How Will the Emissions Trading Scheme Save Cost for Achieving

Applied Energy xxx (2014) xxx–xxx

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Applied Energy

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How will the emissions trading scheme save cost for achieving China’s 2020 carbon intensity reduction target? ⇑ Lian-Biao Cui a,b, Ying Fan b, , Lei Zhu b, Qing-Hua Bi b a School of Management, University of Science and Technology of China, Hefei 230026, China b Center for Energy and Environmental Policy Research, Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China highlights

Establish an inter-provincial emissions trading model of China. The economic performance of carbon emission trading in China is modelled. Total abatement cost could be reduced by 23.67% with the uniﬁed emissions trading market.

The emissions trading market may result in a carbon price of 53 yuan/tCO2 for the 2020 target. article info abstract

Article history: Chinese government has committed to reduce its carbon intensity by 40–45% over the period 2005–2020 Received 31 October 2013 at the 2009 Copenhagen Summit. To achieve the target in a cost-effective way, China is signaling strong Received in revised form 10 April 2014 intentions to establish emissions trading scheme, and presently seven pilots have been established. This Accepted 14 May 2014 paper focuses on the cost-saving effects of carbon emissions trading in China for the 2020 target. First, an Available online xxxx interprovincial emissions trading model is constructed. Then, three kinds of policy scenarios, including no carbon emissions trading among provinces (NETS), the carbon emissions trading only covering the pilots Keywords: (PETS), and the unified carbon emissions trading market (CETS), have been designed. The results show Carbon emissions trading that China needs to reduce its emissions by 819 MtCO for achieving the 42.5% reduction in carbon inten- Abatement cost 2 Cost-saving effects sity over the period 2005–2020. The PETS and the CETS, which may result in a carbon price of 99 yuan/ Computable general equilibrium model tCO2 and 53 yuan/tCO2, could reduce the total abatement costs by 4.50% and 23.67%, respectively. This China paper also finds that the carbon emissions trading could yield different impacts on different provinces, and the cost-saving effects of the eastern and western provinces are more pronounced than the central provinces. Necessary sensitivity analysis is also provided at the end of the research. These findings may be useful for promoting the development of carbon emissions trading in China. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction economic development [1]. However, China has a vast territory, and different provinces in China usually have different resource Chinese government has announced the reduction of its carbon endowments and economic development levels. It implies that emissions per unit of GDP (also called carbon intensity) by 40–45% the marginal abatement cost (MAC) in the different provinces from the 2005 levels by 2020 at the 2009 Copenhagen Summit. To could also be different. Therefore, how to reduce the total costs achieve the commitment, China’s Twelfth Five-Year Plan (2011– by enhancing cooperation between provinces is an important 2015), which adopts a master plan for its economic and social issue. development each ﬁve years, has put forward a national target The emissions trading scheme (ETS) is one of the two main cost- for reducing the nation’s carbon intensity by 17%. This national effective mechanisms for controlling carbon emissions, and the target has been disaggregated at the provincial level, assigning other one is carbon tax. As for ETS, the right to emit carbon is a the responsibility for different levels of carbon intensity reduction tradable commodity, and the participants with high abatement to 31 provinces, and is a mandatory constraint of the provincial costs will spend money on buying emission rights to emit more, while the participants with low abatement costs are being rewarded for their more emissions reduction. As a cost-effective ⇑ Corresponding author. Tel./fax: +86 10 62542627. mechanism, ETS has received increasing attention in different E-mail address: [email protected] (Y. Fan). http://dx.doi.org/10.1016/j.apenergy.2014.05.021 0306-2619/Ó 2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Cui L-B et al. How will the emissions trading scheme save cost for achieving China’s 2020 carbon intensity reduction target? Appl Energy (2014), http://dx.doi.org/10.1016/j.apenergy.2014.05.021 2 L.-B. Cui et al. / Applied Energy xxx (2014) xxx–xxx countries and regions, especially from the European Union [2,3]. ing scheme that only contains the pilots (PETS), and the unified Most recently, China also signals strong intentions to establish a emissions trading market (CETS), have been designed. The rest of national carbon trading system. As a first step, the National Devel- this paper is organised as follows. Section 2 develops an interpro- opment and Reform Commission of China declares that seven vincial emissions trading model. Section 3 introduces the data pilots including the cities of Beijing, Chongqing, Shanghai, Shenz- preparation. Section 4 details the results of different policy scenar- hen, Tianjin, and the provinces of Guangdong and Hubei have been ios. Section 5 provides a sensitive analysis. The conclusions and the approved to establish carbon emissions trading projects during the discussion are presented in Section 6. Twelfth Five-Year Plan. With experience from these pilots, China will promote to establish a unified emissions trading market by 2. Interprovincial carbon emissions trading model 2020 [4]. It can be seen that the ETS will play an important role in reducing carbon emissions for China in the near future. To establish an interprovincial emissions trading model in The research on the ETS has a magnificent foundation in theory. China, we need first estimate the marginal abatement cost (MAC) For example, Coase first proposes the use of property rights to for each province. In this paper, we adopt the coordinate transla- solve externality issues [5]. Dales introduces the concept of tion technique (CTT) to obtain the provincial