1. Introduction
Scholars tend to agree on the adverse effects of large amounts of carbon emissions on the human environment [
1,
2]. For example, excess carbon dioxide intensifies the effect of greenhouse, causing global warming, and accelerating the melting of the polar glaciers in both the north and south, resulting in rising sea levels and shrinking human habitats. Accelerating global temperatures could affect the growth of food crops around the world, diminish people’s quality of life and harm people’s physical health.
Since opening to the exterior, China’s economy has experienced sustained, rapid, and consistent growth over the long term, but its carbon emissions have also increased year by year. In 2011, China’s GDP became the world’s second largest, overtaking that of Japan (Source:
http://jingji.cntv.cn/20110121/105731.shtml, accessed on 1 November 2022). In 2020, China’s GDP reached 101.36 trillion yuan, surpassing 100 trillion yuan for the first time (Source:
https://data.stats.gov.cn/, accessed on 1 November 202). China’s average annual GDP growth rate (1979–2020) was 9.2% (Source:
http://sky.cssn.cn/jjx/jjx_xzyc/202209/t20220913_5492367.shtml, accessed on 1 November 2022). The cost behind China’s economic growth “miracle” is exceptionally high. Its economic development excessively depends on the mode of extensive economic development of “high input, high consumption, and high pollution”, which makes China the greatest carbon emitter in the world [
3].
In the face of the increasingly prominent carbon emission problem, the Chinese government has actively explored reasonable and feasible solutions. As early as 1979, China’s first law on environmental protection was enacted and implemented—
the Environmental Protection Law (Source:
https://www.chinacourt.org/law/detail/1989/12/id/10120.shtml, accessed on 1 November 2022). In 2003, China promulgated
the Regulations on the Collection and Use of Pollutant Discharge Fees, emphasizing the implementation of environmental protection subsidies and encouraging enterprises to conduct pollution control in reverse (Source:
http://www.gov.cn/zhengce/content/2008-03/28/content_5152.htm, accessed on 1 November 2022). In 2011, China issued the
National Environmental Protection Laws, Regulations, and Environmental Economic Policy Construction Plan during the 12th Five-Year Plan Period (Source:
https://www.mee.gov.cn/gkml/hbb/bwj/201111/t20111109_219755.htm, accessed on 1 November 2022). Chinese President Xi Jinping put forward the target of “Carbon Emissions Peaking and Carbon Neutrality” during the 75th United Nations General Assembly on 9 December 2020.
This article studies the effectiveness and mechanism of China’s Carbon Trading Pilot Policy (CTPP) on carbon emissions. In 2011, China released
the Notice on Implementation of the Carbon Trading Pilot Policy (CTPP), which designated Beijing, Shanghai, Tianjin, Chongqing, Hubei, Guangdong, and Shenzhen as pilot carbon trading areas (Source:
https://zfxxgk.ndrc.gov.cn/web/iteminfo.jsp?id=1349, accessed on 1 November 2022). These seven cities and provinces have consecutively implemented CTPP since 2013. In 2016, Sichuan and Fujian began to implement the policy. As of the end of 2022, China’s carbon transaction volume has exceeded RMB 9 billion, with the total turnover exceeding 400 million tons (Source:
http://www.gov.cn/xinwen/2020-10/28/content_5555655.htm, accessed on 1 November 2022). However, China is one of the developing countries implementing CTPP. Compared with developed countries, China’s carbon market has many deficiencies, such as insufficient competition regulation, and weak political and economic constraints [
4,
5]. In this sense, exploring China’s CTPP has important reference significance for other similar developing countries [
6].
CTPP is a market-based environmental policy, distinct from the previous command-based environmental policy. Market-type environmental policy is mainly market-oriented, guiding enterprises to adjust their economic activities and effectively reducing environmental pollution [
7]. Environmental policy is a crucial component of the social and environmental governance system [
8], an essential tool for building an environment-friendly society. The Chinese government often explores appropriate policy tools through policy pilots, and then promotes them nationwide [
9]. The same applies to CTPP. Remarkable progress has been made in China’s carbon emissions. However, is there a significant intrinsic link between China’s CTPP and carbon emissions? What is the transmission mechanism? Previous studies have been controversial, so it is urgent to analyze and test the effect of China’s CTPP.
