Integrated weighting approach to carbon emission quotas: an application case of Beijing-Tianjin-Hebei region

Journal of Cleaner Production xxx (2016) 1e12

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Integrated weighting approach to carbon emission quotas: an application case of Beijing-Tianjin-Hebei region Rong Han a, b, c, Bao-Jun Tang a, b, c, Jing-Li Fan a, d, Lan-Cui Liu a, e, Yi-Ming Wei a, b, c, * a

Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China School of Management and Economics, Beijing Institute of Technology, Beijing, China c Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, China d School of Resources & Safety Engineering, China University of Mining & Technology (Beijing), Beijing, China e Center for Climate and Environmental Policy, Chinese Academy of Environmental Planning, Ministry of Environmental Protection of the People's Republic of China, Beijing, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 31 December 2015 Received in revised form 30 April 2016 Accepted 1 May 2016 Available online xxx

The coordinated development of Beijing-Tianjin-Hebei region has been included in the Chinese ‘12th Five Year Plan’ as a national strategy. Accordingly, the cooperation of energy saving and environmental protection is an important part. At the present stage, establishing carbon emission trading market in China is regarded as an effective measure to reduce and control carbon dioxide emission. Meanwhile, it is an important task for government to allocate carbon emission quotas among regions. In order to explore the cross-provincial carbon trading market mode of China, this paper aims to construct a comprehensive index and use the integrated weighting approach to simulate the carbon quota allocation of BeijingTianjin-Hebei region. The results show that, firstly, the indicator of responsibility, which is about 0.56, is given relatively more weight among the three indicators. By comparison, the weights of capacity and potential are 0.13 and 0.30. Secondly, the provinces with strong economic ability and lower carbon reduction potential may get fewer proportions of carbon emission quotas, such as Beijing. Thirdly, since undertaking the function transfer and dispersal of Beijing and Tianjin, Hebei should have a relatively high carbon emission quotas in the future carbon trading market. These results may provide insightful support for decision makers to promote collaborative carbon reduction and allocate carbon quotas in Beijing-Tianjin-Hebei region of China. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Carbon emission quotas Comprehensive index Integrated weighting approach

1. Introduction From 1995 to 2014, energy consumptions in China are growing rapidly to support its economic development and industrialization. However, energy shortage and climate change issues have greatly influenced China's economy (Wei et al., 2013a, b). Beijing-TianjinHebei region has a long history of industrial, boasting the famous Jingjintang Belt. The total energy consumption of this region is 0.15 billion tons of coal equivalent (tce); in 1995 and 0.44 billion tons of coal equivalent in 2014, representing a rise of three times. During the same period, the related CO2 emissions rise by around 161%. In order to control environmental pollution of the capital economic

* Corresponding author. Beijing Institute of Technology, School of Management and Economics, 5 South Zhongguanccun Street, Haidian District Beijing 100081, China. E-mail address: [email protected] (Y.-M. Wei).

circle and promote rational use of energy, the national 12th (2011e2015) Five Year Plan (FYP) has put the collaborative development of Beijing-Tianjin-Hebei region as an explicit national strategy. As the national capital economic circle, the collaborative development of Beijing-Tianjin-Hebei region has attracted extensive attention from academia, politicians and the public. BeijingTianjin-Hebei region is located in the northeastern coast of mainland China, which includes Beijing municipality, Tianjin municipality and Hebei province as shown in Fig. 1. Among them, Beijing is the capital land and the center of politics, economics and culture in China, Tianjin is one of the four directly governed municipalities of China, Hebei is an important industrial province in northern China. The area of this region is about 0.22 million square kilometers, accounting for 2.2% of the total area of the whole country. The resident population is nearly 0.11 billion people, accounting for 8% of China's total population. The total energy consumption is 0.44 billion tons of coal equivalent (tce), accounting for 10.4% of China's

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Fig. 1. The geographical position of Beijing-Tianjin-Hebei region.

total energy consumption. The CO2 emission is nearly 1.03 billion tons, accounting for 11% of China's total CO2 emissions. As one of the three major industrial regions in China, Beijing-Tianjin-Hebei region contributed 10.4% to the China's GDP in 2014 (Fig. 2). In the process of regional integration, the cooperation of energy saving and environmental protection is an important part. Among them, the effective control of carbon dioxide emissions take center stage of the atmospheric environment management. Many relevant approaches designed to investigate ways to reduce carbon

emissions and to mitigate the impacts of climate change (Huisingh et al., 2015). However, the increase in rapid demand for energy led by rapid urbanization and industrialization makes adjusting and abating the absolute amount of industrial CO2 emissions in China not realistically feasible (Shao et al., 2011). Especially in northern area of China, coal power plants are always responsible for more than 80% of total electricity generation, and carbon emission per kWh from electricity generation is obviously higher than the global average level, so the use of these appliances will also cause

