Impact of Chinese Carbon Emissions Trading Policy on Chongqing’s Carbon Emissions and Economic Development

Abstract

Balancing sustainable economic growth and environmental protection in developing countries is an important way to mitigate climate change. Based on panel data from nine provinces along the Silk Road Economic Belt between 2004 and 2021, this paper takes Chongqing, a pilot city for carbon trading rights, as the experimental group and the remaining eight provinces as the control group; we mainly analyze the impact of the carbon emissions trading policy on Chongqing’s carbon emissions and economic development after its implementation in 2013 using the difference-in-differences method (DID) and propensity score matching difference-in-differences method (PSM-DID). Then we use the synthetic control method (SCM) to test its robustness. The results showed that while maintaining the economic development speed, carbon trading helps to reduce carbon emissions. In addition, we also found that the economic activity effect and the energy intensity effect are the mediating effects of the carbon emissions reduction. Finally, taking the policy effect of the carbon emissions trading in Chongqing as a reference, this article confirmed the importance of aligning the carbon trading pathway with targeted green policies from the government. The government should drive the establishment of a regional carbon market in the nine provinces along the Silk Road Economic Belt, which could help to achieve sustainable development.

1. Introduction

Actively responding to climate change by controlling greenhouse gas emissions has become one of the most important global concerns [1]. In November 2018, the EU adopted the EU 2050 Strategic Long-term Vision, and countries started on the path of green transformation and development. In 2020, global carbon emissions were reduced by 5.9% to 32,079 million tons. As the global economy recovers, energy demand has rebounded sharply. Moreover, the impact of severe weather on the energy market has also significantly increased carbon emissions. In 2021, carbon emissions were 33,884 million tons, up by 5.6% from the previous year. At present, China is the world’s largest carbon emitter, accounting for more than a quarter of the world’s total carbon emissions. Compared to Europe and the US, it would take half as much time for China to achieve the goal of carbon neutrality after reaching its carbon peak. In the face of global environmental challenges, such as the greenhouse gas effect and atmospheric pollution, China, as a responsible nation and global economic power, announced at the 75th Session of the United Nations General Assembly that it would “strive to reach a peak [in carbon dioxide emissions] by 2030 and work towards achieving carbon neutrality by 2060” [2]. Industries that generate the most carbon emissions include manufacturing, livestock, and farming. Scientists are also dedicated to the study and analysis of energy savings and emissions reductions from the perspective of industrial optimization. The use of current and advanced technology is one way to achieve this. Energy conservation measures (ECMs) that are digitalized using advanced sensor technologies are a formal approach that has been widely adopted to reduce the energy consumption of building infrastructure [3]. There is also the use of AI technology combined with deep learning to aid agricultural development and reduce ecological impacts while improving operational efficiency [4].

The adoption of a policy approach is another way of reducing emissions. The rapid development of the carbon trading market has become the main means for carbon emissions reductions in many countries around the world. By 2021, about 38 national jurisdictions and 24 states, territories or cities were operating carbon trading markets, and their carbon trading markets covered 16% of global carbon emissions. In terms of energy savings and emissions reductions, the European Union (EU) has the most developed carbon finance trading in the world; the EU’s empirical data show that the marketization of carbon trading rights can use the market mechanism to curb carbon emissions [5]. The effectiveness of carbon trading policies is also closely related to macroeconomic operations and macroeconomic policies. However, the current situation of environmental problems in China is profound and complex. Some scholars have found that the non-linear relationship between environmental pollution and technological development from the perspective of land resources is sufficient to prove this point [6,7]. In order to achieve the goals of “carbon peaking” and “carbon neutrality”, in July 2021, the online trading of China’s carbon market was officially launched and is the largest carbon market in the world. Therefore, the realization of China’s emission reduction commitments may rely on the support of carbon trading to a large extent to achieve its emissions targets.

On 7 September 2013, General Secretary Xi proposed the international cooperation and development initiative of building the Silk Road Economic Belt, which includes five provinces in the northwest of China, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang, and four provinces in the southwest of China, Chongqing, Sichuan, Yunnan and Guangxi, in the domestic section. The construction of the Silk Road Economic Belt has brought vitality to the development of Western China. Figure 1 shows the regional location of the Silk Road Economic Belt in China.

In 2013, according to the “Notice on the Pilot Work of Carbon Emission Trading”, issued by the China Development and Reform Commission, Chongqing officially launched a pilot carbon trading market. It is the only province among the nine provinces along the Silk Road Economic Belt to have the right to trade carbon emissions. However, the Silk Road Economic Belt is a regional integration strategy that seeks to develop the region overall rather than a single province. Has carbon finance contributed to economic growth? What suggestions can be made for the green development of the provinces along the Silk Road Economic Belt?

