Emission Reduction Effect,Influencing Factors and Economic Impact of China’s Carbon Market: An Empirical Test Based on a Multi-Period DID Model

Abstract

The carbon emission trading market (carbon market) is an important policy tool for China to achieve the goal of “carbon peak and carbon neutrality” through market mechanism. This paper uses panel data of 30 provinces in China from 2005 to 2019 to examine the emission reduction effect (ERE) of China’s carbon market pilots using multi-period Difference-in-Differences (DID)method, and then analyzes the factors influencing ERE and the economic impact of carbon market. According to the Bacon decomposition and a series of robustness tests, the results reveal that China’s carbon market pilots have significant ERE. Both the expansion of the carbon market scale and the improvement of market activity level can significantly boost the carbon market’s ERE. Meanwhile, the establishment of carbon market has no negative impact on the economic growth. Therefore, we propose to accelerate the construction of the national carbon market, incorporating more industries in a systematic manner, expand the scale of the carbon market, steadily promote the innovation of carbon financial instruments, improve the activity of the carbon market, so as to promote the transformation of China’s green and low-carbon economy and achieve the emission reduction target as scheduled.

Keywords

carbon market pilots emission reduction effect (ERE)economic impact multi-period DID model Bacon decomposition

Introduction

Since the 1990s, the global warming caused by the sharp increase of greenhouse gas concentrations has caused serious impact and damage to the natural ecosystem on which humans rely, and it has become a major problem that must be addressed urgently in the development of human society. As the world’s largest emitter of carbon dioxide (BP, 2021; J. Wu et al., 2021), China has prioritized the goal of reducing carbon dioxide emission, listing “carbon peak and carbon neutrality” as a major strategy and strive to achieve the peak of carbon dioxide emission by 2030 which is called “the 2030 goal” and achieve carbon neutrality by 2060 which is called “the 2060 goal,” in order to meet the goals of Paris Agreement (Zhong et al., 2021).

Carbon emission trading market (carbon market) is an innovative environmental solution that establishes carbon emission property rights and “market-oriented” property rights trading mechanism (Hua & Dong, 2019; Hu et al., 2020). Its core mechanism is to generate reasonable carbon price signals through trading and transmit them to enterprises, guiding them to make cost-effective carbon emission reduction decisions, prompting them to eliminate backward production capacity, increase investment in green technology research and development, and finally achieve carbon emission reduction.

In October 2011, the General Office of the National Development and Reform Commission (NDRC) issued the notice on the Pilot Work of Carbon Emission Trading, which was the starting point for the development of China’s carbon trading market. China’s carbon emission trading pilot program has been officially launched in Beijing, Shanghai, Tianjin, Chongqing, Hubei, Guangdong, Shenzhen and Fujian provinces since 2013 (M. Duan et al., 2014; Han et al., 2012; Jotzo, 2013; Lo, 2012). Moreover, the NDRC officially launched the establishment of the national carbon market in 2017. Therefore, on July 16, 2021, the national carbon market officially launched online trading, and 2,225 power generation enterprises were included in the national carbon trading system as the first batch of key emission control units.

In comparison to Western developed countries (Ellerman et al., 2010; Grubb et al., 2011; Mol, 2012; Pezzey & Jotzo, 2013; Spaargaren & Mol, 2013), China’s carbon market development is still in its early stages. There are critical questions for China’s carbon market: the primary metric is how far the establishment of carbon markets can bring environmental dividends? Furthermore, what factors influence the emission reduction effect (ERE) of China’s carbon market? Whether the trade-off between economic growth and environmental protection, which has long plagued developing countries, can be broken (Y. Zhang et al., 2020) These are urgent questions for China to further develop its carbon market. Discussing the above-mentioned questions cannot only provide empirical reference for China to promote the establishment of national carbon market, but can also help in the optimization of China’s sustainable development path and the transition to the green and low-carbon economy.

Our paper is carried out as follows: The second part reviews the relative literature; the third part explains the theoretical mechanism; the fourth part is research design, which introduces the model, variables and data; the fifth part shows the empirical results and robustness test; the sixth part is the analysis of influencing factors and economic effect; the last part is conclusions and policy enlightenment.

Literature Review

Literature Related to Carbon Emission Reduction Mechanism of Carbon Market

The earliest studies on carbon market can be traced back to the property rights theory of Coase (1960). The theory points out that through the clear definition of property rights and the use of market mechanism, external costs can be internalized and external problems can be solved. Inspired by the theory of property rights, Dales (1968) proposed the concept of emission right trading. He believed that under the objective condition of limited environmental resources, the commercialization of emission rights and the establishment of emission rights trading market could optimize the allocation of environmental resources and reduce the total amount of pollution emission.

Based on theoretical research, some Western countries took the lead in implementing a series of carbon-reduction practices. In 2005, the European Union Emissions Trading Scheme (EU ETS) was launched which is the world’s first transnational cap-and-trade system for greenhouse gas emission, and it is a market-based approach to implementing Kyoto Protocol emission reductions (Ellerman et al., 2010; Grubb et al., 2011; Spaargaren & Mol, 2013). Stern (2009) pointed out that the cost difference of carbon emission (CE)would promote the achievement of emission reduction targets via the market mechanism.

Research on the ERE of China’s Carbon Market

Although China’s carbon market is relatively new, it is growing quickly and carbon market pilots have been established in several regions since 2011 (Y. Zhang et al., 2020). Moreover, its first national Emission Trading Scheme (ETS) in the power sector has been established in 2017 and it will continue to expand its carbon market coverage over time. Recently, the effect of carbon market on emission reduction and whether it can help China achieve the goal of “carbon peak and carbon neutrality” have become hot topics of scholars’ research. At present, some scholars believe that China’s carbon market pilots have effectively achieved ERE (Chen et al., 2020; H. Duan et al., 2018; Zhu et al., 2020). Even some scholars are optimistic about the realization of “the 2030 goal” (H. Wang et al., 2019). However, other scholars still believe that the China’s carbon market has a limited impact on emission reduction, and that the peak of China’s CE may occur after 2030 (Cui et al., 2019; J. Wang et al., 2019; Y. Zhang et al., 2020). Only by fully implementing existing and announced policies will China be able to meet “the 2030 goal” on time (Gallagher et al., 2019).

Research on the Influencing Factors of Carbon Market

Most scholars examine the carbon market influencing factors from the standpoint of the deficiencies of current carbon market. For example, Lo (2016) believes that China’s current carbon market pilots are hampered by a lack of domestic demand and effective social investment, which will limit China’s carbon market’s ability to reduce emission. Furthermore, based on the current development status of China’s gradual pilot promotion of the carbon market, some studies have been proposed from the perspective of expanding the scale of the carbon market to promote the linkage of carbon market pilots, reduce the cost of CE, and increase market liquidity (Ibikunle et al., 2016; Ranson & Stavins, 2016).

Research on the Impact of the Carbon Market on Economic Growth

The relationship between environmental protection measures and economic growth has always been the focus of academic discussion. Neoclassical economic theorists believe that environmental protection measures will inevitably bring additional private costs to economic subjects, hinder the improvement of productivity, reduce market competitiveness, and ultimately have a negative impact on economic development (Jorgenson & Wilcoxen, 1990). Porter (1991), however, took a completely different viewpoint, that reasonable but strict environmental protection could inspire innovation, improve production technology, and thereby offset the cost due to environmental governance, increase the competitive advantage of economic entities. The idea proposed by Porter is now known as the Porter hypothesis and has been confirmed by numerous studies (Porter & Linde, 1995). As a measure of environmental protection, whether carbon market will cause economic decline or bring economic dividends has been widely discussed by scholars (Dong et al., 2018; Liu et al., 2017). H. Duan et al. (2018) developed a stochastic energy-economy-environment (3E) synthesis model to evaluate China’s energy and climate goals for 2030. R. Wu et al. (2016) used the static CGE model to evaluate the economic effects of carbon emission trading policies in Shanghai, China, and concluded that carbon cap-and-trade can reduce the negative impact on economic output and employment. However, researchers such as Fujimori et al. (2015) studied synergistic effects of carbon trading on other pollutants and social welfare and found that the emission reduction brought by the establishment of carbon market reduced the welfare loss from 0.7%–0.9% to 0.1%–0.5%.

