Market integration and environmental quality: Evidence from the Yangtze river delta region of China

Journal of Environmental Management 261 (2020) 110208

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Research article

Market integration and environmental quality: Evidence from the Yangtze river delta region of China Ke Zhang a, Shuai Shao b, *, Shuya Fan a a b

Business School, East China University of Political Science and Law, Shanghai, 201620, China School of Urban and Regional Science, Institute of Finance and Economics Research, Shanghai University of Finance and Economics, Shanghai, 200433, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Market integration Environmental pollution emissions Relative price approach Spatial spillover effect China

Based on the panel data of 18 prefecture-level and above cities in the Yangtze River Delta region of China during the period of 2007–2016, this paper uses a relative price approach to calculate the degree of market integration (segmentation), and further adopts the dynamic spatial panel Durbin model and the generalized spatial two-stage least squares method to investigate the effect and its mechanism of market integration on environmental pollution. The results show that the degree of market integration and the total emissions, per capita emissions, and emission intensity of three types of pollutants (i.e., sulfur dioxide, industrial wastewater, and industrial smoke and dust) all show an inverted “U-shaped” curve relationship. When market integration exceeds a certain critical level, market integration will have an emission-reduction effect on these three types of pollutants. Most cities in the Yangtze River Delta region are in an emission-reduction state of market integration. Market inte­ gration facilitates strengthening the emission-reduction effects of technological innovation, environmental regulation, and energy efficiency. Moreover, both environmental pollution and market integration have a sig­ nificant spatial spillover effect. The market integration in neighboring regions is conducive to reducing local pollution emissions. We suggest that China should accelerate market-oriented reform and promote regional market integration, so as to make full use of the emission-reduction effect of market integration.

1. Introduction Since the implementation of reform and opening-up in 1978, China’s market-oriented reform has made great progress in promoting economic development. At the same time, China’s regional market integration has also achieved remarkable results. The Yangtze River Delta region has witnessed the highest degree of market integration in China (Ke, 2015). Market integration is at the core of regional integration, and regional integration strategy has become an important policy tool in the effort to promote China’s regional coordinated development (Zhang, 2018). In 2010, the Guiding Opinions of the State Council on Further Promoting the Reform and Opening Up and Economic and Social Development in the Yangtze River Delta Region was issued and implemented. This document regards the promotion of regional integration as an important devel­ opment goal in the Yangtze River Delta region. Subsequently, the Yangtze River Delta region established the Economic Coordination Committee of Cities in the Yangtze River Delta Region, as well as other cooperation platforms, as a means to promote market integration. Furthermore, in order to contribute to the progress of higher-quality

integration in the Yangtze River Delta region, Shanghai led Jiangsu, Zhejiang and Anhui provinces to set up the Yangtze River Delta Regional Cooperation Office in 2018. With the rapid economic development and the improvement of market integration, the environmental pollution problem in the Yangtze River Delta region has become increasingly prominent (Ming et al., 2017). Environmental pollution control is now one of the key issues facing the region. To some extent, environmental pollution also affects the quality of economic development in the Yangtze River Delta region; pollution also has a negative impact on the life and health of the resi­ dents. Will the continuous improvement of market integration in the Yangtze River Delta region have an important impact on the region’s environmental quality? Theoretically, regional market integration should have a positive impact on environmental pollution (Zhang, 2018). First, the factor mobility and resource allocation efficiency between regions in the pro­ cess of market integration are constantly increasing. On the one hand, the free flow of goods and factors increases the efficiency of resource allocation. On the other hand, marketization promotes the spatial

* Corresponding author. E-mail address: [email protected] (S. Shao). https://doi.org/10.1016/j.jenvman.2020.110208 Received 3 March 2019; Received in revised form 17 January 2020; Accepted 26 January 2020 0301-4797/© 2020 Elsevier Ltd. All rights reserved.

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Journal of Environmental Management 261 (2020) 110208

researches mainly focus on the relationships between regional integra­ tion, marketization, trade integration, industrial agglomeration and environmental pollution. First, in recent years, the environmental effects of regional integra­ tion have attracted the attention of some scholars. For example, Li and Lin (2017) found that regional integration between provinces in China significantly improved energy efficiency and the environment. He et al. (2018) held that the marginal cost of carbon emissions was significantly increased through energy consumption labor productivity and the associated technological advances in China’s provincial economic inte­ gration. Zhang (2018) studied the effect of regional integration on pollution emission convergence in a framework of growth convergence. The study concludes that regional integration significantly promotes the convergence of pollution emission intensity and is beneficial to emission reduction. There are also scholars who approach this issue from the market integration perspective, or the opposite of the market segmen­ tation1 perspective. Market segmentation intensifies competition be­ tween regions, making it difficult for the different regions to reach any agreement on environmental regulations. Local governments sacrifice the ecological environment to increase incentives for economic growth, reducing the threshold of environmental regulation and environmental law enforcement. This can even lead to environmental regulations being a “race-to-the-bottom” state (Bai et al., 2018). Market segmentation also hinders the diffusion and spillover effect of technological innovation and regional technical cooperation. Local protectionism and market segmentation make it difficult for new energy-saving and emission-reduction technologies to be applied and popularized in a market-oriented way. This is not conducive to the improvement of energy use efficiency (Chen and Huang, 2014). Market segmentation between regions is serious; the flow of factors between regions is blocked, the spatial mismatch of resources is prominent, and the relationship between regions is mainly in the competition for growth, the competition in attracting investment, the convergence of industrial structure and repetitive investment. Energy use efficiency is low (Chen and Huang, 2014). In addition to market integration, the overall connotation of regional integration includes transportation integration, industrial employment integration, public service integra­ tion and so on. Therefore, the conclusions of such studies do not directly confirm the relationship between market integration and environmental pollution. Some studies have indirectly confirmed the impact of market integration on environmental pollution. As an important embodiment of inter-country trade market integration, Kellenberg (2009) found that the impact of foreign direct investment (FDI) on the environment depended on the level of marketization. Forslid et al. (2017) maintained that the impact of industrial agglomeration on the environment in the process of regional integration also depended on marketization. Second, the impact of trade integration on environmental pollution is also the focus of some existing studies. Trade integration is the embodiment of market integration at national level. It is generally believed that the impact of international trade on environmental pollution mainly has a scale effect, structural effect, and technological effect (Kellenberg, 2009). Chen and Huang (2014) examined the envi­ ronmental effects of EU trade integration and found that trade integra­ tion significantly improved the environmental quality. The environmental quality was also improved for the same country after joining the EU. Joining the European Union (EU), a trade liberalization region, can improve the environmental quality of both developed and less-developed countries. Hubbard (2014) asserted that the impact of trade integration on environmental pollution was closely related to the degree of the negative effect of regional pollution. Forslid et al. (2017)

realignment of economic activities, which in turn leads to different en­ ergy consumption and pollution-emission requirements (Zhang, 2018). From a microscopic point of view, the free flow of factors is conducive to optimizing the proportion of different factors in the production process. For example, an increase in the proportion of technology and knowledge will effectively reduce the pollution emissions in the production process (He et al., 2018). Second, the integration of the technology market is conducive to the application and promotion of new technologies and is therefore also conducive to emission reduction. For example, new environmental protection technologies can be rapidly spread and applied throughout intermediary technology markets and regional cooperation channels, and this is conducive to coordinated emission-reduction between regions (Shao et al., 2019). Third, market integration is conducive to the formation of uniform industry and product environmental standards (such as the formation of higher product environmental standards), which is conducive to reducing the pollution emissions caused from product use and consumption (Duanmu et al., 2018). Fourth, market integration is conducive to improving the division of industry specialization between regions. Fifth, the integration of resource and energy markets is conducive to improving the efficiency of energy use. Market-oriented reforms of resource products (such as coal) are beneficial to the role of price leverage, optimizing the energy con­ sumption structure and reducing pollution emissions (Li and Lin, 2017). Sixth, the market integration of pollution governance directly helps to restrict the pollution-emission behavior of enterprises. Market integra­ tion is conducive to the establishment of a unified pollution-emission rights trading market across regions, thereby giving play to the con­ straints on the corporate emissions of market-based tools other than government environmental regulation (Bai et al., 2018). An example of such a market-based tool would be the carbon trading market, which is conducive to encouraging companies to reduce emissions. Therefore, market integration may have a certain role to play in improving environmental quality. However, the existing research on how market integration affects environmental quality is yet to receive the necessary attention. From a practical point of view, the study of the relationship between market integration and environmental pollution has great realistic ap­ plications. The report of the Nineteenth National Congress of the Communist Party of China put forward the goals of building a beautiful China and an ecological civilization. At the same time, the regional coordinated development is considered as one of the important foun­ dations for the construction of a modern economic system. As the core of regional integration, increasing market integration is an important way to break administrative divisions and establish benign regional coop­ erative relationships. Therefore, studying the relationship between market integration and environmental pollution can provide both theoretical and empirical evidence for the construction of an ecological civilization and a regional coordinated development strategy. If market integration has the ex­ pected emission reduction effect, then this shows that China’s current regional coordinated development strategy and ecological civilization construction strategy coincide with each other. The promotion of market integration may have the dual policy dividend of promoting regional coordinated development and energy saving and emission reduction at the same time. If market integration exacerbates environmental pollu­ tion, then this shows that China’s regional coordinated development strategy has certain ecological and environmental costs. Therefore, it is necessary to re-examine the regional coordinated development strategy from the perspective of environmental protection. Based on the above theoretical and practical considerations, this paper will use the panel data of prefecture-level and above cities in the Yangtze River Delta re­ gion in China to verify the environmental effect of market integration and its influencing mechanism. At present, very few studies have examined the relationship between market integration and environmental pollution. In addition, the related

1 Market segmentation and market integration are opposite concepts. The higher the level of market integration is, the lower the level of market seg­ mentation will be, and the smaller the resistance of the free flow of factors (Ke, 2015).

