJ) 79,306 51,677 132,215 263,199

Heating (Natural Gas) (TJ) 65,368 42,595 67,551 175,514

Electricity (TJ) 13,938 9,083 64,665 87,686

Emissions Before Savings (ktCO2e) 3,814 2,485 4,469 10,767

SAVINGS (ktCO2e)

Incandescent bulbs to CFL bulbs 16.5 10.8 12.8 40.1

All appliances EnergyStar rated 15.8 10.3 35.4 61.5

R2000 standards in 2012 OBC 14.8 - - 14.8

Mayor's Tower Renewal Retrofits - 49.5 - 49.5

Commercial roof space 10% green - - 0.3 0.3

Total Savings (ktCO2e) 47.1 70.6 48.5 166.2

Total Emissions (ktCO2e) 3,767 2,414 4,421 10,601

Buildings’ Estimation Guideline states that R2000 homes use 30% less energy than

conventional homes. Implementing this standard for low-rise residential buildings would

reduce the increase in space heating energy between 2012 and 2031 by 30%. This energy

savings will correspond to a reduction of 14.8 kilotonnes of GHG emissions.

Completing the Mayor’s Tower Renewal Project

The Mayor’s Tower Renewal Project will aggressively retrofit existing 1960’s-era high-rise

towers, as well as promote neighbourhood revitalization initiatives (Kesik et al. 2008). To

simulate the effects of this, a 30% savings on space heating energy was applied to all

Toronto Community Housing Corporation buildings, as per the ‘Building Retrofits’ Estimation

Guideline. It is important to note that this was a conservative estimate; Kesik et al. (2008)

have simulated much larger savings for some buildings. ICF International (2007) showed

that Community Housing Corporation buildings currently require about 2.9 million GJ of

space heating energy a year. Reducing space heating needs by 30% will save 49.4

kilotonnes of GHG emissions.

Promoting commercial green roofs

The ‘Vegetation’ Estimation Guideline describes green roofs as reducing peak summer

cooling loads by 25% in roofs immediately below the green roof. The green roof initiative

43

targets to cover 10% of commercial buildings with green roofs (City of Toronto 2009b).

Assuming the savings would apply to 10% of the cooling energy used by commercial

buildings, the total percentage savings to space cooling electricity would be 0.25%. This

would result in a GHG savings of 0.3 kilotonnes.

Based on the values presented in Table 12, the initiatives that will have the greatest impact

to GHG emissions are the Mayor’s Tower Renewal Project, requiring ENERGY STAR

appliances, and banning incandescent bulbs. All five initiatives have a combined GHG

savings of about 166 kilotonnes. These initiatives, combined with the lower electrical

emissions intensity resulting from Ontario’s Integrated Systems Plan, will cause Toronto’s

buildings to be responsible for 10.6 megatonnes of GHG emissions in 2031.

Transport

The most significant transport-related government initiative currently planned for Toronto is

the Greater Toronto Area’s Metrolinx Plan (Metrolinx 2008), which will increase availability

of public transport. The three other initiatives that were quantified in this scenario include:

an increased adoption of electric vehicles; an increase to the length of bicycle facility; and a

10% increase in parking price to deter auto use.

Subway and LRT

The Metrolinx Plan will result in numerous upgrades to the current TTC infrastructure. The

Plan will increase subway routes and construct new LRT lines. Assuming the same number

of buses as in 2008, as well as a maintained ratio of transit carriages to track length, the

new Metrolinx infrastructure in 2031 will consist of:

• 1737 Buses

• 248 Streetcars, 65.6 km of Streetcar tracks

• 1063 Subway cars, 102.85 km of Subway tracks

• 452 LRT cars, 126 km of LRT tracks

The calculated VKT of each motorized mode in 2031 is shown in Table 13.

The emissions factor (gCO2e/km) in 2031 of electrically operated motorized modes (shown in

Table 13) will be different from the 2004 Base-Case due to the Province’s Integrated

Systems Plan for electricity supply. The emissions factors for diesel buses remain the same;

44

Table 13: Vehicle kilometres-travelled (VKT), emissions, and planned mode share savings for Toronto's Metrolinx infrastructure in 2031. Values estimated using the

MUNTAG model (Derrible et al. 2010).

Private

Automobiles Bus Streetcar LRT Subway TOTAL

VKT per capita (km) 4,017 28 11 21 54 4,131

Emissions Factor (kgCO2e/km) 0.271 2.01 0.163 0.175 0.130

Per Capita Emissions Before Savings (kgCO2e)

1,087 57 1.8 3.6 7.1 1,156

Emissions Before Savings (ktCO2e)

3,348 176 5.4 11 22 3,562

TECHNOLOGY SAVINGS

Vehicle Technology

20% Battery Electric Vehicles

(emissions factor of 94 gCO2e/km) (ktCO2e)

439 439

ADDITIONAL MODE SHARE SAVINGS

Active Transport

Biking (ktCO2e) 22 2.3 0.07 0.15 0.28 25

Parking Fees

10% Increase in Parking

Price (ktCO2e) 12 -0.2 -0.01 -0.01 -0.02 12

Total Savings (ktCO2e) 473 2.1 0.07 0.14 0.26 476

Total Emissions (ktCO2e) 2,875 174 5.4 11 21 3,086

however, the emissions factor of streetcars, LRTs, and subways changes with a lower GHG

intensity of electricity (10.5 tCO2e/TJ).

Increasing adoption of personal electric vehicles

Current provincial government initiatives aim to increase the market share of electric

vehicles to 5% by 2020 (Office of the Premier 2009). Assuming an exponential increase in

years following, the percentage of private automobile vehicle-kilometres traveled by electric

vehicles was projected to be 20% in 2031. Electric engines operate more efficiently than

internal combustion engines, and their energy will be supplied by electricity generated

according to the Province’s Integrated Systems Plan. Therefore, the emissions factor for

electric vehicles is quite lower than other modes, estimated at 93.55 gCO2e/km.

45

Increasing length of bicycle facility to promote active transport

The bicycle facility in Toronto is planned to increase from 403 km to 1004 km by 2012 (City

of Toronto 2009a). Assuming this length of facility stays constant through to 2031, it will

result in an active-transport mode share of 1.31%. This mode share, applied across all

modes, will result in a total GHG savings of about 25.1 kilotonnes.

Increasing parking price to deter auto use

While official plans to increase parking prices are not known, a conservative estimate of

10% was made. According to the ‘Parking Price Increase’ Estimation Guideline, this will

result in a mode share decrease of 0.70% for private automobiles and a mode share

increase of 0.10% for public transit. The combined effects of these mode share changes will

result in a savings of about 11.7 kilotonnes of GHG emissions.

The final results of the MUNTAG model for the Metrolinx infrastructure, including the GHG

emissions savings from each government initiative, are shown in Table 13. The most

significant savings are associated with changing 20% of personal vehicles to electric

vehicles. When combined, the planned initiatives will reduce transport related GHG

emissions to about 3.1 megatonnes in 2031.

Alternative-Aggressive Scenario for 2031

The Alternative-Aggressive Scenario explores the GHG emissions in 2031 associated with

making aggressive changes to Toronto’s buildings and transport infrastructure. The changes

were drawn from some of the most innovative case studies in Getting to Carbon Neutral

guide, and their impacts were quantified using the corresponding Estimation Guidelines.

This scenario represents one aggressive plan that could help Toronto get closer to carbon

neutral.

Buildings

The 2031 building stock and associated emissions before savings are the same as in the

Planned-Policy Scenario; however, the savings in this scenario are more aggressive. In

addition to expansion of the initiatives described above, changes involving buildings retrofits

and innovative energy systems were applied. Overall, several energy-saving measures were

used to reduce emissions: replacing all light bulbs with LEDs and all appliances with ENERGY

STAR rated appliances; retrofitting all buildings built before 2012; designing all buildings

after 2012 to low-energy standards; implementing Borehole Thermal Energy Storage

46

(BTES) systems, solar water heating, and ground-source heat pumps in low-rise residential

homes; outfitting half of all apartment buildings with Aquifer Thermal Energy Storage

(ATES) systems; and outfitting commercial buildings with solar air heating and 25% green

roof coverage.

LED light bulbs and ENERGY STAR appliances

LED bulbs use less electricity than both incandescent and CFL bulbs. They are approximately

90% more efficient than incandescent bulbs and 60% more efficient than CFL bulbs.

Assuming the same percentages of incandescent lighting as in the Planned-Policy Scenario

(70% of residential lighting energy and 10% of commercial lighting energy), and assuming

the remaining lighting energy is currently met with CFL bulbs, implementing CFL bulbs

would save 81% of lighting electricity in residential buildings and 63% of lighting electricity

in commercial buildings. This would correspond to savings of about 150 kilotonnes of GHG

emissions. Following the previous method for ENERGY STAR appliances, they would again

save about 61.5 kilotonnes of GHG emissions.

Retrofitting pre-2012 buildings

The ‘Building Retrofits’ Estimation Guideline states that retrofitting can reduce energy

demand by 30% for apartments and commercial buildings and can save up to 50% for low-

rise residential homes. Taking the average energy savings to be 30% for all building types,

the potential GHG emissions savings associated with retrofitting all buildings constructed

before 2012 was calculated to be about 2.7 megatonnes.

Designing post-2012 apartments and commercial buildings to low-energy standards

The emergence of accreditation for sustainable buildings has increased the popularity of

low-energy apartments and commercial buildings. According to the ‘New Energy Efficient

Buildings’ Estimation Guideline, these buildings can be designed to consume 60% less

energy than standard. When applied to all apartments and commercial buildings constructed

after 2012, there would be a savings of about 425 kilotonnes.

Designing post-2012 low-rise residential homes to low-energy standards with BTES systems

As demonstrated by the Drake Landing Solar Community in Alberta (Box 5), R2000 homes

combined with a Borehole Thermal Energy Storage system use 90% less space heating

energy than a typical community. If all new low-rise homes in Toronto built after 2012 were

designed with the same specifications – R2000 energy standards combined with a BTES

system – they would save 61.3 kilotonnes of GHG emissions in 2031.

47

Box 5: Drake Landing Solar Community, Okotoks, Alberta

The Drake Landing Solar Community is comprised of 52 single family R-2000 homes. The

homes are connected to a district heating system that includes solar collectors and a

borehole energy storage system. The borehole field consists of 144 boreholes, each 35 m

deep, 150 mm diameter, 2.25 m spacing. The system contains 24 parallel circuits, each

having 6 boreholes in series. During the winter the homes are heated using solar energy

captured during the summer and stored in the boreholes. This system saves more than 110

GJ of energy and 5 tCO2e per home each year. Overall, 90% of the space heating needs are

met by solar thermal energy.

Source: Adapted from Kennedy (2010).

Solar water heating and ground-source heat pumps in pre-2012 low-rise homes

Outfitting low-rise residential homes built before 2012 with solar water heating and ground-

source heat pumps would also decrease fossil-fuel based energy consumption. The ‘Solar

Water Heating’ Estimation Guideline assigns 45% savings to water heating energy needs

with the addition of solar heaters in Toronto. If these savings were applied to all pre-2012

low-rise residential homes in the city, 445 kilotonnes of GHG emissions would be avoided.

Taking an average of the savings described in the ‘Ground Source Heat Pumps’ Estimation

Guideline, outfitting all pre-2012 low-rise residential homes with ground source heat pumps

would save 30% on both space heating and space cooling needs – equivalent to a GHG

emissions savings of 791 kilotonnes.

ATES systems in half of all apartment buildings

The geology in many areas of Toronto is well-suited for Aquifer Thermal Energy Storage

systems, which can provide 25% savings to heating energy and 70% savings to cooling

energy needs. If half of all apartment buildings in Toronto were serviced with an ATES

system, this would result in a total GHG emissions savings of about 223 kilotonnes.

Solar air heating and green roofs on commercial buildings

The Canadair Facility Solarwall (Box 6) is an example of an effective solar air heating

strategy reducing emissions associated with space heating of commercial buildings.

According to the ‘Solar Air Heating’ Estimation Guideline, there is the potential for 25-47%

saving to space heating energy. Using the conservative estimate that 30% energy savings is

48

Box 6: Canadair Facility Solarwall, Dorval, Quebec

Bombardier's Canadair facility in Dorval is home to the world's largest solarwall. The wall is

covered with millions of tiny holes about 1 mm in diameter which allow outside air to pass

through. It is approximately 30 cm away from the main structure of the building which

creates a cavity for air flow. As outside air is drawn into the cavity, it flows upwards and

picks up the solar heat that the wall absorbs. When the heated air reaches the top of the

structure, it is either mixed with recirculated air and used to condition the space, or sent to

the gas fired make up unit if more heat is required. Monitoring results show the combined

effects from the solarwall, reduced heat loss, and destratification of indoor air results in a

savings 720,400 m3 of natural gas per year, reducing annual GHG emissions by 1,342

tCO2e.

Source: Adapted from Kennedy (2010).

possible for commercial buildings in Toronto, solar air heating applied to all commercial

buildings would save about 966 kilotonnes of GHG emissions. The green roof initiative

described previously could be aggressively extended to target to cover 25% of commercial

buildings with green roofs. Assuming the ‘Vegetation’ Estimation Guideline’s 25% savings to

peak cooling needs would again apply to 10% of the cooling energy consumed, this would

result in a savings of 0.7 kilotonnes of GHG emissions.

When combined, all the aggressive savings strategies would result in a reduction of about

5.8 megatonnes of GHG emissions from buildings, as shown in Table 14. The strategies with

the most significant reductions are building retrofits, commercial solar air heating, and low-

rise residential ground source heat pumps. With the aggressive savings, buildings would

account for about 4.9 megatonnes of emissions in 2031 Toronto.

