THESISpyzhang/affluence.pdf · · 2013-10-18This thesis focuses on the link between economic growth and carbon dioxide emissions in China’s transportation sector. Specifically,

REDEFINING AFFLUENCE IN CHINA:

CARBON EMISSION FROM PRIVATE TRANSPORTATION

THESIS

AUTHOR: YUN ZHANG (993718492)

SUPERVISOR: PROFESSOR YU-LING CHENG

DIVISION OF ENGINEERING SCIENCE

FACULTY OF APPLIED SCIENCE AND ENGINEERING

UNIVERSITY OF TORONTO

APRIL 11, 2011

i

Abstract

This thesis focuses on the link between economic growth and carbon dioxide emissions in

China’s transportation sector. Specifically, a bottom-up model is constructed to measure the total

carbon dioxide emissions generated from driving privately-owned vehicles. Three scenarios are

designed to illustrate the potential carbon pathways from 2010 to 2030. All three scenarios

assume that the total number of private vehicles will increase from 50.1 to 342.8 million between

2010 and 2030. Other factors such as vehicle usage, technical efficiency change by different

amounts in three scenarios. In the baseline scenario, carbon emissions in 2010, 2020, and 2030

are estimated to be 201, 566, and 968 MtCO2 respectively. With stricter carbon mitigation

measures, the second and third scenarios are estimated to produce 353 and 231 MtCO2 in 2020

and 476 and 257 MtCO2 in 2030. The result shows that it is possible to decouple carbon

emissions from increase in total vehicle stock. A case study is performed on Beijing and

Shanghai’s transport systems to examine the relationship between economic growth and vehicle

stock growth. The comparison shows that policies related to vehicle registration price and urban

planning – such as investments in public transit and road infrastructure – have significant

influence on private vehicle fleet growth.

ii

Acknowledgement

I want to thank Professor Yu-Ling Cheng for supervising this thesis and showing me how to

think critically from different perspectives. I also want to thank Professor Murray Metcalfe for

his opinions on the final draft of this thesis.

It has been a great pleasure to work with everyone at the Centre for Global Engineering –

Professor Yu-Ling Cheng, Professor Murray Metcalfe, Bev Bradley, Marina Freire Gormaly,

Ryan Hum, and Sean Yamana-Hayes.

iii

Glossary

CO2 Carbon dioxide

CSY China Statistical Yearbook

EIA U.S. Energy Information Administration

EIO Economic Input Output

FYP Five Year Plan

GHG Greenhouse Gas

IEA International Energy Agency

IPCC Intergovernmental Panel on Climate Change

IPCC AR4 IPCC Fourth Assessment Report

IPCC TAR IPCC Third Assessment Report

LCA Life Cycle Analysis

MtCO2 Million tonnes of CO2

WRI World Resources Institute

WtW Well to Wheel (LCA of vehicle fuels)

iv

Contents

Abstract ............................................................................................................................................ i

Acknowledgement .......................................................................................................................... ii

Glossary ......................................................................................................................................... iii

List of Tables and Figures.............................................................................................................. vi

1. Introduction ............................................................................................................................. 1

2. Literature Review .................................................................................................................... 2

2.1 Section Summary .................................................................................................................. 2

2.2 Physical Sciences .................................................................................................................. 2

2.2.1 Evidence of Global Warming ......................................................................................... 2

2.2.2 Anthropogenic Processes and Global Warming ............................................................. 4

2.2.3 Strengthened Greenhouse Effect .................................................................................... 5

2.2.4 Greenhouse Gases – Sources and Sinks ......................................................................... 5

2.3 Quantifying CO2 Emissions.................................................................................................. 7

2.3.1 CO2 and China ............................................................................................................... 7

2.3.2 Illustrating Potential Future Carbon Pathways ............................................................... 9

2.4 Road Transportation .............................................................................................................. 9

2.4.1 Current Status and Future Trends ................................................................................. 10

2.4.2 Mitigation Strategies ..................................................................................................... 11

2.4.3 Road Infrastructure ....................................................................................................... 12

3. The 12th

Five-Year-Plan ........................................................................................................ 14

4. Decoupling Carbon Emission from Vehicle Fleet Growth ................................................... 16

4.1 National Vehicle Ownership .......................................................................................... 16

4.2 Bottom-up Model and Scenario Design ......................................................................... 18

4.1.1 Model (National Transport CO2 Inventory) ................................................................. 18

4.1.2 Scenario Design ............................................................................................................ 22

4.3 Results – 2010 to 2030 ................................................................................................... 23

5. Decoupling Vehicle Population Growth from Rising Income .............................................. 27

v

5.1 Regional Vehicle Ownership ......................................................................................... 27

5.2 Beijing and Shanghai ..................................................................................................... 28

5.2.1 Travel Demand........................................................................................................ 28

5.2.2 Public Transportation .............................................................................................. 30

5.2.3 Policy ...................................................................................................................... 31

5.3 Provincial Vehicle Ownership Data and Municipal Policies in Beijing and Shanghai . 31

6. Summary and Discussion ...................................................................................................... 32

Appendix I – World Vehicle Ownership 2010 – 2030 ................................................................. 33

Appendix II – Scenario Design for Bottom-up Model ................................................................. 34

Bibliography ................................................................................................................................. 37

vi

List of Tables and Figures

Table 1 - 12th Five-Year-Plan Targets (partial), adapted from WRI and Xinhua.net. ................. 14

Table 2 - Targets of 12th FYP that are important for this study ................................................... 15

Table 3 - Basic Assumption for Vehicle Population Forecast, adapted from Hao et al [17]........ 19

Table 4 - Scenario Design ............................................................................................................. 22

Table 5 - Driving Factors of Vehicle Ownership .......................................................................... 28

Table 6 - Cost of Vehicle Ownership, Beijing and Shanghai [23] [24] [25] ................................ 31

Table 7 - CO2 Emissions Projection - EIA Reference Case [12] ................................................. 33

Table 8 – Data for Model Calculation: Scenario 1, Baseline ....................................................... 34

Table 9 - Data for Model Calculation: Scenario 2, Reduced Driving .......................................... 35

Table 10 - Data for Model Calculation: Scenario 3, Integrated Transport Policy ........................ 35

Figure 1 - Variations of the Earth's Surface Temperature over the Last 140 Years and the Last

Millennium, IPCC TAR .................................................................................................................. 3

