# Assessment of the Impact of China’s Carbon Emissions Trading Pilot Policy on the Export Product Quality

Authors: Bing Ma
Journal: Discrete Dynamics in Nature and Society
 (2022)

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## Abstract

This paper carries out the quasi-natural experiment on China’s carbon emissions trading pilot policy to assess the impact of China’s carbon emissions trading pilot policy on the export product quality of enterprises in the pilot areas by using the data of listed companies and China Customs, and employing the DDD method. Meanwhile, it analyzes the heterogeneity of the impact under different ownership attributes and energy-intensive attributes of enterprises. The empirical results are as follows. First, due to the implementation of the carbon emissions trading pilot policy, the enterprises in the included industries bear the emission reduction cost. The export product quality of the enterprises in the included industries in the pilot areas is negatively affected compared with that of nonincluded enterprises in the nonpilot areas. Second, the impact of the carbon emissions trading pilot policy on the export product quality of enterprises may be heterogeneous due to the different ownership attributes of enterprises. The carbon emissions trading pilot policy significantly reduces the export product quality of non-state-owned enterprises, but has no significant effect on state-owned enterprises. Third, the impact of the carbon emissions trading pilot policy on the export product quality of enterprises is heterogeneous due to different energy-intensive attributes of enterprises. The carbon emissions trading pilot policy significantly reduces the export product quality of enterprises in energy-intensive industries, but it does not have a significant impact on enterprises in non-energy-intensive industries.


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## Body

## 1. Introduction

Global warming has become a common challenge for human society in the twenty-first century. As the world’s largest carbon emitter, China plays a significant role in global climate governance. In the Paris Agreement released in 2015, China proposed that its self-imposed contributions to emission reduction should be that the carbon intensity in 2030 will be 60%–65% lower than that in 2005, and carbon dioxide emissions will reach the peak by around 2030 while striving to reach the peak as soon as possible. In 2021, China increased its self-imposed contributions to emission reduction, and promised to achieve peak carbon dioxide emissions by 2030 and carbon neutrality by 2060. Under the dual pressure of international emission reduction commitments and domestic environment, the Chinese government has shifted the critical policy tool for controlling greenhouse gas emissions to a market-based tool, namely the carbon emissions trading market. Since 2005, the European Union, the United States, the United Kingdom, New Zealand, Japan, Australia, South Korea and other countries have initiated or implemented the carbon emissions trading, and achieved remarkable results in promoting emission reduction. From 2013 to 2014, China successively launched the carbon emissions trading pilot policy in Hubei, Guangdong, Beijing, Shanghai, Shenzhen, Tianjin and Chongqing (referred to as “Two Provinces and Five Cities”). In 2017, China’s national carbon emissions trading market was launched, and it only included the electric power industry. In July 2021, the national carbon market was officially launched for online trading, which will gradually include petrochemical, chemical, building materials, steel, non-ferrous metals, paper-making and other industries. In the carbon emissions trading market, the government sets emission reduction targets and total quotas, and allocates initial carbon emission quotas to emission-controlling enterprises through free or auction. Enterprises adjust the surplus and deficit of carbon emission quotas through trading in the carbon market, to achieve low-cost emission reduction and accomplish the goal of total control through the market mechanism.The market internalizes the negative externalities of greenhouse gases such as carbon dioxide, and follows the “polluter pays” principle. Under the condition of setting the total control target, the emission-controlling enterprises obtain the initial carbon quota through free or auction. Furthermore, emission-controlling enterprises have three approaches to achieve emission control targets at the minimal cost: (1) improve energy utilization efficiency, and develop clean and low-carbon technologies to reduce emissions, so that the actual total emissions are less than the total quotas; (2) purchase the carbon quota through the carbon price signal in the carbon market if their actual emissions exceed the initial carbon quota; (3) prefer not to reduce emissions or purchase carbon quotas, and face huge administrative fines when their actual emissions exceeding the initially allocated carbon quota. In these three paths, emission-controlling enterprises choose the way to minimize the emission reduction cost of their enterprises. It is worth noting that the carbon market is the policy tool for environmental regulation, and emission-controlling enterprises belong to carbon-intensive industries. These three ways of fulfilling the target will all generate corresponding emission reduction cost, thereby increasing enterprises’ production costs. According to the traditional economic viewpoint, environmental regulation will reduce the competitiveness of enterprises. However, the “Porter Hypothesis” holds that appropriate environmental regulations will not only harm the competitiveness of enterprises, but also form the effect of “innovation compensation” to enhance the competitiveness of enterprises [1]. So far, China’s carbon pilot policy in “Two Provinces and Five Cities” has been in operation for 7 to 8 years, achieving remarkable emission reduction effects [2, 3]. However, there is no conclusive evidence on how it affects the competitiveness of enterprises, especially international competitiveness [4]. Therefore, this paper studies the impact of the carbon emissions trading pilot policy on the export product quality of enterprises in the industries included. This paper hopes to provide references for adjusting the total quota and allocation mechanism in the market, and balancing the benefits and emission reduction of enterprises. According to the “weak Porter hypothesis”, the carbon trading market helps to form an equilibrium carbon trading price. Incentivize regulated industries or enterprises to adjust and improve their business decisions, product structure, production process, R&D innovation, etc., and gradually transition from the production of pollution-intensive products to green and clean products, thereby reducing carbon emission intensity.


