Regional allocation of carbon emission quotas in China: Evidence from the Shapley value method

Energy Policy 74 (2014) 454–464

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Energy Policy journal homepage: www.elsevier.com/locate/enpol

Regional allocation of carbon emission quotas in China: Evidence from the Shapley value method Yue-Jun Zhang b,c,n, Ao-Dong Wang a,d, Ya-Bin Da a,d a

School of Management and Economics, Beijing Institute of Technology, Beijing 100081, PR China Business School of Hunan University, Changsha 410082, PR China c Center for Resource and Environmental Management, Hunan University, Changsha 410082, P R China d Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, PR China b

H I G H L I G H T S








The The The The The

paper allocates carbon quotas given the collaboration among regions in China. Shapley value method coupled with the entropy and gravity models is adopted. regions with higher GDP, carbon outflow and reduction connection allocate more. Central region has the largest radiation effect on others among all regions. collaboration of the Central and Northern coast regions should have priority.

art ic l e i nf o

a b s t r a c t

Article history: Received 4 June 2014 Received in revised form 6 July 2014 Accepted 12 August 2014 Available online 1 September 2014

It is an important task for China to allocate carbon emission quotas among regions so as to realize its carbon reduction targets and establish the national cap-and-trade carbon market. Meanwhile, it is supposed to be cost-effective to jointly reduce China's carbon emissions through some collaborative activities among regions. Then a natural question is how to allocate the quotas among regions in light of the collaboration. For this purpose, the Shapley value method is adopted and the results show that, first, the regions with higher GDP, higher carbon outflow and higher carbon reduction connection should be allocated more carbon quotas. Moreover, when the collaboration is considered, the optimal allocation of carbon quotas among regions will change significantly compared to the basic quotas by the entropy method; and the Central region is allocated the largest proportion of carbon quota among regions, which indicates its largest radiation effect. Besides, the collaboration between the Central region and Northern coast region, and that between the Central region and the Eastern region should be paid close attention. These results may provide insightful support for decision makers to promote collaborative carbon reduction and allocate carbon quotas in China. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Carbon emissions Shapley value Collaborative emission reduction

1. Introduction It is an important concern in the international society that the accumulation of greenhouse gases in the atmosphere has caused observed global warming and climate change. It is estimated that, among all the greenhouse gases, the contribution of CO2 to atmospheric warming exceeds 50% (IPCC, 2007). Therefore, the priority has been given to control CO2 emissions to relieve the impact of global greenhouse gases (Acaravci and Ozturk, 2010; Zhang et al., 2011). As the largest developing country and also the

n

Corresponding author. Tel./fax: þ 86 731 88822899. E-mail address: [email protected] (Y.-J. Zhang).

http://dx.doi.org/10.1016/j.enpol.2014.08.006 0301-4215/& 2014 Elsevier Ltd. All rights reserved.

biggest energy consumer and carbon emitter in the world, China's action on carbon emission reduction would be of great significance. In 2009, Chinese government has committed to decrease its carbon emissions per unit of GDP (i.e., carbon intensity) by 40–45% by 2020 based on the 2005 level (Zhang et al., 2014). However, the most important premise for this commitment is to reasonably allocate the national target among provinces or regions. Moreover, the National Development and Reform Commission (NDRC) of China has proposed to establish the national carbon trading markets during the 13th Five-Year Plan period (2016– 2020), but its foundation also is to allocate the carbon emission quotas among provinces, based on which China can then form the market supply and demand and guarantee stable and sustainable carbon trading. However, the fact is that China's regional

Y.-J. Zhang et al. / Energy Policy 74 (2014) 454–464

development proves fairly imbalanced in terms of economic level, natural resources endowment, historical emissions, geographical factors and so on (Yu et al., 2012). Therefore, how to design a scientific, effective and feasible method to allocate carbon emission quotas among regions has become an important and urgent task for Chinese central government currently. Besides, it is known that establishing regional alliances to achieve collaborative carbon reduction is an important path to address climate change, not only among countries but also among provinces or regions within a country. In fact, carbon emission reduction conforms to the common interests of all regions in a country, which is the foundation of regional alliances and collaborative carbon emissions reduction. Also, the collaboration of carbon reduction is helpful to share the related resources and curb the costs to cut carbon emissions. Just as the famous Stern Review indicates that the cost of emission reduction may be 1% of GDP if all the countries collaborate and realize the highest carbon reduction potential, but the cost may increase 80% if there are no collaborations (Stern, 2007). As a matter of fact, the regional collaboration in emission reduction has started in China. For example, China's NDRC and other six ministries jointly issued a document named “Implementation Rules for the Control of Air Pollution in Beijing–Tianjin–Hebei and Surrounding Regions” on September 17, 2013,1 which required the six provinces, including Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia and Shandong, should take comprehensive measures to vigorously strengthen the collaborative control and reduction of pollutants and to conscientiously boost air quality. On December 26, 2013, the environmental protection authorities and related enterprises of those six provinces jointly set up an industrial alliance for energy saving and low-carbon environmental protection, which marks a new era of China's inter-regional collaboration in the resource and environment field. The alliance will make efforts to conduct three aspects of work, including forming the policy systems for regional interactive development; supporting the external radiation of Beijing's science and technology resources and the reasonable layout of enterprises' industrial chains; and establishing the integrated markets among regions to break their administrative barriers and play the role of market mechanisms in regional collaboration.2 For another example, the environmental trading institutions of fifteen provinces jointly set up the Chinese Environmental Trading Institution Alliance under the guidance of NDRC in Beijing on January 8, 2014, which will effectively promote the development of China's national carbon trading market.3 In brief, the inter-regional alliances for carbon emission reduction are of great significance to share technical resources, support enterprises' industrial chain layout, build regional integrated market and cut emission reduction costs. However, the outcome of regional collaboration for carbon emission reduction is still unknown because the attempt has just started out; and there still lacks relevant quantitative research about how to regulate the collaborative mechanisms for carbon emission reduction, what influence the collaboration may exert on carbon emission reduction; and in particular, how to allocate the carbon quota in light of regional collaboration in China. These questions prove fundamental not only for the realization of the national carbon emission reduction targets but also for the establishment of the cap-andtrade carbon market mentioned above. And this paper aims to answer these questions. It should be noted that there has been a body of literature considering the allocation of carbon quotas among provinces or

1 2 3

http://www.zhb.gov.cn/gkml/hbb/bwj/201309/t20130918_260414.htm. http://news.xinhuanet.com/energy/2013-12/27/c_125921791.htm. http://news.xinhuanet.com/energy/2014-01/20/c_126029253.htm.