Figure 2 - Radiative Forcing. Adapted from IPCC AR4. ............................................................... 6

Figure 3 - CO2 Emission Level by Country (2007). Data from World Resources Institute .......... 7

Figure 4 - CO2 Emissions Growth, China and the World, 1990-2007. Derived from United

Nations’ Millenium Development Goals Indicators [13] ............................................................... 8

Figure 5 - CO2 Emissions Projection, China and the World, 2008-2030 [12] ............................... 8

Figure 6 - World Vehicle Ownership, adapted from IPCC AR4 [8] ............................................ 11

Figure 7 - World Vehicle Ownership, 1960. Areas of bubbles are proportional to population sizes

[16] ................................................................................................................................................ 16

Figure 8 - World Vehicle Ownership, 2006 [16] .......................................................................... 17

Figure 9 - Bottom-up Model for Measuring CO2 Emission in Transportation ............................ 18

Figure 10 – Adopted from Yan and Crookes: Cradle-to-Grave GHG emissions for various

fuel/propulsion options in China. .................................................................................................. 21

Figure 11 - Carbon emission pathways calculated from bottom-up model .................................. 23

Figure 12 - Comparing Emissions in 2030 (data obtained from this study and EIA) .................. 24

Figure 13 - Carbon emissions by fuel types, all scenarios ............................................................ 24

Figure 14 - Contributions from Four Factors, Scenario 3 ............................................................. 25

vii

Figure 15 - China Vehicle Ownership, by Provinces and Autonomous Cities. Data from CSY

1990-2009. Red: Beijing, Blue: Shanghai, Green: Other provinces/cities. .................................. 27

Figure 16 - Population Density, Beijing and Shanghai [24] [25] ................................................. 29

Figure 17 - Subway Route Density, Beijing and Shanghai [24] [25] ........................................... 30

Figure 18 - Bus Route Density, Beijing and Shanghai [24] [25] .................................................. 30

1

1. Introduction

In 2006, China surpassed US to become the biggest carbon dioxide (CO2) emitter in the world

[1]. Some studies suggest that China will at least double its CO2 emission by 2030 compared to

its emission level in 2005 [2] due to rising income level and rapid urbanization.

Many literature studies have focused on measuring the impact of consumption on CO2 emissions.

One recent study shows that consumer activities account for about 80% of the total carbon

emission in US [3], another study shows how different categories of consumption impact CO2

emissions in China [4]. However, little attention has been given to developing low-carbon

pathways for China’s rising mid- and upper-classes, although current consumption pattern is

already a contributor to high carbon emission [2]. This research gap will be addressed in this

thesis with a specific focus on transportation. The motivations behind this focus are: 1) it is more

desirable to understand one area well rather than briefly mention all consumption categories, and

2) transportation is a major carbon emitting sector,

2

2. Literature Review

2.1 Section Summary

This section summarizes the important work related to global warming, carbon emissions, and

transportation. Since this thesis spans across a number of distinct engineering and science

disciplines, the review is divided into three sub-sections to facilitate understanding: Physical

Sciences, Quantifying CO2 Emission, and Transportation. Physical Sciences section establishes

the foundation. Quantifying CO2 Emission explores a set of top-down and bottom-up

methodologies for accounting carbon emission. Transportation covers a set of transportation

related topics that have been discussed in current literature.

2.2 Physical Sciences

In this first chapter, a few fundamental questions are discussed – what is climate change is and

why is it an important issue? What has happened in the past century? What is the future of

climate change? Each question alone can take years to complete, therefore information presented

in this chapter is from credible scientific discoveries.

These discoveries validate the value of this thesis by stating that climate change attributed to

human activities is evident. Readers should also see how key objectives of this thesis emerge

from this chapter.

2.2.1 Evidence of Global Warming

Temperature has been well documented globally in the past century. A figure is taken from IPCC

(Intergovernmental Panel on Climate Change) 1992 report to show the trend of average surface

temperature in the past 140 years and past millennium respectively [5].

Figure 1(a) (on next page) – Red bars show the Earth's surface temperature from year 1860 to

2000. The black whisker bars represent the 95% confidence interval. Uncertainties of data are

mainly due to data gaps, instrumental errors, bias corrections in ocean surface temperature data,

and adjustments for urbanization. In conclusion, the global average surface temperature

increased 0.6 ± 0.2°C between 1860 and 2000.

Figure 1(b) – Interestingly, scientists were also able to estimate the global surface temperature

through a set of proxies, namely tree rings, corals, ice cores, and other historical records. The

3

blue bars represent historical temperature based on measurement through proxies, and the red

bars represent recorded temperature. Gray bars in this graph shows 95% confidence interval.

These large uncertainties are mainly due to scarcity of proxy data. Readers should notice that red

bars fell uniformly into the 95% interval between year 1900 and 2000, suggesting that measuring

through such proxies are credible. This figure concludes that the last decade of 20th

century had

the highest average temperature in the last millennium.

Global climate change will induce rapid changes in earth’s ecological system. For those who are

interested, information can be found in Peter Vitousek’s work [6].

Figure 1 - Variations of the Earth's Surface Temperature over the Last 140 Years and the Last Millennium, IPCC TAR

4

2.2.2 Anthropogenic Processes and Global Warming

The next question is: to what extent are human activities causing global warming? Public

opinions are divided. There has been an on-going debate on whether human activities have

caused global warming. Arguments mainly concentrate in these areas: (1) whether scientific

consensus exists, (2) whether IPCC result can be viewed as the “standard”, (3) inaccuracy in

temperature measurements and variations in local temperatures, and (4) solar radiation variation.

This section does not examine all of these issues due to their complexity. However, the following

paragraphs do provide the readers an idea about the current scientific consensus.

IPCC 2001 report has stated that:

“An increasing body of observations gives a collective picture of a warming world and

other changes in the climate system... There is new and stronger evidence that most of the

warming observed over the last 50 years is attributable to human activities [7]”

In 2007, IPCC released another report with increased confidence in the causal relationship

between human activities and global warming. According to this report, human activities are

“very likely” – with 90% or greater probability – the main cause for global warming. It is

determined that global warming was characterized by 0.74 ± 0.18 °C increase in earth’s surface

temperature during the 20th

century [8], and it was largely anthropogenic.

