Achieving CO2 emission reduction and the co-benefits of local air pollution abatement in the transportation sector of China

environmental science & policy 21 (2012) 1–13

Available online at www.sciencedirect.com

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Achieving CO2 emission reduction and the co-benefits of local air pollution abatement in the transportation sector of China Xianqiang Mao a,*, Shuqian Yang a, Qin Liu a,b, Jianjun Tu c, Mark Jaccard d a

School of Environment, Beijing Normal University, Beijing 100875, China Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China c Carnegie Endowment for International Peace, Washington, DC 20036-2103, USA d School of Resource and Environmental Management, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada V5A 1S6 b

abstract article info Transportation in China has joined the power generation as well as the steel and iron Published on line 25 April 2012

industries as one of the major CO2 emission sectors. To determine the effective policy

Keywords:

implemented in the near future or have been implemented in China, are examined and

Transportation

compared. These instruments include carbon tax, energy tax, fuel tax, clean energy vehicle

instrument(s) for reducing CO2 emission, various policy instruments, which are likely to be

China

subsidy, and reduction on ticket price. The CIMS model system is employed as the simula-

CO2 emission reduction

tion vehicle to predict the emission dynamics of CO2 and local air pollutants under business-

Co-benefit

as-usual and policy scenarios for the transportation sector of China from 2008 to 2050. The

Policy options

2020 CO2 reduction target is set according to the national carbon intensity reduction pledge

CIMS_China_Transportation model

of China. The policy instruments proposed in the present research can all help mitigate the CO2 emission intensity of the Chinese transportation industry to different extents, and then induce the co-benefits of local air pollutants reduction. Among these policy instruments, energy and fuel taxes, with the tax rates set, are the two most promising instruments for CO2 emission intensity reduction to reach the 2020 carbon intensity reduction targets, whereas subsidies are the least promising options. CO2 tax could be an effective policy tool, but with the suggested low tax rate during discussions in China, it is unlikely that the transportation sector would significantly contribute to achieving a desirable carbon intensity reduction. # 2012 Elsevier Ltd. All rights reserved.

1.

Introduction

Greenhouse gas (GHG) emissions in China are especially difficult to cope with because the heavy reliance of the country on carbon-intensive sources of energy supplies, and the situation will last for decades. In particular, the transportation sector of China is rapidly developing. Since the 1980s, the automobile fleet has been increasing with an annual average growth rate of 12–14%, far exceeding the GDP growth. Passenger transport in China increased from 11.73 billion

person-times (person-time is a unit to measure the quantity of passenger transport, meaning ‘‘one person travel for once’’) in 1995 to 29.77 billion person-times in 2009. The average increasing rates of total freight transportation in 2002 and 2008 was 12.95% (National Bureau of Statistics of China, 2010). In 1994, the transportation sector of China emitted 1.66  108 tons CO2, and the proportion in total CO2 emission was 5.4% (National Climate Change Coordination Committee, 2004). In 2007, the CO2 emission of the transportation sector increased to 4.36  108 tons, and the proportion increased to a total of 7.0% (Cai et al., 2011). According to estimation in

* Corresponding author. Tel.: +86 10 58807812; fax: +86 10 82025600. E-mail address: [email protected] (X. Mao). 1462-9011/$ – see front matter # 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.envsci.2012.03.010

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environmental science & policy 21 (2012) 1–13

the current study, the transportation sector emitted 6.37  108 tons CO2, or 10.6% of the national total, in 2008. Transportation has joined the power generation industry, as well as the steel and iron industries, among others, as one of the major CO2 emission sectors. China has announced a national target of carbon intensity (tons/GDP) reduction by 40– 45% based on 2005 levels by 2020. Hence, reducing CO2 emissions in the transport sector is expected to make an important contribution to achieving this target. The expansion of the transportation sector has also caused tremendous local air pollutant emissions of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and particulate matter (PM), thereby becoming a serious environmental concern. Over 50% of CO, HC, and NOx are from motor vehicle emissions. In some large cities, the proportion is as high as 80% (Zhou, 2009). To resolve the CO2 reduction challenge, new regulations and standards have been initiated and/or discussed. For example, fuel tax was introduced in 2009, and new energy vehicle subsidy pilot schemes were initiated in 2010 for several cities. In Beijing, public transportation subsidy was implemented in the form of price deduction for bus and metro tickets. Carbon tax and energy tax are still under discussion. A research report suggested a CO2 tax rate of 10 Yuan/ton in 2012 (Research Institute for Fiscal Science, 2011). Among all these policy instruments, the one(s) that should be selected and applied to answer this question must be determined and to provide decision-making support. There is an urgent need to pre-examine the effectiveness of various policy options in terms of chocking back the rapid CO2 emission expansion of the transportation sector. Transportation sector is widely studied as a large GHG emitter. Among the various optional policies, taxes and subsidies are the most often examined instrument. Baranzini et al. (2000) reviewed the implementation of energy and carbon taxes in several European countries. They pointed out that only the policies of Norway and Sweden could result in sufficient incentives for energy saving. Ghalwash (2007) performed an empirical analysis of the energy tax impact on consumer preferences, and concluded that the impact is actually uncertain in different fields. The energy tax elasticity is larger than the price elasticity in the heating industry, and the converse is true for the transportation sector. Mao and Yang (2006) reviewed the effects of the energy tax policy in Sweden, and proposed to use international experience in China. Liu et al. (2006) analyzed the different effects of energy and carbon taxes on the choice of clean technologies in the electric power sector. Zhang and Pan (2007) explored the impacts of energy taxation on international trade and environmental pollution. Yang (2010) conducted an economic analysis on how to use the fuel oil tax rationality, and analyzed the impact of fuel tax on the transportation industry. Zhang and Li (2011) indicated that the impact of carbon tax on the economic growth of China considerably varies between different regions. Carbon tax could stimulate economic growth in most eastern regions, but can hinder those of some provinces in the middle and western areas. Morrow et al. (2010) examined different policy scenarios (fuel taxes, continued increases in fuel economic standards, and purchase tax credits for new vehicle purchases) for reducing GHG emissions and oil consumption in the US transportation sector. The