2

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Journal of Environmental Management 261 (2020) 110208

argued that the impact of trade liberalization on pollution emissions depended on market size, tax policies, and other conditions. Anoulise (2016) examined the relationship between trade integration and envi­ ronmental pollution from the perspective of strategic interaction be­ tween countries. The author found that there was a nonlinear relationship between trade integration and environmental pollution. It should be noted that there are significant differences between trade integration among countries and the integration of domestic markets. In addition, geographical and cultural differences, economic development differences, and regional divisions between countries are much higher than the differences and divisions between domestic regions. Therefore, the impact of trade integration on the environment may be different from that of domestic market integration. First of all, domestic market integration does not involve tariff and trade policies. The main obstacles to regional market integration within a country are regional adminis­ trative divisions and local protectionism. Second, other factors affect domestic market integration, including spatial distances, language and cultural differences, technical differences, and environmental policies. The impact of trade integration mechanisms on environmental pollution is in the forms of scale effect, structural effect, and technical effect (Kellenberg, 2009). Meanwhile, the impact of domestic market inte­ gration on environmental pollution is mainly based on technology, en­ ergy efficiency, and environmental policies implemented under regional administrative divisions. Third, the impact of industrial agglomeration on the environment during market integration process has been investigated in some pre­ vious studies. The essence of market integration is the free flow of production factors and products between regions (Onduko, 2013). At the micro level, the flow of factors is related to the location of enter­ prises; the middle level is the spatial transfer of industries. Industrial transfer is ultimately manifested as industrial agglomeration in space, accompanied by the spatial transfer of pollution. Therefore, the rela­ tionship between industrial agglomeration and environmental pollution can, to some extent, reflect the relationship between market integration and environmental pollution. One view is that industrial agglomeration has aggravated the environmental pollution problem. Frank (2001) and Verhoef and Nijkamp (2002) found that industrial agglomeration was an important cause of environmental pollution. Another view holds that agglomeration alleviates environmental pollution. For example, Borrego et al. (2006) and Rodriguez et al. (2016) argued that compact cities were more conducive to air quality improvement than dispersed low-density cities. Lu and Feng (2014) found that the spatial agglomeration of population and economic activities could reduce pollution emission intensity. A third view is that there is a nonlinear relationship between industrial agglomeration and environmental pollution. For example, Newman and Kenworthy (1989), Kamal-Chaoui and Robert (2009), and Glaser and Kahn (2010) all concluded that increasing urban economic density was conducive to reducing carbon emissions, and that there was a nonlinear relationship between them. The concentration of economic activities is conducive to improving energy efficiency. The inconsistency of related conclusions as described above also shows that the relation­ ship between market integration and environmental pollution may not be a simple linear relationship. To sum up, the research on the relationship between market inte­ gration and environmental pollution still does not appear. Based on data from 18 cities in the Yangtze River Delta region from 2007 to 2016, this paper uses a relative price approach to calculate the degree of market segmentation. We further investigate the effect of market integration on environmental pollution for the first time using a dynamic spatial panel Durbin model (DSPDM) and the generalized spatial two-stage least squares (GS2SLS) method. Specifically, the contributions of this paper are mainly reflected in the following aspects. (1) This paper not only proves that regional integration has a nonlinear influence on pollution emissions, but also empirically tests the mechanism of the market in­ tegration’s emission-reduction effect. By doing these, we for the first time provide a literature support and empirical evidence on the

relationship between market integration and environmental pollution. (2) In this paper, we conduct an empirical analysis under the framework of spatial measurement and considering the spatial spillover effect of pollution emissions. At the same time, the effects of market integration on local pollution emissions in neighboring regions are investigated, to enable us to obtain accurate results. (3) Controlling the endogenous problem with the DSPDM and the GS2SLS method, we use three pol­ lutants and several pollution-emission indexes to conduct the empirical test. Therefore, a robust and convincing conclusion can be drawn. The rest of this paper is organized as follows. Section 2 analyzes the theoretical mechanism and proposes the research hypothesis. Section 3 introduces the empirical strategy, including the setting of empirical model and the selection of data and indicators. Section 4 provides and discusses the empirical results. Section 5 presents conclusions and policy implication. 2. Theoretical hypothesis 2.1. Impact of market integration on environmental pollution When the degree of market integration is low, the market segmen­ tation between regions is in a serious state. The flows of economic fac­ tors between regions are blocked, and the spatial mismatch of resources is prominent. Market segmentation intensifies the competition between regions, making it difficult to reach agreement on environmental regu­ lations. Local governments sacrifice the ecological environment to in­ crease incentives for economic growth by reducing the threshold of environmental regulation and environmental law enforcement. This may even cause a “race-to-the-bottom” state of environmental regula­ tions (Bai et al., 2018). To some extent, market segmentation will hinder the diffusion and spillover effects of technological innovation and regional technical cooperation. When the level of market integration is high, the flows of factors between regions is freer. The degree of market convergence and interdependence of economic development between regions is higher; the synergy degree of economic development between regions is also higher. New energy-saving and emission-reduction technology can quickly achieve cross-regional popularization and application. The high levels of market integration and market competition are also conducive to the development of the intermediary technology market and the development of innovation energy-saving and emission-reduction technologies (Duanmu et al., 2018). At the same time, the new energy-saving and emission-reduction technologies can improve energy efficiency and promote energy saving and emission reduction. The willingness to engage in regional environmental protection cooperation and joint pollution control will be strengthened. Environmental regu­ lations will also tend to be more consistent, and having similar envi­ ronmental regulations will benefit the common effort to achieve emission reductions between regions (Zhang, 2018). Market-oriented pollution control is also beneficial to the realization of coordinated regional emission reductions and the sharing of pollution control ben­ efits between regions (Lin and Du, 2015). Such a high degree of mar­ ketization will facilitate the establishment of regional pollution emission trading markets and market-oriented third-party pollution control. The agglomeration and dispersion of factors in different spaces during either regional integration or market integration will have an impact on environmental pollution. The impact of market integration on the flows and spatial agglomeration of factors and the agglomeration and dispersion of economic activities will have different effects on environmental pollution (Wu and Reimer, 2016). Grazi et al. (2016) argued that spatial agglomeration in the process of market integration is an important factor that affects the spatial distribution of environmental pollution. In addition, Grazi et al. (2016) claimed that the endogenous “market-density effect” attracts micro-enterprise spatial agglomeration and causes differences in the spatial distribution of pollution emissions. As a result, we can propose the following hypothesis: 3

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Journal of Environmental Management 261 (2020) 110208

factor, then market integration is conducive to improving the efficiency of energy use, and energy efficiency is conducive to emission reduction (Shao et al., 2018). Market integration is also conducive to the mar­ ketization of resource and energy prices, which will in turn force en­ terprises to use clean energy to reduce production costs and reduce pollution emissions. Market integration helps eliminate distortion in factor markets and improve energy efficiency. Therefore, market inte­ gration is conducive to decreasing high-energy and high-pollution-type energy products, and to optimizing the energy consumption structure in the region. This may enhance the emission-reduction effect of energy efficiency. In summary, we can put forward the following hypothesis:

Hypothesis 1. The relationship between market integration and environmental pollution has an inverted “U-shaped” curve, i.e., when the level of market integration is low, market integration will aggravate environmental pollution, and when market integration exceeds a certain critical level, market integration will exhibit an emission-reduction effect. 2.2. The mechanism First of all, market integration will strengthen the emission reduction effect of technological innovation. In market integration process, the free flows of factors improve the efficiency of resource allocation. In terms of technological innovation, market integration can promote the flows of technological factors and technology spillover effects (Goldman et al., 2016), including the cross-regional application and imitation of energy-saving and emission-reduction technologies. Market integration provides favorable conditions for the cultivation and development of cross-regional technology markets. For example, by establishing a cross-regional intermediary technology market and technology trading platform, the optimal spatial allocation of technologies can be realized. At the same time, regional market integration is beneficial in terms of reducing innovation costs and risks (Zhang et al., 2018). For example, the free flow of factors in the market integration process is manifested as the spatial transfer of enterprises and industries and the agglomeration and dispersion of economic activities. The agglomeration of enterprises is conducive to the development of scale economies and the externalities of agglomeration. Hence, market integration can bring into play the sharing, matching and learning effects of agglomeration and reduce enterprises’ R&D costs and innovation risks (Andersson et al., 2009). In addition, market integration is conducive to fostering technical coop­ eration between regional innovation bodies, inter-regional enterprises, and local governments. Research institutes also can use marketization to achieve the sharing of R&D investment and innovation income and thus give full play to the emission reduction effects of technological inno­ vation (He et al., 2018). In summary, market integration can strengthen the emission-reduction effect of technological innovation, to a certain extent. Second, market integration will strengthen the emission reduction effect of environmental regulation. Market integration will enhance the degree of market convergence and economic dependence between re­ gions. An improvement in market integration and the degree of eco­ nomic correlation will also be beneficial to the construction of an interregional community of interests. Environmental pollution has a typical transboundary effect. The joint prevention and control of environmental pollution is not only an objective need, but also a need that is directly linked to the public interest demands of local governments (Li and Lin, 2017). On the one hand, in regional integration process, with market integration as the core, the environmental policies and environmental protection standards between regions are gradually unified or close to each other. This causes more consistent pollution control costs for en­ terprises, which in turn avoids or at least diminish the transfer of pollution-intensive industries between regions. It is propitious to carry out synergistic treatment of environmental pollution between regions (Zhang, 2018). On the other hand, inter-regional pollution control and emission reduction incentives can be implemented by means of market-oriented tools. Examples of such tools include the construction of cross-regional emissions trading markets and the development of market-oriented third-party pollution controls which make full use of financial market instruments to curb enterprises’ emission behavior (Lin and Du, 2015). Therefore, market integration can provide an effective way to divide environmental pollution control responsibilities and share the ensuing environmental benefits between different regions. These benefits may strengthen the emission-reduction effect of environmental regulations. Finally, market integration will strengthen the emission reduction effect of energy efficiency. If we regard energy as a special production

Hypothesis 2. Market integration will inhibit environmental pollution by strengthening the emission-reduction effects of technological inno­ vation, environmental regulation, and improved energy efficiency. The above two hypotheses are tested in this paper using the panel data of 18 cities in the Yangtze River Delta region of China. 3. Methodology and data 3.1. Benchmark model Considering that environmental pollution has typically geographic cross-border effects and spatial correlation (Qian, 2014), ignoring the spatial correlation of environmental pollution can cause the problems of missing variables. At the same time, considering a time lag effect of the changes in pollution emissions, we introduce the first-order lag term of pollution emissions into the regression equation. The spatial correlation may come from either the explained variables or from the explanatory variables and error terms. The dynamic spatial panel Durbin model (DSPDM) can simultaneously examine the spatial correlation between the explained variable and the explanatory variables. Compared with the spatial lag model (SLM) and the spatial error model (SEM), the DSPDM can effectively prevent missing variables. At the same time, the endogenous problem in this model can also be solved, to some extent. Therefore, this paper uses the DSPDM to perform the empirical research. Without loss of generality, we use the most commonly-used geograph­ ical binary spatial weight matrix. Thus, specific econometric model is built as follows: pit ¼ β0 þ β1 pi;t

1

þ ρ1

n X i¼1

þ ρ3

n X i¼1

wij pjt þ β2 miit þ β3 smiit þ ρ2

n X

wij mijt þ λXit i¼1

wij Xjt þ ui þ εit (1)

where i and t denote the city and year, respectively; p means the pollution emissions, including the total emissions, per capita emissions, and emission intensity of sulfur dioxide (SO2), industrial wastewater (pw), and industrial smoke and dust (pd); mi is the degree of market segmentation. The greater the degree of market segmentation is, the lower the degree of market integration will be. In order to investigate the possible nonlinear relationship between market integration and pollu­ tion emissions, we add the quadratic term smi of market segmentation index. Here, X represents a set of control variables that may affect environmental pollution. Parameter ρ1 , ρ2 , and ρ3 can capture the spatial spillover effects of pollution emissions, market integration, and other control variables, respectively. We focus on the symbols and salience of β2 and β3 . If β2 < 0, β3 > 0, and both are significant, the relationship between market segmentation and pollution emissions will display a “U-shaped” curve. That is to say, market integration and pollution emissions show an inverted “U-shaped” curve relationship.