IPCC’s statement represented not only opinions from a few elite scientists, but also a scientific

consensus. A paper (2010) in the Proceedings of the National Academy of Sciences of the United

States reviewed publication and citation data for 1,372 climate researchers and concluded [9]:

“(i) 97–98% of the climate researchers most actively publishing in the field support the

tenets of ACC (Anthropogenic Climate Change) outlined by the Intergovernmental Panel

on Climate Change, and (ii) the relative climate expertise and scientific prominence of

the researchers unconvinced of ACC are substantially below that of the convinced

researchers.”

Therefore, scientific community has a strong consensus that global warming since the mid-20th

century is mainly caused by human activities.

5

2.2.3 Strengthened Greenhouse Effect

The next question to ask is: how are anthropogenic activities causing global warming?

If the Earth is a perfect blackbody – absorbing and emitting all solar radiation – it would have

had a temperature of 5.3 degrees Celsius. To arrive at this result, first examine the Stefan-

Boltzmann equation:

Equation 1 - Stefan-Boltzmann Law

Variable refers to the amount of radiation per unit area this blackbody emits/absorbs, and is

a constant. Variable is the temperature of this perfect blackbody in Kelvins. The value of can

be calculated based on the known value of solar radiation density. If the Earth were a perfect

blackbody, temperature will turn out to be 5.3 degrees Celsius. Since the Earth is not a perfect

blackbody, this equation can be modified to show that Earth is about -18 °C. However, Earth’s

actual average temperature is about 33°C higher than that. Earth’s has an atmosphere. This layer

of gas atmosphere partially traps solar radiation, and the trapped radiation heats up the Earth’s

surface. This effect is referred to as the greenhouse effect.

Since water’s freezing point is lower than 15°C and surface temperature is a continuous function,

greenhouse effect enables water to exist as liquid and consequently supports all life forms on

earth.

In the past 300 years, humans have gone through several huge leaps in technology. With higher

industrial production intensity, higher population density, and more intense farming activities,

the Earth’s atmosphere is becoming denser and the greenhouse effect is strengthened. Thus the

average surface temperature is higher.

Having understood the concept in this section, one could see that “strengthened greenhouse

effect” is a more accurate term to explain global warming, thus the title of this section.

2.2.4 Greenhouse Gases – Sources and Sinks

The next step is to illustrate how human activities are strengthening greenhouse effect. IPCC’s

Fourth Assessment Report (AR4) stated that "changes in atmospheric concentrations of

greenhouse gases and aerosols, land cover and solar radiation alter the energy balance of the

6

climate system". The figure below illustrates how different components of human activities

contribute to radiative forcing between year 1750 and 2005. Year 1750 is conveniently used in

literature as the beginning year of Industrial Revolution.

Ramaswamy et al. (2001) stated that radiative forcing (RF) is “the change in net (down minus up)

irradiance (solar plus longwave; in W m–2

) at the tropopause after allowing for stratospheric

temperatures to readjust to radiative equilibrium, but with surface and tropospheric temperatures

and state held fixed at the unperturbed values.” [8] Despite the complicated science behind this

phenomenon, a simple equation captures the relationship between Earth’s surface temperature

and radiative forcing:

Equation 2 - Temperature change and radiative forcing

According to scientific experiments [10] [11], change in temperature in Kelvin has

approximately the same numeric value of radiative forcing in Wm2. Equipped with this piece of

knowledge, one can more readily make sense of the following figure.

Figure 2 - Radiative Forcing. Adapted from IPCC AR4.

7

2.3 Quantifying CO2 Emissions

2.3.1 CO2 and China

From this point on, this thesis mainly focuses on the emission problem of carbon dioxide (CO2).

CO2 is the major cause of strengthened greenhouse effect both in the past and in the foreseeable

future. Readers can refer to the previous section and later discussion in this thesis to verify.

According to both World Resources Institute and World Bank, China surpassed United States to

become the top emitter in the world around 2006. The following chart on 2007 emission level is

based data obtained from World Resources Institute [12].

Figure 3 - CO2 Emission Level by Country (2007). Data from World Resources Institute

6,702.60

5,826.70

4,064.50

1,626.30 1,410.40 1,270.10 817.2 583.9 530.2 517.1

10186

CO2 Emission Level by Country (2007)

MtCO2e

8

China is not only the current top emitter, but also one of the fastest growing emitter in the world.

This following graph clearly shows that China’s emission level – driven by rapid urbanization

and economics growth – is rising faster than most of the other top emitters.

Figure 4 - CO2 Emissions Growth, China and the World, 1990-2007. Derived from United Nations’ Millenium

Development Goals Indicators [13]

Figure 5 - CO2 Emissions Projection, China and the World, 2008-2030 [12]

Therefore, it is important that China invests in carbon mitigation strategies.

0.00%

50.00%

100.00%

150.00%

200.00%

250.00%

300.00%

1990 1995 2000 2005

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19

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CO2 Emissions Growth, 1990 - 2007

China

OECD

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2008 2013 2018 2023 2028Pe

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CO2 Emissions Projections, 2008 - 2030

IEA - OECD

IEA - China

IEA - World

9

2.3.2 Illustrating Potential Future Carbon Pathways

A common way of forecasting is to design several different future scenarios. Guan et al [2]

provided the following scenarios for China’s consumption-induced carbon emission: business as

usual, “westernizing”, and “carbon capture and storage”. The first one is a reference scenario,

whereas the other two are extreme cases. Results from these three scenarios are useful because

the extreme cases envelope possible development pathways, while the base case provides a

reasonable prediction.

Hubacek et al [14] used a different approach in scenario design. This approach is more detailed

than the previous one. In specific, the authors designed scenarios based on the growth pattern of

five driving forces of carbon emission. These five forces are: (1) economic growth and per capita

income growth, (2) population dynamics and urbanization, (3) changing consumption patterns, (4)

technical and structural change, and (5) resource consumption efficiency. In other words, the

authors believe that forecasting of total carbon emission is more accurate when scenarios are

based on the trend of individual driving factors. The following equation can better illustrate this

concept:

Equation 3 - CO2 and Economic Activities

∑

Economic growth and per capita income mainly determines the quantity of consumption (second

term in the equation). Factor (2) affects the first term. Factor (3) determines the allocation of

income to different types of goods/services. Factors (4) and (5) relate to the industrial structure

of China’s economy, and they are captured by the last term in equation. In another paper,

Hubacek et al [15] provided more detailed analysis about factor (3) – consumption patterns.

