Does cross-border e-commerce contribute to urban air quality improvement? Evidence from China’s pilot zones

Does cross-border e-commerce contribute to urban air quality improvement? 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Evidence from China’s pilot zones shiwen Luo, Hongsheng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6420572/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As China's rapid urbanization and industrialization accelerate, the issue of deteriorating air quality has gained widespread attention. Cross-border e-commerce (CBEC), as a representative of digital trade, provides a new solution for improving environmental governance performance. Based on panel data from 284 Chinese cities from 2009 to 2022, this paper utilizes the establishment of CBEC pilot zones as a quasi-natural experiment and employs a multi-period Difference-in-Differences method to assess the effect of CBEC on urban sulfur dioxide (SO2) emissions. The results indicate that CBEC significantly improves urban air quality. Heterogeneity analysis shows that this effect is particularly pronounced in eastern cities, especially those with higher levels of digital infrastructure and weaker environmental regulations. Mechanism tests further reveal that CBEC effectively enhances urban air quality by the pathways of green technology innovation, productive service industry agglomeration, and resource allocation optimizing. Therefore, it is essential to accelerate CBEC reforms, promote the construction of CBEC pilot zones, and strengthen policy implementation to fully leverage its potential in improving urban air quality. Business and commerce/Economics Social science/Environmental studies Figures Figure 1 Figure 2 Introduction Since the reform and opening up, China's economy has achieved remarkable achievements that have caught the world's attention. However, this success has also brought about ecological problems such as environmental pollution and excessive resource exploitation. The "2023 China Ecological Environment Bulletin" pointed out that among 339 prefecture-level and above cities nationwide, 136 cities still have air quality (primarily including PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 ) that exceeds the standard, accounting for 40.1%. Air pollution shows a spatial characteristic of "heavier in the east and north, lighter in the west and south," with the most severely polluted cities mainly distributed in the Beijing-Tianjin-Hebei region and the Shandong Peninsula (Lin and Wang, 2016 ). Air pollution not only poses a health crisis for the public and a trust crisis for the government, but also reduces residents' happiness and affects sustainable social development (Sicard et al., 2023 ). To improve urban air quality, and achieve the goal of “carbon peaking” before 2030, Chinese government has prioritized ecological and environmental construction. It has clearly stated the need to establish and improve the environmental governance system, adopting precise, scientific, legal, and systematic pollution control, and promoting pollution and carbon reduction in a coordinated manner to continuously improve air quality. Ecological environmental issues can essentially be attributed to problems with production and lifestyle. As a new driving force and model for foreign trade development, CBEC not only promotes the global allocation of resources but also accelerates the innovation of green technologies and the adoption of low-carbon production methods, thereby profoundly influencing changes in production and lifestyle. From the production side, CBEC has facilitated a shift in production methods, making companies more focused on green production and resource conservation (Liu et al., 2021 ). With the support of CBEC platforms, companies optimize production processes through the adoption of digital tools and intelligent technologies, achieving precise management, thereby improving production efficiency, reducing waste emissions, and lowering energy consumption. At the same time, CBEC has heightened companies' awareness of the growing global demand for green products, compelling them to adjust their production methods and actively transition towards green technologies and sustainable materials in order to meet market demands for environmental protection and sustainable development. On the distribution side, CBEC reduces carbon emissions during the transportation of goods by optimizing logistics and supply chain management (Wang et al., 2020 ). Through technologies such as big data, artificial intelligence, and the Internet of Things, CBEC platforms have significantly improved logistics scheduling efficiency and optimized transportation plans, thereby reducing unnecessary carbon emissions. At the same time, the platforms adopt a model of centralized procurement and decentralized distribution, effectively reducing inventory accumulation and resource waste during the circulation process. Additionally, CBEC platforms encourage companies to adopt green logistics technologies, further reducing energy consumption and carbon emissions during transportation. On the consumption side, CBEC promotes