MACs on the basis property rights into the field of contamination control, and first of the national MAC. MAC reflects the additional costs of reducing proposes the concept of emissions trading scheme [6]. Montgom- the last unit of CO and is upward-sloping, in other words, the ery points out that the efficient equilibrium of ETS is independent 2 marginal abatement cost will increase when more abatement is of initial allocation if the market is in perfect competition [7]. Tie- undertaken [16–19]. tenberg holds on the view that the emissions trading will result in all firms bearing the same marginal abatement costs, and the soci- ety will achieve Pareto optimality in the case of no transaction 2.1. National marginal abatement cost costs [8]. Rose points out that the ETS is not only cost-effective, but also embodies the spirits of equity [9]. Sterner argues that There are two main methods to derive MAC curves. The first the ETS is more effective than administrative commands, but the method is the top-down modelling approach, which uses environmental issues cannot be solved without government macroeconomic models to control carbon emissions through the regulation because of its particularity [10]. imposition of carbon tax [20,21]. The other method is the In addition to theoretical analysis, some researches focused on bottom-up modelling approach. With the detailed technical infor- the application aspects of the ETS. For example, Grubb evaluates mation of emissions reduction, as well as the availability of various the economic implications of achieving the Kyoto Protocol targets, energy technologies and their economic costs, the MAC is esti- the results show that the cost-saving effect is obvious if the global mated with an optimisation model [22–25]. In this research, the emissions trading market could be quickly and effectively former method will be used. The CHINAGEM model, which is a implemented [11]. Rose et al. study the cost-saving effects of the dynamic version of computable general equilibrium (CGE) model inter-regional emissions trading markets in the United States, of China, is used to calculate the national MAC. The bottom-up and found that the overall cost-savings increases with increasing modelling approach will be discussed in the sensitive analysis. geographic scope [12]. Massetti and Tavoni propose a fragmented CHINAGEM is a large system of equations describing the behav- cap-and-trade scheme with a specific regional market in mind iour of economic agents, the linkages between sectors of the econ- for developing Asia, and argue that creating two large trading omy, and between China and the rest of the world. It applies a markets would result in small efficiency losses, while generating nested structure to represent production technology, and energy more reasonable regional incentives and transfers [13]. can be substituted by capital and labour, which is a reasonable As for the ETS in China, the scientific research is still at an initial assumption for Chinese industrial sectors [26]. The core part of stage. For example, Zhou et al. evaluates the economic perfor- CHINAGEM contains widely accepted economic theories, such as mance of an interprovincial emissions trading scheme in China. consumer and producer optimisation behaviours. CHINAGEM sim- They adopt five equity criteria to conduct the initial quote alloca- ulations begin from a base year (i.e. 2002), for which a detailed tion, and the results indicate that the total abatement cost could input–output table is available. The input–output table is used to decrease by over 40% [14]. Zhang et al. discuss the economic construct a model database that portrays a picture of the Chinese impacts of emissions trading scheme in China for achieving the economy for that year, and the model database provides an initial 17% reduction in carbon intensity during the Twelfth Five-Year solution for the CHINAGEM equation system [27]. The construction Plan, and they argue that the unified emissions trading market of the baseline in this paper can be divided into two parts: the his- could result in 25% lower welfare loss relative to the no emissions torical calibration and the forecast simulations. As for the historical trading case [1]. Cui et al. study the cost-saving effects of emissions period (2002–2010), we will calibrate the model with latest data, trading scheme in achieving the reduction targets in the Twelfth and then the policy simulation will be conducted in the forecasting Five-Year Plan, the results show that the national abatement cost period (2011–2020). could decrease more than 20% through implementing the unified emissions trading market [15]. So far, it seems that limited 2.1.1. The baseline construction research focuses on the evaluation of emissions trading markets Regarding the historical simulation, the CHINAGEM model is in China for the 2020 target. calibrated to data from a variety of economic and energy sources. The Twelfth Five-Year Plan will pass soon, and the prospect for a In sum, three kinds of indicators are used in our research. The first unified emissions trading market in China appears distant at one is macroeconomic indicators, including the real GDP, invest- present. As things are, China will most likely establish a unified ment, private consumption and population, and the annual growth emissions trading market in the Thirteenth Five-Year Plan. Against rates of these indicators have been used in our calibration. For the this background, this paper focuses on the cost-saving effects of data sources, we refer to the China Statistical Yearbook 2013 [28]. emissions trading scheme of China in achieving the 2020 target The second one is environmental indicator such as CO2 emissions, (i.e. 40–45% reduction in carbon intensity relative to the 2005 lev- and we adopt the latest information provided by the International els by 2020). First, an interprovincial emissions trading model is Energy Agency (IEA) to calibrate the national emissions of China constructed. Then, three kinds of policy scenarios, including no over the period 2002–2010 [29]. The third one is energy indicators, emissions trading scheme (NETS), the coverage of emissions trad- which consist of coal, crude oil, natural gas, refined oil and