Fig. 2. Present situation of Beijing, Tianjin and Hebei (2014). Note: In the pie charts, yellow stands for Hebei, white stands for Beijing and gray represents Tianjin. b(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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the more carbon emissions (Liu et al., 2011). Thereinto, establishing carbon emission trading market is an effective measure to reduce greenhouse gas emissions worldwide (Liao et al., 2014). On December 26, 2013, the central government of China selects seven province/municipalities (Beijing, Tianjin, Shanghai, Chongqing, Hubei, Guangdong, and Shenzhen) to establish carbon emission trading pilots (NDRC, 2014). In November 2015, Chinese government announces that the national carbon emission trading market is expected to start in 2017. However, the fact is that China's regional development proves fairly imbalanced in terms of economic level, natural resources endowment, historical emissions, geographical factors and so on (Yu et al., 2014). Due to these facts, the establishment of national carbon market should be completed step by step. After setting up seven carbon trading pilots, Chinese government can let some typical regions such as Beijing-TianjinHebei region, Pearl River Delta region and Yangtze River Delta region to build regional carbon trading market in order to explore the cross-provincial carbon trading mode. By comparing the development of the carbon trading pilots and regional carbon markets, decision makers can analyze issues that have emerged in the design process and outline important next steps for the development of national carbon trading system. Therefore, Beijing-Tianjin-Hebei region can establish regional carbon emission trading market based on Beijing and Tianjin pilots to achieve the joint goal of carbon emission reduction and economic growth. Based on China's special circumstances, the appropriate allocation of regional emission reduction targets needs to follow the ‘common but differential’ principle (Miao et al., 2016). Thus, how to design a scientific and effective method to allocate carbon emission quotas has become an important task for Chinese government. Notwithstanding, there has been a body of literature considering the allocation of carbon emission quotas among provinces in China, the results often appear subjective and the indicators tend to be one-fold and isolated (Yi et al., 2011). This study attempts to simulate the carbon emission quotas allocation of Beijing-TianjinHebei region by constructing a comprehensive index and employing the integrated weighting approach. The remainder of the article is organized as follows. Section 2 reviews relevant literature on the perspective of summarizes the allocation mechanism and allocation method. Section 3 introduces data definitions and provides methodologies of allocating carbon emission quotas. Section 4 discusses the allocation results based on the integrated weighting approach. The conclusions and policy implications are drawn in Section 5. 2. Literature review 2.1. Allocation mechanism In carbon emission trading system, the allocation of carbon quotas has the most closely relationship with trading units which directly determines the cost of carbon trading. Initial allowance allocation is fundamental, but it proposes difficulty in terms of the trading mechanism design. The enthusiasm and initiative of the trading units can be influenced by whether the system is designed scientifically, practically and justly. A wealth of literature has raised the discussion about how to allocate carbon quotas, especially about the design of allocation mechanism which mainly include the following three categories (Table 1): The first category is based on the traditional allocation principles such as equity concern and grandfather principle. Tradable emission permits are advocated by economists as an ideal policy instrument to tackle environmental externalities because they would allow separating efficiency from equity considerations. For instance, Bohm and Larsen (1994) state that people have equal rights to use

3

atmospheric resources and find that reduce emissions in proportion to population has a long-term detrimental effect. 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. Besides, 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. As a scheme based on the status quo, grandfathering rule refers to an essential economic idea of public goods distribution. In this scheme, the global space would be allocated top-down among countries, proportional to their actual emissions in the reference year. The second category analyzes carbon quotas by putting forward new schemes. For example, Hu (2008) analyzes the carbon emission reduction responsibility of China based on the Human Development Index and puts forward a road map of the local emission reduction. Also focusing on China, Fan et al. (2010) construct a two-factors and two-product dynamic model to analyze the relationship between saving rate and trade pattern in long-run under oligopolistic market structure, suggesting that the positive effect of high saving rate on China's economic growth should not be underestimated in current time. Similarly, Pan et al. (2014) propose an allocation scheme based on cumulative emission per capita to achieve a globally equitable carbon emission space. Their results indicate that, within this scheme, each country has an equal cumulative emission per capita during the considered time period, and their annual emission per capita would reach the same level in the converged year. In addition, Park et al. (2012) describe a new method for allocating permits in carbon emission trading using the Boltzmann distribution. They point out that if emission permits are allocated to the participating countries free of charge, the Boltzmann allocation is more like the grandfathering method, in which emission permits are distributed freely based on the historical output of each country. If emission permits are allocated to the countries at their expense, then the Boltzmann allocation is similar to auctioning. Based on the perspective of global allocation, Wei et al. (2014) argue that the permits share of most developed countries will sharply shrink when historical responsibilities are taken in through our assessment and comparison of six selected allocation proposals. Then they present a systematic and quantitative method to achieve a common but differentiated responsibility shift, not only between developed and developing countries but also within industrialized countries. We believe that climate policy models played a significant role in studies of climate policy assessment (Wei et al., 2015). The third category allocates carbon quotas using the multi-attribute methods or models. For the estimation of carbon quotas at the national level, 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. Recently, Yu et al. (2014) employ the Shapley value method to decompose the total carbon emissions into an interactive result of four components, which are composed totally by 13 macro influential factors according to the Kaya identity. As discussed above, the methods of previous literature provide important reference for our study. 2.2. Allocation method In the process of solving the problem of multiple attribute decision making (MADM), the weight of attribute has a pivotal role, which is used to reflect the relative importance of attributes. The

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Table 1 Literature classification of the allocation mechanism.