green consumption, enhancing consumers' awareness and choice of sustainable products (Zhang et al., 2023 ). CBEC platforms often have powerful information dissemination capabilities, allowing consumers to learn about green consumption concepts through platform content pushes, thereby encouraging green consumption behaviors. Moreover, the global nature of CBEC enables consumers to access the latest trends and practices in green consumption from different countries, further enhancing their awareness of green consumption. At the same time, the product information and user reviews provided by CBEC platforms help consumers make more rational choices regarding environmentally friendly products, effectively reducing the environmental burden of consumption activities. In summary, CBEC demonstrates a multi-dimensional green effect across the entire supply chain by promoting the optimization of production methods, logistics models, and consumption patterns. Therefore, we speculate that there is likely an inherent logical relationship between CBEC and urban air quality. If CBEC can improve urban air quality, what are the underlying mechanisms? Are there heterogeneous effects? Exploring the environmental welfare effects of CBEC not only contributes to the sustainable development of the industry but also provides important practical references for the formulation of environmental governance policies. Regarding the effect of economic activities on the ecological environment, early research primarily focused on the relationship between economic growth and environmental protection. Grossman and Krueger ( 1995 ) proposed the famous EKC theory, which posits an inverted U-shaped relationship between pollution levels and per capita income, suggesting that pollution intensifies in the early stages of economic development but begins to decrease once per capita income reaches a certain level. In contrast, some scholars have proposed a U-shaped relationship hypothesis, arguing that economic growth may lead to a continued deterioration of pollution (Smulders et al., 2011 ). As globalization and economic integration have accelerated, the research focus has gradually shifted to the relationship between trade and environment, introducing the "Pollution Haven Hypothesis" (Levinson and Taylor, 2008 ) and the "Pollution Halo Hypothesis" (Li and Lu, 2010 ), which explore the transfer and improvement of environmental pollution under different trade patterns. In recent years, with the development of the digital economy, digital trade has emerged as a new mode of economic activity that has a unique effect on the ecological environment. Studies have shown that digital trade can effectively reduce waste and lower carbon emissions by improving resource allocation efficiency and information transparency, thereby alleviating ecological pressure (Ji et al., 2023 ; Li et al., 2024 ). On one hand, digital trade promotes the construction of green supply chains and low-carbon logistics through paperless processes and reduced logistics demands (Saengchai & Jermsittiparsert, 2019); on the other hand, forms of digital trade, such as CBEC, encourage the transformation of consumption patterns and actively guide green consumption (Li and Deng, 2023 ). The environmental effects of digital trade are considered a promising area for research, especially in exploring how to leverage digital technology to achieve a balance between green development and economic growth (Fu et al., 2023 ). As an important manifestation of digital trade, CBEC demonstrates significant potential in promoting industrial transformation and upgrading, as well as green development, and has received widespread attention in recent years. Related studies initially focused on CBEC as a complementary mode to traditional foreign trade, and later gradually expanded to explore its multiple roles in global resource allocation, technology innovation-driven growth, industrial upgrading, and economic development (Chen and Luo, 2024 ; Zhang and Liu, 2021 ). Among them, most studies primarily focus on the economic welfare effects of CBEC, such as its ability to effectively reduce trade costs and cross-border distances (Wang et al., 2017 ; Kim et al., 2017 ), promote trade facilitation and global market expansion (Liang et al., 2021 ), drive regional economic growth (Yang et al., 2023 ), optimize industrial structure (Wang et al., 2022 ), enhance enterprise innovation capabilities (Chen and Luo, 2024 ), improve consumer welfare (Zhang et al., 2023 ), and social welfare (Niu et al., 2022 ), as well as accelerate the application of green technologies and the efficient allocation of resources (Zhang and Liu, 2021 ). Overall, CBEC plays a significant role in the multidimensional development of the economy and society. However, current research on the relationship between CBEC and ecological environment remains limited. Existing literature has primarily explored the effect of CBEC on carbon emission efficiency at the enterprise level (Li et al., 2024 ), or has only preliminarily verified the effect of cross-border e-commerce on the reduction of SO 2 emissions in cities based on panel