Based on the panel data of nine provinces along the Silk Road Economic Belt, from 2004 to 2021, and referring to previous scholars’ research on economic development and environmental protection [8,9], this paper studied carbon emissions and economic growth in the Silk Road Economic Belt from the perspective of carbon trading. Firstly, we constructed a difference-in-differences model (DID) and a propensity score matching difference-in-differences model (PSM-DID) for a benchmark regression and then used the synthetic control model (SCM) for more rigorous testing. Furthermore, we used the intermediary effect model to analyze the specific path of the carbon trading policy impact results. Finally, combined with the relevant research conclusions, this article puts forward relevant suggestions for promoting the development of the Green Silk Road Economic Belt.

In terms of the research’s significance, firstly, we have enriched the study of the development of the Silk Road Economic Belt by using the SCM and PSM-DID models from the perspective of carbon trading; secondly, we further analyzed the impact mechanism of carbon emissions reduction through the intermediary effect. Overall, our research not only provides suggestions for the green development of the Silk Road Economic Belt through carbon trading policy, but also serves as a reference for the sustainable development of other countries along the belt, helping to achieve the common development and progress of all countries in the world and limiting the environmental impact of development.

2. Review of the Literature

2.1. Regional Aspects of the Study

In the study of regions, differences in the levels of regional economic development have always existed and, therefore, regional disparities are an issue of continuous focus for disciplines, such as economics and geography. With the intensification of the contradiction between economic growth and resources and the environment, the green development level of regions and the green development gap between regions have become a topic of great interest in academic circles [10]. Yang [11] found that the level of green development in China is high in the east and low in the west. Liu et al. [12] found that the resource use efficiency of coastal cities in Northern, Southwestern, and Eastern China is higher than that of other regions. In a study on the Silk Road Economic Belt, Chen [13] researched the growth rate of carbon emissions and economic growth in different provinces of the economic belt and found that different provinces have similar trends of change. Jia [14] studied the development of green finance and green bonds in countries along the Silk Road Economic Belt and explored how green finance can facilitate the allocation of financial resources.

2.2. Research Subject Area

In the study of economic development and carbon emissions, most scholars have focused on the green development of industrial economic and enterprise operations and explored the role of technological innovation in this regard. Mathew [15] argues that technological innovation plays a major role in addressing environmental issues, such as global warming and natural disasters.

Chinese scholars have focused more on the construction of ecological provinces, carbon trading, and green finance. Sun [16] applied an environmental Kuznets model to analyze the relationship between carbon emissions and economic growth in Henan Province and concluded that Henan Province is currently in a period of an inverted U-shaped inflection point. Guo et al. [17] argued that promoting the development of green financial instruments is conducive to promoting carbon emissions reductions. Gong et al. [18] suggested that the government should build a joint early warning mechanism to monitor the dynamics of carbon emissions and the agglomeration of primary and secondary industries according to local conditions.

2.3. Research Methodological Aspects

This paper utilized the PSM-DID model, as well as the SCM, which is superior in terms of the evaluation of policies’ effects and has been widely used by domestic and international scholars in recent years. In addition, the PSM-DID model has been increasingly favored in terms of its utilization, given its ability to effectively deal with endogeneity issues. In terms of domestic research, Shi et al. [19] chose the PSM-DID method to more accurately assess the effect of smart city construction on reducing environmental pollution. Ding et al. [20] adopted the PSM-DID method in order to address the endogeneity between green credit and bank cost efficiency. Guo et al. [21] also used the PSM-DID approach to mitigate other differences between the experimental and control groups of firms that might interfere with the study results. In terms of foreign research, Böckerman Petri et al. [22] accurately combined the PSM with the DID model and found that unemployment was not significant in the assessment of self-health status. Volodymyr Bilotkach et al. [23] identified the causal effect of life cycle cost (LCC) entry on international air passenger traffic to and from 30 major airports in Asia using DID and regression discontinuity design (RDD). Acton Blake et al. [24] measured changes in residential property transaction prices within walking distance of a bus rapid transit (BRT) station using a quasi-experimental approach and a propensity score matching model. As for the synthetic control method, based on panel data from 2004 to 2018, Wang et al. [25] evaluated the impact of carbon emissions trading policies on the development of a green economy in pilot areas using the comprehensive control method. Zheng et al. [26] evaluated the impact of WDS on energy utilization efficiency through SCM and found that WDS indirectly improved energy utilization efficiency by accelerating the spatial agglomeration of dominant industries in Western China. Maike B et al. [27] used the comprehensive control method to study the impact of Austria’s Fixed Fuel Price Law on the price level of gasoline and diesel.