Transport

The transport-related emissions quantified in this scenario involve aggressive changes to

transit infrastructure, vehicle technology, and bicycle infrastructure. In addition, aggressive

auto-use deterrents, such as increased parking fees, taxes, and tolls, would provide further

emissions savings.

49

Table 14: Energy use, emissions, and aggressive emissions savings for the Toronto building stock in 2031.

Low-Rise Residential

Apartments Commercial TOTAL

Building Stock (km2) 95.55 76.00 80.13 251.7

Total Energy Before Savings (TJ) 79,306 51,677 132,215 263,199

Heating (Natural Gas) (TJ) 65,368 42,595 67,551 175,514

Electricity (TJ) 13,938 9,083 64,665 87,686

Emissions Before Savings (ktCO2e) 3,814 2,485 4,469 10,767

SAVINGS (ktCO2e)

Incandescent and CFL bulbs to LEDs (ktCO2e)

25.5 16.6 107.8 149.9

All appliances EnergyStar rated (ktCO2e)

15.8 10.3 35.4 61.5

All pre-2012 buildings retrofitted

(ktCO2e) 1,079.7 566.2 1,074.9 2,720.8

Post-2012 buildings follow energy

efficiency standards (ktCO2e) - 301.3 124.0 425.3

Post-2012 homes built to R2000

standards with BTES systems (ktCO2e)

61.3 - - 61.3

Pre-2012 homes outfitted with Solar

Water Heating (ktCO2e) 445.4 - - 445.4

Pre-2012 homes outfitted with Ground Source Heat Pumps (ktCO2e)

790.8 - - 790.8

Half of apartment buildings outfitted with an ATES system (ktCO2e)

- 222.8 - 222.8

Commerical solar air heating (ktCO2e) - - 966.3 966.3

Commerical roofs are 25% green (ktCO2e)

- 0.7 0.7

Total Savings (ktCO2e) 2,418.5 1,117.2 2,309.1 5,844.8

Total Emissions (ktCO2e) 1,396 1,368 2,160 4,922

Improved transit infrastructure

The current Metrolinx plan will promote significant improvements to public transit by 2031.

To examine an aggressive alternative to the current plan, GHG savings were quantified

assuming all planned LRT lines would instead be constructed as subway lines. This would

cause a significant shift away from automobile use to public transit, resulting in a total

emissions savings of about 686 kilotonnes.

50

Complete shift to electric vehicles

Aggressive actions to completely shift vehicle technology from internal combustion engines

using gasoline to electrically powered engines would cause a dramatic reduction in the

overall GHG emissions intensity of automobiles. An advanced electric vehicle infrastructure

network, such as Better Place’s Electric Vehicle Network in Israel (Box 7), would promote

this shift. Replacing all automobiles with electric vehicles in 2031 would save 1.7

megatonnes of GHG emissions.

Improved bicycle infrastructure

The current plan for the bicycle infrastructure in Toronto is to increase the length of the

bicycle facility to 1004 km by 2012. Continuing to increase linearly through to 2031 would

result in a bicycle facility 2431 km in length. With this aggressive increase, the active

transport mode share would be 2.33% applied across all modes, resulting in potentially 26.7

kilotonnes of emissions saved.

Increased parking price

As in the Planned-Policies Scenario, the conservative parking price increase of 10% would

result in a mode share decrease of 0.70% for private automobiles and a mode share

increase of 0.10% for public transit. When applied to the alternative transit infrastructure

proposed in this scenario, this would result in GHG savings of about 6.2 kilotonnes.

Introducing taxes and tolls

When ‘Tolls, Taxes, and Area Pricing’ strategies are applied to Toronto in 2031, the mode

share changes resulting from a VMT tax and a Freeway toll would save 133.6 and 19.2

kilotonnes of GHG emissions respectively. Toronto could also effectively implement a

Beltway Cordon along the city limits and charge vehicles entering the city to further deter

auto use; however, this is not quantified in this scenario, as it would mostly impact

commuters from surrounding areas.

The aggressive methods in this scenario would result in a total of 2.6 megatonnes of

emissions saved (Table 15), with the most significant measures including shifting from

internal combustion to electric vehicles and switching LRT to subway lines. With all

aggressive savings employed, transport would contribute 0.96 megatonnes to Toronto’s

2031 GHG footprint.

51

Summary and Conclusions

The three scenarios presented in this paper represent potential greenhouse gas emissions

situations for the City of Toronto under different levels of climate action. The Base-Case

Scenario acts as a baseline, illustrating the current level of emissions from which emissions

Box 7: Better Place Electric Vehicle Network, all cities in Israel

In addition to future projects in Australia and Denmark, sustainable transportation company

Better Place is developing an electric vehicle network in Israel. The electric car

infrastructure will consist of electric vehicles and innovative battery technology, as well as

battery exchange stations and charging spots powered by renewable energy. Charging spots

will be located around a community, so that batteries are automatically charged as vehicles

are parked. For longer trips (greater than 100 miles), roadside battery switching stations

will replace depleted a battery with a fully charged one. The experience will be automated,

never requiring the driver to leave the vehicle. The business model is similar to that of a

mobile phone carrier whereby users pay for use of the network and the vehicles cost is low.

If electricity is obtained from renewable sources, the electric vehicle network will result in

zero transportation emissions.

Source: Adapted from Kennedy (2010).

52

Table 15: Vehicle kilometres-travelled (VKT), emissions, and mode share savings for Toronto's Metrolinx infrastructure with aggressive transport changes in 2031.

Values estimated using the MUNTAG model (Derrible et al. 2010).

Private

Automobiles Bus Streetcar LRT Subway TOTAL

VKT per capita (km) 4,017 28 11 21 54 4,131

Emissions Factor (kgCO2e/km) 0.271 2.01 0.163 0.175 0.130

Per Capita Emissions Before

Savings (kgCO2e) 1,087 57 1.8 3.6 7.1 1,156

Emissions Before Savings (ktCO2e)

3,348 176 5.4 11 22 3,562

INFRASTRUCTURE AND TECHNOLOGY SAVINGS

Transit Infrastructure

LRT infrastructure changed to

Subway infrastructure (ktCO2e)

701 11 -27 686

Vehicle Technology

100% Battery Electric

Vehicles (emissions factor of 94 gCO2e/km) (ktCO2e)

1,731 1,731

ADDITIONAL MODE SHARE SAVINGS

Active Transport

Biking (ktCO2e) 21 4.1 0.1 0.0 1.1 27

Parking Fees

10% Increase in Parking Price (ktCO2e)

6.4 -0.2 0.0 0.0 0.0 6.2

Taxes and tolls

VMT tax (ktCO2e) 134 134

Freeway toll (ktCO2e) 19 19

Total Savings (ktCO2e) 2,613 3.9 0.1 11 -26 2,603

Total Emissions (ktCO2e) 735 172 5.3 0 47 959

will grow significantly if a business-as-usual approach is taken. However, the municipal and

provincial governments have recognized the importance of action on climate change, and

they have proposed a variety of policies intended to reduce GHG emissions. The GHG

emissions resulting from these policies are projected to 2031 in the Planned-Policies

Scenario.

Assuming the currently planned policies and initiatives will be implemented by 2031,

buildings and passenger transportation will account for 13.7 megatonnes of GHG emissions,

53

or 4.44 tonnes per capita (Table 16). Compared to the 2004 Base-Case Scenario, this

represents a 31% savings in GHG emissions per capita. A large portion of this is due to the

reduced electrical emissions intensity associated with the Province’s Integrated Systems

Plan, as well as reduction in internal combustion automobile use. The other initiatives

outlined in this scenario provide relatively modest GHG savings, which opens potential

opportunities for significant savings to be achieved through more aggressive actions.

The third scenario presented in this paper, the Aggressive-Actions Scenario, offers a bold

infrastructure plan for Toronto that would reduce GHG emissions significantly. The plan goes

far beyond the current climate action proposed by the provincial and municipal

governments, but it is not out of line with global best practices in sustainable infrastructure.

With these aggressive actions taken, Toronto’s buildings and transport related GHG

emissions in 2031 could be reduced to 5.9 megatonnes, or 1.91 tonnes per capita (Table

16). In addition to the Province’s Integrated Systems Plan, the most significant contributors

to savings involve retrofitting all existing buildings, utilizing renewable heating and cooling

systems, and the complete proliferation of electric automobiles. Compared to the 2004

Base-Case Scenario, the aggressive actions suggested in this scenario could reduce GHG

emissions per capita by 71%.

The bold infrastructure plan for Toronto presented in this paper demonstrates that equally

as aggressive plans could be successfully conducted for any municipality in Canada.

Individually, projects invoking these strategies are demonstrating success in cities

throughout the world. When treated as systems working together, these strategies have

allowed for the creation near carbon neutral communities, such as Dockside Green and

BedZED, and even planned carbon neutral cities, such as Dongtan. Implementing bold,

innovative actions that challenge and renew existing infrastructure is a critical component of

an effective climate action plan, and the only way for our cities to get to carbon neutral.

54

Table 16: Comparison of final emissions values for all scenarios (all values in tCO2e).

2004 Base-

Case 2031 Planned-

Policies 2031 Aggressive-

Alternatives

Population (millions) 2.65 3.08 3.08

BUILDINGS (ktCO2e) 13,997 10,601 4,922

Low-Rise Residential (ktCO2e) 4,498 3,767 1,396

Apartments (ktCO2e) 2,114 2,414 1,368

Commercial1 (ktCO2e) 7,386 4,421 2,160

PASSENGER TRANSPORT (ktCO2e) 3,169 3,086 959

Private Automobiles (ktCO2e) 2,895 2,875 735

Bus (ktCO2e) 154 174 172

Streetcar (ktCO2e) 34 5.4 5.3

LRT (ktCO2e) - 11 -

Subway (ktCO2e) 87 21 47

TOTAL (ktCO2e) 17,166 13,687 5,881

TOTAL per capita (tCO2e) 6.48 4.44 1.91

55

References for Chapter 3

City of Toronto. 2009a. “Bicycle Network Project Status.” Available: http://www.toronto.ca/cycling/bikeplan/network-project-status.htm. (accessed July

2009).

City of Toronto. 2009b. “Overview of Green Roof Bylaw.” Available: http://www.toronto.ca/greenroofs/overview.htm. (accessed July 2009).

Derrible, S., S. Saneinejad, L. Sugar, and C.A. Kennedy. 2010. “Macroscopic Model for

Municipalities of Greenhouse Gas Emissions in Urban Transportation.” Journal of the

Transportation Research Board, article in press.

ICF International. 2007. “Greenhouse Gases and Air Pollutants in the City of Toronto:

Toward a Harmonized Strategy for Reducing Emissions.” Available: http://www.toronto.ca/taf/pdf/ghginventory_jun07.pdf. (accessed July 2009).

IPCC. 2006. IPCC Guidelines for National Greenhouse Gas Inventories. Prepared by the National Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa

K., Ngara T. and Tanabe K., Eds. IGES: Japan.

Kennedy, C.A. ed. 2010. Getting to Carbon Neutral: A Guide for Canadian Municipalities. Toronto and Region Conservation Authority: Toronto.

Kesik, T., I. Saleff, R. Wright, G. Stewart, N. Swerdfeger, and J. Kroman. 2008. “Tower

Renewal Guidelines: Project Brief.” Available:

http://www.towerrenewal.ca/MTR_guidelines.pdf. (accessed August 2010).

Love, P. 2009. “Marketplace Regulation: Ontario Building Code.” Conservation Bureau.

Available: http://www.conservationbureau.on.ca/Page.asp?PageID=122&ContentID=810&SiteNodeID=166. (accessed July 2009).

Metrolinx. 2008. “Preliminary Directions and Concepts.” Available:

http://www.metrolinx.com/Docs/WhitePapers/WhitePaper2.pdf. (accessed July 2009).

Ministry of Public Infrastructure and Renewal. 2006. “Schedule 3: Distribution of Population

and Employment for the Greater Golden Horseshoe 2001-2031.” Growth Plan for the

Greater Golden Horseshoe. Government of Ontario: Toronto.

National Resources Canada. 2007. National Energy Use Database (NEUD). Available:

http://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/comprehensive_tables/index.cf

m?attr=0. (accessed August 2010).

Office of the Premier, Government of Ontario. 2009. “Ontario Leading the Charge: McGuinty Government Making it Easier to Buy Electric Vehicles.” Available:

http://www.premier.gov.on.ca/news/event.php?ItemID=7944&Lang=EN. (accessed

July 2009).

Ontario Power Authority. 2006. “Ontario’s Integrated Power Systems Plan.” Available: http://www.powerauthority.on.ca/Storage/24/1922_OPA_-

_IPSP_Scope_and_Overview.pdf. (accessed July 2009).

Toronto Transit Commission. 2008. “Operating Statistics.” Available:

http://www3.ttc.ca/About_the_TTC/Operating_statistics.jsp. (accessed July 2009).

UITP. 2001. Millennium Cities Database for Sustainable Transport. The International

Association of Public Transport: Brussels.

56

UNEP and UNEP SBCI. 2009. “Common Carbon Metric for Measuring Energy Use & Reporting Greenhouse Gas Emissions from Building Operations.”

http://www.unep.org/sbci/pdfs/UNEPSBCICarbonMetric.pdf (accessed April 2010).

