A study of carbon dioxide emissions performance of China's transport sector

Energy 50 (2013) 302e314

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A study of carbon dioxide emissions performance of China’s transport sector Guanghui Zhou a, William Chung b, *, Xiliang Zhang a a b

Institute of Energy, Environment and Economy, Tsinghua University, Beijing, PR China Department of Management Sciences, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 July 2012 Received in revised form 9 October 2012 Accepted 30 November 2012 Available online 4 January 2013

Undesirable output-oriented Data Envelopment Analysis (DEA) models with different return of scales are used to study the CO2 (Carbon dioxide) emissions performance of the transport sector throughout China’s 30 administrative regions. Empirical results show that the number of efficient regions has decreased since 2004, hitting the lowest record in 2006, and improving slightly afterwards. The overall average performance rating reached its peak in 2004 and continuously decreased until it hit its lowest record in 2006. Although it slightly increased in 2007, performance rating has decreased since 2008. This pattern is consistent with the policy guidance of the transport sector in the 11th Five-Year Plan (2006 e2010). In general, Eastern China performed better than Central and Western by adjusting undesirable output (CO2). However, Central performed better than Eastern and Western by adjusting both CO2 emissions and desirable outputs. It may be because that transport infrastructure facilities are heavily concentrated in Eastern and spatial clusters. The clusters are like stair steps decreasing from the higher Eastern to Central then to the lower Western. This indicates that the development of transport infrastructure go hand in hand in China. Hence, Eastern may not have too much improvement room by adjusting desirable outputs.
2012 Elsevier Ltd. All rights reserved.

Keywords: Transport sector Carbon dioxide emissions performance DEA

1. Introduction China is the number one producer of Carbon dioxide (CO2) emissions in the world [1]. In 2009, China was responsible for 23.7% of 28,999 Mt CO2 emission of the world, compared with just 5.7% of a total of 15,624 Mt CO2 global emissions in 1973 [2]. China’s transport energy consumption is also responsible for approximately 10.4% and 15.9% of China’s energy consumption and total final energy consumption, respectively; in addition, its CO2 emission occupies nearly 10% of total final energy consumption in 2008. With the worldwide concern on energy and environmental issues as well as sustainable development, energy efficiency and environment performance evaluation has become more critical. In the 11th Five-Year Plan, China announced that the increasing trend of energy intensity must be reversed, such that energy intensity must decrease by more or less 20% in the next five years. The Government also announced strengthened policy guidance on energy conservation and efficient use, as well as on increased energy conservation efforts. * Corresponding author. Tel.: þ852 3442 7057; fax: þ852 3442 0189. E-mail addresses: [email protected], [email protected] (W. Chung). 0360-5442/$ e see front matter
2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.energy.2012.11.045

Zhou et al. [3] provided a detailed overview of energy and environmental-related policies in China in the past years. In the transport sector, China has launched a series of policies from energy savings to environmental protection from 2003 to 2009: For passenger vehicles: In 2004, China enacted “Fuel Consumption Limits for Passenger Vehicles.” The limits are divided into 16 categories based on kerb mass and are subjected to two phases of enforcement implementation (enforced in July 1, 2007 and January 1, 2009, respectively, with retroactive permission to the production line dated back a year ago). These limits are fit to kerb mass less than 3500 kg, less than 9 seats, and a maximum design speed less than 50 km/h on gasoline and diesel passenger cars. In 2005, China enacted the “Notice about Encouraging the Development of Energy Conservation, Environmental Protection, and Small Displacement Vehicles.” The notice encouraged production of and investment in low-fuel consumption, low emission, small displacement, mini and high-power vehicles; promotion of the implementation of national standard fuel consumption limits for operating vehicles; control of the development of high-fuel consumption vehicles from the origin; improvement on technical standards of energy conservation and environmental protection of small-displacement vehicles; and improvement on safety, energy conservation, and environmental protection.

G. Zhou et al. / Energy 50 (2013) 302e314

Nomenclature CO2 MOT DEA DMUs NIRS VRS CRS PEIndex MEIndex TKM PKM TJ tc cu.m MJ tn MWh tCO2 tce GDP

carbon dioxide Ministry of Transport (China) data envelopment analysis decision making units non-increasing returns of scale variant returns of scale constant returns of scale pure environmental index mixed environmental index tonne kilometers passenger kilometers tera joule tonne carbon cubic metre million joule tonne megawatt hour tonne carbon dioxide tonne coal equivalent gross domestic product

Energy consumption tax: In 2006, China enacted a revised consumption tax, which puts a higher tax burden on larger, energyinefficient vehicles. Ministry of Transport (China) (MOT) enacted “Guiding Suggestions on Building Economized Transportation,” which promoted an economized society. In the same year, it enacted “Guiding Suggestions to Implement ‘Decisions on Strengthening Energy Conservation by the State Council.’” In 2007, it enacted the “Suggestions about Further Strengthening Energy Conservation in the Transport Sector,” and “Notice about Carrying out Energy Conservation Demonstration Activity in Transport Sector.” In the same year, the Chinese Government revised the Conservation Law, which clarified the legal basis for the measures identified in the 11th Five-Year Plan and government authority for energy efficiency in transportation. Also in the same year, MOT enacted “Guiding Suggestions on Energy Conservation and Emission Reduction in Port.” Finally, in 2008, it enacted measures that enforced the Conservation Law in both the road and water transport sectors. For light commercial vehicles: In 2007, China enacted “Fuel Consumption Limits for Light Commercial Vehicle,” which is applicable to vehicles with a total mass less than 3500 kg and to light commercial vehicles with 9e12 seats, including light trucks and buses (enforced in February 1, 2008). For operating passenger buses and trucks: In 2008, China enacted and enforced “Fuel Consumption Limits and Measure Method for Operating Passenger Buses,” and “Fuel Consumption Limits for Operating Trucks.” These enforcements are applicable to vehicles that operate on diesel oil and gasoline fuel as well as to passenger buses and trucks with a total mass greater than 3500 kg. These are also applicable to operating trucks, such as 3500 kge31,000 kg trucks and dump trucks, and semi-trailer combination vehicles with a maximum total mass of 49,000 kg. These fuel economy standards are more stringent than those in the United States, Canada and Australia, but are less stringent than those in Europe and Japan. For vehicle emission standards: Since 2007, China has followed European standards for emission requirements. In fact, the Government has implemented the National Phase III (equivalent to Euro III standards) vehicle emission standards. In 2008, MOT enacted “The Long and Mid-term Planning and Outline about