2.4. Summary

Most of the current research has focused on the national and provincial levels, but relatively little research has been conducted on the nine provinces along the Chinese region of the Silk Road Economic Belt. Meanwhile, previous studies mostly analyzed energy consumption, and most scholars chose carbon emissions as the measurement index, with a single measurement basis and low accuracy. Further, the current time series for the analysis of economic development and carbon emissions is relatively short and needs to be extended in order to increase the accuracy of the research results. Finally, existing studies have mainly focused on the evaluation of emission reduction effects and economic development levels before and after the implementation of carbon trading policies, without using the intermediary effect model to analyze the deep-seated causes.

The overall literature review is provided in Figure 2.

3. Materials and Methods

3.1. Model Construction

Firstly, we chose to use a DID model to assess the magnitude of the impact of the carbon trading policy on carbon emissions and economic growth in Chongqing. However, this may lead to a less accurate result, because there may be other factors that influence regional markets (Tang et al.) [28]. Thus, different from studies that only used the DID method (Zhang et al.) [29], we also used a more rigorous PSM-DID model to ensure the reliability of the results. A baseline regression was first performed using a double-difference DID model, and this model is used to test the policy effects arising from carbon trading policy. We set the basic expression for this experiment as follows:

$$Y_{it}={\beta }_0+{\beta }_{1}Treatment+{\beta }_{2}Time+{\beta }_{3}Treatment\times Time+{\epsilon }_{it}$$

(1)

In the above formula, $Treatment$ indicates whether it is affected by the policy. $Time$ means whether it is after the implementation of the policy. If it is after the implementation of the carbon trading policy (i.e., after 2014), $Time=1$; otherwise, $Time=0$. $Treatment\times Time$ represents the policy interaction effect. Only when the two conditions of “the city is a pilot city” and “the time is 2014 or later” are met, $Treatment\times Time=1$. Otherwise, $Treatment\times Time=0$.

The implementation of the carbon emission trading policy has an impact on carbon dioxide emissions on the one hand and economic development on the other hand. Therefore, the research on the policy effect in this paper mainly focuses on carbon emissions and economy. Following Fan et al. [30], we constructed the following, Equation (2), for the emissions reduction in the carbon trading policy. Moreover, we constructed the following, Equation (3), for the economic effects of the carbon trading policy. $lnC{O}_2$ indicates the growth of carbon emissions and $lnGD{P}_{it}$ indicates the economic development in each area. ${\beta }_1$ and ${\beta }_2$ are used to measure the impact of carbon emissions trading policy on carbon emissions reduction and economic growth, respectively. Furthermore, $X_{1it}$and $X_{2it}$ are control variables. ${\gamma }_{1i}$ and ${\gamma }_{2i}$ denote the regional fixed effects, ${\phi }_{1t}$ and ${\phi }_{2t}$ denote the time fixed effects, and ${\epsilon }_{1it}$ and ${\epsilon }_{2it}$ are the random error terms, where $i$ denotes the $i\_th$ city, and $t$ denotes in year $t$.

$$lnC{O}_2={\alpha }_1+{\beta }_{1}polic{y}_{1it}+{\tau }_{1t}{X}_{1it}+{\gamma }_{1i}+{\phi }_{1t}+{\epsilon }_{1it}$$

(2)

$$lnGD{P}_{it}={\alpha }_2+{\beta }_{2}Polic{y}_{2it}+{\tau }_{2i}{X}_{2it}+{\gamma }_{2i}+{\phi }_{2t}+{\epsilon }_{2it}$$

(3)

3.2. Data Source and Description

Our study used data from nine provinces along the Silk Road Economic Belt, as only Chongqing is a pilot city for carbon emissions trading in the Silk Road Economic Belt; therefore, Chongqing was the treatment group, and the other eight cities were used as the control group. We chose the period of 2004 to 2019 as the analysis time range for which data on the regional gross domestic product (GDP), regional fixed asset investment growth index, and value added of secondary industry as a proportion of regional GDP were obtained, as well as and trade-level and population-related data from the China Statistical Yearbook (NBS 2004–2019) and the China Urban Statistical Yearbook (NBS 2004–2019). The data on regional energy consumption were taken from the China Energy Statistical Yearbook (NBS 2004–2019) and the data on the regional carbon emissions were calculated according to the carbon emissions calculation parameters in the IPCC Guidelines for National Greenhouse Gas Inventories (Qin et al.) [31]. Moreover, as for regional GDP, this paper refers to the market value of all the final products and services produced using production factors within one year by the nine provinces, respectively.