We plot the connection between the number of cities implementing CTPP and carbon emissions in China from 2003 to 2020 (see
Figure 1). From 2003 to 2020, with the increase in the number of cities implementing CTPP, the growth acceleration of China’s carbon emissions shows a trend of gradual slowdown. This trend suggests that CTPP may be effective. Of course, to further determine the connection between CTPP and carbon emissions, we are going to conduct an empirical test through econometrics.
The following three areas will be the highlights and novel aspects of this article. First, CTPP is used as a quasi-natural test. The Difference-in-Difference model (DID) is utilized to investigate the relationship between market-based carbon trading policies and carbon emissions. Different from provincial panel data in most of the previous literature, this paper uses city panel data for empirical analysis and testing. This helps to obtain more precise conclusions. Second, the mechanism analysis and empirical test are carried out in depth. From the standpoint of Green Consumption Transformation (GCT), Ecological Efficiency (EE), and Industrial Structure Upgrading (ISU) investigate the effect of environmental policies on carbon emissions and additionally investigate the relative contributions of various mechanisms to the process of reducing carbon emissions. Third, the heterogeneity of CTPP is discussed from regional and city perspectives, which helps propose targeted policy suggestions.
Below is the remaining content from the article. The second section is a literature review, which reviews the previous relevant literature, and points out the shortcomings of the research and the innovation points of this paper. The third section is the mechanism analysis, which puts forward three hypotheses. Data sources and methods are introduced in the fourth part, introducing the data source and the DID method. The fifth section is the baseline regression results, testing the intermediary mechanism of CTPP affecting carbon emissions. The sixth part is the robustness test, including endogeneity treatment, Propensity Score Matching (PSM), variable substitution, one-period lag, changing the time–bandwidth, and excluding other policy disturbances. The seventh part tests the theoretical mechanism. The eighth section is devoted to some heterogeneity analysis. The final section presents brief conclusions and policy implications.
2. The Literature Review
At present, many scholars and policymakers attach great importance to the issue of carbon emissions. Scholars have made some rich academic achievements in research topics related to the carbon trading market. This paper summarizes the economic and social benefits, emission reduction effect, mechanism path, and other aspects of carbon trading, as follows.
First, scholars evaluated the economic and social benefits of low-carbon policies. For example, Du and Wang (2011) [
10] constructed a low-carbon city evaluation index system and evaluated the low-carbon city construction. Yang and Li (2013) [
11] evaluated the progress of the first eight pilot low-carbon cities in China and proposed specific requirements for the construction of low-carbon cities. Some scholars believe that the carbon trading system can maximize the cost benefit and generate considerable economic benefits [
12], increase the industrial output value by 13.6% [
13], and promote the transformation of the low-carbon economy [
14]. In addition, it can improve energy efficiency [
15], promote industrial structure upgrading [
16], promote enterprise technological innovation [
17] and green and low-carbon innovation [
18,
19], promoting high-quality development of the manufacturing industry [
20], enhancing low-carbon international competition of the industry [
21] and promoting technological maturity of exports [
22]. In addition, CTPP will reduce the level of government green investment due to the substitution effect (Han, 2020) [
23], and so on.
Second, we examined the carbon emission reduction effect of the carbon emission trading system. Scholars from the provincial, prefecture, city, industry, and enterprise levels, respectively, have carried out a relatively sufficient study. Zhang and Shi et al. (2017) [
24] adopted the simulation analysis of China’s provincial panel data and proved that the implementation of carbon emission trading in China can significantly save energy and reduce emissions, which can reduce carbon intensity by 20.06%. Xia and Li et al. (2020) [
25] used provincial data in China and determined that China’s carbon emission trading system would reduce at least 4 million tons of carbon emissions every year. Some scholars constructed panel data of prefecture-level cities and determined that carbon trading systems effectively reduced carbon emissions [
26] and carbon emission intensity per unit GDP [
27]. In addition, scholars study enterprises such as industry [
28] and listed companies [
29,
30]. They discovered the carbon emission reduction effect of the carbon emission trading system. However, some scholars believe that CTPP will lead to carbon leakage, that is, transfer from non-pilot areas to pilot areas through market participation and industrial transfer [
31].
