Market Integration, Industrial Structure, and Carbon Emissions: Evidence from China

Abstract

Against the backdrop of China’s carbon emission reduction targets and the promotion of the construction of a unified domestic market, what kind of carbon emission effect has market integration had in weakening regional barriers and optimizing resource allocation? This paper adopts a two-way fixed effects analysis based on China’s provincial panel data from 2003 to 2019. It uses a mediation model to explore the relationship between market integration and carbon emissions. Furthermore, industrial rationalization and upgrade are the basis for examining whether a competitive or cooperative relationship exists between the carbon emission effects generated and promoting market integration between regions. The study finds a negative relationship between market integration and carbon emissions. In addition, there is significant heterogeneity in the effect of market integration on carbon emissions, and the influence effect is mainly in central China; industrial rationalization can play an enhanced role in the process of the negative impact of market integration on carbon emissions, further enhancing the negative contribution of market integration to carbon emissions. However, market integration can weaken its negative impact on carbon emissions with the industrial upgrade, and there may still be invisible barriers between regions in promoting market integration barriers.

1. Introduction

Market integration aims to eliminate barriers to the flow of resources and factors in each region, break down administrative and trade barriers, form a standardized and orderly market resource-sharing and cooperation platform, and promote equal cooperation and fair competition in economic development between regions. Since the reform and opening up, along with the enhanced flow of resources and factors between regions, the economic development of China has become more and more closely linked, and market integration has become an essential path for economic growth. With the introduction of the carbon peak and neutrality targets, all regions must consider environmental factors in the process of economic development, and try to achieve “zero emissions” in the development process. China has issued guidelines on establishing a unified domestic market, indicating that the unification of factor and resource markets is inevitable, and how to take into account the two-way results of market expansion and carbon emission reduction deserves attention and in-depth analysis.

For a long time, due to the effects of inter-regional competition, regional barriers have deepened, and industrial isomorphism has become increasingly aggravated. This has led to massive energy consumption and exacerbated the rise of carbon emissions, severely hindering green environmental development. The deepening of regional development strategy has produced some policy promotion effects on breaking regional barriers. The guidelines on establishing a unified domestic market have laid a solid foundation for further deepening market integration, and have contributed to the positive environmental effects generated in the process of market integration. Therefore, bringing industrial structure and agglomeration into the framework of discussion, this paper discusses the impact mechanism and effect of market integration on carbon emission levels, and then explores the path and policies to optimize carbon emissions around the construction of market integration.

2. Literature Reviews and Theoretical Analysis

2.1. Literature Reviews

The ability of regional integration strategies to weaken market segmentation and reduce carbon emissions is crucial to achieving China’s carbon emission reduction targets. However, the current literature concerning the impact of market integration on carbon emissions focuses more on its effects on environmental quality, and only a few pay attention to the impact on carbon emissions.

In studies on the impact of trade openness on environmental quality, market integration leads to more unrestricted output flows, implying the elimination of trade barriers. However, reducing trade barriers has a nonlinear effect on the environment [1,2,3,4]. There are two explanations for the nonlinear relationship between trade openness and the environment. One uses the well-known pollution haven hypothesis (PHH) to explain the nonlinear relationship. PHH argues that since pollution often occurs during the transfer of industries from developed to developing countries, low-income developing countries are always the victims of environmental pollution. The environmental impact of trade openness is detrimental to developing countries but benign to developed countries; thus, global environmental performance is conditional [5,6,7,8]. However, trade integration, while increasing the intensity of pollutant emissions, has offset this negative effect by increasing efficiency and promoting cleanliness in its manufacturing sector [9]. Therefore, in the context of trade openness, PHH is often used to test the environmental impact of increased foreign direct investment (FDI). The other explanation has argued that the results of trade openness can be divided into scale, technology, and composition effects [7,10,11,12]. The scale effect refers to the expansion of pollutant emissions as the size of the economy increases due to trade liberalization. The technology effect refers to the upgrading of green technologies to reduce the intensity of emissions, and through stricter environmental regulation, trade openness will raise income levels and increase the demand for a cleaner environment. The composition effect refers to the two-way impact of industrial restructuring. If the high-polluting industrial sector has a comparative advantage, trade openness can damage the environment by making local areas more specialized in high-polluting production. Otherwise, trade openness can improve the environment by making local areas more specialized in cleaner production [2,3,11]. Previous literature has focused extensively on the environmental impacts of trade barriers. However, most have focused on the ecological effects of international trade, while relevant evidence from domestic trade remains scarce.

For studies on the impact of market integration on environmental quality, Li and Lin evaluated the carbon emission performance of provincial regions in China from 1995 to 2012 using a non-radial directional distance function to investigate the effect of regional market integration on carbon emission performance. The study found that regional market integration can significantly contribute to carbon emissions [13]. He et al. confirmed the significant contribution of regional market integration to the marginal abatement cost of carbon emissions in 30 Chinese provinces during 2002–2011 [14]. Lin and Du (2015) used a Tobit regression model to estimate the effect of market-oriented reforms on carbon emissions efficiency [15]. The results show that market-oriented reforms can improve carbon emissions efficiency. The above studies all consider the relationship between regional market integration and carbon emissions. In addition, these studies also proved that the increase in the level of market integration could meaningfully contribute to the strengthening of regional market forces, especially in the area of high-carbon markets, enhancing the competitiveness of enterprises and investment development opportunities [16]. However, it is worth noting that to improve carbon productivity, it is necessary to strengthen inter-regional cooperation further and focus on the coordinated development of carbon productivity in the development process [17]. Certainly, some scholars focus more on the environmental impact of market integration on energy and electricity. They believe the integration process will increase energy and electricity consumption and play a vital role in developing renewable energy. Within the scope of a unified market, technological progress and the strengthening of environmental regulation tends to promote renewable energy development, thereby accelerating the process of sustainable development [18]. On a larger scale, such as regional energy market cooperation that transcends national borders, it faces many additional problems [19].