## 2. Data and Empirical Models

### 2.1. Data Sources and Variable Processing

The data of Shenzhen, a pilot area for the carbon emissions trading, is included in Guangdong Province, and it is not listed separately. In addition, regarding the industries included in the carbon emissions trading market, this paper comprehensively corresponds the industries included in each pilot area to two industries and industries to which the listed companies belong, and obtains a total of 19 included industries.To study whether China’s carbon emissions trading market has an impact on the export product quality of listed companies, this paper matches the WIND database, CSMAR database and China Customs database. The economic characteristic data of the listed companies comes from this matching database. The sample time is from 2000 to 2014. The export product quality of listed companies is calculated according to the relevant data of the customs database.The proxy variable method, price index method, and demand function method are taken to depict product quality. Proxy variables mainly include the ISO9000 dummy variable, the proportion of industry R&D and advertising investment in sales, and unit value. Although proxy variables can be directly observed, they can only reflect the product quality of enterprises to some extent [5–7]. The value index method regards the export price index as the product quality index of enterprises, or decomposes the product quality into unit value and price index. Although the product price reflects the product quality, there are various factors triggering the fluctuation of the product price, and the product quality of enterprises cannot be accurately identified [8]. Therefore, the demand function method is increasingly employed to assess the product quality [9]. In this paper, the demand function method is used to measure the product quality. The demand function set in this paper is similar to the Manova structure:(1)lnqijdtm=lnEijdt+σ−1lnPdt−σlnpijdtm+σ−1lnλijdtm.Among them,λijdtm represents the product quality of the product j exported by the enterprise i to the country d in the period t. qijdtm represents the export volume of the enterprise. Eijdtm represents the consumption level given by exogenous factors. Pdt represents the CES price index. pijdtm represents the price of the product. σ is elasticity of substitution. m represents the trade mode. Therefore, by estimating the following model, the export product quality of the enterprise εijdtm can be obtained:(2)lnqijdtm=aNIdt−σlnpijdtm+εijdtm+δi+δt.In this Equation,NIit represents the income level of the country d that the enterprise faces i in the period t. εijdtm=σ−1lnλijdtm, δi, and δt represent individual effect and time effect. The specific calculation method of NIit is as follows:(3)NIit=∑d,m,jϵMitpijdtmqijdtmlnNIdt∑d,m,jϵMitpijdtmqijdtm,NIit indicates the income level of the destination country.Combined with the customs database and related data, the product quality of the enterprise can be roughly calculated through the regression of the above measurement model:(4)λ^ijdtm=ε^ijdtm+δi^+δt^σ^−1.This paper solves two key problems. Firstly, regardingσ (product elasticity of substitution), Khandelwal et al. [10] assumed that σ (σ  = 4) was the same for all products, which obviously ignored the heterogeneity of products. In this paper, the method of Fan et al. [11] is adopted to allow different industries to have different elasticity of substitution. Secondly, because εijdtm^ contains the product quality information, which means cov (qijdtm, pijdtm) ≠0, endogeneity problems arise when using OLS estimation. Thus, Piveteau and Smaghhue [12] suggested using the real exchange rate fluctuations of the importing country of intermediary products as an instrumental variable for product prices. However, for intermediaries, the correlation between the instrumental variable of product prices and pijdtm is not high, so the data of intermediaries should be excluded from the measurement. However, such methods ignore an essential feature of Chinese export enterprises that intermediaries account for a high proportion of export enterprises (60%–70%). Obviously, the methods of Piveteau and Smaghhue [12] will cause a large number of observed values missing in the customs database, resulting in measurement errors. Instead, this paper uses unobserved demand fluctuations in the export destination country as the instrumental variable for the following reasons. The unobserved demand fluctuations cannot be predicted in advance by enterprises, and enterprises’ production and operation decisions will shift, so the instrumental variable is not correlated with the export product quality of enterprises, but the unobserved demand fluctuations will be reflected in the prices of enterprises’ export products through the price conducting mechanism.(5)covqijdtm,Du=0.Du indicates the unobserved demand fluctuations, and depicts it from enterprise demand change and product demand change. The enterprise demand change is as follows:(6)Dit=∑d,jϵMitexdjitexdjit′/Imdjt∑d,jϵMitexdjit.Among them,Imdjt represents the total import amount and Imdjt represents the adjusted export amount of UN comtrande.Product demand changes are as follows:(7)Djt=ImdjtImdt.To depict the relativity of China’s product quality upgrade, this paper constructs the relative quality index of export productsλ˜ijdtm, and uses it to measure the quality level of China’s export products:(8)λ˜ijdtm=λ˜ijdtmQjtIm=δi^+δt^+ε^ijdtmδd^−1QjtIm.QjtIm represents the world average quality level of the product j in the year t.In this paper, the product quality at the product level is summed up to the enterprise level by taking the proportion of the export volume of a certain product of an enterprise to the total export volume of the enterprise that year as the weight. China’s import and export tariffs are partially adjusted in January 2012. After the adjustment, the total number of import and export tax items increases from 7,977 in 2011 to 8,194. Table1.Table 1
Export product quality of listed companies.
YearObsMeanStd. dev.MinMax20003121.0681.811−2.3813.33020012721.2561.789−1.9093.41920023011.3701.691−1.4943.55820033161.4511.775−1.4653.72820044011.2461.901−1.9993.69420053891.2051.944−2.1573.79220063911.1021.958−2.4103.80620074421.2071.979−2.1144.01720084681.1212.009−2.2883.91020095290.9102.056−2.7603.70720107250.7862.100−2.9683.64420118630.8672.091−2.7343.7542012892-0.03122.256−3.6243.30120138910.8282.013−2.7433.61920149100.8142.057−2.8243.708Considering other enterprise-level factors may have the potential impact on the export product quality, this paper selects a series of important factors affecting the export product quality of enterprises as control variables, including economic growth, technological level, population. Moreover, this paper selects the panel data at the provincial level and the panel data at the industry level as control variables. ① Enterprise age (lnage). The accumulation of business experience may affect the export product quality [13]. ② Enterprise size (lnsize). Enterprises with larger size may export products with better quality because of the obvious competitive advantages in the scale economy and overseas markets. This paper uses the total year-end assets of enterprises to measure the enterprise size. ③ Enterprise production capacity (ln capacity).Enterprises with strong production capacity have advantages in the industry competition. The intensity of competition in the industry will affect the entry of new enterprises, the distribution of the total export volume among existing enterprises, the choice of export destinations and the R&D of new products, thus affecting the export product quality. This paper uses the number of employees in enterprises to measure the ability of enterprises. ④ R&D investment (lnRD). The export products produced by enterprises with higher technical level have higher quality level. ⑤ Enterprise price index (ln wage).The price factor represents the product quality of enterprises to some extent, which is expressed by the employee wage payable in this paper [14].


### 2.2. DDD Baseline Model

To evaluate whether China’s carbon emissions trading pilot policy affects the export product quality of the enterprises in the included industries, this paper compares the differences between the pilot area (treatment group) and the nonpilot area (control group) before and after the implementation of the policy. Based on the traditional difference-in-difference (DD) method, the DDD method is adopted to introduce the attribute of whether the industry of the enterprise is included in the carbon trading market, compare the impact differences between the included enterprises (treatment group) and nonincluded enterprises (control group) in the pilot area before and after the implementation of the policy, identify the effects of the policy from various confounding factors and assess the causal effects of policy. This method can further eliminate the non-time-varying and unobservable factors except the pilot policy, and identify the net effect of carbon market implementation. Thus, the identification of policy treatment effect is relatively accurate and robust [15].The DDD basic model of the impact of the carbon emissions trading pilot policy on the emission reduction and economic performance of regional enterprises is as follows:(9)Yijt=β1ETSi×Sectorj×Postt+β2ETSi×Sectorj+β3ETSi×Postt++β4ETSi×Postt+Xθ+αi+δj+μt+αi∗time+δj∗time+εijt.In the model,i, j, and t indicate the province where the enterprise is located, the industry of the enterprise and the year, respectively. Yijt is an explained variable that represents the export product quality of enterprises. ETSi is a dummy variable of the policy pilot. If the province i where the enterprise is located is a pilot area of carbon market policy (Beijing, Shanghai, Guangdong, Hubei, Tianjin, Chongqing), the value is 1; otherwise, it is 0. Postt is a dummy variable before and after the year of policy implementation. If the year t is the year of policy implementation and subsequent years, the value is 1; otherwise, the value is 0. Since China’s carbon emissions trading pilot has been initiated in succession since 2013, this paper uses 2013 as the starting year of policy implementation. Sectorj is a dummy variable of whether the industry of the enterprise is included in the carbon market. If the industry of the enterprise is included in the carbon trading market, the value is 1; otherwise, the value is 0. X is the control variables, including enterprise age lnage, enterprise size lnsize, enterprise production capacity lncapacity, enterprise price factor lnwage and enterprise R&D investment lnRD. εijt is the random disturbance term. αi, δj and μt are the province fixed effect, industry fixed effect and time fixed effect, respectively. In addition, αi∗time and δj∗time are introduced into the model, respectively, to control the time trend effect of the region and industry. Furthermore, in the regression of the model, the enterprise fixed effect and time trend effect of enterprise are also controlled. Then, the time-invariant and time-varying factors of the regional, industry and enterprise are controlled to better estimate the net effect of the carbon emissions trading market policy on the export product quality of enterprises. In this Equation, the concerned coefficient is the coefficient β1 of the interaction term, ETSi×Sectorj×Postt, which measures the degree of difference in the export product quality between the enterprises in the pilot areas and those in the nonpilot areas before and after implementing the carbon market policy. If the value of β1 is significantly negative, it indicates that the carbon emissions trading pilot policy lowers the export product quality of enterprises in the industry included in the carbon market.