3.3. Variable Selection

Due to the large variation in the values and statistical units of the individual indicator data, some of the data were logged prior to the regression analysis (Volodymyr B. et al.) [23].

Table 1. Variables’ description.

Classification	Description	Variable	Calculation Method
Explained variable	Carbon emission	$lnC{O}_2$	The logarithm of carbon emissions in each region
	GDP per capita growth index	$lnGDP$	The logarithm of regional GDP index (last year = 100)
Explanatory variables	Time	Time	Time = 0 means the year was before 2014 Time = 1 means the year was after 2014 (inclusive)
	Area	Treatment	Treatment = 0 is a non-pilot area Treatment = 1 is the pilot area
	Interactive term	Time × Treatment	Time × Treatment
$\mathrm{Control} \mathrm{variable} X_{1it}$	The level of economic development	$rGDP$	The logarithm of GDP of each region
	Population size level	pop	The logarithm of the resident population of each region
	Level of industrialization	industry	The added value of the secondary industry is logarithmic
	Energy consumption structure	structure	Coal consumption/total energy consumption
	Urbanization rate	urban	Urban resident population in each region/total resident population in each region
$\mathrm{Control} \mathrm{variable} X_{2it}$	Capital level	capital	The logarithm of total capital formation in each region
	Employed population	employ	The logarithm of employed persons in urban units in each region
	Total energy consumption	consume	The logarithm of energy consumption in each region
	Trade level	trade	The logarithm of the total import and export volume of domestic destinations and sources
	Urbanization rate	urban	Urban resident population in each region/total resident population in each region

specifies the variables selected for this study and their definitions, symbols, and calculations. Among them, the explanatory variable was Policy, and the first grouping dummy variable was Treatment. For the experimental group of pilot cities with carbon emissions rights, the value was 1, and for the control group of non-pilot cities, the value was 0; the other time dummy variable was set as Time. If it was after the policy was implemented, the value was 1. If it was before the policy was implemented, the value was 0. In addition, the control variable was the key factor affecting the interpreted variable.

3.4. Statistical Description of the Variables

Before the empirical analysis, the article conducted descriptive statistics on the variables (Cao et al.) [32]. As shown in

Table 2. Statistical description of the variables.

Classification	Variable	Unit	Average	SD	Skewness	Kurtosis	Obs.
Explained variable	$lnC{O}_2$	million tons	9.829	0.055	−0.555	0.567	189
	$lnGDP$	billion	4.703	0.001	0.149	0.061	189
$\mathrm{Control} \mathrm{variable} X_{1it}$	$rGDP$	billion	8.588	0.085	−0.337	−0.430	189
	pop	million	7.838	0.062	−0.687	−0.662	189
	industry	billion	7.781	0.082	−0.374	−0.316	189
	structure	million tons of standard coal	1.262	0.133	0.551	3.189	189
	urban	million	0.446	0.008	0.239	−0.607	189
$\mathrm{Control} \mathrm{variable} X_{2it}$	capital	billion	8.159	0.083	−0.390	−0.613	189
	employ	million	6.989	0.061	−0.599	−0.629	189
	consume	million tons of standard coal	8.705	0.050	−0.488	−0.119	189
	trade	million dollars	15.885	0.112	−0.245	−0.779	189
	urban	million	0.446	0.008	0.239	−0.607	189

, the number of observations of each variable was approximately 189. Due to the logarithmic preprocessing, the average value of all variables was less than 10, which is conducive to eliminating the problem of heteroskedasticity; from a standard deviation perspective, the standard deviation of the data of each variable was less than 0.14, the data dispersion was low, and the accuracy was high; except for the capital level of each region, the absolute value of skewness was less than 0.6, and the kurtosis was close to 3.2 for most variables. The data distribution was more symmetrical and closer to a normal distribution, which also indicates that among all of the variables, the differences in the capital level of each region were larger.

4. Results

4.1. Analysis of the DID Benchmark Regression Results

The results are shown in

Table 3. DID regression results.

