The impact of China's carbon allowance allocation rules on the product prices and emission reduction behaviors of ETS-covered enterprises

Energy Policy 86 (2015) 176–185

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The impact of China's carbon allowance allocation rules on the product prices and emission reduction behaviors of ETS-covered enterprises Yue-Jun Zhang b,c,n, Ao-Dong Wang a,d, Weiping Tan e a

School of Management and Economics, Beijing Institute of Technology, Beijing 100081, PR China Business School of Hunan University, Changsha 410082, PR China c Center for Resource and Environmental Management, Hunan University, Changsha 410082, PR China d Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, PR China e Hologic Corporation, 2585 Augustine Dr, Santa Clara, CA 95054, USA b

H I G H L I G H T S








Analyze the impact of carbon allowance allocation rules on ETS-covered enterprises. For grandfather, self-declaration and auction, they may maximize current profits. For benchmark, they care the effect of current decisions on the coming profits. The optimal product price positively relates to low-carbon awareness and subsidy. Carbon price, low-carbon awareness and subsidy rise leads their emission reduction.

art ic l e i nf o

a b s t r a c t

Article history: Received 6 May 2015 Received in revised form 25 June 2015 Accepted 4 July 2015

It is an important task for China to allocate carbon emission allowance to realize its carbon reduction target and establish carbon trading market. China has designed several allocation rules within seven pilot regions. What influence those rules may cause is closely related with the enthusiasm of emission trading scheme (ETS) covered enterprises' participation in carbon market, and more importantly, with the mechanism design and sustainable development of carbon market. For this purpose, the multi-stage profit model is developed to analyze the ETS-covered enterprises' product prices and emission reduction behaviors under different allocation rules. The results show that, first, under the rules of grandfathering, self-declaration and auctioning, when deciding the optimal product price and optimal carbon emission reduction, those enterprises may focus on maximizing current stage profit; however, under the rule of benchmarking, those enterprises may care more about the impact of current decisions on the profit in next stage. Second, the optimal product price policy is positively correlated with the price of the same kind products, consumers' low-carbon awareness and government subsidy. Finally, along with the increase of carbon price, consumers' low-carbon awareness and government subsidy and the decrease of carbon emission cap, those enterprises tend to reduce carbon emissions. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Carbon emissions Carbon allowance allocation Product prices Emission reduction behaviors

1. Introduction The climate change resulted by global greenhouse gas emission has become a significant issue that drew attention from the international community. Among all kinds of greenhouse gas, the impact of CO2 accounts for over 50% of the total atmosphere warming (IPCC, 2007). Therefore, controlling the emission of greenhouse gas, mainly the CO2, becomes a critical way to alleviate n Corresponding author at: Business School of Hunan University, Changsha 410082, PR China. Fax: þ86 731 88822899. E-mail address: [email protected] (Y.-J. Zhang).

http://dx.doi.org/10.1016/j.enpol.2015.07.004 0301-4215/& 2015 Elsevier Ltd. All rights reserved.

the effect of global warming on human activities (Wei et al., 2008). After the issue of Kyoto Protocol, based on the carbon emission trading, three mechanisms have formed two carbon emission trading markets, i.e., the project-based market and the allowancebased market; and the latter one has been developing very fast. The European Union emission trading scheme (EU ETS) is currently the world's largest and longest-running carbon emission trading market (European Commission, 2014). Since its official launch in 2005, the EU ETS has made remarkable achievements. So far, the EU ETS has entered into the third phase (2013–2020), and will cover more greenhouse gases and sectors. EU attempts to use the average emission of the top 10% enterprises which previously

Y.-J. Zhang et al. / Energy Policy 86 (2015) 176–185

have the best greenhouse gas reduction efficiency, as the benchmarks, to develop a unified carbon emission allowance. The ratio of auction will see a gradual increase, anticipating a 100% auction in 2027 (European Commission, 2009). The development indications of global carbon market show that low carbon economy and development of carbon trading market will be an inexorable trend. However, in China, the coaldominated energy structure and the continuous rapid economic growth have positioned China as the largest CO2 emission country in the world since 2008 (BP, 2014). This led to a great attention on Chinese CO2 emission from academic circles, political circles as well as the social public (Peters et al., 2007; Liang et al., 2013). The carbon emission reduction in China has significant strategic meaning for the effectiveness in slowing down the global climate change, and the sustainable development of social economy (Den Elzen et al., 2011; Van Ruijven et al., 2012). During the APEC meeting in November 2014, China and United States issued a joint statement that China planed to reach the CO2 emission peak around 2030. In fact, China has explicitly put forward the plan to establish a carbon trading market in 2011. According to the roadmap published by the National Development and Reform Commission (NDRC) recently, the establishment of China's national carbon market has been divided into three stages. The first stage from 2014 to 2015 is the preparation stage, during which the relevant laws and legislations will be issued, the technique standards developed and the allowance allocation method formulated. The second stage from 2016 to 2020 is the operation and improvement stage, also the first step of national carbon market setting-up. During this period, NDRC will comprehensively launch the operation of national unified carbon market and improve it at the time. The third stage is the expansion period after the year 2020. During this time, the range of the involved enterprises will be enlarged, as well as the trade variety of carbon market, meanwhile the possibility of matching the international carbon market pilots will be explored. Since 2013, seven ETS pilot regions in China have come up with carbon emission allocation plans one after another, and these regions are Shenzhen, Shanghai, Beijing, Guangdong, Tianjin, Hubei and Chongqing. In accordance with local political, economic and industrial differences, these seven pilot regions adopt inconsistent allocation rules, like benchmarking, grandfathering, and auctioning and self-declaration allocation method under the cap-andtrade system. Their specific information is shown in Table 1. Currently, the carbon emission allowance in Shenzhen, Shanghai, Beijing, Guangdong, Tianjin and Hubei is mainly freely allocated, supplemented with non-gratuitous allocation, and gradually expanding the proportion of non-gratuitous allocation. Chongqing, however, adopts the method as self-declaration allocation under a cap-and-trade system. At the initial stage, total carbon allowance was allocated freely, and will be gradually expanding the proportion of non-gratuitous allocation. Based on local and industrial differences, the allocation rules mainly involve “benchmarking”, “grandfathering”, “auctioning” and “self-declaration allocation method under cap and trade system”. From the national perspective, since 2013, Chinese government