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regions in China, and most of them adopt the multi-attribute decision making method, and the results often appear subjective and the indicators tend to be one-fold and isolated in this way (Yi et al., 2011). Therefore, we use the entropy method to allocate carbon emission increments from 2011 to 2020 among provinces, which proves more objective and comprehensive. Notwithstanding, the entropy method basically cannot explain the situation of regional collaboration to reduce carbon emissions. In order to consider the effect of China's regional collaboration on carbon reduction by 2020, we adopt the cooperative game theory to calibrate the results of the entropy method. Specifically, based on the carbon emission quota allocation results by the entropy method, we further use the Shapley value method to reallocate carbon emission quotas for China's eight regions in light of their collaborative activities and investigate the radiation effect of each region, based on which some policy insights are offered for relevant decision makers. Therefore, the main contribution in this paper is that we use the Shapley value method to allocate the carbon emission quotas among regions in China, in view of their collaborative activities in carbon emission reduction. The rest of the paper is organized as follows. Section 2 reviews relevant literature. Section 3 introduces data definitions and methodologies. Section 4 puts forward the results and discussions. And the conclusions and policy implications are given in Section 5.

2. Relevant literature review China has been the largest carbon emitter in the world since 2008 for its coal dominated energy structure and continuous economic boom (BP, 2013), which has attracted extensive attention from academia, politicians and the public (Peters et al., 2007; Liang et al., 2013). China's effective carbon emission reduction is of strategic significance to mitigate global climate change and drive sustainable social economic development (Den Elzen et al., 2011; Van Ruijven et al., 2012). Therefore, a wealth of literature has raised the discussion about how to allocate carbon quotas among provinces or regions in China to realize the national 40–45% carbon intensity reduction target. However, there are some differences in the principles of carbon quota allocation, which mainly include the following categories. The first category is based on the grandfather principle. Rose et al. (1998) propose the grandfather principle for the problem of global climate change, which allocates free carbon quotas based on the historical emissions and may break the Polluter Governance Principle and generate the distortion of incentives easily. The second category mainly considers the equity concern. For example, Ringius et al. (2002) allocate carbon quotas focusing in particular on three different but complementary notions of distributive fairness: equality, equity and exemption (for parties who lack the capacity to contribute). Park et al. (2012) allocate initial carbon quotas by the Boltzmann distribution method and provide the most probable allocation among multiple countries. The concept of most probable in the physical sciences may be translated into fairness in quota allocation, as the distribution provides a natural and undistorted allocation among participating countries. And some studies take into account the influence of economic structure along with the equity. For example, Gupta and Bhandari (1999) consider that equal per capita emissions, with simple and transparent adjustments for the short-and mediumterm, could be the most equitable basis for allocating emissions rights. Kemp-Benedict (2008) proposes the greenhouse development right framework, a right-based allocation framework, and takes the individual as the basis for national allocations. Moreover, some studies shed light upon the emission reduction capability and obligation principle along with the equity. For example,

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Beckerman and Pasek (1995) argue that the per capita allocation principle should be equally treated with the capability and obligation of a country, given that everyone has the same right to get carbon quotas and those countries with stronger capability should bear more responsibility for carbon emission reduction. Wu et al. (2013) present a modified DEA model for initial emission quota allocation in the cap-and-trade system and the results show that the reduction and reallocation mechanism is fair. Recently, Pan et al. (2014) proposes an allocation scheme based on the cumulative emission per capita to achieve a globally equitable carbon emission space. The third category allocates carbon quotas using the multiattribute methods or models. Some studies allocate carbon quotas given the equity and economic efficiency attributes together. For example, Wei et al. (2012) investigate China's provincial carbon quota allocation from the perspectives of equity and efficiency, and present the optional solutions preferential to equity, to efficiency or to equity and efficiency equally, respectively. Also, some studies allocate carbon quotas on the principles of capability, responsibility and potential jointly. For example, Baer et al. (2007) employ a multi-attribute model to measure carbon emission reduction capability and responsibility, and then allocate carbon emission quotas based on the measurement. Phylipsen et al. (1998) develop a weighted average model based on the comprehensive attributes including per capita carbon emissions, per capita GDP and carbon emissions per unit of industrial value-added. Yi et al. (2011) allocate carbon quotas in light of capability, responsibility and potential with four optional solutions preferential to the three attributes respectively as well as that with equal weights. Zhou et al. (2013) allocate the carbon quotas among provinces based on five attributes, i.e., CO2 emissions, energy consumption, population, GDP and per capita GDP, and then develop a nonlinear programming model to evaluate the economic performance of inter-provincial emission quota trading. The fourth category analyzes carbon emission from the decomposition perspective. For example, Liu et al. (2012) analyze the contribution to greenhouse gas emissions of different sectors by the LMDI approach according to China's carbon emission reduction plan, and also detect the driving factors of greenhouse gases increments. Yu et al. (2014) employ the Shapley value method to decompose the total carbon emissions into an interactive result of four components (i.e., emissions from primary, secondary, and tertiary industries, and from residential areas), which are composed totally by 13 macro influential factors according to the Kaya identity. Besides, Zhang and Da (2013) adopt the PDA decomposition approach to analyze the driving factors of carbon emission increase in China. The fifth category considers the inter-regional carbon outflow. For example, Chen et al. (2013) propose the life cycle carbon emission flow analysis theory of carbon emission embodied in trade and allocate the environmental responsibility among different regions, so as to guarantee the balanced inter-regional development. Recently, from the point of carbon export, Kander and Jiborn (2014) consider the influence of carbon emissions embodied in imports and exports and analyze carbon export efficiency in Sweden, indicating that exports from Sweden are more carbon efficient compared with many other countries. Besides, it should be noted that CO2 emissions quotas can also be allocated from the efficiency perspective. For example, Choi et al. (2012) use the nonparametric efficiency analysis technique to estimate the energy efficiency, potential emission reduction and marginal abatement costs of energy-related CO2 emissions in China. Wang et al. (2012) estimate the environmental efficiency, economic efficiency, economic environmental efficiency and twostage efficiency of different provinces in China by considering CO2 emissions. In fact, the results in these studies may also constitute