Model (1)			Model (2)
	(1)	(2)		(3)	(4)
Time × Treatment1	$-0.264{ }^{**}$	$-{0.219}^{ **}$	Time × Treatment2	$0.014{ }^{**}$	$0.016{ }^{*}$
$rGDP$		$-0.858{ }^{***}$	capital		$0.028{ }^{***}$
pop		0.294	employ		0.023
industry		$-1.556{ }^{**}$	consume		$-0.012{ }^{*}$
efficiency		$-$0.035	trade		$-0.010{ }^{***}$
urban		$-$0.039	urban		−0.018
cons	9.476	4.355	cons	4.355	4.608
Year-fixed effect	Y	Y	Year-fixed effect	Y	Y
City-fixed effect	Y	Y	City-fixed effect	Y	Y
N	189	189	N	189	189
$R^2$	0.570	0.776	$R^2$		0.603

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

. The regression results without the control variables are shown in columns (1) and (3), while the regression results with the control variables are shown in columns (2) and (4). In the regression results of model (1), the coefficients of policy1 were all negative and significant at the 5% level, indicating that the implementation of this policy can significantly reduce the level of CO₂ emissions in the pilot areas compared to the provinces without carbon trading (Guo et al.) [33]. The coefficient of policy1 was at the 10% significance level after the inclusion of the control variables. In the regression results of model (2), the coefficients of policy2 were positive, but the coefficients were small. Even after the inclusion of the control variables, the implementation of this policy only increased carbon emissions by 1.6% in the pilot region. This indicates that the implementation of the carbon trading policy did not have a significant impact on the economic development of Chongqing compared to the non-pilot areas.

4.2. Analysis of the PSM-DID Estimation Results

The experimental and control groups in the DID method were almost identical in all respects, except that the experimental variables for the policy shocks were different. However, this method did not address the endogeneity problem caused by selection bias (Wang et al.) [34]. In the case of pilot cities for carbon emissions trading, the selection was not random; it was influenced by the regional differences and the level of economic development and industrial structure, as well as the reporting and working conditions of the regions concerned. Considering the reasons for the selection of the “carbon trading pilot” will allow us to better evaluate the effects of this policy and reduce the possibility of distorted results.

Thus, based on the policy assessment using the DID method, this paper addressed the issue of endogeneity with the PSM-DID model. Firstly, the PSM was used to match the pilot provinces and cities (i.e., the experimental group) with the control group, and then the change in carbon emissions and economic growth before and after the policy implementation was analyzed again using the DID model. The PSM propensity score matching results are shown in

Table 4. PSM propensity score matching results.

	Variable	Unmatched(U)/Matched (M)	t	p > |t|
Model (1)	$rGDP$	U	1.94	0.054
		M	0.28	0.797
	pop	U	1.00	0.319
		M	0.72	0.513
	industry	U	0.08	0.038
		M	0.59	0.585
	structure	U	1.3	0.173
		M	1.04	0.540
	capital	U	1.11	0.269
		M	0.75	0.458
Model (2)	employ	U	2.19	0.030
		M	1.00	0.030
	consume	U	0.63	0.530
		M	0.99	0.326
	trade	U	2.64	0.009
		M	0.59	0.458

.

As shown in

Table 5. PSM-DID estimation results.

Model (1)		Model (2)
Time × Treatment1	$-{0.466}^{ ***}$	Time × Treatment2	$0.002{ }^{*}$
$rGDP$	0.068	capital	0.004
pop	$0.272{ }^{**}$	Employ	0.007
industry	0.579	consume	${0.004}^{ ***}$
structure	1.062
cons	8.264	cons	4.595
Year-fixed effect	Y	Year-fixed effect	Y
City-fixed effect	Y	City-fixed effect	Y
N	115	N	115
$R^2$	0.711	$R^2$	0.323

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

, in the regression results of model (1), the coefficient of the term of policy1 was significantly negative, indicating that the carbon emissions trading policy could significantly contribute to the reduction in carbon dioxide emissions in Chongqing, which decreased by 46.6% after the implementation of the policy; this would be consistent with the conclusion of the DID model benchmark regression in the previous section. However, in the regression results of model (2), as shown in Table 5, for the term of policy2, there was collinearity between the data for some of the variables, so we rescreened the variables. The regression results for model (2) were obtained after excluding the variable trade, in which policy2 was significant at the 10% level, indicating that in a more rigorous econometric model assessment, the carbon trading policy positively contributed to economic development, but the coefficient values were relatively small, i.e., the effect of the carbon trading policy on increasing the GDP per capita growth was not very significant.

4.3. Robustness Test Based on Synthetic Control Method (SCM)

The PSM-DID model and synthetic control method both play an important role in the analysis of policy effects. However, the application of the DID and PSM models is subject to strict applicability conditions, such as homogeneity, randomness and conditional independence assumptions. However, the synthetic control method is based on panel data weighted to synthesize a control object called “synthetic”, similar to the pilot area. The policy effects are analyzed by comparing the long-run differences between the constructed control object and the pilot area before and after the policy implementation. This has the advantage of avoiding possible errors in the sample selection process, taking into account the whole study area. So in this section, we adopt the synthetic control method for the further robustness testing of the PSM-DID model.

