Global climate change poses a major environmental threat to the sustainable development of society, while the main cause is excessive greenhouse gas emissions. To cope with global climate change, major countries in the world have adopted different measures and policy combinations to achieve energy conservation and emissions reduction targets such as renewable energy subsidies, non-fossil energy replacement, building and equipment efficiency standards, energy intensity targets, differential electricity prices, environmental and resource taxes, and national emission trading systems (denoted as ETS) [1
]. An ETS is a key market-based policy tool for addressing climate change and includes legislative mechanisms, industry coverage, quota allocation methods, monitoring and verification systems, historical data accounting, and specific set-off rules [2
]. An ETS is also a powerful way for all countries to reduce their greenhouse gas emissions and thus mitigate climate change [3
As the largest carbon emitter in the world, China is clearly a main part of the international carbon emission trading market [4
]. It was in recognition of this that the National Development and Reform Commission of China submitted plans to implement the pilot ETS in 2011, which was launched in seven regions since 2013, i.e., Beijing, Tianjin, Shanghai, Chongqing, Guangdong, Hubei, and Shenzhen. Together, these pilot projects involve 1.2 billion tons of carbon dioxide emissions from different sectors, which exceeds all other emission trading mechanisms in the world except for the European Union’s ETS [5
]. These pilot projects have gradually expanded nationwide since 2017. They initially incorporated approximately 3.5 billion tons of carbon dioxide emissions from more than 1700 companies, mainly from the power sector. The transaction volume will surpass EU’s ETS to become the largest ETS in the world [6
]. The Chinese government has made clear the need for a green, open, and innovation-driven low-carbon economy. The industrial sector contributes more than 40% of the GDP, consumes more than 70% of China’s total energy consumption, and occupies a dominant position in China’s ETS. It is essential to point out that China’s first seven pilot regions actually cover eight subsectors, and, except for the air transport sector, the other seven subsectors all belong to industry (see Appendix A
for the details). Could these industrial subsectors achieve low-carbon economic development through the pilot ETS? To answer this research question, this study attempts to assess the environmental and economic effects of ETS on the actual coverage of industrial subsectors in the pilot areas and analyzes the key influencing factors. China’s ETS will gradually upgrade from a regional ETS to a national ETS; the differences in resource endowments among different subsectors in provincial regions will inevitably increase the uncertainty for the successful implementation of a national ETS, at which point the different emitters will need to assume “common but differentiated responsibilities” to achieve the national emission reduction target [7
]. Considering the differences between historical emission liabilities and economic development capacities, this paper investigates whether there are significant differences in environmental responsibility and economic potential among provincial subsectors and discusses their ultimate impact on policy effects, which is another objective of this research.
Next, this paper examines relevant precedent studies which include ETS in major countries around the world and focuses on related research and the modeling methods of China’s ETS, as well as on case studies in the included industrial subsectors. Verbruggen et al. provided a preliminary analysis of the four main components of EU’s ETS: emission reduction measures, regulatory measures, carbon price levels, and emission reduction costs; they found that to achieve the coexistence of the industrial low economy pressure target and the low-carbon environment target, the applicability of the existing ETS needed to be discussed in depth on the basis of its specific design structure [8
]. Nguyen et al. used Japan as an example and assessed the economic viability and environmental efficiency of ETS. Their results show that modest carbon prices and inelastic constraints could reduce carbon emissions by 42%, while the best combination of elastic methods could reduce emissions by 34% [9
]. Nong et al. assessed the impact of the Australian ETS on carbon emissions and economy and found that carbon prices will gradually increase from 4.1 Australian dollars/ton in 2015 to 41.3 Australian dollars/ton in 2030 and that the 28% carbon emission reduction target by 2030 compared to 2005 is achievable. Meanwhile, GDP is expected to decrease by 1.6% in 2030 [10
]. Oke et al. and Diaz et al. conducted similar studies on ETS of sustainable development in South Africa and low-carbon development in New Zealand, respectively [11