Research on the traditional allocation principles

Authors

Methods

Main results

Beckerman and Pasek (1995)

Allocation scheme based on the per capita allocation principle

Rose et al. (1998)

Propose the grandfather principle for the problem of global climate change Auction mechanism

Everyone has the same right to get carbon quotas and those countries with stronger capability should bear more responsibility for carbon emissions reduction. Allocating free carbon quotas based on the historical emissions. The auction is better than that of hereditary quota. Present a scheme that the global space would be allocated top-down among countries, proportional to their actual emissions in the reference year. Evaluates future carbon quota price and Clean Development Mechanism (CDM) potentials under different BAU projections. Put forward a road map of the local emissions reduction of China. The positive effect of high saving rate on China's economic growth should not be underestimated in current time. Allocating permits in emissions trading among the whole world. Redefine the fair carbon emissions related to the carbon Lorentz Curve and carbon Gini coefficient and work out the carbon Gini coefficient. Present an approach to achieve responsibility shift, not only between developed and developing countries but also within industrialized countries. Achieve a globally equitable carbon emissions space.

Cramton and Kerr (2002) Lecocq and Crassous (2003)

Research on the new schemes

Research on the multi-attribute methods or models

Grandfathering rule

Chen (2013)

Carbon emission reduction trading model

Hu (2008)

Develop ‘The Human Development Index’

Fan et al. (2010)

The two-factors and two-product dynamic model

Park et al. (2012)

Boltzmann distribution

Song and Liu (2013)

Based on the perspective of cumulative carbon emissions per capita

Wei et al. (2014)

Provide a new method to account the country-specific historical emissions

Pan et al. (2014)

Allocation scheme based on the cumulative emission per capita Construct comprehensive index

Yi et al. (2011)

Wei et al. (2012)

Use an extended Slacks-Based Measure (SBM) model

Wang et al. (2013)

Improved zero sum gains data envelopment analysis optimization model The Shapley value method

Yu et al. (2014)

weights do not have a clear economic significance, but they influence the results of the analysis (Ma et al., 1999). At present, there are many methods to determine the weight of attribute which can be classified into subjective and objective evaluation methods depending on the information provided (Table 2). The subjective methods select weights based on preference information of attributes given by the decision makers, including Analytic Hierarchy Process (AHP) method, Delphi method and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method etc. The objective methods determine weights based on objective information, involving entropy method, rough set theory, Principle Component Analysis (PCA) method and Fuzzy Clustering Method (FCM) method etc. Weights determined by subjective methods reflect the subjective judgment of decision makers. However, the results based on the subjective weights can be influenced by the knowledge and experience background of decision makers. Objective methods often determine weights by making use of the mathematical models which neglect the subjective judgment information and can not reflect decision makers' preference. Therefore, by integrating subjective and objective attribute weight information, an optimal integrated weighting model in multiattribute decision making is proposed (Zhou and Wu, 2006). The integrated weighting approach can make the results come closer to the actual situation.

Allocate carbon quotas in light of capability, responsibility and potential with four optional solutions preferential to the three attributes respectively. Estimate the carbon reduction potential and marginal abatement costs for 29 provinces of China, find that there exists a large gap in potential reduction capability among different regions. Put forward a new efficient emissions allowance allocation scheme on provincial level for China by 2020. Decompose the total carbon emissions into an interactive result of four components, which are composed totally by 13 macro influential factors.

As to the allocation of resources, a bunch of literature focuses on exploiting the useful information of data to a maximum extent. For instance, Liu et al. (2010) combine the fuzzy mathematics method and the information entropy theory to establish an improved fuzzy comprehensive evaluation method for water quality assessment. Similarly, Sun et al. (2013) propose an allocation method based on the conception of information entropy to solve the challenge between equality and efficiency. They use the method as a standard to judge the equity of allocation of total water pollutants. For the purpose of determining the initial allocation of carbon emission reduction targets of different provinces of China, Zhou et al. (2013) adopt five equity criteria which are respectively linked to the indicators of carbon emissions, energy consumption, population, GDP and per capita GDP. Recently, Chang and Chang (2016) propose the allocation of CO2 emissions increment quotas and carbon intensity reduction burdens based on information entropy method. They find out that those provinces with better economic level, heavier cumulative CO2 emissions, stronger industrial carbon intensity and greater energy consumers may undertake greater shares of carbon intensity reduction targets during 2014e2020. According to the current situation as well as the summary of existing research, it is not hard to find that allocating carbon emission quotas seems not that easy. Based on our review, the mechanism and methods of current study provides the first

Please cite this article in press as: Han, R., et al., Integrated weighting approach to carbon emission quotas: an application case of Beijing-TianjinHebei region, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.05.001

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Table 2 Classification of the weighting methods.