the bases of carbon quota allocation among provinces or regions in China. In addition, a few studies investigate the allocation of carbon emission quotas by the Shapley value based on the cooperative game theory. The Shapley value is a method proposed by Lloyd Stowell Shapley to solve the n-person cooperative game (Shapley, 1953). In fact, as a right for development, carbon quota is suitable to be studied by the Shapley value. For example, through dividing the world into four regions, Filar and Gaertner (1997) allocate carbon quotas by the Shapley value among those four regions. Li and Piao (2013) explore the cost allocation of joint carbon emission reduction in Beijing–Tianjin–Hebei by the Shapley value and confirm the rationality of collaborative mechanisms. Based on the previous literature, we may allocate carbon emission quotas for China's eight geographical regions in this paper, i.e., Northeast, Beijing–Tianjin, North Coast, East Coast, South Coast, Central Region, Northwest and Southwest regions, and then we not only investigate the contribution of each region to the national carbon emission reduction target by 2020, but also consider the interactive influence of collaborative carbon emission reduction among regions and the radiation effect of regional carbon outflows. To sum up, the indicators and methods of previous literature provide important references for this paper, but there are still some problems to be solved. For one thing, many studies give several referenced solutions for carbon emission allocation among regions, but lack a clear and comprehensive solution with objective weights. For another, much literature explores the carbon emission quota allocation among regions or countries but the regions or countries are often independently considered and little literature sheds light upon their collaboration in carbon emission reduction. In fact, different regions have their respective resource advantages and economic features; therefore, it is necessary for different regions to conduct collaborative activities in the process of carbon emission reduction; however, currently it is still unclear that which region is the best partner for a region to achieve optimal carbon emission reduction outcome, and which region has the largest gains through collaborative carbon emission reduction or the largest radiation effect on other regions. For these purposes, we adopt the entropy method to allocate carbon increments from 2011 to 2020 for China's 30 provinces and eight regions, and then we further calibrate the regional carbon emission quotas using the Shapley value coupled with the gravity model. In this way, we aim to provide some references for decision-makers in China regarding the reasonable allocation of carbon quotas, the deployment of national carbon trading market and the achievement of carbon reduction targets.

3. Data definitions and methodologies 3.1. Data definitions Considering the historical responsibility, economic level and the obligation of carbon emission reduction, this paper selects per capita GDP, historical accumulative CO2 emissions, CO2 emissions per unit of industrial value-added as proxies for carbon emission reduction capability, responsibility and potential, respectively, following Ringius et al. (1998). Besides, the latest data of China's regional carbon emissions is back to 2011, which motivates us to define the year of 2011 as the base year and decompose the national carbon emission reduction targets among provinces or regions in 2020 based on the CO2 emissions data in 2011. The GDP and industrial value-added are measured at the constant price in 2005. The specific indicators and principles are shown in Table 1, which are elucidated as follows.

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Table 1 Carbon quotas allocation indicator systema. Indicator

Principle

Interpretation

Dimension

Per capita GDP Vertical equity Reduce emissions in proportion to capacity Capability Historical egalitarian Equal rights to use the atmospheric resources among generations Responsibility Accumulated CO2 emissions Carbon emissions per unit of industrial added value Polluter governance principle Regions with more reduction room should reduce more Potential a According to Ringius et al. (1998), the vertical equity principle is intended to make improvement for those with fewer resources relative to those with more resources; the historical egalitarian principle implies that every individual has the same right to use the atmosphere and should be allowed the same right to emit greenhouse gases; and the polluter governance principle indicates that the burden is distributed in accordance with an individual's contribution to emissions.

Table 2 China's regional divisions. No.

Region

Provinces

1 2 3 4 5 6 7 8

Northeast Beijing–Tianjin Northern coast Eastern coast Southern coast Central region Northwest Southwest

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

(1) Carbon emission reduction capability Per capita GDP often measures carbon emission reduction capability in a province, which reflects the vertical equity principle. Therefore, we single out per capita GDP in 2011 as the indicator for carbon emission reduction capability for each province, and generally, the richer provinces should have heavier reduction burdens. The data comes from the National Bureau of Statistics of China. (2) Carbon emission reduction responsibility The provinces with higher historical accumulated CO2 emission should bear more emission reduction responsibility, which reflects the polluter governance principle. This paper chooses the historical accumulated CO2 emission from 2005 to 2011 to indicate the responsibility of carbon emission reduction according to the latest available data. We refer to IPCC for the specific calculation method (IPCC, 2006; Zhang and Da, 2013) and the data comes from the China Energy Statistical Yearbook 2006–2012. (3) Carbon emission reduction potential China's industrial sector proves carbon-intensive overall and its carbon emissions are almost 10 times larger than those of the service sector up to now. Each province has emitted much CO2 during the industrialization process, and thus there is much carbon emission reduction potential in the industrial sector. Therefore, carbon emission reduction potential is represented by CO2 emissions per unit of industrial valueadded in 2011 in this paper. And the data comes from the Wind database and China Energy Statistical Yearbook. Besides, we divide the 30 provinces in China's mainland into eight regions which come from the State Information Center of China as done by Zhang and Da (2013), as shown in Table 2. It should be noted that this paper does not consider Hong Kong, Macao, Taiwan and Tibet, due to data unavailability. 3.2. Methodologies 3.2.1. The entropy method to allocate carbon emission quotas In order to determine the objective weights in decision making models, the entropy method is frequently used. According to the