177

has implemented seven ETS pilot regions to investigate how to reach the announced reduction target as lowering 40–45% carbon intensity during 2005 to 2020. From the micro- or enterprise perspective, those enterprises in ETS pilot regions have begun to study the impact of carbon allowance allocation rules on their products and emission reduction activities, which has led to adjusting the prices of their products and working out best emission reduction policy, with the objective to realize the goal of maximizing the profits. Much previous literature on carbon emission analyzed carbon emission or carbon allowance allocation from the macro-perspective (such as national, industrial, regional and environmental level) (Cong and Wei, 2010b; Ozturk and Acaravci, 2013; Marleen et al., 2014; Pan et al., 2014; Zhang et al., 2015; Yu et al., 2015). However, rarely has literature analyzed the impact of allocation rules from the micro-perspective, such as the impact on enterprise behaviors. For instance, Yu et al. (2015) propose a multi-factor environmental learning curve estimation model to estimate the carbon intensity abatement potentials of the 30 Chinese provinces; Zhang et al. (2015) propose a non-radial Malmquist index for analyzing the reasons of dynamic changes of carbon emission in transportation industry, pointing out that technical progress is the major influence factor; Pan et al. (2014) allocate carbon allowance according to per capita cumulative carbon emissions, to achieve a globally equitable carbon emission space; Zhang et al. (2014) distribute carbon allowance according to the cooperation relations of the China's eight regions, using the Shapley model. In addition, some literature has studied the carbon emission policies of enterprises under certain carbon allocation rules; for example, Zetterberg (2014) analyze within EU ETS, the impact of benchmarking method on prices of enterprises products and carbon emissions, but the paper does not take into consideration the differences of influences between benchmarking and other methods on enterprises. However, it is important to study enterprises' emission reduction activities under certain or different carbon allocation rules, which can help adopt proper carbon allocation rules according to the different stage of carbon market development. This therefore ensures ETS-covered enterprises to enjoy competitiveness over their non-ETS-covered counterparts, preventing carbon leakage and enterprise migration. According to Paroussos et al. (2015), the total carbon leakage is around 28% over the 2015–2050 period in the world, when the EU acts alone with moderate Armington trade substitution elasticity values. If USA joins the EU effort, the leakage rate may drop only to 25% and if alternatively China joins the EU effort, the leakage rate may drop to 3% over the 2015–2050 period. This paper focuses on enterprises' product price policy and carbon emission policy under different allocation rules. Based on Chinese ETS pilot regions' carbon allowance allocation rules, this paper may further analyze the impact of different allocation rules on enterprises. The contributions of this paper can be summarized as three aspects. First of all, it will explore the optimal product pricing policy for enterprises under the constraint of carbon allowance allocation rules. Under different carbon allocation rules, the costs for enterprises to acquire carbon allowances are different, so are the quantities of allowance, and both the costs and quantities will

Table 1 The carbon allowance allocation rules of Chinese ETS pilot regions.

Grandfather Benchmark Self-declaration Auction

Shenzhen

Shanghai

Beijing

Guangdong

Tianjin

Hubei

√

√ √

√ √

√ √

√ √

√ √

√

√

Chongqing

√ √

√

178

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have an effect on product prices. If the ETS-covered enterprises get more allowances, they will reduce the efforts of emission reduction; therefore the costs of emission reduction will be relatively cheaper or even be negative (by trading the extra allowances in carbon market), thus the price of their products will be more competitive compared with those non-ETS-covered enterprises of the same kind. However, if the cost of acquiring allowances is relatively higher, which increases production costs for ETS-covered enterprises in a covert act, thus they will be at a disadvantage place when competing with those non-ETS-covered enterprises. Second, this paper studies the carbon reduction policy of ETScovered enterprises under different carbon allowance allocation rules. Under different rules, enterprises acquire different carbon emission allowances, which lead the enterprises to decide the effort of emission reduction according to their reduction costs. If the costs of carbon emission reduction are relatively cheaper and the price of market allowance is relatively higher, the ETS-covered enterprises will intensify their effort in reducing emission in order to sell more extra allowances in carbon market. Furthermore, this paper performs a comparative analysis on the impact of different carbon emission allowance allocation rules on the carbon emission reduction activities and development of ETS-covered enterprises, based on the former two parts of contribution. The remaining of this paper is organized as follows. Section 2 introduces the relevant literature review and research methodologies. Section 3 analyzes the research results; and finally Section 4 presents the major conclusions and policy suggestions.