Subjective methods

Methods

Interpretation

Evaluation

Analytic Hierarchy Process (AHP)

It is used to derive ratio scales from both discrete and continuous paired comparisons (Saaty, 1987). It can forecast by converging a possibility value through the feedback mechanism of the results of questionnaires, based on experts' judgment (Ishikawa et al., 1993). It can identify the relevance of the indicators, and indicate the performance difference between indicators (Deng et al., 2000).

Can exert the group wisdom to eliminate the bias of individual judgment bias, but the results have strong subjectivity, thus increasing the burden of decision analysis.

Measure the uncertainty or disorder in an information set. The uncertainty and entropy will be smaller (larger) if the information content is larger (smaller) (Shannon, 1948). Through defining classes of multi-attribute decision problem depending on the structure of their representation to analysis of a vague description of decision situations (Pawlak and Sowinski, 1994). By means of a few principal components, the analysis method of multi variables is explained, and the variance contribution rate of each principal component is used as the weight (Hotelling, 1993). Data elements can belong to one or more cluster and associated with each element is a set of membership levels. Then assigning these membership levels, using them to assign data elements to on or more clusters (Ruspini, 1970). Considering the correlation between different indexes, the correlation coefficient is introduced to determine the weight (Tanabe and Saeki, 1975). Using multiple indexes to measure the scheme is good or bad, and to find the optimal solution under multiple objectives (Hwang, 1979).

It is entirely based on the relationship between data. But without considering decision makers' preference, the result is easy to deviate from the actual situation.

Delphi Method

Objective methods

Technique for order preference by similarity to an ideal solution (TOPSIS) Entropy Method

Rough Set Theory

Principle Component Analysis (PCA)

Fuzzy Clustering Method (FCM)

Correlation Coefficient Method (CCM) Multi-Objective Programming Approach (MOP)

attempt to allocate carbon emission quotas from the perspective of equality and efficiency. Nevertheless, there are still some questions to be solved. On the first ground, little literature considers the coordinate control on carbon emission reduction among regions. What's more, present methods based on the principle of equity, often ignore the difference of regional economy and society situation. Third, some methods can reflect the impact of regional difference to some extent, but they fail to establish strong logical linkage between the allocation result and the index. Overall, many studies give several reference solutions for carbon emission quota allocation among countries or regions, but lack a clear and comprehensive solution with reasonable weights which can reflect decision makers' preference and objective information at the same time. As has been previous mentioned, the allocation of carbon quotas has the most closely relationship with trading units. Thus, it is important that the method or methods be identified for permitting carbon emissions to be allocated appropriately among countries or regions, so as to allow the environmental benefits of cogeneration technologies to be better understood and exploited (Rosen, 2008). Therefore, it is necessary to choose reasonable indicators and determined integrated weighting in the process of allocating regional carbon emission quotas. 3. Methodology 3.1. Basic assumptions and data sources Carbon emission quotas are the prerequisite of carbon trading market, which should be in accord with the regional carbon

emission potential, the direction of future economic development and the positioning of regional carbon trading market etc. Therefore, we select per capita GDP, accumulated CO2 emissions and energy consumption per unit of industrial value-added as proxies for carbon emission reduction capability, responsibility and potential respectively, following Ringius et al. (1998) and Yi et al. (2011) as shown in Table 3. The assumptions of the model are as follows: (1) Carbon emission reduction capacity Generally speaking, ‘with great capacity comes great burden’ means richer province should have heavier reduction burden because the region with higher GDP has greater financial support to promote energy saving and emission reduction. Therefore, we choose per capita GDP as the indicator representing CO2 emission reduction capacity.

Table 3 Interregional intensity reduction allocation equity principles and indicator selection. Indicator

Dimension

Interpretation

Per capita GDP

Carbon emission reduction capacity Carbon emission reduction responsibility

Richer province should have heavier reduction burden. Provinces with higher historical accumulated CO2 emissions should bear more emissions reduction responsibility. Provinces with more reducing room should reduce more.

Historical accumulated CO2 emissions

Carbon emission CO2 emission per unit of industrial reduction potential value-added

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(2) Carbon emission reduction responsibility

province to the regional total CO2 emission per unit of industrial value-added; WA, WB, WC are the weights for the three indicators.