303

Energy Conservation in Road and Water Transport,” choosing road, water transport, and port as key fields. It also put forward overall goals and main tasks in 2015 and 2020. In 2009, the Government enacted “Notice about Demonstration and Promotion of Energy Efficient and Alternative Energy Vehicles.” It also initiated a program called “Ten Cities and Thousand Vehicles,” which aimed at selecting more than 10 cities and introducing more than 1000 alternative energy vehicles in each of the selected cities within three years with support from government subsidy. A total of 25 pilot cities have been selected to participate, and the number is likely to continue to rise since the promotion program is still on pilot mode up to now. In 2009, China enacted “Notice about the Energy Conservation Product Benefit to People Project,” which arranged special funds for government subsidy and supported the promotion of energy conservation products, including energy conservation vehicles. For railways electrification: China propelled railways electrification to reduce the number of oil-based trains. The proportion has increased from 31.2% in 2005 to 46.2% in 2010, and the country’s electrified railways operating mileage is the second in the world [4]. Despite the fact that the rate of increase of energy consumption and emissions has slowed down in recent years, the total absolute quantity is still increasing. Improving energy efficiency and decreasing emissions in the transport sector require more effort. Motivated by the above background information, we study the CO2 emissions performance of China’s transport sector consisting of 30 administrative regions from 2003 to 2009 using the data envelopment analysis (DEA) methodology. A joint production technology is adopted, which treats the undesirable measures by distinguishing between weak and strong disposability. This approach also uses the output directional distance function that explicitly and simultaneously expands desirable outputs and reduces undesirable outputs. This paper is organized as follows. Section 2 is literature review about DEA implemented in environmental performance evaluation, especially the use of output directional distance function method to cope with undesirable outputs. Section 3 describes a series of undesirable, output-oriented DEA models that consider undesirable outputs under different returns to scale. Section 4 presents data about decision-making unit (DMU) selection, non-energy and energy inputs, and desirable and undesirable outputs in the transport sector for 30 provinces, autonomous regions, and municipalities from 2003 to 2009. Section 5 provides some analysis of the CO2 emissions performance of China’s transport sector at the provincial and regional levels. Section 6 discusses the findings and concludes the study. It also provides some current policies and further research suggestions. 2. Literature review Efficiency analysis begins with Farell [5], who first introduce concepts that measure the production efficiency of a DMU relative to the best practice frontier. Charnes et al. [6] assess the relative efficiencies of multi-input and multi-output production units and introduced a powerful methodology that has subsequently been entitled DEA. Cook and Seiford [7] provide a sketch of major research thrusts in DEA over the last three decades since Charnes et al.’s [6] pioneering work. As an effective evaluation method, DEA has gained popularity in energy and environmental (E&E) studies. Zhou et al. [8] review 100 articles that adopted DEA in energy and environmental studies, but many studies do not consider undesirable outputs. Except those, Wang [9] uses DEA to decompose energy productivity change in 23 Organization for Economic Co-operation and Development (OECD) countries between 1980

304

G. Zhou et al. / Energy 50 (2013) 302e314

and 1990, which is based on Shepard output distance function. Wei et al. [10] investigate the energy efficiency change of China’s iron and steel sectors by using DEA-based Malmquist index approach. Mukherjee [11] analyzes the energy efficiency for the aggregate manufacturing sector as well as for the six highest energy consuming 2-digit sectors for the period 1970e2001. Bozoglu et al. [12] examine the energy balance, energy conversion efficiency, and farm-level efficiency of trout and sea bass production in the Black Sea of Turkey during 2005e2006. Lee [13] adopts DEA to evaluate energy performance for cooling by using climate data. Yadav et al. [14] adopt input oriented DEA to evaluate the relative overall efficiency, technical efficiency and scale efficiency of electricity distribution divisions of an Indian hilly state. Mousavi-Avval et al. [15] analyze the efficiency of farmers, discriminate efficient farmers from inefficient ones and identify wasteful uses of energy in order to optimize the energy inputs for apple production in Iran. They further investigate energy use pattern for canola production in Iran in which an input-oriented DEA model is subjected to the data of 130 randomly selected farms to investigate the degree of technical and scale efficiencies of farmers [16]. Lee and Kung [17] use DEA to evaluate building energy performance. They study first adopts cluster analysis to classify the evaluated buildings into different climate clusters, second scale factors are identified by regression analysis. On the other hand, Sarica and Or [18] use DEA to evaluate performance of electricity generation plants in Turkey, where environmental factors are considered through “least squares” methodology (i.e. minimization of the total squared deviation between the curve and data points). Modeling undesirable outputs is necessary in energy and environmental evaluation, and it is done using three perspectives in DEA models. One is to treat undesirable variables as inputs, which is based on the economic argument that both inputs and undesirable outputs incur costs for DMU, and as such, DMUs usually want to reduce both types of variables as much as possible [19,20,21]. Another perspective is the data transformation approach proposed by Seiford and Zhu [22], which integrates undesirable outputs into DEA models through the classification invariance property, where classifications of efficiencies and inefficiencies are invariant to data transformation. The third perspective is the utilization of environmental DEA technology [23,24], which treats undesirable measures by distinguishing between weak and strong disposability; it then uses the output directional distance function that explicitly expands good outputs, while simultaneously reducing undesirable outputs [25,26,27]. This approach is based on the production theory, which has received considerable attention in recent economic literature. Färe et al. [28] introduce an environmental performance indicator by decomposing overall productivity into an environmental index and a productive efficiency index. Tyteca [29] previously provide a comprehensive survey of literature on environmental performance measurement and the later proposes three DEA variants that differ on how they account for undesirable outputs. These three models are later implemented by Tyteca [30] on the same data set used by Färe et al. [31]. In contrast to Tyteca’s works [29,30], where the distance functions are directly used to evaluate environmental performance, Färe et al. [31] propose an environmental performance index, which consists of a pair of ratios of distance functions yielding a ratio of quantity indices. Zhou et al. [32] develop two slacks-based efficiency measures to model environmental performance based on environmental DEA technology. These measures are composite indices that model economic-environmental performance and estimate the impacts of environmental regulations. Zhou et al. [33] present a nonradial DEA approach to measure environmental performance, which consists of a non-radial DEA-based model for multi-lateral environmental performance comparisons and a non-radial Malmquist environmental performance index to model the change of environmental

performance over time. Zhou et al. [34] discuss environmental DEA technologies that exhibit non-increasing returns to scale (NIRS) and variant returns to scale (VRS). Zhou et al. [35] define a non-radial directional distance function, which allows the inputs and outputs to be adjusted non-proportionally, and models energy and CO2 emission performance in electricity generation from the production efficiency point of view. However, extant research on transport energy efficiencies and environmental performance evaluation is rare, especially studies that examine the CO2 emissions performance of China’s transport sector. Ramanathan [36] uses DEA to compare the energy efficiencies of alternative transport modes in India, such as road and rail transport. Ramanathan [37] further extends the application of DEA to estimate energy consumption of rail and/or road transport in India that would result in a pre-specified DEA efficiency.

3. DEA models We adopt the four undesirable output oriented models with different returns of scale presented in [34]. The models are pure and mixed environmental index models denoted by PEIndex and MEIndex respectively. PEIndex only adjusts undesirable outputs and MEIndex adjusts desirable and undesirable outputs simultaneously in order to indicate the CO2 emissions performance. Concerning different returns of scale, Constant Returns of Scale (CRS) means that linearly scale the inputs and outputs without increasing or decreasing efficiency. Non-Increasing Returns of Scale (NIRS) means that linearly scale the inputs and outputs without increasing efficiency. Variant Returns of Scale (VRS) means that linearly scale the inputs and outputs with increasing or decreasing efficiency; see more details in [25]. In short, models with different returns of scale is fit to the evaluate energy and environmental performance of regions under different development stages. For PEIndex models, we adopt CRS, NIRS, and VRS. Due to the development of MEIndex, we only adopt VRS.

3.1. PEIndex under CRS Before describing the models in detail, we first define the joint production technology can be represented as: P ¼ {(xn, xe, yd, yu): (xn, xe) can produce (yd, yu)}, which satisfies the properties (Property 1) below. Property 1: i) Outputs are weakly disposable, i.e., if (xn, xe, yd, yu) ˛ P and 0  d  1, then (xn, xe, dyd, dyu) ˛ P. ii) Desirable outputs and undesirable outputs are null-joint, i.e., if (xn, xe, yd, yu) ˛ P and yu ¼ 0, then yd ¼ 0. Note that CRS environmental DEA technology satisfies the Property 1, and the model is shown below.