In summary, market integration may affect carbon emissions. Market integration is accompanied by the free flow of production factors, which involves economic and technological innovation and carbon emissions [20,21]. However, only a few studies focus on the relationship between market integration and carbon emissions. Similar to other potential influencing factors of carbon emissions, other external conditions may influence the effect of market integration on carbon emissions. The impact of market integration on carbon emissions may vary depending on the external environment.

To address some of these possible gaps in existing studies, this paper verifies the relationship between market integration and carbon emissions from the perspective of domestic trade barriers, using provincial panel data for 30 provinces (excluding Tibet) from 2003 to 2019. The paper aims to contribute to an understanding of the relationship between market integration policies to reduce regional development inequalities and carbon emissions in China and other developing countries.

The potential contributions of this paper can be divided into three aspects. First, we explore the possible negative effects of market integration on carbon emissions in Chinese provinces and cities. Second, unlike most studies examining the environmental impact of international trade, we provide new empirical evidence from the perspective of enhanced factor mobility. Third, we argue that there is a significant correlation between market integration and carbon emissions, with a mediating effect through changes in industrial structure.

2.2. Theoretical Analysis and Research Hypothesis

Market segmentation leads to resource misallocation, which results in the inability to achieve free flow of factor resources within a region, making it challenging to allocate regional resources efficiently, and then adversely affecting the carbon emission intensity in the long run [22]. When local governments engage in integrated cooperation, the level of inter-regional market integration gradually increases, and factors of production can realize a free flow, which will significantly promote the energy-saving and emission-reduction effects of urban agglomerations [23]. Market integration refers to the free flow of goods and factors of production within a framework of consistent rules between regions and industrial sectors, which will lead to scale economy, knowledge sharing, and technology spillovers [24]. In other words, market integration can indirectly affect carbon emissions through economic growth and technological progress [5,25,26,27]. This paper analyzes the impact of market integration on carbon emissions, focusing on the possible scale effects, structural effects, and regional heterogeneity.

Market integration may increase environmental by-products through the expansion of local markets and the promotion of enterprise production, which exacerbates environmental pollution, reflecting the scale effect of market integration. Generally speaking, market integration implies free trade of commodities and removing barriers to factor flow, which is conducive to optimizing the economic structure and developing regional scale effects, thus improving resource allocation efficiency and production technology progress and promoting pollution reduction. While the expansion of trade and market scale caused by market integration may aggravate carbon emissions due to increased production, but also reduce pollution emissions due to the scale effect at the same time, the conclusion depends on comparing different forces [1].

Based on the above theoretical analysis, hypothesis 1 is proposed: Market integration will promote the scale effect and increase production quantity, thus leading to increased carbon emissions.

The environmental Kuznets curve theory suggests that market integration reduces environmental pollution through factors such as industrial agglomeration and industrial restructuring [1]. In the classical new economic geography model, market integration promotes the realization of industrial agglomeration, contributes to the completion of enterprise agglomeration externalities, realizes the diffusion and sharing of environmental protection technology, and contributes to the improvement of green growth efficiency [28,29,30]. In addition, industrial transfer is often accompanied by policy orientation, while industrial restructuring and development in the region are often closely linked to environmental policies, especially for the transfer of heavily polluting industries, and the effect of such policy suppression is evident [21]. Therefore, it is likely to result in competition between regions in policy development and implementation, which is not conducive to the synergy of industries in each region to reduce carbon emissions. Market integration promotes the transformation and upgrading of industrial structure, and reduces the carbon emission intensity of enterprise production [31,32,33,34].

Based on the above theoretical analysis, hypothesis 2 is proposed: Market integration will slow down the carbon emissions increase by influencing industrial restructuring.

The improvement of environmental welfare by market integration also relies on the spatial spillover properties of pollutants and cross-regional pollution coefficients. In particular, with the changing focus of China’s regional development strategy and the accelerated reform process [35], different regions have different economic development statuses, degrees of market integration, and energy structures. The impact of their market integration on carbon emission levels also varies [36].

Based on the above theoretical analysis, Hypothesis 3 is proposed: Regional heterogeneity in the impact of market integration on carbon emission levels.

The remainder of the paper is as follows: Section 3 details the model construction and data description; Section 4 presents and discusses the regression results and conducts robustness tests; Section 5 presents the main conclusions and policy implications.

3. Data and Method

3.1. Data Description

3.1.1. Carbon Emissions Measurement

This paper takes carbon emissions as a dependent variable, and the data comes from China Emission Accounts and Datasets: https://www.ceads.net (accessed on 1 August 2022). It includes carbon emissions from both fossil fuel combustion (i.e., energy-related emissions) and cement production (process-related emissions) in the emission accounts. Energy-related carbon emissions are converted from the carbon content in fossil fuels. We use mass balances to calculate emissions according to the IPCC guidelines (2006), the formula is as follows:

$${\mathrm{CE}}_{\mathrm{i}}={\mathrm{AD}}_{\mathrm{i}}\times {\mathrm{NCV}}_{\mathrm{i}}\times {\mathrm{CC}}_{\mathrm{i}}\times \mathrm{O}$$

(1)

In the equation, CE_i refers to carbon emissions from fossil fuel_i. While China’s statistical energy system has 26 types of fossil fuels, references to the calculation formula and method of carbon emission by existing scholars [37,38], merge them into 17 types due to the small consumption amount and similar quality of certain fuels. Ad_i is the “activity data” used for emission estimation. In the case of energy-related emission accounting, AD_i refers to the combustion volume of fossil fuel i. NCV_i represents the “net caloric value,” which is the heat value per physical unit from the combustion of fossil fuel i. CC_i is the “carbon content” of fuel i, quantifying carbon emissions per net caloric value produced. O refers to “oxygenation efficiency,” which represents the oxidation ratio during fossil fuel combustion of certain fuels.