According to the ‘polluter pays principle’, the reduction burden might be proportional to the historical emission amount, which means the provinces with higher historical accumulated CO2 emissions should bear more emission reduction responsibility. Based on the final energy consumption amount and emission factor for each type of energy (a total of 18 types of final energies), the accumulated energy-related CO2 emissions amount from 1995 to 2014 is calculated as the indicator representing responsibility. (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 2014. China has committed that non-fossil fuels energy source should account for 15% of the entire energy consumption by 2020 in the Energy Development Strategy Action Plan for 2014e2020 and by 2030 according to the U.S.-China Joint Announcement on Climate Change and Clean Energy Cooperation. This means that significant efforts should be made in order to increase the share of renewable and clean energy sources of the entire energy supply structure (Shao et al., 2016a). In the near future, through improving production technologies and making use of clean energy, carbon-intensive sectors can have much carbon emission reduction potential. In other words, higher CO2 emission per unit of industrial value-added means greater carbon emission reduction potential. Based on the above analysis, we select the CO2 emission per unit of industrial value-added in 2014 as the indicator for emission reduction potential. Considering the integrity and availability of related data, we choose the year 2014 as the base year and decompose the regional carbon emission reduction targets among provinces in 2020 based on the CO2 emission data in 2014. The data and processing method are as follows: (1) The GDP and industrial value-added in 2014 are measured at the constant price in 2005. (2) The energy consumption data are from the China Energy Statistical Yearbook (2015), including 18 types of energy sources. The CO2 emission amounts from 1995 to 2014 are calculated by the energy consumption and emission factors (IPCC, 2006). (3) These data mentioned above are mainly from the China Statistical Yearbook (2015), China Energy Statistical Yearbook (2015), Beijing Statistical Yearbook (2015), Tianjin Statistical Yearbook (2015) and Hebei Statistical Yearbook (2015).

3.2. Comprehensive index construction Substantially, carbon quotas allocation is the process of carbon emission reduction target decomposition. Therefore, we construct the comprehensive indexRi, based on the three indicators of capacity, responsibility and potential (Fig. 3). The index of is calculated by the following formula:

Ri ¼ WA Ai þ WB Bi þ WC Ci

(1)

Where Ri is the comprehensive index for i province, representing Beijing municipalities, Tianjin municipalities and Hebei province, respectively; Ai is the proportion of per capita GDP of i province to the regional per capita GDP; Bi is the proportion of accumulated CO2 emissions of i province to regional total emissions; Ci is the proportion of CO2 emission per unit of industrial value-added of i

3.3. Allocation of regional 2020 intensity reduction target In the 2009 Copenhagen climate change summit, Chinese government announced a goal to decrease its carbon emission per unit of GDP (carbon emission intensity) by 40e45% by 2020 compared with the 2005 level. We design three cases which are 7%, 8% and 9% about regional economic growth rate from 2014 to 2020, assuming that the target of 40% carbon intensity reduction (compared with the 2005 level) is realized in 2020. Then in order to estimate the regional carbon emission quotas, we apply the following equations.

GDPt ¼

Et Ct

(2)

Q2020 ¼ 0:6C2005 GDP2020

(3)

DQ ¼ Q2020  Q2014

(4)

Ct is the carbon intensity in year t, Et represents the carbon emissions of Beijing-Tianjin-Hebei region in year t means the gross domestic product in year, which is calculated at the constant price in 2005. Then the regional carbon emission quotas and carbon emission quotas increment DQ from 2014 to 2020 are calculated by Eqs. (3) and (4).

Ei2020 ¼ Ei2014 þ DQi

(5)

Where Ei2020 is the carbon emission of province in 2020; is the carbon emission of province i in 2014; is the carbon quota increment of province from 2014 to 2020. The proportion of carbon emissions of province in the regional total emissions in 2014 are calculated by Eqs. (6)e(8) respectively.

4i ¼

DEi DE

(6) Qi2014

ui2014 ¼ P3

i¼1

Qi2014

Qi2020

ui2020 ¼ P3

i¼1

Qi2020

(7)

(8)

4i is the proportion of carbon emissions of province in the regional total emissions in 2014; ui2014 and ui2020 which are calculated in Eqs. (7) and (8) represent the proportion of carbon quota of province i in the regional total carbon emission quotas in 2014 and 2020.

3.4. Integrated weighting approach (1) Weighting calculation based on AHP method The Analytic Hierarchy Process (AHP) is a theory of measurement for dealing with quantifiable and/or intangible criteria that has found rich applications in decision theory and conflict resolution. It is based on the principle that, to make decisions, experience and knowledge of people is at least as valuable as the data they use. In this paper, based on previous work of Guo et al. (2013), we use the AHP method to construct the judgment matrix according to the importance of the indicator. The weight of indicator is obtained by calculating the maximum eigenvalues of the matrix.

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Fig. 3. The fragment of constructing comprehensive index.

(2) Weighting calculation based on entropy method

WT ¼ xWZ þ yWK

According to the information theory, entropy is a method to measure the uncertainty or disorder in an information set. Specifically, the uncertainty and entropy will be smaller (larger) if the information content is larger (smaller). We can measure the weights of carbon emission reduction capability, responsibility and potential respectively. 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). (3) Weighting calculation based on integrated approach In order to consider decision makers' preference as well as reduce subjective randomness, we use the approach proposed by Guo et al. (2013) for reference. By introducing the distance function, we combine the subjective and objective weights to make the results more scientific and reasonable. As shown in Fig. 4. WZ is subjective weighting and WK is objective weighting. The integrated weightings can be obtained by getting set of these following equations. The distance function can be described in Eq. (9): n 1X dðWZ ; WK Þ ¼ ðWZ  WK Þ2 2

#1 2 =

"

i¼1

Therefore, the integrated weighting WT is:

(9)

(10)

In order to let the difference between different weights be consistent with the distribution coefficient, the distance function and the distribution coefficient should be satisfied with Eqs. (11) and (12).

dðWZ ; WK Þ2 ¼ ðx  yÞ2

(11)

xþy¼1

(12)

Where x, y represent the linear distribution coefficient of subjective weighting and objective weighting, respectively.