information theory, entropy is a method to measure the uncertainty or disorder in an information set (Shannon, 1948). Specifically, the uncertainty and entropy will be smaller (larger) if the information content is larger (smaller). Based on the concept of entropy, we can examine the discrete degree of the three indicators for carbon emission reduction capability, responsibility and potential, respectively. And the larger discrete degree of an indicator is, the larger influence it may have on the comprehensive evaluation, and thus it should be given more weight (Zou et al., 2006; Sun et al., 2013). According to the entropy method, first, we develop the decision making matrix X of the three indicators for China's 30 provinces mentioned above. 2

x11

6 6 x21 X¼6 6 ⋮ 4 x30;1

x12 x22 ⋮ x30;2

x13

3

7 x23 7 7 ⋮ 7 5

ð1Þ

x30;3

where xij represents the value of indicator j for province i (i¼1, 2, 3……30; j¼1, 2, 3). Then we need to conduct the normalization operation as follows because of the difference in dimension among indicators: 30

zij ¼ xij = ∑ xij

ð2Þ

i¼1

As a result, the decision making matrix Z of the three indicators is as follows: 2

z11 6 6 z21 Z¼6 6 ⋮ 4 z30;1

z12 z22 ⋮ z30;2

z13

3

7 z23 7 7 ⋮ 7 5 z30;3

ð3Þ

And the entropy of indicator j is calculated as Eq. (4): Hðxj Þ ¼ 

1 30 ∑ z ln zij ln 30 i ¼ 1 ij

ð4Þ

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Y.-J. Zhang et al. / Energy Policy 74 (2014) 454–464

Then the final weight for indicator j is obtained as Eq. (5) (Wang and Lee, 2009): dj ¼

1  Hðxj Þ 3  Σ j ¼ 1 Hðxj Þ 3

;

ð5Þ

where 0 r dj r1; Σ j ¼ 1 dj ¼ 1. In addition, we assume that China's economic growth rate remains 8% from 2011 to 2020 and the target of 40% carbon intensity reduction (compared with the 2005 level) is realized in 2020. The carbon intensity ηt in year t is defined as Eq. (6) 3

Q ηt ¼ t GDP t

ð6Þ

where Q t is the national CO2 emission in year t, and GDP t is gross domestic product in year t, which is calculated at the constant price in 2005. So China's carbon emission quotas Q 2020 in 2020 and carbon quota increments ΔQ from 2011 to 2020 are calculated by Eqs. (7) and (8) respectively Q 2020 ¼ 0:6η2005 GDP 2020

ð7Þ

ΔQ ¼ Q 2020  Q 2011

ð8Þ

Meanwhile, the carbon quota of province i in 2020, i.e., Q i2020 , is allocated as Eq. (9): Q i2020 ¼ Q i2011 þ ΔQ i

ΔQ i ¼

ΔQ 1 3 3 Σ 30 ð1= Σ z d Þ Σ ij j i¼1 j¼1 j ¼ 1 zij dj

ð9Þ ð10Þ

where Q i2011 is the carbon emission of province i in 2011; ΔQi is the carbon increment of province i from 2011 to 2020. Therefore, the proportion of carbon increment of province i in the national total carbon increment from 2011 to 2020, i.e., λi, the proportion of carbon emissions of province i in the national total emissions in 2011, i.e., θi2011 , and the proportion of carbon quotas of province i in the national total carbon emission quotas in 2020, i.e., θi2020 , are calculated by Eqs. (11), (12) and (13) respectively:

ΔQ i ; λi ¼ ΔQ θi2011 ¼ θi2020 ¼

ð11Þ Q i2011

Σ 30 i ¼ 1 Q i2011 Q i2020

Σ 30 i ¼ 1 Q i2020

;

ð12Þ

:

ð13Þ

3.2.2. Regional carbon quota allocation by the Shapley value method According to the standard economic theory, regional economic connection indicates the interaction and association between regional economic entities, and the value of regional economic connection is the indicator to measure economic relation force among regions (Meng and Lu, 2009), which provides valuable references for this paper. Specifically, this paper defines the regional emission reduction connection to indicate the interaction and association between regions, and also defines the value of regional emission reduction connection to measure the emission reduction relation force between regions, which is also called the spatial interaction value and reflects the radiation effect of carbon emission reduction in one region on surrounding regions as well as the absorption effect of surrounding regions. Moreover, the value of regional emission reduction connection can be divided into the absolute and relative categories. The absolute value means the carbon emission reduction radiation effect of one region on surrounding regions or the magnitude of potential emission reduction association; while the relative value is obtained by

simply dividing the absolute value with the absorption level of regional carbon emission reduction in the region. Concerning the regional economic connection value, the gravity model is commonly used (Anderson, 2011). Therefore, this paper also adopts the gravity model to calculate the absolute value of regional carbon emission reduction connection, i.e., Rmn , between region m and n in 2020 as Eq. (14). pffiffiffiffiffiffiffiffiffiffiffiffipffiffiffiffiffiffiffiffiffiffi Rmn ¼ ð P m Bm P n Bn Þ=D2mn ð14Þ where Pm and Pn are the population size of region m and n in 2020 respectively. This paper forecasts the population of each region in 2020 based on the amount of 2011 with an annual increasing rate of 0.4803%. Here 0.4803% is the national population growth rate in 2011, which comes from National Bureau of Statistics of China. Bm and Bn are the basic carbon quotas of region m and n in 2020 respectively. Dmn is the distance between region m and n. Because of the large scope of those eight regions, it is hard to accurately calculate the regional distance. Then following Han (2011), this paper adopts the average distance among provincial capitals in two regions to represent the region distance. For example, China's Eastern coastal region borders upon Southern coastal region, and then the distance between the two regions adopts the average distance between all the provincial capitals of Eastern coastal region (Nanjing, Shanghai and Hangzhou) and those of Southern coastal region (Fuzhou, Guangzhou and Haikou). Based on the gravity model, we estimate the sum of carbon emission reduction connection value between one region and all of other regions, which is considered to be the region's total external carbon reduction connection, as shown in Eq. (15). Rm ¼ ∑ Rmn ðn; m ¼ 1; 2; 3; :::; 8Þ nam