By aggregating emission results from different energy types, the formula for calculating the total carbon emissions of a province is as follows:

$$\mathrm{TCE}=\sum {\mathrm{CE}}_i$$

(2)

3.1.2. Market Integration Measurement

We use the relative price method proposed by Parsley, Wei and Poncet to measure market integration [26,39]. First, calculate the variance using the absolute value of the relative price of the commodity $\left|\Delta {\mathrm{Q}}_{\mathrm{ijt}}^{\mathrm{k}}\right|$. The formula is as follows:

$$\Delta {\mathrm{Q}}_{\mathrm{ijt}}^{\mathrm{k}}=\ln \left({\mathrm{P}}_{\mathrm{it}}^{\mathrm{k}}/{\mathrm{P}}_{\mathrm{jt}}^{\mathrm{k}}\right)-\ln \left({\mathrm{P}}_{\mathrm{it}-1}^{\mathrm{k}}/{\mathrm{P}}_{\mathrm{jt}-1}^{\mathrm{k}}\right)$$

(3)

By simply morphing, $\Delta {\mathrm{Q}}_{\mathrm{ijt}}^{\mathrm{k}}$ can be expressed as a chain index of commodity prices ${\mathrm{P}}_{\mathrm{it}}^{\mathrm{k}}/{\mathrm{P}}_{\mathrm{jt}}^{\mathrm{k}}$ and ${\mathrm{P}}_{\mathrm{it}-1}^{\mathrm{k}}/{\mathrm{P}}_{\mathrm{jt}-1}^{\mathrm{k}}$. The formula is as follows:

$$\Delta {\mathrm{Q}}_{\mathrm{ijt}}^{\mathrm{k}}=\ln \left({\mathrm{P}}_{\mathrm{it}}^{\mathrm{k}}/{\mathrm{P}}_{\mathrm{it}-1}^{\mathrm{k}}\right)-\ln \left({\mathrm{P}}_{\mathrm{jt}}^{\mathrm{k}}/{\mathrm{P}}_{\mathrm{jt}-1}^{\mathrm{k}}\right)$$

(4)

To calculate and measure the market segmentation level more accurately and reflect its actual situation, therefore, in a further transformation of the equation, the non-additive effects due to commodity heterogeneity $\left|\Delta {\mathrm{Q}}_{\mathrm{ijt}}^{\mathrm{k}}\right|$ are first hypothesized by means using the mean value method:

$$\left|\Delta {\mathrm{Q}}_{\mathrm{ijt}}^{\mathrm{k}}\right|={\mathsf{\alpha }}^{\mathrm{k}}+{\mathsf{\epsilon }}_{\mathrm{it}}^{\mathrm{k}}$$

(5)

To eliminate ${\mathsf{\alpha }}^{\mathrm{k}}$, we need to find the mean value of $\left|\Delta {\mathrm{Q}}_{\mathrm{ijt}}^{\mathrm{k}}\right|$, and subtract that mean by $\left|\Delta {\mathrm{Q}}_{\mathrm{ijt}}^{\mathrm{k}}\right|$. The difference between the two is obtained as ${\mathrm{q}}_{\mathrm{ij}}^{\mathrm{k}}$. After that, the factors affecting ${\mathrm{q}}_{\mathrm{ij}}^{\mathrm{k}}$ are mainly focused on the market score and some random factors. Calculate the variance $\mathrm{Var}\left({\mathrm{q}}_{\mathrm{ij}}^{\mathrm{k}}\right)$ using the ${\mathrm{q}}_{\mathrm{ij}}^{\mathrm{k}}$ values. Variance $\mathrm{Var}\left({\mathrm{q}}_{\mathrm{ij}}^{\mathrm{k}}\right)$ indicates the change in the arbitrage space due to market segmentation. If this arbitrage space is smaller, it shows a higher level of current market integration and vice versa. The mean value of $\mathrm{Var}\left({\mathrm{q}}_{\mathrm{ij}}^{\mathrm{k}}\right)$ of a province and city and all bordering provinces and cities can be used to express the degree of market segmentation of this province and city, that is:

$$\mathrm{Var}\left({\mathrm{q}}_{\mathrm{m}}^{\mathrm{k}}\right)=\left({\sum }_{\mathrm{i}\ne \mathrm{j}}\mathrm{Var}\left({\mathrm{q}}_{\mathrm{ij}}^{\mathrm{k}}\right)\right)∕\mathrm{N}$$

(6)

Among them, j denotes all provinces and cities bordering province i, m represents the name of the province and city, and N represents the number of combinations of provinces and cities bordering province and city i. Finally, the market integration index is built on top of the existing segmentation index (expressed as MI, m still indicates the name of the province or city), and the formula for the integration index is defined as follows:

$${\mathrm{MI}}_{\mathrm{m}}=\sqrt{\frac{1}{{\mathrm{Var}}_{\mathrm{m}}^{\mathrm{k}}}}$$

(7)

Therefore, the relationship between the two indices of market segmentation and market integration has an inverse trend. After calculating each provincial and municipal integration index, the average value of all provincial and municipal market integration values in the region can be calculated to measure the market integration level of a particular region. We selected eight categories of: food, tobacco and alcohol; clothing; housing; household goods and services; transportation and communication; education; culture and entertainment; and health care. These eight categories of consumer price index are measured; all data are from provincial statistical yearbooks and the National Bureau of Statistics.

3.1.3. Industry Change Measurement

Industrial rationalization. We refer to existing scholarly practice to measure [40]. The first step is to calculate the structural deviation factor:

$${\mathrm{INDR}}^{*}={\displaystyle \sum }_{\mathrm{j}}\frac{{\mathrm{Y}}_{\mathrm{j}}}{\mathrm{Y}}\left|\frac{{\mathrm{Y}}_{\mathrm{j}}/{\mathrm{L}}_{\mathrm{j}}}{\mathrm{Y}/\mathrm{L}},-,1\right|(\mathrm{j}=1,2,3)$$

(8)

where i represents each province, j = 1,2,3 represents the three industries respectively, Y_j represents the value added of the industry in that year, and L_j represents the number of employees in that year. INDR* is the degree of industrial structure rationalization, measured by the degree of structural deviation in province i. The summed numbers indicate the relative degree of imbalance between the respective value-added shares of the three industries and the employment shares. In this case, the higher the value of INDR^*, the lower the degree of industrial structure rationalization of the province. Since all data in the index are from the same year, we omit the time subscript t for symbolic simplicity, so as to not cause conceptual confusion.