PEIndexðCRSÞ ¼ Minq0 s.t. J X j¼1 J X j¼1

lj xnkj  xnk0 ; k ¼ 1; .; K

(1)

lj xelj  xel0 ; l ¼ 1; .; L

(2)

G. Zhou et al. / Energy 50 (2013) 302e314 J X j¼1 J X j¼1

lj ydmj  ydm0 ; m ¼ 1; .; M

(3)

lj yunj ¼ q0 yun0 ; n ¼ 1; .; N

(4)

lj  0; j ¼ 1; 2; .; J

J X

lj ¼ b0 ;

(9)

j¼1

(5)

where the subscript “0” represents the DMU to be evaluated and there are J DMUs exist, whose energy efficiency measures need to be measured. For the jth DMU, xnj ¼ ðxn1j ; xn2j ; .; xnKj Þ represents non-energy inputs, xej ¼ ðxe1j ; xe2j ; .; xeKj Þ represents energy inputs, denotes desirable outputs, and ydmj ¼ ðyd1j ; yd2j ; .; ydMj Þ yunj ¼ ðyu1j ; yu2j ; .; yuNj Þ represents undesirable outputs. Hence, PEIndex(CRS) is an aggregated and standardized environmental performance index, which only adjusts undesirable outputs, and lies in the interval (0,1). If a specific DMU has a larger PEIndex(CRS), it has a better environmental performance under the CRS environmental DEA technology.

0  b0  1; lj  0; j ¼ 1; 2; .; J:

(10)

It can be easily verified that VRS environmental performance index PEIndex(VRS) satisfies Property 2. PEIndex(VRS) is also an aggregated and standardized environmental performance index which lies in the interval (0,1). 3.4. Mixed environmental performance index under VRS Different from the PEIndex models that only adjust undesirable outputs, a mixed environmental performance index (MEIndex) adjusts desirable and undesirable outputs simultaneously, measures the production scale of desirable outputs to undesirable outputs, and indicates the environmental scale size.

MEIndexðVRSÞ ¼ Min

q0 r0

s.t. (1), (2), and

3.2. Pure environmental performance index under NIRS The NIRS environmental performance index PEIndex(NIRS) can be formulated by imposing the restrictions of intensity variables on the CRS environmental performance index. The joint production technology of NIRS can be represented in the same manner as that for CRS since it satisfies the Property 1.

J X j¼1 J X j¼1

PEIndexðNIRSÞ ¼ Minq0 s.t. (1), (2), (3), (4), (5), and

J X

J X

j¼1

lj  1:

305

lj ydmj  r0 a0 ydm0 ; m ¼ 1; .; M

(11)

lj yunj ¼ q0 a0 yun0 ; n ¼ 1; .; N

(12)

lj ¼ 1

(13)

(6)

j¼1

a0  1; lj  0; j ¼ 1; 2; .; J

PEIndex(NIRS) is also an aggregated and standardized environmental performance index which lies in the interval (0,1).

Removing the adjusting parameter a0 would have no influence on its optimal objective value:

3.3. Pure environmental performance index under VRS

MEIndexðVRSÞ ¼ Min

Different from CRS and NIRS, the joint production technology under VRS is represented as: P ¼ {(xn, xe, yd, yu): (xn, xe) can produce (yd, yu)}, which satisfies the Property 2 below. Property 2: i) Outputs are weakly disposable, i.e., if (xn, xe, yd, yu) ˛ P and 0 < d  1, then (xn, xe, dyd, dyu) ˛ P. ii) Desirable outputs and undesirable outputs are null-joint, i.e., if (xn, xe, yd, yu) ˛ P and yu / 0, then yd / 0.

(14)

q0 r0

s.t. (1), (2), (4), (5), (13), and J X j¼1

lj ydmj  r0 ydm0 ; m ¼ 1; .; M:

(15)

The equivalent LP programming can be denoted as:

MEIndexðVRSÞ ¼ Minq0 s.t. (3), (4), (5), (7), (8), (9) MEIndex(VRS) is also an aggregated and standardized environmental performance index which lies in the interval (0,1].

The corresponding model is shown below.

PEIndexðVRSÞ ¼ Min q0

4. Data selection and DMU s.t. (3), (4), and J X j¼1 J X j¼1

lj xnkj



b0 xnk0 ;

k ¼ 1; .; K;

lj xelj  b0 xel0 ; l ¼ 1; .; L;

(7)

(8)

Data are collected from the China Statistical Yearbook “2004e 2010”, China Energy Statistical Yearbook “2004e2010”, and Yearbook of China Transportation and Communications “2004e2010”. With data availability and consistency, the CO2 emissions performance of the transport sector in 30 administrative regions (i.e., provinces, autonomous regions, and municipalities) shown in Table 1 are examined.

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G. Zhou et al. / Energy 50 (2013) 302e314

Table 1 Distribution of 30 administrative regions in the three areas of China. Area

Administrative regions (provinces, autonomous regions, and municipalities)

Eastern

Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan Sichuan, Chongqing, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Guangxi, Inner Mongolia

Central Western

China’s statistical data are classified by industries and sectors. The final energy consumption is divided into the following industries: (1) manufacturing; (2) construction; (3) transport, storage and postal; (4) agriculture, forestry, animal husbandry, fishery and water conservation; (5) wholesale, retail trade and hotels and restaurants; (6) residential consumption; and (7) others. The transport industry and corresponding data in this paper refers to (3) the transport, storage, and postal industries.1 From the data sources, we can have the following input and output data for conducting DEA. Non-energy input: Labor and Capital stock should be the nonenergy inputs. Since data of capital stock is not available in China’s transport sector, we consider labor as the only non-energy input. Labor statistical data are recorded at the end of each year, and the number of employees of the current year is assumed as the average number of employees at the end of the current and previous years.

CO2 emission

factor

¼ Carbonemission

Table 2 Conversion factors for calculating CO2 emissions from different fuel types. Fuel type

Unit

Carbon Carbon CO2 content oxidation emission (tc/TJ) factor (%) factor (kgCO2/TJ)a

Fuel calorific value (MJ/tn or MJ/cu.m)

Raw coal Cleaned coal Other washed Briquettes Coke Coke oven Other gas Crude oil Gasoline Kerosene Diesel oil Fuel oil LPG Natural gas Other petroleum

104tn 104tn 104tn 105tn 106tn 108cu.m 108cu.m 104tn 104tn 104tn 104tn 104tn 104tn 108cu.m 104tn

25.8 25.8 25.8 26.6 29.2 12.1 12.1 20 18.9 19.5 20.2 21.1 17.2 15.3 20

20,908 26,344 8363 20,908 28,435 16,726 5227 41,816 43,070 43,070 42,652 41,816 50,179 38,931 41,816

a

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100

87,300 87,300 87,300 87,300 95,700 37,300 37,300 71,100 67,500 69,700 72,600 75,500 61,600 54,300 72,200

Lower end-point of the 95% confidence interval.