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3.2. Research sample

3.3. Variable selection

The samples in this paper are 18 prefecture-level and above cities in the Yangtze River Delta region (Shanghai, Nanjing, Zhenjiang, Suzhou, Changzhou, Wuxi, Yangzhou, Taizhou (in Jiangsu), Nantong, Yancheng, Hangzhou, Jiaxing, Shaoxing, Ningbo, Zhoushan, Jinhua, Taizhou (in Zhejiang), and Huzhou). The reasons for selecting only 18 cities in this paper are as follows: first, the above 18 cities appear in the “Yangtze River Delta Urban Agglomeration Development Plan”, which was announced by the National Development and Reform Commission in 2016. They are the core cities in the Yangtze River Delta region. Second, given the limited availability of data in several cities, such as Lishui and Xuzhou, there are serious shortages in the data of the eight major commodity price indexes. Third, from a geographical perspective, the above 18 cities have the similar characteristics of humanities and ge­ ography, and the degree of economic integration is relatively high. This is also a region with more prominent environmental problems, which can more easily convey the environmental effects of market integration. Most of the data of these 18 cities before 2007 are missing. Considering the uniformity, completeness, and availability of data, the sample time of this paper is chosen from 2007 to 2016. The used data are derived from the China City Statistical Yearbook and the statistical yearbooks of various cities. The Yangtze River Delta region is the region with the highest degree of both integration and environ­ mental pollution (Zhang, 2018). The market integration process in the Yangtze River Delta region has changed from a low to relatively high degree of integration. The environmental pollution in the Yangtze River Delta region has also changed during the study period. moving from serious pollution to the present state of improvement (Zhang, 2018). Therefore, the Yangtze River Delta region is an ideal sample to study the relationship between market integration and environmental pollution.

Considering the need for robustness analysis, three indexes (i.e., the total emissions, per capita emissions, and emission intensity of sulfur dioxide, industrial wastewater, and industrial smoke and dust) are adopted to reflect the degree of environmental pollution. The degree of market integration is measured by a market segmentation index. Given that many factors affect environmental pollution, a group of control variables X are added, including regional development level (pgdp), technology innovation (pat), industrial structure (ind), environmental regulation (pr), opening degree (open), and energy efficiency (ee) (see Table 1). 3.4. Measurement and statistical observation of market integration Market segmentation and market integration are two relative con­ cepts. The more serious the market segmentation is, the lower the degree of market integration will be, and vice versa. Therefore, a market seg­ mentation index (mi) can reflect the degree of market integration. The measurement methods of market segmentation include the input-output based trade flow method, specialization index method, and relative price method. The relative price method based on the law of one price can directly reflect the integration degree and dynamic trend of the commodity market. The data needed for this method are easy to obtain. Therefore, the relative price method is used to calculate the market segmentation index in this paper. With the increasing degree of market integration, the transportation costs and market segmentation between regions will continue to decline; the relative price differences between regions will also show a narrowing trend. The relative price method uses three-dimensional (n�t�k) panel data, where n represents the city, t represents the time, and k represents the type of commodity. The sample is 18 cities in the Yangtze River Delta region (n¼18). In view of the integrity and availability of data, we choose eight types of commodities, including food, beverages, tobacco and liquor, garments, shoes and hats,

Table 1 Descriptive statistics of variables. (1) Regional development level (pgdp): we use per capita GDP to measure this variable. The classical environmental Kuznets curve (EKC) hypothesis holds that the relationship between per capita income and environmental pollution is an inverted “U-shaped” curve (Grossman and Krueger, 1995). Therefore, the quadratic spgdp of per capita GDP is introduced into the regression model. (2) Technological innovation (pat): we use the amount of granting patents of 10000 persons to measure this variable. On the one hand, technological innovation may improve energy efficiency and thus reduce pollution emissions in production process (Zhang et al., 2013). On the other hand, technological progress may enhance productivity and increase scale to aggravate environmental pollution (Arouri et al., 2012). (3) Industrial structure (ind): we use the share of the secondary industry added value in GDP to measure this variable. The secondary industry is an important source of pollution emissions. The larger the proportion of industry in the industrial structure is, the more serious the pollution will be (Qian, 2014). (4) Environmental regulations (pr): we use the proportion of environmental investment to GDP to measure this variable. Higher environmental standards will force enterprises to reduce emissions or re-locate. Environmental regulation is also an important factor that affects the emission behavior of enterprises (Popp, 2006). (5) Opening degree (open): we use the share of FDI in GDP to measure this variable. The higher the opening degree is, the easier it is will be to attract foreign enterprises. Foreign enterprises have relatively advanced environmental protection technologies and concepts, which are conducive to emission reduction (He, 2006). (6) Energy efficiency (ee): we use the ratio of industrial output value to industrial energy consumption with the unit of tons of standard coal to measure this variable. Improving energy efficiency plays an important role in reducing environmental pollution (Zhang et al., 2013). Variable

Name

Observation

Unit

Mean

Standard deviation

Minimum

Maximum

Total SO2 emissions Per capita SO2 emissions SO2 emission intensity Total industrial wastewater discharge Per capita industrial wastewater discharge Industrial wastewater discharge intensity Total industrial smoke and dust emissions Per capita industrial smoke and dust emissions Industrial smoke and dust emission intensity Market segmentation degree Regional development level Technical innovation Industrial structure Environmental regulation Opening degree Energy efficiency

SO2 pcSO2 iSO2 pw pcpw ipw pd pcpd ipd mi pgdp pat ind pr open ee

180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180

Tons Tons/person Tons/yuan 10000 tons 10000 tons/person 10000 tons/yuan Tons Tons/person Tons/yuan N.A. Yuan/person Number of Patents/10000 persons % % % 10000 yuan/ton of standard coal

73367 0.014 0.002 22148 0.004 0.001 31845 0.006 0.001 0.066 86808 30.716 0.504 0.004 0.005 4.613

64029 0.009 0.001 17697 0.003 0.000 22760 0.004 0.001 0.053 44645 26.500 0.059 0.006 0.003 2.450

1925 0.001 0.000 1439 0.001 0.000 2066 0.001 0.000 0.037 17796 0.481 0.298 0.001 0.000 0.930

496377 0.078 0.007 80468 0.012 0.002 131433 0.031 0.004 0.286 145771 151.705 0.621 0.030 0.014 13.736

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pollution emissions

household appliances and music and video equipment, cultural and of­ fice appliances, articles for daily use, transportation and communication applications, and building materials and hardware, as typical com­ modities. First, 18 cities are paired. Then, the retail price index (P) of each type of commodity is compared with the first-order difference of relative prices to obtain ΔQkijt as follows: ! ! ! ! Pkjt Pki;t 1 Pk Pk ΔQkijt ¼ ln k it ln k ¼ ln itk ln k (2) Pi;t 1 Pi;j 1 Pjt Pi;j 1 � � � � Next, the absolute value �ΔQkijt � is logarithmically processed to alle­

Wastewater per capita discharge Wastewater discharge intensity Total wastewater discharge MI

viate the heteroscedasticity and skewness of the data. Based on the commodity retail price index data of eight types of commodities in 18 cities from 2007 to 2016, the relative prices of 12240 differential forms (8*153*10) can be obtained, of which the paired cities are C218 ¼ 153. can be acquired by � � � k� �ΔQijt �:

� k� �ΔQ � t

(3)

Based the value of Rkijt , we can judge the fluctuations in market prices

in different cities. The larger the standard deviation of Rkijt (VarðRkijt Þ) is, the more serious the market segmentation will be, and the lower the degree of market integration will be (Ke, 2015). We merge the price differentials of eight types of commodities mentioned above. Finally, we P can get a city-level market segmentation index miijt ¼ 8k¼1 VarðRkijt Þ. In order to initially investigate the relationship between market integration and environmental pollution in the Yangtze River Delta re­ gion, this paper takes the annual average of the market segmentation index of 18 sample cities as a representation of the degree of market integration in the Yangtze River Delta region. As shown in Figs. 1–3, the market segmentation index of the Yangtze River Delta region in 2007–2016 shows a slowly decreasing trend. That is to say, the degree of market integration is increasing. At the same time, the marketization of the Yangtze River Delta region was continuing to increase during the period of 2007–2016, the emission indicators of the three pollutants showed an overall downward trend. In other words, the market inte­ gration and the overall trend of pollution emissions were negatively correlated. However, many practical factors affect pollution emissions. We cannot now conclude that a causal relationship exists between market integration and pollution emissions. Nevertheless, this trend is consistent with our expectation. In recent years, the degree of market integration in the Yangtze River Delta region has been increasing. The commodity price index in the Yangtze River Delta region has generally shown a convergence trend, and the environmental quality in the Yangtze River Delta region has also improved, to a certain extent. We will further verify the relationship between market integration and environmental pollution through econometric analysis methods.

Pollution emissions

0.068

0.2

0.066

0.1

0.064

0

0.062 Sulfur dioxide emissions per capita Sulfur dioxide emission intensity Total sulfur dioxide emissions MI

0.069 0.068 0.067 0.066 0.065 0.064 0.063

0.1 0.05 0 Smoke and dust emissions per capita Smoke emission intensity Total smoke and dust emission MI

Fig. 3. Trends of market segmentation index and industrial smoke and dust emissions. Note: The unit of per capita industrial smoke and dust emissions is tons/person; the unit of industrial smoke and dust emission intensity is tons/ million yuan; the unit of the total smoke and dust emissions is tons*10 5.

4. Results and discussion 4.1. Test of Hypothesis 1 To conduct a robustness and comparative analysis, we also report the estimation results of the generalized spatial two-stage least squares (GS2SLS). In order to overcome the inconsistent estimation problem and the defects of poor robustness when the maximum likelihood estimation does not meet the independent identical distribution (i.i.d) assumption, the advantage of the GS2SLS is that, even if heteroscedastic and nonnormal distributions exist, the same consistent estimate can be ob­ tained. The GS2SLS uses the spatial lag term as an instrumental variable, and the two-stage least squares (2SLS) can be used to obtain a consistent estimate. Specifically, the GS2SLS performs spatially generalized least squares (GLS) after the 2SLS estimate. That is to say, a spatial CochraneOrcutt transform is performed. Detailed steps for the GS2SLS can be found in Kelejian and Prucha (1998). Before the parameter estimation, the spatial correlation test of the residuals of the corresponding ordinary least squares (OLS) estimates should be carried out. As shown in Table 2, the same as the results of the Moran’s index (Moran’s I), the values of LM-lag and LM-err tests are all statistically significant at least at a 5% level, indicating that spatial correlation exists between the pollution discharges of the explanatory variables. The LM (robust) statistic of the SLM is significant at least at a 5% level, while the LM (robust) statistic of the SEM is not significant in most cases, indicating that the SLM is superior to the SEM. In order to further test whether the spatial Durbin model (SDM) can be degraded to the SLM or the SEM, the Wald and LR tests are also performed in this paper. As shown in Table 2, the Wald and LR statistics are both signif­ icant at least at a 5% level, indicating that the SDM is a suitable choice. Moreover, the correlation coefficients between explanatory variables

Market segmentation index

0.07

0.3

0.15

Market segmentation index

� � � � Rkijt ¼ �ΔQkijt �

Fig. 2. Trends of market segmentation index and industrial wastewater discharge. Note: The unit of per capita industrial wastewater discharge is 10000 tons/person; the unit of industrial wastewater discharge intensity is tons/yuan; the unit of the total wastewater discharge is tons*10 5.