data from 106 Chinese cities between 2010 and 2018 (Ma and Zhang, 2022 ),which is still a lack of robustness tests, mechanism analysis, and in-depth heterogeneity analysis. In summary, existing literature on CBEC primarily focuses on its economic welfare effects, providing valuable references for analyzing the relationship between CBEC and the ecological environment. However, as a significant driving force in global trade, understanding how CBEC leverages its advantages to optimize resource allocation, accelerate green technology innovation, and enhance the agglomeration of productive services to improve urban air quality holds substantial practical significance for high-quality economic development. Therefore, it is essential to conduct an in-depth exploration of this topic. Based on this, this paper utilizes the quasi-natural experiment of CBEC pilot zones, employing panel data from 284 cities in China from 2009 to 2022. The study systematically analyzes the improvement effect of CBEC on urban air quality using a multi-period Difference-in-Differences (DID) method, while also investigating its transmission mechanisms and heterogeneous effects. The marginal contributions of this research are primarily reflected in three aspects: First, from the unique perspective of the establishment of CBEC pilot zones, this study systematically evaluates the effect of CBEC on urban air quality, providing new theoretical perspectives and empirical evidence for research on the relationship between CBEC and environment. Second, from the perspectives of agglomeration, technological, and resource allocation effects, this study deeply analyzes the mechanisms through which CBEC influences urban air quality, enriching the theoretical framework of the environmental impact pathways of CBEC. Finally, from dimensions such as geographic location, digital infrastructure, and environmental regulation, this study examines the heterogeneity of CBEC in improving urban air quality, revealing the diversified effect of CBEC on urban air quality under different environmental conditions. Policy background and research hypotheses Policy background. As an innovative trade model combining "Internet + international trade," CBEC has played an increasingly significant role in expanding foreign trade and related areas. In recent years, China’s CBEC has achieved rapid growth. According to statistics from China Customs, the import and export value has increased from CNY 36 billion in 2015 to CNY 2.37 trillion in 2023, with an average annual growth rate of 19.38%.This rapid growth has been largely driven by policy support. To promote the regulated development of CBEC, the Chinese government has consistently included it in its annual government work reports for ten consecutive years. Since 2012, the government has introduced a series of policy documents, including the “Notice on Promoting Healthy and Rapid Development of E-Commerce,” the “Notice on Pilot Programs for CBEC Foreign Exchange Payment by Payment Institutions,” and the “Opinions on Promoting Stable Growth and Structural Adjustment of Imports and Exports.” These policy measures, such as the facilitation of financial services, integration of tax administration, and standardization of market supervision, have created a high-quality business environment conducive to the prosperity of CBEC. To address the deep-rooted contradictions and institutional challenges in the development of CBEC and to improve management systems that adapt to and lead global CBEC development, the Chinese government introduced the policy of CBEC pilot zones. In 2015, Hangzhou became the first pilot city in the country. The Hangzhou CBEC pilot zones achieved significant success in areas such as industrial clusters, brand promotion, and ecosystem development, and established the “Hangzhou Experience” with its core "Six Systems and Two Platforms" model. Building on this, since 2016, the State Council has approved the establishment of 165 CBEC Comprehensive Pilot Zones in seven batches by the end of 2022, covering 31 provinces. From a policy perspective, these CBEC pilot zones have made significant progress in building the “Six Systems and Two Platforms,” effectively reducing trade costs, optimizing the business environment, creating a complete industrial chain and ecosystem, and promoting the clustering of CBEC industries. This has injected strong momentum into the development of high-quality trade. The key to high-quality development lies in supply-side structural reform and green sustainable development. Under the new circumstances, China's CBEC must not only achieve a transition from traditional to new growth drivers in trade but also play an active role in environmental protection. Research hypotheses The effect of CBEC on urban air quality In the process of promoting the transformation and upgrading of foreign trade, CBEC not only emphasizes the convenience of import and export trade but also increasingly focuses on the green and environmentally friendly characteristics of import and export goods. By strengthening cross-border industrial cooperation and forming industrial alliances, CBEC enterprises