57

Chapter 4 Synergies Between Adaptation and Mitigation in

Development: Case Studies from Amman, Jakarta, and

Dar es Salaam

Introduction

The majority of the world’s population now lives in cities and, for many of those urban

residents, poverty is widespread. Inadequate access to water, energy, food, and

employment are all realities of daily life. A changing climate will exacerbate the issues faced

by the urban poor: climate change is a problem primarily caused by the rich will affect poor

most severely (Bartlett et al. 2009; Hoornweg et al. 2010). Urban development must

improve the quality of life for the urban poor, now and as the climate changes, without

contributing greenhouse gas (GHG) emissions.

The potential to combine strategies for mitigation and adaptation is of great interest in the

literature – especially in the urban context. Experts contend that mitigation and adaptation

need not compete for development resources, for they are complementary pursuits

(Wilbanks and Sathaye 2007). Wilbanks et al. (2003) describe how mitigation and

adaptation target different geographical and temporal scales: mitigation is global with long-

term impacts, and adaptation is local with short-term impacts. Therefore, if mitigation can

keep climate change moderate, adaptation can take case of the rest. Such a synergetic

approach is essential to incorporate into urban planning practices (Susskind 2010). Venema

and Rehman (2007) and Simon (2010) bring up challenges unique to Africa: energy is a

sector to target. Energy poverty, if left unaddressed, contributes to climate change with

deforestation and biomass burning, while making poor populations even more vulnerable to

climate impacts. It becomes clear that in developing regions, strategies that can address

both mitigation and adaptation simultaneously will be the most efficient use of limited

resources.

This paper presents case studies of three cities: Amman, Jakarta, and Dar es Salaam. The

objective of the case studies is to address the dual problem of mitigation and adaptation,

providing insight into the climate vulnerabilities, greenhouse gas impacts, and low-carbon,

adaptive development opportunities of urbanizing regions in developing countries. The

58

challenges of sustainable development today are immense; each city is at a different stage

in the development process and faces unique everyday circumstances.

Amman is the largest city in Jordan: home to 2.8 million people. The population growth in

the city accelerated in the later half of the last century, as Amman has been a place of

refuge for those escaping from neighbouring conflict zones. The majority of the population

in Jordan is under the age of 25 (WDI 2008). The climate is moderate with seasonal

variations and cool winter months. The annual per-capita income in Jordan is about

US$3,600 on average (WDI 2008), and the economy in Amman is primarily service-based.

Jakarta is on the northwest coast of Java, Indonesia; with a population of nine million, it is

the archipelago’s largest city. The climate is tropical, with warm temperatures and an eight-

month long rainy season. Economic activity centres on services, finance, and

manufacturing. The average income per capita in Indonesia is about US$2,250, growing at a

rate of 6.1% per year (WDI 2008).

Dar es Salaam is the largest city in Tanzania, and although not the capital of the country, it

is considered a hub for business, government, and transportation. The population is

approaching three million, growing recently at a rate of 6% per year (Kimbisa 2010). The

climate is tropical, and the average income per capita of Tanzania is about US$500 per year

(WDI 2008). The local job market centres on manufacturing and natural resources, such as

fishing, and many people support themselves with entrepreneurial enterprises.

The amount and severity of urban poverty in each city varies. In all three cities, the poor

live primarily in unplanned areas that are not connected to basic municipal services. In

Amman, while the economy is flourishing, there are still portions of the population that live

in unsafe squatter settlements without access to reliable services. The poor in Amman are

primarily refugees from conflicts in neighbouring regions (Tavernise 2007), and the

government has made housing upgrade projects and infrastructure access for poor

communities a development priority (Gerlach and Franceys 2009; Bisharat and Tewfik

1985). Unplanned settlements in Jakarta are known as kampungs. The kampungs are not

equipped with reliable water or energy infrastructure, some are located in flood-prone

areas, and they are not exclusive to the urban poor: some kampung residents have low- to

mid-range incomes (Argo and Laquian 2007; Rustamadji 1992).

In Dar es Salaam, the contrast between the rich and the poor is most extreme. The rich live

in comfortable, planned developments with access to water, electricity, and cars. The poor

59

majority (nearly 70%) inhabits over populated, unplanned slum settlements without basic

municipal infrastructure, including roads, water, and sewerage (Government of Tanzania

2000; Tweehuysen and Hayes 2006). The conditions in the slums are worsened by everyday

behaviour caused by desperation. Biomass is burned in the home for cooking, which

produces soot that deteriorates interior air quality (Sanderson 2000). Solid waste, human

waste, and wastewater are dumped in the streets to decompose, where they contaminate

the meagre water supply and spread dysentery and disease (Victor et al. 2008). While the

problems in Dar es Salaam are seemingly overwhelming, city officials have successfully

taken steps to upgrade some areas.

The three case studies demonstrate different interactions with the issue of climate change.

This paper will first outline the chief issues associated with climate change for each city.

Next, greenhouse gas inventories for each city are presented, as well as associated data

and methodological issues. Finally, the cities’ current plans to address climate change are

presented, along with some further suggestions of synergetic opportunities for mitigation,

adaptation, and development.

The Vulnerabilities of Cities to Climate Change

The Intergovernmental Panel on Climate Change (IPCC) has given some dire predictions of

the consequences of climate change (IPCC 2007). Some consequences include: a decrease

of water availability in dry areas by up to 30%; sea-level rise; and increased ocean

temperatures that disrupt ecosystems and intensify storms. For cities, these consequences

take on a life of their own. The infrastructure in cities, or lack thereof, has profound impacts

on how severely the consequences of climate change are experienced. In cities with strong

emergency systems, diverse power sources, and well-coordinated drainage and water

systems, climate change may be more easily managed. However, the majority of cities in

developing regions of the world do not even have the infrastructure to provide for all of their

citizens on a daily basis, let alone adjust to disruptions caused by climate change. It is in

these cities that the impacts of climate change will be most severe.

In Amman, the most climate-sensitive municipal service is water. Jordan is exceptionally

dry, and nearly 15% of all electricity consumption is by the Water Authority of Jordan (GTZ

2004). Accordingly, Amman uses approximately 825 GWh of electricity to pump water each

year (see the urban metabolism diagram for Amman, Figure 7). With climate change, water

scarcity will become more severe. While Amman has nearly a one-hundred percent

60

Figure 7: Amman's urban metabolism.

Water Supply

Fossil Fuels

Natural Gas Fuel Oil LPG Kerosene Diesel Oil Gasoline Jet Kerosene

Marine Fuel Oil

2,143 TJ 12,998 TJ 1,910 TJ 1,640 TJ 26,236 TJ 20,187 TJ 12,709 TJ 668 TJ

180 MCM/a

73 MCM/a

Electricity Generation

Consumption

5,500 GWh

T&D Losses

770 GWh

Wastewater

Aviation & Marine

3,766 ktCO2e

983 ktCO2e

1,012 ktCO2e

Landfill Waste

669 kt

Greenhouse Gas Emissions

92 ktCO2eNitrous Oxide

1,029 ktCO2eMethane

9,136 ktCO2eCarbon Dioxide

10,256 ktCO2e

20.4 MJ/m2

Total Radiation

42 ktOther Materials

136 ktPlastics, Glass, Metal

197 ktPaper, Cardboard, Textiles

294 ktOrganic Waste

19 GWhDiesel Oil

3,472 GWhNatural Gas

2,744 GWhFuel Oil

34 GWhRenewables

6,270 GWh

61 ktCO2e

1,008 ktCO2e

520 ktCO2e

2,906 ktCO2e

Cropland

Manufacturing & Industry

Commercial, Institutional & Residential Road

Transport

Water Supply

Fossil Fuels

Natural Gas Fuel Oil LPG Kerosene Diesel Oil Gasoline Jet Kerosene

Marine Fuel Oil

2,143 TJ 12,998 TJ 1,910 TJ 1,640 TJ 26,236 TJ 20,187 TJ 12,709 TJ 668 TJ

180 MCM/a

73 MCM/a

Electricity Generation

Consumption

5,500 GWh

T&D Losses

770 GWh

Wastewater

Aviation & Marine

3,766 ktCO2e

983 ktCO2e

1,012 ktCO2e

Landfill Waste

669 kt

Greenhouse Gas Emissions

92 ktCO2eNitrous Oxide

1,029 ktCO2eMethane

9,136 ktCO2eCarbon Dioxide

10,256 ktCO2e

20.4 MJ/m2

Total Radiation

42 ktOther Materials

136 ktPlastics, Glass, Metal

197 ktPaper, Cardboard, Textiles

294 ktOrganic Waste

19 GWhDiesel Oil

3,472 GWhNatural Gas

2,744 GWhFuel Oil

34 GWhRenewables

6,270 GWh

61 ktCO2e

1,008 ktCO2e

520 ktCO2e

2,906 ktCO2e

Cropland

Manufacturing & Industry

Commercial, Institutional & Residential Road

Transport

61

connection rate to the municipal water supply, providing a consistent water flow is

something authorities are already struggling with (Gerlach and Franceys 2009).

The projected sea-level rise and flooding are risks in Jakarta. The city has already

experienced extreme flooding, with flooding being most severe in low-elevation kampungs

(Texier 2008). Floods, landslides, and sea-level rise are all risks for Jakarta (Bowo 2010).

The climate vulnerabilities associated with sea-level rise are intensified by local actions:

parts of Jakarta are already subsiding because of an over-exploitation of ground water and

soil compression from heavy construction (van Sluis and van Aalst 2006).

In Dar es Salaam, alternating floods and droughts are climate impacts already experienced.

The city is witnessing an unprecedented influx of climate refugees from rural areas (Kimbisa

2010), which puts increased pressure on already strained services. Flooding in Dar es

Salaam is intensified by the poor drainage infrastructure (Mwandosya et al. 1998), causing

roads to be washed out and economic activity to be put on hold. The droughts are also

impacting Tanzania’s electricity infrastructure: a low water level disrupts hydroelectricity

production, requiring new natural gas infrastructure to maintain power. Not only is this

response expensive, it also increases greenhouse gas emissions that further the climate

problem.

The climate vulnerabilities of the three cities point to a common theme: infrastructure that

is resilient to extreme circumstances will be the best response to climate change. As

demonstrated by the example of electricity in Tanzania, development responses should not

exacerbate the climate change problem by producing greenhouse gas emissions. Similarly,

the water situation in Amman shows that infrastructure access and supply need to be

considered in concert. A big picture perspective is needed that address impacts as well as

contributions to climate change.

Determining Greenhouse Gas Emissions from Cities

Greenhouse gas emissions from developing cities, though sometimes challenging to

quantify, can give insight into urban activities that are contributing to the climate change

problem. Cities in developing economies to not generally emit as many emissions per capita

as cities in developed regions, simply because the quality of life and consumption practices

are lower on average. However, conducting an inventory of emissions by sector is the first

step towards targeting the most effective low-carbon development strategies for a given

city.

62

Methodology and Data Sources

The greenhouse gas inventories for Amman, Jakarta, and Dar es Salaam were conducted

following the methodology of Kennedy et al. (2010) and the Standard for Determining

Greenhouse Gas Emissions from Cities (UNEP et al. 2010), which is a city-scaled version of

the Intergovernmental Panel on Climate Change (IPCC) methodology for nations (IPCC

2006). The urban GHG inventory is a hybrid of a consumption and production inventory;

that is, it includes GHG emissions produced within the city boundary, as well as emissions

that are a direct result of urban activity. For example, the inventory includes emissions from

heating, industrial, and transportation fuels, as well as electricity consumption (though

electricity production often takes place outside the urban boundary).

The IPCC defines four categories of emissions: Energy (stationary and mobile combustion of

fossil fuels); Industrial Processes and Product Use (non-energy related emissions);

Agriculture, Forestry, and Other Land Uses (AFOLU); and Waste. In cities, the most

significant categories of emissions are Energy (fossil fuel combustion and electricity

consumption) and Waste. Some cities have high Industrial Process emissions (e.g. Chinese

cities, see Chapter 2), but these emissions are not considered for the three cities studied in

this paper. Amman and Dar es Salaam do not have significant industrial activity, and the

industrial data for Jakarta is currently unavailable. Similarly, AFOLU emissions are not

included for lack of available data; however, they are estimated to be insignificant for the

three cities based on previous studies conducted in other cities (Kennedy et al. 2009).

In general, GHG emissions are calculated as follows:

FactorEmissionDataActivityEmissionsGHG ×= (8)

Activity data varies with inventory component; for example, the amount of energy

consumed and the amount of waste produced are both forms of activity data for their

respective sectors. Both the activity data and GHG intensity are required to complete

calculations. For the three cities studied, GHG intensities are either based on national

averages or IPCC default values.

The greenhouse gas inventories for Amman, Jakarta, and Dar es Salaam were conducted in

collaboration with city officials and local consultants to give an ‘on-the-ground’ advantage to

data collection. Officials were able to access statistical data and engage local experts to

provide reasonable estimations. For Amman, city officials from the Greater Amman

Municipality were able to provide data for all necessary sectors; when needed, national data

63

was scaled to values appropriate to the local context and confirmed with city officials. Data

for Jakarta was readily available, as local consultants had recently conducted a thorough air

quality report outlining sources of carbon dioxide, methane, and nitrous oxide (Suhadi

2009). The inventory data for Dar es Salaam was the most difficult of the three cities to

acquire, for it is where the conditions of poverty are most extreme. Local officials,

academics, and development agencies were able to provide some data, and the remaining

data was scaled from national statistics to the urban context. It is important to note that the

three urban GHG inventories presented in this paper are an important first step; future

inventories will improve as urban data collection becomes more thorough and the practice of

inventorying becomes a higher priority to cities.