Pollution emissions

According to Shao et al. (2018), dispersion Rkijt � � removing the average price dispersion �ΔQkt � from

0.069 0.068 0.067 0.066 0.065 0.064 0.063

0.12 0.1 0.08 0.06 0.04 0.02 0

Market segmentation index

Journal of Environmental Management 261 (2020) 110208

Fig. 1. Trends of market segmentation index and sulfur dioxide emissions. Note: The unit of per capita sulfur dioxide emissions is tons/person; the unit of sulfur dioxide emission intensity is tons/million yuan; the unit of the total sulfur dioxide emissions is tons*10 5. 6

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Table 2 Spatial correlation and results of LM and LR tests. Moran’s I LM-lag LM-lag (robust) LM-err LM-err (robust) Wald (lag) LR-lag Wald (err) LR-err

so2

pcso2

iso2

pw

pcpw

ipw

pd

pcpd

ipd

0.301*** [0.00] 8.226*** [0.00] 2.120** [0.04] 2.952* [0.08] 0.359* [0.41] 17.301*** [0.00] 22.320** [0.00] 15.114** [0.04] 18.230** [0.02]

0.214** [0.04] 6.175** [0.02] 3.655** [0.05] 1.147 [0.21] 1.022 [0.29] 19.231** [0.02] 29.112** [0.03] 19.022** [0.02] 22.369** [0.01]

0.182** [0.03] 10.124** [0.03] 1.786** [0.04] 7.335*** [0.00] 2.145* [0.08] 8.320** [0.03] 11.306** [0.04] 10.104** [0.05] 20.320** [0.03]

0.185** [0.05] 9.020*** [0.00] 4.512*** [0.00] 2.121* [0.08] 1.332 [0.18] 23.114** [0.01] 27.234** [0.02] 16.855** [0.02] 21.034** [0.01]

0.232** [0.02] 8.119** [0.04] 6.250** [0.02] 2.024* [0.07] 1.147 [0.22] 8.244*** [0.00] 9.013*** [0.00] 7.384*** [0.00] 8.320** [0.02]

0.241** [0.03] 6.303** [0.03] 3.342** [0.03] 1.542 [0.35] 1.032 [0.44] 7.320** [0.04] 9.652** [0.01] 6.142** [0.03] 7.058** [0.03]

0.356*** [0.00] 8.006*** [0.00] 4.165*** [0.00] 1.514* [0.09] 1.236 [0.19] 11.148** [0.02] 18.228** [0.01] 16.632** [0.04] 19.205** [0.01]

0.296** [0.03] 7.602*** [0.00] 3.129** [0.03] 2.021** [0.03] 1.141 [0.20] 10.335** [0.01] 15.004** [0.01] 10.320** [0.03] 15.204** [0.02]

0.301** [0.04] 6.115** [0.03] 1.792** [0.04] 1.620 [0.41] 0.912 [0.56] 15.620*** [0.00] 17.210*** [0.00] 14.112*** [0.00] 16.302** [0.01]

Notes: The values in brackets are probabilities; ***, **, and * represent significant levels of 1%, 5%, and 10%, respectively; LM and LR denote the Lagrange multiplier test and the likelihood ratio test, respectively; lag and err denote the SLM and the SEM, respectively.

are less than 0.6, while the variance inflation factor is less than 10, and thus the multicollinearity problem can be ignored. In addition, the variables for non-percentage (proportional) measures are taken as nat­ ural logarithms, in order to reduce the degree of data dispersion. The regression results in Tables 3–5 show that the estimation co­ efficients’ signs of the core variables (mi and its quadratic term smi) in the DSPDM are consistent with those based on the GS2SLS method, which are only slightly different in significant levels. The coefficient of market segmentation index (mi) is negative and significant at least at a 10% level, while the coefficient of its quadratic term (smi) is positive and significant at least at a 10% level. This finding shows that a “U-shaped” curve relationship exists between the market segmentation and these

three types of pollution emissions (reflected by total emissions, per capita emissions, and emission intensity). In other words, the degree of market segmentation within a certain range will be conducive to emis­ sion reduction. Since the opposite of market segmentation is market integration, such results also indicate that market integration and these three types of pollution emissions all show an inverted “U-shaped” curve relationship. That is to say, as the degree of market integration continues to increase, pollution emissions present a trend of first increasing and then decreasing. Thus, Hypothesis 1 is verified. When the degree of market integration exceeds a certain critical level, it will promote environmental quality improvement. In the Yangtze River Delta region, the degree of market integration has

Table 3 Impact of market integration on sulfur dioxide emissions. Variable

Total sulfur dioxide emissions (SO2)

Per capita sulfur dioxide emissions (pcSO2)

Sulfur dioxide emission intensity (iSO2)

GS2SLS

GS2SLS

GS2SLS

L.SO2 mi smi pgdp spgdp pat ind pr open ee w.SO2 w.mi Constant Observations Adj. R2

4.150* (-1.72) 0.496** (2.32) 4.371 (1.01) 0.218 (-1.10) 0.003* (-1.78) 3.955 (1.20) 0.016* (-1.70) 0.015 (-0.46) 0.609** (-1.98) 0.214*** (6.39) 5.526 (-0.23) 180 0.615

DSPDM 0.085 (0.76) 2.216** (-2.30) 0.271* (1.78) 8.472** (2.31) 0.410** (2.31) 0.002* (-1.66) 3.401 (1.09) 0.032* (-1.89) 0.001 (-0.02) 1.360*** (-3.04) 0.229*** (18.19) 0.160** (2.30) 7.372 (-1.05) 180 0.744

DSPDM 0.071 (0.76) 3.241* (1.75) 0.402** (2.16) 1.982 (1.55) 0.150 (-1.46) 0.007* (-1.89) 11.345** (2.29) 0.037* (-1.68) 0.066 (-0.45) 4.818* (-1.89) 0.126*** (4.31) 0.117** (2.20) 15.390** (-1.99) 180 0.714

1.253* (-1.74) 0.151* (1.69) 1.672* (1.69) 0.281*** (-3.62) 0.001* (-1.68) 2.493 (1.29) 0.042* (-1.78) 0.044 (-1.07) 1.545* (-1.73) 0.117* (1.65) 5.854 (-0.98) 180 0.702

3.264** (-2.10) 0.396** (2.34) 4.790** (2.27) 0.866* (-1.66) 0.006* (-1.81) 3.093 (1.32) 0.052** (-2.36) 0.022 (-0.47) 2.211** (-2.10) 0.172*** (2.70) 10.687 (-1.55) 180 0.656

DSPDM 0.608** (2.24) 3.323** (-2.39) 0.405* (1.87) 3.276 (1.60) 0.507 (-1.27) 0.005* (-1.75) 4.810* (1.90) 0.024** (-2.05) 0.055 (-1.25) 1.417* (-1.86) 0.182*** (2.79) 0.107** (2.04) 3.898 (-1.17) 180 0.713

Notes: The values in parentheses are t or z values; ***, **, and * represent significant levels of 1%, 5%, and 10%, respectively; L.SO2 represents the first-order lag term of sulfur dioxide emissions (total, per capita, and intensity); w.SO2 represents the spatial lag term of sulfur dioxide emissions (total, per capita, and intensity; w.mi represents the spatial lag term of market segmentation; the estimation results of the spatial lag terms of other control variables are omitted. 7

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Table 4 Impact of market integration on industrial wastewater discharge. Variable

Total industrial wastewater discharge (pw)

Per capita industrial wastewater discharge (pcpw)

Industrial wastewater discharge intensity (ipw)

GS2SLS

GS2SLS

GS2SLS

L.pw mi smi pgdp spgdp pat ind pr open ee w.pw w.mi Constant Observations Adj. R2

1.421* (-1.77) 0.175* (1.66) 5.569** (2.44) 0.448** (-2.34) 0.002* (-1.78) 1.697 (1.22) 0.001* (-1.77) 0.022 (-1.39) 0.253* (-1.76) 0.043** (2.44) 21.721* (-1.85) 180 0.722

DSPDM 0.403 (1.44) 2.362** (-2.03) 0.285* (1.89) 4.013** (1.96) 0.367* (-1.92) 0.001* (-1.66) 0.961 (0.63) 0.002* (-1.79) 0.034* (-1.71) 0.647** (-2.41) 0.043** (2.40) 0.292** (2.28) 13.137 (-1.26) 180 0.763

0.766** (-2.41) 0.095* (1.68) 1.284*** (3.36) 0.358*** (-3.17) 0.004* (-1.90) 4.630 (0.81) 0.010* (-1.90) 0.055 (-0.72) 1.290* (-1.91) 0.213*** (2.67) 17.537*** (-3.41) 180 0.819

DSPDM 0.807** (2.09) 0.628** (-2.38) 0.078* (1.88) 0.181 (1.06) 0.031 (-0.26) 0.002* (-1.77) 1.520 (0.61) 0.002* (-1.78) 0.005 (-0.43) 0.312* (-1.84) 0.047** (2.01) 0.507*** (3.40) 0.324 (0.23) 180 0.753

1.057* (-1.72) 0.128* (1.70) 4.395*** (3.43) 0.196*** (-3.36) 0.001* (-1.68) 1.131 (1.32) 0.003* (-1.88) 0.002 (-0.22) 0.017** (-2.07) 0.301*** (6.40) 25.974*** (-3.55) 180 0.798

DSPDM 0.911*** (6.94) 0.291* (-1.79) 0.035* (1.82) 0.776 (1.00) 0.032 (-0.96) 0.001* (-1.68) 0.078 (0.50) 0.003* (-1.86) 0.0003 (-0.13) 0.108* (-1.86) 0.194*** (7.21) 0.297** (2.15) 1.102 (1.16) 180 0.623

Notes: The values in parentheses are t or z values; ***, **, and * represent significant levels of 1%, 5%, and 10%, respectively; L.pw represents the first-order lag term of industrial wastewater discharge (total, per capita, and intensity); w.pw represents the spatial lag term of industrial wastewater discharge (total, per capita, and in­ tensity; w.mi represents the spatial lag term of market segmentation; the estimation results of the spatial lag terms of other control variables are omitted. Table 5 Impact of market integration on industrial smoke and dust emissions. Variable

Total industrial smoke and dust emissions (pd)

Per capita industrial smoke and dust emissions (pcpd)

Industrial smoke and dust emission intensity (ipd)

GS2SLS

GS2SLS

GS2SLS

L.pd mi smi pgdp spgdp pat ind pr open ee w.pd w.mi Constant Observations Adj. R2

1.991** (-2.52) 0.243* (1.65) 0.341** (2.12) 0.037* (-1.75) 0.002* (-1.67) 0.851 (0.51) 0.018* (-1.65) 0.011 (-0.51) 0.394** (-1.98) 0.221* (1.95) 2.122 (-1.61) 180 0.702

DSPDM 1.216*** (10.49) 6.714* (-1.70) 0.805* (1.68) 15.355** (2.16) 0.717** (-2.20) 0.001* (-1.66) 0.821 (0.34) 0.039* (-1.74) 0.008 (-0.47) 0.380* (-1.78) 0.239** (2.16) 0.365*** (3.14) 1.009 (-1.41) 180 0.649

4.498* (-1.93) 0.541** (2.43) 2.504 (0.10) 0.362 (-0.31) 0.003* (-1.74) 1.367 (1.31) 0.208* (-1.74) 0.026 (-0.59) 4.653* (-1.76) 0.309*** (4.86) 21.073 (1.07) 180 0.621

DSPDM 0.675*** (6.18) 1.275** (-2.06) 0.155* (1.79) 6.919* (1.70) 1.761* (-1.74) 0.006* (-1.86) 1.916 (1.32) 0.151* (-1.68) 0.015 (-0.97) 3.919* (-1.94) 0.511*** (8.36) 0.469** (2.33) 16.273 (1.62) 180 0.614

0.615* (-1.76) 0.075* (1.73) 0.396 (1.17) 0.224 (-1.55) 0.003* (-1.75) 0.627 (1.31) 0.023* (-1.65) 0.003 (-1.21) 0.142** (-2.42) 0.151*** (2.71) 2.118 (-0.50) 180 0.642

DSPDM 0.937*** (5.22) 0.223* (-1.94) 0.027* (1.94) 0.579* (1.83) 0.324* (1.80) 0.001* (-1.66) 0.656 (1.39) 0.031* (-1.76) 0.001 (-0.37) 0.089* (-1.76) 0.189*** (6.97) 0.257** (2.06) 1.990 (-0.97) 180 0.740

Notes: The values in parentheses are t or z values; ***, **, and * represent significant levels of 1%, 5%, and 10%, respectively; L.pd represents the first-order lag term of industrial smoke and dust emissions (total, per capita, and intensity); w.pd represents the spatial lag term of industrial smoke and dust emissions (total, per capita, and intensity; w.mi represents the spatial lag term of market segmentation; the estimation results of the spatial lag terms of other control variables are omitted.