authors showed that all policies fail to meet the goal of reducing GHG emissions by 14% below the 2005 levels by 2020. Kim et al. (2011) estimated the amount of GHG emission reduction by carbon taxation in the transportation sector in Korea using the price elasticity of gasoline. They confirmed that the amount of GHG emissions could be reduced under different carbon tax rate scenarios. Hu et al. (2010) presented an overview of the challenges encountered in road transportation development in China. They showed that if the current pattern continues, by the year 2030, the vehicle population in China will be 400 million, and the fuel demand will be 350 million tons per year. Yan and Crookes (2010) predicted that the rapid growth of road transportation in China will likely continue in the next two to three decades, and policy measures bringing about favorable behavioral changes are required. They suggested that strategic planning and acting early are the keys to tackling the energy and environment challenges faced by the road transport sector. These aforementioned studies have widely conducted policy analyses in China and around the world, and have mostly focused on a single instrument. Studies tailored for the transportation sector of China are still very limited. In the current research, various instruments, namely, carbon tax, energy tax, fuel tax, clean energy vehicle subsidy (CEVS), and reduction on ticket price (RTP) policies, are examined and compared. The purpose is to determine the effective policy instrument(s) to achieve the CO2 intensity reduction target, and disclose the co-benefits of local air pollutants reduction when applying these policy instruments. The remaining parts of the current research are organized as follows. The methods and data are presented in Section 2. The policy instrument scenarios are set for the simulation and evaluation in Section 3. The effectiveness analysis of the policy instruments is detailed in Section 4. The policy implications are summarized in Section 5.

2.

Methodology and data

The current research applies the CIMS model as a policy impact simulation tool. CIMS is a hybrid simulation model developed by the Energy and Materials Research Group (EMRG) at Simon Fraser University (SFU), Canada. CIMS was designed to help policy-makers to better understand the effect of policy alternatives (Tu et al., 2007). The CIMS model is widely applied in Canada, United States, Australia, China, among others (Rivers and Jaccard, 2005; Murphy et al., 2007). CIMS starts from a bottom-up model with an explicit representation of technologies, including their capital costs, operating and maintaining costs, fuel consumption, life spans, as well as CO2 emissions. However, unlike a conventional bottom-up model, the CIMS model combines the strengths of the bottom-up and top-down approaches to realize three key modeling criteria, namely, technological explicitness, behavioral realism, and the ability to capture equilibrium feedback (Murphy et al., 2007). The strength of CIMS lies in the fact that its basic modeling principle is to simulate technological competition and substitution processes for an industry. CIMS smartly assumes that, with old technologies/facilities dying out of the market

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and/or market expansion, new technologies/facilities fill the emerging market vacancy. Consequently, CIMS simulates the dynamics of the allocation of an emerging market share to all available technologies, decided by the ratio of the life-cycle cost (LCC) of a specific technology to the total LCC of all available technologies in the market. The allocation of a certain technology of an additional market share is determined by the following equation (1). MS j ¼ PK

½CC j ðr=ð1  ð1 þ rÞn j ÞÞ þ MC j þ EC j þ i j 

k¼1 f½CCk ðr=ð1

nk

 ð1 þ rÞ

v

(1)

v

ÞÞ þ MCk þ ECk þ ik  g

where MSj is the proportion of technology j accounting for the additional market share, CCj is the capital cost of technology j, MCj is the maintenance cost of technology j, ECj is the energy cost of technology j, nj is the average life span of technology j, r is the social discount rate, ij is the intangible cost, v is a variable (describing heterogeneity in the market), and k is the number of technologies.

The total emissions in the transportation sector are calculated as follows.

SUM p ¼

K X S j e j

(2)

k¼1

where SUMp is the total emission of pollutant p, Sj is the stock of technology j, ej is the emission factor per stock of technology j, and K is the number of technologies. Although CIMS model was developed in Canada, its bascial way of thought of modelling the real world can capture the common natural of the energy–economy–environment system beyond the nation border. When the country specific parameters being given according to the country facts, it could be amended to be a modeling system suitable for China. For example, CIMS model has been used to simulate the clean development mechanism (CDM) potential, technology transformation, etc., for studies in China (Tu, 2004; Tu et al., 2007).

PDL_Water PDL_Air

PDL

Gasoline

PDL_Rail Diesel

PD

PDS Bus

Passenger

PDS_MRT

PDS_Personal

PDS

PDS_Taxi

PI

PDS_Other

CNG LPG Electricity Gasoline_HEV Biodiesel

FDRoad_H Gasoline Transportaon

FDRoad_M

FD_

Diesel

Road

FDRoad_L FDRoad_Mi

FD

CNG LPG

FD_Water

Biodiesel

FD_Air

Gasoline-HEV

FD_Pipeline FDRail_Diesel FD_Rail

FDRail_Electricity FI_Marine Freight FI

FI_Rail

FI_M_Balc Handysize

FI_Road

FI_M_Handysize

FI_Air

FI_M_Panamax FI_M_Capesize

Fig. 1 – Constructed dendritical structure of the technologies in CIMS_China_Transportation model.