Results

The urban characteristics of the cities are well represented in their GHG inventories. In the

study year, Amman, Jakarta, and Dar es Salaam produced 10.3 megatonnes, 44.6

megatonnes, and 1.6 megatonnes of carbon dioxide equivalent emissions respectively

(Figure 8; Table 17; full standard tables for Amman, Jakarta, and Dar es Salaam are shown

in Appendices D-F). The major contributors of emissions are highly dependent on the

amount and type of urban infrastructure in each city.

Emissions from road transportation were quite high in all three cities, accounting for 28%,

27%, and 49% of the total emissions from Amman, Jakarta, and Dar es Salaam

respectively. In all cities, there is little in terms of a public transportation infrastructure: the

public transportation networks are still growing, and privately owned automobiles are still

the mode of choice for those who can afford them. In all the cities, mass transit takes the

form of buses or mini-buses, which give mobility access to more people but do not

necessarily take private cars off the road.

Electricity consumed in the cities had varying impacts on the greenhouse gas inventories,

primarily due to differing levels and methods of generation (Table 18). Emissions from

electricity for Amman and Jakarta were quite high: 37% and 59% respectively. This can be

attributed to high levels of electricity generated using fossil fuels. Dar es Salaam, however,

had the lowest percentage of emissions from electricity (7%), chiefly because electricity is

generated primarily from hydropower and service is inconsistent.

Waste in all three cities is managed at landfill sites, with a small portion composted in

Jakarta. The percentages of emissions from waste are quite low in Amman and Jakarta:

64

Figure 8: Total urban greenhouse gas emissions by sector for Jakarta, Dar es Salaam, and Amman.

JAKARTA

44,562 ktCO2e

DAR ES SALAAM

1,628 ktCO2e

AMMAN

10,256 ktCO2e

37%

15%

28%

10%

10%

37% Electricity

Heating & Industrial

Ground Transport

Aviation & Marine

Industrial Processes

Waste

59%

7%

27%

7%

7%

49%

12%

32%

JAKARTA

44,562 ktCO2e

DAR ES SALAAM

1,628 ktCO2e

AMMAN

10,256 ktCO2e

37%

15%

28%

10%

10%

37% Electricity

Heating & Industrial

Ground Transport

Aviation & Marine

Industrial Processes

Waste

59%

7%

27%

7%

7%

49%

12%

32%

65

Table 17: Per-capita GHG emissions (tCO2e/capita) by sector for Amman, Jakarta, and Dar es Salaam.

Amman Jakarta

Dar es Salaam

ENERGY

a) Stationary Combustion

Electricity 1.35 2.91 0.04

Commercial, Institutional, and Residential 0.19 0.10 -

Manufacturing and Construction 0.36 0.23 -

Other 0.02 - -

b) Mobile Combustion

Road Transportation 1.04 1.32 0.27

Aviation and Marine 0.35 - 0.07

WASTE 0.36 0.36 0.18

TOTAL (tCO2e/capita) 3.66 4.92 0.56

Table 18: Fuel supply and emission factors for electricity generation for Amman, Jakarta,

and Dar es Salaam.

Amman Jakarta Dar es Salaam

Electricity Generation Method

Renewables, incl. Hydro 0.6% - 60.1%

Fuel Oil 43.8% 28.1% 0.9%

Diesel Oil 0.3% 25.9% -

Natural Gas 55.4% 46.0% 36.2%

Coal - - 2.7%

Emission Factor (tCO2e/GWh) 601 891 241

10% and 7% respectively. However, waste in Dar es Salaam produced 32% of total

emissions. It is important to note that waste emissions only account for waste that is

collected and managed by the cities; in reality, waste in unplanned settlements is collected

rarely and may not be collected at all.

Given that these inventories are the first for all three cities, there are some emissions that

are left unaccounted for. Emissions from fossil fuel combustion in the industrial, commercial,

and residential sectors were available for Amman and Jakarta (15% and 7% respectively),

but not for Dar es Salaam as the data was not available. Similarly, emissions from the

aviation and marine sectors were not calculated for Jakarta because the ports lie outside the

66

city boundary, and assigning emissions based on city-related port use was not possible due

to lack of available data. However, given their statuses as regional business and commercial

hubs, aviation in Amman and Dar es Salaam were significant, accounting for 10% and 12%

of total emissions respectively.

In examining the greenhouse gas inventories of the three cities, it is important to revisit the

living conditions of the urban poor. Those living in poverty do not have access to the same

energy, water, or waste management infrastructure as other urban residents. Therefore, it

becomes important to question to what degree the inventories reflect the consumption

habits of the entire city. One may argue that the inventories focus primarily on residents

living in well-connected areas of the city, though this is a topic requiring further research.

An understanding of the attribution of emissions within the city will help inform sectors best

suited for either mitigation measures or low-carbon development.

Development Opportunities

As demonstrated in previous sections, the level of service access, climate change

vulnerabilities, and greenhouse gas emissions in the study cities are all different, which

demands unique and innovative solutions. The most efficient use of development resources

will address all three issues simultaneously. For some examples: decentralized renewable

energy addresses energy poverty, reduces greenhouse gas emissions, and enhances

resilience of poor communities (Venema and Rehman 2007); improved water and waste

management services improve resilience while avoiding emissions from untreated

decomposition; and slum upgrading from dangerous areas to safe, energy efficient homes

reduces emissions and vulnerabilities to extreme events. Current development plans in each

city begin to address these issues. Further recommendations of specific strategies that will

address mitigation, adaptation, and development are provided for each city.

Officials in Amman have been working to improve urban life for a number of decades. In the

1980s, the city introduced housing upgrading programs that legalized land tenure in

squatter settlements and built household connections to water, sewage, and electricity

infrastructure (Bisharat and Tewfik 1985). The upgrades made the settlements less prone to

washout and lessened safety and health risks. Today, authorities are continually addressing

the problem of water scarcity, approaching the issue from multiple angles including water

management infrastructure projects (GTZ 2004) and public-private partnerships (Gerlach

and Franceys 2009). Recently, some of the most significant projects have focused on the

city’s social fabric: sidewalks, park benches, and pedestrian walkways with trees are

67

improving infrastructure resiliency and quality of life while “tearing down walls between rich

and poor” (Stockman 2010). Mobility is increasingly important for the young population, and

creating connectivity and curbing sprawl are top priorities.

The greatest contributor to the GHG inventory in Amman is the electricity sector, and the

greatest climate vulnerability is the water supply. The latter issue exacerbates the former:

the electricity consumption for water pumping is already high, and it will grow with climate

change. Therefore, a recommendation strategy for climate change mitigation, adaptation,

and service provision in Amman is an increased share of photovoltaic electricity production,

especially for energy needs surrounding water supply. Amman receives a high amount of

solar radiation each year (20.4 MJ/m2; see Figure 7), making photovoltaic electricity a

viable renewable energy option. Water technologies, such as deep groundwater pumping or

desalination, are very energy intensive, and meeting those energy needs in a resilient,

carbon neutral manner is essential.

In Jakarta, the tradition of community-based development is also applied in response to

climate change (see Karamoy and Dias 1986). The municipal government is working with

communities on disaster preparedness and budget management programs, as well as water

treatment, waste management, and waste-to-energy programs that are currently in

progress (Bowo 2010). Some major successes in Jakarta have been the conversion of users

of kerosene to LPG, car free days, emissions testing for cars, expansion of green spaces,

and plans to shift public transportation to alternate fuels (Bowo 2010). Not only do these

efforts reduce emissions, they also improve the resilience of Jakarta’s communities:

diversity in energy sources reduces vulnerability to energy infrastructure disruption and

shortages, and increases in vegetation provide adaptive ecosystem services that improve

community life.

Jakarta’s highest emitting sectors are electricity and road transportation. The government’s

current work to enhance public transportation and reduce vehicle use targets greenhouse

gas emissions as well as local air pollution. However, one of the greatest areas of

vulnerability in Jakarta is flooding and sea-level rise, specifically for the urban poor living in

unsafe, flood-prone areas. A synergetic recommendation for Jakarta addressing mitigation,

adaptation, and development is advanced slum upgrading: relocation of the at-risk poor to

safe areas with energy efficient homes connected to public transit and decentralized,

community-based electricity generation. There are a variety of vernacular, Southeast Asian

architecture techniques that take advantage of passive day-lighting and ventilation

strategies. Combined with electricity produced nearby using renewable resources, the

68

resultant community would be more resilient to climate change with a minimal carbon

footprint.

The municipal government of Dar es Salaam has a number of programs that address

development and climate change mitigation. For example, the government has programs in

place to encourage waste-to-energy briquettes and cooking stoves (as an energy alternative

to charcoal), tree planting, methane flaring at the local dumpsite, as well as a planned Bus

Rapid Transit project (Kimbisa 2010). International development banks also have projects in

Dar es Salaam, such as the World Bank’s Community Infrastructure Upgrading Program

(CIUP) that has built roads with drainage systems, street lights, water kiosks, and solid

waste containers (World Bank 2002). These programs improve resiliency to flooding and

landslides, diversify energy resources, promote economic activity, and encourage waste

management.

The World Bank’s CIUP projects in Dar es Salaam are excellent ways to improve the

resiliency of poor communities to flooding. Building on this strategy, a recommendation for

Dar es Salaam focuses on community habits of waste disposal. Waste emissions are highest

in the city’s inventory, which only represents waste collected and disposed in landfills. The

waste left to decompose in the streets not only produces emissions, it also causes

contamination of the water supply and disease during floods. A synergetic way to address

this issue is to implement waste dumping practices combined with community-based waste-

to-energy facilities. This would reduce climate change impacts and vulnerabilities while

addressing energy poverty in poor communities.

Summary and Conclusions

The need to address climate change mitigation and adaptation simultaneously is quite

pronounced in developing cities. The strategies to manage and mitigate risks are

complementary: reducing emissions now will ensure climate change does not surpass a

level manageable by adaptation in the future. This is especially relevant for the urban poor,

for they will experience the effects of climate change most severely. Cities in developing

regions are faced with the tasks of carbon neutral development, climate change protection,

and increased service access for their citizens.

The cities presented in this paper are examples of three unique urban circumstances.

Amman has a young population with a primarily service-based economy; Jakarta is the

most population city in Indonesia with economic activity focusing on services, finance, and

69

manufacturing; and Dar es Salaam has a rapidly growing population and many support

themselves with small-scale entrepreneurship. The severity of the living conditions of the

urban poor varies, but an important commonality remains: the poor live in unplanned

developments with little access to municipal infrastructure, putting them highly at risk to

the extreme events associated with climate change.

On a global scale, climate change is considered to cause a variety of extreme events,

including flooding, droughts, sea-level rise, and intense storms. The population density and

prevalence of physical infrastructure makes cities particularly vulnerable. The climate

change vulnerabilities in the three study cities have the potential to drastically impact the

way of life of urban residents. In Amman, water scarcity is a problem that worsens each

year, and it will continue to as the region experiences a reduction in precipitation and an

increase in population. In Jakarta, flooding and sea-level rise have the potential to alter the

normal way of life. Alternating droughts and floods in Dar es Salaam challenge the resilience

of already fragile infrastructure. In all cases, the unplanned developments are at a

particularly high risk of experiencing the most devastating consequences.

A greenhouse gas inventory is the first step to effective emissions reduction strategies. The

GHG inventories for the three study cities were conducted according to a city-scaled version

of IPCC methodology, revealing the following: Amman was responsible for 10.3 megatonnes

of greenhouse gases, Jakarta for 44.6 megatonnes, and Dar es Salaam for 1.6 megatonnes.

The sectors responsible for the highest percentage of emissions in the three cities were

generally road transportation, electricity generation, and waste. This pattern raises an issue

that warrants further investigation: the highest emissions result from services and

infrastructure that are typically inaccessible to the urban poor. The local distribution of

emissions sources could be a useful element in future development planning.

In all three cities studied, an aggressive reduction in greenhouse gas emissions will need to

target the lifestyles of the rich; nevertheless, climate change adaptation and access to basic

services remain priorities for the poor. Strategies that address mitigation, adaptation, and

development together will be the most effective in cities in developing regions. Officials in

Amman, Jakarta, and Dar es Salaam have recognized the importance of including climate

change in their development strategies, and they are implementing projects that address

both climate change and the development needs of their residents. A recommendation of a

synergetic climate change strategy was made for each city. For Amman, solar energy has

the potential to meet many electricity needs, particularly those related to water pumping.

The relocation of the poor from unsafe areas is a top priority in Jakarta, and alternative

70

housing could be met with energy efficient homes connected to transit and community-

generated electricity. In Dar es Salaam, upgrading the waste collection in unplanned

settlements and using the waste to produce energy locally is a potential solution to both

waste dumping and energy poverty. In all three recommendations, key adaptive

requirements have been addressed along with heavy emitting sectors. Similar synergic

approaches could be developed for other cities as well.

In developed and developing regions of the world, it is increasingly important for cities to

address climate change mitigation and adaptation as part of their strategic development

plans. All cities are unique in their infrastructure, municipal priorities, and social fabric;

therefore, local involvement in research and assessment is crucial. Sustainable development

requires not only an understanding of climate change science, but also leadership that

encourages creativity and innovation in project implementation. Perhaps most importantly,

it will be essential for cities to disseminate their experiences, allowing cities to learn from

each other and explore new ideas.

71

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Chapter 5 Moving Forward

Each of the three papers in this thesis provides a unique urban perspective on the response

to climate change. “Greenhouse Gas Emissions from Chinese Cities” illustrates the situation

in China, where urbanization is rapid and the economy is extremely carbon intensive. The

case in an established, developed city that is starting to take steps to reduce emissions is

shown in “A Low-Carbon Infrastructure Plan for Toronto, Canada.” Finally, the challenges

experienced in developing cities, where poverty is a chief concern, are depicted in

“Synergies between Adaptation and Mitigation in Development: Case Studies of Amman,

Jakarta, and Dar es Salaam.”