In the second step, a numerical extreme difference transformation is used to normalize the range of indicators to a specific interval and convert them into positive indicators.

$${\mathrm{INDRM}}_{\mathrm{i}}=\frac{\underset{\mathrm{k}}{\max }{\mathrm{mind}}_{\mathrm{k}}^{*}-{\mathrm{indrL}}_{\mathrm{i}}^{*}+1}{\underset{\mathrm{k}}{max}{\mathrm{indr}}_{\mathrm{k}}^{*}-{\mathrm{minindr}}_{\mathrm{k}}^{*}}\alpha$$

(9)

In the third step, we first use the structural deviation to calculate the degree of rationality of the industrial structure, which is given by:

$$\mathrm{INDRS}={\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}\left|\frac{{\mathrm{Y}}_{\mathrm{i}}/{\mathrm{L}}_{\mathrm{i}}}{\mathrm{Y}/\mathrm{L}},-,1\right|={\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}\left|\frac{{\mathrm{Y}}_{\mathrm{i}}/\mathrm{Y}}{{\mathrm{L}}_{\mathrm{i}}/\mathrm{L}},-,\mathrm{1}\right|{}^{\mathrm{L}}$$

(10)

In this equation, Thiel’s index is introduced, and the formula is as follows:

$$\mathrm{INDR}={\displaystyle \sum }_{\mathrm{i}=\mathrm{1}}^n\left(\frac{{\mathrm{Y}}_{\mathrm{i}}}{\mathrm{Y}}\right)\ln \left(\frac{{\mathrm{Y}}_{\mathrm{i}}}{{\mathrm{L}}_{\mathrm{i}}}/\frac{\mathrm{Y}}{\mathrm{L}}\right)$$

(11)

where, INDR denotes the Thiel index, which indicates the level of industrial structure upgrading. y denotes output, L denotes employment, i denotes industry, and n denotes the number of industrial sectors; Y/L indicates productivity, Y_i/Y denotes the output structure, and L_i/L indicates the employment structure, and the value range of INDR is (0,LnN). When INDR = 0, it means that the industrial development is very reasonable, and the smaller the INDH value is, the more reasonable the industrial structure is, and the development of each internal factor is balanced.

Industrial upgrade. The upgrading of industries involves the evolution of proportional relations and the improvement of labor productivity. When the share of industries with higher labor productivity in a country or region is more prominent, it indicates a stronger industrial chain heightening in that region. Therefore, following the approach of Liu Wei et al. [41], the connotation of industry chain heightening (INDH) is defined as the weighted value of the product of the proportional relationship between industries and the labor productivity of each industry. This shows the essential characteristics of the evolution of industry chains as higher proportions of labor productivity. The specific formula for measuring the quality of industry chain heightening is:

$${\mathrm{INDH}}_{\mathrm{it}}={\displaystyle \sum }_{\mathrm{m}=1}^3\frac{{\mathrm{Y}}_{\mathrm{itm}}}{{\mathrm{Y}}_{\mathrm{it}}}\times \frac{{\mathrm{Y}}_{\mathrm{itm}}}{{\mathrm{L}}_{\mathrm{itm}}},\mathrm{m}=1,2,3$$

(12)

Here, Y_itm denotes the value added of industry m in period t in region i, L_itm represents the number of people employed in industry m in period t in region i. Y_itm/Y_it denotes the labor productivity of industry m in period t in region i. Considering that labor productivity has a quantitative dimension, this paper adopts the mean value method for dimensionless treatment. All data are from provincial statistical yearbooks, the National Bureau of Statistics and CSMAR Database.

Other control variables. To control external factors in different regions, government input (GOVI), the level of openness to the outside world (FDI), the level of technological innovation (ZLSP), and the level of regional economic development (GDP) are selected as control variables. Government input, the level of openness to the outside world, the level of technological innovation, and the level of regional economic development not only affect carbon emissions [42,43] but also affect the impact of market integration on carbon emissions [44,45].

Government input (GOVI) is measured by the annual fiscal revenue of each province; the level of openness to the outside world (FDI) is measured by the sum of total annual import and export and foreign investment in each province; the level of technological innovation (ZLSP) is measured by the annual number of patents granted in each province, and the level of regional economic development (GDP) is measured by the annual gross product of each province. The relevant data were logarithmically processed. All data are from provincial statistical yearbooks.

3.2. Methods

3.2.1. Two-Way Fixed Effects Model

This paper’s two-way fixed effects model was constructed to investigate the linear relationship between market integration and carbon emissions adopting panel data from 2003 to 2019. Considering the estimated coefficients of the double logarithmic, market integration on the carbon emissions equation can be treated as the elasticities of the dependent variables [46]. To eliminate heteroscedasticity effectively, concerning the independent variables, we used TCE to represent the level of carbon emissions, MI is the level of market integration, and X represents the other control variables. We conducted a double logarithmic function as shown below:

$${\mathrm{TCE}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1{\mathrm{MI}}_{\mathrm{it}}+{\mathsf{\beta }}_2{\mathrm{X}}_{\mathrm{it}}+{\mathsf{\mu }}_{\mathrm{it}}+{\mathsf{\epsilon }}_{\mathrm{it}}$$

(13)

3.2.2. Analysis of the Mechanism

In this paper, a mechanism analysis model was constructed, and based on the benchmark model, indicators related to industrial structure change were added to test the mechanism of action between market integration, industrial structure change, and carbon emissions, and further analyze the impact of market integration on carbon emissions. The model is set as follows.