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experienced a process of moving from low to high. During the period of low market integration, cities in the Yangtze River Delta region were at a stage of rapid industrialization and urbanization. The competition for growth between regions intensified market segmentation. Such competition is not conducive to improving market integration, the diffusion of technological innovation, and technological spillover effect. In fact, environmental regulations tend to a “race-to-the-bottom” state, where energy use efficiency is relatively low. These factors are not conducive to emission reduction. In recent years, regional integration in the Yangtze River Delta region has rapidly advanced. Transportation integration, industrial employment integration, the integration of public services, and the integration of environmental protection policies have, to some extent, all promoted market integration in the Yangtze River Delta region. The technology trading market in the Yangtze River Delta region is becoming more and more mature, and the degree of the mar­ ketization of environmental and energy factors is increasing. For example, with the establishment of the Shanghai Environment and En­ ergy Exchange, the emission-reduction effect of market integration in the Yangtze River Delta region has become increasingly prominent. The spatial lag terms’ coefficients of three types of pollution emis­ sions are all positive and statistically significant at least at a 10% level. This finding indicates that a significant spatial correlation exists be­ tween pollution emissions between cities in the Yangtze River Delta region. A joint defense must be formed against environmental pollution. The spatial spillover sources of environmental pollution are as follows. First, environmental pollution typically has a cross-border pollution effect. Pollutants can affect the environmental quality of neighboring regions through natural factors, such as the atmosphere, river channels, groundwater circulation systems, and wind power. Second, there is a symbiosis between pollution emissions and industrial production. En­ terprises are constantly clustering in central market areas, mostly in order to obtain local market effects. The pollution that accompanies production is also concentrated in these central market areas, thus forming a spatial correlation between regional pollution emissions. Third, due to the regional competition of economic growth that is a main cause of pollution emissions, the pollution emissions between regions also show a corresponding spatial correlation. Fourth, in the process of a company’s pursuing the local market effect, the central and surrounding areas are prone to form a related pollution-oriented industry. In this case, the pollution emissions between regions will also be spatially correlated (Zhang, 2018). The spatial lag term’s coefficient of market segmentation is positive and significant at least at a 5% level, indicating that market segmenta­ tion in neighboring regions promotes local pollution emissions, to some extent. That is to say, market integration in neighboring regions is conducive to promoting local emission reduction. First, when the level of market integration in neighboring regions is high, local goods and factors can enter the neighboring regions relatively freely. In addition, local factors and resources can achieve higher allocation efficiency in neighboring regions, thereby optimizing the portfolio ratio of local factors and resources, to some extent. All of this is conducive to local emission reduction. Second, a significantly positive correlation exists between the level of market integration in neighboring regions and that of local market integration. In other words, the marketization of neighboring regions is conducive to improving the level of local mar­ ketization. When the level of local market integration exceeds a certain critical point, an inhibitory effect on local pollution emissions will be formed. Table 6 shows the critical points of the emission-reduction ef­ fects of market integration on three types of pollutants. The results show that most cities in the Yangtze River Delta region are within the market segmentation threshold. That is to say, most cities in the Yangtze River Delta region are in an emission-reduction state of market integration. At the same time, market integration has also been found to have little difference in the critical points of the emission-reduction effects on the different indicators of three types of pollutants. This also indicates that the conclusions in this paper are robust.

Table 6 Critical points of the emission-reduction effects of market integration on three types of pollutants. Variable

GS2SLS

DSPDM

Mean

Total sulfur dioxide emissions Per capita sulfur dioxide emissions Sulfur dioxide emission intensity Total industrial wastewater discharge Per capita industrial wastewater discharge Industrial wastewater discharge intensity Total industrial smoke and dust emissions Per capita industrial smoke and dust emissions Industrial smoke and dust emission intensity

0.0656 0.0563 0.0616 0.0580 0.0563 0.0621 0.0601 0.0639 0.0603

0.0597 0.0634 0.0605 0.0630 0.0560 0.0639 0.0647 0.0611 0.0602

0.0634 0.0598 0.0611 0.0605 0.0562 0.0630 0.0624 0.0625 0.0612

The slight differences in the impacts of market integration on the critical point of emission reductions of these three pollutants may be caused by the following aspects. First, sulfur dioxide is mainly derived from the combustion of sulfur fuels (such as coal and petroleum) and industrial production processes. Industrial wastewater is mainly derived from industrial processes. Industrial smoke and dust are mainly derived from the process of fuel combustion and the transportation and milling of raw materials. Second, there are differences in the regulatory stan­ dards and difficulties related to these three pollutants. In recent years, China has paid more attention to air pollution and water pollution, but regulating industrial smoke and dust is relatively difficult. For example, industrial smoke and dust produced during transportation are more difficult to control than those produced during production processes. The marketization of pollution governance in the process of market integration can be an important way to make up for regional differences and the difficulties associated with pollutant regulation. Third, the pollution-emission characteristics of different industries are different. For example, industrial wastewater accounts for a relatively high pro­ portion of pollutants in the chemical industry, while the power industry emits huge amounts of sulfur dioxide. It should be pointed out that these differences indicate the need for policy makers to develop differentiated market integration strategies for different pollutants. The above also indicates that, when a number of pollutants are considered, the difficulty of achieving the integrated emission-reduction effects of market inte­ gration also increases. The level of market integration can, to some extent, reflect the interaction effect of regional environmental regulation strategies. When the level of market segmentation is high (i.e., when the level of market integration is low), the relationship between neighboring regions is dominated by competition. Given the incentive of fiscal decentralization and promotions for local governments, it is easy for regions to reduce their environmental standards in order to increase their competitiveness and attract investment. i.e., a “race-to-the-bottom” phenomenon. The interaction behavior of a local government’s competition strategy can be reflected by the level of local pollution emissions, to some extent. The spatial spillover effect of pollution emissions between regions may present the phenomenon of “You have many emissions, and I also have many” (Zhang et al., 2016). Therefore, market integration in neigh­ boring regions may have a certain impact on local pollution emissions. Market integration promotes the free flow of goods and factors, and the free flow of goods and factors provides a channel for establishing cooperation between regions and enterprises. In a market integration environment, price signals can pass through various economic associa­ tions between regions and on to the local market. As such, a positive correlation exists between marketization in the neighborhood and local marketization (Johansson and Ljungwall, 2009). Market integration also has a significant role to play in promoting economic growth (Ke, 2015), and environmental pollution is a by-product of economic growth. Therefore, market integration in neighboring regions has an impact on local environmental pollution. Regarding the estimation results of control variables, the coefficients of technological innovation, environmental regulation, and energy 9

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Journal of Environmental Management 261 (2020) 110208

efficiency are all significantly negative. This finding indicates that technological innovation, environmental regulation, and energy effi­ ciency significantly inhibit pollution emissions. This conclusion is consistent with Huang (2018). The coefficients of industrial structure are positive, but most of them are not significant. This finding indicates that industrial structure in the Yangtze River Delta region are not an important factor for environmental pollution. In recent years, the in­ dustrial transformation and upgrading of the Yangtze River Delta region has been very rapid, but this growth has not had a significant impact on the levels of environmental pollution. The coefficient of openness is negative but not significant, indicating that an export-oriented eco­ nomic mode in the Yangtze River Delta region is not a cause of envi­ ronmental pollution. It is noteworthy that a certain factor can directly affect local envi­ ronmental pollution, and this factor can also affect the local environ­ ment by affecting the environmental pollution of other regions. According to LeSage and Pace (2009), we can further decompose the impact of market integration on environmental pollution into direct and indirect effects. The matrix of the partial derivative of the explained variable with respect to the explanatory variables can explain the direct and indirect effects. The direct effect is defined as the mean of the main diagonal elements, reflecting the average effect of the changes in a certain explanatory variable in one region on the explained variable in this region. The indirect effect is defined as the mean of the non-diagonal elements, i.e., the average cross-partial derivative, reflecting the average effect of the changes in a certain explanatory variable in other regions on the explained variable in the local region (LeSage and Pace, 2009). The results in Table 7 show that market integration has both direct and indirect effects on three types of pollution emissions. Regarding the estimation coefficients, the direct effects of market integration on environmental pollution are far greater than the indirect effects. This finding indicates that the direct effect of regional integration on local environmental pollution is greater than the indirect effect of regional integration on local environmental pollution. The potential reason is that there is a certain market segmentation between different regions, and the transmission channels of the effect of market integration in other regions on local environment pollution are longer and more complex. Taking sulfur dioxide emissions as an example (see Table 7), the direct and indirect effects of market integration (mi) are 2.014 and 0.315, and the direct and indirect effects of the quadratic term (smi) of market integration are 0.202 and 0.044. The direct effects of market integration and its quadratic term are 6.39 times and 4.59 times of the indirect ef­ fects of market integration and its quadratic term, respectively.