## 2.1. Data Sources and Variable Processing

The data of Shenzhen, a pilot area for the carbon emissions trading, is included in Guangdong Province, and it is not listed separately. In addition, regarding the industries included in the carbon emissions trading market, this paper comprehensively corresponds the industries included in each pilot area to two industries and industries to which the listed companies belong, and obtains a total of 19 included industries.To study whether China’s carbon emissions trading market has an impact on the export product quality of listed companies, this paper matches the WIND database, CSMAR database and China Customs database. The economic characteristic data of the listed companies comes from this matching database. The sample time is from 2000 to 2014. The export product quality of listed companies is calculated according to the relevant data of the customs database.The proxy variable method, price index method, and demand function method are taken to depict product quality. Proxy variables mainly include the ISO9000 dummy variable, the proportion of industry R&D and advertising investment in sales, and unit value. Although proxy variables can be directly observed, they can only reflect the product quality of enterprises to some extent [5–7]. The value index method regards the export price index as the product quality index of enterprises, or decomposes the product quality into unit value and price index. Although the product price reflects the product quality, there are various factors triggering the fluctuation of the product price, and the product quality of enterprises cannot be accurately identified [8]. Therefore, the demand function method is increasingly employed to assess the product quality [9]. In this paper, the demand function method is used to measure the product quality. The demand function set in this paper is similar to the Manova structure:(1)lnqijdtm=lnEijdt+σ−1lnPdt−σlnpijdtm+σ−1lnλijdtm.Among them,λijdtm represents the product quality of the product j exported by the enterprise i to the country d in the period t. qijdtm represents the export volume of the enterprise. Eijdtm represents the consumption level given by exogenous factors. Pdt represents the CES price index. pijdtm represents the price of the product. σ is elasticity of substitution. m represents the trade mode. Therefore, by estimating the following model, the export product quality of the enterprise εijdtm can be obtained:(2)lnqijdtm=aNIdt−σlnpijdtm+εijdtm+δi+δt.In this Equation,NIit represents the income level of the country d that the enterprise faces i in the period t. εijdtm=σ−1lnλijdtm, δi, and δt represent individual effect and time effect. The specific calculation method of NIit is as follows:(3)NIit=∑d,m,jϵMitpijdtmqijdtmlnNIdt∑d,m,jϵMitpijdtmqijdtm,NIit indicates the income level of the destination country.Combined with the customs database and related data, the product quality of the enterprise can be roughly calculated through the regression of the above measurement model:(4)λ^ijdtm=ε^ijdtm+δi^+δt^σ^−1.This paper solves two key problems. Firstly, regardingσ (product elasticity of substitution), Khandelwal et al. [10] assumed that σ (σ  = 4) was the same for all products, which obviously ignored the heterogeneity of products. In this paper, the method of Fan et al. [11] is adopted to allow different industries to have different elasticity of substitution. Secondly, because εijdtm^ contains the product quality information, which means cov (qijdtm, pijdtm) ≠0, endogeneity problems arise when using OLS estimation. Thus, Piveteau and Smaghhue [12] suggested using the real exchange rate fluctuations of the importing country of intermediary products as an instrumental variable for product prices. However, for intermediaries, the correlation between the instrumental variable of product prices and pijdtm is not high, so the data of intermediaries should be excluded from the measurement. However, such methods ignore an essential feature of Chinese export enterprises that intermediaries account for a high proportion of export enterprises (60%–70%). Obviously, the methods of Piveteau and Smaghhue [12] will cause a large number of observed values missing in the customs database, resulting in measurement errors. Instead, this paper uses unobserved demand fluctuations in the export destination country as the instrumental variable for the following reasons. The unobserved demand fluctuations cannot be predicted in advance by enterprises, and enterprises’ production and operation decisions will shift, so the instrumental variable is not correlated with the export product quality of enterprises, but the unobserved demand fluctuations will be reflected in the prices of enterprises’ export products through the price conducting mechanism.(5)covqijdtm,Du=0.Du indicates the unobserved demand fluctuations, and depicts it from enterprise demand change and product demand change. The enterprise demand change is as follows:(6)Dit=∑d,jϵMitexdjitexdjit′/Imdjt∑d,jϵMitexdjit.Among them,Imdjt represents the total import amount and Imdjt represents the adjusted export amount of UN comtrande.Product demand changes are as follows:(7)Djt=ImdjtImdt.To depict the relativity of China’s product quality upgrade, this paper constructs the relative quality index of export productsλ˜ijdtm, and uses it to measure the quality level of China’s export products:(8)λ˜ijdtm=λ˜ijdtmQjtIm=δi^+δt^+ε^ijdtmδd^−1QjtIm.QjtIm represents the world average quality level of the product j in the year t.In this paper, the product quality at the product level is summed up to the enterprise level by taking the proportion of the export volume of a certain product of an enterprise to the total export volume of the enterprise that year as the weight. China’s import and export tariffs are partially adjusted in January 2012. After the adjustment, the total number of import and export tax items increases from 7,977 in 2011 to 8,194. Table1.Table 1
Export product quality of listed companies.
YearObsMeanStd. dev.MinMax20003121.0681.811−2.3813.33020012721.2561.789−1.9093.41920023011.3701.691−1.4943.55820033161.4511.775−1.4653.72820044011.2461.901−1.9993.69420053891.2051.944−2.1573.79220063911.1021.958−2.4103.80620074421.2071.979−2.1144.01720084681.1212.009−2.2883.91020095290.9102.056−2.7603.70720107250.7862.100−2.9683.64420118630.8672.091−2.7343.7542012892-0.03122.256−3.6243.30120138910.8282.013−2.7433.61920149100.8142.057−2.8243.708Considering other enterprise-level factors may have the potential impact on the export product quality, this paper selects a series of important factors affecting the export product quality of enterprises as control variables, including economic growth, technological level, population. Moreover, this paper selects the panel data at the provincial level and the panel data at the industry level as control variables. ① Enterprise age (lnage). The accumulation of business experience may affect the export product quality [13]. ② Enterprise size (lnsize). Enterprises with larger size may export products with better quality because of the obvious competitive advantages in the scale economy and overseas markets. This paper uses the total year-end assets of enterprises to measure the enterprise size. ③ Enterprise production capacity (ln capacity).Enterprises with strong production capacity have advantages in the industry competition. The intensity of competition in the industry will affect the entry of new enterprises, the distribution of the total export volume among existing enterprises, the choice of export destinations and the R&D of new products, thus affecting the export product quality. This paper uses the number of employees in enterprises to measure the ability of enterprises. ④ R&D investment (lnRD). The export products produced by enterprises with higher technical level have higher quality level. ⑤ Enterprise price index (ln wage).The price factor represents the product quality of enterprises to some extent, which is expressed by the employee wage payable in this paper [14].


## 2.2. DDD Baseline Model

To evaluate whether China’s carbon emissions trading pilot policy affects the export product quality of the enterprises in the included industries, this paper compares the differences between the pilot area (treatment group) and the nonpilot area (control group) before and after the implementation of the policy. Based on the traditional difference-in-difference (DD) method, the DDD method is adopted to introduce the attribute of whether the industry of the enterprise is included in the carbon trading market, compare the impact differences between the included enterprises (treatment group) and nonincluded enterprises (control group) in the pilot area before and after the implementation of the policy, identify the effects of the policy from various confounding factors and assess the causal effects of policy. This method can further eliminate the non-time-varying and unobservable factors except the pilot policy, and identify the net effect of carbon market implementation. Thus, the identification of policy treatment effect is relatively accurate and robust [15].The DDD basic model of the impact of the carbon emissions trading pilot policy on the emission reduction and economic performance of regional enterprises is as follows:(9)Yijt=β1ETSi×Sectorj×Postt+β2ETSi×Sectorj+β3ETSi×Postt++β4ETSi×Postt+Xθ+αi+δj+μt+αi∗time+δj∗time+εijt.In the model,i, j, and t indicate the province where the enterprise is located, the industry of the enterprise and the year, respectively. Yijt is an explained variable that represents the export product quality of enterprises. ETSi is a dummy variable of the policy pilot. If the province i where the enterprise is located is a pilot area of carbon market policy (Beijing, Shanghai, Guangdong, Hubei, Tianjin, Chongqing), the value is 1; otherwise, it is 0. Postt is a dummy variable before and after the year of policy implementation. If the year t is the year of policy implementation and subsequent years, the value is 1; otherwise, the value is 0. Since China’s carbon emissions trading pilot has been initiated in succession since 2013, this paper uses 2013 as the starting year of policy implementation. Sectorj is a dummy variable of whether the industry of the enterprise is included in the carbon market. If the industry of the enterprise is included in the carbon trading market, the value is 1; otherwise, the value is 0. X is the control variables, including enterprise age lnage, enterprise size lnsize, enterprise production capacity lncapacity, enterprise price factor lnwage and enterprise R&D investment lnRD. εijt is the random disturbance term. αi, δj and μt are the province fixed effect, industry fixed effect and time fixed effect, respectively. In addition, αi∗time and δj∗time are introduced into the model, respectively, to control the time trend effect of the region and industry. Furthermore, in the regression of the model, the enterprise fixed effect and time trend effect of enterprise are also controlled. Then, the time-invariant and time-varying factors of the regional, industry and enterprise are controlled to better estimate the net effect of the carbon emissions trading market policy on the export product quality of enterprises. In this Equation, the concerned coefficient is the coefficient β1 of the interaction term, ETSi×Sectorj×Postt, which measures the degree of difference in the export product quality between the enterprises in the pilot areas and those in the nonpilot areas before and after implementing the carbon market policy. If the value of β1 is significantly negative, it indicates that the carbon emissions trading pilot policy lowers the export product quality of enterprises in the industry included in the carbon market.