Methodological Overview of Carbon Market-Related Research

From the perspective of research methods, Computable General Equilibrium simulation (CGE) model (Tan et al., 2018; Tang et al., 2020; R. Wu et al., 2016), Propensity Score Matching with Difference-in-Differences (PSM-DID) model (Gao et al., 2020; Xuan et al., 2020; Y. Zhang et al., 2020), Data Envelopment Analysis (DEA) method (Jaraitė & Di Maria, 2012; S. S. Zhang et al., 2016) have been widely used by scholars to measure the ERE of carbon market.

Gap in Literature

To summarize, existing literature has yielded numerous research findings on the ERE and theoretical mechanism of carbon market, but there is still room for further research. It is embodied in the following aspects: (1) In terms of research methods, most existing studies treat pilot regions in a rough way, and usually set a unified policy implementation time when exploring the ERE of carbon market pilots. However, the different timing of the establishment of China’s carbon market pilot will undoubtedly affect the accuracy and credibility of the empirical results of DID model with a single policy implementation time to a certain extent (Xu, 2021; Y. J. Zhang & Liu, 2020). (2) In terms of research objectives, most scholars focus on testing the ERE of the carbon market, with few studies delving deeper into the influencing factors of the carbon market; therefore, improvement is still needed in the variables selection. (3) According to the latest econometric literature of DID theory, bidirectional fixed effect estimators are biased in the presence of time-heterogeneous processing effects when used in the design of multi-period DID studies (Callaway & Sant’Anna, 2021; Goodman-Bacon, 2021). However, most studies that used the DID method to examine the ERE of China’s carbon market pilots did not diagnose or correct these errors.

Contribution of This Paper

Based on the analysis above, our paper’s marginal contribution is reflected in the following aspects: (1) Based on the launch time of each carbon market pilot, multi-period DID model is developed to more accurately evaluate the ERE of carbon emission trading policies. Simultaneously, in order to alleviate the endogeneity problem, the randomness of the selection of the policy treatment group and the implementation time are discussed in the research design part. (2) In terms of exploring the factors influencing the carbon market, our paper examines the influence of relative market scale and relative market activity level on the ERE in the pilot area, which adds to the research on the factors that influence the carbon market’s ability to reduce emission. (3) Bacon decomposition method is first applied in the multi-period DID study to diagnose the bidirectional fixed effect bias in the estimation of ERE in China’s carbon market pilots, which more rigorously demonstrates the robustness of the baseline regression results in our paper.

Theoretical Mechanism

Carbon Emission Reduction Mechanism of Carbon Market

The carbon market is an institutional arrangement designed to reduce the environmental and economic costs of emission reduction while effectively promoting the transition to low-carbon economy (Zhu et al., 2020). The internal logic is as follows: the government determines the annual total emission quota according to the regional carbon emission cap and the current stage of regional economic development. On this basis, taking into account the historical emissions of the specific enterprise and the advanced emission level of the industry, CE quota is allocated to the enterprise. To maximize profits and meet their own development needs, enterprises can buy or sell carbon quota on the carbon market (Anger, 2008; Fernández-Amador et al., 2017).

Under the control of relevant rules formulated by the government, enterprises that fail to implement the contract will be punished more than the market transaction cost to encourage enterprises to actively participate in carbon market transactions. The enterprises are controlled and face the pressure of fulfilling the contract as a result of the synergistic effect of government regulation and market mechanism; on the other hand, they can choose to buy or sell quotas in the market based on their own circumstances. More specifically, when the marginal cost of emission reduction is lower than the market price of carbon quotas, as a “rational economic man,” enterprises can reduce CE by improving the process, implementing green technology innovation, or other means. Furthermore, they can earn extra money by selling excess carbon quotas on the carbon market. When the price of the carbon quota in circulation is lower than the marginal cost of emission reduction, market participants will buy the carbon quota from other enterprises in the market to complete contracts on time at the lowest possible cost. Carbon emission trading, when implemented through a market mechanism, can improve the enthusiasm of each participant in emission reduction, reduce emission reduction costs, and, ultimately, reduce the CE level of the entire society. Therefore, the goal of carbon market is to achieve a reasonable allocation of resources among all participants through free trade and market circulation based on the commodity attribute of carbon emission rights. From the aforementioned analysis, we propose hypothesis 1.

Hypothesis 1: China’s carbon market pilots have significant ERE.

The Influence Mechanism of the Carbon Market Scale and Its Activity Level

Furthermore, if the carbon market has ERE, which factors affect this effect? For this issue, it has been pointed out in the literature that the larger the scale of the carbon market is, the more beneficial it is to reduce CE in pilot areas (Hu & Ding, 2020). However, due to the differences in regional characteristics, the trading scale of carbon market pilots in different regions cannot be directly compared. It is more scientific to combine the total amount of regional carbon quota trading the regional CE level. The greater the proportion of carbon quota circulation in local CE, the greater the impact of carbon market on regional ERE. Therefore, with the expansion of the relative market scale, there are more and more market participants, and their influence is also increasing, which is conducive to discovering the true marginal cost of emission reduction, encouraging participants’ enthusiasm for emission reduction, and thus strengthening the market mechanism.

The relative liquidity of carbon market is an important factor (Y. Wu et al., 2021). Sufficient liquidity is a key factor in the formation of effective price, which will greatly affect the reduction decisions of market participants. The lack of liquidity means that the market is not active enough and participants do not trade with sufficient enthusiasm, which ultimately undermines the carbon market’s ability to reduce emissions. Therefore, we propose the following two hypotheses.

Hypothesis 2: The larger the relative market scale of the carbon market is, the more significant the ERE will be.

Hypothesis 3: The higher the relative market activity level of the carbon market is, the more significant the ERE will be.

The Impact of the Carbon Market on Economic Growth

The establishment of carbon market pilot will first restrict the emission behavior of participants, and most of the emission control subjects included in the carbon market are traditional industrial sectors with high carbon emission characteristics that their economic activities are usually accompanied by higher emission behaviors. As a result, the carbon market may be inhibiting regional economic growth as well as reducing emission. Indeed, some scholars found through empirical research that carbon markets work by reducing regional economic output (H. Zhang et al., 2019). On the other hand, we should not ignore the incentive effect of carbon market emission limits on enterprises. The Porter hypothesis states that appropriate external environmental regulations will give enterprises more incentives to engage in R&D and innovation activities, improve their production efficiency, offset the external costs brought by environmental regulations, and ultimately promote the development of the regional economy (Porter, 1991). Participants are not only restricted by carbon market policies but also obtain external incentives, which means joining the carbon market makes enterprises realize that it is a long-term strategy to obtain more economic profits from the market to reduce emission through technological innovation (Lokhov & Welsch, 2008; Wei & Ren, 2021).

Therefore, influenced by the above two factors, the carbon market may not have a significant inhibiting effect on regional economic growth. Based on the above analysis, we propose hypothesis 4.

Hypothesis 4: The carbon market never restrains regional economic growth.

Research Design

Model

Our paper regards China’s carbon market pilot policy as a quasi-natural experiment. Since the start-up time of each carbon market pilot is not consistent, our paper uses the multi-period DID model to evaluate the ERE of the carbon market. Under the premise that the treatment group and the control group meet the parallel trend hypothesis, the multi-period DID model can more accurately test the policy effect of the carbon market.