While the perspectives in the papers are unique, three commonalities tie them together.

They illustrate three important and interconnected processes: economic growth,

infrastructure development, and the response to climate change. The early stages of

economic growth are shown in Amman, Jakarta, and Dar es Salaam, followed by an

economy in transition illustrated by Chinese cities, then finally an established economy

shown in Toronto. The infrastructure development priorities vary accordingly: basic access

to services is paramount in Amman, Jakarta, and Dar es Salaam; rapid infrastructure

construction corresponding to accelerated urbanization is the case in Chinese cities; and in

Toronto the priority is strategic infrastructure enhancement. The climate change response

process, from conducting assessment of climate change impacts (i.e. a greenhouse gas

inventory) or vulnerabilities to strategic planning for mitigation or adaptation, can be

followed through the papers.

The overall picture presented by the three papers is one of diversity. The cities presented

face unique challenges when it comes to climate change, as do cities throughout the world.

However, if responses to climate change are to be efficient and effective, cities around the

world must learn from the unique circumstances and best practices of other cities. Cities

must respond to climate change at various scales and adopt different financial mechanisms

to do so. As the response process to climate change moves forward, these issues will

become increasingly important targets of innovation.

74

Comparison and Dissemination

Disseminating experiences and enabling cities to learn from each other requires a certain

amount of city to city comparison, which is not always favoured by city officials. Officials

often worry that comparison of their city to another may result in a competition of

“goodness”. While a certain amount of competition may drive change, when comparing

metrics from cities, such as per capita greenhouse gas emissions, it is important to

acknowledge the inherent differences captured in those values. Differing levels of

development, gross domestic products, physical geographies, political systems, economic

sectors, and population demographics will impact the level of emissions and the success of

strategic mitigation and adaptation projects in a city. Successful dissemination will come

from finding commonalities among these differences.

Interesting work addressing the issue of comparison and dissemination is that of Xuemei Bai

and colleagues (Bai et al. 2010). By comparing 30 innovative best practices in urban

sustainability in Asia, the authors were able to pinpoint the successes, barriers, and

necessary circumstances to maximize project success and transferability. In finding

commonalities among projects, issues of varying economic, social, and political

circumstances were able to be resolved. On-going analysis in Asia and other global regions

would be of great benefit to strategic planning, enabling prediction and promoting

successful project management.

Progress in the dissemination and comparison of urban responses to climate change will also

be manifested in terms of learning platform. Workshops, conferences, and publications are

the traditional methods of communication; however, the networking capabilities of the

internet are promoting new possibilities. Currently in progress, the Carbon Neutral City

Planner tool based on Getting to Carbon Neutral (Kennedy 2010) will be accessible as a

piece of online software, allowing planners and citizens to conduct various scenarios for low-

carbon development for Canadian cities. The strategies are based on the best practices of

others, but applied to the unique geographic circumstances of each city. A suggestion for

further work is a similar online platform for global cities (perhaps through the C40 or

another international group), where an analysis similar to that of Bai et al. (2010) is

conducted for numerous projects. This would provide urban planners and developers with

searchable listings of key commonalities for the success of different types of climate change

mitigation and adaptation projects.

75

Scale of Strategic Response

The strategic mitigation and adaptation responses to climate change presented in this thesis

highlight infrastructure and policy development, and these may occur at various spatial

scales. Strategies may target change at the level of the individual, the community or

neighbourhood, or the city as a whole. Strategies should be catered to the scale they

address, and multiple scales may be addressed at once to maximize the success of a

strategic response.

At the level of the individual, personal choice and lifestyle can impact greenhouse gas

emissions as well as vulnerability to climate change. People may choose to live energy-

intensive lifestyles, or they may be in a situation of poverty where they live in dangerous

squatter settlements. For example, in the developed city context the choices of the

individual play a very important role in climate change mitigation. An organization currently

addressing this is the Zerofootprint Foundation. (Zerofootprint 2010). Through social

networking and an online carbon calculator, Zerofootprint is creating software that brings

awareness to individuals of their greenhouse gas impact, and encourages them to work as

members of an online community to reduce emissions. As difficult as it is to mobilize

individuals, in democratic systems they have immense political and consumer power to

drive change.

The need to understand the climate change response on a neighbourhood level is another

area for further work, previously mentioned in the Chapter 4. The type and availability of

infrastructure varies most on the neighbourhood scale, resulting in varying levels of

greenhouse gas emissions and climate change vulnerabilities. The attribution of emissions

varies greatly by neighbourhood; for example, per-capita emissions in a high-rise complex

close to transit in Toronto are one tenth that of an isolated neighbourhood of single-family

homes in the suburbs (Hoornweg et al. 2011). Further research is needed, especially in

developing cities, to understand these neighbourhood differences and to encourage climate

change responses to be tailored appropriately.

Financial Mechanisms

One essential aspect of the climate change response that was not addressed in this thesis is

the costs and sources of funding for mitigation and adaptation strategies. While this topic is

beyond the scope of this thesis, it is important to mention in a holistic look at the responses

to climate change. Technology costs will vary over time, indicating that some strategies that

76

are not feasible now will be in the very near future. Financial mechanisms are an essential

area where future research is needed; financing will be a continuous source of innovation in

the response to climate change.

The development of carbon markets has supported the development of innovative financial

mechanisms for climate change mitigation strategies. Putting a price on carbon creates

costs associating with emitting greenhouse gas emissions. Therefore, in addition to savings

associated with reducing energy consumption, there are now monetary savings associated

with reducing emissions. Carbon finance and the Clean Development Mechanism have

enabled projects throughout developing countries. While the costs of technology will change

over time, a study of the capital costs and greenhouse gas reductions of various CDM

projects revealed that waste projects are currently the most efficient (Kennedy et al. 2010).

Additional innovative financial mechanisms are emerging in developing regions, such as the

work of Grameen Shakti (Grameen Shakti 2009) on the microfinance of decentralized

renewable energy projects.

The financial mechanisms associated with mitigation are enabled by one very important

characteristic: the amount of greenhouse gas emissions avoided by a project is a metric

that can be quantified. However, for adaptation no such metric exists yet. This is one area

where future research is desperately needed: for some cities, adaptation is a far more

pressing issue than mitigation. The development of a financial mechanism for adaptation,

similar to that of carbon markets for mitigation, will enable a more rapid uptake of the

adaptation strategies and projects that will save countless lives in some of the poorest

regions of the world.

Conclusions

The process of responding to climate change is dynamic, and it will continue to change over

time. The papers presented in this thesis provide a snapshot of the process, as it exists at

the time of publication, in different cities throughout the world. Topics covered include:

issues of assessing climate change impacts and responsibilities, including greenhouse gas

emissions inventories; strategic development scenarios that addressing climate change

mitigation; and the need to consider synergic development strategies for mitigation and

adaptation. The three papers point to one important conclusion: there is more work to be

done. We need to better understand how to enable cities to learn from each other, how to

target efforts at the appropriate scale, and how to finance necessary infrastructure

developments. The single most important driver of the future responses to climate change

77

will be innovation. Climate change will require us all to think differently about our

consumption habits and the nature of our economies; accordingly, we must think differently

about our cities. The fight against climate change will be won or lost in cities, and now is the

time for innovative action.

78

References for Chapter 5

Bai, X., B. Roberts, and J. Chen. 2010. “Urban sustainability experiments in Asia: patterns and pathways.” Environmental Science & Policy 13(4): 312-25.

Grameen Shakti. 2009. “Grameen Shakti.” Available: http://www.gshakti.org. [Accessed

September 2010].

Hoornweg, D., L. Sugar, C.L. Trejos-Gomez. 2011. “Cities and Greenhouse Gas Emissions: Moving Forward.” Environment and Urbanization 23(1). (Article in press).

Kennedy, C.A. ed. 2010. Getting to Carbon Neutral: A Guide for Canadian Municipalities. Toronto and Region Conservation Authority: Toronto.

Kennedy, C.A., D. Bristow, S. Derrible, E. Mohareb, S. Saneinejad, R. Stupka, L. Sugar, R.

Zizzo, and B. McIntyre. 2010. “Getting to Carbon Neutral: A Review of Best Practices

in Infrastructure Strategy” in Bose, R.K. (ed). 2010. Energy Efficient Cities:

Assessment Tools and Benchmarking Practices. World Bank: Washington, DC.

Zerofootprint Foundation. 2010. “Zerofootprint for Individuals.” Available:

http://www.zerofootprintfoundation.org/individuals. [Accessed September 2010].

79

Appendix A: Standard GHG Reporting Tables for Beijing

Table 1: Community Information

Name of city or local region Beijing Country China

Inventory year 2006

Reporting date June 15, 2010

Population (year round residents) 15,810,000

Land area (sq. kilometers) 16,800

Name, status and address of

reporter

Lorraine Sugar, University of Toronto

Name, status and address of third

party verifier (if applicable)

Other information, e.g., websites of

fuller inventory report or emissions

reduction program

.

Note for Tables: ND = not determined; Neg. = negligible

TOTAL EMISSIONS: 172,577 kt CO2 e PER CAPITA EMISSIONS: 10.9 t CO2 e (Note: now includes industrial process emissions from cement and steel)

80

Table 2: Greenhouse Gas Emissions by Sector

SCOPE CO2 CH4 N2O HFCs PFCs SF6s TOTAL GWP (1) ( 21 ) ( 310 )

Units kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e.

ENERGY a) Stationary Combustion

Electricity (incl. T&D losses)i 1,2,3 60,761 13.9 278.9 ND ND ND 61,054

District energy and CHPii 1,2 12,351 3.8 50.0 ND ND ND 12,405

Energy from waste 1 ND ND ND ND ND ND ND

Commercial & Institutionaliii 1 3,197 6.3 8.1 ND ND ND 3,212

Residentialiv 1 8,094 356 27.4 ND ND ND 8,478

Manufacturing Industries & Constructionv

1 37,449 56.7 131 ND ND ND 37,637

Othervi 1 10,765 81.6 38.9 ND ND ND 10,885

b) Mobile Combustion

Road transportation: LDVsvii 1

Road transportation: trucks 1

Railways 1

13,906 69.1 389 ND ND ND 14,364

Domestic aviationviii 3

International aviation 3 7,092 1.04 61.5 ND ND ND 7,155

Domestic marineix 3

International marine 3 Neg. Neg. Neg. ND ND ND Neg.

Other 1 ND ND ND ND ND ND ND

c) Fugitive Sources ND ND ND ND ND ND ND

INDUSTRIAL PROCESSES

Mineral industryx 1 3,693 Neg. Neg. ND ND ND 3,693

Chemical industry 1 ND ND ND ND ND ND ND

Metal industryxi 1 8,773 Neg. Neg. ND ND ND 8,773

Electronics industry 1 ND ND ND ND ND ND ND

Other 1 ND ND ND ND ND ND ND

Solvent and product use 1 ND ND ND ND ND ND ND

AFOLU 1 ND ND ND ND ND ND ND

WASTE

Solid waste disposal on landxii 1,3 Neg. 4,869 20.2 ND ND ND 4,889

Wastewater handling 1,3 ND ND ND ND ND ND ND

Waste incineration 1,3 30.9 Neg. 1.15 ND ND ND 32.1

TOTAL 166,112 5,457 2,152 ND ND ND 172,577

81

Notes on Table 2

i Includes thermal power generated and consumed in the city, as well as electricity imported from outside the city (Chinese average

thermal power intensities were used for imported electricity); emissions factors for energy industries stationary combustion were used.

ii Includes steam for heating generated and consumed within the city; emissions factors for energy industries stationary

combustion were used.

iii Includes consumption for “transportation, telecommunications, and postal” and “wholesale, retail, and catering” categories in the Chinese Energy Yearbook; emissions factors for commercial/institutional stationary combustion were used.

iv Includes consumption for the “residential” category in the Chinese Energy Yearbook; emissions factors for residential/agriculture

stationary combustion were used.

v Includes consumption for the “industry” and “consumption” categories in the Chinese Energy Yearbook; emissions factors for manufacturing industries & construction stationary combustion were used.

vi Includes consumption from the “agriculture” and “other” categories in the Chinese Energy Yearbook; emissions factors for

commercial/institutional stationary combustion were used for the “other” categories, and emissions factors for residential/agriculture stationary combustion were used for the “agriculture” category.

vii Includes emissions from all ground transportation, specifically consumption of gasoline and diesel fuel; emissions factors from

mobile combustion were used.

viii Includes emissions from international and domestic aircraft, including fuel loaded onto foreign vessels at city airports; emissions factors for mobile combustion were used.

ix Includes emissions from international and domestic vessels, including fuel loaded onto foreign vessels at city ports; emissions factors for mobile combustion were used.

x Includes cement production; emissions factors determined based on IPCC default with 60% clinker content.

xi Includes steel production; emissions factors determined based on IPCC defaults.

xii Includes waste disposed in landfills and by compost; emissions factors determined based on the IPCC default waste composition

values for East Asia.