## 3. Empirical Result Analysis

### 3.1. The Impact of the Carbon Emissions Trading Pilot Policy on the Export Product Quality

According to the constructed baseline model (1) and the DDD regression model, this paper first verifies whether the implementation of the carbon emissions trading pilot policy significantly affects the export product quality of enterprises in the pilot areas. The estimation results are shown in Table2. Among them, Column (1) presents the estimation results that only industry fixed effect, enterprise fixed effect, and time fixed effect are controlled. Column (2) shows the estimation results that only the province fixed effect, industry fixed effect, enterprise fixed effect, and time fixed effect are controlled. Column (3) displays the estimation results that only the province fixed effect, industry fixed effect, and enterprise fixed effect are controlled. Column (4) demonstrates the estimation results that only province fixed effect, industry fixed effect, enterprise fixed effect, and time fixed effect are controlled. Column (5) shows the estimation results that province fixed effect, industry fixed effect, enterprise fixed effect, time fixed effect, as well as the time trends of the province and industry are controlled. Column (6) displays the estimation results that the time trends of province, industry and enterprise are controlled on the basis of Column (5).Table 2
Impact of the carbon emissions trading pilot policy on export product quality of enterprises.
Export product quality(1)(2)(3)(4)(5)(6)ETSi×Sectorj×Postt−0.717∗∗∗ (0.177)−0.687∗∗∗ (0.177)−0.692∗∗∗ (0.175)−0.717∗∗∗ (0.177)−0.556∗∗∗ (0.177)−0.561∗∗∗ (0.178)ETSi×Postt0.621∗∗∗ (0.088)0.619∗∗∗ (0.088)0.599∗∗∗ (0.084)0.621∗∗∗ (0.088)0.917∗∗∗ (0.113)0.919∗∗∗ (0.113)ETSi×Sectorj0.435 (0.305)0.126 (0.252)0.425 (0.305)0.435 (0.305)0.504 (0.303)0.517 (0.303)Sectorj×Postt0.776∗∗∗ (0.087)0.745∗∗∗ (0.085)0.752∗∗∗ (0.083)0.776∗∗∗ (0.087)0.962∗∗∗ (0.110)0.963∗∗∗ (0.110)lnage−0.749∗∗∗ (0.173)−0.757∗∗∗ (0.173)−0.855∗∗∗ (0.129)−0.749∗∗∗ (0.173)−0.683∗∗∗ (0.171)−0.723∗∗∗ (0.176)lnsize0.344∗∗∗ (0.108)0.331∗∗∗ (0.108)0.315∗∗∗ (0.103)0.344∗∗∗ (0.108)0.369∗∗∗ (0.107)0.358∗∗∗ (0.108)lncapacity−0.123 (0.086)−0.121 (0.086)−0.119(0.086)−0.123(0.086)−0.124 (0.085)−0.115 (0.085)lnRD−0.039 (0.051)−0.033 (0.051)−0.046(0.051)−0.039(0.051)−0.011 (0.051)−0.006 (0.051)lnwage0.083∗(0.044)0.081∗(0.044)0.080∗(0.044)0.083∗(0.044)0.074∗ (0.043)0.072∗ (0.043)Observed value359035903590359035903590R20.0630.0610.0620.0630.0800.081Province fixed effectYYYYYIndustry fixed effectYYYYYEnterprise fixed effectYYYYYYTime fixed effectYYYYYProvince∗time fixed effectYYIndustry∗time fixed effectYYEnterprise∗time fixed effectYNote. The standard deviation is within brackets. ∗, ∗∗, and ∗∗∗ represent significance levels of 10%, 5%, and 1%, respectively.The regression results in Table2 show that the implementation of the carbon emissions trading pilot policy has significantly reduced the export product quality in the pilot areas. Columns (1), (2), and (3) in Table 2 show the estimation results that three kinds of province fixed effect, industry fixed effect, enterprise fixed effect, and time fixed effect are controlled, respectively. Column (4) shows the estimation results that these four kinds of fixed effects are all controlled. In these cases, the regression coefficients of the DDD interaction item, ETSi×Sectorj×Postt have little difference, and remain between −0.69 and −0.72 at the significance level of 5%. However, when the time trends of the province, industry and enterprise are controlled in Column (5) and Column (6), the regression coefficients of the DDD interaction item ETSi×Sectorj×Postt is about -0.56 at the significance level of 5%. This indicates that it is necessary to consider the time trends of province, industry and enterprise in the model estimation of this paper, and the time-varying factors of province, industry and enterprise will be further excluded to estimate the net effect of the implementation of the carbon trading pilot policy. The estimation results show that the establishment of the carbon trading market does have a negative impact on the export product quality of enterprises in included industries compared with enterprises in nonincluded industries in nonpilot areas. Moreover, the carbon emissions trading pilot policy reduces the export product quality index of enterprises in industries included by nearly 0.56.The carbon emissions trading is a market-based environmental regulation policy tool. Under this policy regulation, the enterprises in the included industries will undertake additional emission reduction tasks compared to those in nonincluded industries. In other words, faced by a certain initial carbon quota, they need to reduce emissions to fulfill their obligations, or pay economic costs to buy quotas in the carbon market for emissions. Then, enterprises in the included industries will bear additional emission reduction cost whether they invest capital, equipment, manpower, improve management efficiency to reduce emissions, or purchase carbon quotas in the carbon market [16]. This leads to an increase in production costs for enterprises in the included industries, and the cost increase effect brought by the carbon emissions trading market is greater than the innovation effect, which ultimately leads to the decline in the export product quality [17]. Enterprises have to reduce the quality of products to maintain profits. In the meantime, they also reduce the quality of products to maintain the market share without raising prices. The findings of this paper are consistent with the results of Chapple et al. [18], which state that the EU carbon trading system reduces the competitiveness of emission-controlling enterprises and increases the cost of enterprises from the perspective of market share. This paper finds that China’s carbon emissions trading pilot more or less reduces the export competitiveness of enterprises from the perspective of the export product quality.