2.1.2. The baseline simulation To get the provincial MACs curves, this paper adopts the CTT The baseline scenario of China’s economic over the period 2002– approach [34]. Based on the estimation by Nordhaus, Bohm and 2020 is displayed in Fig. 1, which shows that the national GDP Larsen derive the MACs for other countries by modifying that of increases from 29.19 trillion yuan (the constant price in 2002, the the United States, taking the difference of the national carbon same hereinafter) in 2010 to 54.76 trillion yuan in 2020, and mean- intensities into account [34]. With the same approach, Okada while, the carbon emissions increase from 7.20 GtCO2 (Giga tons of investigates the international environmental agreements in a CO2, the same hereinafter) to 10.26 GtCO2, thus the carbon inten- game-theory framework, and Li et al. discuss how to allocate sity decreases from 2.47 tCO2/10,000 yuan to 1.87 tCO2/ emissions allowance among provinces in China with the minimum 10,000 yuan, and a decline of 24.07% has been achieved, with an total cost, and Cui et al. study the cost-saving effects of emissions average of 2.72%. The national carbon intensity will decrease by trading scheme in achieving the reduction targets in the Twelfth 37.51% relative to the 2005 levels by 2020, which is smaller than Five-Year Plan [15,35,36]. the 40–45% target. It implies that more additional measures are To simplify the research, several assumptions have been made. needed for China to achieve the proposed intensity target. Firstly, this paper assumes that all the reduction behaviour will The natural decline of carbon intensity in the baseline is reason- only occur in 2020. It can be used for avoiding discussing the dis- able for two reasons. Firstly, the national carbon intensity has tribution burdens in various years. Secondly, it is assumed that the reduced by 17.46% during the Eleventh Five-Year Plan, this is relative relationship between provincial CO2 intensities remain because China has adopted many mandatory measures, including unchanged with different carbon intensity targets, which may be an industrial energy efficiency mandate, targets for the deploy- reasonable in a short period. Thirdly, we assume that the interpro- ment of renewable and nuclear electricity generation, and reduced vincial emissions trading are in perfect competition and complete subsidies to China’s energy-intensive, export-oriented sectors [1]. information, and transaction costs will not be involved. The reduction becomes more difficult when more abatement is Fig. 2 illustrates the national MAC curve, and the abscissa is the undertaken, therefore, the 10.73% reduction in carbon intensity ratio of carbon intensity reduction, while the ordinate represents during the Twelfth Five-Year Plan should be less than that in the the marginal cost. Let e denotes the national carbon intensity, Eleventh Five-Year Plan. Secondly, IEA shows that the average which can be regarded as the weighted average of provincial car- annual decline of carbon intensity over the period 1995–2005 bon intensities. Provinces with lower carbon intensities are (China hasn’t adopted stringent abatement measures during this assumed to have taken measures to reduce their carbon intensities. period, thus it can be used as a reference for the natural decline Therefore, their MACs are assumed to begin further up to the right of carbon intensity) is 2.82%, which is closer to 2.72% in the along national MAC curve where the curve is steeper. Similarly, forecasting period [29]. provinces with higher carbon intensities are assumed to have their MACs starting at a lower point where the curve is flatter. 2.1.3. National MAC of China in 2020 Analogy to the previous studies, for the province, l, with lower As for the MAC curve, this paper adopts a logarithmic functional (or higher) carbon intensity, assuming the abscissa of the starting 0 form [33], point Rl is rlðrl > 0orrl < 0Þ, such that eð1 rlÞ¼el; where el is the carbon intensity. The shift distance r tells us by how much per- MACðRÞ¼a þ b lnð1 RÞð1Þ l cent the country would have to reduce its national carbon intensity

Fig. 1. GDP, CO2 emission and carbon intensity in the baseline.

Table 1 Ai ¼ Eið1 riÞ½1 expðt=bÞ ð8Þ National marginal abatement cost in 2020 unit: Yuan (2002 price)/tCO2. P P With the constraint condition iAi = iqi, the equilibrium car- Reduction percentage (%) National MAC in 2020 bon price t⁄ satisﬁes, 1 6.69 P 5 34.14 P iqi 10 70.14 t ¼ b ln 1 ð9Þ iEið1 riÞ 30 237.35 50 463.31 To summarise, the problem can be simpliﬁed to, 70 824.39 8 P q > P k k >A ¼ Eið1 riÞ > i E ð1r Þ > k k k <> P P q q P k k P k k pi ¼bEið1 riÞ bEið1 riÞln 1 ð10Þ > Ekð1rkÞ Ekð1rkÞ > k k > P > qk :þbq ln 1 P k i E ð1r Þ k k k