Third, we examined the research on the carbon emission reduction mechanism of CTPP. The literature in this area is not sufficient, and the research conclusions are quite diverse. Based on the correlation between Beijing’s industrial structure adjustment and emissions of carbon dioxide, Mi et al. discovered that the former significantly affects the latter [
32]. Nevertheless, Zhang et al. analyzed data from China’s 281 prefecture-level cities from 2006 to 2016 using a dynamic spatial panel model [
33]. They discovered that improving industrial structure insignificantly affects the intensity of carbon emissions (CEI). Hong and Cui et al. (2022) [
15] concluded that China’s carbon trading system could improve cities’ total factor energy efficiency through green innovation and resource allocation. Wang and Huang et al. (2022) [
34], from the perspectives of economy, politics, culture, society, and ecological civilization, concluded that CTPP could reduce carbon emissions through industrial structure adjustment, low-carbon policy coordination, cultural communication, green space construction, energy intensity reduction, and other aspects. Regarding the mechanism of carbon emission reduction, scholars have different opinions. Some believe that the carbon-trading mechanism promotes carbon emission reduction through energy consumption structure [
35] rather than industrial structure [
36]. Some scholars also believe that carbon quota price and the number of enterprises participating in carbon trading are key factors affecting carbon emission reduction [
37]. Chen and Shi et al. (2020) [
38] constructed provincial panel data and determined that CTPP reduces carbon emissions through technical, structural, and configuration effects.
Fourth, we studied the link between environmental policy and carbon emissions. Prior research primarily examined environmental policies that were based on market forces and orders from above. Scholars have studied different environmental policy instruments and reached different conclusions. For example, Blackman and Kildegaard studied the effectiveness of mandatory environmental regulation policies in Mexico. They theorized that those environmental policies did not effectively stimulate the green technology innovation of the enterprises, but increased their pollution emissions [
39]. Zheng and Shi studied China’s environmental regulation policies and determined that pollution reduction targets had not been achieved [
40]. Wang et al. concluded that CTPP could not reduce sulfur dioxide emissions [
41]. In contrast to this view, Marconi examined the effect of China’s and the EU’s mandated environmental regulations on the emissions of pollution-intensive businesses and discovered that these regulations had a favorable influence on lowering carbon emissions [
42]. According to Cheng et al., a market-based emissions trading system will have a negative effect on Guangdong Province’s carbon emissions, which are expected to fall to two-thirds of their 2010 levels by 2020 [
43]. Liao et al. used the Shanghai CTPP as an illustration and discovered that the application of environmental policies greatly affects the decrease in regional carbon emissions [
44].
To sum up, emissions of carbon have been extensively examined in the past literature. Previous studies have comprehensively elaborated on the characteristics and operating mechanisms of the carbon trading market. However, there are shortcomings, as follows. First, in the space and time range of the previous studies, the scholars mainly focused on provincial or industrial carbon emission reduction effects [
14,
18,
45], and there is a lack of nationwide environmental policy testing. The findings are inconsistent and controversial, especially regarding the transmission mechanism. Second, the current research focuses on carbon emission reduction, but the empirical methods ignore the parallel trend test and endogeneity treatment, and so on. These may reduce the reliability of the conclusions. Third, the previous focus of the relevant literature studies is only on testing the influence of environmental regulating policies on the decrease in carbon emissions and analysis of mechanisms, but not on the measurement of the contribution of various mechanisms to carbon emission reduction. Finally, a lack of literature exists on the urban grade heterogeneity of environmental regulation policies, and a vast bulk of the literature centers on the repercussions of environmental regulating laws on lowering carbon emissions.
Compared with previous studies, this paper has certain uniqueness. First, this article revisits the repercussions of environmental policies on carbon emissions from another angle. The previous literature has examined chiefly the impacts of command-based environmental policies. Therefore, this article is founded on the pilot policy of market-based carbon trading (CTPP). Second, this article researches both the policy influence and the theoretical mechanism. From the standpoint of green consumption, industrial structure, and ecological efficiency, this article investigates the connection between the CTPP and carbon emissions, which is rarely seen in the previous studies and would enrich the relevant body of literature. Third, endogenous problems, such as reverse causality and variable omission, are tested by the instrumental variable method, and the probability of selection bias is dealt with by the Propensity Score Matching (PSM) method. The paper enriched the body of the literature related to empirical detection. Fourth, we test not only the mechanisms but also the contribution of each mechanism.
6. Robustness Test
6.1. Endogeneity Treatment
The ventilation coefficient is used in this article as an instrumental variable to assess potential endogeneity, taking into account endogeneity issues such as reverse causation or other omissions of other key variables.