Based on the classical DID model set by Imbens and Wooldridge (2009) and a series of extended DID models (Dong et al., 2019; Shen et al., 2017; H. Zhang et al., 2019), we construct a multi-period DID model as follows (Xu, 2021):

\begin{matrix} Y_{it} = & β_{0} + β_{1} (polic y_{i} * pos t_{it}) + β_{2} contro l_{it - 1} + μ_{i} \\ + v_{t} + ε_{it} \end{matrix}

(1)

In model (1), subscripts $i$ and $t$ represent region and year respectively. $Y_{it}$ is the dependent variable, including CE and carbon emission intensity (CEI). Variable $polic y_{i}$ is a dummy variable used to identify whether the specific region is a pilot region for carbon market. If it is a pilot region for carbon emission trading, variable $polic y_{i}$ . is 1; otherwise, it is 0. Variable $pos t_{it}$ is also a dummy variable, used to identify the grouping variable before or after the launch of the carbon market pilot. Specifically, when $i$ represents Beijing, Tianjin, Shanghai, Hubei, Chongqing, Guangdong, or Fujian, $polic y_{i}$ is assigned as 1. In other cases, $polic y_{i}$ is equal to 0. When $i$ represents Beijing, Tianjin, Shanghai, Guangdong and $t \geq 2013$ , or $i$ represents Hubei and Chongqing and $t \geq 2014$ , or $i$ represents Fujian and $t \geq 2016$ , the value of $pos t_{it}$ is 1; in other cases, the value of $pos t_{it}$ is 0.

$μ_{i}$ represents the individual fixed effect. $ν_{t}$ stands for time fixed effect. $ε_{it}$ is the random perturbation term. $β_{1}$ is the coefficient that our paper focuses on. If $β_{1}$ is significantly negative, it proves hypothesis 1 proposed in this paper is confirmed. Furthermore, to obtain the unbiased estimator of $β_{1}$ , control variables reflecting regional characteristics should be added into model (1). In other words, after the addition of control variables, the randomness prerequisites of regional selection and time selection of carbon market pilot should be satisfied as much as possible. To further alleviate the endogeneity problem of characteristic variables, the lag of regional characteristic variables $contro l_{i, t - 1}$ is incorporated in our model.

Test of Recognition Conditions

To make the setting of the DID model in our paper conform to the premise assumptions of “random grouping” and “random event,” the following two issues should be discussed: (1) the randomness of the selection of carbon market pilots and (2) the randomness of the establishment time of the carbon market.

Test of Random Selection of Carbon Market Pilot Areas

Based on existing literature research (Dietz & Rosa, 1994; Y. Duan et al., 2016; Gao et al., 2020; Shao et al., 2011; Yang, 2015), we select the following variables that may affect the selection of the carbon market: regional economic development level ( $lnpgdp$ ), industrial structure development level ( $industry$ ), population size ( $lnpop$ ), opening-up level ( $fdi$ ), technology level ( $rd$ ), carbon sink capacity of trees ( $lnforest$ ), energy conservation and environmental protection expenditure level ( $ecology$ ). Our paper takes whether the province is a pilot province of the carbon market ( $ETprovince$ ) as the dependent variable. If a province is selected as a pilot, the value of $ETprovince$ is 1; otherwise, the value is 0. We construct a Logit model, and all independent variables are regressive with first-order lag terms. Table 1 shows the results.

Table 1.

Test of the Preliminary Factors Selected for the Pilot Carbon Market.

	ETprovince
	(1)	(2)
lngdp	1.4932 (1.2811)	1.4932 (2.4555)
industry	−2.8906*** (1.1199)	−2.8906 (2.1641)
lnpop	−2.7248*** (0.6479)	−2.7248** (1.3624)
fdi	2.3925 (1.6451)	2.3925 (3.4683)
rd	3.7706*** (0.9964)	3.7706* (1.9289)
lnforest	3.1755*** (0.8252)	3.1755* (1.7932)
ecology	−34.7920*** (9.1105)	−34.7920*** (13.4971)
Constant	−9.0612 (12.4762)	−9.0612 (24.1469)
Year fixed effect	Yes	Yes
Observations	210	210
Pseudo R²	.6750	.6750

Note. Column (1) reports robust standard errors, and column (2) reports standard errors adjusted by clustering at the provincial level.

***

, ** and *indicate significance at the 1%, 5%, and 10% levels, respectively.

The results show that the selection of carbon market pilot is mainly influenced by variables $pop$ , $rd$ , $lnforest$ , and $ecology$ . Therefore, the first-order lag term of the above-mentioned variables should be added into regression model (1) to control for possible endogenous problems. Meanwhile, to alleviate the estimation bias caused by the omission of variables as much as possible, the first-order lag terms of other regional characteristic variables mentioned in Table 1 except for the above four variables are also included in model (1).

Discussion on the Randomness of the Establishment Time of the Carbon Market

The carbon market is an institutional innovation for China to carry out industrial upgrading and green economic transformation in the face of international pressure on emission reduction, which can be considered to be unpredictable. There is no landmark event that directly leads to the establishment of carbon market pilot. Therefore, it can be considered that the successive carbon market pilots established during this period are relatively random. To make the discussion more rigorous, our paper will test the expected effect of carbon market in the robustness analysis.

Variables and Data

The Dependent Variable

Following the relative papers (Mahmood, 2020; Zhao et al., 2013), $lnc o_{2}$ , $lngc o_{2}$ are used as dependent variables.

The Core Independent Variable

According to the classic DID model (Imbens & Wooldridge, 2009) and a series of extended DID models (Dong et al., 2019; Shen et al., 2017; Xu, 2021; H. Zhang et al., 2019), $polic y_{i} * pos t_{it}$ is taken as the core independent variable in our model.

Control Variables

Based on the above analysis of the preliminary factors selected for the pilot regions of carbon market, control variables selected in our paper include: (1) $lnpgdp$ , our paper takes 2005 as the base period to calculate the real per capita GDP and takes the logarithm of the value; (2) $industry$ , we use the proportion of the added value of the tertiary industry and the secondary industry in the gross regional product to measure the industrial structure; (3) $lnpop$ , the logarithm of permanent population in each region is used to measure population size; (4) $fdi$ , the ratio of regional FDI to regional GDP is used to measure the level of opening-up, where the total regional FDI is converted into RMB at the annual average exchange rate;(5) $rd$ , our paper selects the ratio of R&D investment to regional GDP to measure regional technology level; (6) $lnforest$ , our paper uses log value of forest coverage rate in each region to measure carbon sequestration capacity of each region; (7) $ecology$ , our paper uses the ratio of the amount of investment completed in regional industrial pollution control to regional GDP to measure the expenditure level of energy conservation and environmental protection in the region.

Data

We construct panel data of 30 provinces in China from 2005 to 2019 to test the ERE of the carbon market. The carbon emission data of each region were collected from the China Carbon Emission Accounting Database (CEADs), and the R&D investment data were collated from The Statistical Bulletin of National Investment in Science and Technology. The amount of completed investment in industrial pollution control were from the China Environmental Statistics Yearbook in each year. The rest of the relevant data were from the China Statistical Yearbook. The descriptive statistical results of the main variables are reported in Table 2.

Table 2.

Descriptive Statistics of the Main Variables.