jointly promote green sustainable development across the entire industry, sharing green technologies and successful experiences to reduce environmental effect (Wang et al., 2017 ). CBEC enterprises not only actively promote the construction of green supply chains but also take on greater social responsibility. They give back to society and enhance their public image by participating in public welfare activities and implementing environmental protection projects, gradually establishing a brand image associated with green and environmental protection (Tu and Shangguan, 2018 ). These initiatives effectively reduce environmental pollution in areas such as production, transportation, and packaging, contributing to improved urban environmental quality. At the policy level, governments and industry organizations have also taken a series of measures to promote the green transformation of CBEC. For example, local governments encourage green development in the industry by promoting green packaging, green warehousing, and environmentally friendly transportation. The newly revised national standard for "Express Packaging Materials" further improves the original standards from the perspectives of "reduction," "greening," and "recyclability," aiming to reduce excessive packaging and advance the greening process in the express delivery sector. With the greening transformation of the express delivery industry, the widespread use of environmentally friendly transportation tools, such as electric vehicles and bicycles, has effectively reduced exhaust emissions and noise pollution, significantly improving urban air quality. Moreover, the establishment of CBEC pilot zones is regarded as an important policy tool for promoting green development in CBEC. Specifically regarding urban environments, Ma and Zhang ( 2022 ) have preliminarily verified that CBEC has a significant positive effect on reducing SO 2 emissions in cities, with a more pronounced effect in economically developed cities and those with a higher proportion of the secondary industry.This indicates that CBEC is not only an important force for economic development but can also play a positive role in green transformation and urban environmental governance. Based on the above analysis, the following hypothesis is proposed: H 1 CBEC can improve urban air quality. CBEC, green technology innovation, and urban air quality In the past, local governments in China commonly exhibited a phenomenon of "race to the bottom" in economic development, where short-term economic growth was pursued at the expense of environmental protection. This resulted in a misallocation of environmental resources, leading to a serious imbalance between economic development and environmental protection, further exacerbating air pollution (Liu and Xu, 2019 ). The establishment of CBEC pilot zones aims to eliminate policy barriers, create a fair competitive market environment, and reduce excessive government intervention in the market, allowing the market to play a decisive role in resource allocation (Liu et al., 2021 ). As the degree of marketization continues to increase, previously misallocated environmental resources will be optimized, gradually guiding enterprises to adjust their production behaviors and promoting green technology innovation. To support the construction of these CBEC pilot zones, various favorable policies have been implemented, aiming to create a conducive policy environment for the development of CBEC and related industries. These policy measures not only attract the agglomeration of production factors such as labor and capital but also create a "policy gap effect," further driving the development of the CBEC industry (Lu and Wang, 2016 ). The agglomeration of production factors provides ample technical talent and intermediate inputs for green technology innovation, while also intensifying market competition pressure, prompting enterprises to gain competitive advantages through green technology innovation (Zeng et al., 2021 ). When engaging in green technology innovation activities, transaction costs are one of the important constraints faced by enterprises. The development of CBEC effectively reduces the spatial and temporal barriers to information exchange and material exchange both between and within enterprises, significantly lowering external market transaction costs and internal management costs. This reduction in costs releases more resources for enterprises, which can be further invested in green technology innovation (Yang et al., 2023 ), thereby enhancing the level of green technology innovation in cities. Research indicates that green technology innovation not only promotes economic growth but also effectively reduces atmospheric pollutant emissions and improves environmental conditions (Luo et al., 2019 ; Li and Bai, 2021 ). The widespread application of green technology helps enterprises optimize production methods, enhance energy utilization efficiency, and reduce energy consumption (Javed et al., 2023 ). Additionally, green technology innovation accelerates the development and utilization of new energy sources, further optimizing the energy