4. Results and discussions 4.1. Indicators weighting based on the integrated weighting approach According to the basic principle of the integrated weighting approach, the weights of the three indicators carbon emissions reduction capability (WA), responsibility (WB) and potential (WC) are shown in Table 4. We find that the indicator of responsibility which is about 0.56 is given relatively more weight among the three indicators. By comparison, the indicator of capacity which is approximately 0.13 takes the smallest proportion of all the three

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Fig. 4. The flow chart of calculating indicator weights based on integrated weighting approach.

indicators. The assignment of indicators weight directly determines the final result of carbon quota allocation. As shown in Table 4, according to the integrated weighting approach, carbon emission reduction responsibility processes the biggest proportion as 56%. In a long time series, fossil fuels are the main power to promote the industrialization process. The use of carbon-rich fossil fuels derived from coal oil, and natural gas results in carbon emissions into the atmosphere, mainly in the form of CO2 emissions. Therefore, developed provinces, like Beijing and Tianjin, have a majority of the regional responsibility to contribute to the regional CO2 emission solution due to their historical responsibility for the environmental problem and their present financial ability to contribute to a regional solution. On the contrary, developing province, such as Hebei, is held to a lesser degree of responsibility due to their lack of historical responsibility coupled with their present financial inability to contribute to a national solution (Weisslitz, 2002). This is also consistent with the ‘Common but Different Responsibilities Table 4 Weight of indicators determined by different weighting approaches.

subjective weighting method objective weighting method integrated weighting method

WA

WB

WC

0.1466 0.1195 0.1333

0.6571 0.4331 0.5628

0.1963 0.4474 0.2996

(CBDR)’ advocated by China government in the international climate negotiations. Carbon emission reduction potential processes the second place of proportion which is calculated as 0.30. More reduction room should reduce more carbon emissions. The regional carbon intensity is related with the quality of economic growth, energy efficiency and energy structure. From the perspective of social economic development, under the same economic growth rate, the more developed the economies, the fewer the carbon intensity decline. Less developed areas, like Hebei province, per unit of industrial value-added consume more energy and emit more CO2 due to the financial inability and technology immaturity. Therefore, the construction of regional carbon trading market can make full use of marketing mechanisms to minimize marginal abatement costs and push the capital flow to greater carbon reduction potential provinces. The inflow of capital has a significantly positive effect on the economy of less developed provinces. In this way, we can achieve the goal of energy saving and emission reduction without affecting the regional economic development. The carbon emission reduction capacity which is about 13%, takes the smallest proportion among the three indicators. For instance, Beijing and Tianjin have sufficient funds to improve related technology and promote technology developing of energy saving and emission reduction. By constructing regional carbon trading market, Beijing and Tianjin can buy carbon quotas from

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Hebei to achieve the carbon emission reduction target and realize the regional complementary advantages.

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Table 6 The carbon quota allocation of Beijing-Tianjin-Hebei region from 2014 to 2020. Carbon quota allocation (million tons)

4.2. The distribution of carbon quotas in Beijing-Tianjin-Hebei region Based on the objectives of carbon dioxide emissions per unit of GDP decreased 40e45% to 2020, this paper assumes that the 40% decrease can be achieved. According to the results, when the rate of economic growth remains 7%, 8% and 9%, the total increment of Beijing-Tianjin-Hebei region's carbon emissions increment is about 0.35, 0.48 and 0.63 billion tons respectively. Among them, Hebei has the largest increment which equals to 1.4 times of the sum of Beijing and Tianjin. This result is consistent with the actuarial situation that the carbon emissions of Beijing and Tianjin are much less than that of Hebei province. As shown in Table 6, we allocate carbon emission quotas among Beijing, Tianjin and Hebei based on the comprehensive index. When the rate of economic growth remains 7%, 8% and 9%, our calculation shows that the carbon quota of Beijing is the smallest, which means Beijing may take the highest reduction burdens. On the contrary, Hebei has the largest quota. Compared with the carbon emissions increment from 2014 to 2020, we can directly see that there exists a large gap between carbon emissions increment and carbon quota allocation of Beijing. According to Fig. 5, the D-value of Beijing is negative. Conversely, the Dvalue of Tianjin and Hebei is positive. That indicates in the regional carbon trading market, if CO2 emissions are held at current levels, Beijing have to buy about 2025 million tons carbon quota from Tianjin or Hebei to satisfy the demand for carbon emissions. From Tables 5 and 6 and Fig. 5, it can be found that, the provinces with higher per capita GDP like Beijing and Tianjin are most likely to be allocated with lower proportion of carbon quotas increment during 2014e2020. It is mainly because the provinces with higher per capita GDP usually have been crossing the early stages of industrialization. In other words, they have relatively larger carbon emissions historically. These facts indicate that regions with high accumulated CO2 emissions may be responsible for the environmental problems existing after their development and may shoulder greater burdens of emission reduction to save more emission space for the economic development of less developed regions. Additionally, these provinces also have stronger carbon reduction capacity due to their advanced technology and sufficient capital. Meanwhile, due to the industrialization starts relatively late, Hebei has the highest CO2 emission per unit of industrial value-added among these provinces. This implies that in the near future, by technology innovation, Hebei will have greater carbon emission reduction room. Thus, Hebei can have more carbon emission quotas. As discussed above, the allocation result is in line with the reality development of the Beijing-Tianjin-Hebei region.