82

Table 3: Greenhouse Gas Emissions by Fuel or Activity Type

Activity Data Emissions Factor Total GHGs Value Units Tier Value Units Tier t CO2 e ENERGYi

Electricity (on-site renewable) ND GWh N/A 0 t CO2 e / GWh N/A 0

Electricity (grid)ii 61,899 GWh 1 986 t CO2 e / GWh 2 61,053,570

Steam (heating supply)iii 121,816 TJ 1 102 t CO2 e / TJ 2 12,405,238

Coal Briquettes 3,128 TJ 1 99.3 t CO2 e / TJ 1 310,696

Coal Cleaned 8 TJ 1 99.0 t CO2 e / TJ 1 783

Coal Gas not Coke Source 37,120 TJ 1 44.5 t CO2 e / TJ 1 1,650,037

Coal Raw 297,933 TJ 1 100.3 t CO2 e / TJ 1 29,886,033

Coal Washed 15 TJ 1 99.0 t CO2 e / TJ 1 1,492

Coke 64,371 TJ 1 107.7 t CO2 e / TJ 1 6,931,112

Coke Other Products 1,885 TJ 1 107.7 t CO2 e / TJ 1 202,937

Coke Oven Gas 15,499 TJ 1 44.5 t CO2 e / TJ 1 688,963

Diesel 75,541 TJ 1 75.4 t CO2 e / TJ 1 5,695,095

Fuel Oil 8,022 TJ 1 77.6 t CO2 e / TJ 1 622,893

Gasoline 119,898 TJ 1 72.3 t CO2 e / TJ 1 8,669,200

Jet Keroseneiv 99,194 TJ 1 72.1 t CO2 e / TJ 1 7,154,900

Kerosene 276 TJ 1 72.2 t CO2 e / TJ 1 19,931

Liquid Petroleum Gas 23,030 TJ 1 63.2 t CO2 e / TJ 1 1,456,002

Marine Fuel Oilv Neg. TJ 1 78.2 t CO2 e / TJ 1 Neg.

Natural Gas 121,303 TJ 1 56.2 t CO2 e / TJ 1 6,820,305

Petroleum Other Products 141,652 TJ 1 73.5 t CO2 e / TJ 1 10,418,363

Refinery Gas 20,858 TJ 1 57.7 t CO2 e / TJ 1 1,202,523

INDUSTRIAL PROCESSES

Cementvi 11,838 kt 1 0.312 t CO2 e / t 1 3,693,456

Steelvii 8,276 kt 1 1.06 t CO2 e / t 1 8,772,560

WASTE

Solid waste disposal on landviii 4,288,000 t 1 1.14 t CO2 e / t 1 4,889,168

Wastewater handling ND ND ND

Waste incineration 74,000 t 1 0.43 t CO2 e / t 1 32,066

AFOLU ND ND ND

83

Notes on Table 3

i Emissions factors are a weighted average of sector-specific emissions factors used for each fuel.

ii Includes electricity produced and consumed in the city, as well as electricity imported; emissions factor takes both into account.

iii Includes steam heat produced and consumed in the city; emissions factor is a reflection of the steam production mix.

iv Includes an import/export adjustment, as determined by the Chinese Energy Yearbook.

v Includes an import/export adjustment, as determined by the Chinese Energy Yearbook.

vi Emissions factors determined based on IPCC default with 60% clinker content.

vii Emissions factors determined based on IPCC defaults.

viii Includes waste disposed in landfills and by compost; emissions factors determined based on the IPCC default waste composition values for East Asia.

84

Table 4: Upstream (Embodied) Greenhouse Gas Emissions - ND Activity Data Emissions Factor Total

GHGs Value Units Value Units t CO2 e. ENERGY

Electricity (on-site

renewable)

GWh t CO2 e / GWh

Electricity (grid) GWh t CO2 e / GWh

Natural gas TJ t CO2 e / TJ

Fuel oil TJ t CO2 e / TJ

Coal TJ t CO2 e / TJ

Gasoline TJ t CO2 e / TJ

Diesel TJ t CO2 e / TJ

Jet Fuel TJ t CO2 e / TJ

Marine Fuel TJ t CO2 e / TJ

<add fuels as appropriate> TJ t CO2 e / TJ

WATER ML t CO2 e/ ML

BUILDING MATERIALS

Cement kt t CO2 e / kt

Steel kt t CO2 e / kt

Bricks kt t CO2 e / kt

<add building materials as appropriate>

FOOD

Cereals kt t CO2 e / kt

Fruits kt t CO2 e / kt

Meat kt t CO2 e / kt

Seafood kt t CO2 e / kt

Dairy kt t CO2 e / kt

Other kt t CO2 e / kt

85

Appendix B: Standard GHG Reporting Tables for Shanghai

Table 1: Community Information

Name of city or local region Shanghai Country China

Inventory year 2006

Reporting date June 15, 2010

Population (year round residents) 18,150,000

Land area (sq. kilometers) 6,200

Name, status and address of

reporter

Lorraine Sugar, University of Toronto

Name, status and address of third

party verifier (if applicable)

Other information, e.g., websites of

fuller inventory report or emissions reduction program

.

Note for Tables: ND = not determined; Neg. = negligible TOTAL EMISSIONS: 235,499 kt CO2 e PER CAPITA EMISSIONS: 13.0 t CO2 e (Note: now includes industrial process emissions from cement and steel)

86

Table 2: Greenhouse Gas Emissions by Sector

SCOPE CO2 CH4 N2O HFCs PFCs SF6s TOTAL GWP (1) ( 21 ) ( 310 )

Units kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e.

ENERGY a) Stationary Combustion

Electricity (incl. T&D losses)i 1,2,3 88,834 20.7 401 ND ND ND 89,255

District energy and CHPii 1,2 6,617 2.05 27.7 ND ND ND 6,646

Energy from waste 1 ND ND ND ND ND ND ND

Commercial & Institutionaliii 1 2,397 5.61 5.99 ND ND ND 2,408

Residentialiv 1 4,150 91.9 8.86 ND ND ND 4,251

Manufacturing Industries & Constructionv

1 73,172 104 245 ND ND ND 73,522

Othervi 1 1,585 3.79 4.98 ND ND ND 1,594

b) Mobile Combustion

Road transportation: LDVsvii 1

Road transportation: trucks 1

Railways 1

19,674 73.7 477 ND ND ND 20,225

Domestic aviationviii 3

International aviation 3 8,479 1.25 73.5 ND ND ND 8,553

Domestic marineix 3

International marine 3 4,951 9.40 39.7 ND ND ND 5,000

Other 1 ND ND ND ND ND ND ND

c) Fugitive Sources ND ND ND ND ND ND ND

INDUSTRIAL PROCESSES

Mineral industryx 1 2,246 Neg. Neg. ND ND ND 2,246

Chemical industry 1 ND ND ND ND ND ND ND

Metal industryxi 1 20,436 Neg. Neg. ND ND ND 20,436

Electronics industry 1 ND ND ND ND ND ND ND

Other 1 ND ND ND ND ND ND ND

Solvent and product use 1 ND ND ND ND ND ND ND

AFOLU 1 ND ND ND ND ND ND ND

WASTE

Solid waste disposal on landxii 1,3 Neg. 873 46.7 ND ND ND 919

Wastewater handling 1,3 ND ND ND ND ND ND ND

Waste incineration 1,3 428 Neg. 15.9 ND ND ND 444

TOTAL 232,969 1,185 1,346 ND ND ND 235,499

87

Notes on Table 2

i Includes thermal power generated and consumed in the city, as well as electricity imported from

outside the city (Chinese average thermal power intensities were used for imported electricity); emissions factors for energy industries stationary combustion were used.

ii Includes steam for heating generated and consumed within the city; emissions factors for energy industries stationary combustion were used.

iii Includes consumption for “transportation, telecommunications, and postal” and “wholesale, retail, and catering” categories in the Chinese Energy Yearbook; emissions factors for commercial/institutional stationary combustion were used.

iv Includes consumption for the “residential” category in the Chinese Energy Yearbook; emissions factors for residential/agriculture

stationary combustion were used.

v Includes consumption for the “industry” and “consumption” categories in the Chinese Energy Yearbook; emissions factors for manufacturing industries & construction stationary combustion were used.

vi Includes consumption from the “agriculture” and “other” categories in the Chinese Energy Yearbook; emissions factors for

commercial/institutional stationary combustion were used for the “other” categories, and emissions factors for residential/agriculture stationary combustion were used for the “agriculture” category.

vii Includes emissions from all ground transportation, specifically consumption of gasoline and diesel fuel; emissions factors from

mobile combustion were used.

viii Includes emissions from international and domestic aircraft, including fuel loaded onto foreign vessels at city airports; emissions factors for mobile combustion were used.

ix Includes emissions from international and domestic vessels, including fuel loaded onto foreign vessels at city ports; emissions

factors for mobile combustion were used.

x Includes cement production; emissions factors determined based on IPCC default with 60% clinker content.

xi Includes steel production; emissions factors determined based on IPCC defaults.

xii Includes waste disposed in landfills and by compost; emissions factors determined based on the IPCC default waste composition

values for East Asia.

88

Table 3: Greenhouse Gas Emissions by Fuel or Activity Type

Activity Data Emissions Factor Total GHGs Value Units Tier Value Units Tier t CO2 e ENERGYi

Electricity (on-site renewable) ND GWh N/A 0 t CO2 e / GWh N/A 0

Electricity (grid)ii 99,015 GWh 1 901 t CO2 e / GWh 2 89,255,031

Steam (heating supply)iii 63,004 TJ 1 105 t CO2 e / TJ 2 6,646,392

Coal Briquettes 595 TJ 1 99.0 t CO2 e / TJ 1 58,843

Coal Cleaned 3,885 TJ 1 99.0 t CO2 e / TJ 1 384,551

Coal Gas not Coke Source 11,688 TJ 1 44.5 t CO2 e / TJ 1 520,315

Coal Raw 211,567 TJ 1 99.4 t CO2 e / TJ 1 21,023,258

Coal Washed Neg. TJ 1 Neg. t CO2 e / TJ 1 Neg.

Coke 175,922 TJ 1 107.7 t CO2 e / TJ 1 18,942,382

Coke Other Products 10,924 TJ 1 107.7 t CO2 e / TJ 1 1,176,235

Coke Oven Gas 37,513 TJ 1 44.5 t CO2 e / TJ 1 1,667,548

Diesel 157,129 TJ 1 75.4 t CO2 e / TJ 1 11,846,080

Fuel Oil 57,016 TJ 1 77.7 t CO2 e / TJ 1 4,427,693

Gasoline 115,887 TJ 1 72.3 t CO2 e / TJ 1 8,379,219

Jet Keroseneiv 118,582 TJ 1 72.1 t CO2 e / TJ 1 8,553,406

Kerosene 806 TJ 1 72.2 t CO2 e / TJ 1 58,189

Liquid Petroleum Gas 45,026 TJ 1 63.2 t CO2 e / TJ 1 2,845,243

Marine Fuel Oilv 63,966 TJ 1 78.2 t CO2 e / TJ 1 5,000,025

Natural Gas 64,666 TJ 1 56.2 t CO2 e / TJ 1 3,633,488

Petroleum Other Products 323,104 TJ 1 73.6 t CO2 e / TJ 1 23,767,739

Refinery Gas 56,712 TJ 1 57.7 t CO2 e / TJ 1 3,269,568

INDUSTRIAL PROCESSES

Cementvi 7,200 kt 1 0.312 t CO2 e / t 1 2,246,306

Steelvii 19,280 kt 1 1.06 t CO2 e / t 1 20,436,376

WASTE

Solid waste disposal on landviii 1,199 kt 1 0.77 t CO2 e / t 1 919,357

Wastewater handling ND ND ND

Waste incineration 1,024 kt 1 0.43 t CO2 e / t 1 443,723

AFOLU ND ND ND

89

Notes on Table 3

i Emissions factors are a weighted average of sector-specific emissions factors used for each fuel.

ii Includes electricity produced and consumed in the city, as well as electricity imported; emissions factor takes both into account.

iii Includes steam heat produced and consumed in the city; emissions factor is a reflection of the steam production mix.

iv Includes an import/export adjustment, as determined by the Chinese Energy Yearbook.

v Includes an import/export adjustment, as determined by the Chinese Energy Yearbook.

vi Emissions factors determined based on IPCC default with 60% clinker content.

vii Emissions factors determined based on IPCC defaults.

viii Includes waste disposed in landfills and by compost; emissions factors determined based on the IPCC default waste composition values for East Asia.

90

Table 4: Upstream (Embodied) Greenhouse Gas Emissions - ND Activity Data Emissions Factor Total

GHGs Value Units Value Units t CO2 e. ENERGY

Electricity (on-site

renewable)

GWh t CO2 e / GWh

Electricity (grid) GWh t CO2 e / GWh

Natural gas TJ t CO2 e / TJ

Fuel oil TJ t CO2 e / TJ

Coal TJ t CO2 e / TJ

Gasoline TJ t CO2 e / TJ

Diesel TJ t CO2 e / TJ

Jet Fuel TJ t CO2 e / TJ

Marine Fuel TJ t CO2 e / TJ

<add fuels as appropriate> TJ t CO2 e / TJ

WATER ML t CO2 e/ ML

BUILDING MATERIALS

Cement kt t CO2 e / kt

Steel kt t CO2 e / kt

Bricks kt t CO2 e / kt

<add building materials as appropriate>

FOOD

Cereals kt t CO2 e / kt

Fruits kt t CO2 e / kt

Meat kt t CO2 e / kt

Seafood kt t CO2 e / kt

Dairy kt t CO2 e / kt

Other kt t CO2 e / kt

91

Appendix C: Standard GHG Reporting Tables for Tianjin

Table 1: Community Information

Name of city or local region Tianjin Country China

Inventory year 2006

Reporting date June 15, 2010

Population (year round residents) 10,750,000

Land area (sq. kilometers) 11,300

Name, status and address of

reporter

Lorraine Sugar, University of Toronto

Name, status and address of third party verifier (if applicable)

Other information, e.g., websites of fuller inventory report or emissions

reduction program

.