Variables	Observations	Mean	Standard deviation	Minimum	Maximum
lnco₂	450	5.51	0.793	2.682	7.438
lngco₂	450	10.132	0.769	8.042	12.091
lnpgdp	450	10.219	0.564	8.688	11.676
industry	450	1.188	0.663	0.527	5.234
lnpop	450	8.185	0.747	6.306	9.433
fdi	450	0.422	0.521	0.048	5.799
rd	450	1.472	1.101	0.17	6.31
lnforest	450	3.303	0.721	1.386	4.202
ecology	450	0.263	0.63	0.002	6.561

Results

Baseline Regression Results

Table 3 reports results of model (1). When only individual fixed effect and year fixed effect are controlled, the coefficients of $polic y_{i} * pos t_{it}$ affecting CE and CEI are significantly negative at the significance of 5% and 1%, respectively which are shown in column (1) and column (3) in Table 3. After adding control variables, the coefficients of $polic y_{i} * pos t_{it}$ affecting CE and CEI are significantly negative at the significance of 5%, indicating that the carbon market can effectively reduce regional CE and lower CEI. Specifically, the establishment of the carbon market has reduced CE and CEI by 19.30% and 18.95% in the pilot areas, respectively. Therefore, hypothesis 1 in our paper is confirmed.

Table 3.

Regression Results of the Baseline Model.

	lnco₂		lngco₂
	(1)	(2)	(3)	(4)
policy*post	−0.2169** (0.0861)	−0.1930** (0.0800)	−0.2296*** (0.0827)	−0.1895** (0.0793)
lnpgdp		−0.2037 (0.6023)		−1.1656* (0.6032)
industry		−0.2745* (0.1558)		−0.2704* (0.1557)
lnpop		0.3283 (0.8870)		−0.6366 (0.8785)
fdi		−0.0182 (0.0212)		−0.0211 (0.0210)
rd		−0.0985 (0.0823)		−0.0990 (0.0803)
lnforest		0.4456 (0.4816)		0.4313 (0.4777)
ecology		−0.0779*** (0.0190)		−0.0788*** (0.0192)
Constant	5.4961*** (0.0084)	3.9270 (11.7615)	10.1713*** (0.0081)	26.2319** (11.6975)
Year fixed effect	Yes	Yes	Yes	Yes
Individual fixed effects	Yes	Yes	Yes	Yes
Observations	450	420	450	420
R ²	.9586	.9692	.9513	.9673

Note. Values in brackets are robust standard errors for clustering adjustment at the provincial level.

***

, **, and * are significant at the levels of 1%, 5%, and 10%, respectively.

Test of Parallel Trend and Analysis of Policy Dynamic Effect

The most important premise of DID model is to satisfy the parallel trend hypothesis (Bertrand et al., 2004). Therefore, to estimate the “pure” policy effect, we conduct parallel trend test and policy dynamic effect analysis.

First, to visually display the difference between the treatment group and the control group, our paper draws the time trend chart of CE and CEI. As shown in Figures 1 and 2, both dependent variables in the treatment group are lower than those in the control group. Before 2011, the carbon emissions of the two groups basically showed a parallel trend. However, after 2011, the two independent variables of the treatment group showed a significant downward trend.

Figure 1.

Time trend of carbon emission (CE).

Figure 2.

Time trend of carbon emission intensity (CEI).

To test the parallel trend hypothesis more accurately and analyze the policy dynamic effect, our paper uses the event study method (Huang, 2018; W. Wang et al., 2018; Y. Wu et al., 2021). Specifically, 7 years before the establishment of the carbon market pilot in each region are taken as the comparison benchmark to construct a parallel trend test model, as shown below:

\begin{matrix} Y_{it} = & β_{0} + \sum_{j = 1}^{6} β_{pre_j} D_{it}^{j} + β_{current} D_{it} + \sum_{k = 1}^{6} β_{after_k} D_{it}^{k} \\ + β_{2} contro l_{it - 1} + μ_{i} + v_{t} + ε_{it} \end{matrix}

(2)

In model (2), $D_{it}^{j}$ is a dummy variable, indicating that if the province is a pilot carbon market area and is in the year before the establishment of the carbon market (only the first 6 years are considered), the value is 1; otherwise, it is 0. Similarly, $D_{it}$ equals 1 only when the provinces and cities set up carbon market pilots in the year; otherwise, it is 0. $D_{it}^{k}$ equals 1 if the province is the pilot region of the carbon market and is in the year after the establishment of the carbon market; otherwise, the value is 0. Other variables have the same meaning as in model (1). Therefore, the coefficient $β_{pre_j}$ , $β_{current}$ , $β_{after_k}$ . measures the difference in the change trend of carbon emission characteristics between the control group and the treatment group before, during and after the establishment of the carbon market. The regression results of model (2) are shown in Table 4.

Table 4.

The Results of the Parallel Trend Test.

	lnco₂	lngco₂
	(1)	(2)
pre_6	0.0169 (0.0592)	0.0079 (0.0593)
pre_5	0.0037 (0.0782)	0.0021 (0.0789)
pre_4	−0.0523 (0.0835)	−0.0553 (0.0834)
pre_3	−0.0608 (0.0849)	−0.0631 (0.0838)
pre_2	−0.1189 (0.1121)	−0.1108 (0.1090)
pre_1	−0.1968 (0.1345)	−0.1881 (0.1330)
current	−0.2199* (0.1289)	−0.2148 (0.1279)
after_1	−0.2441* (0.1385)	−0.2409* (0.1373)
after_2	−0.2721* (0.1525)	−0.2704* (0.1509)
after_3	−0.2878* (0.1650)	−0.2836* (0.1636)
after_4	−0.3517** (0.1523)	−0.3389** (0.1514)
after_5	−0.3437** (0.1526)	−0.3322** (0.1525)
after_6	−0.3335* (0.1734)	−0.3261* (0.1726)
Constant	0.1754 (12.6691)	22.7070* (12.6462)
Control	Yes	Yes
Year fixed effect	Yes	Yes
Individual fixed effects	Yes	Yes
Observations	420	420
R ²	.9702	.9682

Note. Values in brackets are robust standard errors for clustering adjustment at the provincial level.

***

, **, and * are significant at the levels of 1%, 5%, and 10%, respectively .

In Table 4, the corresponding coefficients $β_{pre_j}$ from 1 year to 6 years before the establishment of the carbon market failed to pass the test of significance, indicating that before the establishment of the carbon market, there is no significant difference in the variation trend of CE and CEI between two groups, which satisfies the parallel trend hypothesis. Therefore, the above estimation of the carbon market ERE is reasonable and feasible.

Furthermore, the policy effect coefficients during and after the establishment of the carbon market in Table 4 provide data support for our paper to investigate the dynamic effects of carbon market policies. For both CE and CEI, the estimated coefficients after the policy implementation time are significantly negative at the 5%−10% level. Specifically, from the current period of policy implementation to the first 3 years after implementation, although the regression coefficient did not pass the significance test of 5%, its absolute value increased year by year. However, in the fourth and fifth years after the implementation of the policy, the absolute value of the estimated coefficient still shows an upwards trend, and its significance reaches 5%, which indicates that the reduction effect of carbon market may have a certain time lag and be cumulative over a long time. A reasonable explanation is that with the gradual development of carbon market, the corresponding management measures will be improved, and the role of the market mechanism continues to emerge, and its influence on regional emission reduction is also deepening and strengthening. At the same time, to more intuitively show the long-term impact of the ERE of carbon market, our paper draws a dynamic effect diagram of the carbon market pilot policy (Figures 3 and 4). It is observed that in the period after policy implementation, the regression coefficient shows a downwards trend overall, indicating that ERE of carbon market is persistent, cumulative and time-lagged to a certain extent.

Figure 3.

Results of parallel trend test and policy dynamic effect when the dependent variable is CE.

Figure 4.

Results of parallel trend test and policy dynamic effect when the dependent variable is CEI.

Diagnosis of Bidirectional Fixed Effect Estimator Bias

Our paper uses Bacon decomposition to conduct a bidirectional fixed effect bias test for the estimators obtained above. This method can decompose the total multi-period DID estimator into three parts: (1) provinces with carbon market pilots (treatment group) and provinces with no carbon market pilot (control group); (2) provinces that set up carbon market pilots earlier (treatment group) and provinces that set up carbon market pilots later (control group); and (3) provinces that set up carbon market pilots later (treatment group) and provinces that set up carbon market pilots earlier (control group).