In this paper, the Ventilation Coefficient (
ln_venti) of cities was chosen as the instrumental variable of environmental policy variables with reference to Hering and Poncet [
75]. The Ventilation Coefficient is considered the determinant of the diffusion rate of air pollution in standard box models of air pollution [
76]. In the case of a particular total carbon emission, the smaller the cities’ Ventilation Coefficient is, the greater the air pollution concentration is monitored. Therefore, the government is likely to raise the bar for environmental oversight, and the city is more likely to be selected as the city for CTPP implementation, which satisfies the correlation hypothesis. In addition, since Ventilation Coefficient is determined by large-scale weather systems, there is no other action mechanism between the Ventilation Coefficient and carbon emission. Therefore, as an instrumental variable of CTPP, the ventilation coefficient satisfies the exogeneity hypothesis.
Table 3 displays the outcomes of endogenic processing. In Column (2), the ventilation coefficient (
ln_venti) was the explanatory variable and CTPP is the explained variable, and the regression coefficient was −0.2973, and
p < 0.01. This means that the smaller the ventilation factor, the more likely it is to be selected as a CTPP city.
The CTPP’s coefficient is −3.5167, which is still strongly negative according to Column (1) of
Table 3. The F statistic is greater than 10 at 29.965. The Ventilation Coefficient (
ln_venti) is a valid instrumental variable as a result. This suggests that even after the endogeneity test, the baseline regression results in this work are still accurate.
6.2. PSM-DID Test
6.2.1. PSM Process
To test possible sample bias, Propensity Score Matching (PSM) was used for processing, followed by DID regression analysis. The outcome of the PSM method is to make the policy the only factor that distinguishes cities that adopt policies from those that do not. Propensity score values are obtained by Logit regression on the CTPP dummy variable using one-to-one matching with replacement. Matches are not tied, and if the propensity score is the same, the selection is sorted according to the data. The most important characteristic variables of matching are the Level of Opening-up (ln_fdigdp), Investment in Science and Technology (ln_sciep), Investment in Education (ln_edue), Economic Status (ln_gdppop), Information Status (ln_internetp), Population Density (ln_popden), Energy Consumption (ln_gasp, ln_liqgasp, ln_elecp).
The common value test and the matching balance test were used to evaluate the impact of PSM therapy. Take 2015, the middle of the policy’s implementation. The value zones of the treatment and the control group overlapped before matching, as seen in
Figure 4a, demonstrating that the assumption of common value was met.
After PSM, the sample distribution of cities with and without policy implementation tends to be significantly consistent (See
Figure 4b). The absolute values of standard deviations after PSM treatment were all less than 20% (see
Figure 5 and
Table 4). These matching results are valid [
77], and the results meet the requirements of the matching balance test. All P-values in
Table 3 exceed 0.1, indicating that the two types of variables are indifferent, so the results of the PSM are valid.
6.2.2. PSM-DID Regression Results
The data were first processed using the Propensity Score Matching (PSM) approach, and then regression analysis was performed using the DID method. The results of stepwise regression using the DID technique are displayed in
Table 5. With a confidence level of 1%, the coefficients of the Carbon Trading Pilot Policy (CTPP) are all significantly negative from Column (1) to Column (5), showing that the policy’s adoption significantly decreased carbon emissions. This indicates that there is no sample selection bias in baseline regression. These outcomes are in line with the baseline regression and once more support its results.
6.3. Variable Substitution, Lag Phase, and Time–Bandwidth
In order to test the benchmark regression’s reliability, the robustness test is further conducted from the following aspects: explanatory variable replacement, explanatory variable lagging one period, and changing the time window of regression.
First, this paper replaces the explanatory variable, namely the CTPP, with the representation of the Proportion of Environmental Statements (PES) in the Government Work Report. The higher the ratio, the more stringent the local government is on environmental issues, including carbon emissions. Second, considering the hysteresis of policy implementation in the benchmark regression, a robustness test was conducted for the CTPP with a lag of one year (L1_CTPP). Finally, to examine the impact of policy implementation time on carbon emissions, 2003–2017, 2003–2018, and 2003–2019 were selected as regression time ranges.
The Proportion of Environmental Statements (PES) in the Government Work Report is used as the explanatory variable in Column (1) of
Table 6 instead.
PES’s coefficient is −0.0693, with a confidence level of 1%. This shows a negative correlation between the importance of environmental protection and carbon emissions in the government work report.
Column (2) is the explanatory variable CTPP lagged by one year (L1_CTPP), and the coefficient of L1_CTPP is −0.0618, with a confidence level of 1%. This demonstrates the robustness of the baseline regression result.