$${\mathrm{TCE}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1{\mathrm{MI}}_{\mathrm{it}}++{\mathsf{\beta }}_2{\mathrm{INDR}}_{\mathrm{it}}+{\mathsf{\beta }}_3\mathrm{Interact}{1}_{\mathrm{it}}+{\mathsf{\beta }}_4{\mathrm{X}}_{\mathrm{it}}+{\mathsf{\mu }}_{\mathrm{it}}+{\mathsf{\epsilon }}_{\mathrm{it}}$$

(14)

$${\mathrm{TCE}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1{\mathrm{MI}}_{\mathrm{it}}+{\mathsf{\beta }}_2{\mathrm{INDH}}_{\mathrm{it}}+{\mathsf{\beta }}_3\mathrm{Interact}{2}_{\mathrm{it}}+{\mathsf{\beta }}_4{\mathrm{lnX}}_{\mathrm{it}}+{\mathsf{\mu }}_{\mathrm{it}}+{\mathsf{\epsilon }}_{\mathrm{it}}$$

(15)

where $\mathrm{TCE}$ is the carbon emission level, MI is the market integration level, INDR is the level of industrial rationalization level, INDH is the level of industrial upgrade, Interact1 is the interaction term between market integration (MI) and industrial rationalization (INDH), Interact2 is the interaction term between market integration (MI) and industrial upgrade (INDH), and X represents other control variables.

3.2.3. Intermediary Effect Model

We constructed a mediating effect model to test the relationship between market integration, industrial structure change, and carbon emissions. The impact of market integration development on carbon emissions was quantitatively analyzed. Then we tested whether industrial structure change mediates the market integration process affecting carbon emissions. In the next step, the extent of the mediating impact was studied under the premise that there is a mediating effect, and industrial rationalization and upgrade were taken as mediating variables. In summary, the empirical panel data regression model constructed in the article is as follows:

$${\mathrm{TCE}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1{\mathrm{MI}}_{\mathrm{it}}+{\mathsf{\beta }}_3{\mathrm{X}}_{\mathrm{it}}+{\mathsf{\mu }}_{\mathrm{it}}+{\mathsf{\epsilon }}_{\mathrm{it}}$$

(16)

$${\mathrm{Medi}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1{\mathrm{MI}}_{\mathrm{it}}++{\mathsf{\beta }}_3{\mathrm{X}}_{\mathrm{it}}+{\mathsf{\mu }}_{\mathrm{it}}+{\mathsf{\epsilon }}_{\mathrm{it}}$$

(17)

$${\mathrm{TCE}}_{\mathrm{it}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1{\mathrm{MI}}_{\mathrm{it}}+{\mathsf{\beta }}_2{\mathrm{Medi}}_{\mathrm{it}}+{\mathsf{\beta }}_3{\mathrm{X}}_{\mathrm{it}}+{\mathsf{\mu }}_{\mathrm{it}}+{\mathsf{\epsilon }}_{\mathrm{it}}$$

(18)

4. Result and Discussion

4.1. Spatial and Temporal Trends

4.1.1. Spatial and Temporal Trends in Market Integration

Through the measurement of market integration levels, it is found that each region’s overall market integration level shows a fluctuating upward trend, and most regions have the phenomenon of rising and then falling in Figure 1, such as Beijing, Tianjin, Inner Mongolia, etc. During the sample examination period, most regions reached a peak market integration level in 2016. They then fluctuated, indicating to a certain extent that the inter-regional market integration promotes factor flow, and that there is a limited value in the collaborative division of labor. In the market integration process, especially after the local benefits or industrial system have formed a stable situation with a particular economic foundation, most regions focused more on maximizing the local economy, thus gradually lowering the goal of inter-regional cooperation with the risk that market segmentation will rise again. Some regions, such as Shanghai, Zhejiang, and Guangdong, gradually rebounded after showing a downward trend in 2016, and the degree of inter-regional regional cooperation was further strengthened. The level of market integration in the region was continuously enhanced after 2016.

From a sub-regional perspective, the overall difference in market integration levels among the eastern, central, western, and northeastern regions at the initial stage is slight in Figure 2, the eastern pre-coastal regions are far more demanding than other regions in China in terms of opening and cooperation, are more dependent on industrial chain development and foreign trade cooperation, and are more in need of a market. It is necessary to pursue the role of a win-win or leading role in the process of market integration. On the other hand, after 2012, with the central government’s slogan “lucid waters and lush mountains are invaluable assets,” the demand for green economic development in this region is increasing, indirectly promoting inter-regional market-level integration. Although the eastern region maintained an upward trend until 2012, it rose slowly, and unlike other areas, it showed a downward trend after 2012 and rebounded in 2016. Still, the gap in the level of market integration in 2019 compared with the central and western regions is evident. It may be because the eastern region, as the frontier region of China’s economic development, has shown a high level of inter-regional cooperation and development in the early stage of development and tends to converge in the market integration process. With the central government proposing industrial transformation and structural adjustment, the eastern region may have been in a temporary period of pain since 2012, and it will recover the level of market integration in due course.

The central region always showing an upward trend and maintaining a leading position until 2009 in Figure 3, which may also be closely related to the strategic development of the rise of central China that the Chinese government has advocated.

The level of market integration in the western region has always been on the rise in Figure 4, showing a slower growth between 2003 and 2012, but between 2012 and 2016, the level of market integration in the western region achieved a significant increase, which may be attributed to the enhanced binding of resources and environment. The western region must seek a path that fits ecological and environmental protection with economic development, so the past development model that relied on environmental resources needs to be improved to rapidly promote the transformation of the region’s economic structure through effective inter-regional cooperation.