The decomposition results of the total DID estimator without adding control variables are shown in Table 5. The results of the total DID estimator are the same as the baseline regression results in Table 4, and the weighted average of the average DID estimator in each group is equal to the total DID estimator. Furthermore, we note that the “unsatisfactory” subgroup of “provinces that set up carbon market pilots later (treatment group) and provinces that set up carbon market pilots earlier (control group)” has a weight of only 1.3%, indicating that this subgroup has little influence on the total bidirectional fixed effect estimator. The group with the greatest influence on the total bidirectional fixed effect estimator is “provinces with carbon market pilots (treatment group) and provinces with no carbon market pilot (control group),” which is also the focus of the research design of our paper, accounting for 96.6% of the weight.

Table 5.

Results of Bacon Decomposition.

Dependent variable	Category		Total DID estimator	Average DID estimator	Weight (%)
Dependent variable	Treatment group	Control group	Total DID estimator	Average DID estimator	Weight (%)
lnco₂	Earlier Group Treatment	Later Group Treatment	−0.217	−0.149	2.2
	Later Group Treatment	Earlier Group Treatment		0.111	1.3
	Treatment	Never Treated		−0.223	96.6
lngco₂	Earlier Group Treatment	Later Group Treatment	−0.23	−0.058	2.2
	Later Group Treatment	Earlier Group Treatment		0.053	1.3
	Treatment	Never Treated		−0.237	96.6

Note. “Earlier Group Treatment” indicates that provinces that set up carbon market pilots earlier; “Later Group Treatment “indicates that provinces that set up carbon market pilots later; “Treatment” represents provinces with carbon market pilots; “Never Treated” represents provinces with no carbon market pilot.

Therefore, even if there is an “unsatisfactory” control group in this study, it only lowers the total bidirectional fixed effect estimator to a very small extent, and this study may underestimate the ERE of China’s carbon market to a very small extent.

To more intuitively present the weight of each potential category and its influence on the total bidirectional fixed effect estimator, weight diagrams of the Bacon decomposition are presented in our paper, as shown in Figures 5 and 6.

Figure 5.

Weight diagram of Bacon decomposition when the dependent variable is CE.

Figure 6.

Weight diagram of Bacon decomposition when the dependent variable is CEI.

Placebo Test

To make the baseline results more robust, a placebo test is carried out. The specific operation is as follows: non-repeated random sampling was conducted for 30 provinces and policy time in the sample. Seven provinces were selected each time as the virtual treatment group and its corresponding random policy time point, and the remaining provinces were taken as the virtual control group. This process was repeated1,000 times to obtain1,000 estimated coefficients of $new_policy * new_post$ . The cumulative distribution of coefficients are shown in Figures 7 and 8.

Figure 7.

Empirical cumulative distribution of placebo test coefficients when the dependent variable is CE.

Figure 8.

Empirical cumulative distribution of placebo test coefficients when the dependent variable is CEI.

As seen from the figures above, when the dependent variables are CE and CEI, the empirical cumulative distribution of coefficients of $new_policy * new_post$ is approximately normal, mainly distributed near 0, which is significantly different from the actual regression coefficients in Table 3 (−0.1930 and −0.1895). It shows that the influence of other policies or factors on the above empirical results are excluded in both the time dimension and the individual dimension.

Robustness Test

Specify the Implementation Time of the Policy

Considering the specific month difference in the establishment time of the carbon market pilots, to investigate the policy effect in a more precise way, our paper refers to the method of Lu et al. (2017) to test the robustness of the baseline results34. A new rule is as follows: we reassign the variable $pos t_{it}$ according to the proportion of months affected by the policy in the first year of the carbon market pilot. For example, Hubei carbon market pilot was established in April 2014, so Hubei was affected by the policy for 9 months from April to December in 2014. Therefore, when Hubei is in 2014, $pos t_{it}$ is 3/4; after 2014, $pos t_{it}$ is 1; and before 2014, $pos t_{it}$ is 0. Put the reassigned $pos t_{it}$ into the model for regression. Table 6 presents relative results. There is no significant difference in the estimated coefficient of variable $policy * post$ , which proves that the estimated result of the baseline model is robust.

Table 6.

Regression Results Considering the Monthly Difference of Policy Implementation Time.

	lnco₂	lngco₂
	(1)	(2)
policy*post	−0.2044** (0.0854)	−0.2013** (0.0845)
lnpgdp	−0.1992 (0.5972)	−1.1609* (0.5981)
industry	−0.2703* (0.1550)	−0.2662* (0.1548)
lnpop	0.2961 (0.8702)	−0.6668 (0.8616)
fdi	−0.0161 (0.0215)	−0.0190 (0.0210)
rd	−0.0992 (0.0819)	−0.0995 (0.0798)
lnforest	0.4880 (0.4863)	0.4732 (0.4823)
ecology	−0.0777*** (0.0188)	−0.0785*** (0.0190)
Constant	4.0000 (11.5945)	26.2858** (11.5304)
Year fixed effect	Yes	Yes
Individual fixed effects	Yes	Yes
Observations	420	420
R ²	.9692	.9673

Note. Values in brackets are robust standard errors for clustering adjustment at the provincial level.

***

, **, and * are significant at the levels of 1%, 5%, and 10%, respectively.

Expected Effect Test

To further test the randomness of the carbon market, our paper conducts the expected effect test to examine whether CE are affected by the expectations of the establishment of the carbon market. Table 7 reveals the regression results. The coefficient of interaction variable $polic y_{i} * pos t_{it - 1}$ is not significant, and both the symbol and significance of the coefficient of variable $polic y_{i} * pos t_{it}$ do not change after controlling the interaction variable $polic y_{i} * pos t_{it - 1}$ . This result shows that the carbon market policy does not have the expected effect.

Table 7.

Results of Expected Effect Test.

	lnco₂	lngco₂
	(1)	(2)
policy*post	−0.1395** (0.0608)	−0.1363** (0.0605)
policy*post_t-1	−0.0695 (0.0496)	−0.0690 (0.0485)
lnpgdp	−0.1981 (0.5999)	−1.1601* (0.6008)
industry	−0.2710* (0.1554)	−0.2670* (0.1552)
lnpop	0.3439 (0.8822)	−0.6212 (0.8736)
fdi	−0.0170 (0.0213)	−0.0198 (0.0209)
rd	−0.0970 (0.0820)	−0.0974 (0.0799)
lnforest	0.4655 (0.4887)	0.4510 (0.4848)
ecology	−0.0774*** (0.0189)	−0.0783*** (0.0192)
Constant	3.6716 (11.6943)	25.9781** (11.6298)
Year fixed effect	Yes	Yes
Individual fixed effects	Yes	Yes
Observations	420	420
R ²	.9693	.9674

Note. Values in brackets are robust standard errors for clustering adjustment at the provincial level.

***

, **, and * are significant at the levels of 1%, 5%, and 10%, respectively.

Robustness Test Excluding Some Special Samples

We further conduct a robustness test by eliminating some special samples. The eliminated samples were as follows: (1) Beijing, Shanghai and Guangzhou. The economies of Beijing, Shanghai and Guangzhou are the strongest among China. In addition to the carbon market pilot policy, these three regions may have implemented other energy-saving and emission reduction policies that may interfere with the identification of the effects of the carbon market pilot policy. (2) Chongqing. Among all the carbon market pilot regions in China, only the carbon market in Chongqing adopts the enterprise independent reporting system for quota allocation. To avoid excessive costs, enterprises tend to declare more CE, which leads to relatively low activity of the carbon market in Chongqing and may affect our baseline regression results. (3) Fujian. The carbon market in Fujian was launched late, and the market was not fully developed, which may also affect the overall evaluation effect of the carbon market policy.