Columns (3) to (5) represent the impact of the CTPP on carbon emissions during 2003–2017, 2003–2018, and 2003–2019 respectively, and their coefficients are −0.0586, −0.0594, and −0.0618, respectively, with a confidence level of 1%. The influence increases gradually with the coefficient, indicating that the impact of CTPP increases gradually with the increase in the implementation time.
6.4. Excluding Other Policy Interference
The repercussions of the CTPP on carbon emissions may be related to other environmental protection policies, which may be the direct or combined effect of other environmental protection policies. Other environmental policies, such as Low-carbon Cities and Smart Cities Policy, may also have carbon-reducing effects.
(1) Low-carbon Cities Policy (only one prefecture-level city that implemented the policy was deleted. In addition, Sanya city is repeated with the second list of cities, Yuxi city is repeated with the first list of cities, Ankang city is repeated with the first list of cities, and the duplicate prefecture-level cities are deleted. The sample in Smart Cities Policy is also treated). The Chinese government has proposed this development strategy as a proactive response to climate change and to encourage low-carbon development. The policy refers to the three batches of cities announced by relevant departments of the Chinese government from 2010 to 2017. The first batch was announced on 19 July 2010, involving 72 prefecture-level cities (Source:
https://www.ndrc.gov.cn/xxgk/zcfb/tz/201008/t20100810_964674.html?code=&state=123, accessed on 1 November 2022). The second batch was announced on 26 November 2012, adding 24 prefecture-level cities (Source:
http://gongyi.sina.com.cn/greenlife/2012-12-04/095739489.html, accessed on 1 November 2022). The third batch, announced on 7 Jan 2017, consists of 27 prefecture-level cities (Source:
https://www.ndrc.gov.cn/xxgk/zcfb/tz/201701/t20170124_962888.html?code=&state=123, accessed on 1 November 2022).
(2) Smart Cities Policy. The Smart Cities Policy is the advanced development stage of urban digitalization, which promotes smart industry clusters and expands the application ecological scenarios of clean industries. Smart Cities Policy can encourage green and low-carbon development [
78].
The first group of 90 “Smart Cities” in China, comprising 37 prefecture-level cities, was unveiled in December 2012 (Source:
https://www.mohurd.gov.cn/xinwen/jsyw/201301/20130131_221676.html, accessed on 1 November 2022). In May 2013, 83 cities and districts, 20 counties or towns, and 9 cities and districts made up the second batch of “Smart Cities”, which was enlarged from the initial batch of pilot cities in 2012 (Source:
https://www.mohurd.gov.cn/xinwen/gzdt/201308/20130808_214670.html, accessed on 1 November 2022). On 7 April 2014, the third batch of China’s Smart Cities list, which included 97 cities, counties, or districts, was made public (Source:
https://www.mohurd.gov.cn/xinwen/gzdt/201504/20150414_220664.html, accessed on 1 November 2022).
Table 7 shows the above two kinds of policy regression results. The CTPP coefficient is obviously considered negative in the two types of policy samples, and the impact of this policy is stronger in low-carbon cities than in non-low-carbon cities and in smart cities than in non-smart cities. These findings show that CTPP significantly contributes to lowering carbon emissions. The reduction in carbon emissions is also related to Low-carbon Cities and Smart Cities Policies. Still, there is a possibility of the combined effect of these two kinds of policies.
9. Discussion
In 2013, Shenzhen took the lead in launching CTPP in China. Later, six provinces, and cities including Beijing, Shanghai and Guangdong set up CTPP. In 2016, Sichuan and Fujian provinces joined CTPP. These provinces and cities are the sample space range of this paper. China’s carbon trading policy market has achieved remarkable results. Like most of the literature, the results of our study are consistent with the actual policy outcomes of CTPP in China.
However, at the academic level, the conclusion of the CTPP policy effect is not completely consistent with that of other scholars. The different performance is mainly reflected in the following aspects. First, the effect of carbon reduction is inconsistent. In this study, it is suggested that China’s CTPP reduces carbon emissions by 6.21%, while some studies by other scholars show that it reduces carbon emissions by 15.5% (Hu and Ren et al., 2020) [
28], and 20.06% (Zhang and Shi et al., 2017) [
24]. This may be related to different scholars’ research spaces, data, or model methods. Second, the transmission mechanism is inconsistent. This paper holds that the mechanism of CTPP to reduce carbon emissions is Green Consumption Transformation (GCT), improving Ecological Efficiency (EE), and promoting Industrial Structure Upgrading (ISU). Our study does not detect that technological innovation is the mediating mechanism of CTPP, which is consistent with the study of Xia and Li et al. (2020) [
25], but Liu, Ma, and Xie (2020) [
86] believe that technological innovation is the mediating mechanism. Third, heterogeneity analysis is inconsistent. In addition, to sample regression from east, central, and west in China, this paper also conducts subsample regression of core and peripheral cities, hoping to obtain more detailed conclusions.