Guangdong, Guangxi, and Hainan); and (5) TKM of pipeline by fuel oil consumption. Undesirable outputs: We consider CO2 emissions as the undesirable output, and calculated the CO2 emission factor (kgCO2/TJ) of different fuel types using the following formulation:

factor *Carbonoxidation factor *CO2 gasification coefficient

Energy input: Transport fuel is diversified in China, and different administrative regions use various forms of transport energy end use. We classify different areas and administrative regions into the following categories for easy comparison: “Coal” (including raw coal, cleaned coal, other washed coal, briquettes, coke, coke oven gas), “Gasoline,” “Kerosene,” “Diesel oil,” “Electricity”, and “Others” (including fuel oil, liquefied petroleum gas, natural gas, other petroleum products, other gas, crude oil, heat, and other energy). The energy unit used is million tonne coal equivalence (Mtce). Desirable outputs: These are transport services that can be classified into passenger kilometers (PKM) and tonne kilometers (TKM) for passenger and freight services, respectively. Note that in China, the PKM of air as well as TKM of air, railway luggage and parcel, ocean, and pipeline are not splintered into the provincial level. In this study, we made the following divisions based on the energy consumption feature of different Chinese transport modes: (1) PKM of air into provincial level by provincial kerosene consumption; (2) TKM of air by provincial kerosene consumption; (3) TKM of railway luggage and parcel by provincial diesel consumption; (4) TKM of ocean by 11 coastal provincial consumption of fuel oil, diesel, and gasoline (these provinces includes Tianjin, Hebei, Liaoning, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian,

1 With its statistical criteria difference, China’s statistical data are classified by industries, not sectors. That is, China’s statistical data on transport only refers to the transport industry, not the entire transport sector; moreover, the transport, storage, and postal industries are bracketed together (See Ref. [38]). Some constituents (for example, the non-operating transport) of energy consumption, manpower and transport services from other industries, such as construction and agriculture, belong to the transport sector; similarly, some constituents from the transport industry belong to other sectors.

(16)

where Carbonemission factor and Carbonoxidation factor are based on [39], and CO2 gasification coefficient is equal to 44/12. Then, we can calculate the CO2 emissions for different fuel types using the following:

CO2 emissions ¼ CO2 emission

factor *Fuel

calorific value;

(17)

where fuel calorific value is based on the China Energy Statistical Yearbook 2010, which may vary in different administrative regions. Table 2 shows the factors for (16) and (17). CO2 emissions from electricity use is considered in the current study using the emission factors of coal-fired power generation (1186 kgCO2/MWh) as well as the regional distribution of percentage of thermal (assumed approximately equal to that of coal-fired), hydro, and nuclear power generation for 2005 as reported by [40]. Table 3 Approximation of CO2 intensity of power generation in different regions. Administrative regions

Thermal (%)

Beijing, Tianjin, Hebei, Liaoning, Shandong, Jilin, Heilongjiang Shanghai, Jiangsu, Zhejiang Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Inner Mongolia Fujian, Guangdong, Hainan Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan Sichuan, Chongqing, Guizhou, Yunnan, Guangxi

99

1

1.17414

94 80

6 20

1.11484 0.9488

77 70

23 30

0.91322 0.8302

55

45

0.6523

Source: [40].

Non-thermal (%, hydro and nuclear)

CO2 intensity (t CO2/MWh)

G. Zhou et al. / Energy 50 (2013) 302e314

Tables 4 and 5 show the summary and average of different types of input and output data for DEA models, respectively.

Table 4 Different inputs and outputs for DEA models. Input

Output

307

Non-energy input (xn) Energy input (xe)

Labor Coal Gasoline Kerosene Diesel oil Electricity Other energy Passenger kilometers (PKM) Tonne kilometers (TKM) CO2 emissions

Desirable output (yd) Undesirable output (yu)

Such emission factors and distributions are assumed to have no significant changes from 2003 to 2009. Hence, these factors and distributions could be used to calculate CO2 emissions from electricity use from 2003 to 2009. Table 3 summarizes the factors and distributions.

Table 5 The average values of inputs and outputs. Variables

Unit\year

Labor Coal Gasoline Kerosene Diesel oil Electricity Other energy PKM TKM CO2 emissions

Thousand person 204.9 217.3 215.5 215.5 217.9 219.2 230.9 Million tce 0.3 0.2 0.3 0.3 0.3 0.3 0.3 Million tce 1.1 1.3 1.7 1.8 1.8 1.9 2.0 Million tce 0.3 0.4 0.5 0.6 0.6 0.7 0.7 Million tce 1.6 1.8 2.5 2.8 3.3 3.8 4.0 Million tce 0.2 0.2 0.2 0.2 0.2 0.2 0.3 Million tce 0.3 0.4 0.6 0.6 0.7 0.9 1.0 Billion 46.0 54.3 58.2 63.9 71.9 77.2 82.7 Billion 179.4 231.4 267.4 296.4 337.9 367.6 407.0 Million tn. 7.6 8.8 11.8 13.0 14.3 15.9 16.9

2003 2004 2005 2006 2007 2008 2009

5. Empirical studies Three belts scheme: Various regions of China are generally agreed to have huge differences, so the study chose regions as the DMUs (Decision Making Units) to examine these differences between them. In the 7th Five-Year Plan (1986e1900), the “three belts” scheme has been used to classify three areas in China, namely, Eastern, Western, and Central areas. Each area consists of different administrative regions. In 2000, Inner Mongolia and Guizhou have been classified into the Western area, because their average GDP growth rates are similar to those of other Western administrative regions. The following facts are also well known: (1) the Eastern area has a higher growth rate and more foreign direct investments than most of the central and western provinces; (2) the Central area is a home base for farming with a large population; and (3) the Western area has comparatively low population density and is the least developed area of the three. However, we note that the regional classification in China for different administrative regions does not need to be based on the official definition stated above. For instance, Chen and Fleisher [41] divide China into two areas (i.e., coastal and non-coastal) in order to study regional income inequality and economic growth. Hu and Wang [42] adopt the old three belts scheme of the 7th Five-Year Plan to study regional energy efficiency. Gelb and Chen [43] use the official Western area to provide a progress report of the Great Western Development Strategy being touted by the Chinese government, more descriptions about regional disparities in China could be found in He and Duchin [44] and Yu et al. [45], which examine regional transportation development in China.

Table 6 DEA results of PEIndex(CRS). Area

Region

2003

2004

2005

2006

2007

2008

2009

Average

E E E E E E E E E E E C C C C C C C C W W W W W W W W W W W Eastern Central Western

Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Sichuan Chongqing Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Guangxi Inner Mongolia (Average) (Average) (Average)

0.2902 1.0000 1.0000 0.2548 1.0000 0.4604 1.0000 1.0000 0.4944 1.0000 1.0000 1.0000 0.3481 1.0000 1.0000 1.0000 1.0000 0.2193 0.6793 0.4787 0.5523 1.0000 0.1785 0.4221 0.2832 1.0000 1.0000 0.3513 1.0000 1.0000 0.7727 0.7808 0.6606

1.0000 1.0000 1.0000 0.3654 1.0000 0.4779 1.0000 0.5585 0.4897 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6214 1.0000 0.3101 0.7273 0.6419 0.3356 1.0000 1.0000 1.0000 0.4821 1.0000 1.0000 0.4722 1.0000 0.3424 0.8083 0.8324 0.7522

0.4958 1.0000 1.0000 0.2625 1.0000 0.6268 1.0000 0.4765 0.2565 1.0000 1.0000 1.0000 0.3930 0.4907 1.0000 1.0000 1.0000 0.2751 1.0000 1.0000 0.3337 0.5613 0.2342 0.4833 0.4878 1.0000 0.2405 0.3289 1.0000 1.0000 0.7380 0.7699 0.6063

0.5013 1.0000 1.0000 0.2768 1.0000 0.7585 1.0000 0.5124 0.2704 1.0000 1.0000 0.3797 0.3484 0.3586 1.0000 1.0000 1.0000 0.2811 1.0000 0.6224 0.3413 0.5169 0.2328 0.5035 0.5470 1.0000 0.2349 0.3232 1.0000 0.2013 0.7563 0.6710 0.5021