3. Data preparation

3.1. The calculation of provincial shift distances

In Section 2.2, we detail how to derive the provincial MACs by modifying that of the nationwide. The shift distance is an important parameter need to be estimated. As illustrates in For- mula (3), the shift distance for each province is determined by the difference between its carbon intensity and the national carbon intensity. The national carbon intensity in 2020 can be obtained from the baseline simulation. To estimate the provincial carbon

intensity, this paper simply assumes the provincial CO2 emissions Fig. 2. Derivation of provincial MACs [15,35,36]. in 2020 equal to the national emissions in 2020 multiplied by their emissions shares in 2010, and similarly the provincial GDP in 2020 if it were to reach the steepness of the MAC curve of province l. in baseline can be estimated as the national GDP in 2020 multi- Hence, plied by their GDP shares in 2010. e r ¼ 1 l ð3Þ l e 3.2. Provincial CO2 reduction targets allocation Then, for province i (no matter low carbon intensity or high carbon intensity), the marginal abatement cost MCi is computed as, To allocate the national carbon intensity reduction targets ratio- nally across Chinese provinces, many studies have been conducted R MC ðR Þ¼MACðR þ r ÞMACðr Þ¼b ln 1 i ð4Þ while there are still debates on this issue [36–38]. For example, Li i i i i i 1 r i et al. adopt a nonlinear programming model to obtain the optimal where Ri is the ratio of emissions reduction, MAC is the national CO2 abatement allocation among provinces in China with a mini- marginal abatement cost. Formula (4) can be written in another mum total abatement cost [36]. Wei et al. develops a CO2 abate- form, ment capacity index (ACI) based on weighted equity and efficiency indexes, and they find that there exists a large gap in Ai MCiðAiÞ¼b ln 1 ð5Þ potential reduction capability and marginal abatement cost among Eið1 riÞ the eastern, middle and western regions [37]. Yu et al. propose a PSO–FCM–Shapley approach to allocate carbon emission reduction where Ai is the quantified emissions reduction, Ei is carbon emis- target among 30 provinces, and the results indicate that provinces sions in the benchmark. Therefore, the total abatement cost Ci satisfies, with high cardinality of emissions have to shoulder the largest Z reduction, whereas provinces with low emission intensity met Ai x the minimum requirements for emission in 2010 [38]. In sum, C ðA Þ¼ b ln 1 dx i i E ð1 r Þ assigning targets at the provincial level is a very complicated 0 i i problem. The reasonable allocation may need a wide variety of Ai ¼b½Eið1 riÞAi ln 1 bAi ð6Þ considerations—marginal cost of abatement, equity, perceptions Eið1 riÞ of fairness, sector structure, and level of development, and so on. This research assumes that the approach to allocate carbon 2.3. Carbon emissions trading model emissions reduction target among provinces in the Twelfth Five- Year Plan will be employed to 2020 as well. Actually, to achieve The total costs for province i in ETS include two parts. The first the 17% reduction in the carbon intensity over the period 2010– one is the abatement cost of emissions reduction Ai. The other one 2015 effectively, the Chinese government has assigned different is the expenditure to buy emission rights through emissions trad- levels of reduction targets to 31 provinces with negotiation among ing market. The objective function is, stakeholders (see Table A1), taking the difference of the provincial development stages into account, which is intended to ease the pi ¼ CiðAiÞþt ðq AiÞð7Þ i pressure on less affluent regions or regions targeted for accelerated where qi is the initial reduction quota, and t is the carbon price. development. Therefore, this allocation partial embodies the @p Regarding the first order conditions, i ¼ 0, we have, principal of fairness. However, the provincial targets are intensity @Ai Please cite this article in press as: Cui L-B et al. How will the emissions trading scheme save cost for achieving China’s 2020 carbon intensity reduction target? Appl Energy (2014), http://dx.doi.org/10.1016/j.apenergy.2014.05.021 L.-B. Cui et al. / Applied Energy xxx (2014) xxx–xxx 5 targets, and it is necessary to transform them into quantified Table 3 emission reduction volume. Cui et al. addressed this issue and The cost-saving effects of trading markets with different reduction targets. transformed the intensity targets into quantified targets for all NETS PETS CETS 31 provinces in the Twelfth Five Year Plan (see Table A1) [15]. Total emissions reduction (MtCO2) 819.01 819.01 819.01 We assumed that the provincial reduction targets in 2020 are pro- Abatement cost (million yuan) 28,153 26,886 21,489 portional to their reduction burdens in the Twelfth Five-Year Plan. Cost-saving (%) 0 4.50 23.67 Carbon price (yuan/tCO2) – 98.87 53.17

Emissions trading (MtCO2) – 27.76 147.55 4. Empirical results and analysis

4.1. Policy scenarios In NETS, the marginal abatement costs of the eastern coastal In this paper, the target of 42.5% reduction in national carbon provinces (with higher per-capita GDP, higher emissions share, intensity relative to the 2005 level by 2020, which is the average but lower emission intensity) are much higher than those of the of the 40–45% commitment of China at the 2009 Copenhagen western provinces. For example, the marginal abatement costs of