2.4 Road Transportation

The main focus of the thesis is the carbon emission from transportation activities in China.

According to Dargay et al [16], “China’s vehicle stock will increase nearly twenty-fold, to 390

million in 2030”. This fast expansion implies a potential dramatic increase in carbon emission.

10

2.4.1 Current Status and Future Trends

On the global level, transport relies almost entirely (95%) on petroleum. In 2004, transport was

“responsible for 23% of world energy-related GHG emissions with about three quarters coming

from road vehicles.” In addition, the GHG emission from transportation has increased at a rate

faster than any other energy intensive sector [8].

Despite China’s economic development in the past twenty years, almost thirty-three percent of

China’s population still does not have access to all-weather transport [8]. This means that there is

huge potential for China’s vehicle fleet to grow in number (shown in the following figure).

Indeed, multiple factors are driving up China’s per capita vehicle ownership. First, more and

more families are financially capable of owning private cars. Secondly, China’s currency is faced

with great appreciation pressure, which in turn will increase Chinese consumers’ purchasing

power. Thirdly, Chinese government has strategically set the car manufacturing industry to be a

“pillar industry” in the previous Five-Year-Plan (2005-2010), and the government encourages

consumer to purchase more cars. In addition, cars have become a symbol of power and status in

some areas. This attitude has put more upward pressure to private vehicle stock. In current

literature, Hao et al. has provided the following statistics [17]:

“Domestic automotive production and sales have risen from 2.07 and 2.08 million in

2000 to 13.79 and 13.64 million in 2009”, equivalent to “annual growth rates of 23.5%

and 23.3%” respectively.

“Chinese vehicle stock has risen from 23.8 million in 2003 to 62.9 million in 2009”,

equivalent to “annual growth rate of 16.3%”.

Various papers have also documented current carbon emission from the vehicle fleet and

forecasted future trend. In the work done by He et al [18], it is estimated that China’s oil

consumption reached 210 million tons in 2000, and the oil demand will likely grow up to 363

million tons in 2030. Translating this into emission, 1000 million tons of CO2 will be emitted

from direct gasoline consumption on road. This number is likely to increase by at least 10% [19],

if entire life cycle assessment is added to the production, distribution, and disposal of vehicle.

1000 million tons of CO2 emission is equivalent to 15% of China’s total CO2 emission in 2007.

Carbon emission from constructing road/highway systems have not been considered in these

11

studies. Since cement production is extremely carbon intensive [20], the total carbon emission

reported by He et al is likely underestimated.

Figure 6 - World Vehicle Ownership, adapted from IPCC AR4 [8]

2.4.2 Mitigation Strategies

Transportation carbon emission can be reduced by various means. Comparing existing mitigation

strategies can yield insights. Before reviewing papers about mitigation strategies, this equation is

designed to conceptually capture the various contributors of carbon emissions:

Equation 4 - CO2 from Transportation

∑∑

12

Wu et al [21] analyzed seven categories of emission control policies in Beijing, namely (1)

emission control on new vehicles; (2) emission control on in-use vehicles; (3) fuel quality

improvements; (4) alternative fuel and advanced vehicles; (5) economic policies; (6) public

transport; and (7) temporal traffic control measures. The study discovered that Beijing’s road

transportation emission increased from 1995 to 1998, but decreased from 1999 to 2009 due to

aggressive pursuit of technical efficiency. However, efficiency gain is bounded, and the

continued rapid growth of vehicle stock is a challenging problem.

In another two studies [22] [23], researchers discussed the unique challenges faced by Shanghai

policy-makers. Similar to Beijing, Shanghai has one of the highest income levels in China.

Shanghai is also home to a nascent automotive industry. The problem was further complicated

by the national government’s decision to use automobile industry to drive economic growth.

Such policy has put strong upward pressure on the level of motorization in Shanghai.

Yet Shanghai’s vehicle ownership remained remarkably low, especially compared with other

highly developed urban centres in China. The reasons can be summarized into (1) high

population density; (2) aggressively introduced competition into bus supply system; (3)

congestion already a problem with large number of 2-wheel vehicles; (4) high registration and

license fees.

These studies did not provide any quantitative analysis regarding Shanghai or Beijing’s vehicle

ownership. Chapter 5 will explore this topic based on information found in, for example, national

and municipal Statistical Yearbooks [24] [25].

A number of other studies also confirmed these discoveries about the driving forces and blocking

agents in private vehicle ownership [26] [27].

2.4.3 Road Infrastructure

Road infrastructure is an important foundation for transport development and economic

development in general. For inter-city transport, China’s total highway length and total

expressway length increased from 0.9 to 3.6 million km (1978-2007) and 0.1 to 53.9 million km

(1988-2007) respectively. China’s total urban road network length increased from 29,000 km to

246,000 km between 1980 and 2007, and total urban network area increased from 300 to 4200

km2

in the same period of time [28].

13

Despite the rapid construction of road transportation network, infrastructure change still could

not keep pace with the vehicle fleet growth. With vehicle fleet still expanding at a high rate, it is

unlikely that infrastructure construction will catch up. In fact, building more roads is not a long

term solution to congestion problem. The more extensive the road network is, the more people

will buy and drive their cars. This is especially true in most urban centres in China due to high

population densities [29].

Road infrastructure also has a strong link with CO2 emissions. Guan et al. calculated that capital

investment contributed to about half of China’s CO2 emissions in 2007, and 63% of it comes

from construction [2].

Therefore, carefully urban planning, restriction on private vehicle use, and promotion of public

transit will therefore result in carbon mitigation in three ways – reduction of construction,

reduction in use of private vehicles, and higher utilization rate of energy- and carbon-efficient

public transit system.

The next chapter examines China’s 12th

Five-Year-Plan Draft, released in March 2011. It

includes plans for tackling some of the environmental challenges discussed in this chapter.

14

3. The 12th Five-Year-Plan

A draft of the much anticipated 12th

Five-Year-Plan (FYP) was released on March 5th

, 2011 in

the National People’s Congress in China. One observation about this FYP is notable: not only

did this FYP mention climate change for the first time in the history of FYP’s, but it also put

climate change at the beginning of the environmental section.

The following table lists the policies that will impact the topic presented in this thesis.

Information was mostly obtained from the official national news website and other organizations’

websites [30] [31].

Table 1 - 12th Five-Year-Plan Targets (partial), adapted from WRI and Xinhua.net.