### 3.2. Robustness Test Analysis

The empirical results of the baseline model show that the carbon emissions trading pilot policy reduces the export product quality of enterprises in the included industries. To further verify whether this effect indeed comes from the implementation of the carbon trading policy, rather than other policy that may affect the change of export product quality, this paper further sets the year without the implementation of the carbon pilot policy as the year with the implementation of the policy through the placebo test. It also conducts the robustness test by comparing the significant difference between the treatment effect after the implementation of the carbon emissions trading pilot policy and that before the implementation of the policy, as follows:(10)Yijt=λ0ETSi×Sectorj×Post2008+λ1ETSi×Sectorj×Post2009+λ12ETSi×Sectorj×Post2010+β1ETSi×Sectorj×Postt,+β2ETSi×Sectorj+β3ETSi×Postt+β4ETSi×Postt+Xθ+αi∗time+δj∗time+μt+εijt.Among them,Post2008, Post2009, and Post2010 are time dummy variables, which, respectively, mean that the value is 1 when the year is 2008, 2009 and 2010; otherwise it is 0. It should be noted that the actual year of implementing the carbon pilot policy adopted in this paper is 2013. In 2011, China announced the launch of the carbon emissions trading pilot. The launch of the carbon emissions trading pilot policy may have a corresponding impact in 2011 and 2012. Given this, 2011 and 2012 are set as the year with the dummy implementation of the policy in the placebo test. The meanings of other variables are consistent with (9). When the coefficient β1 of the interaction term ETSi×Sectorj×Postt is significant and the coefficients of interaction term ETSi×Sectorj×Post2008, ETSi×Sectorj×Post2009, and ETSi×Sectorj×Post2010 are not significant, it indicates that the export product quality has not changed significantly before the implementation of the carbon trading policy, but it varies significantly after the implementation of carbon trading policy. Therefore, the trend variation of the export product quality of enterprises indeed comes from the carbon emissions trading pilot policy implemented in 2013 and later, rather than other policy factors.According to (10), the dummy years of the carbon market policy implementation in 2008, 2009, and 2010 are constructed. Furthermore, whether the emission reduction effect of the carbon market is really due to the establishment of the carbon market in 2013 is analyzed. If the export product quality of enterprises in the pilot areas in 2008, 2009 and 2010 is significantly different from that of enterprises in the nonpilot areas, the variation of export product quality is not caused by the carbon pilot policy. On the contrary, if the export product quality of the enterprises in the pilot areas in 2008, 2009 and 2010 is significantly different from enterprises in the nonpilot areas, but it changes significantly compared with the enterprises in the nonpilot areas however after the implementation of the pilot policy in 2013, it suggests that the carbon emissions trading pilot policy indeed causes the change in the export product quality of enterprises.The results of the robustness test are shown in Table3. The results reveal that in the placebo test, the export product quality of enterprises in the included industries in the pilot areas has not changed significantly compared with enterprises in the nonincluded industries in the nonpilot areas whether considering the dummy carbon market policy in 2008, 2009, and 2010, or the dummy carbon market policy in 2009 and 2010. However, after the implementation of the carbon trading pilot in 2013, the export product quality of enterprises in the pilot areas is significantly lower than that of enterprises in the nonpilot areas at a significant level of 5%. The coefficients of interaction terms are not significant before 2013, but are significant after 2013, indicating that the decrease of export product quality is indeed due to the establishment of the carbon market in 2013. Hence, this further proves the credibility of the previous regression results of the baseline model.Table 3
Placebo test.
Export product quality(1)(2)(3)(4)ETSi×Sectorj×Postt−0.561∗∗∗ (0.181)−0.565∗∗∗ (0.181)−0.569∗∗∗ (0.183)−0.571∗∗∗ (0.183)ETSi×Sectorj×Post20100.183 (0.222)0.185 (0.222)0.165 (0.232)0.172 (0.232)ETSi×Sectorj×Post2009−0.483 (0.302)−0.471 (0.302)−0.500 (0.305)−0.491 (0.303)ETSi×Sectorj×Post2008−0.101 (0.378)−0.076 (0.380)Observed value3590359035903590R20.0830.0830.0830.083Province fixed effectYYYYIndustry fixed effectYYYYEnterprise fixed effectYYYYTime fixed effectYYYYProvince∗time fixed effectYYYYIndustry∗time fixed effectYYYYEnterprise∗time fixed effectYYNote. The standard deviation is within brackets. ∗, ∗∗, and ∗∗∗ represent significance levels of 10%, 5% and 1%, respectively. The economic characteristic variables of enterprises, including total assets, number of employees, age of enterprises, R&D investment, as well as wage, and the interaction items are all controlled, and no report will not be presented here due to the limited space.


## 3.1. The Impact of the Carbon Emissions Trading Pilot Policy on the Export Product Quality

According to the constructed baseline model (1) and the DDD regression model, this paper first verifies whether the implementation of the carbon emissions trading pilot policy significantly affects the export product quality of enterprises in the pilot areas. The estimation results are shown in Table2. Among them, Column (1) presents the estimation results that only industry fixed effect, enterprise fixed effect, and time fixed effect are controlled. Column (2) shows the estimation results that only the province fixed effect, industry fixed effect, enterprise fixed effect, and time fixed effect are controlled. Column (3) displays the estimation results that only the province fixed effect, industry fixed effect, and enterprise fixed effect are controlled. Column (4) demonstrates the estimation results that only province fixed effect, industry fixed effect, enterprise fixed effect, and time fixed effect are controlled. Column (5) shows the estimation results that province fixed effect, industry fixed effect, enterprise fixed effect, time fixed effect, as well as the time trends of the province and industry are controlled. Column (6) displays the estimation results that the time trends of province, industry and enterprise are controlled on the basis of Column (5).Table 2
Impact of the carbon emissions trading pilot policy on export product quality of enterprises.
Export product quality(1)(2)(3)(4)(5)(6)ETSi×Sectorj×Postt−0.717∗∗∗ (0.177)−0.687∗∗∗ (0.177)−0.692∗∗∗ (0.175)−0.717∗∗∗ (0.177)−0.556∗∗∗ (0.177)−0.561∗∗∗ (0.178)ETSi×Postt0.621∗∗∗ (0.088)0.619∗∗∗ (0.088)0.599∗∗∗ (0.084)0.621∗∗∗ (0.088)0.917∗∗∗ (0.113)0.919∗∗∗ (0.113)ETSi×Sectorj0.435 (0.305)0.126 (0.252)0.425 (0.305)0.435 (0.305)0.504 (0.303)0.517 (0.303)Sectorj×Postt0.776∗∗∗ (0.087)0.745∗∗∗ (0.085)0.752∗∗∗ (0.083)0.776∗∗∗ (0.087)0.962∗∗∗ (0.110)0.963∗∗∗ (0.110)lnage−0.749∗∗∗ (0.173)−0.757∗∗∗ (0.173)−0.855∗∗∗ (0.129)−0.749∗∗∗ (0.173)−0.683∗∗∗ (0.171)−0.723∗∗∗ (0.176)lnsize0.344∗∗∗ (0.108)0.331∗∗∗ (0.108)0.315∗∗∗ (0.103)0.344∗∗∗ (0.108)0.369∗∗∗ (0.107)0.358∗∗∗ (0.108)lncapacity−0.123 (0.086)−0.121 (0.086)−0.119(0.086)−0.123(0.086)−0.124 (0.085)−0.115 (0.085)lnRD−0.039 (0.051)−0.033 (0.051)−0.046(0.051)−0.039(0.051)−0.011 (0.051)−0.006 (0.051)lnwage0.083∗(0.044)0.081∗(0.044)0.080∗(0.044)0.083∗(0.044)0.074∗ (0.043)0.072∗ (0.043)Observed value359035903590359035903590R20.0630.0610.0620.0630.0800.081Province fixed effectYYYYYIndustry fixed effectYYYYYEnterprise fixed effectYYYYYYTime fixed effectYYYYYProvince∗time fixed effectYYIndustry∗time fixed effectYYEnterprise∗time fixed effectYNote. The standard deviation is within brackets. ∗, ∗∗, and ∗∗∗ represent significance levels of 10%, 5%, and 1%, respectively.The regression results in Table2 show that the implementation of the carbon emissions trading pilot policy has significantly reduced the export product quality in the pilot areas. Columns (1), (2), and (3) in Table 2 show the estimation results that three kinds of province fixed effect, industry fixed effect, enterprise fixed effect, and time fixed effect are controlled, respectively. Column (4) shows the estimation results that these four kinds of fixed effects are all controlled. In these cases, the regression coefficients of the DDD interaction item, ETSi×Sectorj×Postt have little difference, and remain between −0.69 and −0.72 at the significance level of 5%. However, when the time trends of the province, industry and enterprise are controlled in Column (5) and Column (6), the regression coefficients of the DDD interaction item ETSi×Sectorj×Postt is about -0.56 at the significance level of 5%. This indicates that it is necessary to consider the time trends of province, industry and enterprise in the model estimation of this paper, and the time-varying factors of province, industry and enterprise will be further excluded to estimate the net effect of the implementation of the carbon trading pilot policy. The estimation results show that the establishment of the carbon trading market does have a negative impact on the export product quality of enterprises in included industries compared with enterprises in nonincluded industries in nonpilot areas. Moreover, the carbon emissions trading pilot policy reduces the export product quality index of enterprises in industries included by nearly 0.56.The carbon emissions trading is a market-based environmental regulation policy tool. Under this policy regulation, the enterprises in the included industries will undertake additional emission reduction tasks compared to those in nonincluded industries. In other words, faced by a certain initial carbon quota, they need to reduce emissions to fulfill their obligations, or pay economic costs to buy quotas in the carbon market for emissions. Then, enterprises in the included industries will bear additional emission reduction cost whether they invest capital, equipment, manpower, improve management efficiency to reduce emissions, or purchase carbon quotas in the carbon market [16]. This leads to an increase in production costs for enterprises in the included industries, and the cost increase effect brought by the carbon emissions trading market is greater than the innovation effect, which ultimately leads to the decline in the export product quality [17]. Enterprises have to reduce the quality of products to maintain profits. In the meantime, they also reduce the quality of products to maintain the market share without raising prices. The findings of this paper are consistent with the results of Chapple et al. [18], which state that the EU carbon trading system reduces the competitiveness of emission-controlling enterprises and increases the cost of enterprises from the perspective of market share. This paper finds that China’s carbon emissions trading pilot more or less reduces the export competitiveness of enterprises from the perspective of the export product quality.