0.6742 1.0000 1.0000 0.2959 1.0000 0.7349 1.0000 0.6120 0.2793 1.0000 1.0000 0.4005 0.3255 1.0000 1.0000 1.0000 0.8136 0.4363 1.0000 0.6267 0.3943 0.4954 0.2620 1.0000 1.0000 0.4132 0.2583 0.3800 1.0000 0.2059 0.7815 0.7470 0.5487

1.0000 0.5852 1.0000 0.4412 1.0000 0.3955 1.0000 0.5483 1.0000 1.0000 1.0000 0.3262 1.0000 1.0000 1.0000 1.0000 1.0000 0.2640 0.8055 1.0000 0.3126 1.0000 0.2540 0.4863 1.0000 1.0000 0.3874 0.3425 1.0000 0.2717 0.8155 0.7995 0.6413

1.0000 1.0000 1.0000 0.3338 1.0000 0.3886 1.0000 0.3700 1.0000 1.0000 1.0000 0.7505 1.0000 1.0000 1.0000 1.0000 1.0000 0.2641 0.6545 0.5039 0.3513 0.6339 0.2664 0.3020 1.0000 1.0000 0.4727 0.3178 1.0000 0.1878 0.8266 0.8336 0.5487

0.7088 0.9407 1.0000 0.3186 1.0000 0.5489 1.0000 0.5825 0.5415 1.0000 1.0000 0.6938 0.6307 0.8356 1.0000 0.9459 0.9734 0.2929 0.8381 0.6962 0.3744 0.7439 0.3468 0.5996 0.6857 0.9162 0.5134 0.3594 1.0000 0.4584 0.7856 0.7763 0.6086

308

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Table 7 DEA results of PEIndex(NIRS). Area

Region

2003

2004

2005

2006

2007

2008

2009

Average

E E E E E E E E E E E C C C C C C C C W W W W W W W W W W W Eastern Central Western

Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Sichuan Chongqing Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Guangxi Inner Mongolia (Average) (Average) (Average)

0.2902 1.0000 1.0000 0.2548 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3481 1.0000 1.0000 1.0000 1.0000 0.2193 1.0000 0.4787 0.5523 1.0000 0.1785 0.4221 0.2832 1.0000 1.0000 0.3513 1.0000 1.0000 0.8677 0.8209 0.6606

1.0000 1.0000 1.0000 0.3654 1.0000 1.0000 1.0000 0.5585 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.6214 1.0000 0.3101 1.0000 0.6691 0.3356 1.0000 1.0000 1.0000 0.4821 1.0000 1.0000 0.4722 1.0000 0.3424 0.9022 0.8664 0.7547

1.0000 1.0000 1.0000 0.2635 1.0000 1.0000 1.0000 0.4765 0.4198 1.0000 1.0000 1.0000 0.3930 0.4907 1.0000 1.0000 1.0000 0.2751 1.0000 1.0000 0.3337 0.5613 0.2342 0.4833 0.4878 1.0000 0.2405 0.3289 1.0000 1.0000 0.8327 0.7699 0.6063

1.0000 1.0000 1.0000 0.2953 1.0000 1.0000 1.0000 0.5124 1.0000 1.0000 1.0000 0.3797 0.3484 0.3586 1.0000 1.0000 1.0000 0.2811 1.0000 1.0000 0.3413 0.5169 0.2328 0.5035 0.5470 1.0000 0.2349 0.3232 1.0000 0.2013 0.8916 0.6710 0.5364

1.0000 1.0000 1.0000 0.3194 1.0000 1.0000 1.0000 0.6120 0.3937 1.0000 1.0000 0.4005 0.3255 1.0000 1.0000 1.0000 1.0000 0.4363 1.0000 1.0000 0.3943 0.4954 0.2620 1.0000 1.0000 0.4132 0.2583 0.3800 1.0000 0.2059 0.8477 0.7703 0.5826

1.0000 0.5852 1.0000 1.0000 1.0000 0.4412 1.0000 0.5483 1.0000 1.0000 1.0000 0.3262 1.0000 1.0000 1.0000 1.0000 1.0000 0.2640 0.8330 1.0000 0.3126 1.0000 0.2540 0.4863 1.0000 1.0000 0.3874 0.3425 1.0000 0.2717 0.8704 0.8029 0.6413

1.0000 1.0000 1.0000 0.5165 1.0000 0.4364 1.0000 0.3700 1.0000 1.0000 1.0000 0.7505 1.0000 1.0000 1.0000 1.0000 1.0000 0.2641 0.6871 1.0000 0.3513 0.6339 0.2664 0.3020 1.0000 1.0000 0.4727 0.3178 1.0000 0.1878 0.8475 0.8377 0.5938

0.8986 0.9407 1.0000 0.4307 1.0000 0.8397 1.0000 0.5825 0.8305 1.0000 1.0000 0.6938 0.6307 0.8356 1.0000 0.9459 1.0000 0.2929 0.9314 0.8783 0.3744 0.7439 0.3468 0.5996 0.6857 0.9162 0.5134 0.3594 1.0000 0.4584 0.8657 0.7913 0.6251

Table 8 DEA results of PEIndex(VRS). Area

Region

2003

2004

2005

2006

2007

2008

2009

Average

E E E E E E E E E E E C C C C C C C C W W W W W W W W W W W Eastern Central Western

Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Sichuan Chongqing Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Guangxi Inner Mongolia (Average) (Average) (Average)

0.3167 1.0000 1.0000 0.2637 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.4239 1.0000 1.0000 1.0000 1.0000 0.2246 1.0000 0.4932 0.6416 1.0000 0.2097 0.4764 0.3292 1.0000 1.0000 0.3886 1.0000 1.0000 0.8709 0.8311 0.6853

1.0000 1.0000 1.0000 0.3695 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.3101 1.0000 0.6691 0.3389 1.0000 1.0000 1.0000 0.5022 1.0000 1.0000 0.4932 1.0000 0.3460 0.9427 0.9138 0.7590

1.0000 1.0000 1.0000 0.2635 1.0000 1.0000 1.0000 0.5402 0.4198 1.0000 1.0000 1.0000 0.4386 1.0000 1.0000 1.0000 1.0000 0.2751 1.0000 1.0000 0.3456 1.0000 0.2393 0.4904 0.5330 1.0000 1.0000 0.3444 1.0000 1.0000 0.8385 0.8392 0.7230

1.0000 1.0000 1.0000 0.2953 1.0000 1.0000 1.0000 0.5423 1.0000 1.0000 1.0000 0.4955 0.3682 0.3857 1.0000 1.0000 1.0000 0.2811 1.0000 1.0000 0.3850 1.0000 0.2364 0.5092 0.5860 1.0000 1.0000 0.3341 1.0000 0.2137 0.8943 0.6913 0.6604

1.0000 1.0000 1.0000 0.3194 1.0000 1.0000 1.0000 0.6288 0.3937 1.0000 1.0000 0.5222 0.3755 1.0000 1.0000 1.0000 1.0000 0.4363 1.0000 1.0000 0.4176 0.5607 0.2671 1.0000 1.0000 1.0000 1.0000 0.4022 1.0000 0.2352 0.8493 0.7918 0.7166

1.0000 1.0000 1.0000 1.0000 1.0000 0.4412 1.0000 0.6055 1.0000 1.0000 1.0000 0.3985 1.0000 1.0000 1.0000 1.0000 1.0000 0.2640 0.8330 1.0000 0.3418 1.0000 0.2809 1.0000 1.0000 1.0000 1.0000 0.3942 1.0000 0.2983 0.9133 0.8119 0.7559