Indicator Relevant Key Targets (2015)

Economic GDP to grow by 7 percent annually on average

Urbanization 51.5% (rising 4%)

Environment &

Clean Energy

Non-fossil fuel to account for 11.4 percent of primary energy consumption

“Strategic emerging industries” include: Energy efficiency technologies, recycling,

and waste management; advanced nuclear energy, wind, solar, smart grids and

biomass; and hybrid and pure electric vehicles.

Energy consumption per unit of GDP to be cut by 16 percent;

Carbon dioxide emission per unit of GDP to be cut by 17 percent;

Population To be less than 1.39 billion

According to this FYP, the hybrid and electric vehicle industry is promoted to be strategically

important. As the next chapter of the thesis shows, this dedication on clean technology will play

a key part in reducing roadside carbon dioxide emission in China, as well as creating other co-

benefits such as reducing China’s reliance on imported oil and fostering healthier environment in

rapidly growing urban centres.

The World Resource Institute [32] has summarized other targets in the transportation sector as

the following:

“… what is striking is the commitment to rail, both long distance and in urban mass transit…

There are also plans to improve subway and light rail in cities that already have urban transit

15

systems, building new systems in at least nine other cities, and making plans for six or more

cities.”

Although public transportation is not a key focus in this thesis, the development of public

transport systems inevitably influences the trajectory of carbon emission from private vehicle

usage. More details will be explored in the next chapter.

In the next chapter, a bottom-up model will be introduced in illustrating the future carbon

pathways of private transportation. Three scenarios will be introduced – one baseline scenario

and two carbon-saving scenarios. Key targets from the 12th

FYP influences the design of the two

carbon-saving scenarios.

The following table puts some of the targets in specific numbers.

Table 2 - Targets of 12th FYP that are important for this study

2008 (CSY [28]) 2015 (calculated from 12th

FYP target)

Population (million) 1328 <1390

GDP (billion RMB, 2008 Price) 30,067 48,281

Urbanization Rate 45.7 51.5

16

4. Decoupling Carbon Emission from Vehicle Fleet Growth

After setting up the context (Chapter 2) and introducing high level goals from the 12th

FYP

(Chapter 3), this chapter explores a bottom-up model that quantifies China’s current (2010)

carbon emission from private transportation, and shows three different emission pathways from

2010 to 2030.

4.1 National Vehicle Ownership

The following two figures show the vehicle ownership level in 1960 for 45 countries, including

the United States and China. For the purpose of this thesis, the most important discovery from

comparing these two graphs is that China’s vehicle stock has huge potential to grow in the future.

Figure 7 - World Vehicle Ownership, 1960. Areas of bubbles are proportional to population sizes [16]

United States

China

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Vehicle Ownership, 1960

17

Figure 8 - World Vehicle Ownership, 2006 [16]

With the objective of minimizing future carbon emission, three questions can be asked to address

the three dimensions of this problem:

1) Is there a way to keep vehicle stock low? In other words, can China’s vehicle ownership

level not go to where US is when its GDP/capita rises?

2) Is there a way to maintain or decrease vehicle usage?

3) To what extent can scientists, policy makers, and business leaders drive up technical

efficiency and provide alternative-fuel vehicles?

It is likely that solutions to CO2 problem in transportation will involve strategies in all three

dimensions. This section focuses on the second and third questions. In other words, this chapter

attempts to answer – or at least provides insights about – whether carbon emissions can be

decoupled from vehicle fleet growth. Chapter 5 will explore the other link – decoupling vehicle

fleet growth from economic growth.

Can

USA

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Vehicle Ownership, 2006

18

4.2 Bottom-up Model and Scenario Design

A bottom-up model generally has the advantage of being able to provide more insights about

different components in a system. In this case, bottom-up model provides a more robust way of

analyzing how different factors contribute to total emission. On the other hand, bottom-up

approach usually suffers from the boundary problem. For this thesis, the bottom up model only

examines private transportation in China, and it does not capture emission embedded in

manufacturing these privately-owned vehicles. Carbon emission embedded in producing the

fuels is included (the so-called Well-to-Wheel approach). According to Hendrickson et al [33],

the manufacturing and delivery of cars are not major contributors of carbon dioxide emission.

Furthermore, since the thesis focuses on comparing different emission pathways within the same

bottom-up model, it is not significant to include the entire life-cycle emission of vehicles.

4.1.1 Model (National Transport CO2 Inventory)

The following diagram summarizes the bottom-up model:

Figure 9 - Bottom-up Model for Measuring CO2 Emission in Transportation

Notice that this model embodies the CO2 and Economic Activities equation in Chapter 2. Vehicle

population is analogous to population; vehicle usage (km/vehicle/year) is the same as “service

Transportation CO2

Vehicle Population

Income Population

Vehicle Usage

Urban Structure

Public Transit

Fuel Efficiency

Vehicle Weight

Technology

Carbon Intensity of Fuels

Fuel Type

19

per capita”; and fuel efficiency and carbon intensity of fuels correspond to carbon intensity/unit

of service.

Vehicle Population

In China Statistical Yearbook [28], private vehicles show up in two different places. Take CSY

2009 as an example. Section 15-27 of the Yearbook is dedicated to “Private Vehicle Ownership”.

In one dimension, vehicles are divided into “Passenger Vehicles” and “Trucks”. Within each of

the two categories, vehicles are further divided into four different subgroups according to vehicle

length. In another dimension, vehicles are sorted by the province in which they are registered.

Another place that shows private vehicle ownership is section 9-14 in CSY 2009. This section is

named “Ownership of Durable Consumer Goods per 100 Urban Households at Year-end by

Level of Income”. In this section, private vehicles are represented by “motorcycle”, “hand car”,

and “automobile”. There is another section dedicated to durable goods in rural households,

however, it does not mention the number of vehicles owned per house. Therefore, vehicle

ownership in rural households are extrapolated from urban household data.