4.2. Test of Hypothesis 2 According to the above empirical results, when the degree of market integration exceeds a certain critical point, certain emission-reduction effects will be produced. This is precisely the point of interest of this paper. Then, what is the mechanism of the emission-reduction effect of market integration? The above empirical results show that technological innovation, environmental regulation, and energy efficiency in the Yangtze River Delta region have a significant inhibitory effect on three types of pollution emissions. In addition, market integration has a certain role to play in promoting technological innovation, environ­ mental regulation, and energy efficiency. Market integration may also enhance emission reduction through technological innovation, envi­ ronmental regulation, and energy efficiency. In order to further identify the emission-reduction mechanism of market integration, this paper adds the interaction terms of the market segmentation index and technology innovation, environmental regula­ tion, and energy efficiency (mi*pat, mi*pr, and mi*ee), respectively, on the basis of the baseline model. The above transmission mechanism can be verified by estimating the coefficients of these interaction terms. We still use the GS2SLS method and DSPDM to conduct the empirical tests of the transmission mechanism. The specific econometric model is as follows: pit ¼ α0 þ α1 pi;t

Core explanatory variable

Direct effect

Indirect effect

Total effect

SO2

mi smi mi smi mi smi mi smi mi smi mi smi mi smi mi smi mi smi

2.014 0.202 3.209 0.144 3.114 0.379 2.314 0.266 0.564 0.061 0.274 0.022 1.914 0.235 1.132 0.142 0.210 0.019

0.315 0.044 0.189 0.017 0.147 0.015 0.045 0.023 0.014 0.016 0.021 0.010 0.037 0.023 0.024 0.012 0.027 0.007

2.329 0.246 3.398 0.161 3.621 0.394 2.359 0.289 0.578 0.077 0.295 0.032 1.951 0.248 1.156 0.154 0.237 0.026

pcSO2 iSO2 pw pcpw ipw pd pcpd ipd

þ ρ1

n X

wij pjt þ α2 miit � patit þ α3 miit � prit þ α4 miit � eeit

i¼1 0

þρ2

n X i¼1

0

0

wij mijt þ λ Xit þ ρ3

n X i¼1

0

0

wij Xjt þ ui þ εit (4)

The regression results of Tables 8–10 show that the coefficients of the interaction terms of market segmentation and technological innovation (mi*pat), market segmentation and environmental regulation (mi*pr), and market segmentation and energy efficiency (mi*ee) are all positive and statistically significant at least at a 10% level. This finding indicates that when the degree of market segmentation is higher (or the degree of market integration is lower), the promotion effects of technological innovation, environmental regulation, and energy efficiency on these three types of pollutants are greater. That is to say, the higher the degree of market integration is, the larger the emission-reduction effects of technological innovation, environmental regulation, and energy effi­ ciency on these three types of pollutants. Hence, Hypothesis 2 is verified. With the increasing market integration level in the Yangtze River Delta region, technological innovation cooperation and technology spillover effects within the Yangtze River Delta region are becoming more and more obvious. The emission-reduction effects of technological innova­ tion are also becoming larger. Meanwhile, in the background of market integration, the environ­ mental regulation standards in the Yangtze River Delta region have become stricter and more enforced; more and more market-orientated pollution control tools are adopted. Consequently, environmental reg­ ulations have gradually curbed pollution. Meanwhile, the marketization of production factors in the Yangtze River Delta region has gradually increased, and the leverage role of production factor prices has become increasingly obvious. In particular, the role of energy efficiency in suppressing pollution emissions has also become more obvious. The coefficients of the spatial lag terms of these three types of pollution emission indicators and market segmentation are all significantly posi­ tive. This indicates that there is a significant spatial spillover effect of both pollution emissions and market segmentation in neighboring re­ gions, which significantly promotes local pollution emissions. Table 11 reports the direct, indirect and total effects of the influ­ encing mechanism of market integration on pollution emissions. The results show that the direct effect of market integration through tech­ nological innovation, environmental regulation, and energy efficiency on environmental pollution is greater than its indirect effect. The

Table 7 Direct, indirect and total effects of market integration on three types of pollution emissions. Explained variable

0

1

10

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Table 8 Influencing mechanism of market integration on sulfur dioxide emissions. Variable

Total sulfur dioxide emissions (SO2)

Per capita sulfur dioxide emissions (pcSO2)

Sulfur dioxide emission intensity (iSO2)

GS2SLS

GS2SLS

GS2SLS

L.SO2 mi*pat mi*pr mi*ee pgdp spgdp ind open w.SO2 w.mi Constant Observations Adj. R2

0.001* (1.89) 0.008** (2.42) 0.194* (1.77) 7.165** (2.47) 0.356*** (-2.65) 2.378 (1.11) 0.046* (-1.91) 0.189*** (7.95) 30.677** (-2.20) 180 0.870

DSPDM 0.022 (0.47) 0.001* (1.90) 0.008** (2.45) 0.287* (1.88) 7.260 (1.61) 0.349** (-1.71) 0.576 (0.23) 0.028 (-0.78) 0.111*** (8.94) 0.114** (2.19) 30.267 (-1.28) 180 0.583

DSPDM 0.072 (0.87) 0.001** (2.05) 0.009* (1.75) 1.383* (1.68) 7.685 (1.46) 0.900 (-1.37) 5.790** (2.50) 0.253 (-0.70) 0.125*** (4.25) 0.121** (2.45) 43.470* (-1.66) 180 0.606

0.006* (1.66) 0.001** (2.01) 1.678** (2.42) 8.876** (2.16) 0.790 (-1.10) 5.539* (2.12) 0.350 (-1.06) 0.088** (2.46) 52.261** (-2.35) 180 0.734

0.002* (1.65) 0.010** (2.44) 0.468* (1.95) 4.538** (2.53) 0.893 (-1.33) 4.332** (2.07) 0.016 (-0.34) 0.047* (1.70) 41.363 (-1.37) 180 0.801

DSPDM 0.597** (2.13) 0.001* (1.94) 0.002** (2.04) 0.335* (1.86) 1.122 (1.11) 0.011 (-1.02) 5.159** (2.11) 0.053 (-1.22) 0.140*** (5.68) 0.109** (2.07) 30.732 (-0.13) 180 0.721

Notes: mi*pat, mi*pr, and mi*ee represent the interaction terms of market segmentation and technological innovation, market segmentation and environmental regulation, and market segmentation and energy efficiency, respectively, and others are the same as Table 3. Table 9 Influencing mechanism of market integration on industrial wastewater discharge. Variable

Total industrial wastewater discharge (pw)

Per capita industrial wastewater discharge (pcpw)

Industrial wastewater discharge intensity (ipw)

GS2SLS

GS2SLS

GS2SLS

L.pw mi*pat mi*pr mi*ee pgdp spgdp ind open w.pw w.mi Constant Observations Adj. R2

0.003* (1.80) 0.049* (1.74) 0.058* (1.90) 5.575*** (2.81) 0.250*** (-2.72) 1.625 (1.17) 0.026 (-1.61) 0.044*** (2.70) 24.016** (-2.29) 180 0.634

DSPDM 0.404 (1.54) 0.001* (1.74) 0.434* (1.75) 0.098* (1.82) 7.307** (2.34) 0.332* (-2.34) 0.294 (0.17) 0.017 (-0.82) 0.096** (2.02) 0.285** (2.26) 19.329* (-1.93) 180 0.559

0.001* (1.67) 0.092* (1.74) 0.267* (1.84) 7.855*** (3.13) 1.201*** (-2.94) 10.835* (1.82) 0.093 (-1.13) 0.135*** (2.67) 16.916*** (-3.49) 180 0.816

DSPDM 0.913** (3.03) 0.001* (1.78) 0.047** (2.16) 0.240* (1.66) 5.495** (1.98) 2.472 (-2.07) 6.343 (0.81) 0.015 (-0.19) 0.023** (2.46) 0.512*** (3.42) 24.439* (-1.86) 180 0.709

0.002* (1.66) 0.018** (2.09) 0.011** (2.13) 4.144*** (3.16) 0.186*** (-3.31) 0.686 (0.86) 0.003 (-0.32) 0.267*** (7.40) 12.567** (-2.42) 180 0.827

DSPDM 0.834** (2.09) 0.001** (2.15) 0.004* (1.69) 0.058* (1.72) 1.644 (0.21) 0.005 (0.15) 5.712 (0.60) 0.005 (-0.43) 0.438* (1.85) 0.288** (2.11) 6.687 (1.39) 180 0.731

Notes: mi*pat, mi*pr, and mi*ee represent the interaction terms of market segmentation and technological innovation, market segmentation and environmental regulation, and market segmentation and energy efficiency, respectively, and others are the same as Table 4.

potential reason is that local market integration has a more direct and effective impact on local technological innovation, environmental regulation, and energy efficiency, and therefore has a greater effect on local environmental pollution. However, the transmission process of market integration in neighboring regions affecting local environmental pollution through technology innovation, environmental regulation, and energy efficiency is more complicated. Such an effect is indirect and relies on the degree of interaction between regions. Taking sulfur di­ oxide emissions as an example (see Table 11), the direct effects of the

interaction terms of market segmentation and technological innovation, market segmentation and environmental regulation, and market seg­ mentation and energy efficiency are 0.0009, 0.0069, while 0.2014, and their indirect effects are 0.0003, 0.0012, and 0.0898, respectively. The direct effects are 3 times, 5.75 times, and 2.24 times of the indirect ef­ fects, respectively.

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Table 10 Influencing mechanism of market integration on industrial smoke and dust emissions. Variable

Total industrial smoke and dust emissions (pd)

Per capita industrial smoke and dust emissions (pcpd)

Industrial smoke and dust emission intensity (ipd)

GS2SLS

GS2SLS

GS2SLS

L.pd mi*pat mi*pr mi*ee pgdp spgdp ind open w.pd w.mi Constant Observations Adj. R2

0.001** (2.08) 0.057** (2.27) 0.120* (1.70) 0.436 (0.15) 0.016 (-0.12) 0.958 (0.25) 0.009 (-0.39) 0.023** (2.00) 6.815 (0.44) 180 0.504

DSPDM 0.939*** (6.49) 0.002* (1.80) 0.016* (1.69) 0.270** (2.29) 3.241 (-0.41) 0.140 (0.42) 0.780 (0.13) 0.001 (-0.15) 0.161*** (8.95) 0.374*** (3.20) 2.327 (0.49) 180 0.524

0.007* (1.74) 0.322** (2.05) 1.950* (1.90) 8.125 (0.33) 0.638 (-0.55) 2.458 (1.19) 0.100 (-0.46) 0.347*** (5.69) 1.785 (1.44) 180 0.519

DSPDM 0.678*** (6.46) 0.001** (2.01) 0.229* (1.83) 1.027* (1.95) 4.828 (-0.75) 1.911 (0.78) 2.557 (0.20) 0.041 (-0.20) 0.215*** (8.66) 0.471** (2.38) 73.216 (0.71) 180 0.619

0.002* (1.86) 0.021** (1.74) 0.095* (1.90) 2.024 (-1.55) 0.139 (0.19) 0.872 (0.53) 0.009 (-0.38) 0.108** (2.33) 9.381* (1.69) 180 0.556

DSPDM 0.919*** (4.98) 0.001* (1.71) 0.026* (1.93) 0.579* (1.83) 5.710 (-0.82) 0.234 (0.79) 0.221 (0.16) 0.006 (-0.30) 0.189*** (7.10) 0.262** (2.19) 8.594 (0.86) 180 0.678

Notes: mi*pat, mi*pr, and mi*ee represent the interaction terms of market segmentation and technological innovation, market segmentation and environmental regulation, and market segmentation and energy efficiency, respectively, and others are the same as Table 5.

represents the spherical distance between city i and city j, and d is the geographical threshold. Second, there may be the impact of sample heterogeneity, due to the differences in the administrative level and scale of 18 cities in the Yangtze River Delta region. Shanghai, Hangzhou, and Nanjing are municipality or provincial capitals, whose administra­ tive levels and scales are higher and greater than those of other cities,2 respectively. The degree of the autonomy of these three cities in the urban development process is higher than those of ordinary cities. Considering that such a heterogeneity may affect the empirical estima­ tion results, we remove these three cities and re-estimate the regression model to check the robustness of the above results. Tables 12–14 report the estimation results after changing the spatial weight matrix. Tables 15–17 show the estimation results after excluding Shanghai, Hangzhou, and Nanjing. The results show that the coefficients of market segmentation and its quadratic term are negative and positive, respectively, at least at a significance level of 10%. This finding once again confirms the inverted “U-shaped” curve relationship between market integration and environmental pollution. Thus, Hypothesis 1 in this paper is still valid. The inflection point value of market integration in the estimation results based on the geological threshold distance spatial weight matrix is between 0.058 and 0.065. This is basically consistent with the above estimation results based on the geographical binary spatial weight matrix. However, after eliminating Shanghai, Hangzhou, and Nanjing, the inflection point value of market integration exceeds 0.07, which is greater than the above estimation results based on the whole sample. The reason for this finding may be that environmental regulations in Shanghai, Hangzhou, and Nanjing are relatively stricter than those in other cities. The formulation and implementation of environmental policies in these three cities can play a demonstration role for other cities. Moreover, the degree of the marketization of these three cities is higher than that of other cities. The higher level of market integration makes the transition to the emission-reduction mechanism of