Summit, is assumed the emission reduction target. Three policy Beijing and Guangdong are almost 155 yuan/tCO2, while Qinghai scenarios of no emissions trading scheme among provinces (NETS), and Tibet have zero abatement costs. The huge differences in mar- emissions trading scheme only containing the pilots (PETS), and ginal abatement costs among provinces imply a high level of the unified national emissions trading market (CETS), is designed national abatement cost, which indicates the necessity of the emis- and analysis. It should be noticed that in PETS, Shenzhen was inte- sions trading market in China. grated into Guangdong for data availability. More details about the policy scenarios please refer to Table 2. (2) In PETS, six pilot provinces can reduce their cost by partici- pating in the ETS. As Table 4 shows, the cost-savings of 4.2. Simulation results Guangdong is 572 million yuan, which is the largest of all. This is followed by Hubei with 406 million yuan. The 4.2.1. The cost-saving effects cost-saving effects of Chongqing and Beijing are 151 and Table 3 illustrates the cost-saving effects of the emissions trad- 119 million yuan. In PETS, the cost-saving effects of Shang- ing scheme in China. To achieve the 42.5% reduction in carbon hai and Tianjin are not obvious. The reason is that the mar- intensity over the period 2005–2020, the national emissions need ginal abatement costs for the two cities are not very far off to be reduced by about 819 MtCO2, accounting for 7.98% of the from the equilibrium carbon price. total emissions in 2020. If there is no emissions trading among provinces (NETS), the total abatement cost is about 28 153 million Fig. 4 with details in PETS shows that, Guangdong, Beijing and yuan, accounting for 0.05% of national GDP in 2020. In the case of Shanghai are the emissions rights buyers, while Guangdong takes PETS, the total abatement cost is 26 886 million yuan, which over 75% of the market share. To the contrary, Tianjin, Chongqing, implies that a 4.50% cost-saving is achieved, and the equilibrium and Hubei will sell emission rights to benefit from PETS, and carbon price is about 99 yuan /tCO2. If the unified emissions almost 68% of the certified emission reductions will be provided trading market could be implemented (CETS), the total abatement by Hubei. Overall, the carbon trading volume among pilot prov- cost would be 21 486 million yuan, which suggests that a 23.67% inces in PETS is about 28 MtCO2, and accounts for 17% of the total cost-saving is achieved, and the equilibrium carbon price is CO2 reduction in the pilots. The cost-saving is 1 268 million yuan,

53.17 yuan/tCO2. accounting for 14% of total abatement costs of the pilots.

4.2.2. Provincial behaviours in ETS (3) In CETS, all provinces could buy (or sell) emission rights in In this section, the effect of the carbon emissions trading on the national market. However, different provinces provincial behaviours will be discussed. experience different cost-saving effects, as they have different marginal abatement costs. As shown in Table 5, the cost- (1) In NETS scenario, provinces achieve their reduction targets saving effects of the eastern and western provinces are more on their own. Fig. 3 shows that different provinces have dif- pronounced than the central provinces of China. For exam- ferent reduction burdens and different abatement costs. ple, the cost-saving effects of the eastern provinces such as More specially, the ﬁve provinces with the largest reduction Guangdong and Jiangsu are 1 945 and 831 million yuan,

in CO2 emissions are Shandong (82 MtCO2), Jiangsu (71 and the cost-saving effects of the western provinces such MtCO2), Hebei (68 MtCO2), Guangdong (63 MtCO2), and Lia- as Xinjiang and Guizhou are 560 and 187 million yuan, while oning (54 MtCO2), while some western provinces such as the middle provinces such as Anhui and Henan have limited Xinjiang and Tibet experience zero burdens. The reason for cost-saving effects, almost approaching zero. the results is that the reduction burdens for some western provinces are relatively small that they are able to achieve For the emissions trading, the eastern provinces will buy emis- by relying on the technology progress. sion rights to reduce their abatement costs, while the western provinces may earn beneﬁts by reducing more. When the market

Table 2 Policy scenarios.

Reduction target Denotes Policy description 42.5% reduction in carbon intensity relative to 2005 levels NETS No emissions trading market, the reference case by 2020 PETS The coverage of emissions trading market contains Beijing, Chongqing, Shanghai, Tianjin, Guangdong and Hubei CETS All provinces involve in trading market, and a uniﬁed national emissions trading market has been achieved

Fig. 3. provincial reduction in the case of no emissions trading among provinces.

Table 4 provincial reduction in the case of PETS.

a CO2 reduction Emissions trading Abatement cost Total cost Cost-savings

(MtCO2) (MtCO2) (Million yuan) (Million yuan) (Million yuan) Beijing 8.33 4.25 402 823 119 Tianjin 19.30 1.43 931 789 6 Shanghai 25.29 2.62 1220 1478 14 Hubei 45.10 18.74 2175 322 406 Guangdong 41.65 20.89 2009 4074 572 Chongqing 19.92 7.58 961 211 151 Total 159.60 0.00 7698 7698 1268

a Note: the positive stands for buying emission rights, the negative stands for selling emission rights.

Fig. 4. Carbon emissions trading in PETS.

reaches equilibrium, Guangdong (40 MtCO2), Jiangsu (33 MtCO2) Meanwhile, the total abatement costs increase from 6 602 million and Zhejiang (22 MtCO2) are the three largest buyers, and more yuan to 680 655 million yuan. than 60% of emission rights will be purchased by these three prov- Fig. 5 illustrates the cost-saving effects of the uniﬁed emissions inces. To the contrary, the three largest emission right sellers are trading scheme with different intensity targets. It shows that along