In addition, after 2009, the market integration level in the northeast region significantly increased but sharply declined after 2016 in Figure 5. This may be because the promotion of the revitalization strategy of the northeast stimulated cooperation and exchange between regions to a certain extent. Still, due to the solidification of the industrial foundation and many difficulties in transformation, the industrial transformation may not be able to match further the integration process in the late stage of the market integration process. As a result, the level of market integration in the region showed a downward trend in the late sample period. The level of market integration in the western region changed more slowly before 2012, while it showed a significant upward trend after 2012 and a slight decline after 2016. This may be caused by the fact that most western regions belong to important ecological protection areas, which, to a certain extent, restricts the possibility of inter-regional economic collaboration, and guides these regions to invest more in environmental protection.

4.1.2. Spatial and Temporal Trends in Carbon Emission

Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10 visualize the level of carbon emissions in each region, and it is evident from the data that the extent of the carbon emissions is closely linked to the area. It can be seen from the emissions that Hebei, Shanxi, Shandong, and Inner Mongolia are among the country’s top regions in terms of carbon emission levels, which is also related to the pillar industries and development patterns of the areas in Figure 6. For example, in the results of the sub-region, heavy industries, such as coal mines, metal, and petroleum have relatively high carbon emissions.

Eastern China has always maintained the rising trend of carbon emission levels, keeping the second position in Figure 7. Obviously, as an economically developed region, the eastern region maintains a high level in terms of carbon emissions, both in terms of production activities and living agglomeration, but with the transformation and upgrading of the economic structure, more and more energy-consuming industries gradually move to the central and western regions, thus making the total amount of carbon emissions in the eastern region show a slight downward trend.

The carbon emission level in central China has shown a slight downward trend since 2012 in Figure 8, resulting from the fact that most provinces in central China are located in the Yangtze River economic belt, the ecological protection requirements of which may have a particular impact on the carbon emission level in central China.

It is noteworthy that western China’s carbon emission level has always shown a significant upward trend in Figure 9, although its carbon emission level ranked last in 2013. However, by 2019, western China’s overall carbon emission level had jumped to the top of the four areas. On the one hand, with the deepening of the western development strategy, the development of western China has shown an upward trend. On the other hand, along with the phenomenon of industrial transfer, the level and scale of industries undertaken by the part of the west in eastern and central China are also rising, which leads to a gradual increase in the carbon emission level of western China.

The northeast region has maintained a higher carbon emission position for a long time in Figure 10, which may be related to its regional characteristics; it carries more old industrial industries, and the transformation of economic and industrial development after 2012 may have had a particular impact on its carbon emission level. It is evident in the graph that the carbon emission level has fallen back since 2012.

4.2. Analysis of Empirical Results

4.2.1. Regression Analysis Results

Table 1. Results of the benchmark return.

	ALL	EAST	CENTRAL	WEST	NE
	TCE	TCE	TCE	TCE	TCE
MI	0.0787 **	−0.0262	0.437 ***	0.0111	0.0361
	(2.18)	(−0.50)	(4.04)	(0.25)	(0.87)
GOVI	0.507 ***	0.543 ***	0.289	0.605 ***	0.0150
	(4.78)	(3.23)	(1.15)	(5.56)	(0.10)
ZLSP	−0.0328	−0.141 **	0.151 *	−0.183 ***	0.0213
	(−0.88)	(−2.30)	(1.83)	(−4.46)	(0.43)
FDI	0.173	5.832 ***	−3.447 **	−0.962 *	3.315 ***
	(0.36)	(4.22)	(−2.41)	(−1.83)	(3.61)
GDP	−0.0615	−0.328 *	−0.0308	0.158	0.114
	(−0.47)	(−1.71)	(−0.10)	(1.05)	(0.61)
_cons	1.917 *	−10.22 ***	9.606 ***	3.900 ***	−4.761 *
	(1.66)	(−2.90)	(2.93)	(3.42)	(−2.00)
N	510	170	102	187	51
r2	0.765	0.725	0.732	0.860	0.850

Notes: t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

indicates that under the baseline regression, market integration significantly increases carbon emissions without considering the impact of changes in industry structure. However, its impact is far less than that of government input (i.e., each 1% increase in the level of market integration will lead to a 0.0787% increase in carbon emissions), while every 1% increase in government input will lead to a 0.507% increase in carbon emissions. The increase in the level of market integration will help break down local barriers, accelerate the flow of various factors, and reduce transaction costs, thus promoting the scale effect to increase total production and consumption. Therefore, with other conditions unchanged, enterprises can produce more goods, and consumers’ willingness and ability to pay are also enhanced to a certain extent. The increase in these production factors and the acceleration of commodity flow will increase carbon emissions [47].

To further analyze whether there is regional heterogeneity in the impact of market integration on carbon emissions, regression analysis is carried out for eastern, central, western, and northeastern China without considering the factors of the industrial structure change. The results show that the impact of market integration on carbon emissions presents significant regional differences. The eastern, western, and northeastern regions have no significant impact, and only in the central area has market integration promoted the level of carbon emissions. The elasticity coefficient is 0.437; (i.e., for every 1% increase in market integration, carbon emissions will increase by 0.437%). As for other control variables, both government input and foreign investment levels in eastern China significantly contribute to the increase in carbon emissions, with elasticities of 0.543 and 5.832, respectively. In contrast, the technology and local economic development levels have a suppressive effect on carbon emissions, with elasticities of −0.141 and −0.328, respectively. Only western China negatively impacts carbon emissions in government input, with an elasticity coefficient of 0.605. At the same time, technology and foreign investment have a positive relationship, with elasticities of −0.183 and 0.962, respectively. Northeast China only shows a negative relationship with carbon emissions in foreign investment, with an elasticity coefficient of 3.315.