2. Methods 2.1. Relevant literature review The establishment of EU carbon trading market shows that, along with the increase of emission reduction activities, carbon allowance allocation rules gradually created a transition from the grandfathering method to the benchmarking method, then completely to the auctioning method (Ellerman et al., 2014). Meanwhile, Chinese ETS pilot regions attempt to adopt the benchmarking method under the condition of available data, while expanding the proportion of the auctioning method. For example, Guangdong required all the ETS-covered enterprises purchase at least 3% of the total allowance from 2013 to 2014, and the lowest price was 60 RMB (about 9.59 USD) per ton CO2. Otherwise, enterprises were not able to get the rest allowances for free. The ratio will increase to 10% in 2015.1As can be seen, Chinese carbon market will realize the transition of carbon allowance allocation rules just as the EU carbon market. As the design focus of carbon trading market, the allocation rules not only decide the operating mechanism of the market, but also impact the emission allowance of the ETS-covered enterprises, which may affect their emission reduction policies and carbon allowance trading and in turn affect their production costs and eventually influence their product pricing and emission reduction decision-making. Consequently, under the constraint of different carbon allowance allocation rules, the pricing process, the emission reduction decision-making of the ETS-covered enterprises, and the effect differences of individual rules, have become important and urgent issues nowadays. So far, the research of carbon allowance allocation rules can be roughly divided into the following categories. The first category mainly includes allocation rules like grandfathering and benchmarking methods in light of fairness. For

example, Rose et al. (1998) propose the grandfathering allocation norm on the issue of global climate change, and this norm allocates carbon allowance freely in accordance with the historical cumulative emissions. However, this norm goes against the polluter-pays principle, prone to distort incentives. Grandfathering, as a gratis allocation method, is easier for enterprises to accept at the early stage of carbon market, therefore Chinese ETS pilot regions mainly adopt grandfathering allocation at present, as shown in Table 1. Meanwhile, some scholars take history and other aspects into consideration on the basis of fairness. For example, Pan et al. (2014) allocate carbon allowance according to per capita cumulative carbon emissions to achieve a globally equitable carbon emission space; Ringius et al. (2002) put forward a comprehensive index which includes individual equality, sovereign equality and polluter-pays principle to allocate. There are also scholars proposing the benchmarking method which can avoid the distort incentives triggered by grandfathering. For example, Zetterberg (2014) analyzes the carbon allowance allocation using the benchmarking method, as well as the emission reduction policy and the pricing policy of enterprises. Analyzing the carbon market policies of the EU ETS and North America, Zetterberg et al. (2012) believes that benchmarking should be used as the transitive method from grandfathering to auctioning. Benchmarking method can avoid the distort incentives, and most of the Chinese ETS pilot regions are also trying benchmarking as shown in Table 1. The second category is based on efficiency. Many scholars have widely believed that auctioning is better than the traditional grandfathering (Pezzey and Park, 1998; Cramton and Kerr, 2002; Böhringer and Lange, 2005), because auctioning can avoid the low efficiency allocation brought by grandfathering (Cong and Wei, 2010a, 2012; Betz et al., 2006). Auctioning guarantees the efficiency, transparency, and simplicity of the trading system, which can greatly stimulate the investment of low carbon economy. Moreover, it conforms best to the polluter-pays principle, avoiding large amount of free emission from polluters to gain profit (European Commission, 2008). Currently, several Chinese ETS pilot regions, including Guangdong, Hubei, Shenzhen and Shanghai, have tried the auctioning method. Guangdong took the lead in allocating allowance through auctioning. Hubei conducted the first auction on 31st March, 2014, selling 2 million tons of carbon dioxide with the price as 20 RMB (about 3. 198 USD) per ton CO2.2 Shenzhen conducted the first allowance auction on 6th June, 2014 and sold successfully 74.974 thousand tons of allowance, pricing 35.43 RMB (about 5.663 USD) per ton CO2.3 Shanghai's first auction carried out on 30th June, 2014, and through bidding, two pilot enterprises purchased the annual allowance for the year of 2013 altogether 7220 t.4 From the perspective of efficiency, some scholars also consider the carbon emission relations between the regions to allocate carbon allowance. For example, Kander and Jiborn (2014) analyze the carbon export productivity of Sweden, pointing out that Sweden has higher carbon efficiency. Zhang et al. (2014) analyze the cooperative relationship in carbon emission reduction among China's eight regions and propose to use the Shapley model to allocate carbon allowance for each region. Besides, DEA model allocation method is well recognized as an carbon allowance allocation method based on efficiency, and some authors have shed light upon this. For instance, Feng et al. (2015) propose two-step method consults the overall and individual interests in the meantime. In the first step, a new centralized DEA model is employed to allocate CEAs to participating countries, aiming at maximizing the total potential GDP. In the second step, 2

http://www.chinanews.com/sh/2014/04-01/6016279.shtml http://bxg.f139.com/bxg/detail/1811160.html 4 http://www.shdrc.gov.cn/main?main_colid ¼ 357&top_id¼ 316&main_ artid¼ 24609 3