Table 5 The carbon emissions increment of Beijing-Tianjin-Hebei region from 2014 to 2020. Carbon emissions increment (million tons)

Beijing-Tianjin-Hebei Beijing Tianjin Hebei

7%

8%

9%

34 688.96 8372.18 5952.36 20 364.43

48 113.85 10 183.19 8302.81 29 627.86

62 12 10 39

Note: 7%, 8% and 9% refers to the rate of economic growth.

715.25 152.91 859.23 703.11

Beijing Tianjin Hebei

R

7%

8%

9%

0.1637 0.2192 0.6127

6346.82 8497.97 23 751.62

8544.95 11 441.12 31 977.65

10 935.71 14 642.19 40 924.58

Note: R is the comprehensive index. 7%, 8% and 9% refers to the rate of economic growth.

4.3. Analysis of the carbon trading function from the regional characteristics The urban area of Beijing takes the first place in all of the biggest cities of China, and it takes the important political function. Nevertheless, the high centralize of non-capital functions put great pressure on Beijing, causing series urban diseases. In contrast, Hebei province has resource endowment and potential development opportunities, but do not have conditions to cross the threshold for regional development. Thus, the function transfer of Beijing is an inevitable way to realize cooperative development of Beijing-Tianjin-Hebei region. Economic activity in Beijing is highly concentrated and its total energy consumption has been increasing rapidly since 2000. The average annual growth rate of GDP is 16.04% from 2000 to 2011. Accordingly, Beijing's energy consumption increased at a rate of 4.88% during this period (Mi et al., 2015). In 2011, Beijing emitted about 50 Mt more than in 2002, of which 93.8% was from energy consumption (Huisingh et al., 2015). Zhang et al. (2015) evaluate the attributes of the energy consumption structure and determine the required carbon emissions reduction by each sector. From 2000 to 2010, the emission efficiency of Beijing's energy consumption structure fluctuated, but with an overall trend toward higher emission efficiency. As shown in Fig. 6, due to transferring a large amount of relatively low efficient, low added value and low radiation of the economic sector, Beijing's carbon emissions is the smallest among the whole region. Therefore, Beijing has the smallest carbon quotas. However, Beijing and Tianjin who has lighter industrial emissions intensity and energy intensity will face huge emission reduction pressure during 2014e2020 because of greater marginal abatement costs and then incline emissions reduction burdens because of lower emission reduction potential and lower energy saving space. On the contrary, since undertaking the function transfer and dispersal of Beijing and Tianjin, Hebei has relatively high carbon emissions. For example, according to the central government's arrangement, Shougang group which is China's iconic iron and steel factory has been moved from Beijing to Hebei since 2005. In the future regional carbon market transaction, for industry transfer area like Beijing and Tianjin, the next point of work is to adjust the industrial structure, develop more stringent standards on industrial emissions reduction and optimize the structure of regional industry in order to prevent the impact of cutting production capacity on regional economic and social development. For Hebei province, where industry transfer in, should emphasis on improving energy use efficiency and reducing energy intensity. 5. Conclusions and policy implications 5.1. Main conclusions The Chinese government has put the collaborative development of Beijing-Tianjin-Hebei region as an explicit national strategy. As the largest capital economic circle in the world, the advanced

Please cite this article in press as: Han, R., et al., Integrated weighting approach to carbon emission quotas: an application case of Beijing-TianjinHebei region, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.05.001

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Fig. 5. The per capita GDP and carbon quota increment proportion of Beijing-Tianjin-Hebei region. Note: D-value refers to the difference between carbon emission increment and carbon quota allocation from 2014 to 2020.