In our paper, the above three groups of special samples are removed step by step to test the robustness of the baseline regression results. In Table 8, column (1) and (4) are the regression results excluding Beijing, Shanghai and Guangzhou, column (2) and (5) are the regression results excluding Chongqing, and column (3) and (6) are the regression results excluding Fujian. After the elimination of special samples step by step, the estimated coefficient of the core independent variable $polic y_{i} * pos t_{it}$ is still significant at the level of 1%−5%, which further verifies the robustness of the baseline regression results.

Table 8.

Regression Results Excluding Special Samples.

	lnco₂			lngco₂
	(1)	(2)	(3)	(4)	(5)	(6)
policy*post	−0.1966** (0.0893)	−0.1741* (0.0901)	−0.2431*** (0.0802)	−0.1867** (0.0880)	−0.1702* (0.0893)	−0.2385*** (0.0794)
lnpgdp	−0.1650 (0.6201)	−0.0900 (0.6911)	−0.2031 (0.5904)	−1.1327* (0.6230)	−1.0488 (0.6944)	−1.1654* (0.5916)
industry	−0.0664 (0.1530)	−0.2738 (0.1623)	−0.2367 (0.1541)	−0.0597 (0.1525)	−0.2684 (0.1623)	−0.2337 (0.1541)
lnpop	0.6671 (0.9447)	0.4589 (0.9645)	0.3109 (0.8726)	−0.3399 (0.9375)	−0.5031 (0.9576)	−0.6533 (0.8640)
fdi	0.0047 (0.0191)	−0.0160 (0.0227)	−0.0122 (0.0211)	0.0028 (0.0185)	−0.0184 (0.0226)	−0.0151 (0.0206)
rd	−0.0934 (0.1030)	−0.1142 (0.0851)	−0.0944 (0.0829)	−0.0915 (0.1012)	−0.1164 (0.0832)	−0.0950 (0.0807)
lnforest	0.5294 (0.5800)	0.4465 (0.4931)	0.5367 (0.5000)	0.5291 (0.5711)	0.4308 (0.4893)	0.5204 (0.4955)
ecology	−0.0792*** (0.0193)	−0.0775*** (0.0192)	−0.0792*** (0.0189)	−0.0798*** (0.0194)	−0.0785*** (0.0195)	−0.0800*** (0.0191)
Constant	0.2201 (11.8225)	1.7477 (13.2591)	3.7430 (11.5074)	22.8774* (11.7910)	24.0013* (13.2390)	26.0539** (11.4434)
Year fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Individual fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Observations	378	406	406	378	406	406
R ²	.9688	.9690	.9699	.9584	.9669	.9673

Note. Values in brackets are robust standard errors for clustering adjustment at the provincial level.

***

, **, and * are significant at the levels of 1%, 5%, and 10%, respectively.

Further Analysis

Analysis of the Impact of Carbon Market Scale and Carbon Market Activity

The above regression results indicate that the carbon market has significant ERE. However, China’s carbon market is still in its early stages, and various problems are emerging in the operation process: for example, due to the imperfection of the market mechanism and the unreasonable allocation of quotas, the due date effect is relatively common; the carbon price in the pilot areas is generally low and deviates greatly from the marginal emission reduction cost of enterprises; the fluctuation of carbon price is large, the carbon market has not fully explored the price discovery function, and the expectation of quota value is not clear. Therefore, to test the influencing factors of the carbon market ERE, referring to Moser and Voena (2012), we construct a continuous DID model for further research. In other words, continuous variables that can reflect the characteristics of the carbon market are used to replace the dummy variables of policy grouping to form the interaction term with the dummy variable of policy time. To better identify the impact of the characteristics of the carbon market itself on the reduction effect, model (3) is constructed as follows:

Y_{it} = β_{0} + β_{1} (E_{it} * pos t_{it}) + α_{s} contro l_{it - 1} + μ_{i} + v_{t} + ε_{it}

(3)

In model (3), $E_{it}$ represents the influencing factors of the carbon market itself, specifically referring to the relative scale of the market ( $rscale$ ) and relative market activity level ( $activation$ ). The relative market scale is measured by the ratio of the total amount of carbon quota trading in the pilot region, while the relative market activity level is measured by the ratio of the nonzero trading days to the total trading days in the pilot region. Instructions of other variables are the same as model (1).

The results of Table 9 show that the larger the relative market scale of the carbon market is, the higher the relative market activity level, and the stronger the ERE. Specifically, from the dimension of market scale, coefficients of $rscale * post$ are both significantly negative at 1%, whether the dependent variable is CE or CEI, which indicates that relative market scale can significantly enhance the carbon markets’ reduction effect. In terms of the market activity level, the coefficient of $activation * post$ is negative at the significance level of 10% whether the dependent variable is CE or CEI, which means that as the relative market activity level increases, the ERE of the pilot carbon market will also increase. Results in Table 9 confirm hypothesis 2 and hypothesis 3.

Table 9.

Analysis of Influencing Factors of ERE.

	lnco₂		lngco₂
	(1)	(2)	(3)	(4)
rscale*post	−5.7162*** (1.6078)		−5.8220*** (1.6193)
activation*post		−0.1679* (0.0932)		−0.1709* (0.0910)
lnpgdp	−0.2234 (0.6197)	−0.2708 (0.6247)	−1.1818* (0.6199)	−1.2301* (0.6248)
industry	−0.2566* (0.1464)	−0.2927* (0.1556)	−0.2512* (0.1458)	−0.2879* (0.1554)
lnpop	0.0026 (0.8883)	0.0497 (0.9137)	−0.9501 (0.8797)	−0.9022 (0.9043)
fdi	−0.0165 (0.0199)	−0.0242 (0.0210)	−0.0189 (0.0191)	−0.0268 (0.0208)
rd	−0.1072 (0.0874)	−0.0977 (0.0885)	−0.1067 (0.0853)	−0.0969 (0.0866)
lnforest	0.4325 (0.4746)	0.3765 (0.4773)	0.4205 (0.4703)	0.3635 (0.4736)
ecology	−0.0779*** (0.0195)	−0.0784*** (0.0196)	−0.0787*** (0.0196)	−0.0792*** (0.0198)
Constant	6.8147 (12.0338)	7.1259 (12.2140)	28.9723** (11.9634)	29.2905** (12.1353)
Year fixed effect	Yes	Yes	Yes	Yes
Individual fixed effects	Yes	Yes	Yes	Yes
Observations	420	420	420	420
R ²	.9687	.9683	.9669	.9665

Note. Values in brackets are robust standard errors for clustering adjustment at the provincial level.

***

, **, and * are significant at the levels of 1%, 5%, and 10%, respectively.

Analysis of the Economic Impact of the Carbon Market

Carbon emission trading policy may affect local economic by influencing business activities. On one hand, enterprises subjected to emission restrictions may reduce their production scale to a certain extent to reduce emission reduction costs and complete compliance; on the other hand, under the pressure of external environmental regulations, participants will have greater motivation and incentives to carry out technological innovation, thus improving the production efficiency of enterprises and enhancing competitiveness and profitability. Therefore, to test whether carbon market pilots have negative impact on regional economic growth, on the basis of model (1), our paper constructs the following model (4):

\begin{matrix} lngd p_{it} = & β_{0} + β_{1} (polic y_{i} * pos t_{it}) + β_{2} contro l_{it - 1} \\ + μ_{i} + v_{t} + ε_{it} \end{matrix}

(4)

In model (4), the dependent variable is replaced by $lngd p_{it}$ , which represents the logarithm of the actual GDP of each province in different years based on 2005. To avoid excessive control, the control variables of model (4) do not include $pgdp$ , and the meanings of other variables are the same as those in model (1).