1

http://www.gddpc.gov.cn/xxgk/tztg/201311/t20131126_230325.htm

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two schemes are provided to compensate participants according to their CEA contributions or their optimal efficiencies calculated by the performance evaluation model, while keeping the total GDP obtained in the first step. The third category considers both fairness and efficiency in allocating allowance. For example, Zhang and Hao (in press) put forward a comprehensive allocating indicator system and apply the TOPSIS approach to allocate China's 40–45% carbon emission intensity (carbon emission per unit of GDP) reduction target by 2020 according to the combined principles of fairness and efficiency. Wei et al. (2012) focus on the problem of provincial responsibility of CO2 abatement. From the perspectives of equity and efficiency, they provide referential solutions as equity emphasis, efficiency emphasis and equity and efficiency balancing allocation method to distribute carbon allowance. Baer et al. (2008) propose using comprehensive index model to measure the abatement culpability and responsibility of the nation, and they allocate carbon allowance from the perspective of culpability, responsibility and potential. In addition, Golombek et al. (2013) believe that if the output-based allocation (OBA) of quotas is used, gas power production is then substantially higher than if quotas are grandfathered; moreover, the welfare costs of attaining a fixed emission target are significantly higher. Cong and Brady (2012) examine several alternative subsidy systems (the pure loan, the harvest tax and the income contingent loan) to find a possible way to distribute subsidies more efficiently and equitably. In practice, Chinese ETS pilot region Chongqing adopts the self-declaration allocation method under a cap-and-trade system, which takes fairness and efficiency into consideration in essence. The ETS-covered enterprises will decide to buy in or sell out carbon allowance based on the cost of carbon emission reduction and carbon market price, and through the cap-and-trade system, the emission which the ETS-covered enterprises achieve will be capped. Then, according to the carbon market price, the ETS-covered enterprises may determine their carbon emission reduction amount flexibly, therefore this cap-and trade system may minimize the cost of carbon reduction. In addition, most of the current literature focuses on exploring new carbon allowance allocation method. For example, FAIR 2.0-A analyzes 10 solutions and calculates the carbon allowance of 17 regions worldwide under such 10 solutions (denElzen and Lucas, 2003). Pan et al. (2014) divide the world into 8 major regions, and analyze the carbon allowance of these 8 regions through 20 kinds of carbon allowance allocation methods, pointing out that different allocation rules may lead to different results, and the topdown emission abatement promise is the only way to realize the climate deal. There is also some literature studying the impact of carbon allowance allocation rules on enterprises. For example, Ma et al. (2014) analyze the optimal pricing and carbon emission policies of enterprises in a cap-and-trade system. Zetterberg (2014) analyzes carbon emission allowance allocation, product pricing and emission reduction policy of enterprises under the benchmarking method, and indicates that the benchmarking method based on the latest production and benchmarking coefficient can give stronger emission reduction incentives to ETS-covered enterprises than that based on early production and benchmarking coefficient. As previously mentioned, it is of vital importance to research on the impact difference of different allocation rules on enterprises, and their emission reduction performance. Therefore, this paper focuses on the different allocation rules of Chinese ETS pilot regions and their current situation after adopting different rules, based on the demand prediction of ETS-covered enterprises' production under customers' low-carbon awareness hypothesis, to solve the sale proceeds of such enterprises. Considering the cost of emission reduction and trading profit of carbon allowance market,

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this paper gives the following equation: The overall revenue function of ETS-covered enterprises ¼product sale proceeds þcarbon market trading profits-emission reduction costs. Meanwhile, under the constraint of different allocation rules, combining the derivatives of the overall revenue function of ETScovered enterprises with respect to product prices, this paper gets the best products pricing under different allocation rules. Based on the product sale proceeds function of ETS-covered enterprises, the emission reduction function and the carbon market price, this paper uses the equation like: marginal reduction costs ¼marginal reduction earnings to determine the best reduction amount. Moreover, from the perspective of ETS-covered enterprises, this paper analyzes the product pricing policy and emission reduction policy of enterprises for the purpose of achieving best benefits under different allocation rules, as well as the impact of different allocation rules on revenue. Based on that, this paper gives a further comparative analysis of the abatement differences of enterprises, under individual allocation rules. 2.2. Methodologies 2.2.1. Allocation rules 2.2.1.1. Grandfathering. Compared with other allocation rules, grandfathering is relatively easy to accept in practice and more feasible (Groenenberg and Blok, 2002). In the early stage of Chinese carbon market, grandfathering is also the most widely used allocation method in the ETS pilot regions. The allowance the emission subjects acquired was benchmarked against their historical emission level. Adopting grandfathering in allocating carbon allowance can meet the former production requirement of enterprises, and in a general sense, it will not heavily impact the operation of enterprises. Moreover, since the allowance is a valuable transferable voucher, enterprises who reduce their emission can sell the surplus allowance in exchange for profits, thus enjoy the abatement flexibility of the emission trading market. The allowance allocated by grandfathering method equals the product of the base year emission and the reduction factor,5 as Eq. (1):

e = fec − ante

(1)

where e is the gratis carbon allowance of the current period acquired by enterprises; f is the reduction factor; and ec − ante is emission amount in the base year. 2.2.1.2. Benchmarking. Among the same kind of performance, benchmarking is a method which expresses comparison, and enterprises can compare themselves with peers using benchmarking (Groenenberg and Blok, 2002). Chinese ETS pilot regions have all tried the benchmarking method when the data were available. Through choosing industry benchmarks, this method further awards the most efficient enterprises. When comparing the performance of the same kind, benchmarking usually adopts a single referential benchmark to draw a comparison, which is generally the best performance among all. The carbon allowance of enterprise e equals the benchmark setting bsector multiplies quantity index q, as shown by Eq. (2), where q denotes the demanded quantity, the capacity or the input of enterprise products, and this paper picks the value of product demand (Zetterberg, 2014)

e = qbsector

(2)

In addition, under the benchmarking rule, the enterprise allowance for the second stage e2 is granted based on the benchmark for the second stage bsector 2, also referring to the quantity index of the previous year, i.e., q1, as shown by Eq. (3) 5

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Y.-J. Zhang et al. / Energy Policy 86 (2015) 176–185

e2 = q1bsector2

(3)

2.2.1.3. Self-declaration allocation method in the cap-and-trade system. Self-declaration under the cap-and-trade system is a peculiar allocation method in Chongqing. Chongqing uses the sum of the highest annual emission of existing capacity of ETS-covered enterprises during the year 2008 to 2012 as the benchmark total allowance. Before the year 2015, the annual carbon allowance declines 4.13% year by year. Based on the self-declaration of product demand, the carbon allowance allocates as Eq. (4):

e=q

E qsector

(4)

where E indicates the sector carbon emission cap and qsector represents the quantity demand of the whole sector which the enterprises belong to. Here, this paper assumes that the ETS-covered enterprises declare the emission amount according to the demand of their products, and the allowance they get is the product of emission cap and the ratio of product demand to total demand of the sector. 2.2.1.4. Auctioning. Cramton and Kerr (2002) argue that auctioning is better than all the other allocation rules, because auctioning can reduce tax distortions, stimulate technology innovation and have more flexibility during the allocation process. Under the auctioning rule, all the carbon allowance should be bought from the market, and the gratis carbon allowance is as Eq. (5):

e=0

(5)