## 3.2. Robustness Test Analysis

The empirical results of the baseline model show that the carbon emissions trading pilot policy reduces the export product quality of enterprises in the included industries. To further verify whether this effect indeed comes from the implementation of the carbon trading policy, rather than other policy that may affect the change of export product quality, this paper further sets the year without the implementation of the carbon pilot policy as the year with the implementation of the policy through the placebo test. It also conducts the robustness test by comparing the significant difference between the treatment effect after the implementation of the carbon emissions trading pilot policy and that before the implementation of the policy, as follows:(10)Yijt=λ0ETSi×Sectorj×Post2008+λ1ETSi×Sectorj×Post2009+λ12ETSi×Sectorj×Post2010+β1ETSi×Sectorj×Postt,+β2ETSi×Sectorj+β3ETSi×Postt+β4ETSi×Postt+Xθ+αi∗time+δj∗time+μt+εijt.Among them,Post2008, Post2009, and Post2010 are time dummy variables, which, respectively, mean that the value is 1 when the year is 2008, 2009 and 2010; otherwise it is 0. It should be noted that the actual year of implementing the carbon pilot policy adopted in this paper is 2013. In 2011, China announced the launch of the carbon emissions trading pilot. The launch of the carbon emissions trading pilot policy may have a corresponding impact in 2011 and 2012. Given this, 2011 and 2012 are set as the year with the dummy implementation of the policy in the placebo test. The meanings of other variables are consistent with (9). When the coefficient β1 of the interaction term ETSi×Sectorj×Postt is significant and the coefficients of interaction term ETSi×Sectorj×Post2008, ETSi×Sectorj×Post2009, and ETSi×Sectorj×Post2010 are not significant, it indicates that the export product quality has not changed significantly before the implementation of the carbon trading policy, but it varies significantly after the implementation of carbon trading policy. Therefore, the trend variation of the export product quality of enterprises indeed comes from the carbon emissions trading pilot policy implemented in 2013 and later, rather than other policy factors.According to (10), the dummy years of the carbon market policy implementation in 2008, 2009, and 2010 are constructed. Furthermore, whether the emission reduction effect of the carbon market is really due to the establishment of the carbon market in 2013 is analyzed. If the export product quality of enterprises in the pilot areas in 2008, 2009 and 2010 is significantly different from that of enterprises in the nonpilot areas, the variation of export product quality is not caused by the carbon pilot policy. On the contrary, if the export product quality of the enterprises in the pilot areas in 2008, 2009 and 2010 is significantly different from enterprises in the nonpilot areas, but it changes significantly compared with the enterprises in the nonpilot areas however after the implementation of the pilot policy in 2013, it suggests that the carbon emissions trading pilot policy indeed causes the change in the export product quality of enterprises.The results of the robustness test are shown in Table3. The results reveal that in the placebo test, the export product quality of enterprises in the included industries in the pilot areas has not changed significantly compared with enterprises in the nonincluded industries in the nonpilot areas whether considering the dummy carbon market policy in 2008, 2009, and 2010, or the dummy carbon market policy in 2009 and 2010. However, after the implementation of the carbon trading pilot in 2013, the export product quality of enterprises in the pilot areas is significantly lower than that of enterprises in the nonpilot areas at a significant level of 5%. The coefficients of interaction terms are not significant before 2013, but are significant after 2013, indicating that the decrease of export product quality is indeed due to the establishment of the carbon market in 2013. Hence, this further proves the credibility of the previous regression results of the baseline model.Table 3
Placebo test.
Export product quality(1)(2)(3)(4)ETSi×Sectorj×Postt−0.561∗∗∗ (0.181)−0.565∗∗∗ (0.181)−0.569∗∗∗ (0.183)−0.571∗∗∗ (0.183)ETSi×Sectorj×Post20100.183 (0.222)0.185 (0.222)0.165 (0.232)0.172 (0.232)ETSi×Sectorj×Post2009−0.483 (0.302)−0.471 (0.302)−0.500 (0.305)−0.491 (0.303)ETSi×Sectorj×Post2008−0.101 (0.378)−0.076 (0.380)Observed value3590359035903590R20.0830.0830.0830.083Province fixed effectYYYYIndustry fixed effectYYYYEnterprise fixed effectYYYYTime fixed effectYYYYProvince∗time fixed effectYYYYIndustry∗time fixed effectYYYYEnterprise∗time fixed effectYYNote. The standard deviation is within brackets. ∗, ∗∗, and ∗∗∗ represent significance levels of 10%, 5% and 1%, respectively. The economic characteristic variables of enterprises, including total assets, number of employees, age of enterprises, R&D investment, as well as wage, and the interaction items are all controlled, and no report will not be presented here due to the limited space.


## 4. Heterogeneity Analysis

### 4.1. Enterprise Ownership

The impact of the carbon emissions trading pilot policy on the export product quality of enterprises may be heterogeneous due to the different ownership attributes of enterprises. Based on the baseline model, this paper divides the whole sample into two sub-samples of state-owned enterprises and non-state-owned enterprises according to the attributes of enterprise ownership. Furthermore, it investigates the different effects of the carbon emissions trading policy implementation on the export product quality of state-owned enterprises and non-state-owned enterprises in pilot areas compared with those of non-state-owned enterprises and non-state-owned enterprises in nonpilot areas. This paper aims to explore whether the carbon emissions trading policy generates heterogeneous effects on the export product quality of enterprises with different ownership attributes.The estimation results are shown in Table4. Whether controlling the province fixed effect, industry fixed effect, time fixed effect, province time trend, industry time trend or enterprise time trend, the DDD coefficients are not significant in the subsamples of state-owned enterprises, but significantly negative in the subsamples of non-state-owned enterprises. This shows that the impact of the carbon emission trading pilot policy on the export quality product of enterprises does have heterogeneity differences in the ownership attributes of enterprises. The carbon trading pilot policy does not significantly affect the export quality product of state-owned enterprises, but significantly reduces that of non-state-owned enterprises. The possible reasons mainly come from two aspects. First, most state-owned enterprises are traditional industries, and they often need to follow the development path of high pollution and high emissions; However, the state-owned enterprises are generally large in scale and volume, and often bear a greater responsibility for the local economy, and also have a relatively underlying relationship with the government. Then, the environmental regulation policies, such as the carbon emission trading, may have a weak regulatory effect on the state-owned enterprises, and its impact on their export quality product may be small [19]. Secondly, compared with non-state-owned enterprises, state-owned enterprises enjoy abundant capital investment, financial subsidies, tax relief and financing facilities, etc. Compared with non-state-owned enterprises, they have enough ability and strength to deal with environmental regulation policies, such as carbon emission trading, without affecting their export quality product. On the contrary, non-state-owned enterprises have to confront the environmental regulation of the carbon emission trading. Moreover, under the carbon emissions trading, they will face more intense market competition, so it is difficult to consider improving the productivity of enterprises and their export quality product.Table 4
Heterogeneity analysis of considering enterprise ownership attribute.
State-owned enterpriseNon-state-owned enterprises(1)(2)(3)(4)ETSi×Sectorj×Postt0.008 (0.083)0.005 (0.083)−0.488∗∗∗ (0.131)−0.488∗∗∗ (0.131)ETSi×Postt0.221∗∗∗ (0.052)0.223 (0.052)0.560∗∗∗ (0.084)0.560∗∗∗ (0.084)ETSi×Sectorj0.247 (0.144)0.255 (0.144)0.168 (0.228)0.168 (0.228)Sectorj×Postt0.253∗∗∗ (0.052)0.254 (0.052)0.563∗∗∗ (0.081)0.563∗∗∗ (0.081)Observed valueYYYYR2YYYYProvince fixed effectYYYYIndustry fixed effectYYYYEnterprise fixed effectYYYYTime fixed effectYYYYProvince∗time fixed effectYYNote. The standard deviation is within brackets. ∗, ∗∗, and ∗∗∗ represent significance levels of 10%, 5%, and 1%, respectively. The economic characteristic variables of enterprises, including total assets, number of employees, age of enterprises, R&D investment, as well as wage, and the interaction items are all controlled, and no report will not be presented here due to the limited space.