ð15Þ

where Rm is the total external carbon reduction connection of region m and reflects the degree of the region's carbon emission reduction connection to others. Then the relative carbon emission reduction connection value of region n to m, i.e., amn , is defined as Eq. (16). amn ¼

Rmn  100% Rm

ð16Þ

Then, we assume the regional set G ¼ ½g 1 ; g 2 ; g 3 ; g 4 ; g 5 ; g 6 ; g 7 ; g 8 , where g m (m¼1,2,……,8) represents China's eight regions respectively as shown in Table 2, and its regional subsets are defined as Gy (y¼1, 2, ……, 256). In theory, the regional alliances will get more collaborative room for carbon emission reduction and more collaborative returns if they have higher GDP, more inter-regional carbon outflows and bigger relative carbon emission reduction connection values; otherwise, the regional alliances may only have limited collaborative room and returns (Filar and Gaertner, 1997). In this way, this paper defines the joint returns of collaborative alliances in 2020 as Eq. (17). pðGy Þ ¼ ∑ GDP m ∑ Em m A Gy

m A Gy

∑ amn

m;n A Gy

ð17Þ

where GDP m is the GDP of region m in 2020, which is projected at the annual growth rate of 8% based on the 2011 level; Em is the carbon outflow of region m to other seven regions in 2020. It should be noted that this paper employs the input–output method to calculate the inter-regional carbon outflows, based on the Input–Output Table 2007 of China with eight regions and seventeen sectors (Yang and Liang, 2013).4 Then we project the carbon outflow of a region to other regions in 2020 at the annual growth rate of φ based on its carbon outflow in 2007, and it should be noted that the specific value of φ does not affect the final carbon quota allocation results. On this basis, we can calculate the carbon emission quota proportions of all regions 4 Now the latest and most complete input-output table in China is the edition released in 2007.

Y.-J. Zhang et al. / Energy Policy 74 (2014) 454–464

in light of collaborative carbon reduction as follows: ðq  uÞ!ðu  1Þ! ; wm ¼ q!

ð18Þ

Sm ¼ ∑ wm ðpðGy Þ  pðGy \mÞÞ;

ð19Þ

m

Cm ¼

Sm

Σ m Sm

;

ð20Þ

where wm is the weighting factor; q is the number of regions, i.e., 8; u is the number of elements in regional subset Gy ; (
)! is the factorial operator; Sm is the Shapley value of region m; and Cm is the carbon emission quota proportion of region m in 2020.

4. Results and discussions 4.1. Regional carbon quota allocation based on the entropy method We first calculate the weights of carbon emission reduction capability, responsibility and potential based on Eqs. (4) and (5), i.e., d1 ¼ 0:226, d2 ¼ 0:495, d3 ¼ 0:279 respectively. It can be found that the indicator of responsibility is given relatively more weight, among the three indicators, which indicates that the carbon reduction responsibility has been given priority when carbon emission quota is allocated. And the importance of reduction capability and potential indicators follows. Actually, the entropy method has also been used as a weighting method in the literature of composite indicators, e.g. composite environmental index. Therefore, based on the weighted results of the three indicators above, we calculate the proportion of every province's carbon quota increment in the national total increment, i.e., λi, from 2011 to 2020 based on Eqs. (10) and (11), the proportion of every province's carbon emissions in the national total carbon emissions in 2011, i.e., θi2011 , and the proportion of every province's carbon emission quota in the national total carbon emission quotas in 2020, i.e., θi2020 , based on Eqs. (9), (12) and (13), as shown in Figs. 1 and 2. From Figs. 1 and 2, it can be found that, first, the provinces with higher energy consumption are most likely to be allocated lower proportions of carbon quota increment during 2011–2020. It is mainly because the provinces with higher energy consumption often have stronger carbon reduction capability or larger carbon reduction responsibility or higher carbon reduction potential. Therefore, they are likely to be allocated smaller proportions of carbon quota increment. As shown in Table 3, the carbon emission proportions in Shandong, Inner Mongolia, Shanxi, Hebei, Jiangsu and Henan all surpassed 6% of the total in 2011, and those provinces can be considered as the first-level energy consumers. And the results indicate that due to the higher historical emissions

459

and per capita GDP in Shandong, Inner Mongolia and Jiangsu, they undertake more carbon reduction responsibility and possess stronger carbon reduction capability, and they are allocated 1.6%, 2.1%, 2.2% of carbon quota increment during 2011–2020 respectively, which are the relatively smallest proportions. Meanwhile, due to the higher historical emissions and carbon emissions per unit of industrial value-added, as well as the resulting heavier carbon reduction responsibility and higher carbon reduction potential in Shanxi, it is also allocated pretty smaller proportion of carbon quota increment, i.e., 1.9%. Similarly, Hebei province has relatively higher historical emissions and undertakes heavier carbon reduction responsibility; as a result, it is only allocated 2.2% of carbon quota increment. Second, the provinces with lower energy consumption tend to be allocated higher proportions of carbon quota increment during 2011–2020. This is mainly because those provinces with lower energy consumption often have weaker carbon reduction capability or less carbon reduction responsibility or lower carbon reduction potential. As shown in Table 4, the carbon emissions in nine provinces, including Chongqing, Guangxi, Ningxia, Jiangxi, Tianjin, Gansu, Beijing, Qinghai and Hainan, all account for less than 2% of the total, which stay in the lowest level in China's regions.

Fig. 2. The proportions of provincial carbon emissions in 2011 and carbon quotas in 2020.