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environmental science & policy 21 (2012) 1–13

For the parameters in the model, in Eq. (1), ‘r’, discount rate, and ‘i’, intangible cost, are determined based on authors’ investigation on transportation sector of China. While parameter ‘v’, heterogeneity coefficient, refers to the EMRG’s research (Nyboer, 1997; Tu et al., 2007). Sensitivity analysis made by Tu (2004) and made in this study all indicates that the model’s response to the changes of ‘‘v’’ and the uncertainty are small. Based on the open and dynamic analytical framework provided by CIMS, a CIMS_China_Transportation model tailored for the carbon emission reduction policy study on the Chinese transportation sector could be constructed, which is essentially a Chinese-characterized transportation module nested in the CIMS model. To form a dendritical structure of transportation technologies is the first step. And then proper energy consumption and emission factors for every competing technology involved in the model are to be employed, to reflect the real picture of transportation industry of China. Fig. 1 shows the dendritical structure of the sub-sectors within the Chinese transportation sector, constructed specially for the study. The standard that we use to classify Chinese transportation vehicles into various types is whether it can differentiate those technologies with different characteristics of its usages (passenger vs. freight), energies, sizes, etc. Taking passenger road transportation as an example, public bus and personal car transportation are different types with large differences in energy consumption and emissions factors. The constructed CIMS_China_Transportation model simulates the competition of technologies at each energy service node (Fig. 1) based on a comparison of their LCC and technology-specific constraints, such as the maximum market share of a technology due to physical, technical, or regulatory barriers. A total of 95 technologies in the transportation sector are involved in the model, covering passenger and freight transportations, roads, railways, as well as water, air, and pipeline transportations. Eight categories of energies, namely, gasoline, diesel, compressed natural gas (CNG), liquefied petroleum gas (LPG), electricity, gasoline used by hybrid electric vehicles, biodiesel and kerosene, as well as CO2 and four local air pollutants (CO, HC, NOx, and PM) are considered. The emission factors of each technology are calculated by dividing the total emission of technology j by its transport turnover. The emissions data are drawn from the China Vehicle Emission Control Annual Report (Ministry of Environmental Protection of the People’s Republic of China, 2010a,b), and the transportation turnover data is drawn from the China Statistical Yearbook (National Bureau of Statistics of China, 2009,

2010). All costs/prices in the present study are based on the 2008 price term. The indirect emission from the generation of electricity power consumed by transportation is not considered in the current research, or it is assumed to be zero CO2 emission for electricity consumed in transportation. This is to avoid the complexity of discussion and prediction of the electricity power generation structure dynamics, which basically will decide the emission density of power generation and the indirect emission induced. In order to simplify the simulation and analysis, we assume that the energy consumption factor and emissions factors of a transportation technology keep constant during the simulating period, rather than allowing them to change in the process. For different major technologies in transportation sector (whether those have been widely used or those are under development) we have known for now, the energy consumption factors and emission factors are given in the model. The differences between technologies from different transportation modes (e.g. road and water transportation, or bus and mass rapid transit (MRT)) are much larger than those between technologies within the same transportation mode (e.g. sedan of different brands). When applying the different policies, the technology competition and substitution will be affected and then lead to emission reduction. That’s how the effectiveness of different policy impacts could be simulated. To avoid over-complication of the study, the energy price and technology cost are assumed to be exogenous and remain unchanged in the simulation period. Due to the nature of CIMS model the transportation demand must be exogenously given and input to the model, and allow a competition process of different technologies with different life cycle cost. The transportation demand for the simulation period is exogenously given (see Table 1). The simulation period is set for 2008–2050, and 2008 is the base year. The price used in the research is also based on the 2008 price term, other than being specified. Considering the commitment of China to carbon intensity reduction by 40– 45% by 2020 compared with the year 2005 levels, the CO2 emission intensity of the transportation sector in 2005 is also cited wherever CO2 emission intensity is mentioned for the convenience of comparison. To ensure data and simulation accuracy, CIMS has a calibration function that only under a certain condition can the accuracy of the constructed CIMS_China_Transportation model be accepted. The condition is when the percentage differences between the simulated base year values and the observed values of energy and emissions are less than 5%.

Table 1 – Predicted market stocks of freight and passenger transportation from 2008 to 2050. Year Passenger (pkt) Freight (tkt)

2008

2014

2020

2026

2032

2038

2044

2050

2.32  1012 1.10  1013

4.35  1012 1.68  1013

7.91  1012 2.51  1013

1.32  1013 3.54  1013

2.09  1013 4.82  1013

3.00  1013 6.14  1013

4.04  1013 7.49  1013

5.26  1013 8.94  1013

Data source: ERI (2009). Note: pkt means ‘‘passenger kilometer travel’’ and tkt means ‘‘ton kilometer travel’’, which measure the quantity of passenger and freight transportation, respectively.

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environmental science & policy 21 (2012) 1–13

Table 2 – Energy prices under BAU and fuel tax scenarios (Yuan/GJ). Scenarios

Diesel

Biodiesel

Gasoline

LPG

CNG

Electricity

G_HEV

Kerosene

BAU Fuel tax Fuel tax Fuel tax Fuel tax

119.68 131.65 155.59 179.52 239.36

120.58 120.58 120.58 120.58 120.58

145.62 160.18 189.30 218.43 291.23

186.10 186.10 186.10 186.10 186.10

138.76 138.76 138.76 138.76 138.76

94.44 94.44 94.44 94.44 94.44

145.62 145.62 145.62 145.62 145.62

112.45 112.45 112.45 112.45 112.45

(10% rate) (30% rate) (50% rate) (100% rate)

Note: The prices are based on the 2008 price term. Since only diesel, biodiesel and gasoline are taxed, the other energy prices keep unchanged as the same as BAU.

3.

Policy scenarios design

3.5.

3.1.

Business-as-usual (BAU) scenario

Chinese government has implemented subsidies for new energy vehicles to promote the development of clean energy vehicles. For example, the plug-in hybrid electricity vehicle can get 50,000 Yuan subsidy at most, and other clean energy vehicles can get 3000 Yuan subsidy (Ministry of Finance of the People’s Republic of China, 2010). For public bus, the present subsidy in Beijing for pure electric bus is 500,000 Yuan/vehicle. In this scenario, subsidies are assumed to be given to those vehicles using clean energy such as HEV (Hybrid Electric Vehicle), CNG_V (Compressed Natural Gas Vehicle), LPG_V (Liquid Petroleum Vehicle), and BDV (Bio-Diesel Vehicle) with reference to China’s current CEVS policy (Ministry of Finance of the People’s Republic of China, 2010). The subsidy rate varies with different energies and technologies (Table 4).