Inner Mongolia (36 MtCO2), Shanxi (23 MtCO2) and Xinjiang with the increasing of intensity targets, the cost-saving effects (21MtCO2), sponsoring more than 50% of the selling market. become more obvious. More specifically, with the carbon intensity reduction increases from 40% to 60%, the cost-saving effects 4.2.3. The cost-saving effects of CETS with more ambitious targets increase from 23% to 32%, and the equilibrium carbon price The previous study has shown how China could reduce the total increases from 25 yuan/tCO2 to 269 yuan/tCO2. In particular, for abatement costs by establishing emissions trading scheme, espe- the 45% target, the total abatement cost will be reduced by 24% cially in the case of CETS. In this section, several more ambitious if the unified emissions trading market could be established, and targets will be investigated. It is assumed that the unified carbon the carbon price is almost 78 yuan/tCO2. emissions trading could be established in 2020, and the initial provincial emissions reduction shares maintain unchanged with above 5. Sensitive analyses analysis. Five cases have been considered for the simulation, and we 5.1. Changes in annual GDP growth assume 40%, 45%, 50%, 55%, and 60% reduction in carbon intensity relative to the 2005 levels by 2020 for each case. As Table 6 shows, The previous analysis assumes that the annual GDP growth rate with the intensity targets increase from 40% to 60%, the quantified is 7.5% over the period 2011–2015 and then decreases to 5.5% over emissions reduction increases from 408 MtCO2 to 3692 MtCO2. the period 2016–2020. To analyse the sensitivity effect of the

Table 5 Provincial reduction in the case of CETS.

a CO2 reduction (MtCO2) Emissions trading (MtCO2) Abatement cost (Million yuan) Total cost (Million yuan) Cost-savings (Million yuan) Guangdong 23.15 39.51 607 647 1945 Jiangsu 37.78 33.39 991 1025 831 Zhejiang 19.57 22.34 513 536 724 Shanghai 14.06 13.89 369 383 388 Shandong 70.82 11.22 1858 1869 49 Beijing 4.63 7.98 122 130 397 Tianjin 10.73 7.16 281 289 134 Fujian 12.58 7.01 330 337 109 Sichuan 19.73 6.75 518 524 64 Hunan 21.61 2.92 567 570 11 Hubei 25.07 1.29 658 659 2 Chongqing 11.07 1.27 291 292 4 Jiangxi 13.88 1.21 364 365 3 Shaanxi 17.94 0.19 471 470 0 Guangxi 12.75 0.85 334 334 2 Anhui 23.55 1.06 618 617 1 Hainan 1.55 1.55 41 39 42 Tibet 1.60 1.61 42 40 43 Jilin 22.97 4.01 603 599 19 Qinghai 4.33 4.35 114 109 117 Gansu 13.03 4.81 342 337 48 Heilongjiang 21.20 5.20 556 551 35 Liaoning 60.04 5.80 1575 1570 15 Henan 51.62 5.84 1354 1349 18 Ningxia 13.06 6.58 343 336 90 Yunnan 25.57 8.33 671 663 74 Hebei 79.32 11.80 2081 2069 48 Guizhou 29.41 14.23 772 757 187 Xinjiang 20.79 20.86 546 525 560 Shanxi 60.89 22.73 1598 1575 231 Inner Mongolia 74.70 36.17 1960 1924 475 Total 819.00 0.00 21,489 21,489 6664

a Note: the positive stands for emission rights buyer, the negative stands for emission rights seller.

Table 6 Emissions reduction effects with different intensity reduction targets.

Reduction in CO2 intensity (%) Quantiﬁed reduction (MtCO2) Relative to 2020 levels (%) Total abatement costs (million yuan) Relative to 2020 GDP (%) 40 408 3.98 6602 0.01 45 1229 11.98 62,520 0.11 50 2050 19.98 182,802 0.33 55 2871 27.98 380,493 0.69 60 3692 35.98 680,655 1.24

Table 7 Nature decline of carbon intensity with different GDP growth assumption.

S1 (%) S2 (%) S3 (%) S4 (%) Annual GDP growth (2011–2015) 6.5 7.5 8 9 Annual GDP growth (2016–2020) 4 5.5 6 6.5 Carbon intensity decline (2010–2020) 38.34 37.51 37.32 37.17

of carbon intensity decreases from 38.34% to 37.17%. Therefore, the natural decline of carbon intensity is not sensitive to GDP growth assumptions. Fig. 6 illustrates the cost-saving effects of the carbon emissions trading with different GDP growth scenarios. The cost-saving Fig. 5. The cost-saving effects with ambition reduction target. effects of the uniﬁed emissions trading market will increase from 21% to 26% when the annual GDP growth rates vary from S1 to

S4, while carbon prices show some decrease from 58 yuan/tCO2 annual GDP growth rate, four GDP growth scenarios are designed. to 48 yuan/tCO2, with a modest change. In particular, we assume that the annual GDP growth rate increases from 6.5% to 9% (see Table 7) during the Twelfth Five-Year Plan, 5.2. The shape of the national MAC and the corresponding annual GDP growth rate increases from 4% to 6.5% over the period 2016–2020. Table 7 presents the natural The national MAC curve is a key input to derive the provincial decline of carbon intensity with different scenarios. As we can see, MAC curves. Different MACs may result in different carbon prices. with the GDP growth rate varies from S1 to S4, the natural decline To investigate the effects of the shapes of national MAC on the