2.2.2. Product demand prediction of ETS-covered enterprises Lots of research has proved that consumers will be more satisfied with those enterprises who assume social responsibility. The reputation of these enterprises will be improved, so does their competitive edge, and their financial value will be consequently enhanced (Sayedeh et al., 2015). In China, the enterprises' awareness of social responsibility starts rather late, but with the appearance of a series of social ecological problems, resource issues, product quality problems, the attention of the public has begun to switch from “cheap and fine”, “high quality” to the social responsibility of enterprises. Therefore, along with the enhancement of consumers' low-carbon awareness, enterprises assuming the responsibility of carbon emission reduction will improve customer satisfaction as well. This paper assumes that the consumers in the market have certain low-carbon awareness, and they will make their consumption choice according to the enterprises' efforts of emission reduction. Suppose the cognitive degree of market's consumers towards the ETS-covered enterprises' reduction efforts comply with the uniform distribution of [ δ , δ ], and δ means the strongest lowcarbon awareness, when consumers accept completely the products of enterprises which gave great effort in carbon reduction and buy them, while δ means no low-carbon awareness at all, when consumers do not recognize carbon reduction and will not buy the products of ETS-covered enterprises. Suppose the government subsidy towards consumers' purchase of the products of low carbon emission enterprises is as follows: ξ = serd (6)

erd = em − ec

(7)

where em is the carbon emission cap of an enterprise; ec is the actual emission amount; erd is the emission reduction of enterprises; and s is the government subsidy coefficient. Government can change the amount of subsidies by adjusting the subsidy

coefficient s . Whether consumers buy products of certain enterprises or not depends on the consumption effect they get from the purchase. Consider a group of consumers who have a general sense of low-carbon awareness but not a strong one, and their low-carbon awareness is δ . When they face different prices of two kinds of products, and decide which to buy, they hold neutralizing attitude toward the products of ETS-covered enterprises. For them, the consumption effect of the two kinds of products is the same, so we can get Eq. (8):

P − P0 = k (δ − δ ) + serd

(8)

where k is the consumer low-carbon awareness constant; P is the market price of ETS-covered enterprises products; P0 is the market price of products from similar enterprises which ETS does not cover; and qsector is the total demand of the sector, which includes the products of both ETS-covered and non-covered enterprises. According to Eq. (8), we further get Eq. (9):

δ=

P − P0 − serd + k δ k

(9)

Meanwhile, the product demand of ETS-covered enterprises in the market can be shown as Eq. (10):

q = qsector

∫δ

δ

serd + P0 − P 1 dx = qsector (1 + ) δ − δ k (δ − δ )

(10)

2.2.3. Profit analysis Under the allocation rules of grandfathering, auctioning, and self-declaration in cap-and-trade system, the market demand of products at an earlier stage will not affect the gratis carbon allowance. Maximizing the profit of the current phase (as the single one phase) is the only consideration. However, under the allocation rule of benchmarking, gratis carbon allowance is affected by the market demand of the previous stage. Due to the fact that price determines the supply–demand relationship of the products, when making decisions, enterprises have to consider the impact of the product price of the first phase on the carbon allowance of the second phase, which is to maximize the profits of two phases. 2.2.3.1. Single phase. Under the situation of maximizing the profit of a single phase, an enterprise's decision of emission reduction and product price only needs to consider maximizing the profit of the current phase. The total profit of the enterprise, i.e., ∏, is as Eq. (11):

∏ = (P − c ) q − cd (erd ) + ε (e + erd

− em )

(11)

where P , as a control variable, is the market price of ETS-covered enterprise’s products; c is the unit production cost excluding the cost of carbon reduction technology; q is the market demand of the ETS-covered enterprise’s products; cd (erd ) is the carbon reduction cost function when the amount of carbon reduction is erd ; and ε is the price of carbon market transaction. The best price policy of an ETS-covered enterprise is in accordance with Eq.(12):

( ∏ )′P = 0

(12)

2.2.3.2. Two phases. '∏1 + ∏2, which can be estimated as follows:

∏1 = (P − c ) q1 − cd (erd1) + ε1(e1 + erd1 − em )

(13)

∏2 = (P − c ) q2

(14)

− cd (erd2 ) + ε2 (e2 + erd2 − em )

where ∏1 and ∏2 are the profit of ETS-covered enterprise in the first and second stage, respectively; q1 and q2 are the market demand of enterprises' products in the first and second stage,

Y.-J. Zhang et al. / Energy Policy 86 (2015) 176–185

respectively; erd1 and erd2 are carbon emission reduction of the enterprises in the first and second stage, respectively; e1 and e2 are the gratis carbon allowance of the enterprises in the first and second stage, respectively; ε1 and ε2 are the carbon prices in the first and second stage, respectively. The best price policy of an enterprise is in accordance with Eq. (15)

( ∏1 +

∏2 )′P = 0

(15)

2.2.4. The optimal product pricing 2.2.4.1. Grandfathering. Under the allocation rule of grandfathering, the carbon allowance is determined by historical emission amount. The optimal product price P ⁎ of ETS-covered enterprises can maximize carbon reduction profit, which is based on the derivative of profit function Eq. (11) with respect to product price. In accordance with Eqs. (1), (11) and (12), we get Eq. (16):

P⁎

1 = (c + P0 + serd + k (δ − δ )) 2

(16)

2.2.4.2. Benchmarking. Under the allocation rule of benchmarking, the carbon allowance is related to benchmarks, which usually refer to the carbon emission condition of the previous phase. Therefore, an enterprise has to consider maximizing profit for two phases when deciding the product price under the benchmarking rule, which is based on the derivative of profit function of two phases ∏1 + ∏2 with respect to product price. In accordance with Eqs. (2), (3) and (15), we get the optimal product price as Eq. (17):

P⁎ =

1 1 [c + P0 + k (δ − δ )] + (serd1 + serd2 − bsector2 ε2 ) 2 4

(17)