From the statistics of international energy agencies, which show global carbon emissions of up to 31.6 billion tons in 2012, the developed countries have achieved less than 20% of the global reduction target, while developing countries are responsible for the rest. As the largest developing country, China accounts for nearly 50% of the world’s potential for emission reductions [13
]. Therefore, many scholars have used relevant models to study the policy effects of China’s ETS, mainly discussing the environmental and economic effects. Wang et al. used inter-provincial panel data to analyze the policy effects of the pilot ETS by using the propensity score matching-difference in differences model (denoted as PSM-DID). Their results indicated that the ETS could achieve both environmental and economic benefits and the low-carbon economy transformation target [14
]. Yu et al. used the data envelopment analysis model (denoted as DEA) to analyze the potential benefits of the ETS and found that it generated a 21.0% average potential environmental benefit and a 92.0% average potential economic benefit for industry [15
]. Liu et al. adopted the computable general equilibrium model (denoted as CGE) and analyzed the environmental and economic effects for Hubei province as the exemplary pilot area. Their results indicated that in 2014, the ETS reduced the carbon emissions of Hubei by 1%, while the economy decreased by 0.06% [16
]. Some scholars have used hybrid models in their research. For instance, Zhu et al. combined the PSM-DID model with the DEA model and discussed the impacts of the ETS on green development efficiency in China. Their conclusions show that the ETS has a significant positive impact of 4.25% on green development efficiency [17
]. Zhang et al. combined the DID model with the stochastic frontier approach (denoted as SFA) and analyzed the effects of the pilot ETS on carbon intensity and carbon emissions. It was found that the pilot ETS decreased industrial carbon intensity and carbon emissions by 0.78% and 10.1%, respectively [18
]. As can be seen from the above literature, scholars mainly used the DID, DEA, and CGE models from a bottom-up perspective, to analyze the policy effects of ETS regions and whole sectors in China. Almost all of the studies found that the ETS had a significant inhibitory impact on carbon emissions, but the conclusions related to economic effect were different [19
]. In 1991, Porter proposed that reasonable environmental regulations could send positive signals to enterprises, that resource allocations were inefficient and that the technology needed to be improved, which would autonomously stimulate the “innovation compensation” effect. Therefore, Porter’s hypothesis could not only offset the “compliance cost” of enterprises but also achieve both environmental and economic benefits by improving productivity and international competitiveness [25
]. This raises the question of whether the industrial subsectors covered by China’s ETS can achieve a win-win situation for the environment and the economy by promoting technological optimization and innovation-driven development. To answer this question, the limited literature, which discussed the policy effects of ETS in specific coverage sectors, mainly began at the enterprise and subsector levels. Focusing on the panel data of listed enterprises in seven energy-intensive industries in China from 2010 to 2017, Zhang and Liu used the DID model to select the listing age, firm size, capital structure, liquidity, and R&D investment to analyze the economic effect of ETS on enterprises. Their conclusions indicated that the regulatory policy had a positive economic impact on electric power enterprises and a negative economic impact on non-ferrous metal enterprises, and thus showed clear industrial heterogeneity; it had positive economic impacts on paper production and aviation enterprises with a lag of two to four years and showed long-term profitability on the whole [26
]. Zhang et al. used the same method and selected the listing age, per capita fixed assets, and enterprise ownership to discuss the effects of ETS on technological innovation by enterprises; they found that the policy had positive impacts on the technological innovation of power and aviation enterprises but had negative impacts on the other six industries and thus indicated clear heterogeneity [27
]. There was also research on the total factor productivity of manufacturing enterprises, denoting that the ETS did not achieve the ideal “Porter effect” [28
]. The literature from the enterprise perspective mainly concluded that ETS policy effects have significant industrial heterogeneity for the listed enterprises in the covered industries and mainly selected financial indicators to analyze specific reasons such as the scale of the enterprise and the years of listing. Zhang et al. used the DEA model to analyze policy effects on carbon emissions and GDP in industrial subsectors. They concluded that the time-restricted sector trading scenario and the unrestricted sector trading scenario had positive impacts on industrial added value of 55.17% and 73.76%, respectively, from 2006 to 2015, and reduced carbon emissions by 58.30% and 65.25%, respectively [29