It suggests that the advancement of market integration in eastern China may not substantially impact carbon emissions. Meanwhile, it may be due to the higher level of economic development in eastern China, which has achieved inter-regional opening and integration earlier than other regions of the country, and the barriers between regions are relatively low. On the contrary, factors such as technology levels and government input may have a more significant impact on carbon emissions, while we consider that changes in industrial structure may have a relatively more significant impact on regional carbon emissions. Since 2005, the strategy for the rise of central China has led to a gradual increase in inter-regional cooperation. Therefore, in this context, the further opening of the market has accelerated the flow of factors and commodity transactions in central China, which has led the region to increase the amount of carbon emissions. At the same time, along with the requirements of national green development, central and western China have gradually tightened their investment requirements. As a result, it is not difficult to understand that the western and central regions have significantly suppressed the increase in carbon emissions in terms of foreign investment, but with the deepening of regional cooperation and further breaking of market barriers in central China, the spillover of technology is more likely to be concentrated in non-high-tech. The development of the technology level in this region is mainly applied to industrial production sectors such as manufacturing, thus showing that technological progress has significantly promoted the increase in carbon emissions.

4.2.2. Analysis of the Mechanism

To examine the moderating role played by industrial structure changes in the market integration process on carbon emissions, the interaction terms of market integration and industrial rationalization, and the interaction terms of market integration and industrial advancement, are incorporated into the model, respectively. The results are shown in

Table 2. Analysis of moderator effects.

	(1)	(2)	(3)	(4)
	TCE	TCE	TCE	TCE
MI	0.0798 **	0.0949 ***	0.0629 *	0.0633 *
	(2.21)	(2.66)	(1.79)	(1.79)
INDR	0.0916	0.160
	(0.62)	(1.10)
INDH			−0.250 ***	−0.250 ***
			(−5.17)	(−5.18)
GOVI	0.501 ***	0.459 ***	0.491 ***	0.492 ***
	(4.70)	(4.37)	(4.75)	(4.76)
ZLSP	−0.0301	−0.0160	−0.0674 *	−0.0666 *
	(−0.81)	(−0.44)	(−1.84)	(−1.81)
FDI	0.169	0.0717	−0.213	−0.216
	(0.35)	(0.15)	(−0.45)	(−0.46)
GDP	−0.0637	−0.115	−0.125	−0.127
	(−0.49)	(−0.90)	(−0.99)	(−1.00)
Intreact1		0.692 ***
		(4.23)
Intreact2				0.0148 (0.26)
_cons	−0.0148	−0.118	−0.228 *	−0.230 *
	(−0.13)	(−1.00)	(−1.89)	(−1.90)
N	510	510	510	510
r2	0.765	0.774	0.778	0.778

Notes: t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

.

Columns (1) and (2) show the results of the effect of market integration on carbon emissions after considering industrial rationalization. In the main effects regression, the impact of market integration on carbon emissions is significantly negative, and the coefficient of the effect of market integration on carbon emissions is 0.0798, which means that every 1% increase in the level of market integration will lead to a 0.0798% increase in the level of carbon emissions. After adding the interaction term, the result of the interaction term is significantly negative; That is, industrial rationalization further enhances the impact of market integration on carbon emissions. This indicates that as the level of industrial rationalization among regions increases, it helps accelerate the optimization of the inter-regional division of labor and efficient collaboration, and can effectively improve the utilization of resources and energy. However, more production and consumption may increase carbon emissions and weaken the carbon reduction effect brought by improving resource and energy efficiency. Columns (3) and (4) show the results of the impact of market integration on carbon emissions after considering advanced industrialization. In the main effects regression, the effect of market integration on carbon emissions is also significantly negative; namely, every 1% increase in market integration will lead to a 0.0629% increase in carbon emissions. In addition, industrial upgrading has a significant positive effect on carbon emissions; every 1% increase in industrial upgrading will lead to a 0.25% decrease in carbon emissions.

It suggests that although industrial upgrading can play its unique advantage and role in promoting carbon emission reduction, it may play a limited role in market integration. With the promotion of market integration, different regions may have different paths and ways to achieve industrial upgrading. In promoting market integration, due to differences in levels or similar political demands, the strategic objectives and positioning of industrial restructuring may have a convergence effect. Therefore, industrial upgrading has not played a role in strengthening or weakening the impact of market integration on carbon emissions.

4.2.3. Intermediary Effect Analysis

We used industrial rationalization and industrial upgrade as mediating variables to explore whether market integration has a mediating effect on carbon emissions, and tested that market integration can additionally affect carbon emissions by influencing changes in industrial structure. The results of

Table 3. Results of mediating effects of industrial upgrades.

	(1)	(2)	(3)
	TCE	INDH	TCE
MI	0.397 ***	−0.279 ***	0.268 ***
	(4.78)	(−3.50)	(3.560)
INDH			−0.462 ***
			(−10.94)
GOVI	0.39 ***	0.957 ***	0.832 ***
	(3.19)	(8.14)	(7.120)
ZLSP	−0.245 ***	−0.0896 *	−0.287 ***
	(−4.66)	(−1.77)	(−6.050)
FDI	−4.243 ***	2.429 ***	−3.121 ***
	(−7.56)	(4.50)	(−6.080)
GDP	1.023 ***	−1.128 ***	0.502 ***
	(10.09)	(−11.57)	(4.890)
_cons	5.585 ***	0.498	5.815 ***
	(4.48)	(0.420)	(5.20)
Sobel Test	0.129 *** (3.331)
Indirect effect	0.2682
Direct effect	0.3972

Notes: t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

indicate that using industrial upgrade as a mediating variable, it passes the Sobel Test (i.e., market integration can further affect the carbon emission level by influencing the change of industrial upgrading). The results show that the proportion coefficient of the intermediary effect of market integration on carbon emissions is 0.6654. Although the impact of market integration on carbon emissions is still negative after considering the level of an industrial upgrade as a mediating variable, it can be seen from the coefficient change that the coefficient of the effect of market integration on carbon emissions decreases from 0.397 to 0.268 with the intervention of industrial upgrade (i.e., every 1% increase in market integration level will lead to a 0.268% increase in carbon emissions). However, the carbon emission level will increase by 0.397% without industrial upgrade intervention. Interestingly, in the intermediary transmission process, we find that market integration has a negative effect on industrial upgrading, which may indicate that in promoting market integration and removing barriers between regions, the rapid transformation to industrial upgrading may not be achieved in the short term. The rapid development of local trade, industry, etcetera may be preferred, which will hinder or slow down the process of industrial structure upgrading. This phenomenon can be seen in the above regional heterogeneity analysis, and the process of industrial advancement is not the same between regions. Market integration cannot achieve a pull effect on the overall industrial progress in the short term. However, it is undeniable that market integration can still weaken carbon emissions by upgrading industrial structures. The rationalization of industrial structures failed to pass the intermediary effect test, indicating that regions may still fail to take industrial rationalization as the primary choice for regional industrial development in promoting market integration. Therefore, this invisible competition relationship may still exist among regions. This relationship cannot effectively promote the scientific and reasonable change of industrial structure, and there are risks of resource reuse, resource waste, and carbon emissions in the market integration process.