Table 10 shows the regression results. Whether or not to join the control variables, the estimated coefficient of interaction variable is always positive but not significant. This shows that carbon market pilots have no significant inhibiting impact on regional economic output. According to the above theoretical analysis, this may be the result of mutual offset between the inhibiting and promoting effects of carbon market policies on regional economic output. Empirically speaking, it can be concluded that the carbon market effectively reduces regional CE and CEI without significant negative impact on the regional economy. Therefore, the carbon market mainly achieves ERE by reducing regional CEI. Hypothesis 4 is valid.

Table 10.

Analysis of the Economic Impact of the Carbon Market.

	Lngdp
	(1)	(2)
policy*post	0.0127 (0.0439)	0.0300 (0.0317)
industry		−0.1785*** (0.0309)
lnpop		0.1443 (0.1426)
fdi		−0.0131** (0.0055)
rd		−0.0224 (0.0369)
lnforest		0.0634 (0.1401)
ecology		−0.0004 (0.0062)
Constant	9.1403*** (0.0043)	8.0444*** (1.1166)
Year fixed effect	Yes	Yes
Individual fixed effects	Yes	Yes
Observations	450	420
R ²	0.9970	0.9979

Note. Values in brackets are robust standard errors for clustering adjustment at the provincial level.

***

, **, and * are significant at the levels of 1%, 5%, and 10%, respectively.

Conclusions and Policy Implications

Our paper reveals that: (1) Overall, China’s carbon market can effectively reduce CE and CEI in pilot areas. (2) In terms of influencing factors, the expansion of the relative market scale and the improvement of the relative market activity level can significantly enhance the ERE of the carbon market in pilot areas. (3) In terms of economic impact, the carbon market has no significant inhibiting effect on the economic growth.

The following policy implications are proposed: (1) Summarize regional pilot experience and accelerate the development of the national carbon market. At present, the regional carbon market continues to play its role, providing experience for the construction of the national carbon market, and promoting the coordination between the pilot areas and the national carbon market in terms of the depth of docking, trading process, quota allocation and quota transfer held by enterprises. (2) Expand market scale and include more industries in an orderly manner. At present, only the electric power industry is included in the national carbon market. Actually, the high energy consumption industries should also be included, such as chemical, iron and steel, paper making, and civil aviation. (3) In terms of ensuring economic growth, the innovation of carbon financial products should be gradually promoted. At the present stage, the expansion of the market scale and the improvement of market activity are largely limited by the characteristics of the single trading object (carbon quota spot) and the single participant (enterprises). Therefore, it is necessary to innovate financial products, introduce financial instruments such as carbon futures and carbon forwards. In the long run, the entry of other diversified players is indispensable. The participation of diversified players can more effectively stimulate the activity of the carbon market, improve market liquidity, and thus form a reasonable and effective carbon price. It must be emphasized that financial innovation in the carbon market should be steadily promoted. Risk control should be combined with the introduction of diversified market players, and carbon financial instruments should be fully utilized to promote the construction and improvement of the national carbon market.

This study can be further developed from the following aspects: (1) Further explore the paths and mechanisms by which China’s carbon market exerts its ERE; (2) Further study the impact of the carbon market on the innovation ability of enterprises and thus enrich the research on the Porter’s hypothesis.

Supplemental Material

sj-do-1-sgo-10.1177_21582440231206626 – Supplemental material for Emission Reduction Effect, Influencing Factors and Economic Impact of China’s Carbon Market

Supplemental material, sj-do-1-sgo-10.1177_21582440231206626 for Emission Reduction Effect, Influencing Factors and Economic Impact of China’s Carbon Market by Heng Zhang, Ziwei Zhang, Keyuan Sun and Yutong Zou in SAGE Open

Supplemental Material

sj-dta-2-sgo-10.1177_21582440231206626 – for Emission Reduction Effect, Influencing Factors and Economic Impact of China’s Carbon Market

sj-dta-2-sgo-10.1177_21582440231206626 for Emission Reduction Effect, Influencing Factors and Economic Impact of China’s Carbon Market by Heng Zhang, Ziwei Zhang, Keyuan Sun and Yutong Zou in SAGE Open

This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors are grateful for the research support from the following foundations:

(1) Liaoning Provincial Department of Education Project, grant number (LJKMR20220425);

(2) Liaoning Province Economic and Social Development Research Project,grant number(2024lsljdybkt-001).

ORCID iD

Ziwei Zhang

Supplemental Material

Supplemental material for this article is available online.

References

Anger

(2008). Emissions trading beyond Europe: Linking schemes in a Post-Kyoto World. Energy Economics, 30, 2028–2049.

Bertrand

Duflo

Mullainathan

(2004). How much should we trust differences-in-differences estimates? The Quarterly Journal of Economics, 119, 249–275.

BP. (2021). Statistical review of world energy. BP. https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2020-full-report.pdf.2021

Callaway

Sant’Anna

P. H. C.

(2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200–230.

Chen

Shi

Wang

(2020). Carbon emission curbing effects and influencing mechanisms of China’s emission trading scheme: The mediating roles of technique effect, composition effect and allocation effect. Journal of Cleaner Production, 264, 121700.

Coase

R. H.

(1960). The problem of social cost. The Journal of Law and Economics, 3(4), 1–44.

Cui

Song

Zhu

(2019). Can China achieve its 2030 energy development targets by fulfilling carbon intensity reduction commitments? Energy Economics, 83, 61–73.

Dales

(1968). Pollution, property and prices. University of Toronto Press.

Dietz

Rosa

E. A.

(1994). Rethinking the environmental impacts of population, affluence and technology. Human Ecology Review, 1(2), 277–300.

10.

Dong

Dai

Zhang

Long

(2019). Can a carbon emission trading scheme generate the porter effect? Evidence from pilot areas in China. The Science of the Total Environment, 653, 565–577.

11.

Dong

Long

Wang

Dai

Yang

Chen

(2018). How can China allocate CO₂ reduction targets at the provincial level considering both equity and efficiency? Evidence from its Copenhagen Accord pledge. Resources Conservation and Recycling, 130, 31–43.

12.

Duan

Fan

Wang

(2018). Achieving China’s energy and climate policy targets in 2030 under multiple uncertainties. Energy Economics, 70, 45–60.

13.

Duan

Pang

Zhang

(2014). Review of carbon emissions trading pilots in China. Energy & Environment, 25(3-4), 527–549.

14.

Duan

(2016). Analysis of the relationship between China’s IPPU CO₂ emissions and the industrial economic growth. Sustainability, 8, 426.

15.

Ellerman

A. D.

Convery

F. J.

Perthuis

C. D.

(2010). Pricing carbon: The European Union emissions trading scheme. Cambridge University Press.

16.

Fernández-Amador

Francois

J. F.

Oberdabernig

D. A.

Tomberger

(2017). Carbon dioxide emissions and economic growth: An assessment based on production and consumption emission inventories. Ecological Economics, 135, 269–279.

17.

Fujimori

Masui

Matsuoka

(2015). Gains from emission trading under multiple stabilization targets and technological constraints. Energy Economics, 48, 306–315.

18.

Gao

Xue

Liu

(2020). Evaluation of effectiveness of China’s carbon emissions trading scheme in carbon Mitigation. Energy Economics, 90, 104–872.

19.

Gallagher

K. S.

Zhang

Orvis

Rissman

Liu

. (2019). Assessing the policy gaps for achieving China’s climate targets in the Paris agreement. Nature Communications, 10(1), 1256.

20.

Goodman-Bacon

(2021). DID with variation in treatment timing. Journal of Econometrics, 225(02), 254–277.

21.

Grubb

Laing

Counsell

Willan

(2011). Global carbon mechanisms: Lessons and implications. Climatic Change, 104, 539–573.

22.