In the BAU scenario, the transportation industry is assumed to keep developing as the current trajectory. Table 1 shows the stocks of freight and passenger transportation from the base year 2008–2050, which is exogenously provided with reference to the research report of the Energy Research Institute (ERI), National Development and Reform Commission of China (ERI, 2009).

3.2.

CO2 tax scenarios

CO2 taxation is assumed to be levied on the CO2 emission quantity. CO2 tax rates of 10 Yuan/ton to 300 Yuan/ton are tested. The starting level of 10 Yuan/ton is the CO2 tax rate suggested by a study hosted by the Ministry of Finance (Research Institute for Fiscal Science, 2011). Considering both the current CO2 tax rates of Great Britain, Sweden, Denmark, Canada, etc., and the current low per capita income level of China, the highest CO2 rate in the current study is set at 300 Yuan/ton (Qiao and Li, 2010).

3.3.

Reduction on ticket price (RTP) scenario

In this scenario, the ticket price of public transportations (including public buses and the subway) are assumed to be discounted by 60%, in reference to the Beijing public transportation subsidy in the form of a 60% reduced ticket price.

Fuel tax scenarios

Referring to the ‘‘fuel pricing and taxation reform’’ enforced in China since 2009, the fuel tax base is assumed to cover only gasoline, diesel, and kerosene consumption, but is not applied to the other energies. Various provisions of tax rates are set and tested in the model simulation (Table 2).

3.4.

3.6.

Clean energy vehicle subsidy (CEVS) scenario

Energy tax scenarios

The tax bases of energy tax include not only gasoline, diesel, and kerosene, but also biodiesel, CNG, LPG, G-HEV and electricity (Table 3).

4.

Results and analysis

4.1.

BAU scenario

4.1.1.

Total CO2 emission

The total CO2 emission will keep increasing from 2008 to 2050 (Fig. 2) under the BAU scenario. In 2008 (base year), the transportation sector of China emitted 6.37  108 tons CO2. In 2050, the number will be 8 times larger than that in 2008. However, the annual growth rate of CO2 will initially increase and then decrease after 2020.

Table 3 – Energy prices under BAU and energy tax scenarios (Yuan/GJ). Scenarios BAU Energy Energy Energy Energy

tax tax tax tax

(10% rate) (30% rate) (50% rate) (100% rate)

Diesel

Biodiesel

Gasoline

LPG

CNG

Electricity

G_HEV

Kerosene

119.68 131.65 155.59 179.52 239.36

120.58 132.64 156.75 180.87 241.16

145.62 160.18 189.30 218.43 291.23

186.10 204.71 241.90 279.1 372.20

138.76 152.63 180.38 208.13 277.51

94.44 103.89 122.80 141.70 188.90

145.62 160.18 189.30 218.43 291.23

112.45 123.69 146.18 168.67 224.90

Note: The prices are based on the 2008 price term.

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Table 4 – Subsidies for different technologies in CEVS scenario (Yuan/vehicle). Technologies FD_Road_H_BD FD_Road_M_BD FD_Road_L_BD FD_Road_Mini_BD FI_Road_H_BD PDS_Bus_H_CNG PDS_Bus_H_LPG PDS_Bus_H_BD PDS_Bus_H_HEV PDS_Bus_M_CNG PDS_Bus_M_LPG PDS_Bus_M_BD PDS_Bus_M_HEV

Subsidy amount

Technologies

Subsidy amount

Technologies

Subsidy amount

100,000 80,000 60,000 40,000 100,000 100,000 100,000 100,000 420,000 80,000 80,000 80,000 350,000

PDS_Bus_L_CNG PDS_Bus_L_LPG PDS_Bus_L_BD PDS_Bus_L_HEV PDS_Bus_Mini_CNG PDS_Bus_Mini_LPG PDS_Bus_Mini_BD PDS_Bus_Mini_HEV PDS_PC_H_CNG PDS_PC_H_LPG PDS_PC_H_BD PDS_PC_H_HEV PDS_PC_M_CNG

60,000 60,000 60,000 250,000 40,000 40,000 40,000 150,000 30,000 30,000 30,000 50,000 20,000

PDS_PC_M_LPG PDS_PC_M_BD PDS_PC_M_HEV PDS_PC_L_CNG PDS_PC_L_LPG PDS_PC_L_BD PDS_PC_L_HEV PDS_TA_CNG PDS_TA_LPG PDS_TA_BD PDS_TA_HEV

20,000 20,000 40,000 10,000 10,000 10,000 30,000 20,000 20,000 20,000 40,000

Freight transportation emits much more CO2 than passenger transportation (Fig. 3). Between 2008 and 2050, the CO2 emission of passenger transportation will increase at a higher annual growth rate than that of freight.

4.1.2.

CO2 intensity

The CO2 intensity of freight transportation will continue to decrease from 2008 to 2050. In 2005, the CO2 intensity of freight transportation is 5.13  1005 tons/ton kilometer travel (tkt). In 2050, the CO2 intensity will decrease to 4.30  1005 tons/tkt (Fig. 4). This result can be attributed to the increased share of

water transportation, which has a low CO2 emission factor, and of domestic road transportation, which involves low CO2 intensity technologies, such as heavy duty trucks and buses. For passenger transportation, the CO2 intensity will not change much in 2050 compared with that in 2005 (Fig. 5). Between 2005 and 2050, the CO2 intensity of passenger transportation initially increases and then deceases. This finding corresponds to the prediction that the personal car stock of China will rapidly increase until 2020, and its share in total passenger domestic market will enlarge. After 2020, with the stagnant share of private cars and the share of public transportation being re-enhanced, the CO2 intensity will then decrease in the following years.

4.1.3.

Fig. 2 – CO2 emissions and annual increasing rates under the BAU scenario from 2008 to 2050.

4.2.

Fig. 3 – Total CO2 emission and annual CO2 increasing rates under the BAU scenario for freight and passenger transportation.