5.3. Initial allocation of emissions reduction targets

The paper assumes that the reduction targets for provinces in the Twelfth Five-Year Plan will be applied for 2020 as well. These reduction targets are also a key determinant of the cost-saving due to emissions trading. To investigate the effects of the initial allowance allocation on the results, this research introduces three more approaches. The ﬁrst one is the Grandfathering approach, which uses provincial previous emissions levels to set their future permits. The second one is the population criterion, which assumes

that all people have equal right to emit CO2. The third one is the Fig. 6. The cost-saving effects of the CETS with different annual GDP growth equal intensity reduction approach, which assumes that all assumptions. provinces experience the same decline of carbon intensity over the period 2010–2020. It should be noted that the emission reduction targets for some provinces are zero under the above approaches. Actually, the neg- results, we introduce two more curves. The first one is McKinsey ative targets imply that these provinces are allowed to emit more MAC curve, which is a kind of bottom-up approaches that depend than their baseline emission levels in 2020. To avoid this, we follow on the marginal cost and emissions reduction potential of all [40] to set the emission reduction targets for these provinces at available technical options [22,24]. The other one is MIT Emissions zero and the emission reduction targets for other provinces are Prediction and Policy Analysis (EPPA) MAC curve, which is a kind of then proportionally adjusted [14]. top-down approaches that generate MAC curves by recording the Fig. 8 shows that the cost-saving effects are sensitive to the emission reductions achieved at different marginal costs [21]. initial allocation methods. More specially, if the provincial The MAC curves for the above two approaches are measured in emission allowances are allocated based on the population 2002 price of Chinese currency Yuan for consistency. Following the approach, the total abatement costs could decrease by 48%, which McKinsey approach, the national MAC of China in 2020 was is the largest of all. In the case of the equal intensity reduction McKinsey estimated as MAC2020 ðRÞ¼2884:73 lnð1 RÞ720:06. The approach, China could reduce its total cost by 11%, which is the simulation data is from the research provided by Hanaoka and smallest of all. The cost-savings in the Grandfathering approach EPPA Kainuma [39]. On the other hand, we have MAC2020ðRÞ¼303:82 is about 12%, which is almost half of that in the base case. This lnð1 RÞ16:70 by fitting the data from the analysis of Morris comparison implies the complexity of the allocation of initial emis- et al. [21]. This calculation shows that the MAC in the McKinsey sion permits, while the approach China adopted for the Twelfth approach is much larger than that in the EPPA approach. The rea- Five Year Plan is somehow not bad. son is that China has maximum technically feasible abatement potential in the former method. For example, as stated by Hanaoka 5.4. The provincial MACs and Kainuma, China’s carbon emissions could not reduce more than 3.4 GtCO in 2020 [40]. 2 We have no reliable alternative but to use CTT approach to Fig. 7 illustrates the cost-saving effects of the carbon emissions derive provincial MACs on the basis of the national MAC. Although trading with different national MACs. It should be mentioned that some scholars estimated the cost-saving effects of the interprovin- the CTT approach is used in these three curves to derive provincial cial emissions trading scheme of China, the provincial MACs are MACs. The cost-saving effect changes little, while carbon prices not presented in their papers [1,15,41]. One exception is the vary substantially across different national MACs. More specially, analysis provided by Zhou et al., which gives an interesting estima- the McKinsey approach will result in a carbon price of 225 yuan/ tion of provincial MACs in China [14]. However, different from the tCO , which is nearly 4 times of that in the base case, while EPPA 2 concept of MAC, the independent variable of the provincial MACs model achieves a carbon price of 24 yuan/tCO , which is less than 2 in Zhou et al.’s analysis was defined as the amount of cumulative half of that in the base case. CO emission reduction since 1996, rather than the emissions In a word, the cost saving effect of this paper shows is close to 2 abatement relative to a target year (i.e. 2020). This definition those of other MAC approaches, while carbon prices among differ- makes it difficult to be compared with the results of this paper. ent MACs are significantly different, the price in this paper is Yet, to investigate the rationality of provincial MACs of this between McKinsey approach and EPPA approach. paper, we present an interesting comparison with fresh pilot prices in Table 8. With the CCT approach, Cui et al. once studied the cost- savings of carbon emissions trading in China for achieving the 17% target during the Twelfth Five-Year Plan [15], and they find that

Fig. 7. The cost-saving effects of the CETS with different national MACs. Fig. 8. The cost-saving effects of the CETS with different allocation approaches.

Table 8 about 819 MtCO2, accounting for 7.98% of the total emissions in The carbon prices for the pilots in the observation period. 2020. Secondly, the partial emissions trading market (PETS) and