In Figure 3, it is easy to see that after the implementation of carbon trading policy in 2014, the gap of lnCO₂ between “Chongqing” and the “synthetic Chongqing” obviously shows a widening trend; the gap is expanding with time. This proves that the implementation of the carbon trading policy has been effective in reducing carbon emissions in Chongqing. It is also worth noting that the trend of the widening carbon emissions gap first appeared in 2011, and Wang et al. [25] suggested that this was due to the fact that the National Development and Reform Commission announced information about the carbon trading pilot policy in late 2011. Therefore, this phenomenon can be interpreted as an overall adjustment adopted by Chongqing to better implement the carbon trading policy.

To enhance the credibility as well as the validity of the SCM, we perform the permutation test method. It is assumed that both the pilot area as well as non-pilot area adopted the carbon trading policy. The policy effect is compared by comparing the policy effect of the pilot area with that of all non-pilot areas, and the policy effect is considered significant if the policy effect of the pilot area is significantly larger than that of the non-pilot area that is assumed to have implemented the policy.

Among them, in the non-pilot area where the policy is assumed to have been implemented, if the root-mean-square prediction error (RMSPE) is a lot higher than that of the pilot area, it means that the fitting result is not suitable to be adopted to the ranking test for analysis. Here, compared to other provinces, the RMSPE value of Qinghai is 1.270. while the RMSPE of Chongqing is 0.424, and all other seven provinces also remain at a level less than 1. Therefore, after dropping Qinghai province, the results are shown in Figure 4. By comparing the trend between the solid line and dashed lines, the carbon reduction effect of the pilot area is proven to be significant. This supports the robustness of the PSM-DID and SCM.

4.4. Placebo Test

The previous empirical results show that “carbon emission reduction” is indeed affected by the policy effect from the “carbon trading policy”. There was no significant difference between the experimental and control groups before the implementation of the policy, but for whether the policy effect estimated by the DID was affected by other policies or factors, a placebo test was necessary. The most common method of placebo testing is to narrow the study sample to the period before the policy was implemented and to randomize a policy implementation year. The study sample for this paper was from 2004 to 2019, and the year of full policy implementation was 2014; therefore, this placebo assumed that the policy time occurred before 2013. As shown in

Table 6. Placebo test estimation results.

Variable	Implemented in 2013	Implemented in 2012	Implemented in 2011
Time × Treatment1	−0.153 *	−0.076	0.003
period	−0.011	0.009	−0.005
treated	−0.306	−0.289	−0.294
$rGDP$	0.497 ***	−0.488 ***	0.488 ***
pop	0.129	0.147	0.150
industry	0.622 **	0.664 ***	0.657 ***
structure	0.696 **	0.680 **	0.733 ***
urban	−0.224	−0.233	−0.224
cons	3.672 ***		3.689 ***
Year-fixed effect	Y	Y	Y
City-fixed effect	Y	Y	Y
N	117	117	117
$R^2$	0.9433	0.9415	0.9411

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

, similar to the study by Lv et al. [35], the study sample was set between 2004 and 2014, and the policy years were set for 2013, 2012 and 2011. Moreover, as shown in Table 6, con is a constant term.

As can be seen from the results in Table 6, the policy implementation was only significant at the 10% level of significance when it was advanced by one year, while after two to three years, the level of significance decreased significantly, and the cross-term coefficient became positive. The cross-term coefficient changed from negative to positive as the policy implementation was advanced to a longer time, and the corresponding significance level tended to decrease. Thus, excluding other potentially unobservable factors, the carbon trading policy did have a significant positive impact on the emissions reduction effect in Chongqing, indicating that the policy effects estimated in this paper are valid and robust.

4.5. Mediating Effects Analysis

To answer the question of how carbon trading affects the level of carbon emissions and economic development, we used a mediating effects analysis model. In the study of the drivers of carbon emissions growth in economic development, Wang [36] classified them into four areas: energy intensity effects, structural effects, activity intensity effects, and scale effects. In the case of the carbon market, its construction was based on carbon financial products and the trading of carbon emission rights so that the combined effects of energy and economy roughly indicate the direction of the variables affecting carbon emissions. Zeng [37] evaluated the emissions reduction effect and the mechanism of China’s carbon trading. The carbon trading policy can reduce the carbon intensity of the pilot area and have a synergistic pollution reduction effect, reducing sulfur dioxide emissions as well. In addition, Zhang [38] introduced low-carbon technological and industrial structure coordination as a dual intermediary variable. Based on the double mediation effect, the research results verify the technological innovation, industrial structure coordination, and carbon emission rights trading mechanisms.