Han

Olsson

Hallding

Lunsford

(2012). China’s carbon emission trading: An overview of current development. FORSE and Stockholm Environment Institute.

23.

Huang

(2018). Does the carbon emission trading scheme promote carbon mitigation? Journal of Arid Land Resources and Environment, 9, 32–36.

24.

Hua

Dong

(2019). China’s carbon market development and carbon market connection: A literature review. Energies, 12, 1663.

25.

Ding

(2020). Can the carbon emission trading mechanism give consideration to enterprise benefits and green efficiency. China Population Resources and Environment, 30(03), 56–64.

26.

Ren

Wang

Chen

(2020). Can carbon emission trading scheme achieve energy conservation and emission reduction? Evidence from the industrial sector in China. Energy Economics, 85, 104–590.

27.

Ibikunle

Gregoriou

Hoepner

A. G. F.

Rhodes

(2016). Liquidity and market efficiency in the world’s largest carbon market. The British Accounting Review, 48(4), 431–447.

28.

Imbens

G. W.

Wooldridge

J. M.

(2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5–86.

29.

Jaraitė

Di Maria

(2012). Efficiency, productivity and environmental policy: A case study of power generation in the EU. Energy Economics, 34, 1557–1568.

30.

Jorgenson

D. W.

Wilcoxen

P. J.

(1990). Environmental regulation and U.S. economic growth. Journal of Economics, 21(2), 314–340.

31.

Jotzo

(2013). Emissions trading in China: Principles, design options and lessons from international practice (CCEP Working Paper 1303, May 2013). Crawford School of Public Policy, The Australian National University.

32.

Liu

Tan

X. J.

S. Z.

(2017). Assessment of impacts of Hubei pilot emission trading schemes in China – A CGE-analysis using TermCO₂ model. Applied Energy, 189, 762–769.

33.

A. Y.

(2012). Carbon emissions trading in China. Nature Climate Change, 2, 765–766.

34.

A. Y.

(2016). Challenges to the development of carbon markets in China. Climate Policy, 16(1), 109–124.

35.

Tao

Zhu

(2017). Identifying FDI spillovers. Journal of International Economics, 107, 75–90.

36.

Lokhov

Welsch

. (2008). Emissions trading between Russia and the European Union: A CGE analysis of potentials and impacts. Environmental Economics and Policy Studies, 9(1), 1–23.

37.

Mahmood

(2020). CO₂ emissions, financial development, trade, and income in North America: A spatial panel data approach. Sage Open, 10(4), 1–15.

38.

Mol

A. P. J.

(2012). Carbon flows, financial markets and climate change mitigation. Environmental Development, 1(1), 10–24.

39.

Moser

Voena

(2012). Compulsory licensing: Evidence from the trading with the enemy act. The American Economic Review, 102(1), 396–427.

40.

Pezzey

J. C. V.

Jotzo

. (2013). Carbon tax needs thresholds to reach its full potential. Nature Climate Change, 12(3),1008–1011.

41.

Porter

M. E.

(1991). America’s green strategy. Scientific American, 264(4), 193–246.

42.

Porter

M. E.

Linde

C. V. D.

(1995). Toward a new conception of the environment competitiveness relationship. The Journal of Economic Perspectives, 9(4), 97–118.

43.

Ranson

Stavins

R. N.

(2016). Linkage of greenhouse gas emissions trading systems: learning from experience. Climate Policy, 16(3), 284–300.

44.

Shao

Yang

(2011). Estimation, characteristics, and determinants of energy-related industrial CO₂ emissions in Shanghai (China), 1994–2009. Energy Policy, 39, 6476–6494.

45.

Shen

Huang

Liu

(2017). A study of the micro-effect and mechanism of the carbon emission trading scheme. Journal of Xiamen University, 1, 13–22.

46.

Spaargaren

Mol

A. P. J.

(2013). Carbon flows, carbon markets, and low-carbon lifestyles: Reflecting on the role of markets in climate governance. Environmental Politics, 22(1), 174–193.

47.

Stern

(2009). A blueprint for a safer planet: How to manage climate change and create a new era of progress and prosperity. Random House.

48.

Tang

B. J.

C. J.

Y. J.

Tan

J. X.

Wang

X. Y.

(2020). Optimal carbon allowance price in China’s carbon emission trading system: Perspective from the multi-sectoral marginal abatement cost. Journal of Cleaner Production, 253, 119–945.

49.

Tan

Liu

Cui

(2018). Assessment of carbon leakage by channels: An approach combining CGE model and decomposition analysis. Energy Economics, 74, 535–545.

50.

Wang

Deng

Sun

Nielsen

C. P.

Liu

Zhu

McElroy

M. B.

(2019). China’s CO₂ peak before 2030 implied from characteristics and growth of cities. Nature Sustainability, 2, 748–754.

51.

Wang

Tukker

Rodrigues

J. F. D.

(2019). The impact of regional convergence in energy-intensive industries on China’s CO₂ emissions and emission goals. Energy Economics, 80, 512–523.

52.

Wang

Xie

Luo

Zhao

(2018). The key elements analysis from the mitigation effectiveness assessment of Chinese pilots carbon emission trading system. China Population Resources and Environment, 28, 26–34.

53.

Wei

Ren

(2021). Can carbon emission trading promote enterprise green technology innovation – from the perspective of carbon price. Lanzhou Academic Journal, 7, 91–110.

54.

Cui

Mei

Zhang

(2021). Migration of manufacturing industries and transfer of carbon emissions embodied in trade: Empirical evidence from China and Thailand. Environmental Science and Pollution Research, 1–13.

55.

Dai

Geng

Xie

Masui

Tian

(2016). Achieving China’s INDC through carbon cap-and-trade: Insights from Shanghai. Applied Energy, 184, 1114–1122.

56.

Xian

Chen

(2021). The carbon emission reduction effect of China’s carbon Market——From the perspective of the coordination between market mechanism and administrative intervention. China Industrial Economy, 401(08), 114–132.

57.

Xuan

Shang

(2020). Can China’s policy of carbon emission trading promote carbon emission reduction? Journal of Cleaner Production, 270, 122–383.

58.

(2021). The impact and influencing path of the pilot carbon emission trading market——Evidence from China. Frontiers in Environmental Science, 9, 787655.

59.

Yang

X. L.

(2015). Spatio-temporal characteristics of water pollution accidents and the verification of environmental Kuznets curve in Yangtze River basin. Applied Mechanics and Materials, 15(2), 28–291.

60.

Zhang

Duan

Deng

(2019). Have China’s pilot emissions trading schemes promoted carbon emission reductions? The evidence from industrial sub-sectors at the provincial level. Journal of Cleaner Production, 234, 912–924.

61.

Zhang

S. S.

Lundgren

Zhou

W. C.

(2016). Energy efficiency in Swedish industry A firm-level data envelopment analysis. Energy Economics, 55, 42–51.

62.

Zhang

Luo

Gao

(2020). The effect of emission trading policy on carbon emission reduction: Evidence from an integrated study of pilot regions in China. Journal of Cleaner Production, 265, 121–843.

63.

Zhang

Y. J.

Liu

J. Y.

(2020). Overview of research on carbon information disclosure. Frontiers of Engineering Management, 7(1), 47–62.

64.

Zhao

Zhang

Yang

(2013). Energy endowment, human capital and China’s green economic performance. Modern Economic Science, 35(04), 74–84.

65.

Zhong

Xin

Shen

Chen

(2021). Effects of land urbanization and Internet penetration on environmental sustainability: A cross-regional study of China. Environmental Science and Pollution Research, 28, 66751–66771.

66.

Zhu

Zhang

Huang

Wang

Wei

Y. M.

(2020). Exploring the effect of carbon trading mechanism on China’s green development efficiency: A novel integrated approach. Energy Economics, 85, 104–601.

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