consumption structure (Zhang et al., 2023 ), thus improving urban air quality. Therefore, green technology innovation can contribute to the improvement of urban air pollution conditions. Based on the above analysis, the following hypothesis is proposed: H 2 CBEC can improve urban air quality by the pathway of green technology innovation. CBEC, resource allocation, and urban air quality The main causes of air pollution lie in extensive production methods and inefficient resource utilization, while improving resource allocation efficiency can effectively reduce undesirable outputs in production, thereby improving air quality (Guo et al., 2024 ). As a green, sustainable, and high-quality foreign trade model, CBEC helps optimize resource allocation, enhance overall production efficiency, and reduce negative effects on the environment. First, CBEC promotes the deep development of data elements and international cooperation, facilitating the integrated development of global e-commerce supply chains. This not only achieves effective integration of global resources but also enhances resource utilization efficiency by optimizing resource allocation in international markets (Wang et al., 2020 ). Second, supported by digital technology, CBEC platforms integrate various resources such as suppliers, manufacturers, and logistics service providers. By utilizing technologies like big data and cloud computing, these platforms achieve precise matching of resources, thus increasing the overall utilization rate of resources. As reforms in CBEC deepen, the construction of CBEC pilot zones relies on an integrated innovation model of trial and exploration. This not only accelerates the interregional flow of traditional production factors but also expands the supply scale of data elements, optimizes their allocation structure, and enhances the capacity for resource reallocation. On one hand, the improved intelligence level of CBEC pilot zones enables many processes in cross-border trade to be replaced by artificial intelligence technologies, with intelligent systems significantly outperforming traditional mechanical automation in substituting low-skilled labor (Koomey et al., 2013 ). This acceleration of technological substitution effectively optimizes the structure of resource factor allocation and enhances overall factor allocation efficiency. On the other hand, the high-quality business environment created by CBEC pilot zones provides fertile ground for innovations in digital information technology. The demand for new digital jobs brought about by this technological innovation, especially the surge in knowledge-intensive labor, further enhances the potential for high-quality factor allocation. Moreover, CBEC pilot zones promote the deep integration of data elements with traditional factors by expanding the application scenarios of digital elements (Bingbing et al., 2023 ). This integration not only optimizes resource allocation efficiency but also provides strong support for promoting green development and a low-carbon economy. Based on the above analysis, the following hypothesis is proposed: H 3 CBEC can improve urban air quality by the pathway of resource allocation optimizing. CBEC, productive service industry agglomeration, and urban air quality The agglomeration of the productive service industry has been proven to be an effective pathway for reducing environmental pollution and improving environmental quality (Zhuang et al., 2021 ). The productive service industry consists of supporting service industries closely related to manufacturing, covering various fields such as research and development, logistics and transportation, information services, and financial services. To better serve the manufacturing sector and share production information, technology, and natural resources, the productive service industry often naturally concentrates in specific regions, resulting in agglomeration effects. This concentration significantly enhances the production efficiency of manufacturing by providing high value-added, high-tech, low-energy, and low-pollution services, thereby reducing resource consumption and facilitating green transformation, which effectively decreases pollution emissions (Bartolomeo et al., 2003). Productive service industry agglomeration also promotes production specialization, driving the development of capital and knowledge-intensive production. This agglomeration effect helps improve the utilization efficiency of production factors, reduce unnecessary emissions during the production process, and strengthen environmental pollution control (Yang et al., 2021 ). Furthermore, the economies of scale and resource-sharing effects brought about by the agglomeration of the productive service industry contribute to enhancing total factor productivity, reducing unit energy consumption, and improving energy utilization efficiency (Hosoe and Naito, 2006 ). The combined effects of these factors not only reduce pollution emissions and ecological damage but also significantly improve urban air quality. In practice, the development of CBEC often supports and attracts leading enterprises, drawing in