Vehicle population forecast in this model is based on a study by Hao et al [17], which is in turn

based on the statistics provided by CSY, such as section 9-14. The following table lists the key

assumptions used by Hao et al:

Table 3 - Basic Assumption for Vehicle Population Forecast, adapted from Hao et al [17]

2008 2010 2020 2030

Overall Population (million) 1328 1360 1440 1470

Households (million) 388 414 476 504

Urbanization rate (%) 45.7 49 63 70

GDP (billion RMB, 2008 Price) 30,067 36,163 80,864 160,715

Urban Population (million) 607 666 907 1029

Persons per household 2.91 2.88 2.8 2.75


Rural Population (million) 721 694 533 441

Persons per household 4.01 3.8 3.5 3.4


20

Notice that the assumptions for population, GDP, and urbanization rate by Hao et al. are close to

the 2015 targets set by 12th

FYP (chapter 3).

Based on these assumptions, and other socio-economic parameters obtained from CSY, Hao et al

estimated that the vehicle population by 2010, 2020, and 2030 are 30.19, 134.4, and 307.2

million respectively.

Vehicle Usage

Vehicle usage is not documented in CSYs. Despite the lack of data, the study still remains useful

if it has (1) different scenarios to capture the range of possible outcomes by 2030, and (2) base

the vehicle usage levels on realistic basis. Numbers in this study is based on two other Asian

countries – Korea (South) and Japan. More details are explored in the scenario design section.

Fuel Efficiency

In 1990, China started policy and scientific research on climate change with the establishment of

National Climate Change Coordination Committee [34]. Since then, China has implemented a set

of policies on fuel intensity standards for new vehicles. These standards are strictly enforced and

are likely having beneficial effects on CO2 emissions.

Fuel efficiency, or in other words Vehicle Kilometers Travelled (VKT) per unit time, depends on

the weight of the vehicle and the propulsion technology. According to Ng and Schipper [35], the

average weight of new Chinese cars is 1,500 kilograms (between 3,000 kilograms of a Hummer

and 750 kilograms of a Mercedes Smart). He et al. estimated the current fuel economy is about

9.1liter/100km [18].

Carbon Intensity of Fuels

Yan and Crookes [36] summarized carbon intensities of various vehicle fuels in China. The

following figure is taken from their work – life cycle assessment of CO2 emissions from various

vehicle fuels in China.

21

Figure 10 – Adopted from Yan and Crookes: Cradle-to-Grave GHG emissions for various fuel/propulsion options in

China.

22

4.1.2 Scenario Design

The following table illustrates key points from the scenario design. A more detailed explanation

of parameters used in each scenario is presented in Appendix II.

Table 4 - Scenario Design

Scenario 1:

Baseline

Scenario 2:

Reduced driving

Scenario 3:

Integrated Transport Policy

Vehicle

Population

Vehicle fleet rises from 30.1 million in 2010 to 342.8 million in 2030

Vehicle Use

By 2030

30% decrease in

Vehicle Kilometer

Travelled (VKT)

40% decrease in VKT 50% decrease in VKT

(Japan level)

Fuel Types

By 2030

90% Gasoline

3% CNG

7% Electric/hybrid

70% Gasoline

5% CNG

25% Electric/hybrid

20% Gasoline

20% CNG

60% Electric/hybrid

Carbon

intensity of

Fuels

No change 20% decrease in WtW

emissions per 10-year period

25% decrease in WtW

emissions per 10-year period

Vehicle population growth is the same across all three scenarios. The forecast for vehicle

population growth is based on Hao et al. [17]. Three scenarios use the same base because the

main question for this chapter is whether carbon emissions can stay low as the number of cars

increases.

The design of Scenario 3’s vehicle fuel types – almost 60% electric/hybrid vehicles. Even though

the current market penetration of electric and hybrid vehicles is limited at the moment, it is

possible that China will have a large market for electric and hybrid vehicles in the next twenty

years [31].

Although the carbon contents of some fuels are not subject to technological improvement, the

processes in which fuels are extracted, produced, and transmitted can be improved. For example,

23

electric and hybrid vehicles take electricity as input, and the amount of carbon emission

embedded in electricity depends on how electricity is generated in China and distributed to the

charging stations. As China drops its overall carbon intensity [31], the WtW emissions will drop

as well.

The last thing to note is the 30% reduced driving in the baseline scenario. This is a likely case.

The big decrease is largely due to the fact that VKT for 2010 is calculated for all private vehicles

including taxis. Since taxis generally travel much more than family vehicles, VKT will likely

shrink as the percentage of taxis drops.

4.3 Results – 2010 to 2030

Figure 11 - Carbon emission pathways calculated from bottom-up model

In 2010, all scenarios have the same starting point – 201.03 million tonnes CO2. By 2020,

emissions will be 566.3, 353.1 and 231.3 million tonnes respectively. And by 2030, emissions

will be 967.8, 475.5, and 256.8 million tonnes respectively.

To put the Scenario 1 result in perspective, the following diagram compares Scenario 1 emission

(calculated in this thesis) with China and World’s total emission in 2030 (adapted from EIA [37])

0

0.2

0.4

0.6

0.8

1

1.2

2010 2020 2030

Gig

a-to

n C

O2

Transportation CO2, China 2010 - 2030

Scenario I

Scenario II

Scenario III

24

Figure 12 - Comparing Emissions in 2030 (data obtained from this study and EIA)

One important discovery is that, despite the dramatic growth (600%) in vehicle fleet, carbon

emission in Scenario 3 does not increase more than 30%. In order to understand such feature,

graphs are shown below to demonstrate each factor puts upward/downward pressure on total

carbon emissions.

Figure 13 - Carbon emissions by fuel types, all scenarios

In order to understand the way each factor influences total carbon emissions, the following

equation is designed to decompose the influences from each factor:

0

200

400

600

800

1000

1200

2010 2020 2030 2010 2020 2030 2010 2020 2030

Baseline Reduced Driving Integrated TransportPolicy

MtC

O2

Carbon Emissions by Fuel Types, all Scenarios

Battery Electric

Hybrid Electric-Gasoline

CNG

Gasoline

25

Equation 5 - Change in carbon emissions over time – contributions from four factors

Variable is vehicle population, is vehicle kilometres travelled per year, is a vector

representing the percentages of four fuel types, and is a vector representing the WtW carbon

intensities of four fuel types. Right hand side of the equation presented above is not the only way

to decompose the change in CO2 from time to time . In fact, there are in total

first-order decompositions [2]. The focus of this study is not to compare all 24 possibilities.

Figure 14 - Contributions from Four Factors, Scenario 3

Despite the promising case of Scenario 3, it is not hard to see that improvements in vehicle usage,

fuel-types, and carbon intensity of fuels can potentially be exhausted. On the contrary, vehicle

ownership level in China by 2030 (233 cars per 1000 people) will still be well below the USA

and even European levels (812 and about 600 cars per 1000 people respectively in 2006).