4.2.4. Robustness Test

We tested the robustness of the benchmark regression by replacing variables, changing measures standards, extracting years, and other methods in

Table 4. Robustness test results.

	(1)	(2)	(3)
	TCE	TCE	TCE
MI	0.0282 ***	0.0818 **	0.0738 **
	(0.0095)	(0.0348)	(0.0369)
Control Variables	YES	YES	YES
Constant	1.9195 ***	2.4699 **	2.3137 *
	(0.3527)	(1.1846)	(1.2899)
N	510	450	450
r2		0.6822	0.7349

Notes: t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

. The results show that the impact of market integration on carbon emissions is always negative, but only slightly varies in the magnitude and coefficient of significance, indicating that the empirical results are reliable. Column (1) tests the model by the GLS method, and the results show that the effect of market integration on carbon emissions is significantly negative (i.e., every 1% increase in market integration will lead to a 0.0282% increase in the level of carbon emissions). Column (2) shows that after bringing the market integration lag into the model as the core explanatory variable, the effect of market integration on carbon emissions is still significantly negative (i.e., every 1% increase in market integration will lead to a 0.0282% increase in carbon emission level). Column (3) shows that after adjusting the data by excluding the data from 2003 and from 2019, the model regression results still indicate that the effect of market integration on carbon emissions is significantly negative (i.e., every 1% increase in market integration will lead to a 0.0738% increase in the level of carbon emissions). The results of the robustness test on the model indicate the stability of the regression analysis and the reliability of the results.

5. Conclusions

This paper adopts a two-way fixed effects analysis based on Chinese provincial panel data from 2003 to 2019. It uses a mediation model to explore the relationship between market integration and carbon emissions, the carbon emission effects generated in promoting market integration between regions, and the shock effects caused by changes in industrial structure based on industrial rationalization and upgrade. The specific conclusions are as follows:

First, market integration significantly increases carbon emissions, however, there was significant regional heterogeneity. While the advancement of market integration in eastern China may not have a substantial impact on carbon emissions, the further opening of markets in central China accelerates the flow of factors and trade of commodities in central China, making this region demonstrate characteristics of market integration significantly increasing the number of carbon emissions.

Second, market integration will increase the scale of production through industrial upgrades, which will further increase carbon emissions; the results of the moderating effect show that market integration has a significant negative contribution on carbon emissions, and that industrial structure change can play a moderating role. Industrial rationalization can further enhance this negative effect. However, industrial upgrading does not have an effective regulatory result on this effect, but it significantly positively impacts carbon emissions. Therefore, it can be seen that along with improving industrial rationalization, it is conducive to improving the efficiency of production division and accelerating the circulation of commodity factors among regions. The increase in carbon emissions brought by this scale efficiency may be much more significant than the reduction effect brought by the improvement of resource and energy utilization efficiency, thus further enhancing the impact of market integration on carbon emission. As for industrial advancement, its effects on carbon emissions are more reflected in itself, but the role played by industrial upgrades in the process of market integration may be limited.

Third, industrial upgrading can reduce carbon emissions, but there are still some obstacles to market integration in the process of promoting industrial restructuring. We find that market integration has a negative effect on industrial upgrading, which may indicate that promoting market integration between regions and breaking down inter-regional barriers may not realize the rapid transformation to industrial upgrading in the short term. Market integration cannot promote the upgrading of the overall industry in a short time. However, it is undeniable that market integration can still weaken carbon emissions by upgrading the industrial structure. The rationalization of the industrial structure failed to pass the intermediary effect test, indicating that in promoting market integration, regions may still fail to take industrial rationalization as the primary choice for regional industrial development. Therefore, this invisible competition relationship may still exist among regions. This relationship cannot effectively promote the scientific and reasonable change of industrial structure, and there are risks of resource reuse, resource waste, and carbon emissions in the process of market integration.

Based on these findings, we propose several recommendations to the Chinese government to promote the development of a low-carbon economy and achieve China’s carbon reduction targets.

First, the government should consider the two-way effect of low-carbon and market integration. With the expansion of market scale, the increase in production brought by the increase in the degree of market integration will undoubtedly lead to a short-term increase in carbon emissions. Therefore, it is necessary to take full advantage of the policy dividend of market integration to accelerate the rationalization of industrial transfer and locally advanced upgrading between areas to improve the various levels of technical exchanges, applications, and complementarities among regions.

Second, in the process of promoting market integration, each region should choose an industrial change path that is in line with the actual regional development, consolidate the various primary conditions for industrial development, achieve the valid promotion of increment in actual production and development, and effectively realize the recyclable mode of emission reduction. In particular, the central government should accelerate the implementation of diversified local assessment standards, optimize local officials’ promotion options and ways as soon as possible, and ultimately break the rough inspection system of regional economic development only.

Finally, in promoting market integration, we should fully use spillover advantages, play the role of inter-regional transmission of technological innovation, and realize a good model of increasing output without increasing carbon in the market integration process. At the same time, central and western China should further strengthen the management of foreign direct investment, attach importance to the introduction and support of green industries, and form a sustainable industrial development pattern.