Pilots Product Observation period Carbon price (yuan/tCO2) the uniﬁed national trading market (CETS), which may result in a Min Max Average carbon price of 99 yuan/tCO2 and 53 yuan/tCO2, could reduce the total abatement costs by 4.50% and 23.67%, respectively. Thirdly, Beijing BEA 2013.11.28–2014.4.3 50.00 57.00 52.75 Tianjin TJEA 2013.12.26–2014.4.3 25.48 50.11 30.88 the emissions trading scheme could yield different cost-saving Shanghai SHEA13 2013.11.26–2014.4.3 27.00 50.90 33.94 effects for different provinces, and the cost-saving effects of the Guangzhou GDEA 2013.12.19–2014.4.3 60.00 66.00 62.54 eastern and western provinces are more pronounced than the cen- Shenzhen SZA 2013.6.18–2014.4.3 28.00 130.90 75.31 tral provinces of China. Hubei HBEA 2014.4.2–2014.4.3 21.00 24.20 22.60 This paper provides a preliminary evaluation of the cost-saving Note: Chongqing was not included because its carbon trading project has not been effects of carbon emissions trading in China, and the results show implemented. that the emissions trading scheme will play an important role in

promoting China to reduce CO2 emissions in a cost-effective way. the partial emissions trading (PETS) may result in a carbon price of In fact, the proposed 17% intensity target during Twelfth Five-Year 70.55 yuan/tCO2. Most recently, 6 pilots of China have started their Plan is just the first try for China to take quantified reduction mea- carbon emissions trading projects, and the released trading infor- sures. With more stringent targets and the increase in marginal mation may be useful to evaluate the validity of modelling results. abatement cost, the carbon trading volume will be enlarged. The The carbon prices in Table 8 are from the official websites of 6 emissions trading scheme will play an increasing important role pilots. It shows that carbon prices vary substantially across differ- in helping China to reduce CO2 emissions cost effectively in the ent pilots. More specially, the city of Shenzhen is the first pilot in future. China to introduce ETS, and the carbon prices range from This research employs a single-region CGE model, CHINAGEM, 28.00 yuan/tCO2 to 130.90 yuan/tCO2, with an average of to simulate the effects, and the climate policies of other countries 75.31 yuan/tCO2, which is the largest of all. The carbon prices in have not been involved. In fact, the emissions reduction measures Guangzhou range from 60.00 yuan/tCO2 to 66.00 yuan/tCO2, and adopted in other countries will change China’s MAC through trade the average level is 62.54 yuan/tCO2. Hubei starts its emissions effects. However, Ellerman and Decaux indicate that the EPPA- trading project on April 2014 and the carbon prices vary around based curves are very stable and thus robust to other countries’ 22.60 yuan/tCO2. We are happy to find that the modelling result behaviours [42], and this finding based on a multi-region CGE 70.55 yuan/tCO2 falls in the price interval of these pilots. The inter- model may be useful to reduce the concerns of climate targets esting finding may support the validity of the provincial MACs abroad on China’s emissions trading market. This paper does not from CTT approach. consider the possible influence of Clean Development Mechanism (CDM) on local ETS due to data availability. In fact, although China 6. Conclusion and discussion used to be a dominant provider of the CDM credits, the EU has adjusted its CDM policy in the third phase, which stipulates that The emission trading scheme is regarded as a cost-effective way only Least Developed Countries (LDCs) as sources of post-2012 for mitigating CO2 emissions, and presently China signals strong new project credits [43,44]. It implies that the CDM credits from intentions to establish a national emissions trading market. This the new projects registered in China can’t be sold in the EU-ETS research explores on the cost-saving effect of carbon emissions anymore from 2013. trading in China for achieving its 2020 intensity reduction target. There are other limitations in our research. Firstly, this paper Firstly, an interprovincial emissions trading model is constructed. focuses on the cost-saving effects of interprovincial carbon Then, three kinds of policy scenarios, including no carbon emis- emissions trading, and do not consider the potential for emissions sions trading among provinces, an emission trading scheme only trading among industries or enterprises. Secondly, referring to covering the pilots, and a unified national emissions trading other research, this paper estimates the provincial MACs on the scheme, have been analysed followed by a few sensitivity analysis. basis of national levels with CTT approach, and more accurate mea- With the simulation, this paper finds some interesting results. sures about provincial MACs in 2020 will result in more convincing Firstly, to achieve 42.5% reduction in carbon intensity over the per- results. Thirdly, transaction costs haven’t been considered in this iod 2005–2020, the national emissions need to be reduced by research. As a matter of fact, transaction costs do decrease the

Table A1 The provincial reduction targets in the Twelfth Five-Year Plan.

a a Province Intensity reduction (%) Quantiﬁed reduction (MtCO2) Province Intensity reduction (%) Quantiﬁed reduction (MtCO2) Beijing 18 9.83 Hubei 17 20.57 Tianjin 19 13.95 Hunan 17 19.14 Hebei 18 52.73 Guangdong 19.5 48.82 Shanxi 17 29.84 Guangxi 16 9.29 Inner Mongolia 16 30.17 Hainan 11 0.00 Liaoning 18 42.35 Chongqing 17 9.63 Jilin 17 14.81 Sichuan 17.5 20.66 Heilongjiang 16 12.51 Guizhou 16 11.88 Shanghai 19 21.78 Yunan 16.5 13.48 Jiangsu 19 55.47 Tibet 10 0.00 Zhejiang 19 32.66 Shannxi 17 13.86 Anhui 17 17.56 Gansu 16 6.43 Fujian 17.5 15.27 Qinghai 10 0.00 Jiangxi 17 11.78 Ningxia 16 5.08 Shandong 18 64.01 Xinjiang 11 0.00 Henan 17 33.75

a Note: the provincial quantiﬁed emissions reduction targets are from Cui et al.’s estimation [15].

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