Fig. 6. Carbon emissions and energy intensity of Beijing, Tianjin and Hebei from 1995 to 2014.

experiences of cooperative environmental management in BeijingTianjin-Hebei region will provide reference for the sustainable development of other regions in China. To allocate the carbon emission quotas to the provinces, Beijing-Tianjin-Hebei region may consider equity principles and a regional development strategy. Based on the integrated weighting approach, this paper calculates the weights of these three indicators and allocates regional carbon emission quotas. Some main conclusions are obtained as follows: (1) Per capita GDP, historically accumulated CO2 emissions and CO2 emission per unit of industrial value-added have significant impacts on the allocation of emissions quotas during 2014e2020 and the three indicators have greater provincial divergences in regional carbon emission trading system. According to integrated weighting approach, we calculate

that the weight of carbon reduction responsibility, potential and capacity is 0.56, 0.30 and 0.13, respectively. The estimation results reveal that the province with heavier carbon reduction responsibility and higher carbon reduction potential may get greater proportions of carbon emission quotas by 2020. For instance, the allocation proportions of carbon emission quotas in Hebei surpass 50% of the regional CO2 emission increments during 2014e2020. Furthermore, responsibility is the relatively most important indicator among the three indicators. This is also consistent with the ‘Common but Different Responsibilities (CBDR)’ advocated by China government in the international climate negotiations. (2) According to the results, when the rate of economic growth remains 7%, 8% and 9%, the total increment of Beijing-TianjinHebei region's carbon quotas is about 0.35, 0.48 and 0.63 billion tons respectively. Among them, Hebei has the largest quota increment which equals to 1.4 times of the sum of Beijing and Tianjin. Based on Interim Measures of Emission Trading Management (IMETM), future target allocation of provincial emission reduction may comprehensively consider carbon emission reduction capacity, responsibility and potential. Thus, Beijing and Tianjin who has lighter industrial emissions intensity and energy intensity will face huge emission reduction pressure during 2014e2020 because of greater marginal abatement costs and then incline emissions reduction burdens because of lower emission reduction potential and lower energy saving space. (3) The function transfer of Beijing is an inevitable way to realize cooperative development of Beijing-Tianjin-Hebei region. Since undertake the function transfer and dispersal of Beijing and Tianjin, Hebei has a relatively high carbon emissions. Thus Hebei should have more carbon emission quotas in the future carbon trading market.

Please cite this article in press as: Han, R., et al., Integrated weighting approach to carbon emission quotas: an application case of Beijing-TianjinHebei region, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.05.001

R. Han et al. / Journal of Cleaner Production xxx (2016) 1e12

5.2. Policy implications Obviously, a high and mandatory emission reduction task will undermine China's economic growth and social stability (Shao et al., 2016b). With the improvement of living standards, there are increasing concerns from different stakeholders about environmental quality, especially in the developed eastern regions of China (Wang et al., 2011). Carbon emission reduction is a long-term strategy for China to promote its economic and social development. Emission trading as a useful policy instrument may help different provinces achieve their emission reduction targets cost-effectively. If the regional carbon trading market established in Beijing-TianjinHebei region in the near future, the carbon emission quotas can achieve efficient configuration by market-oriented way. Therefore, in the process of carbon allocation, how to guide the rational competition among local governments to avoid ‘free riding’ and ‘tragedy of the commons’ phenomenon is worth thinking for decision makers. Based on the conclusions above, we provide some policy implications for related decision makers in China. (1) In the development plan, there is no certain and explicit targets and methods for carbon emissions reduction. Since policy targets could significantly offset the carbon emissions (Wang and Chen, 2010), an explicit and vigorous carbon emissions target should be proposed for Beijing-TianjinHebei region from a holistic perspective. Decision makers should design reasonable mechanisms or policies to facilitate collaborative carbon emission reduction among provinces. For example, they should consider each province's carbon emission reduction responsibility, capacity and potential before allocating emission increment quotas. (2) In the process of allocating carbon quotas, policy makers should choose an approach which is suitable for their regional needs. Each quota allocation approach has its own advantages and disadvantages. According to our research, if a carbon emissions trading market is established in future in Beijing-Tianjin-Hebei region, the choice preference on responsibility may be chosen for emission quotas allocation. In this way, the provinces with strong economic ability could purchase the extra emission allowances from other provinces to achieve the emission reduction target. (3) Based on our research, in the regional carbon market, Beijing may buy some quotas from Tianjin and Hebei to satisfy its emission need. Therefore, Tianjin and Hebei who sell their quotas could benefit economically from the trading. In order to promote the long-term development of regional energy saving, policy makers can take steps to earmark a fund which is gained from carbon trading market for developing energy saving and emission reduction technology, training the related professional talents and so on. (4) Efforts should focus on how to optimize local energy structure and promote energy saving in the whole region, especially in the local industries. Renewable or cleaner energy sources, such as natural gas, wind power, solar power, and geo-thermal power, should be fully supported by considering the local energy endowments. Acknowledgment The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China under the grant nos. 71521002, 71273031 and 71573013. We are also thankful for the support and help provided by CEEP-BIT colleagues. The authors gratefully acknowledge Professor Donald Huisingh and the anonymous referees for their helpful suggestions and corrections on the

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Please cite this article in press as: Han, R., et al., Integrated weighting approach to carbon emission quotas: an application case of Beijing-TianjinHebei region, Journal of Cleaner Production (2016), http://dx.doi.org/10.1016/j.jclepro.2016.05.001