### 4.2. Energy-Intensive Attribute

The impact of the carbon emissions trading pilot policy on the export product quality of enterprises may also be heterogeneous due to the different energy-intensive attributes of enterprises. The carbon emissions trading policy is to regulate the total carbon emissions of energy-intensive industries, and achieve the goal of promoting emission reduction of enterprises with heavy emissions through marketization. For enterprises in energy-intensive and non-energy-intensive industries, the carbon market makes them face different pressures and emission reduction cost, so the impact on their product quality is different. To study the heterogeneity of the impact of the carbon emissions trading pilot policy on the export product quality in included energy-intensive enterprises and non-energy-intensive enterprises, industries with energy intensity higher than the average energy intensity of all included industries are set as energy-intensive industries, while industries with energy intensity lower than the average energy intensity of all included industries are set as non-energy-intensive industries. Furthermore, based on the baseline model, the samples in the included industries are divided into sub-samples of energy-intensive industries and non-energy-intensive industries according to the energy-intensive attributes of industries to study whether the carbon emissions trading policy has different effects on the export product quality of enterprises in energy-intensive and non-energy-intensive industries.The estimation results are shown in Table5. Whether controlling the province fixed effect, industry fixed effect, time fixed effect, province time trend, industry time trend or enterprise time trend, the DDD coefficients are significant in the subsamples of energy-intensive industries, but not significant in the subsamples of non-energy-intensive industries. The result shows that the impact of the carbon emissions trading pilot policy on the export product quality of enterprises does have heterogeneity differences in the energy-intensive attributes of industries. Compared with the nonincluded industries in nonpilot areas, the carbon emissions trading pilot policy significantly reduces the export product quality of enterprises in the included energy-intensive industries, but does not have a significant impact on the export product quality of enterprises in the non-energy-intensive industries. This result is also in line with expectations. For the energy-intensive enterprises included in the carbon market pilot policy, they bear great pressure to reduce emissions. They need to upgrade equipment or develop new technologies to improve energy efficiency or energy consumption structure to reduce emissions, or they need to buy a certain amount of carbon quotas in the carbon market to meet their own emission needs, and this extra emission reduction cost is likely to force enterprises to lower their product quality for the sake of ensuring sufficient income. For enterprises in non-energy-intensive industries, the pressure of emission reduction is smaller than that in energy-intensive industries, and the export product quality is not affected by the cost of emission reduction.Table 5
Heterogeneity analysis of considering energy-intensive attribute.
Energy-intensive industryNon-energy-intensive industry(1)(2)(3)(4)ETSi×Sectorj×Postt−0.057∗ (0.032)−0.056∗ (0.032)0.008 (0.087)0.008 (0.087)ETSi×Postt−0.009 (0.019)−0.010 (0.019)0.072 (0.051)0.072 (0.051)ETSi×Sectorj0.046 (0.054)0.043 (0.054)−0.565∗∗∗ (0.154)−0.566 (0.154)Sectorj×Postt0.130∗∗∗ (0.020)0.131∗∗∗ (0.020)0.778∗∗∗ (0.055)0.778 (0.0550)Observed valueYYYYR2YYYYProvince fixed effectYYYYIndustry fixed effectYYYYEnterprise fixed effectYYYYTime fixed effectYYYYProvince∗time fixed effectYYNote. The standard deviation is within brackets. ∗, ∗∗, and ∗∗∗ represent significance levels of 10%, 5%, and 1%, respectively. The economic characteristic variables of enterprises, including total assets, number of employees, age of enterprises, R&D investment, as well as wage, and the interaction items are all controlled, and no report will not be presented here due to the limited space.


## 4.1. Enterprise Ownership

The impact of the carbon emissions trading pilot policy on the export product quality of enterprises may be heterogeneous due to the different ownership attributes of enterprises. Based on the baseline model, this paper divides the whole sample into two sub-samples of state-owned enterprises and non-state-owned enterprises according to the attributes of enterprise ownership. Furthermore, it investigates the different effects of the carbon emissions trading policy implementation on the export product quality of state-owned enterprises and non-state-owned enterprises in pilot areas compared with those of non-state-owned enterprises and non-state-owned enterprises in nonpilot areas. This paper aims to explore whether the carbon emissions trading policy generates heterogeneous effects on the export product quality of enterprises with different ownership attributes.The estimation results are shown in Table4. Whether controlling the province fixed effect, industry fixed effect, time fixed effect, province time trend, industry time trend or enterprise time trend, the DDD coefficients are not significant in the subsamples of state-owned enterprises, but significantly negative in the subsamples of non-state-owned enterprises. This shows that the impact of the carbon emission trading pilot policy on the export quality product of enterprises does have heterogeneity differences in the ownership attributes of enterprises. The carbon trading pilot policy does not significantly affect the export quality product of state-owned enterprises, but significantly reduces that of non-state-owned enterprises. The possible reasons mainly come from two aspects. First, most state-owned enterprises are traditional industries, and they often need to follow the development path of high pollution and high emissions; However, the state-owned enterprises are generally large in scale and volume, and often bear a greater responsibility for the local economy, and also have a relatively underlying relationship with the government. Then, the environmental regulation policies, such as the carbon emission trading, may have a weak regulatory effect on the state-owned enterprises, and its impact on their export quality product may be small [19]. Secondly, compared with non-state-owned enterprises, state-owned enterprises enjoy abundant capital investment, financial subsidies, tax relief and financing facilities, etc. Compared with non-state-owned enterprises, they have enough ability and strength to deal with environmental regulation policies, such as carbon emission trading, without affecting their export quality product. On the contrary, non-state-owned enterprises have to confront the environmental regulation of the carbon emission trading. Moreover, under the carbon emissions trading, they will face more intense market competition, so it is difficult to consider improving the productivity of enterprises and their export quality product.Table 4
Heterogeneity analysis of considering enterprise ownership attribute.
State-owned enterpriseNon-state-owned enterprises(1)(2)(3)(4)ETSi×Sectorj×Postt0.008 (0.083)0.005 (0.083)−0.488∗∗∗ (0.131)−0.488∗∗∗ (0.131)ETSi×Postt0.221∗∗∗ (0.052)0.223 (0.052)0.560∗∗∗ (0.084)0.560∗∗∗ (0.084)ETSi×Sectorj0.247 (0.144)0.255 (0.144)0.168 (0.228)0.168 (0.228)Sectorj×Postt0.253∗∗∗ (0.052)0.254 (0.052)0.563∗∗∗ (0.081)0.563∗∗∗ (0.081)Observed valueYYYYR2YYYYProvince fixed effectYYYYIndustry fixed effectYYYYEnterprise fixed effectYYYYTime fixed effectYYYYProvince∗time fixed effectYYNote. The standard deviation is within brackets. ∗, ∗∗, and ∗∗∗ represent significance levels of 10%, 5%, and 1%, respectively. The economic characteristic variables of enterprises, including total assets, number of employees, age of enterprises, R&D investment, as well as wage, and the interaction items are all controlled, and no report will not be presented here due to the limited space.