Fig. 1. The proportions of provincial carbon quota increments from 2011 to 2020.

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Y.-J. Zhang et al. / Energy Policy 74 (2014) 454–464

Table 3 The carbon quota allocation of provinces with higher energy consumption. Province

Carbon emission proportion in 2011 (%)

Shandong 9.1 Shanxi 6.7 Inner 14 Hebei Jiangsu Henan

Mongolia

6.4 6.2 6.0

Carbon quota proportion in 2020 (%)

Carbon quota increment proportion (%)

Per capita GDP Historical Carbon emissions per unit of industrial ranking emissions ranking value-added ranking

7.0 5.4 7.1

1.6 1.9 5.8

10 18 2.1

1 2 6

23 10 6

5.3 5.1 5.0

2.2 2.2 2.3

14 4 23

3 5 4

19 29 18

Table 4 The carbon quota allocation for provinces with lower energy consumption. Province

Carbon emission proportion in 2011 (%)

Carbon quota proportion Carbon quota in 2020 (%) increment (%)

Per capita GDP ranking

Historical emissions ranking

Carbon emissions per unit of industrial value-added ranking

Chongqing Guangxi Ningxia Jiangxi Tianjin Gansu Beijing Qinghai Hainan

1.7 1.7 1.6 1.6 1.5 1.4 1.1 0.4 0.4

2.4 2.5 2.0 2.6 2.1 2.3 1.8 1.6 1.5

12 27 16 24 1 28 3 21 22

22 24 28 23 25 26 27 29 30

11 15 1 21 26 8 25 5 4

4.2 4.9 2.9 5.2 3.7 4.7 3.9 4.8 4.4

For instance, the per capita GDP and carbon emissions per unit of industrial value-added are fairly lower in Jiangxi, indicating its weaker carbon reduction capability and lower carbon reduction potential. As a result, it has 5.2% of carbon quota increment, with the highest proportion among all provinces. Meanwhile, the per capita GDP and historical emissions are also lower in Guangxi, suggesting its weaker carbon reduction capability and less carbon reduction responsibility, and thus it is allocated 4.9% of carbon quota increment, a relatively higher proportion. Besides, Chongqing has lower historical emissions, indicating its less carbon reduction responsibility, and therefore it is allocated 4.2% of carbon quota increment, which is also a relatively higher proportion and larger than the average (3.3%). Similar case occurs in Gansu, Qinghai and Hainan, with higher carbon reduction potential but less carbon reduction responsibility and weaker carbon reduction capability, and they are allocated 4.7%, 4.8% and 4.4% of carbon quota increment respectively, much higher than the proportions for the first-level energy consumers. Besides, the provinces with higher (lower) energy consumption currently will still be allocated higher (lower) proportions of carbon quotas in 2020; however, basically, the carbon quota proportions of the provinces with higher (lower) energy consumption in 2020 will be lower (higher) than their respective carbon emission proportions in 2011. This is mainly because the provinces with higher energy consumption tend to undertake heavier carbon emission reduction obligations with smaller proportions of carbon quota increment during 2011–2020, but they have higher carbon emission bases; as a result, their carbon quota proportions are still higher than the provinces with lower energy consumption in 2020. Similarly, due to the lower carbon emission bases in provinces with lower energy consumption, their carbon quota proportions are still lower in 2020. It should be noted that this paper adopts the entropy method to allocate carbon quotas among regions in China and the results are consistent with the results by Yi et al. (2011) and Yu et al. (2014) to some extent. For example, Yi et al. (2011) enhance carbon intensity reduction targets for provinces with higher energy consumption, such as Inner Mongolia, Shanxi, Hebei, in other

words, they should take more reduction burdens; meanwhile, they also relax the targets for provinces with lower energy consumption, such as Hainan, Qinghai, Tianjin. And Yu et al. (2014) argue that the carbon quota proportions of the provinces with higher energy consumption in 2020 should be lower than their respective carbon emissions proportions in the base year, including Shandong, Jiangsu, Henan etc.; whereas the provinces with lower energy consumption should have larger carbon quota proportions in 2020 than those in the base year, including Hainan, Qinghai, Beijing and so on. In addition, from the regional perspective, the carbon quota proportion tends to be more convergent in 2020 than the carbon emission proportion in 2011 because of the different reduction obligations among regions. We figure out the basic allocation of carbon quotas in 2020 among regions based on the entropy method (see Table 5). It can be found that the Central region may get the relatively largest proportion of carbon emission quota among regions, i.e., 22.6%; while the Beijing–Tianjin region may get the smallest proportion, i.e., 4.0%. As for the allocation of carbon emission increments from 2011 to 2020, the Southwest and Central regions may have the largest proportions, i.e., 19.4% and 19.3% respectively. 4.2. Regional carbon quota allocation based on the Shapley value method Based on the carbon quota allocation results among eight regions using the entropy method above, we further consider the radiation effect of carbon emission reduction among regions and their collaborative activities, and employ the Shapley value method coupled with the gravity model to calibrate (reallocate) the carbon quotas among the eight regions (see Fig. 3). As for the reallocation results using the Shapley value method, it can be found that regional GDP, inter-regional carbon outflow and regional carbon emission reduction connection are the key factors affecting the reallocation. Because the Shapley value method considers the collaboration among regions in carbon emission reduction, so it is easy to understand that the regions

Y.-J. Zhang et al. / Energy Policy 74 (2014) 454–464

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Table 5 Regional carbon quotas by the entropy method.