Table 11 Direct, indirect and total effects of the influencing mechanism. Explained variable

Explanatory variable

Direct effect

Indirect effect

Total effect

SO2

mi*pat mi*pr mi*ee mi*pat mi*pr mi*ee mi*pat mi*pr mi*ee mi*pat mi*pr mi*ee mi*pat mi*pr mi*ee mi*pat mi*pr mi*ee mi*pat mi*pr mi*ee mi*pat mi*pr mi*ee mi*pat mi*pr mi*ee

0.0009 0.0069 0.2014 0.0010 0.0081 1.2471 0.0009 0.0018 0.3317 0.0010 0.3925 0.0852 0.0009 0.0040 0.2105 0.0010 0.0031 0.0468 0.0015 0.0134 0.2141 0.0010 0.2116 0.8743 0.0009 0.0193 0.5624

0.0003 0.0012 0.0898 0.0004 0.0020 0.1321 0.0002 0.0006 0.0109 0.0003 0.0485 0.0139 0.0002 0.0011 0.0307 0.0004 0.0011 0.0116 0.0007 0.0033 0.0571 0.0002 0.0185 0.1531 0.0002 0.0071 0.0168

0.0012 0.0081 0.2912 0.0014 0.0111 1.3792 0.0011 0.0024 0.3426 0.0014 0.4410 0.0991 0.0011 0.0051 0.2412 0.0014 0.0042 0.0584 0.0022 0.0167 0.2712 0.0012 0.2301 1.0274 0.0011 0.0264 0.5792

pcSO2 iSO2 pw pcpw ipw pd pcpd ipd

4.3. Robustness test The robustness test of this study will be conducted from two aspects. The first is to consider that whether the selection of different spatial weights has an evident impact on the estimation results, especially when capturing the spatial spillover effects of environmental pollution. An important factor in the extent of this is the boundary. Therefore, the geographical threshold distance spatial weight matrix is used:wij ¼ 0,

2 Market segmentation and market integration are opposite concepts. The higher the market integration is, the lower the market segmentation will be, and the smaller the resistance to the free flow of factors will be (Ke, 2015).

when i ¼ j or dij > d; wij ¼ 1=dij , when i 6¼ j and dij < d. Variable dij

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Table 12 Impact of market integration on sulfur dioxide emissions after changing the spatial weight matrix. Variable

Total sulfur dioxide emissions (SO2)

Per capita sulfur dioxide emissions (pcSO2)

Sulfur dioxide emission intensity (iSO2)

GS2SLS

GS2SLS

GS2SLS

L.SO2 mi smi pgdp spgdp pat ind pr open ee w.SO2 w.mi Constant Observations Adj. R2

4.220* (-1.75) 0.511** (2.33) 3.662 (1.14) 0.240 (-1.25) 0.004* (-1.80) 3.985 (1.37) 0.019* (-1.71) 0.017 (-0.47) 0.588* (-1.86) 0.225*** (6.41) 6.309 (-1.04) 180 0.685

DSPDM 0.180 (0.82) 2.204** (-2.20) 0.269* (1.74) 8.041** (2.27) 0.402** (2.30) 0.003* (-1.68) 3.441 (1.16) 0.037* (-1.91) 0.022 (-0.54) 1.449*** (-2.88) 0.104** (2.26) 0.177** (2.32) 9.264 (-1.31) 180 0.748

DSPDM 0.154* (1.85) 1.335* (1.79) 0.162** (2.18) 12.021 (1.33) 2.778 (-1.34) 0.005* (-1.80) 10.220** (2.16) 0.084* (-1.79) 0.172 (-0.56) 3.413* (-1.80) 0.152*** (4.29) 0.123** (2.07) 8.242* (-1.74) 180 0.792

1.250* (-1.73) 0.152* (1.67) 1.695* (1.66) 0.079*** (-3.45) 0.001* (-1.67) 2.469* (1.71) 0.071* (-1.72) 0.005 (-1.22) 1.569* (-1.75) 0.224** (1.98) 5.907 (-1.04) 180 0.711

DSPDM 0.647** (2.30) 2.696** (-2.03) 0.327* (1.79) 2.216 (1.45) 0.602 (-1.52) 0.008* (-1.84) 2.779* (1.65) 0.039** (-2.12) 0.042 (-1.20) 1.224* (-1.67) 0.248*** (5.20) 0.165** (2.22) 4.250 (-1.05) 180 0.721

2.776** (-2.12) 0.334** (2.13) 3.449* (1.71) 0.774** (-2.02) 0.007* (-1.83) 3.445* (1.70) 0.044** (-2.21) 0.031 (-0.55) 1.562** (-2.03) 0.186*** (2.74) 9.110 (-1.09) 180 0.667

Notes: The geographical threshold is 130 km according to the significance levels of the Moran’s index, and others are the same as Table 3. Table 13 Impact of market integration on industrial wastewater discharge after changing the spatial weight matrix. Variable

Total industrial wastewater discharge (pw)

Per capita industrial wastewater discharge (pcpw)

Industrial wastewater discharge intensity (ipw)

GS2SLS

GS2SLS

GS2SLS

L.pw mi smi pgdp spgdp pat ind pr open ee w.pw w.mi Constant Observations Adj. R2

1.415* (-1.74) 0.175* (1.67) 4.256** (2.33) 0.524** (-2.19) 0.003* (-1.78) 1.707 (1.27) 0.001* (-1.76) 0.018 (-1.32) 0.240* (-1.72) 0.051** (2.47) 18.550* (-1.82) 180 0.736

DSPDM 0.550* (1.69) 2.374** (-2.08) 0.290* (1.92) 5.040** (2.03) 0.392* (-1.90) 0.001* (-1.67) 1.042 (0.92) 0.003* (-1.80) 0.030* (-1.70) 0.626** (-2.25) 0.041** (2.44) 0.310** (2.30) 9.559 (-1.33) 180 0.779

0.801** (-2.45) 0.098* (1.69) 1.255*** (3.30) 0.358** (-2.09) 0.005* (-1.92) 1.302 (1.03) 0.012* (-1.91) 0.059 (-0.75) 1.214* (-1.85) 0.210*** (2.65) 10.110*** (-3.25) 180 0.850

DSPDM 0.824** (2.14) 0.681** (-2.40) 0.084* (1.91) 0.490 (1.18) 0.046 (-0.79) 0.003* (-1.77) 1.559 (1.26) 0.003* (-1.80) 0.007 (-0.59) 0.301* (-1.80) 0.055** (2.03) 0.522*** (3.42) 7.559 (-1.55) 180 0.796

1.075* (-1.79) 0.132* (1.73) 4.250*** (3.17) 0.220*** (-3.40) 0.002* (-1.70) 1.120 (1.30) 0.004* (-1.90) 0.008 (-0.90) 0.085** (-2.09) 0.342*** (6.49) 14.770*** (-3.29) 180 0.805

DSPDM 0.902*** (4.50) 0.309* (-1.81) 0.038* (1.84) 0.809 (1.07) 0.041 (-0.99) 0.001* (-1.69) 0.120 (0.74) 0.005* (-1.89) 0.001 (-0.42) 0.122* (-1.89) 0.159*** (4.35) 0.315** (2.17) 9.259** (-2.36) 180 0.692

Notes: The geographical threshold is 130 km according to the significance levels of the Moran’s index, and others are the same as Table 4.

environmental pollution smoother. After eliminating these three cities, a higher level of market integration is needed to reach the critical emis­ sion reduction level. In addition, in Tables 12–17, the coefficients of the spatial lag terms of market segmentation and three types of pollution

emissions keep positive and significant at least at a 5% level, indicating that the estimation results in this paper are very robust.

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Table 14 Impact of market integration on industrial smoke and dust emissions after changing the spatial weight matrix. Variable

Total industrial smoke and dust emissions (pd)

Per capita industrial smoke and dust emissions (pcpd)

Industrial smoke and dust emission intensity (ipd)

GS2SLS

GS2SLS

GS2SLS

L.pd mi smi pgdp spgdp pat ind pr open ee w.pd w.mi Constant Observations Adj. R2

1.672** (-2.39) 0.130* (1.68) 0.332** (2.10) 0.036* (-1.76) 0.003* (-1.69) 0.665 (0.75) 0.020* (-1.67) 0.017 (-0.64) 0.322* (-1.85) 0.259** (1.99) 1.026 (-1.52) 180 0.685

DSPDM 1.310*** (11.05) 4.850* (-1.69) 0.581** (2.09) 4.285** (2.02) 0.625** (-2.14) 0.006* (-1.69) 0.810 (0.55) 0.037* (-1.72) 0.012 (-0.57) 0.745* (-1.67) 0.264** (2.06) 0.350*** (3.10) 1.024 (-1.46) 180 0.632

4.466* (-1.94) 0.544** (2.45) 2.114 (1.16) 0.445 (-1.02) 0.005* (-1.76) 1.447 (1.35) 0.176* (-1.70) 0.033 (-1.02) 4.019* (-1.89) 0.324*** (4.63) 10.332 (1.20) 180 0.618

DSPDM 0.682*** (6.52) 1.264** (-2.08) 0.155* (1.80) 10.315* (1.72) 1.205* (-1.74) 0.010* (-1.87) 1.612 (1.40) 0.165* (-1.66) 0.014 (-0.90) 3.256* (-1.72) 0.420*** (5.09) 0.427** (2.31) 14.021 (1.47) 180 0.604

0.601* (-1.78) 0.073* (1.74) 0.485 (1.35) 0.350 (-1.50) 0.012* (-1.85) 0.627 (1.29) 0.012* (-1.67) 0.008 (-1.10) 0.119** (-2.02) 0.169*** (2.68) 2.320 (-0.56) 180 0.641

DSPDM 0.922*** (5.10) 0.240* (-1.90) 0.029* (1.88) 0.590* (1.85) 0.424* (1.82) 0.015* (-1.82) 0.644 (1.37) 0.021* (-1.79) 0.005 (-0.87) 0.107* (-1.86) 0.190*** (5.05) 0.262** (2.14) 1.885 (-0.95) 180 0.729

Notes: The geographical threshold is 130 km according to the significance levels of the Moran’s index, and others are the same as Table 5. Table 15 Impact of market integration on sulfur dioxide emissions after excluding Shanghai, Hangzhou, and Nanjing. Variable

Total sulfur dioxide emissions (SO2)

Per capita sulfur dioxide emissions (pcSO2)

Sulfur dioxide emission intensity (iSO2)

GS2SLS

GS2SLS

GS2SLS

L.SO2 mi smi pgdp spgdp pat ind pr open ee w.SO2 w.mi Constant Observations Adj. R2