## 4.2. Energy-Intensive Attribute

The impact of the carbon emissions trading pilot policy on the export product quality of enterprises may also be heterogeneous due to the different energy-intensive attributes of enterprises. The carbon emissions trading policy is to regulate the total carbon emissions of energy-intensive industries, and achieve the goal of promoting emission reduction of enterprises with heavy emissions through marketization. For enterprises in energy-intensive and non-energy-intensive industries, the carbon market makes them face different pressures and emission reduction cost, so the impact on their product quality is different. To study the heterogeneity of the impact of the carbon emissions trading pilot policy on the export product quality in included energy-intensive enterprises and non-energy-intensive enterprises, industries with energy intensity higher than the average energy intensity of all included industries are set as energy-intensive industries, while industries with energy intensity lower than the average energy intensity of all included industries are set as non-energy-intensive industries. Furthermore, based on the baseline model, the samples in the included industries are divided into sub-samples of energy-intensive industries and non-energy-intensive industries according to the energy-intensive attributes of industries to study whether the carbon emissions trading policy has different effects on the export product quality of enterprises in energy-intensive and non-energy-intensive industries.The estimation results are shown in Table5. Whether controlling the province fixed effect, industry fixed effect, time fixed effect, province time trend, industry time trend or enterprise time trend, the DDD coefficients are significant in the subsamples of energy-intensive industries, but not significant in the subsamples of non-energy-intensive industries. The result shows that the impact of the carbon emissions trading pilot policy on the export product quality of enterprises does have heterogeneity differences in the energy-intensive attributes of industries. Compared with the nonincluded industries in nonpilot areas, the carbon emissions trading pilot policy significantly reduces the export product quality of enterprises in the included energy-intensive industries, but does not have a significant impact on the export product quality of enterprises in the non-energy-intensive industries. This result is also in line with expectations. For the energy-intensive enterprises included in the carbon market pilot policy, they bear great pressure to reduce emissions. They need to upgrade equipment or develop new technologies to improve energy efficiency or energy consumption structure to reduce emissions, or they need to buy a certain amount of carbon quotas in the carbon market to meet their own emission needs, and this extra emission reduction cost is likely to force enterprises to lower their product quality for the sake of ensuring sufficient income. For enterprises in non-energy-intensive industries, the pressure of emission reduction is smaller than that in energy-intensive industries, and the export product quality is not affected by the cost of emission reduction.Table 5
Heterogeneity analysis of considering energy-intensive attribute.
Energy-intensive industryNon-energy-intensive industry(1)(2)(3)(4)ETSi×Sectorj×Postt−0.057∗ (0.032)−0.056∗ (0.032)0.008 (0.087)0.008 (0.087)ETSi×Postt−0.009 (0.019)−0.010 (0.019)0.072 (0.051)0.072 (0.051)ETSi×Sectorj0.046 (0.054)0.043 (0.054)−0.565∗∗∗ (0.154)−0.566 (0.154)Sectorj×Postt0.130∗∗∗ (0.020)0.131∗∗∗ (0.020)0.778∗∗∗ (0.055)0.778 (0.0550)Observed valueYYYYR2YYYYProvince fixed effectYYYYIndustry fixed effectYYYYEnterprise fixed effectYYYYTime fixed effectYYYYProvince∗time fixed effectYYNote. The standard deviation is within brackets. ∗, ∗∗, and ∗∗∗ represent significance levels of 10%, 5%, and 1%, respectively. The economic characteristic variables of enterprises, including total assets, number of employees, age of enterprises, R&D investment, as well as wage, and the interaction items are all controlled, and no report will not be presented here due to the limited space.


## 5. Conclusions and Policy Implications

This paper carries out the quasinatural experiment on China’s carbon emissions trading pilot policy to assess the impact of China’s carbon emissions trading pilot policy on the export product quality of enterprises in the pilot areas by using the data of listed companies, and the DDD method, and analyze the heterogeneity of the impact under different ownership attributes and the energy-intensive attributes of enterprises. The empirical results are as follows. First, due to the cost of emission reduction, the implementation of China’s carbon emissions trading pilot policy has a negative impact on the export product quality of the enterprises in the pilot areas, compared with the nonincluded enterprises in the nonpilot areas. Second, the impact of the carbon emissions trading pilot policy on the export product quality of enterprises may be heterogeneous due to the different ownership attributes of enterprises. The carbon emissions trading pilot policy significantly reduces the export product quality of non-state-owned enterprises, but has no significant impact on the export product quality of state-owned enterprises. Third, the impact of the carbon emissions trading pilot policy on the export product quality of enterprises is heterogeneous due to different energy-intensive attributes of enterprises. The carbon emissions trading pilot policy significantly reduces the export product quality of enterprises in energy-intensive industries, but it does not have a significant impact on enterprises in non-energy-intensive industries.To help the enterprises in the included industries to complete the task of emission reduction while maintaining sustained international competitiveness, the research findings of this paper have the following implications:For emission-controlling enterprises, the scarcity of carbon emission rights leads to the extra carbon emission cost for included enterprises when the total carbon emission of the industry is restricted. This increases the production cost of enterprises in the short term, resulting in the decline of product quality. In the long run, enterprises, especially those in non-state-owned enterprises and energy-intensive industries, should shift to a clean, low-carbon and efficient development model to maintain competitiveness in the international market. First of all, enterprises should carry out the reform, abandon the extensive development mode, improve the productivity and energy efficiency of enterprises, eliminate or update backward equipment and technology, promote energy saving and efficiency improvement of key equipment, effectively save energy and reduce carbon emission intensity. Secondly, enterprises in energy-intensive industries should optimize the energy consumption structure. To further control the production costs increased by emission reduction cost, enterprises, especially energy-intensive enterprises, should actively carry out the transformation of energy consumption structure, promote the substitution, transformation and upgrading of coal consumption, increase renewable energy electricity consumption, and promote the transformation of energy consumption patterns. Finally, enterprises in the included industry should develop or introduce energy-saving and low-carbon technologies, and transform to the low-carbon development strategy. The current quota allocation method of the carbon emission rights pilot trading is mainly allocated free of charge. In the future, the carbon market will reduce the total amount of quotas and increase the proportion of auction allocation. Therefore, enterprises included in the pilot carbon market should lay out their measures of transformation and emission reduction in advance, and cope with the further increase in emission reduction cost in the future with a higher potential for emission reduction, especially for non-state-owned energy-intensive enterprises.For the government, the implementation of the carbon market under the total carbon emission limit relies on the price signal reflected by the carbon price to encourage or constrain the emission-controlling enterprises to make decisions in emission reduction. Enterprises with different emission levels obtain different carbon emission quotas, leading to differences in emission reduction costs and production costs. Different quota allocation methods will have a more significant impact on the quotas obtained by enterprises. If the quota allocation is too tight, it will cause tremendous economic losses to the production and operation of enterprises. If the quota allocation is too loose, enterprises will not be motivated enough to reduce emissions. Therefore, the government authorities should take into account the fairness and efficiency of quota allocation and further improve the quota allocation mechanism. Secondly, the carbon price reflects the carbon emission reduction of the included enterprises to some extent. Excessive carbon price makes the enterprise unable to bear the excessive emission reduction cost, resulting in the carbon leakage risk of relocation. Low carbon price cannot effectively motivate enterprises to reduce emissions, so stable and reasonable carbon price is the fundamental guarantee for enterprises to achieve emission control. Government authorities should design a systematic price stability mechanism and take reasonable price management measures to ensure the sound and stable operation of the carbon market.



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*Source: 6458398-2022-03-21.xml*