Northeast Beijing–Tianjin Northern coast Eastern coast Southern coast Central region Northwest Southwest

Carbon emission proportion in 2011 (%)

Carbon quota proportion in 2020 (%)

Allocation of carbon quota increment (%)

10.6 2.6 15.5 12.2 8.1 23.9 16.0 11.1

10.2 4.0 12.3 11.2 8.9 22.6 17.5 13.4

9.2 7.6 3.8 8.4 11.1 19.3 21.2 19.4

Fig. 3. Regional carbon quota proportions.

with higher GDP, larger inter-regional carbon outflow and stronger regional carbon emission reduction connection may dominate the carbon quota allocation, considering the collaboration. For example, the Central region, Northern coast and Eastern coast get 53.3% carbon emission quotas totally. In contrast, the regions with lower GDP, smaller inter-regional carbon outflow and weaker regional carbon emission reduction connection may obtain fewer carbon emission quotas. For example, the Northeast, Beijing–Tianjin and Southwest regions get only 25.2% of carbon emission quotas in total. Specific findings are as follows: First, the inter-regional alliance with higher GDP may get more returns through collaborative carbon emission reduction. For example, the proportions of GDP in the Central and Eastern regions are 20.1% and 19.3%, respectively, and they are the largest economic regions in China. Meanwhile, their proportions of carbon emission quotas are relatively higher among all regions, which are 23.6% and 13.7% respectively. Second, the inter-regional alliance with larger carbon outflow basically may get more returns through collaborative carbon emission reduction. For example, the inter-regional carbon outflows of the Northwest region and Central region account for 20.4% and 18.7% of the total respectively. Meanwhile, their proportions of carbon emission quotas are relatively higher, with 10% and 23.6%, respectively. Third, carbon emission reduction connection is the key factor for the central government to promote collaborative carbon emission reduction and for regions to single out collaborative partners. According to the gravity model, the absolute and relative inter-regional carbon emission reduction connection values are shown in Tables 6 and 7, respectively. And we find that, on the one hand, the Central region has relatively largest absolute carbon emission reduction connection among all the regions, i.e.,

256.24 ton million people/km2, and the Northern coast region follows, which indicates their relatively higher radiation effect of carbon emission reduction overall. This is mainly because their relatively more basic carbon emission quotas by the entropy method may have stronger reduction radiation effect on other regions around. Moreover, as shown in Table 6, the absolute carbon emission reduction connection between the Central region and the Northern coast reaches 83.78 ton million people/km2, which is the largest value in Table 6 and indicates the strongest carbon emission reduction radiation among various regional collaboration. And the absolute carbon emission reduction connection between the Central region and the Eastern coast follows. The results may guide the central government in China to deploy the policy and economic resources to promote collaboration in carbon emission reduction on the national level. On the other hand, the relative carbon emission reduction connection in Table 7 may provide valuable help for all regions to single out their respective reasonable collaborative partners, in order to realize the maximum returns from the alliances. For instance, as for the Central region, the Northern coast and Eastern coast regions have relatively stronger carbon emission reduction connection with it, i. e., 32.7% and 30.7%, respectively. Therefore, under the same conditions, the Central region may get more returns in the regional collaboration for carbon reduction if it builds the alliance with the regions including the Northern coast and Eastern coast. These results may provide important guide for the government to make policies to promote collaborative carbon reduction among regions in China in an optimal way. Finally, as for the contribution of the three factors on carbon emission quota allocation, there are no consistent results among the eight regions. As shown in Table 8, the carbon emission reduction connection plays the dominant role in the quota allocation in six regions, including Northeast, Northern coast, Southern coast, Central region, Northwest and Southwest regions, while in the Beijing–Tian region, carbon outflow appears the main contributor and in the Eastern coast, the role of GDP proves more important for its carbon emission quota allocation. 4.3. Comparisons of carbon quota allocation using the entropy and Shapley value methods The regional GDP, inter-regional carbon outflow, the basic carbon quotas by the entropy method and the reallocated carbon quotas by the Shapley value method are shown in Table 9. It can be found that the regions with higher GDP, higher carbon outflow and higher basic carbon quotas by the entropy method may get more carbon emission quotas based on the Shapley value method; otherwise, they may get fewer carbon emission quotas based on the Shapley value method, which considers the collaborative activities. This is mainly because the region with higher basic carbon quotas may often have higher carbon emission reduction connection, and thus tends to get higher proportion of carbon

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Table 6 The absolute carbon emission reduction connection among regions in China (ton million people/km2).

Northeast Beijing–Tianjin Northern coast Eastern coast Southern coast Central region Northwest Southwest

Northeast

Beijing–Tianjin

Northern coast

Eastern coast

Southern coast

Central region

Northwest

Southwest

Total

0.00 5.95 13.59 4.71 1.65 10.77 3.32 2.62

5.95 0.00 64.30 3.53 1.12 12.35 2.85 1.57

13.59 64.30 0.00 29.57 5.63 83.78 12.53 1.88

4.71 3.53 29.57 0.00 10.34 78.66 5.81 8.21

1.65 1.12 5.63 10.34 0.00 23.80 2.98 12.13

10.77 12.35 83.78 78.66 23.80 0.00 19.68 27.20

3.32 2.85 12.53 5.81 2.98 19.68 0.00 7.33

2.62 1.57 1.88 8.21 12.13 27.20 7.33 0.00

42.61 91.67 211.28 140.83 57.65 256.24 54.5 60.94

Table 7 The relative carbon emission reduction connection among regions in China (%).

Northeast Beijing–Tianjin Northern coast Eastern coast Southern coast Central region Northwest Southwest

Northeast

Beijing–Tianjin

Northern coast

Eastern coast

Southern coast

Central region

Northwest

Southwest

0 6.5 6.4 3.3 2.9 4.2 6.1 4.3

14 0 30.4 2.5 1.9 4.8 5.2 2.6

31.9 70.1 0 21 9.8 32.7 23 3.1

11.1 3.9 14 0 17.9 30.7 10.7 13.5

3.9 1.2 2.7 7.3 0 9.3 5.5 19.9

25.3 13.5 39.7 55.9 41.3 0 36.1 44.6

7.8 3.1 5.9 4.1 5.2 7.7 0 12

6.1 1.7 0.9 5.8 21 10.6 13.4 0

Table 8 The contribution of the three key factors to carbon quota allocation.