]. Focusing on the panel data of inter-provincial industrial subsectors from 2005 to 2015, Zhang and Duan used the DID model to select the output, state-owned asset ratios, fixed asset ratios, and profitability as control variables to discuss the effects of ETS on the total output and employment of industrial subsectors. Their conclusion is noteworthy in that the pilot ETS significantly reduced GDP and would lead to significant employment declines in related subsectors, but would not produce a “decoupling” of carbon emissions and GDP in the short term [30
]. Zhang et al. used the DID model and selected the same control variables to analyze the effects of ETS. Their conclusion demonstrated that China’s ETS would reduce carbon emissions and show an increasing trend over the years but would fail to effectively reduce the carbon intensity of the covered subsectors. They further found that the main reason for reductions in carbon emissions was to reduce production, so the proportion of free quota should be tightened to facilitate technological innovation and effectively reduce carbon intensity in the future [31
]. The literature on the effects of ETS on the covered sectors has also mainly selected relevant financial indicators for analysis and argued that the ETS could promote different levels of carbon emission reductions, but there are some disagreements regarding whether the economic effect could be achieved.
To summarize, the literature on the policy effects of ETS was mainly based on whole sectors in China and relevant pilot areas. As sector studies focused on the industrial sectors with the highest emission levels, research on the specific coverage of ETS in China was sufficiently in-depth; the research methods used mainly included the DID, DEA, and CGE models, and there was little research using multiple models from different perspectives. Previous research has mainly addressed the environmental and economic effects of ETS, and much of this research concluded that an ETS will produce significant emission reductions, but the economic effect remains to be studied. In view of the complexity of data collection, the variables selected by the relevant literature for industrial subsectors were relatively few in number, as the financial indicators were mainly selected at both the enterprise and departmental levels. These studies were less concerned with significant technical and innovation indicators to achieve low-carbon economic development, and thus had certain limitations.
Based on the existing research results, this paper makes three main contributions. First, instead of examining the provincial industrial sector as a whole, because China’s ETS initially covered the industrial high-emission subsectors of the pilot areas, provincial industrial sectors are subdivided into 37 subsectors, according to the industrial classification code of national economic activities (GB/T 4754-2011), and seven industrial subsectors involved in the pilot areas are defined as the treatment group and reflect the actual coverage of the pilot ETS. Second, while most studies used a single model, we used both the PSM-DID model according to different matching methods, to eliminate the selection bias of large sample sets, and the triple difference model (denoted as DDD), introduced on the basis of PSM-DID research to construct a new control group to fix trend differences, thereby obtaining unbiased estimations of the treatment effect [32
]. Third, while previous studies on industrial subsectors mainly chose the financial indicators, from the perspectives of economic development, technological optimization, and innovation-driven development, we used the panel data of provincial subsectors from 2005–2017 to select representative variables, in order to analyze the environmental and economic effects of the pilot ETS from multiple perspectives. More importantly, on the basis of the above results, we considered the differences in environmental responsibility and economic potential among subsectors, and then evaluated the influence of developmental heterogeneity on the ultimate policy effects, which are further discussed by considering the imbalance of resource endowments among different subsectors to provide supporting evidence for the national ETS future plan in China.
The remainder of this research is organized as follows. In Section 2
, the research design, methods, and data are introduced. In Section 3
, empirical results are presented, including PSM matching results, DID benchmark regression results, a robustness test, and the triple difference model (DDD) regression results. Then we discuss and analyze all these empirical results and propose some implications and suggestions. In Section 4
the conclusion of the research is summarized, putting forward the policy proposals.