Emissions of local air pollutants

According to ‘‘China Vehicle Emission Control Annual Report 2010’’, the motor vehicles of China emitted 3.11  107 tons CO, 3.59  106 tons HC, 5.30E  106 tons NOx and 5.61  105 tons PM in 2009 (Ministry of Environmental Protection of the People’s Republic of China, 2010a,b). Besides, disclosed by the authoritative ‘‘Communique of the First Countrywide Census of Pollution Sources’’, the motor vehicles emitted 3.95  107 tons CO, 4.79  106 tons HC, 5.50  106 tons NOx and 5.91  105 tons PM in 2007 (Ministry of Environmental Protection of the People’s Republic of China, 2010a,b). In this research, according to the CIMS model simulation, the transportation sector of China emitted 7.32  107 tons CO, 9.35  106 tons HC, 1.50  107 tons NOx and 1.77  106 tons PM in the base year 2008. And the motor vehicles emitted 3.09E  107 tons CO, 3.59  106 tons HC, 5.39  106 tons NOx and 5.74  105 tons PM. In the subsequent decades, local air emissions will keep increasing yearly for all the pollutants, as indicated in Fig. 6.

CO2 emission reduction target for 2020

China has announced a national goal of a 40–45% cut in carbon intensity (tons/GDP) below the 2005 levels by 2020. In the current research, a CO2 emission reduction target is set for the transportation sector according to the national target for 2020, by calculating the total emissions and reductions that are equivalent to achieving the national CO2 intensity reduction target. Table 5 shows the projected total CO2 emission and intensity by 2020 in China. In 2005, the GDP of China was 18.31 trillion Yuan (National Bureau of Statistics of China,

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Table 5 – GDP, total CO2 emission, and CO2 intensity in China in 2005 and 2020 (based on the 2005 price). Year

GDP (Trillion Yuan, based on the 2005 price)

CO2 (billion tons)

18.31 64.80

5.10 9.92**

2005 Projected quantity of 2020 * **

Fig. 4 – CO2 intensity of freight transportation in BAU scenario.

Intensity (ton/ Yuan, based on the 2005 price) 0.0002785 0.0001531*

CO2 intensity of 45% reduction compared with that of 2005. Total CO2 emission with 45% reduction in CO2 intensity.

Assuming that all sectors contribute the same CO2 intensity reduction rates, for the transportation sector of China, the CO2 emission in 2020 should be no more than 1.95 times that of 2005. In other words, the index of carbon2020/2005 (the ratio of the CO2 emission in 2020 to the CO2 emission in 2005) should be lower than 1.95.

4.3.

Policy instruments to achieve CO2 reduction

Five policy scenarios are simulated to compare their CO2 emission reduction effects. They are CO2 tax, fuel tax, energy tax, CEVS and RTP.

4.3.1.

Fig. 5 – CO2 intensity of passenger transportation in BAU.

CO2 tax

Fig. 7 shows the CO2 emissions under the BAU and CO2 tax scenarios from 2008 to 2050. The CO2 emission increases yearly under all scenarios from 2008 to 2050. However, under CO2 tax scenarios, CO2 emissions are expected to be slightly lower than those under BAU, and higher CO2 tax rates cause more CO2 reduction. The CO2 emission in 2050 under the BAU scenario is predicted to be 5.84  109 tons, and that under the 10 Yuan/ton CO2 tax scenario will be reduced by 5.50  107 tons, with a reduction rate of 0.9%. Under the 300 Yuan/ton CO2 tax scenario, the reduction rate will be as high as 19.8%. The transportation sector of China will emit 1.30  1011 tons CO2 emission during 2008–2050 in BAU. 10 Yuan/ ton CO2 emission tax will lead to a reduction of 1.18  109 tons, whereas the 300 Yuan/ton CO2 tax will result in a reduction of as high as 2.52  1010 tons.

Fig. 6 – Predicted local air pollutant emission of the transportation sector under the BAU scenario from 2008 to 2050.

2006, based on the 2005 price), and the total CO2 emission was 5.10 billion tons (Cheng, 2009). Thus, the CO2 intensity in 2005 was 0.0002785 ton/Yuan. To achieve the CO2 intensity reduction target, CO2 intensity by 2020 should be lower than 0.0001531 ton/Yuan. The GDP of China will be 64.80 trillion Yuan in 2020, with an average annual increasing rate of 8.8% (Zhang and Lou, 2009). Achieving the CO2 intensity reduction target by 2020 means that the total emission must be less than 9.92 billion tons, which is 1.95 times of that in 2005.

Fig. 7 – CO2 emissions under the BAU and CO2 tax scenarios from 2008 to 2050.

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environmental science & policy 21 (2012) 1–13

Table 6 – CO2 emissions under the BAU and CO2 tax scenarios. CO2 tax scenarios

CO2 emission (ton) Carbon2020/2005

BAU in 2005

BAU in 2020

10 Yuan/ ton in 2020

50 Yuan/ ton in 2020

100 Yuan/ ton in 2020

150 Yuan/ ton in 2020

200 Yuan/ ton in 2020

300 Yuan/ ton in 2020

4.76  10 8 –

1.41  10 9 2.97

1.40  10 9 2.95

1.36  10 9 2.86

1.31  10 9 2.76

1.27  10 9 2.67

1.23  10 9 2.59

1.16  10 9 2.44

Table 7 – CO2 emissions and the carbon2020/2005 ratio under the BAU and fuel tax scenarios. Fuel tax scenarios CO2 emission (ton) Carbon2020/2005

BAU in 2005

BAU in 2020

4.76  10 8 –

1.41  10 9 2.97

10% Fuel tax in 2020

30% Fuel tax in 2020

50% Fuel tax 2020

100% Fuel tax in 2020

1.29  10 9 2.71

1.09  10 9 2.29

9.38  10 8 1.97

7.04  10 8 1.48

Note: The bold value indicates the index of carbon2020/2005 in 100% fuel tax scenario is lower than 1.95, showing that this policy can achieve the CO2 reduction target.