2.2.4.3. Self-declaration. Under the allocation rule of self-declaration, the carbon allowance is related to the total carbon emission cap and the volume of the enterprise's production. The optimal product price to maximize the enterprise's profit is based on the derivative of profit function Eq. (11) with respect to product price. In accordance with Eqs. (4), (11) and (12), we get the optimal product price as Eq. (18):

P⁎ =

1 E [c + P0 + serd + k (δ − δ ) − ε ] 2 qsector

(18)

2.2.4.4. Auctioning. Under the allocation rule of auctioning, the carbon allowance of an enterprise is acquired through auction. The optimal product price to maximizing the enterprise's profit is based on the derivative of profit function Eq. (11) with respect to product price. In accordance with Eqs. (5), (11) and (12), we get the optimal product price as Eq. (19):

P⁎ =

1 [c + P0 + serd + k (δ − δ )] 2

(19)

2.2.5. The optimal carbon reduction amount When involved with carbon emission reduction, the ETS-covered enterprises have to deal with the cost of carbon reduction, the profit from carbon emission allowance and the profit from government subsidy. Based on the principle of maximizing profit, when marginal profit equals marginal cost, enterprises can achieve optimal profit, as when the enterprise's marginal emission reduction cost is the addition of marginal carbon emission allowance profit and marginal government subsidy profit, the enterprise will achieve optimal profit in participating carbon emission reduction. Generally speaking, if the enterprise does not participate in carbon reduction, with little or no carbon reduction technology input,

181

then it has larger carbon emission amount and less carbon emission reduction cost; on the contrary, if the enterprise actively participates in the carbon reduction, with great input of carbon reduction technology, then it has smaller carbon reduction amount and more carbon emission reduction cost. Focusing on a specific enterprise, the determination of its cost of carbon reduction is impacted by many factors, and the optimal carbon reduction amount will be achieved when it reaches the optimal carbon reduction profit, as Eq. (20):

cd′ (erd ) = ε + [(P − c ) q]′e rd

(20)

3. Results and discussions 3.1. The optimal product price analysis for ETS-covered enterprises Based on the calculation and discussion of Section 2.2.4, this paper gets the optimal product prices for ETS-covered enterprises under different allocation rules, as shown in Table 2. According to the results in Table 2, several findings are identified. First of all, the optimal product price of ETS-covered enterprises is affected by production cost, the product price of similar enterprises which are not covered by the ETS, the subsidy amount and the low-carbon awareness of customers; under the allocation rule of benchmarking, the ETS-covered enterprises are also impacted by benchmark coefficient, and the carbon price; the enterprises ruled by self-declaration allocation method are also impacted by carbon emission cap and carbon market price. Second, the optimal product prices of grandfathering and auctioning are the same; however, the cost of acquiring carbon allowance through auctioning is relatively higher. When all the other elements are same, enterprises get more profits acquiring carbon allowance through grandfathering allocation method. Therefore, currently in China, the auction of carbon allowance takes only a small proportion. The practice of auctioning will also encounter high resistance due to enterprises favoring acquiring gratis allowance through grandfathering. Third, compared with grandfathering, under the rules of benchmarking and self-declaration, the product price will decline because of carbon price, benchmark coefficient and the ratio of emission cap to sector output, respectively. Compared with grandfathering, the profit of enterprises will also be smaller through benchmarking and self-declaration. Therefore, in the circumstance which the subsidy does not change, the enterprises would like to acquire carbon allowance through grandfathering. In addition, Schmidt and Heitzig (2014) analyze that in the earlier stage of cap-and-trade carbon market, grandfathering can avoid the migration of covered enterprises; this opinion is in agreement with the second and third findings above. Fourth, the enterprises migration caused by government external control and the insufficient subsidy is detrimental for both the enterprises and the local government, and will lead to the Table 2 Optimal product prices. Allocation rule

Optimal product price

Grandfather

P⁎ =

Benchmark

P⁎

=

Self-declaration Auction

1 (c 2 1 [c 2

+ P0 + serd + k (δ − δ )) + P0 + k (δ − δ )] +

1 [serd1 + 4

P⁎ =

1 (c 2

+ P0 + serd + k (δ − δ ) − ε

P⁎ =

1 (c 2

+ P0 + serd + k (δ − δ ))

serd2 − bsector 2 ε2 ] ∧ E

qsector

)

Y.-J. Zhang et al. / Energy Policy 86 (2015) 176–185

{

k (δ − δ ) s2qsector ε1 + [16k (δ − δ ) − 3s2qsector ] ε2 sqsector bsector 2 ε2 [k (δ − δ ) + P 0 − c − ]+ 2 4 8βk (δ − δ ) − s2qsector 4βk (δ − δ ) − s2qsector

}

failure of carbon market. Martin et al. (2014) believe that when the government interferes with the market with negative externality, enterprises will ask for government subsidy to offset the harm of its competitiveness resulted by the interference. If adopting auctioning method completely at the early stage of the practice in Chinese ETS pilot regions, the ETS-covered enterprises would migrate to other non-ETS regions to avoid bearing too much regulatory burden, which will lead to carbon leakage. From the perspective of government, the migration of enterprises will take away large amount of employment opportunity, tax income and carbon emission allowance under the climate policy; from the perspective of enterprises, the cost of migration is quite high, and the adaptation in a new environment will also be a problem. Finally, under different allocation rules, enterprises may take different regulatory burden. Compared with grandfathering, under 1 the rule of benchmarking, the optimal product price is 4 bsector 2 ε2 lower due to benchmark coefficient and carbon price, which will damage enterprises’ profit. The optimal product price declines εE under the rule of self-declaration due to carbon emission 2qsector