-1000

-500

0

500

1000

1500

2010 2020 2030

MtC

O2

Contributions from Four Factors (Scenario 3)

Vehicle Population

Vehicle Usage

Vehicle Fuel Types

WtW Carbon Intensityof Fuels

Total Change in CO2Emissions

26

Therefore, it is important to also examine the other link – relationship between income growth

and vehicle ownership growth. If such link can be weakened, carbon emissions will become even

less dependent economic growth than Scenario 3. This is the motivation behind Chapter 5 –

Decoupling Vehicle Population Growth from Rising Income.

27

5. Decoupling Vehicle Population Growth from Rising Income

5.1 Regional Vehicle Ownership

The following graph plots vehicle ownership levels against income levels in 31 Chinese

provinces and autonomous cities for year 2008. In addition, data for Beijing and Shanghai is over

multiple years. This graph has two important implications:

1) Disparity in income level and vehicle ownership level is huge among Chinese cities and

provinces. It is likely that if provinces are further broken into cities, more disparities will

show up.

2) Shanghai and Beijing had very different development pathways. This confirms the two

studies in literature review. Those two studies showed that in order to control traffic

related emissions, Shanghai focused on controlling vehicle stock, while Beijing has

mainly focused on controlling the efficiency level of vehicles.

Figure 15 - China Vehicle Ownership, by Provinces and Autonomous Cities. Data from CSY 1990-2009. Red: Beijing,

Blue: Shanghai, Green: Other provinces/cities.

Tianjin

Hebei Shanxi Jiangsu

Zhejiang

Shandong

Guangdong

Guizhou Shanghai 2003

Shanghai 2008

Beijing 2003

Beijing 2008

0

20

40

60

80

100

120

140

0 1 2 3 4 5 6 7 8

Ve

hic

les/

10

00

Pe

op

le

GDP/capita 10,000 RMB

Vehicle ownership by regions

Beijing 1995

Shanghai 1995

28

The other provinces loosely form two clusters – one on the north-east side, and the other on the

south-west. The north-east group of provinces, such as Guangdong, Zhejiang, Jiangsu, and

Shandong, are mostly provinces on the coastal line. They constitute the so-called Eastern China

which has been the main focus of economic development in the past thirty years.

5.2 Beijing and Shanghai

Beijing and Shanghai’s vehicle ownership pathways are analyzed in more details. In order to

make more concrete recommendations, the following driving factors of vehicle ownership will

be quantified:

Table 5 - Driving Factors of Vehicle Ownership

Factors Potential Proxies

Travel Demand (+) Population density

Income (+) GDP/capita

Public Transportation (-) Per area route length, government investment per capita

Other Alternatives (-) Routes for bicycles and other 2W

Cost of ownership (-) Vehicle price + fees + parking and gasoline

Roads (+) Per area road length, government investment per capita

Each factor is followed by (+)/(-), indicating their positive/negative effect on car ownership level.

5.2.1 Travel Demand

Among Chinese cities, Beijing and Shanghai have similar levels of income levels per capita and

levels development. Therefore the population density alone serves as a reasonable proxy for

travel demand.

29

Figure 16 - Population Density, Beijing and Shanghai [24] [25]

This graph clearly shows that Shanghai’s population density is much higher than Beijing’s. From

this number, three points can be made: 1) Shanghai is likely to have “denser” infrastructure, 2)

Shanghai is likely to have narrower roads and more compact road system, and 3) parking spots

per unit area in Shanghai is potentially more limited. All these factors limited the growth of

vehicle stock in Shanghai. In fact, interview with immigrants from Shanghai and Beijing have

confirmed the first two points. The third point is indirectly confirmed by the fact that Shanghai

has extremely high parking price in downtown area.

0

500

1000

1500

2000

2500

3000

3500

2003 2004 2005 2006 2007 2008 2009

Population Density (persons/km2)

Beijing

Shanghai

30

5.2.2 Public Transportation

Figure 17 - Subway Route Density, Beijing and Shanghai [24] [25]

Figure 18 - Bus Route Density, Beijing and Shanghai [24] [25]

0

0.01

0.02

0.03

0.04

0.05

0.06

2003 2004 2005 2006 2007 2008 2009

km/k

m2

Subway length/km2

Beijing

Shanghai

0

0.5

1

1.5

2

2.5

3

3.5

4

2003 2004 2005 2006 2007 2008 2009

km/k

m2

Bus Route length/km2

Beijing

Shanghai

31

Using public transit route length per km2 as a proxy, Shanghai and Beijing’s development of

public transit is quantified in the previous two graphs. Due to the lack of data, three questions

remain unaddressed. They are recommended as work for future research. The two major

challenges are (1) gathering a set of reliable data that reflects the public transport and private

transport development pathways in various cities, and (2) normalizing the data to eliminate

“noise” from the difference in urban structure.

1) No clear indication of causality;

2) No clear indication of the direction of causality;

3) Service length/area is not a perfect representation of public transit level. For example,

price and service frequency also affects people’s choice of whether to take public transit

or not.

5.2.3 Policy

The following table summarizes four municipal policies related to cost of vehicle ownership in

Shanghai and Beijing:

Table 6 - Cost of Vehicle Ownership, Beijing and Shanghai [23] [24] [25]

Cost of Vehicle Ownership Beijing Shanghai

License Fee <$50 $5,000 to 10,000

Tax 5% 10%

Gasoline price $1/Litre Similar to Beijing’s level

Parking Comparable to North America $3 to 10 in downtown

5.3 Provincial Vehicle Ownership Data and Municipal Policies in Beijing

and Shanghai

Vehicle ownership and real GDP of 45 sample countries are calculated from the United Nations

and International Monetary Foundation data.

Province-level data is taken from Chinese Statistical Yearbooks (1990 – 2009). Information

regarding carbon emission is taken from World Resource Institute, World Bank, and United

Nations. Data on Beijing and Shanghai’s municipal development (vehicle ownership, GCP,

public transportation) is taken from Beijing Statistical Yearbooks (1990 – 2009) and Shanghai

Statistical Yearbooks (1990 – 2009).

32

6. Summary and Discussion

This thesis first presented a range of possible emission pathways in China’s private transport up

to 2030, and concluded that carbon emissions can be decoupled from vehicle fleet growth. Then

a case study analyzes how different Chinese cities have different vehicle fleet growth patterns.