The negative externality of greenhouse gases may hinder the sustainable development of the economy and society. In this study, instead of grouping all sectors together, seven industrial subsectors, which are covered by the pilot ETS in the initial pilot areas in China, were taken as the treatment group. Based on data availability, representative variables were selected from the perspectives of economic development, technological optimization, and the innovation-driven development of provincial panel data from 2005 to 2017. A comprehensive analysis of the environmental and economic effects of industrial subsectors covered by the pilot ETS was conducted by using the PSM-DID model. Empirical results show three important findings, which are the three main contributions of the research. First, in the early stage of the pilot ETS in China, the carbon emissions of the included industrial subsectors were significantly reduced, by 14.5%, by adding key control variables to exclude the interference from other policies while the GDP fell by 4.8%; the policy effects remained robust during the experimental period, and hence the pilot ETS did not achieve the development of a low-carbon economy. The main reason for the carbon emission reduction was probably the decline of production in the included industrial subsectors. Therefore, the government should make more harmonized adjustments between the economic and environmental policies, resulting in environmentally friendly efforts. Second, the factors of GDP and gearing ratio of economic development had significant positive impacts on carbon emissions. Among them, if GDP increased by 1%, carbon emissions increased by 0.878%, and if the gearing ratio increased by one unit, carbon emissions increased by 0.017 units. Technology optimization factors such as labor productivity and energy efficiency and innovation drivers such as R&D ratio and R&D intensity have significant negative impacts on carbon emissions. Among them, if labor productivity and energy efficiency increased by one unit, carbon emissions would decrease by 0.005 units and 0.026 units, respectively, while if the R&D ratio and R&D intensity increased by one unit, carbon emissions would decrease by 0.078 units and 0.010 units, respectively. Economic effect indicators, such as assets, labor and energy consumption all had significant positive impacts on GDP. That is, if assets and energy consumption increased by 1%, GDP would increase by 0.731% and 0.107%, respectively, while if labor increased by one unit, GDP would increase by 0.019 units. We found that the pilot ETS in China does achieve environmental benefits through improved technology and innovation, but the decreases in labor and energy consumption during the experimental period may result in economic decline, implying that the regulatory policies require customized fine tuning among the subsectors of industries, especially in labor-intensive industries. Third, it is noteworthy that the pilot ETS had the stronger inhibitory impacts on carbon emissions and GDP in the subsectors with greater environmental responsibilities. In other words, the pilot ETS produced a 60.1% carbon emission inhibition effect and 23.2% GDP inhibition effect on the subsectors with greater environmental responsibilities when compared with the subsectors with fewer environmental responsibilities. Moreover, differences in economic potential had no significant impact on policy effects. This means that the pilot ETS may hinder the production and business activities of the covered high-emission subsectors while promoting carbon emission reduction, and thus exert a negative impact on GDP. Many papers on Chinese environmental policies supported the Porter hypothesis, but in our paper, the regulation policies always entailed some other unavoidable costs, and thus the Chinese government should promote a general, nationwide emission trading system, customized according to the individual characteristics of industries. For example, labor-intensive industries should not aim at ambitious targets, given their excessive potential damage.
Based on the above research conclusions, we propose the following targeted policy recommendations: (1) Establishing a reasonable distribution system and expanding the coverage of the ETS. The implementation of the ETS has significantly reduced carbon emissions, and it is necessary to extend the policy coverage to additional regions and sectors in order to determine a reasonable total allocation in accordance with different distribution principles, which would achieve large-scale energy conservation and the emission reduction targets. (2) Intensifying technological innovation and research investment. Since technological optimization and innovation-driven development are key drivers for developing a low-carbon economy, policy makers should formulate relevant incentive policies and increase R&D investment to fully stimulate the compensation effect, which would achieve the environmental benefits and economic benefits of a win-win situation in the future. (3) Developing differentiated emission reduction strategies. Considering the differences of historical environmental responsibilities and economic potential of different emitters, the Chinese government should prudently formulate differentiated emission reduction measures based on the resource endowments of different sectors in different regions, which would achieve a low-carbon economy across the country.