On this basis, combined with the results of the above analysis, we constructed a mediating effects model with the economic activity effect ($m\_1$) and the energy intensity effect ($m\_2$) as the mediating variables, $M$, where the economic benefits from the market exchange of carbon emission rights, $GD{P}_{it}$, is the core explanatory variable, $X$, and the logarithm of carbon emissions, $lnC{O}_2$, is the explanatory variable, $Y$. The specific path is shown in Figure 5.

In the variable composition of the energy intensity effect and the activity effect, we introduced two indicators in which the total energy consumption ($con$) and energy utilization rate ($eff$) strengthened the explanatory power of the $m\_2$ mediating variable; in $m\_1$, we introduced three indicators in which the urbanization rate ($urb$), trade level ($tra$) and economic development level ($rGDP$) strengthened their explanatory power. Carbon emissions, $Y$, was the explanatory variable, and the level of economic development, $X$, was the explanatory variable.

Following Chen et al. [39], taking into account both indirect and direct tests—the Sobel, bootstrapping, and Markov chain Monte Carlo methods—this paper finally used a combination of sequential tests and the bootstrap method to further analyze the selected mediating variables. Similar to Li et al. [40], the causality behind the experiments was further strengthened by designing three experiments: X→M(m_1,m_2), M(m_1,m_2)→Y, and X→Y. In addition, if the regression model is used to express the above steps, it can be roughly summed up as Formulas (4) and (5):

$$GD{P}_{it}=a_{1}X+b_1\left(com+eff\right)+e_1$$

(4)

$$lnC{O}_2=a_{2}X+b_2\left(urb+tra+rGDP\right)+e_2$$

(5)

In Equations (4) and (5), $\alpha$represents the effect of the core explanatory variable $X$, and $b$ reflects the effect of the mediating variable. Lastly, $e_1-e_2$ is the regression residual resulting from conducting the regression.

The model fitting related evaluation index results were as follows: ${\chi }^2=7.234, df=2, p=0.00269, CFI=0.965, TLI=0.897, \mathrm{and} RMSEA=0.031. \mathrm{The} CFI$, $TLI$and $RMSEA$ are the comparative fit index, Tucker–Lewis index and root-mean-square error of approximation, respectively. Each index is relatively ideal, indicating that the mediating effects analysis model is effective.

As shown in

Table 7. Bootstrap-mediated effect results.

Pathway	Regression Coefficient	SE	Confidence Interval
$X\to m\_1\to Y$	−0.116	0.005	<0.001
Mediating effect
$X\to m\_2\to Y$	−0.255	0.003	0.002
Mediating effect
$X\to Y$	−0.029	0.007	<0.001
Direct effect

, we present the final results obtained from the above analysis and the mediating effects test model.

The results of the bootstrap method, in terms of the regression coefficients and confidence intervals of less than 0, indicate that the overall effect values (namely, the regression coefficients in the table) can be considered to have passed the bootstrap test and the sequential test at a 90% confidence level (Li et al.) [41]. Thus, similar to the conclusion of Yang et al. [42], the “energy intensity effect” and the “economic activity effect” had a significant mediating effect between the construction of the carbon market and the reduction in carbon emissions.

5. Discussion

From the PSM-DID model and SCM above, it can be seen that for Chongqing, as a pilot province of the carbon emissions trading policy, compared with the provinces without carbon emissions trading, the implementation of the carbon emission trading policy significantly reduces the carbon dioxide emissions level of the pilot areas but cannot significantly improve the economic development level of the pilot areas. In addition, from the results of the mediating effect model, it can be seen that the energy intensity effect constructed with the total energy consumption and energy utilization efficiency as indicators was the intermediary effect of the carbon emissions reduction effect.

In research on the impact of carbon trading policy on sustainable development, Wang et al. [43] took Hebei Province, China, as an example to study the effect of carbon trading policy on haze weather. During the economic development process from 2000 to 2016, the implementation of the pilot carbon trading policy made the PM2.5 drop by 10% in the five years of the pilot phase, which is significant at least at the level of 10%. Moreover, Wang et al. [25] analyzed the mechanism and effect of a carbon emissions trading policy on green economy development through the SCM and mediating effect model. Based on the synthetic control method, the results show that carbon emission trading, which is affected by the economic base and location conditions, has a significant effect on the green economy. These research result are consistent with ours above; that is, the implementation of a carbon trading policy can promote the sustainability of a region to a certain extent.