1.0000 1.0000 1.0000 0.5165 1.0000 0.4364 1.0000 0.4077 1.0000 1.0000 1.0000 0.7505 1.0000 1.0000 1.0000 1.0000 1.0000 0.2641 0.6871 1.0000 0.4982 1.0000 0.2957 0.3379 1.0000 1.0000 1.0000 0.3801 1.0000 0.2155 0.8510 0.8377 0.7025

0.9024 1.0000 1.0000 0.4326 1.0000 0.8397 1.0000 0.6749 0.8305 1.0000 1.0000 0.7381 0.6580 0.9122 1.0000 1.0000 1.0000 0.2936 0.9314 0.8803 0.4241 0.9372 0.3613 0.6877 0.7072 1.0000 1.0000 0.3910 1.0000 0.4727 0.8800 0.8167 0.7147

G. Zhou et al. / Energy 50 (2013) 302e314

309

Table 9 DEA results of MEIndex(VRS). Area

Region

2003

2004

2005

2006

2007

2008

2009

Average

E E E E E E E E E E E C C C C C C C C W W W W W W W W W W W Eastern Central Western

Beijing Tianjin Hebei Liaoning Shanghai Jiangsu Zhejiang Fujian Shandong Guangdong Hainan Shanxi Jilin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Sichuan Chongqing Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang Guangxi Inner Mongolia (Average) (Average) (Average)

0.3167 1.0000 1.0000 0.2637 0.4363 0.4040 0.7109 1.0000 0.3495 1.0000 1.0000 0.8353 0.4239 0.6205 1.0000 0.4198 1.0000 0.2128 0.5488 0.4597 0.6416 1.0000 0.2097 0.4764 0.3292 1.0000 1.0000 0.3886 0.5835 0.5348 0.6801 0.6326 0.6021

0.5000 1.0000 1.0000 0.3695 0.3569 0.4651 0.5541 0.4841 0.4675 1.0000 1.0000 1.0000 0.6269 1.0000 1.0000 1.0000 0.7699 0.3101 0.6596 0.5798 0.3389 1.0000 1.0000 0.7031 0.5022 1.0000 1.0000 0.4932 1.0000 0.3460 0.6543 0.7958 0.7239

0.4391 1.0000 1.0000 0.2625 0.3866 0.5399 0.6766 0.5402 0.2565 1.0000 1.0000 1.0000 0.4386 1.0000 1.0000 1.0000 0.7863 0.2751 0.6302 0.6108 0.3456 1.0000 0.2393 0.4904 0.5330 1.0000 1.0000 0.3444 1.0000 0.3024 0.6456 0.7663 0.6242

0.4099 1.0000 1.0000 0.2768 0.4173 0.6146 0.6713 0.5423 0.2704 0.5660 1.0000 0.4955 0.3682 0.3857 1.0000 1.0000 0.7673 0.2811 0.7609 0.5634 0.3850 1.0000 0.2364 0.5092 0.5860 1.0000 1.0000 0.3341 1.0000 0.2137 0.6153 0.6323 0.6207

0.5294 1.0000 1.0000 0.2959 0.3712 0.6713 0.6753 0.6288 0.2793 0.5632 1.0000 0.5222 0.3755 1.0000 1.0000 1.0000 0.7999 0.4363 0.7574 0.5755 0.4176 0.5607 0.2671 1.0000 0.8005 1.0000 1.0000 0.4022 1.0000 0.2352 0.6377 0.7364 0.6599

0.4758 0.7580 0.9378 0.4248 1.0000 0.3955 1.0000 0.6044 0.4395 0.5708 1.0000 0.3985 0.8403 1.0000 1.0000 1.0000 1.0000 0.2640 0.7800 0.5544 0.3418 1.0000 0.2809 1.0000 0.9124 1.0000 1.0000 0.3942 1.0000 0.2983 0.6915 0.7854 0.7075

0.4943 1.0000 0.9083 0.3148 0.6231 0.3886 0.8822 0.4077 0.3240 0.5357 1.0000 0.7505 0.7709 1.0000 1.0000 1.0000 0.8159 0.2641 0.6279 0.4783 0.4686 1.0000 0.2957 0.3379 0.8992 1.0000 1.0000 0.3801 1.0000 0.2155 0.6253 0.7787 0.6432

0.4522 0.9654 0.9780 0.3154 0.5131 0.4970 0.7386 0.6011 0.3410 0.7480 1.0000 0.7146 0.5492 0.8580 1.0000 0.9171 0.8485 0.2919 0.6807 0.5460 0.4199 0.9372 0.3613 0.6453 0.6518 1.0000 1.0000 0.3910 0.9405 0.3066 0.6500 0.7325 0.6545

This paper adopts the official classification to make our results more relevant for policymakers. Table 1 shows the classification of the 30 administrative regions for study, and we will present some results area by area. All DEA models were solved using MATLAB 2009a.

(2) Central Area: Anhui and Henan consistently achieved 100% performance, while Hubei obtained the lowest average of 29.3%. (3) Western Area: Guangxi consistently achieved 100% performance, while Yunnan obtained the lowest average of 34.7%.

5.1. Results of DEA models

From Table 8 of PEIndex(VRS) results:

From Table 6 of PEIndex(CRS) results, we can have the following observations: (1) Eastern Area: Hebei, Shanghai, Zhejiang, Guangdong, and Hainan consistently achieved 100% performance, while Liaoning obtained the lowest average of 31.9%. (2) Central Area: Anhui consistently achieved 100% performance, while Hubei obtained the lowest average of 29.3%. (3) Western Area: Guangxi consistently achieved 100% performance, while Yunnan obtained the lowest average of 34.7%.

(1) Eastern Area: Tianjin, Hebei, Shanghai, Zhejiang, Guangdong, and Hainan consistently achieved 100% performance, while Liaoning obtained the lowest average of 43.3%. (2) Central Area: Anhui, Jiangxi, and Henan consistently achieved 100% performance, while Hubei obtained the lowest average of 29.4%.

Number of efficient regions 25

From Table 7 of PEIndex(NIRS) results: (1) Eastern Area: Hebei, Shanghai, Zhejiang, Guangdong, and Hainan consistently achieved 100% performance, while Liaoning obtained the lowest average of 43.1%.

20 PEIndex(CRS)

15

PEIndex(NIRS) Table 10 Summary of the best and worst regions.

MEIndex(VRS)

Area

Best performer

Worst performer

Eastern

Hebei; Shanghai; Zhejiang; Guangdong; Hainan Anhui Guangxi; Qinghai; Ningxia

Liaoning

Central Western

PEIndex(VRS)

10

5

0 Hubei Yunnan; Inner Mongolia

2003

2004

2005

2006

2007

2008

2009

Fig. 1. Number of efficient regions.

310

G. Zhou et al. / Energy 50 (2013) 302e314

5.2. Number of efficient regions

Average performance rating 0.9

0.85 0.8 PEIndex(CRS)

0.75

PEIndex(NIRS) 0.7

PEIndex(VRS) MEIndex(VRS)

0.65 0.6

Fig. 1 shows that the number of CO2 emissions efficient regions has been decreasing since 2004, hitting the lowest record in 2006, and then slightly increasing afterwards. Similarly, Fig. 2 shows that the overall average rating for CO2 emissions performance peaked in 2004, decreased and hit the lowest record in 2006, increased slightly and then decreased again since 2008. These results are consistent with the policy guidance stated in the 11th Five-Year Plan (2006e2010), through which the trend of energy intensity increase is reversed, energy conservation and environmental

(a)

0.55 2003

2004

2005

2006

2007

2008

0.9

2009

Fig. 2. Overall average performance rating.