}

⁎ = erd 2

cap and sector output, which will also damage profit. Under the role of auctioning, optimal product price lowers the enterprises' profit εec due to the cost of acquiring carbon allowance. Government should offset the ETS-covered enterprises' loss of profit to a certain level, in line with different carbon emission allocation rules, to avoid the enterprises migration which will further lead to the failure of carbon market. 3.2. The optimal carbon emission reduction analysis for ETS-covered enterprises

s 2qsector

8k (δ − δ ) 8k (δ − δ ) sqsector bsector2 ε2 = [k (δ − δ ) + P0 − c − ] + ε2 2k (δ − δ ) 4

(23)

From the results above, first of all, under the rule of grandfathering and auctioning, the optimal carbon emission reduction policy is the same. The carbon emission reduction amount of ETScovered enterprises is easily impacted by carbon price ε , the lowcarbon awareness of consumers and the government subsidy. The emission reduction amount will be higher as long as any of the

4βk (δ − δ ) − s2qsector

=

4βk (δ − δ ) − s2qsector

2kε (δ − δ ) + sqsector [k (δ − δ ) + P 0 − c ]

2kε (δ − δ ) + 2Esε + sqsector [k (δ − δ ) + P 0 − c ]

⁎ = erd

⁎ erd

Auction

] − erd1

Self-declaration

3s 2qsector

(22)

Benchmark

8k (δ − δ ) 8k (δ − δ ) sqsector bsector2 ε2 = [k (δ − δ ) + P0 − c − ] + ε1 2k (δ − δ ) 4

erd2 [2β −

⁎ = erd 1

s 2qsector

Grandfather

] − erd2

Allocation rule

3s 2qsector

Table 3 Optimal carbon emission reduction.

erd1 [2β −

4βk (δ − δ ) − s2qsector

where β is the adjustment coefficient and erd is the reduction amount of ETS-covered enterprises. The calculations of optimal carbon emission reduction amount of grandfathering, self-declaration and auctioning are based on Eq. (20), and the results are shown in Table 3. Benchmarking involves with two phases, so when calculating optimal carbon reduction amount, it takes Eqs. (22) and (23), and the results of two phases are shown in Table 3.

2kε (δ − δ ) + sqsector [k (δ − δ ) + P 0 − c ]

(21)

⁎ = erd

Optimal carbon emission reduction

cd (erd ) = (βerd )2

{

Due to the complex relations between the specific costing of ETS-covered enterprises and the carbon emission reduction, the only certain issue is that the cost of carbon reduction will be higher if more efforts are put into it, as the larger the emission reduction the higher the cost. Moreover, a larger reduction amount will also lead to the increase of marginal cost of carbon emission reduction. Based on the AJ model (D’Aspremont and Jacquemin, 1988), this paper assumes that the reduction cost of ETS-covered enterprises complies with Eq. (21):

k (δ − δ ) s2qsector ε2 + [16k (δ − δ ) − 3s2qsector ] ε1 sqsector bsector 2 ε2 [k (δ − δ ) + P 0 − c − ]+ 2 4 8βk (δ − δ ) − s2qsector 4βk (δ − δ ) − s2qsector

182

Y.-J. Zhang et al. / Energy Policy 86 (2015) 176–185

above three factors increase. The increase of carbon market price will get ETS-covered enterprises more profit through selling surplus allowance in the market. The improvement of consumers' low-carbon awareness will lead them to choose the products which are produced by enterprises that contribute more to lowcarbon environment, and further increase the demand of such enterprises and benefit them thereafter. The increase of government subsidy will give ETS-covered enterprises more subsidized income, which benefits the enterprises. Second, under the rule of self-declaration in a cap-and-trade system, except the above three factors, the ETS-covered enterprises are also affected by carbon emission cap E . The larger E is, the lower the initial carbon reduction cost of ETS-covered enterprises may be, and therefore, the enterprises are more willing to enlarge the amount of carbon reduction to acquire extra allowance to sell. Moreover, enlarging carbon emission reduction also helps increase the sale volume of products, further increase the enterprises' revenue. Finally, under the carbon emission allowance allocation rule of benchmarking, the carbon emission reduction is affected by two phases, and the impact of the second phase is greater than the first one. 3.3. Discussion on the impact of basic parameters In this paper, the consumers' low-carbon awareness constant k , the government subsidy coefficient s , the consumers' low-carbon awareness distribution δ − δ and the carbon market price ε are all varying basic parameters. The product demand of ETS-covered enterprises q, the optimal product price P ⁎, the optimal carbon ⁎ , the overall profits of enterprises ∏ emission reduction amount erd are all affected by the variation of the basic parameters. Then, this ⁎ paper takes q , P ⁎, erd and ∏ to operate derivatives with respect to k ,

s , δ − δ , and ε , respectively. For example, if Table 4 is “ þ”; if

∂q ∂k

∂q ∂k

> 0, then the mark in

= 0, then the mark in Table 4 is “x”; and if

∂q ∂k

< 0, then the mark in Table 4 is “  ”. The specific impact of basic parameters on analyzed variable is shown in Table 4, and several statements can be concluded as follows. First and foremost, along with the increase of consumers' lowcarbon awareness constant k , the market consumption trend favors low-carbon environmental protection, therefore the ETScovered enterprises will shoulder more carbon reduction responsibility to enlarge the carbon reduction amount. The products of ETS-covered enterprises will get the upper hand in the market, ⁎ and its optimal price P ⁎ and erd presents positive correlation with k ; however, the product demand q remains unchanged, thus the overall profits of ETS-covered enterprises ∏ will increase. Second, along with the increase of government subsidy coefficients , government enhances the support to low-carbon environmental protection, thus the ETS-covered enterprises that shoulder the responsibility of low-carbon environmental protection will get advantages in the market, and its optimal price P ⁎ and ⁎ erd presents positive correlation with k ; however, the product demand q remains unchanged, thus the overall profits of ETSTable 4 The impact of basic parametersa.

k s δ− δ ε

q

∂P ⁎ ∂k

х х  х

þ þ x 

>0

⁎ erd

∏

þ þ х þ

þ þ  n

a х, þ ,  and n represent uncorrelated, positive correlation, negative correlation and uncertainty, respectively.