Note for Tables: ND = not determined; Neg. = negligible

TOTAL EMISSIONS: 131,654 kt CO2 e PER CAPITA EMISSIONS: 12.2 t CO2 e (Note: now includes industrial process emissions from cement and steel)

92

Table 2: Greenhouse Gas Emissions by Sector

SCOPE CO2 CH4 N2O HFCs PFCs SF6s TOTAL GWP (1) ( 21 ) ( 310 )

Units kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e.

ENERGY a) Stationary Combustion

Electricity (incl. T&D losses)i 1,2,3 42,326 9.22 198 ND ND ND 42,533

District energy and CHPii 1,2 13,776 2.98 64.9 ND ND ND 13,844

Energy from waste 1 ND ND ND ND ND ND ND

Commercial & Institutionaliii 1 1,680 3.48 5.75 ND ND ND 1,689

Residentialiv 1 2,517 102 7.91 ND ND ND 2,627

Manufacturing Industries & Constructionv

1 42,663 76.9 173 ND ND ND 42,913

Othervi 1 1,520 24.8 6.83 ND ND ND 1,551

b) Mobile Combustion

Road transportation: LDVsvii 1

Road transportation: trucks 1

Railways 1

11,638 37.6 264 ND ND ND 11,939

Domestic aviationviii 3

International aviation 3 410 0.06 3.56 ND ND ND 413

Domestic marineix 3

International marine 3 1,220 2.32 9.78 ND ND ND 1,232

Other 1 ND ND ND ND ND ND ND

c) Fugitive Sources ND ND ND ND ND ND ND

INDUSTRIAL PROCESSES

Mineral industryx 1 1,620 Neg. Neg. ND ND ND 1,620

Chemical industry 1 ND ND ND ND ND ND ND

Metal industryxi 1 10,126 Neg. Neg. ND ND ND 10,126

Electronics industry 1 ND ND ND ND ND ND ND

Other 1 ND ND ND ND ND ND ND

Solvent and product use 1 ND ND ND ND ND ND ND

AFOLU 1 ND ND ND ND ND ND ND

WASTE

Solid waste disposal on landxii 1,3 Neg. 1,040 Neg. ND ND ND 1,040

Wastewater handling 1,3 ND ND ND ND ND ND ND

Waste incineration 1,3 122 Neg. 4.54 ND ND ND 127

TOTAL 129,618 1,299 738 ND ND ND 131,654

93

Notes on Table 2

i Includes thermal power generated and consumed in the city, as well as electricity imported from outside the city (Chinese average

thermal power intensities were used for imported electricity); emissions factors for energy industries stationary combustion were used.

ii Includes steam for heating generated and consumed within the city; emissions factors for energy industries stationary combustion were used.

iii Includes consumption for “transportation, telecommunications, and postal” and “wholesale, retail, and catering” categories in the Chinese Energy Yearbook; emissions factors for commercial/institutional stationary combustion were used.

iv Includes consumption for the “residential” category in the Chinese Energy Yearbook; emissions factors for residential/agriculture

stationary combustion were used.

v Includes consumption for the “industry” and “consumption” categories in the Chinese Energy Yearbook; emissions factors for manufacturing industries & construction stationary combustion were used.

vi Includes consumption from the “agriculture” and “other” categories in the Chinese Energy Yearbook; emissions factors for

commercial/institutional stationary combustion were used for the “other” categories, and emissions factors for residential/agriculture stationary combustion were used for the “agriculture” category.

vii Includes emissions from all ground transportation, specifically consumption of gasoline and diesel fuel; emissions factors from

mobile combustion were used.

viii Includes emissions from international and domestic aircraft, including fuel loaded onto foreign vessels at city airports; emissions factors for mobile combustion were used.

ix Includes emissions from international and domestic vessels, including fuel loaded onto foreign vessels at city ports; emissions

factors for mobile combustion were used.

x Includes cement production; emissions factors determined based on IPCC default with 60% clinker content.

xi Includes steel production; emissions factors determined based on IPCC defaults.

xii Includes waste disposed in landfills; emissions factors determined based on the IPCC default waste composition values for East

Asia.

94

Table 3: Greenhouse Gas Emissions by Fuel or Activity Type

Activity Data Emissions Factor Total GHGs Value Units Tier Value Units Tier t CO2 e ENERGYi

Electricity (on-site renewable) ND GWh N/A 0 t CO2 e / GWh N/A 0

Electricity (grid)ii 44,573 GWh 1 954 t CO2 e / GWh 2 42,533,264

Steam (heating supply)iii 113,663 TJ 1 122 t CO2 e / TJ 2 13,844,266

Coal Briquettes Neg. TJ 1 Neg. t CO2 e / TJ 1 Neg.

Coal Cleaned 32,531 TJ 1 99.0 t CO2 e / TJ 1 3,219,731

Coal Gas not Coke Source 5,129 TJ 1 44.5 t CO2 e / TJ 1 228,009

Coal Raw 192,136 TJ 1 99.6 t CO2 e / TJ 1 19,134,911

Coal Washed Neg. TJ 1 Neg. t CO2 e / TJ 1 Neg.

Coke 154,051 TJ 1 107.7 t CO2 e / TJ 1 16,587,460

Coke Other Products 1,059 TJ 1 107.7 t CO2 e / TJ 1 114,037

Coke Oven Gas 11,207 TJ 1 44.5 t CO2 e / TJ 1 498,272

Diesel 105,494 TJ 1 75.4 t CO2 e / TJ 1 7,953,300

Fuel Oil 20,360 TJ 1 77.6 t CO2 e / TJ 1 1,580,965

Gasoline 55,130 TJ 1 72.3 t CO2 e / TJ 1 3,986,155

Jet Keroseneiv 5,735 TJ 1 72.1 t CO2 e / TJ 1 413,704

Kerosene 1,320 TJ 1 72.3 t CO2 e / TJ 1 95,342

Liquid Petroleum Gas 7,833 TJ 1 63.2 t CO2 e / TJ 1 495,134

Marine Fuel Oilv 15,767 TJ 1 78.2 t CO2 e / TJ 1 1,232,497

Natural Gas 38,589 TJ 1 56.2 t CO2 e / TJ 1 2,168,492

Petroleum Other Products 52,502 TJ 1 73.6 t CO2 e / TJ 1 3,861,578

Refinery Gas 5,582 TJ 1 57.7 t CO2 e / TJ 1 321,806

INDUSTRIAL PROCESSES

Cementvi 5,192 kt 1 0.312 t CO2 e / t 1 1,619,748

Steelvii 9,553 kt 1 1.06 t CO2 e / t 1 10,125,968

WASTE

Solid waste disposal on landviii 873 kt 1 1.19 t CO2 e / t 1 1,040,214

Wastewater handling ND ND ND

Waste incineration 293 kt 1 0.43 t CO2 e / t 1 126,964

AFOLU ND ND ND

95

Notes on Table 3

i Emissions factors are a weighted average of sector-specific emissions factors used for each fuel.

ii Includes electricity produced and consumed in the city, as well as electricity imported; emissions factor takes both into account.

iii Includes steam heat produced and consumed in the city; emissions factor is a reflection of the steam production mix.

iv Includes an import/export adjustment, as determined by the Chinese Energy Yearbook.

v Includes an import/export adjustment, as determined by the Chinese Energy Yearbook.

vi Emissions factors determined based on IPCC default with 60% clinker content.

vii Emissions factors determined based on IPCC defaults.

viii Includes waste disposed in landfills; emissions factors determined based on the IPCC default waste composition values for East Asia.

96

Table 4: Upstream (Embodied) Greenhouse Gas Emissions - ND Activity Data Emissions Factor Total

GHGs Value Units Value Units t CO2 e. ENERGY

Electricity (on-site

renewable)

GWh t CO2 e / GWh

Electricity (grid) GWh t CO2 e / GWh

Natural gas TJ t CO2 e / TJ

Fuel oil TJ t CO2 e / TJ

Coal TJ t CO2 e / TJ

Gasoline TJ t CO2 e / TJ

Diesel TJ t CO2 e / TJ

Jet Fuel TJ t CO2 e / TJ

Marine Fuel TJ t CO2 e / TJ

<add fuels as appropriate> TJ t CO2 e / TJ

WATER ML t CO2 e/ ML

BUILDING MATERIALS

Cement kt t CO2 e / kt

Steel kt t CO2 e / kt

Bricks kt t CO2 e / kt

<add building materials as appropriate>

FOOD

Cereals kt t CO2 e / kt

Fruits kt t CO2 e / kt

Meat kt t CO2 e / kt

Seafood kt t CO2 e / kt

Dairy kt t CO2 e / kt

Other kt t CO2 e / kt

97

Appendix D: Standard GHG Reporting Tables for Amman

Table 1: Community Information

Name of city or local region Greater Amman Municipality

Country Jordan

Inventory year 2008

Reporting date July 6, 2010

Population (year round residents) 2,800,000

Land area (sq. kilometers)

Name, status and address of

reporter

Inventory Data:

Greater Amman Municipality

Inventory Conducted by:

Lorraine Sugar, University of Toronto

Name, status and address of third

party verifier (if applicable)

Other information, e.g., websites of

fuller inventory report or emissions reduction program

.

Note for Tables: ND = not determined; Neg. = negligible

TOTAL EMISSIONS: 10,256 kt CO2 e PER CAPITA EMISSIONS: 3.7 t CO2 e

98

Table 2: Greenhouse Gas Emissions by Sector

SCOPE CO2 CH4 N2O HFCs PFCs SF6s TOTAL GWP (1) ( 21 ) ( 310 )

Units kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e.

ENERGY a) Stationary Combustion

Electricity (incl. T&D losses)i 1,2,3 3,758 2.37 6.15 ND ND ND 3,766

District energy and CHP 1,2 ND ND ND ND ND ND ND

Energy from waste 1 ND ND ND ND ND ND ND

Commercial & Institutionalii 1

Residential 1 517 1.39 1.15 ND ND ND 520

Manufacturing Industries & Construction

1 1,005 0.82 2.43 ND ND ND 1,008

Otheriii 1 61.2 0.13 0.69 ND ND ND 61.4

b) Mobile Combustion

Road transportation: LDVsiv 1

Road transportation: trucks 1 2,821 12.2 73.3 ND ND ND 2,906

Railways 1 ND ND ND ND ND ND ND

Domestic aviationv 3

International aviation 3 908.7 0.13 7.88 ND ND ND 916.7

Domestic marinevi 3

International marine 3 65.4 0.11 0.64 ND ND ND 66.1

Other 1 ND ND ND ND ND ND ND

c) Fugitive Sources ND ND ND ND ND ND ND

INDUSTRIAL PROCESSES

Mineral industryvii 1 Neg. Neg. Neg. Neg. Neg. Neg. Neg.

Chemical industry 1 Neg. Neg. Neg. Neg. Neg. Neg. Neg.

Metal industry 1 Neg. Neg. Neg. Neg. Neg. Neg. Neg.

Electronics industry 1 Neg. Neg. Neg. Neg. Neg. Neg. Neg.

Other 1 Neg. Neg. Neg. Neg. Neg. Neg. Neg.

Solvent and product use 1 Neg. Neg. Neg. Neg. Neg. Neg. Neg.

AFOLU 1 ND ND ND ND ND ND ND

WASTE

Solid waste disposal on land 1,3 Neg. 1,012 Neg. ND ND ND 1,012

Wastewater handling 1,3 ND ND ND ND ND ND ND

Waste incineration 1,3 ND ND ND ND ND ND ND

TOTAL 9,136 1,029 92.2 ND ND ND 10,256

99

Notes on Table 2

i Electricity GHG intensity is based on the national electricity production mix, using energy industries stationary combustion

emissions factors; transmission and distribution losses were taken to be 14%.

ii Includes heating fuel for commercial, institutional, and residential sectors; emissions factors for commercial/institutional and

residential stationary combustion were used. Sector weighting is based on national sector-based consumption of fuels

iii Includes agriculture fuel consumption; emissions factors for residential/agriculture stationary combustion were used.

iv Includes all gasoline and diesel fuel consumed in the transportation sector; emissions factors for mobile combustion were used.

v Includes 99% of national jet fuel consumption, used for both domestic and international air travel; emissions factors for mobile combustion were used.

vi Includes all marine fuel consumption, international and domestic; emissions factors for mobile combustion were used.

vii For all of Jordan, 99% of industrial process emissions are from cement production; however, no industrial activity is located in Amman.

100

Table 3: Greenhouse Gas Emissions by Fuel or Activity Type

Activity Data Emissions Factor Total GHGs Value Units Tier Value Units Tier t CO2 e ENERGYi

Electricity (on-site renewable)ii ND GWh N/A ND t CO2 e / GWh N/A ND

Electricity (grid)iii 6270 GWh 1 601 t CO2 e / GWh 1 3,766,275

Diesel Oil 26,221 TJ 1 75.1 t CO2 e / TJ 1 1,970,361

Fuel Oil 10,820 TJ 1 77.6 t CO2 e / TJ 1 840,136

Gasoline 20,187 TJ 1 72.3 t CO2 e / TJ 1 1,459,614

Jet Kerosene 12,709 TJ 1 72.1 t CO2 e / TJ 1 916,693

Kerosene 1,640 TJ 1 72.3 t CO2 e / TJ 1 118,534

LPG 1,910 TJ 1 63.2 t CO2 e / TJ 1 120,784

Marine Fuel Oil 668 TJ 1 78.2 t CO2 e / TJ 1 52,209

INDUSTRIAL PROCESSESiv Neg. kt Neg. t CO2 e / kt Neg.