The cause of the above differences may have the following several aspects. First, the sample selection space is different. Most of the previous studies were carried out at the provincial level, less at the prefecture-level city level. Second, data sources and index construction are different. Third, the measurement method is different. Some used the synthetic control method (Chen and Lin, 2021) [
87], others used the data simulation method, but most papers used DID method for empirical research. DID is effective in testing policy impact. Fourth, the robustness test is different. Unlike the previous literature, in order to improve the reliability of the conclusions, our paper carries out a variety of robustness tests, such as parallel trend test, endogeneity treatment, and selection bias test by using PSM. However, the previous literature does not offer such a comprehensive analysis.
In general, more detailed data and more rigorous empirical analysis on the carbon reduction effect of CTPP are expected from future scholars.
10. Conclusions and Policy Enlightenment
10.1. Conclusions
Adopting environmental policy to reduce carbon emissions is a crucial measure of environmental governance. This study builds the balance panel data of 285 Chinese cities between 2003 and 2020, involving 5130 samples. This study uses the Difference-in-Difference (DID) method to explore the effect of the CTPP on carbon emissions reduction. The conclusion is as follows.
First, according to the findings, the CTPP implementation considerably lowers carbon emissions by 6.21%.
Second, through a series of tests, we determine that the conclusion is robust. (1) To test possible reverse causality and variable omission, the ventilation coefficient was selected as the instrumental variable of CTPP for the endogeneity test, and regression analysis showed that the conclusion was still valid. (2) The data were processed using Propensity Score Matching (PSM) and the DID method for regression analysis to evaluate for potential sample selection bias, and the result was still robust. (3) In addition, by replacing the explanatory variable, the explanatory variable lags one stage and changes the sample’s time–bandwidth. The conclusion are still consistent. (4) With the increase in time–bandwidth, the effect of CTPP gradually increases. The paper distinguishes other environmental policies, including the Low-carbon Cities Policy and Smart Cities Policy on carbon emissions. We report that the effect of CTPP in these two types of cities is more excellent, indicating that there is a superimposed effect of environmental policies.
Third, the mechanism test and analysis show that CTPP can reduce carbon emissions through three intermediary mechanisms: Green Consumption Transformation (GCT), Ecological Efficiency (EE), and Industrial Structure Upgrading (ISU). The further contribution decomposition shows that among the three mechanisms, the contribution of green consumption transformation is the largest, with a value of 7.5%, followed by ecological efficiency and industrial structure upgrading, with 2.25% and 1.66%, respectively.
Fourth, the heterogeneity analysis shows that CTPP has the biggest marginal impact on reducing carbon emissions in central cities and peripheral cities.
10.2. Enlightenments
According to the empirical research of this paper, we propose the following policy recommendations.
First, expand the scope of the CTPP and accelerate the establishment of a unified national carbon emission trading market. CTPP is conducive to promoting carbon reduction in cities and is worth promoting nationwide. China is the largest carbon emitter, and the successful implementation of CTPP has made great contributions to global greenhouse gas emission reduction.
Second, establish a reasonable carbon allocation system. We should guide enterprises and society to take an active role in carbon trading, increase the popularity of carbon trading, improve the market management regulatory system for carbon trading, and boost the effectiveness of environmental law enforcement, oversight, and governance.
Third, adopt a variety of carbon reduction measures and provide full play to the integrated role of policies. As consumers, we should vigorously advocate the concept of low-carbon consumption and low-carbon life. In terms of regional economic development, we should guide regional industrial structure upgrading and develop low-carbon industries. On the energy front, technological reform should be carried out to improve energy efficiency. In addition, cities’ carbon emission reduction should be considered in combination with Low-carbon City Policies and Smart Cities Policies to exert the effect of policy superposition.
Fourth, policy measures should pay attention to urban and regional heterogeneity. In order to better promote the construction of low-carbon cities and accelerate the construction of an ecologically friendly society, local governments must pay attention to the differences in CTPP in different regions and cities and consider adopting targeted policies.