183

covered enterprises ∏ will increase. Third, the consumers' low-carbon awareness distribution interval δ − δ shows the distribution range under the “neutral”. The smaller the δ − δ , the better the level of low-carbon awareness of consumers group as a whole, and consumers will prefer products produced by enterprises that shoulder more carbon reduction re⁎ sponsibility, while optimal product price P ⁎ and erd remains unchanged, thus the overall profits of ETS-covered enterprises ∏ will increase. Finally, the increase of carbon price ε will lead the enterprises to enlarge carbon emission reduction amount in order to have more surplus carbon allowance to sell in the market. The profit of emission trading market will increase, and correspondingly, the cost of carbon reduction will also increase. The extent of carbon market profit increase is larger than that of the increase of carbon reduction cost; however, the optimal product price P ⁎ decreases in response, the product demand q remains unchanged, and thus the overall profits of ETS-covered enterprises ∏ is uncertain.

4. Conclusions and policy suggestions Based on the current carbon allocation rules in China, we analyze the optimal product pricing policies and optimal carbon emission reduction policies for ETS-covered enterprises, in order to maximize their overall profits. Taking into account the discussions above, several important conclusions are drawn as follows. (1) Due to the existence of government low-carbon subsidy, ETScovered enterprises should thoroughly consider the marginal carbon emission reduction cost, market carbon price and subsidy profits, to make the optimal carbon emission reduction policy, as “marginal carbon emission reduction cost¼carbon market priceþmarginal subsidy profit”. Under the allocation rules of grandfathering, self-declaration method in the cap-and-trade system and auctioning, when deciding the optimal product price and optimal carbon emission reduction amount, the product price and carbon reduction amount of the current phase cannot be affected by the previous or the later phase. Therefore, the ETScovered enterprises adopt the price and emission reduction policy as maximizing current stage profit. Under the rule of benchmarking, when deciding the optimal product price and optimal carbon emission reduction amount, the ETS-covered enterprises have to take into consideration the impact of current price and carbon emission reduction amount on product demand and later phase carbon allowance; therefore, two-phase profit maximization is taken into account. (2) The ETS-covered enterprises should decide the product price which maximizes the profit according to the optimal emission reduction policy and the specific carbon allocation rule. The optimal price policy is positively correlated with the unit production cost, the price of the same kind of product, the consumers' low-carbon awareness and the government lowcarbon subsidy. Meanwhile, the benchmarking method is positively correlated with the benchmark coefficient and carbon market price; and the self-declaration method is positively correlated with the ratio of sector carbon emission cap to product demand and the carbon market price. (3) The optimal carbon emission reduction policy is the same under the allocation rules of grandfathering and auctioning. Along with the increase of carbon price, consumers' low-carbon awareness and government subsidy, the ETS-covered enterprises tend to augment their carbon emission reduction. Meanwhile, under the allocation rule of benchmarking, the optimal carbon emission reduction policy is negatively correlated with benchmark coefficient. Specifically, if the enterprises' benchmark coefficient

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increases, their dependency of the first phase will increase, and the optimal carbon emission reduction amount of the two phases will both decline. Based on the conclusions above and the actual situation of Chinese carbon emission trading market and enterprises' carbon emission reduction, we also put forward some important policy suggestions as follows: (1) When gradually enlarging the proportion of auctioning, and bringing enterprises with higher energy efficiency into the ruling of auctioning, Chinese government should think about the industrial competitiveness cautiously. The nation should give low-carbon subsidy to enterprises with higher energy efficiency, to help them maintain competitive power and let the polluters pay the bill. Only when the market receives the signal from the government that the future policy will be stringent, then the investment will flow to higher energy efficiency or other carbon emission reduction measures. (2) In the circumstances that customers gradually improve their low-carbon awareness and government increases the lowcarbon subsidy, the ETS-covered enterprises should put more efforts in the carbon emission reduction, increasing the price of low carbon products and maximizing the overall profit of the enterprises. Under the allocation rule of benchmarking, enterprises can also adjust the carbon emission reduction amount and the product price of two phases in line with the benchmark coefficient. Under the allocation rule of self-declaration method in a cap-and-trade system, enterprises should adjust product price according to the ratio of carbon emission cap to product demand, and adjust optimal carbon emission reduction amount according to carbon emission cap, and the product of carbon price and government subsidy coefficient. (3) The ETS-covered enterprises should take advantage of the leverage as government subsidy coefficient to maximize the profit. Government subsidy is the guide for enterprises to accelerate the input of carbon emission reduction. The changes of government subsidy coefficient will lead to the parallel changes of product demand, strategic price and the optimal carbon emission reduction amount, imposing leverage effect on enterprises profit. As for the future work, this paper takes the social responsibility of ETS-covered enterprises into consideration; however, due to the difficulty in the quantification of consumers' low-carbon awareness, no quantitative index has been proposed towards the social responsibility of ETS-covered enterprises, and this will be an important direction of our future effort. Considering the establishment of Chinese carbon trading market has not been long yet, and the information disclosure of policies and regulations of ETS pilot regions is yet inadequate, targeted research based on the integrated information of ETS pilot regions will also be the focus of future studies. Moreover, we will also further consider the carbon reduction cooperation issues among enterprises based on different carbon allowance allocation rules, such as the cooperation among upstream and downstream enterprises, and among counterpart enterprises. In particular, with the increase of market data, we can focus on the enterprises of a certain sector. Acknowledgments We gratefully acknowledge the financial support from the National Natural Science Foundation of China (Nos. 71001008, 71273028, 71322103).

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