The CO2 emissions in 2005 and 2020, as well as the carbon2020/2005 ratio under the BAU and CO2 tax scenarios, are shown in Table 6. Even when the CO2 tax is as high as 300 Yuan/ ton, carbon2020/2005 is still much higher than 1.95. This finding indicates that the CO2 emission reduction target set for 2020 cannot be achieved. For China, the CO2 tax policy is still under argumentation and is proposed to be 10–20 Yuan/ton. Thus, the CO2 tax rate of 300 Yuan/ton or higher does not seem to be a realistic choice in the near future. If the CO2 tax is only 10 Yuan/ ton to 20 Yuan/ton, there is absolutely no way that the transportation sector would contribute anything significant to achieving the target. Therefore, achieving the CO2 intensity reduction target will require a much higher CO2 tax rate.

4.3.2.

Fuel tax

Fig. 8 shows the CO2 emissions under the BAU and fuel tax scenarios from 2008 to 2050. The CO2 emissions under the fuel tax scenarios are all much lower than that under BAU, and higher fuel tax rates will lead to more CO2 reduction. Under the 10% fuel tax scenario, the CO2 emission in 2050 will be 5.41  109 tons. Compared with that in BAU, there will be 4.33  108 tons CO2 emission reduction, and the reduction rate is 7.4%. CO2 emission under the 30% fuel tax scenario in 2050 will be 4.63  109 tons, with 1.21  109 tons CO2 reduction and a reduction rate of 20.8%. With 100% fuel tax rates, the CO2 reduction amount and rate will be as high as 2.80  109 tons and 47.9%, respectively. From 2008 to 2050, 10% and 30% fuel tax will result in 1.01  1010 tons and 2.79  1010 tons CO2 reduction. The 100% fuel tax will lead to a 6.33  1010 tons reduction. The CO2 emissions in 2005 and 2020, as well as the carbon2020/ 2005 ratio under the fuel tax scenarios, are shown in Table 7. When the fuel tax rate is higher than 53%, the transportation sector will reach its CO2 reduction target for 2020.

4.3.3.

CO2 emission under the 30% energy tax scenario in 2050 will be 4.72  109 tons, with 1.12  109 tons and 19.2% CO2 reduction. For the 100% energy tax rate, the CO2 reduction amount and rate will be 2.63  109 tons and 45.0%, respectively. During 2008 and 2050, 10% and 30% energy taxes will lead to 9.97  109 and 2.58  1010 tons CO2 emission reduction in the transportation sector, and in 100% energy tax scenario, the CO2 reduction will be 5.97  1010 tons. According to Table 8, under the 100% energy tax scenario, the carbon2020/2005 ratio will be 1.56, and an energy tax rate higher than 60% can already lead to reaching the CO2 emission reduction target.

Fig. 8 – CO2 emissions under the BAU and fuel tax scenarios from 2008 to 2050.

Energy tax

Fig. 9 shows the CO2 emissions under the BAU and energy tax scenarios. Similar to the fuel tax scenarios, the CO2 emissions under the energy tax scenarios are all lower than those under BAU. A higher fuel tax rate will lead to more CO2 reduction. By 2050, CO2 emission under the 10% energy tax scenario will be 5.41  109 tons, compared with that under BAU, wherein there will be 4.31  108 tons CO2 reduction, a reduction rate of 7.4%.

Fig. 9 – CO2 emissions under the BAU and energy tax scenarios from 2008 to 2050.

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Table 8 – CO2 emissions and the carbon2020/2005 ratio under the BAU and energy tax scenarios. Energy tax scenarios CO2 emission (ton) Carbon2020/2005

BAU in 2005

BAU in 2020

4.76  10 8 –

1.41  10 9 2.97

10% Energy tax in 2020

30% Energy tax in 2020

50% Energy tax in 2020

100% Energy tax in 2020

1.30  10 9 2.73

1.11  10 9 2.34

9.69  10 8 2.04

7.41  10 8 1.56

Note: The bold value indicates the index of carbon2020/2005 in 100% energy tax scenario is lower than 1.95, showing that this policy can achieve the CO2 reduction target.

Table 9 – Comparison and equivalent of CO2 tax and energy tax (based on the 2008 price). CO2 tax (Yuan/ton)

Energy tax (Yuan/L gasoline)

Gasoline price (Yuan/L gasoline)

Energy tax rate

0.02 0.11 0.22 0.33 0.44 0.55 0.66 1.31

4.39 4.39 4.39 4.39 4.39 4.39 4.39 4.39

0.5% 2.5% 5.0% 7.5% 10.0% 12.5% 15.0% 30.0%

10 50 100 150 200 250 300 600

In 2050, under the 10 Yuan/ton CO2 tax scenario, the annual CO, HC, NOx, and PM emissions will be reduced by 0.68%, 0.66%, 0.55% and 0.49% compared with BAU. Under the 300 Yuan/ton CO2 tax rate scenario, they will be reduced by

Data source: Research Institute for Fiscal Science (2009).

Table 10 – CO2 emissions under the BAU, CEVS, and RTP scenarios in 2020. CEVS and RTP scenarios CO2 emission (ton) Carbon2020/2005

BAU in 2005

CEVS in 2020

RTP of 60% in 2020

RTP of 100% in 2020

4.76  10 8

1.40  10 9

1.41  10 9

1.40  10 9

–

2.94

2.96

2.95

Why does CO2 taxation seem far less effective than fuel and energy taxations? Table 9 may provide an explanation. A CO2 tax of 10 Yuan/ton is just roughly equal to 0.5% energy tax, and a CO2 tax of 600 Yuan/ton CO2 is equal to 30% energy tax. Psychologically, an energy tax rate of 30% would be much acceptable than a 600 Yuan/ton CO2 of carbon tax. Hence, the seemingly ineffectiveness of CO2 taxation is explained by the actual low level of the tax rates examined in the current research.

4.3.4.

Fig. 10 – CO2 emissions under the BAU, CEVS, and RPT scenarios from 2008 to 2050.

Fig. 11 – Local air pollutants under the BAU and CO2 tax scenarios in 2050.