PEIndex(CRS) by area

0.85 0.8 0.75

(3) Western Area: Qinghai, Ningxia, and Guangxi consistently achieved 100% performance, while Yunnan obtained the lowest average of 36.1%. From Table 9 of MEIndex(VRS) results:

0.7 0.65 0.6 0.55 0.5

(1) Eastern Area: Hainan consistently achieved 100% performance, while Liaoning obtained the lowest average of 31.5%. (2) Central Area: Anhui consistently achieved 100% performance, while Hubei obtained the lowest average of 29.2%. (3) Western Area: Qinghai and Ningxia consistently achieved 100% performance, while Inner Mongolia obtained the lowest average of 30.7%.

0.45 2003 2004 2005 2006 2007 2008 2009 Eastern

(b)

Central

Western

PEIndex(NIRS) by area

0.95 It should be noted that Hubei obtained the lowest average in all models. Table 10 is the summary to the regions with the best and worst CO2 emissions performance by area.

0.85 0.75

Table 11 Summary of energy and environmental policies in transport sector. Year

Energy and environmental policy in transport sector

2004 2005

 Fuel consumption limits for passenger vehicles  Notice about encouraging the development of energy conservation, environmental protection, and small displacement vehicles  Guiding suggestions on building economized transportation  Guiding suggestions to implement “Decisions on strengthening energy conservation by the state council”  Suggestions about further strengthening energy conservation in the transport sector  Notice about carrying out energy conservation demonstration activity in transport sector  Guiding suggestions on energy conservation and emission reduction in port  Fuel consumption limits for light commercial vehicle  The government has implemented the national phase III vehicle emission standard  Fuel consumption limits and measure method for operating passenger buses  Fuel consumption limits for operating trucks  The long and midterm planning and outline about energy conservation in road and water transport  Notice about demonstration and promotion of energy efficient and alternative energy vehicles (initial program of ten cities and 1000 vehicles)  Notice about the energy conservation product benefit to people project  China propelled railways electrification to reduce the number of oil-based trains

2006

2007

2008

2009

0.65 0.55 0.45 2003 2004 2005 2006 2007 2008 2009 Eastern

Central

Western

(c) PEIndex(VRS) by area 1 0.95 0.9 0.85 0.8 0.75 0.7 0.65 0.6 2003 2004 2005 2006 2007 2008 2009 Eastern

Central

Western

Fig. 3. (a). PEIndex(CRS) by area. (b). PEIndex(VRS) by area. (c). PEIndex(VRS) by area.

G. Zhou et al. / Energy 50 (2013) 302e314

protection efforts are increased, and a series of new policies in the transport sector is implemented. Table 11 summarizes that the key policies were implemented in 2007e2008. However, another decrease in 2008 shows that CO2 emissions performance in some regions worsened, and did not operate at their environmental scale size based on the mixed environmental performance index.

311

MEIndex(VRS) 0.85 0.8 0.75 Eastern 0.7

5.3. Performance of different areas

Central

0.65

On average, following the PEIndex results, Eastern performed better than Central which performed better than Western. However,

Western

0.6 0.55 2003

(a)

2004

2005

Eastern Area

2006

2007

2008

2009

Fig. 5. MEIndex(VRS)by area.

Index (2003 = 100)

240

180

based on MEIndex results, Central performed better than Western which performed better than Eastern. It implies that Central performed better in adjusting desirable and undesirable outputs simultaneously than other areas. Fig. 3(a, b, c) describe the pure environmental performance index with different returns of scale of the three areas, respectively. Except in some years, Central Area performed better than Eastern Area with constant returns of scale. In general, Eastern Area performed better than Central Area and Western Area. In addition, from Fig. 4(a, b, c) we can observe that the growth of CO2 emissions was in line with that of energy consumption. Hence, Eastern always performed better because the latter are the least developed areas with relatively backwards transport facilities mentioned Yu et al. [45]. Fig. 5 describes the mixed environmental performance index shows that Central was better than that of Eastern and Western. From Fig. 4(a, b, c), the growth of Central’s TKM is faster than that of Eastern’s and Western’s. Hence, the MEIndex of Central was better than that of Eastern and Western since MEIndex shows the performance of adjusting desirable and undesirable outputs simultaneously.

130

5.4. Regions’ performance below 50%

220 200 180 160 140 120 100 2003 2004 2005 2006 2007 2008 2009 Labor

Energy Use

PKM

TKM

(b)

CO2 emissions

Central Area

Index (2003 = 100)

280 230

80 2003 2004 2005 2006 2007 2008 2009 Labor

Energy Use

PKM

TKM

(c)

CO2 emissions

From PEIndexes, we can find that the average CO2 emissions performance of (E) Liaoning, (C) Hubei, (W) Chongqing, Yunnan, Xinjiang and Inner Mongolia are below 50% in all PEIndex models. Figs. 6 and 7 describe the percentage of energy use, PKM, TKM, and CO2 emissions of Anhui and some low performance regions to whole country respectively. To make a comparison, Anhui has a high

Western Area

Anhui 6%

230

5% 4%

180

Percenage

Index (2003 = 100)

280

130

3% 2% 1%

80 2003 2004 2005 2006 2007 2008 2009 Labor

Energy Use

PKM

TKM

CO2 emissions

Fig. 4. (a). Growth of inputs and outputs in eastern area. (b). Growth of inputs and outputs in central area. (c). Growth of inputs and outputs in western area.

0% 2003

2004

Energy use

2005 PKM

2006 TKM

2007

2008

2009

CO2 emissions

Fig. 6. Percentage of energy use, PKM, TKM, and CO2 emissions of Anhui to whole country.

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G. Zhou et al. / Energy 50 (2013) 302e314

performance, and unit score with different returns of scale. The figures show that the percentage of Anhui’s CO2 emissions to whole country is less than that of Anhui’s PKM and TKM to whole country. However, the percentage of those low performance regions’ CO2 emissions to whole country is more than the percentage of those low performance provinces’ PKM and TKM to whole country. The results show that the energy intensity of those regions is too high, so cause plenty of CO2 emissions. However, from MEIndex(VRS), it shows that the average environmental performance of (E) Beijing, Liaoning, Jiangsu, and Shandong, (C) Hubei, (W) Chongqing, Yunnan, Xinjiang, and Inner Mongolia are inefficient with the performance below 50%. We can found those regions showing inefficient with the performance below 50% under PEIndex are also showing inefficient with the performance below 50% under MEIndex. It is worth to note that except some regions in Western area has low environmental scale size (the production scale of desirable outputs to undesirable outputs), some developed regions in Eastern area also have low environmental scale size, such as Beijing, the Capital of China, a huge city with developed road, rail, and air transportation. The

vehicle population of Beijing increases from 2.58 million in 2005 to nearly 5 million in 2010. Now Beijing becomes the most serious traffic congestion city in China, which imposes serious influence to energy efficiency and environment performance. Frequent haze weather caused by transportation pollution has become an important environmental problem in Beijing now. Liaoning is a coastal province, and is one of the China old industrial bases in Northeast area. It is the access to the sea of Northeast area, and Dalian, a city in Liaoning province is an international shipping center. In addition, its vehicle populations reach 5.35 million in 2010. Jiangsu and Shandong are two coastal and industrial provinces located in Eastern area. Road transportation is main mode in these two provinces, which has serious effect to environmental performance. Shandong’s vehicle populations reach 20.37 million in 2010, which rank number one in China. Jiangsu’s vehicle populations reach 14.58 million in 2010. Located in the Central area, Hubei is a province along the Yangtze River with developed water, road transportation. Hubei’s vehicle populations reach 7.24 million in 2010.