3.174* (-1.66) 0.369** (2.01) 4.145 (1.20) 0.324 (-1.26) 0.002* (-1.70) 2.144 (1.06) 0.023* (-1.67) 0.010 (-0.41) 0.704** (-1.99) 0.103** (2.39) 4.662 (-0.49) 150 0.635

DSPDM 0.146 (1.45) 2.425** (-2.34) 0.283* (1.73) 6.352** (2.21) 0.520** (2.40) 0.003* (-1.72) 1.522 (1.44) 0.027* (-1.75) 0.008 (-0.37) 1.205** (-2.01) 0.241*** (3.20) 0.256** (2.41) 7.621 (-1.42) 150 0.761

DSPDM 0.180 (1.57) 1.335* (1.78) 0.157** (2.10) 1.456 (1.50) 0.201 (-1.53) 0.006* (-1.85) 2.312 (1.27) 0.039* (-1.82) 0.017 (-1.28) 2.302* (-1.82) 0.225*** (4.46) 0.250** (2.37) 5.744 (-1.52) 150 0.720

1.240* (-1.72) 0.145* (1.67) 1.736* (1.70) 0.212 (-1.60) 0.004* (-1.74) 2.055 (1.16) 0.035* (-1.77) 0.014 (-1.25) 2.119* (-1.76) 0.120** (2.17) 4.124 (-1.33) 150 0.730

3.352** (-2.15) 0.392** (2.35) 3.225* (1.80) 0.465 (-1.46) 0.010* (-1.90) 3.611 (1.61) 0.025* (-1.69) 0.044 (-1.42) 1.572** (-2.22) 0.206*** (2.71) 9.652 (-1.60) 150 0.683

DSPDM 0.423** (2.07) 3.442** (-2.43) 0.405* (1.89) 2.854 (1.63) 0.566 (-1.50) 0.013* (-1.92) 4.127* (1.78) 0.030* (-1.78) 0.049 (-1.47) 1.482* (-1.83) 0.235*** (2.91) 0.225** (2.28) 5.612 (-1.15) 150 0.732

Note: The same as Table 3.

5. Concluding remarks

market integration’s emission-reduction effect. Based on the data of 18 prefecture-level and above cities in the Yangtze River Delta region from 2007 to 2016, we use the relative price method to measure the degree of market segmentation in the Yangtze River Delta region. The dynamic

This paper examines the relationship between market integration and environmental pollution, as well as the transmission mechanism of 14

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Table 16 Impact of market integration on industrial wastewater discharge after excluding Shanghai, Hangzhou, and Nanjing. Variable

Total industrial wastewater discharge (pw)

Per capita industrial wastewater discharge (pcpw)

Industrial wastewater discharge intensity (ipw)

GS2SLS

GS2SLS

GS2SLS

L.pw mi smi pgdp spgdp pat ind pr open ee w.pw w.mi Constant Observations Adj. R2

1.465* (-1.76) 0.172* (1.68) 3.419** (2.04) 0.620** (-2.15) 0.001* (-1.66) 1.203 (1.15) 0.004* (-1.79) 0.012 (-1.25) 0.251* (-1.74) 0.197*** (3.44) 12.201 (-1.63) 150 0.662

DSPDM 0.620** (2.02) 2.201** (-2.01) 0.255* (1.81) 4.201** (2.11) 0.416* (-1.88) 0.002* (-1.68) 1.002 (0.84) 0.005* (-1.84) 0.021 (-1.63) 0.640** (-2.27) 0.135*** (2.85) 0.264** (2.11) 7.154 (-1.38) 150 0.701

DSPDM 0.770** (2.10) 0.665** (-2.32) 0.078* (1.69) 0.820 (1.24) 0.104 (-0.85) 0.002* (-1.72) 1.201 (1.17) 0.013* (-1.90) 0.010 (-0.52) 0.421* (-1.85) 0.149** (2.14) 0.350** (2.41) 6.215 (-1.39) 150 0.707

0.766** (-2.47) 0.089* (1.78) 1.113*** (2.82) 0.517** (-2.19) 0.006* (-1.93) 2.218 (0.95) 0.011* (-1.88) 0.045 (-0.70) 1.114* (-1.69) 0.252*** (2.70) 11.226*** (-3.14) 150 0.863

1.191* (-1.82) 0.140* (1.76) 2.120** (2.08) 0.101 (-1.46) 0.003* (-1.74) 1.023 (1.26) 0.005* (-1.84) 0.006 (-0.88) 0.090** (-2.03) 0.355*** (6.70) 8.621** (-2.35) 150 0.795

DSPDM 0.821*** (3.12) 0.349* (-1.85) 0.041* (1.87) 1.256 (1.19) 0.204 (-1.60) 0.002* (-1.70) 0.110 (0.72) 0.009* (-1.91) 0.002 (-0.45) 0.130* (-1.90) 0.235*** (4.64) 0.339** (2.29) 7.230** (-2.15) 150 0.744

Note: The same as Table 4. Table 17 Impact of market integration on industrial smoke and dust emissions after excluding Shanghai, Hangzhou, and Nanjing. Variable

Total industrial smoke and dust emissions (pd)

Per capita industrial smoke and dust emissions (pcpd)

Industrial smoke and dust emission intensity (ipd)

GS2SLS

GS2SLS

GS2SLS

L.pd mi smi pgdp spgdp pat ind pr open ee w.pd w.mi Constant Observations Adj. R2

1.662** (-2.31) 0.195* (1.69) 0.385** (2.18) 0.042* (-1.79) 0.002* (-1.67) 0.691 (0.84) 0.035* (-1.70) 0.015 (-0.61) 0.301* (-1.82) 0.328** (2.21) 1.250 (-1.64) 150 0.672

DSPDM 0.665*** (4.11) 2.115** (-2.44) 0.248* (1.72) 1.059* (1.72) 0.047** (-2.16) 0.005* (-1.68) 0.872 (0.70) 0.038* (-1.72) 0.010 (-0.52) 0.729* (-1.66) 0.285** (2.10) 0.366*** (3.23) 1.345 (-1.55) 150 0.644

4.512** (-2.04) 0.529** (2.48) 2.256 (1.16) 0.490 (-1.19) 0.007* (-1.82) 1.480 (1.49) 0.180* (-1.82) 0.027 (-1.00) 3.455* (-1.74) 0.345*** (4.74) 7.149 (1.34) 150 0.601

DSPDM 0.709*** (5.49) 1.271** (-2.10) 0.149* (1.83) 7.599* (1.74) 1.233* (-1.80) 0.012* (-1.89) 1.622 (1.45) 0.171* (-1.69) 0.013 (-0.90) 3.114* (-1.69) 0.433*** (5.17) 0.450** (2.34) 11.259 (1.38) 150 0.612

0.640* (-1.88) 0.075* (1.79) 0.504 (1.44) 0.172 (-1.20) 0.010* (-1.87) 0.614 (1.21) 0.040* (-1.73) 0.006 (-1.07) 0.105** (-2.01) 0.180*** (2.76) 2.101 (-0.48) 150 0.651

DSPDM 0.681*** (4.23) 0.265* (-1.92) 0.031* (1.90) 0.652* (1.89) 0.509* (1.91) 0.012* (-1.77) 0.692 (1.41) 0.038* (-1.72) 0.003 (-0.85) 0.102* (-1.83) 0.250*** (5.24) 0.277** (2.19) 1.707 (-0.88) 150 0.717

Note: The same as Table 5.

spatial panel Durbin model and the generalized spatial two-stage least squares method are used to investigate the spatial nonlinear relationship between market integration and environmental pollution. Finally, the influencing mechanism of market integration on environmental

pollution is verified. We conclude that (1) the degree of market inte­ gration and the total emissions, per capita emissions, and emission in­ tensity of sulfur dioxide, industrial wastewater, and industrial smoke and dust all show an inverted “U-shaped” curve relationship. In other 15

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Journal of Environmental Management 261 (2020) 110208

difficult to obtain product price and pollution emission data at the Chinese enterprise level. Third, the market integration index of this paper is calculated based on the price index of eight major commodities in the region, which may cover the heterogeneity between different product markets. For example, the degree of the market integration of different products may vary. Future research can examine the degree of the price differentiation of a specific product in different regions, or specific markets in different regions could be selected as research­ objects, which may yield more relevant policy implication.

words, when the degree of market integration is relatively low, market integration will promote environmental pollution. When the degree of market integration is high to reach a certain critical level, market inte­ gration will have an emission-reduction effect on environmental pollu­ tion. (2) Most cities in the Yangtze River Delta region are in an emissionreduction state of market integration. (3) Market integration can strengthen the emission-reduction effects of technological innovation, environmental regulation, and energy efficiency, to a certain extent. (4) Pollution emissions have a significant spatial spillover effect, and mar­ ket segmentation (as opposed to market integration) in neighboring regions has a promotion effect on local pollution emissions. That is to say, market integration in neighboring regions is conducive to reducing local pollution emissions. Such conclusions have some important policy implications for pro­ moting regional market integration and joint pollution governance. As a region in China with a high level of market integration, the Yangtze River Delta region is also at the frontier of China’s reform. Therefore, research that focuses on the Yangtze River Delta region has important policy implications for market-oriented reforms and pollution gover­ nance at national level. First, when the degree of market integration exceeds a certain critical level, the emission-reduction effects will become obvious. Therefore, policymakers should give full play to the emission-reduction effect of market integration. The process of market integration in the Yangtze River Delta region should be further pro­ moted, and the role of market integration in optimizing the spatial allocation of factors should be strengthened. The government should improve the coordination of its environmental policies, environmental regulatory standards, environmental legislation, and industrial plan­ ning, and the coordination and implementation of pollution control policies should be rigorously enforced. Second, policymakers should take full advantage of the roles of technological innovation, environmental regulation, and energy effi­ ciency in the market integration process. Market integration should be used to develop trans-regional technology and intellectual property transaction markets, and full play should be given to the incentive of financial market tools for trans-regional technological innovation cooperation. Interregional economic connections, industrial integration, and economic dependence should be reinforced, and the integration of interregional environmental regulations should be promoted. In addi­ tion, a joint trading market for factors and energy in the Yangtze River Delta region can be built. Finally, market integration and environmental pollution control must have an element of global awareness. Regional environmental pollution control simply cannot be achieved “independently”. Joint defenses need to be formed for inter-regional pollution control; envi­ ronmental regulations and joint pollution controls must gradually become unified across regions. A pollution emissions trading market should be established as soon as possible, in order to promote the effective allocation of pollution emission rights across regions, thereby achieving overall emission reductions. As soon as possible, the necessary inter-regional cross-border pollution accounting work should be carried out, in order to clarify the joint responsibilities for cross-border pollu­ tion control between regions. In particular, in the process of promoting the integration of the local market, the integration of markets in the adjacent regions should also be supported. Regional planning and in­ dustrial planning between regions should be linked, in order to gradu­ ally eliminate the market segmentation effect of environmental pollution. The limitations of this study are as follows: first, the study is based on the data from prefecture-level cities in the Yangtze River Delta region. There is a lack of research on smaller spatial scales. From a spatial perspective, China’s market integration and segmentation are more re­ flected at the county level. Second, the micro-subject of market inte­ gration is the enterprise. It may be more practical to analyze interregional market integration and corporate pollution emission behavior from the perspective of corporate behavior. At present, it is more

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