CEVS and RTP

Fig. 10 shows that CEVS and RPT can contribute very little to the CO2 reduction of the transportation sector of China. This finding is essentially due to the very small proportion of the industry that these two policy instruments could address. According to Table 10, the CEVS and RTP scenarios set in the present report probably cannot effectively help the transportation sector to achieve the 2020 CO2 reduction target.

4.4.

Co-benefit of local air pollutants reduction

CO2 tax, fuel tax, energy tax, and subsidies (CEVS and RTP) can also result in co-benefits of local air pollutants reduction. Figs. 11–14 show the local air pollutants under BAU and policy scenarios in 2050.

Fig. 12 – Local air pollutants under the BAU and fuel tax scenarios in 2050.

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Fig. 13 – Local air pollutants under the BAU and energy tax scenarios in 2050.

It is observed that, the fuel tax is more effective than energy tax in terms of local air pollutants abatement. The implication of the difference between energy tax and fuel tax is significant: in fuel tax scenario, when gasoline, diesel and kerosene consumption are taxed, consumers will tend to choose cleaner technologies powered by electricity, natural gas, etc., which will lead to more reduction of local air pollutants. CEVS and RTP will not bring about considerable co-benefit for the entire industry, but will still contribute to the air pollution reduction in densely populated urban areas. Under the CEVS policy scenario, the annual CO, HC, NOx, and PM emissions in 2050 will be reduced by 5.89  106, 3.78  105, 6.60  105, and 6.36  104 tons compared with those under BAU, whose abatement rates are 0.45%, 0.24%, 0.36%, and 0.33%, respectively. A 100% RTP will result in a larger abatement on local air emissions, where CO, HC, NOx, and PM emissions in 2050 will be reduced by 0.49%, 0.53%, 0.38% and 0.27%, respectively.

5.

Fig. 14 – Local air pollutants under the BAU, CEVS, and RTP scenarios in 2050.

15.1%, 14.1%, 10.1% and 8.3%, respectively, compared with BAU. The accumulate CO, HC, NOx, and PM emissions of the transportation sector from 2008 to 2050 will be abated by 3.79  109, 4.18  108, 3.66  108 and 3.19  107 tons, respectively in 300 Yuan/ton CO2 tax scenario. By fuel taxation, much notable reductions in CO, HC, NOx, and PM emissions will be achieved by the year 2050, where the annual emissions of the four pollutants will be decreased by 6.8%, 6.3%, 4.6%, and 3.8%, respectively under the 10% tax rate, as well as by 43.9%, 38.9%, 24.3%, and 16.9% under the 100% fuel tax. The total local air emissions under the 100% fuel tax scenario from 2008 to 2050 will be reduced by 1.11  1010, 1.15  109, 8.65  108, and 6.32  107 tons compared with those under BAU, wherein the reduction rates are about 42.1%, 36.6%, 21.6%, and 14.6%, respectively. In 2050, the annual CO, HC, NOx, and PM emission reduction under the 10% energy tax scenario are expected to be 5.9%, 5.6%, 4.1%, and 3.3% lower than that under BAU, respectively. Under the 100% energy tax scenario, their annual emission reduction rates will be 39.4%, 36.1%, 21.6%, and 14.3%, respectively. The accumulate local air emissions from 2008 to 2050 in 100% energy tax scenario will be reduced by 9.95  109, 1.06  109, 7.62  108 and 5.18  107 tons separately, with reduction rates of 37.7%, 33.8%, 19.0% and 12.0%.

Conclusion and discussion

The emission of CO2 and local air pollutants under current trajectory (BAU scenario) and policy scenarios for the transportation sector of China from 2008 to 2050 are simulated. The purpose is to compare the effectiveness of the policy instruments of CO2 tax, fuel tax, energy tax, CEVS, and RTP, which have already been implemented or are likely to be implemented in the near future. The policy instruments proposed in the present research can all help mitigate the CO2 emission intensity in the Chinese transportation sector to different extents. The co-benefits of local air pollutants reduction are also induced. Among these instruments, energy and fuel taxes are the two most promising instruments for CO2 emission intensity reduction, and subsidies are the least promising options. CO2 tax could be an effective policy tool. However, with the low suggested tax rate currently being discussed in China, there is no way that the transportation sector would significantly contribute to achieving carbon intensity emission reduction. CEVS and RTP scenarios set in the present report probably cannot effectively help the transportation sector to achieve the 2020 CO2 reduction target. For the three taxation instruments, definitely, tax rate is very important, because it is the source of the extra life cycle cost (LCC) exerted on the different technologies. But CO2 tax, energy tax, and fuel tax are different kinds of tax due to their different taxation bases. CO2 tax is based on carbon emission. Energy tax is based on energy consumption. Fuel tax is exerted on a few specific fuels, gasoline, diesel, and kerosene consumption. These differences are important and have very significant political and economic implication for the governmental taxation departments and to the consumers. Since their taxation bases are different, the caused consumer behavior change will be different. That is why there are a lot of dispute on which tax should be used, other than how much rate should be applied. Even though CO2 tax can achieve the CO2 and local air pollutants reduction, it is of higher political sensitivity than energy and fuel tax, and its tax rate in discussion is unlikely to be set at higher than 20 Yuan/ton

environmental science & policy 21 (2012) 1–13

Table 11 – CGE estimation of energy tax impacts on the macro-economy of China based on 2002 data. Energy tax rate (Yuan/ton SCE) Real GDP change rate (%) Total investment change rate (%) Capital factor income change rate (%) Labor factor income change rate (%) Import change rate (%) Export change rate (%)

50

100

150

11

Acknowledgments

200

0.18 0.25

0.38 0.38

0.58 0.43

0.79 0.42

0.57

1.09

1.56

2.00

1.17

2.21

3.14

3.99

0.25 0.24

0.50 0.46

0.74 0.68

0.96 0.89

Source: Yang et al. (2009). Note: Price is in 2002 Yuan.