GDP (%) Carbon outflow (%) Carbon emission reduction connection (%)

Northeast

Beijing–Tianjin

Northern coast

Eastern coast

Southern coast

Central region

Northwest

Southwest

8.5 6.7 84.8

42.4 46.3 11.3

4.9 27.4 67.7

52.8 34.1 13.1

14.7 37.6 47.7

20.2 16.2 63.6

19.9 33.0 47.1

26.1 28.4 45.5

Table 9 Allocation results by the Shapley value and entropy methods. Northeast Beijing–Tianjin Northern coast Eastern coast Southern coast Central region Northwest Southwest GDP share (%) 8.7 Carbon outflow share (%) 13.2 Carbon emission reduction connection share (%) 4.7 Allocation by the entropy method (%) 10.2 Allocation by the Shapley value method (%) 8.9

5.3 4.4 10.0 4.0 6.9

13.4 15.5 23.1 12.3 15.9

emission quotas. In fact, the result also reflects the close relationship between the entropy method and Shapley value method. For example, this paper allocates carbon emission quotas among regions in 2020 by the entropy method considering carbon reduction capability, responsibility and potential, while the Shapley value method introduces the inter-regional carbon emission reduction connection, which is dependent upon the basic carbon quotas by the entropy method, regional geography and population etc. The two regions with more basic carbon emission quotas, closer distance and more population may get stronger interregional carbon emission reduction connection. Therefore, the basic carbon quotas by the entropy method often have positive effect on the reallocated carbon emission quotas by the Shapley value method. Moreover, the carbon emission quota allocation results using the Shapley value method, which considers the collaborative carbon reduction, have some changes compared with those using the entropy method. Specifically, the proportions of the Northeast, Northwest and Southeast regions have declined while those of other regions have increased when the Shapley value method is employed. Even so, the Central region always has the relatively largest proportion among all the regions no matter using the

19.3 8.0 15.4 11.2 13.7

14.1 9.3 6.3 8.9 10.7

20.1 18.7 28.0 22.6 23.6

8.1 20.4 6.0 17.5 10.0

11.0 10.4 6.7 13.4 10.2

Shapley value method or the entropy method. Therefore, we may say that the collaborative activities in carbon emission reduction may exert significant impact on carbon quota allocation. Besides, the basic carbon emission quotas by the entropy method may affect the carbon emission reduction connection among different regions. Generally, the regions with more basic carbon emission quotas have stronger carbon emission reduction connection, and they may be allocated more final carbon emission quotas by the Shapley value method. For instance, the Central region has 22.6% basic carbon emission quotas by the entropy method, and its carbon emission reduction connection accounts for 28.0%, the largest share among eight regions.

5. Conclusions and policy implications This paper allocates regional carbon emission quotas based on the entropy method, the gravity model and the Shapley value approach in light of the inter-regional collaborative activities for carbon emission reduction. And some main conclusions are obtained as follows.

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First, when we allocate carbon quotas among provinces in China, the provinces with stronger carbon reduction capability, heavier carbon reduction responsibility and higher carbon reduction potential may get smaller proportions of carbon emission increment quotas, and responsibility is the relatively most important indicator among the three indicators. For example, the provinces undertaking heavier responsibility like Shandong, Shanxi, Inner Mongolia, Hebei, Jiangsu and Henan are allocated the least carbon increment quotas among the 30 provinces. Overall, those provinces with higher (lower) energy consumption currently may have lower (higher) proportions of carbon quotas by 2020, compared to their respective carbon emission proportions in 2011. For example, the carbon quota proportions of Shandong, Shanxi, Inner Mongolia, Hebei, Jiangsu and Henan all drop by 1 point more, while those of Chongqing, Guangxi, Ningxia, Jiangxi, Tianjin, Gansu, Beijing, Qinghai and Hainan all increase by over 0.4 point. Second, compared with the carbon emission quota allocation results using the entropy method and the Shapley value method, we find that when the collaboration in carbon emission reduction is considered, the proportions of carbon emission quotas among regions in China have changed a lot. As shown in Table 9, the three regions with the largest proportions of carbon emission quotas are the Central, Southwest and Northwest regions using the entropy method, while the Central, Northern coast and Eastern coast regions using the Shapley value method. It should be noted that the Central region always has the relatively largest proportion among all the regions, no matter using the entropy method or the Shapley method. Finally, in the collaborative carbon emission reduction across the nation, the Central region and Northern coast region may be the two dominant regions and the main beneficiaries, with more than one-third carbon emission quotas allocated to them in total. Specifically, the Central region has the stronger radiation effect of carbon emission reduction than other regions, which shows its significant possible returns through collaborative activities in carbon emission reduction. Based on the conclusions above, we can provide some policy implications for related decision makers in China. For one thing, some valuable factors should be taken into account when the carbon emission quota allocation is conducted among regions in light of collaborative activities, such as carbon reduction capability, responsibility, potential, economic level, carbon outflows and carbon emission reduction connection. For another, China's central government is expected to design reasonable mechanisms or policies to facilitate collaborative carbon emission reduction among regions with strong carbon emission reduction connection, which is beneficial not only for the regions themselves but also for the realization of the national carbon emission reduction target. For instance, the collaboration between the Central region and Northern coast region, and that between the Central region and the Eastern region should be paid more attention. In particular, now China's central government has provided relatively explicit and distinctive strategies for the regional economic development across the nation. For instance, the six provinces in the Central regions are endowed with the “Rising Strategy of Central China” while the three provinces in the Eastern coast have the “Priority Strategy of Eastern China”. However, the collaboration among regions is also important, although they have different positions in the nationwide and developing strategies. What is more, the central government is advised to shape efficient policies to integrate the regional collaboration in economic development and carbon emission reduction, such as the inter-regional low-carbon industrial development. Besides, it should be noted that carbon quota allocation appears the key problem now for China to address climate change, and it still needs a lot of attention in the future. For instance, this paper

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allocates the carbon emission quotas in light of the collaboration in carbon emissions reduction among regions, but does not shed light upon the relationship among industries within a region, which may be an important research direction in the future.

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