The case study concludes that vehicle fleet growth can be decoupled from economic growth.

Any plausible solution to mitigating transportation emissions will likely address many different

areas. Specifically, emissions level is sensitive to (1) income levels and vehicle prices, (2)

vehicle registration and maintenance cost, (3) private vehicle usage and public transit

development, (4) fuel types, and (5) road system planning. Due to the long implementation

period of most urban policies such as those related to public transportation, fuel efficiency, road

infrastructure, cities and provinces must pay close attention to these factors at the planning phase.

Many other cities in developing countries face similar challenges from traffic capacity,

congestion, air pollution and carbon emission. However, Schipper [38] pointed out that even in

U.S. and European cities where congestion level is relatively low, CO2 externality (at $85/ton) is

still much less than the cost of congestion, accidents or local air pollution per km. Therefore,

future research that examines CO2 as a co-benefit of, for example, congestion reduction, would

provide a new perspective on carbon pathways.

China, among other developing countries, does not have the most mature data aggregation

system. For example, transportation activities are still grouped under “transportation and

communications” in China Statistical Yearbook. Travel distances are only included in an over-

aggregated section that includes national total of rail, road, and aviation travels. Consistent effort

needs to be put into improvement of managing statistical information.

33

Appendix I – World Vehicle Ownership 2010 – 2030

Table 7 - CO2 Emissions Projection - EIA Reference Case [12]

EIA Reference Case, MtCO2e (from energy): 2010-2030

Country 2010 2030 Avg. Annual Growth

Total Growth

World 30,362 39,268 1.30% 29.30% Annex I 14,777 14,909 0.00% 0.90% non-Annex I 15,545 24,358 2.30% 56.70% United States of America 5,889 6,176 0.20% 4.90% Canada 573 609 0.30% 6.20% Mexico 447 641 1.80% 43.50%

OECD Europe 4,280 4,052 -0.30% -5.30% Japan 1,199 1,085 -0.50% -9.50% South Korea 523 687 1.40% 31.30% Australia/New Zealand 501 546 0.40% 8.90% Total OECD 13,416 13,796 0.10% 2.80% Russia 1,655 1,715 0.20% 3.60% Other Non-OECD Europe and Eurasia 1,236 1,327 0.40% 7.40% China 6,787 11,945 2.90% 76.00% India 1,459 2,079 1.80% 42.50% Other Non-OECD Asia 1,817 2,882 2.30% 58.70%

Middle East 1,662 2,450 2.00% 47.40% Africa 1,063 1,461 1.60% 37.40% Brazil 424 682 2.40% 61.00% Other Central/South America 796 931 0.80% 16.90% Total Non-OECD 16,912 25,472 2.10% 50.60%

34

Appendix II – Scenario Design for Bottom-up Model

Table 8 – Data for Model Calculation: Scenario 1, Baseline

Year 2010 2020 2030

Part I - Socio-economic Parameters

GDP (billion RMB) 36163 80864 160715

Population (billion) 1.36 1.44 1.47 Urbanization % 49 63 70 GDP/capita 26590 56156 109330

Part II - Vehicle Fleet and Usage

Vehicle Population (million) 50.1 165.8 342.8 Vehicles per 1000 people 36.83824 115.1389 233.1973

Vehicle Milage (km/vehicle/year) 14496 12484 10472

Total distance travelled (billion km/year) 726.2496 2069.847 3589.802

Part III - Vehicle Types (%)

Gasoline 97% 94% 90%

CNG 1% 2% 3% Hybrid Electric-Gasoline 1% 2% 4% Battery Electric 1% 2% 3%

Part IV - Carbon Intensity (g CO2e/km)

Gasoline 280.0 280.0 280.0 CNG 220.0 220.0 220.0 Hybrid Electric-Gasoline 200.0 200.0 200.0 Battery Electric 100.0 100.0 100.0

Part V - Emission (Million tCO2e) Year 2010 2020 2030


35

Table 9 - Data for Model Calculation: Scenario 2, Reduced Driving

Reduced Driving

Year 2010 2020 2030

Part I - Socio-economic Parameters GDP (billion RMB) 36163 80864 160715

Population (billion) 1.36 1.44 1.47 Urbanization % 49 63 70 GDP/capita 26590.44 56155.56 109329.9

Part II - Vehicle Fleet and Usage Vehicle Population (million) 50.1 165.8 342.8

Vehicles per 1000 people 36.83824 115.1389 233.1973



Part III - Vehicle Types (%) Gasoline 0.97 0.8 0.7

CNG 0.01 0.05 0.05 Hybrid Electric-Gasoline 0.01 0.1 0.15 Battery Electric 0.01 0.05 0.1

Part IV - Carbon Intensity (g CO2e/km) Gasoline 280 224 179.2

CNG 220 176 140.8

Hybrid Electric-Gasoline 200 160 128 Battery Electric 100 80 64



Table 10 - Data for Model Calculation: Scenario 3, Integrated Transport Policy

Integrated Transport Policy

36

Year 2010 2020 2030

Part I - Socio-economic Parameters GDP (billion RMB) 36163 80864 160715

Population (billion) 1.36 1.44 1.47 Urbanization % 49 63 70 GDP/capita 26590.44 56155.56 109329.9

Part II - Vehicle Fleet and Usage Vehicle Population (million) 50.1 165.8 342.8

Vehicles per 1000 people 36.83824 115.1389 233.1973



Part III - Vehicle Types (%) Gasoline 0.97 0.5 0.2

CNG 0.01 0.1 0.2 Hybrid Electric-Gasoline 0.01 0.2 0.3 Battery Electric 0.01 0.1 0.3

Part IV - Carbon Intensity (g CO2e/km) Gasoline 280 210 157.5

CNG 220 165 123.75 Hybrid Electric-Gasoline 200 150 112.5 Battery Electric 100 75 56.25


Gasoline 197.2494 152.764 75.69538 CNG 1.597749 24.00577 59.47494

Hybrid Electric-Gasoline 1.452499 43.64685 81.1022 Battery Electric 0.72625 10.91171 40.5511

37

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Documents

THESISpyzhang/affluence.pdf · · 2013-10-18This thesis focuses on the link between economic growth and carbon dioxide emissions in China’s transportation sector. Specifically,