WASTE

Solid waste disposal on land 669.3 kt 1 1.512 t CO2 e / t 2 1,011,953

Wastewater handling ND kt BOD ND t CO2 e / kt BOD ND

Waste incineration ND kt ND t CO2 e / kt ND

AFOLU ND ND ND

101

Notes on Table 3

i Emissions factors are a weighted average of sector-specific emissions factors used for each fuel; fuel oil and LPG are estimated for

Amman according to national energy consumption values.

ii Renewable technologies include hydropower, wind power, and biogas; these comprise 0.55% of the national electricity production

mix.

iii Electricity GHG intensity is based on the national electricity production mix, using energy industries stationary combustion

emissions factors; transmission and distribution losses were taken to be 14%.

iv For all of Jordan, 99% of industrial process emissions are from cement production; however, no industrial activity is located in Amman.

102

Table 4: Upstream (Embodied) Greenhouse Gas Emissions - ND Activity Data Emissions Factor Total

GHGs Value Units Value Units t CO2 e. ENERGY

Electricity (on-site

renewable)

GWh t CO2 e / GWh

Electricity (grid) GWh t CO2 e / GWh

Natural gas TJ t CO2 e / TJ

Fuel oil TJ t CO2 e / TJ

Coal TJ t CO2 e / TJ

Gasoline TJ t CO2 e / TJ

Diesel TJ t CO2 e / TJ

Jet Fuel TJ t CO2 e / TJ

Marine Fuel TJ t CO2 e / TJ

<add fuels as appropriate> TJ t CO2 e / TJ

WATER ML t CO2 e/ ML

BUILDING MATERIALS

Cement kt t CO2 e / kt

Steel kt t CO2 e / kt

Bricks kt t CO2 e / kt

<add building materials as appropriate>

FOOD

Cereals kt t CO2 e / kt

Fruits kt t CO2 e / kt

Meat kt t CO2 e / kt

Seafood kt t CO2 e / kt

Dairy kt t CO2 e / kt

Other kt t CO2 e / kt

103

Appendix E: Standard GHG Reporting Tables for Jakarta

Table 1: Community Information

Name of city or local region Jakarta Country Indonesia

Inventory year 2008

Reporting date July 8, 2010

Population (year round residents) 9,057,632

Land area (sq. kilometers)

Name, status and address of

reporter

Name, status and address of third party verifier (if applicable)

Data collected & inventory conducted by: Dollaris R. Suhadi

Tables compiled by: Lorraine Sugar, University of Toronto

Other information, e.g., websites of

fuller inventory report or emissions reduction program

.

Note for Tables: ND = not determined; Neg. = negligible

TOTAL EMISSIONS: 44,562 kt CO2 e PER CAPITA EMISSIONS: 4.9 t CO2 e

104

Table 2: Greenhouse Gas Emissions by Sector

SCOPE CO2 CH4 N2O HFCs PFCs SF6s TOTAL GWP (1) ( 21 ) ( 310 )

Units kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e.

ENERGY a) Stationary Combustion

Electricity (incl. T&D losses)i 1,2,3 26,323 6.85 49 ND ND ND 26,379

District energy and CHP 1,2 ND ND ND ND ND ND ND

Energy from waste 1 ND ND ND ND ND ND ND

Commercial & Institutional 1

Manufacturing Industries &

Construction

1 2,053 0.73 16 ND ND ND 2,069

Residential 1 891 1.8 1.1 ND ND ND 894

Other 1 ND ND ND ND ND ND ND

b) Mobile Combustion ND ND ND ND

Road transportation: LDVsii 1

Road transportation: trucks 1 11,722 151 123 ND ND ND 11,997

Railways 1 ND ND ND ND ND ND ND

Domestic aviation 3 ND ND ND ND ND ND ND

International aviation 3 ND ND ND ND ND ND ND

Domestic marine 3 ND ND ND ND ND ND ND

International marine 3 ND ND ND ND ND ND ND

Other 1 ND ND ND ND ND ND ND

c) Fugitive Sources ND ND ND ND ND ND ND

INDUSTRIAL PROCESSES

Mineral industry 1 Neg. Neg. Neg. ND ND ND Neg.

Chemical industry 1 ND ND ND ND ND ND ND

Metal industry 1 TBD TBD TBD ND ND ND TBD

Electronics industry 1 ND ND ND ND ND ND ND

Other 1 ND ND ND ND ND ND ND

Solvent and product use 1 ND ND ND ND ND ND ND

AFOLU 1 ND ND ND ND ND ND ND

WASTE

Solid waste disposal on landiii 1,3 1,659 1,564 Neg. ND ND ND 3,223

Wastewater handling 1,3 ND ND ND ND ND ND ND

Waste incineration 1,3 ND ND ND ND ND ND ND

TOTAL 42,648 1,724 189 ND ND ND 44,562

105

Notes on Table 2

i Electricity is based on consumption and a regional emissions factor; specific emissions by gas are based on the distributions of

national electricity emissions.

ii Road transportation emissions are calculated based on estimates of vehicle-kilometers traveled.

iii Solid waste disposal on land includes landfill and compost disposal.

106

Table 3: Greenhouse Gas Emissions by Fuel or Activity Type

Activity Data Emissions Factor Total GHGs Value Units Tier Value Units Tier t CO2 e ENERGYi

Electricity (on-site renewable) ND GWh N/A ND t CO2 e / GWh N/A ND

Electricity (grid)ii 29,606 GWh 891 t CO2 e / GWh 26,378,946

Diesel Oil (Stationary) 153,318 kL 2.17 t CO2 e / kL 332,888

Fuel Oil 69,972 kL 2.48 t CO2 e / kL 173,679

Kerosene 115,340 kL 2.56 t CO2 e / kL 295,245

LPG 357,600 t 2.94 t CO2 e / t 1,049,993

Natural Gas 532,888 1000 m3 2.09 tCO2e / 1000 m3 1,111,199

Road Vehicle-Kilometers 53,768 106 km 223 tCO2e / 106 km 11,996,603

INDUSTRIAL PROCESSES

Steel Production TBD kt TBD t CO2 e / kt TBD

WASTE

Solid waste disposal on landiii 1,966,620 t 1.64 t CO2 e / kt 3,222,937

Wastewater handling ND kt BOD ND tCO2e / kt BOD ND

Waste incineration ND kt ND t CO2 e / kt ND

AFOLU ND ND ND

Notes on Table 3

i Emissions factors are weighted averages to account for all sectors.

ii Electricity is based on consumption and a regional emissions factor.

iii Solid waste disposal on land includes landfill and compost disposal.

107

Table 4: Upstream (Embodied) Greenhouse Gas Emissions - ND Activity Data Emissions Factor Total

GHGs Value Units Value Units t CO2 e. ENERGY

Electricity (on-site

renewable)

GWh t CO2 e / GWh

Electricity (grid) GWh t CO2 e / GWh

Natural gas TJ t CO2 e / TJ

Fuel oil TJ t CO2 e / TJ

Coal TJ t CO2 e / TJ

Gasoline TJ t CO2 e / TJ

Diesel TJ t CO2 e / TJ

Jet Fuel TJ t CO2 e / TJ

Marine Fuel TJ t CO2 e / TJ

<add fuels as appropriate> TJ t CO2 e / TJ

WATER ML t CO2 e/ ML

BUILDING MATERIALS

Cement kt t CO2 e / kt

Steel kt t CO2 e / kt

Bricks kt t CO2 e / kt

<add building materials as appropriate>

FOOD

Cereals kt t CO2 e / kt

Fruits kt t CO2 e / kt

Meat kt t CO2 e / kt

Seafood kt t CO2 e / kt

Dairy kt t CO2 e / kt

Other kt t CO2 e / kt

108

Appendix F: Standard GHG Reporting Tables for Dar es Salaam

Table 1: Community Information

Name of city or local region Dar es Salaam Country Tanzania

Inventory year 2009

Reporting date July 6, 2010

Population (year round residents) 2,928,502

Land area (sq. kilometers)

Name, status and address of reporter

Lorraine Sugar, University of Toronto

Name, status and address of third party verifier (if applicable)

Other information, e.g., websites of fuller inventory report or emissions reduction program

.

Note for Tables: ND = not determined; Neg. = negligible

TOTAL EMISSIONS: 1,628 kt CO2 e PER CAPITA EMISSIONS: 0.55 t CO2 e

109

Table 2: Greenhouse Gas Emissions by Sector

SCOPE CO2 CH4 N2O HFCs PFCs SF6s TOTAL GWP (1) ( 21 ) ( 310 )

Units kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e. kt CO2 e.

ENERGY a) Stationary Combustion

Electricity (incl. T&D losses)i 1,2,3 119.7 0.04 0.15 ND ND ND 119.9

District energy and CHP 1,2 ND ND ND ND ND ND ND

Energy from waste 1 ND ND ND ND ND ND ND

Commercial & Institutional 1 ND ND ND ND ND ND ND

Residential 1 ND ND ND ND ND ND ND

Manufacturing Industries & Construction

1 ND ND ND ND ND ND ND

Other 1 ND ND ND ND ND ND ND

b) Mobile Combustion ND ND ND

Road transportation: LDVsii 1

Road transportation: trucks 1 770.2 3.66 21.0 ND ND ND 794.9

Railways 1 ND ND ND ND ND ND ND

Domestic aviationiii 3

International aviation 3 189.7 0.03 1.65 ND ND ND 191.3

Domestic marine 3 ND ND ND ND ND ND ND

International marine 3 ND ND ND ND ND ND ND

Other 1 ND ND ND ND ND ND ND

c) Fugitive Sources ND ND ND ND ND ND ND

INDUSTRIAL PROCESSES

Mineral industry 1 ND ND ND ND ND ND ND

Chemical industry 1 ND ND ND ND ND ND ND

Metal industry 1 ND ND ND ND ND ND ND

Electronics industry 1 ND ND ND ND ND ND ND

Other 1 ND ND ND ND ND ND ND

Solvent and product use 1 ND ND ND ND ND ND ND

AFOLU 1 ND ND ND ND ND ND ND

WASTE

Solid waste disposal on landiv 1,3 Neg. 522 Neg. ND ND ND 522

Wastewater handling 1,3 ND ND ND ND ND ND ND

Waste incineration 1,3 ND ND ND ND ND ND ND

TOTAL 1,080 525.7 22.8 ND ND ND 1,628

110

Notes on Table 2

i Electricity GHG intensity is based on the national electricity supply mix, including transmission and distribution losses.

Consumption is based on national per-capita urban electricity consumption.

ii All gasoline and diesel fuel was considered to be for road transportation, a 60-40 gasoline-diesel split by volume was taken;

emissions factors for mobile combustion were used.

iii All avgas and jet kerosene loaded onto planes at the airport were used, assumed to include domestic and international air travel;

emissions factors for mobile combustion were used.

iv Emissions factors were determined using IPCC defaults for waste composition.

111

Table 3: Greenhouse Gas Emissions by Fuel or Activity Type

Activity Data Emissions Factor Total GHGs Value Units Tier Value Units Tier t CO2 e ENERGY

Electricity (on-site renewable) ND GWh N/A 0 t CO2 e / GWh N/A 0

Electricity (grid)i 497.8 GWh ND 240.8 t CO2 e / GWh 1 119,861

Gasolineii 6264 TJ 1 72.3 t CO2 e / TJ 1 452,919

Diesel 4536 TJ 1 75.4 t CO2 e / TJ 1 341,973

Jet Fuel 2632.5 TJ 1 72.1 t CO2 e / TJ 1 189,884

Avgas 20.88 TJ 1 69.9 t CO2 e / TJ 1 1,460

INDUSTRIAL PROCESSES ND kt ND t CO2 e / kt ND

WASTE

Solid waste disposal on landiii 426 kt 1 1.22 t CO2 e / t 1 522,000

Wastewater handling ND kt BOD ND tCO2e / kt BOD ND

Waste incineration ND kt ND t CO2 e / kt ND

AFOLU ND ND ND

Notes on Table 3

i Electricity GHG intensity is based on the national electricity supply mix, including transmission and distribution losses.

Consumption is based on national per-capita urban electricity consumption.

ii All gasoline and diesel fuel was considered to be for road transportation, a 60-40 gasoline-diesel split by volume was taken; emissions factors for mobile combustion were used.

iii Emissions factors were determined using IPCC defaults for waste composition.

112

Table 4: Upstream (Embodied) Greenhouse Gas Emissions - ND Activity Data Emissions Factor Total

GHGs Value Units Value Units t CO2 e. ENERGY

Electricity (on-site

renewable)

GWh t CO2 e / GWh

Electricity (grid) GWh t CO2 e / GWh

Natural gas TJ t CO2 e / TJ

Fuel oil TJ t CO2 e / TJ

Coal TJ t CO2 e / TJ

Gasoline TJ t CO2 e / TJ

Diesel TJ t CO2 e / TJ

Jet Fuel TJ t CO2 e / TJ

Marine Fuel TJ t CO2 e / TJ

<add fuels as appropriate> TJ t CO2 e / TJ

WATER ML t CO2 e/ ML

BUILDING MATERIALS

Cement kt t CO2 e / kt

Steel kt t CO2 e / kt

Bricks kt t CO2 e / kt

<add building materials as appropriate>

FOOD

Cereals kt t CO2 e / kt

Fruits kt t CO2 e / kt

Meat kt t CO2 e / kt

Seafood kt t CO2 e / kt

Dairy kt t CO2 e / kt

Other kt t CO2 e / kt