Based on the conclusion that the economic development level of the pilot area cannot be significantly improved, we can see two problems: First, the impetus of a carbon trading policy for economic development is still lacking. Second, how to realize the path of economic and low-carbon development needs to be discussed. For these two aspects, Yin et al. [44] addressed how to realize the transition from coal power under China’s emissions trading scheme and analyzed how Chinese provinces can promote the transformation potential of coal power through carbon trading policies. This paper points out that the transformation path through carbon capture, utilization, and storage (CCUS) is worth considering, and an effective carbon trading management system can be used to stabilize the carbon price. However, it is also necessary to consider the heterogeneity of the provinces and formulate appropriate policies to achieve a comprehensive low-carbon transition.

Most of the current studies mainly explored the relationship between economic growth and carbon emissions by selecting some countries or cities. This paper took the implementation of the carbon trading policy as the starting point to explore the effect of the carbon trading policy on achieving sustainable economic development. In addition, to research the relationship between carbon emissions and the level of economic development, we also analyzed the specific impact path of the effects of carbon emissions reductions through intermediary effects.

6. Conclusions

Based on the emissions reduction effect of the carbon trading policy on Chongqing, there was also a positive contribution of the carbon trading policy to economic development, as shown by the PSM-DID model and SCM. We suggest that the provinces and cities along the Silk Road Economic Belt should take advantage of their resources and regional advantages and continue to build carbon market pilots in other cities on the basis of the carbon market in Chongqing and gradually construct a regional carbon emissions trading market. For regions that do not have the capacity to establish a carbon emissions trading market, they should seek other suitable development paths for the regions. For example, in Xinjiang and other energy-rich provinces, CCUS technology can be used to help in the development of a green economy.

Global warming is not only an environmental problem but also a global problem related to economic and social development, which must be solved through global cooperation. The core of a carbon emissions trading policy is the internalization of external costs. Based on the panel data of nine provinces along the Silk Road Economic Belt from 2004 to 2019, this paper empirically studied the emissions reduction effect and the mechanism of a carbon emissions trading policy by using double difference and intermediary effect models. The research results show that a carbon trading policy can significantly reduce the carbon emissions of pilot cities, and this conclusion was still valid after a series of robustness tests. Through further research, it was shown that the carbon trading policy had an emissions reduction effect on pilot cities but had no economic effect. That is, after the trading of carbon emissions rights, while maintaining a basically stable growth rate, Chongqing’s carbon emissions significantly decreased. Finally, through the construction of the intermediary effect model of the carbon emissions reduction mechanism, the results showed that the intermediary effect of carbon emissions reduction was an economic activity effect constructed with the urbanization rate, trade level and economic development as indicators, and the energy intensity effect was constructed with the total energy consumption and energy utilization efficiency as indicators.

In addition to providing empirical evidence for the implementation effect of the carbon trading policy, this paper also has obvious policy implications. First, a carbon trading policy has a significant emissions reduction effect. Therefore, we should further improve the national carbon trading market and help China, which urgently needs to reduce carbon emissions in economic development to achieve the goal of the “dual carbon” policy based on market orientation. On this basis, the nine provinces along the entire Silk Road Economic Belt should also develop a green economy on the basis of ecological protection integration so that ecology and economy can go hand in hand, seek common ground while reserving any differences, and achieve win–win cooperation and common development in the region. That is to say, under the overall guidance of the “Silk Road Economic Belt”, the nine provinces along the line should strive to develop a green economy and jointly solve ecological problems.

Secondly, considering the intermediary role of the urbanization rate, trade level, economic development, total energy consumption and energy utilization efficiency, we believe that the nine provinces should adjust measures to local conditions according to their own development conditions and actual situation. That is to say, the nine provinces should scientifically formulate their own green development strategies, improve their own development paths, and help the Silk Road Economic Belt continue to flourish in the 21st century. Specifically, we can deepen the reform of the energy consumption system, increase investment in research and development, build a green industry growth mechanism in each province, promote the transformation and upgrading of industrial structures, gradually realize the benign development from the introduction of technology to technology imitation to independent innovation, adopt more subdivided dynamic and differentiated environmental regulation strategies to adapt to the new scene, effectively promote the realization of local emissions reduction goals, and realize the construction of the “Green Silk Road Economic Belt” as soon as possible.

Finally, in order to achieve full low-carbon economic development in the Silk Road Economic Belt at an early date, the government should also propose targeted and dynamic green policies. As China’s socialist construction progresses, the modernization of governance capacity is a dynamic process, as is the improvement in the government’s ecological functions. In order to realize the people’s growing need for a better life, as long as there are still ecological shortcomings, the government needs to improve its ecological functions and further promote the improvement of the ecological civilization system. In addition, the government should make full use of the power generated by the public spontaneously, guide the public to establish the low-carbon development concept, actively advocate green and low-carbon living concepts and consumption behaviors, and achieve the dual-carbon policy goal as soon as possible.