Liaoning

Hubei

8%

8% 7% 6%

Percentage

Percentage

6% 4% 2%

5% 4% 3% 2% 1%

0% 2003 2004 2005 2006 2007 2008 2009 Energy use

PKM

TKM

0% 2003 2004 2005 2006 2007 2008 2009

CO2 emissions

Yunnan 4%

3%

4%

Percentage

Percentage

Chongqing 3%

2% 2% 1% 1%

3% 3% 2% 2% 1% 1% 0%

0%

2003 2004 2005 2006 2007 2008 2009

2003 2004 2005 2006 2007 2008 2009

Xinjiang

Inner Mongolia

3%

5% 5% 4%

2%

Percentage

Percentage

3%

2% 1%

4% 3% 3% 2% 2% 1%

1%

1% 0%

0% 2003 2004 2005 2006 2007 2008 2009

2003 2004 2005 2006 2007 2008 2009

Fig. 7. Percentage of energy use, PKM, TKM, and CO2 emissions of some low performance regions to whole country.

G. Zhou et al. / Energy 50 (2013) 302e314

6. Conclusion 6.1. Concluding remarks In this study, we employ undesirable-output-oriented DEA models to study the CO2 emissions performance of China’s transport sector from 2003 to 2009. Thirty administrative regions are considered. Environmental DEA technology is adopted with different returns of scale. From the empirical results, the number of environmentally efficient regions has been decreasing since 2004, hitting the lowest record in 2006, and then slightly increasing afterwards. The overall average performance rating peaked in 2004, decreased and hit the lowest record in 2006 and, after a slight increase, decreased again since 2008. The results are consistent with the policy guidance stated in the 11th Five-Year Plan (2006e 2010), in which a series of new policies is enacted in the transport sector. In general, Eastern performed better than Central Area and Western Area based on the pure environmental performance index that only adjusts undesirable output. The Eastern Area performed worse than the Central and Western Areas, except in 2003 and 2005, and the Central Area performed better than Western area based on the mixed environmental performance index that adjusts both desirable and undesirable outputs simultaneous. As expected, the distinguishing power of MEIndex is bigger than that of PEIndex mentioned in [34]. However, it is difficult to have a simple conclusion that MEIndex is better than PEIndex. As shown in our results, PEIndex indicates that Eastern is the best area while MEIndex shows that Central is the best. Further investigation shows that Eastern is the best in adjusting CO2 emissions (not include TKM and PKM) because transport infrastructure facilities are heavily concentrated in Eastern and spatial clusters. The clusters are like stair steps decreasing from the higher Eastern to Central then to the lower Western. This indicates that the development of transport infrastructure go hand in hand in China [45]. Hence, Eastern may not have more room to have an improving adjustment in desirable outputs (TKM and PKM). 6.2. Current policies and further research China is entering the 12th Five-Year Plan period (2011e2015), and is currently building an energy-efficient, environmentfriendly society. The Chinese Government endeavors to conserve energy and reduce emissions through a series of new Plan. Prior to the 2009 UN climate change conference in Copenhagen, Denmark, China has announced that the target CO2 emission reduction per unit GDP in 2020 would be 40%e45% compared with the 2005 level [46]. A series of new policies have been launched. In November 2010, for passenger vehicles, China enacted the “Fuel Consumption Evaluation Method and Standards for Passenger Vehicles” (the third phase), which is enforced from January 2012. It aims to decrease average fuel consumption for passenger vehicles by 15% compared with 2006, and reach a rate of 7L/100 km around 2015. Vehicle manufacturers, not the single vehicle type, are chosen as evaluation objects, and their fuel consumption targets are constructed. Enforcement steps start from 2012, wherein the ratios of “each vehicle manufacturer average fuel consumption” to “vehicle manufacturers average fuel consumption” target are as follows: 2012: 109%; 2013: 106%; 2014: 103%; 2015 and afterwards: 100%. In November, 2011, for heavy commercial vehicles, the Chinese Government enacted the “Fuel Consumption Limits for Heavy Commercial Vehicle,” to be enforced in 2012 for maximum designed total mass of over 3500 kg trucks (not including dump trucks), buses (not including city buses), and semi-trailer towing vehicles. In 2012, China revised the “Ambient Air Quality Standard,” which added PM2.5 and ozone (O3) concentration limits to the 8-h

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monitoring index in order to enhance vehicle pollution prevention and control, and improve both vehicle fuel quality and emission standards. On the other hand, new DEA analysis should be conducted further if there are new polices related to the spatial aspects. i) Transportation infrastructure construction is strengthened (such as passenger rapid transit system, city and rural transportation system). The regional imbalances are narrowed in transport sector, such as relatively backwards transport facilities in Central and Western Area. ii) The conflict of Central and Local governments is reduced by push local government on the environment front. Indeed, the conflict of polices between central and local governments was an important factor to have different regional performance. For example, Zhang [47] mentioned that since 1990, China cities have put restrictions on small vehicles (with engines of less than 1.3 L). Local governments viewed small cars as slower, less reliable, and more polluting. However, it may not be true now because manufacturers of small cars have upgraded their engines so that they are much more energy efficient and environmentally friendly. Hence, the central government began promoting energy-efficient small cars since 1996. In early January 2006, it was reported that 84 cities in 22 provinces still had such restrictions on small cars. These restrictions different from one region to another: some regions took the form of limiting routes open to small cars and/or banning the use of small cars as taxis. iii) Policies are related to encourage public transportation, and control individual transportation, guide huge traffic population and congestion in some huge cities in Eastern Area. Acknowledgments Financial support for Guanghui Zhou’s work came from the National Postdoctoral Science Foundation of China (Grant No. 2011M500340). The authors thank the referees for their valuable suggestions, which greatly improved the paper. References [1] Hu JL, Lee YC. Efficient three industrial waste abatements for regions in China. International Journal of Sustainable Development and World Ecology 2008; 15(1):132e44. [2] IEA (International Energy Agency). Key world energy statistics 2011. Paris: OECD/IEA; 2011. [3] Zhou N, Levine MD, Price L. Overview of current energy efficiency policies in China. Energy Policy 2010;38(11):6439e52. [4] NBSC (National Bureau of Statistics of China). Economic and social development achievements series report in 11th Five-Year Plan, seventh: outstanding achievements in transportation. Available from: http://www.stats.gov.cn/tjfx/ ztfx/sywcj/t20110304_402707886.htm; 2011 [Chinese edition]. [5] Farrell MJ. The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A (General) 1957;120:253e81. [6] Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978;2(6):429e44. [7] Cook WD, Seiford LM. Data envelopment analysis (DEA) e thirty years on. European Journal of Operational Research 2009;192(1):1e17. [8] Zhou P, Ang BW, Poh KL. A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research 2008; 189(1):1e18. [9] Wang C. Decomposing energy productivity change: a distance function approach. Energy 2007;32(8):1326e33. [10] Wei YM, Liao H, Fan Y. An empirical analysis of energy efficiency in China’s iron and steel sector. Energy 2007;32(12):2262e70. [11] Mukherjee K. Energy use efficiency in US manufacturing: a nonparametric analysis. Energy Economics 2008;30(1):76e96. [12] Bozoglu M, Ceyhan V. Energy conversion efficiency of trout and sea bass production in the black sea turkey. Energy 2009;34(2):199e204. [13] Lee WS. Benchmarking the energy performance for cooling purposes in buildings using a novel index-total performance of energy for cooling purposes. Energy 2010;35(1):50e4.

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