presently in China, which is a very small impact to the energy cost. The implication of the difference of energy tax and fuel tax are also significant. In fuel tax scenario, when gasoline, diesel and kerosene consumption are taxed, consumer will tend to choose cleaner technologies powered by electricity and natural gas, which will lead to more significant reduction of local air pollutants. So, in this research, we make conclusion of which policy would be more ‘‘effective’’ rather which one is ‘‘better’’, based on the policy realistic and effectiveness itself, but not based on the level of tax. In the current research, the energy prices from 2008 to 2050 are assumed to be constant to neglect the impact from inflation which will complicate the analysis. However, the fuel prices in China have undergone a large increase since China set a new fuel pricing mechanism in January 1, 2009. More flexibility is granted to allow the fuel price to keep up with the pace of fluctuations in the international market. Since the simulation of CIMS is based on life cycle costs and technology competition, the consequence of increasing fuel price is very much similar with that of tax scenarios. So the scenario of increasing energy prices is not done in this research. CIMS model cannot simulate the macro economy consequence due to its ‘‘partial-equilibrium and technologybased’’ nature. Readers may worry that such taxes mentioned in the research, if successfully implemented, would bring the economy to a halt. Our previous studies (Yang et al., 2009) using computable general equilibrium (CGE) model to quantify the impact of energy tax on macro economy indicates that, GDP of China will drop by a small percentage with a tax rate of 200 Yuan/ton SCE (see Table 11). Other researches, such as Zhou et al. (2011a,b) and Yuan et al. (2011), also suggest that tax instruments should be applied step by step to avoid any sharp impacts on the economy and social welfare. Although the present research provides quite an extensive output and policy indications on the effects of the policy instruments on pollution emission reduction, there are still some more work that needs to be done in future studies. For example, the total transportation demand in the present research is exogenously given, and the price-demand elasticity is not considered. The lack of demand elasticity is the shortage of almost all bottom-up models. In future research, a linkage between the CIMS and top-town models such as CGE will be established to fill the gap.

We sincerely thank the anonymous reviewers for their invaluable comments to this paper. We would also like to thank those who made this study possible, namely, IDRC/ EEPSEA (particularly, Dr. Herminia Francisco, EEPSEA director, and David Glover, former EEPSEA director), who provided the research grant to the research team; Dr. Benoit Laplante for his invaluable comments; all the members of the EEPSEA Workshop Groups to whom this study was presented for their comments and recommendations; and Ms. Cathy Ndiaye, Ms. Teresa Lum, Mr. Canesio, D. Predo, et al. for their great efforts in supporting this project. We also sincerely appreciate the help of the EMRG at the School of Resource and Environmental Management at SFU for the CIMS modeling support. Sincere gratitude is also given to the National Social Science Foundation of China (Grant No. 11BJY065).

Appendix A. Data sources Data on technological details and energy efficiency factors of the technologies in the CIMS_China_Transportation model are collected from extensive data sources. The published official statistical materials are used, for example, China Statistical Year Book. For air and water transportation, we also collected data from the annual corporation reports, market investigation reports, etc. The demands of transportation during 2008–2050 are drawn from the research report of the Energy Research, Institute (ERI, 2009). The energy consumption factors and emission factors are inferred from the very rich data and information of ‘‘Year Book of China Transportation and Communications’’, extensive literatures review, transportation industry investigation, experts review, etc. Some of them are listed below: (1) Energy–economy–environmental model databases, such as IPAC, LEAP, and MARKAL (Jiang et al., 2008; Zhu and Jiang, 2002; Chen and Wu, 2001); (2) National Bureau of Statistics of China. China Statistical Yearbook (2006–2009). Beijing: China Statistical Publishing House; (3) Yearbook of China Transportation and Communications (2006–2009). Beijing: China Statistical Publishing House; (4) Literature review (Xie et al., 2000, 2006; Zhou et al., 2011a,b), etc.; (5) Online databases: http://www.gov.cn/jrzg/2007-06/03/content_634545.htm, http://www.mof.gov.cn/zhengwuxinxi/ caizhengxinwen/201006/t20100601_320713.html, etc.; (6) CIMS-Canada database (Jaccard et al., 2004; Horne et al., 2005; Mau et al., 2008); (7) Interviews with experts and government transportation departments. (8) Market investigation reports such as ‘‘The deep forecast and investment analysis report of the road transportation industry in China’’, ‘‘The deep investigation and prospect consultant analysis report of the high speed railway market in China’’, and ‘‘The investment opportunity and

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development analysis report of the waterway transportation market in China’’, etc. (9) The annual corporation reports such as ‘‘The Boeing Company Annual Report’’, ‘‘Air China Company Annual Report’’ and ‘‘China Southern Airlines Company Annual Report’’.

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Ms. Shuqian Yang is master student at the School of Environment, Beijing Normal University. She is majored in natural resource and environmental economics. She got bachelor’s degree in 2009 majoring in environmental engineering. Her research topic focuses on co-benefit of CO2 reduction, local air pollutants reduction and energy conservation in transportation sector. Qin Liu right now is a foreign affairs officer of Science and Technology Division in the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (2009-today). She got her master degree in 2009 from the School of Environment, Beijing Normal University. Her master degree thesis was focused on energy saving and pollution abatement in transportation industry of China. Jianjun Tu is a senior associate in the Carnegie Energy and Climate Program. He was a nonresident research fellow at the Canadian Industrial Energy End-use Data and Analysis Centre. He served as senior energy and environmental consultant from 2004 to 2011 for M.K. Jaccard and Associates, a premier energy and climate consulting firm in Vancouver. He was a director of marine operations at Sino-Benny LPG from 2001-2004. Dr. Mark Kenneth Jaccard is professor of environmental economics in the School of Resource and Environmental Management (REM) at Simon Fraser University. He served as Chair and CEO of the B.C. Utilities Commission (1992 to 1997), on the Intergovernmental Panel on Climate Change (1993 to 1996; Nobel Peace Prize in 2007), and on the China Council for International Cooperation on Environment and Development (1996 to 2001). Currently, he was a lead author on the Global Energy Assessment (due in 2011), a member of Canada’s National Roundtable on the Environment and the Economy and a special advisor to the Canadian Council of Chief Executives.