logistics, customs clearance, information security, legal services, and other supporting productive service companies to settle in CBEC pilot zones, thereby promoting the agglomeration of the productive service industry. Relying on its institutional advantages and location benefits, CBEC pilot zones combine with local characteristic industrial clusters to focus on building the CBEC industry chain and ecosystem, further promoting the agglomeration of related enterprises in production, operations, logistics, and warehousing, thereby forming CBEC industrial clusters (Dai and Min, 2023 ). This agglomeration effect not only improves the collaborative efficiency of the industry chain but also promotes green and low-carbon development by efficiently integrating various factor resources. CBEC pilot zones also leverage efficient government services and investment trade facilitation measures to utilize the cumulative advantages of other policies, gathering various high-quality factor resources. Through innovative institutional guarantees and policy support, these regions can quickly attract a large number of productive service enterprises, forming an industry ecosystem of collaborative development (Lu and Wang, 2016 ). Based on the above analysis, the following hypothesis is proposed: H 4 CBEC can improve urban air quality by the pathway of productive service industry agglomeration. Research design Model. To address the institutional barriers and deep-rooted contradictions faced in the development of cross-border e-commerce, the Chinese government has successively established CBEC pilot zones since 2015. By November 2022, 165 CBEC pilot zones had been established across 31 provinces. Considering the continuity of the sample interval data, this paper selects the cities from the first five batches of CBEC pilot zones as the sample. This paper considers the establishment of CBEC pilot zones as a policy shock, and based on theoretical analysis, employs the DID method to evaluate its impact on urban air quality. The specific model setup is as follows: $$\:{Uaq}_{it}={\alpha\:}_{0}+{\alpha\:}_{1}{Policy}_{it}+{\alpha\:}_{2}{Controls}_{it}+{\delta\:}_{i}+{\mu\:}_{t}+{\epsilon\:}_{it}$$ 1 Where, \(\:i\) , \(\:\:t\:\) denote cities and years respectively, \(\:Uaq\) represents urban air quality, \(\:{\alpha\:}_{0}\:\) is the intercept term. The explanatory variable \(\:{Policy}_{it}\) is composed of the interaction term of \(\:{Treat}_{i}\:\) and \(\:{Post}_{t}\) , representing the policy effect of the establishment of CBEC pilot zones. \(\:{Treat}_{i}\) is a policy group dummy variable, where \(\:Treat=1\) for cities with pilot zones and \(\:Treat=0\) for those without. \(\:Post\) is a time dummy variable, where \(\:Post=0\) before the establishment and \(\:Post=1\) during and after the establishment year. \(\:{Controls}_{it}\) is a set of city-level control variables. \(\:{\delta\:}_{i}\:\) denotes city fixed effects, \(\:{\mu\:}_{t}\:\) denotes time fixed effects, and \(\:{\epsilon\:}_{it}\:\) is the random disturbance term. Variable settings Dependent variable ( \(\:Uaq\) ). The dependent variable is urban air quality. Based on the availability of urban-level air pollution data, this study follows Guo et al. ( 2024 ) and uses the total industrial sulfur dioxide emissions ( \(\:ln\text{s}{o}_{2}\) ) and per capita industrial sulfur dioxide emissions ( \(\:lnp\text{s}{o}_{2}\) ) to measure urban air quality. Explanatory variable ( \(\:Policy\) ). The explanatory variable \(\:Policy\) represents the policy of the establishment of CBEC pilot zones, defined as \(\:{Treat}_{i}\times\:\) \(\:{Post}_{t}\) . The policy dummy variable \(\:{Treat}_{i}\) is generated based on whether the sample city becomes a pilot zone, and \(\:{Post}_{t}\) is generated based on the establishment time. The interaction term \(\:{Treat}_{i}\times\:\) \(\:{Post}_{t}\) indicates the policy effect. Mechanism variables (1) Green technology innovation ( \(\:Greentec\) ).The measurement of \(\:Greentec\) primarily relies on data integrated from the National Intellectual Property Office's patent database, along with further identification of green patents using the "Green Patent List" published by the World Intellectual Property Organization (WIPO) in 2010. Specifically, patents that meet the definition and classification criteria for green technology innovation are selected from the database, and the number of green patent applications per 10,000 people in each city is calculated to assess the level of green technology innovation. The related measurement methods reference the studies by Zhang et al. ( 2020 ) and Bilal et al. ( 2021 ), using the number of green patents as the core indicator of green technology innovation. (2) Resource allocation ( \(\:Ra\) ). \(\:\:\:Ra\) is measured using the total factor productivity of cities, primarily calculated using the Malmquist index. The Malmquist index has been widely applied in the field of productivity measurement, particularly for efficiency change analysis in multi-period data (Fare et al., 1994 ). By conducting a comparative analysis of productivity changes across different periods, it can effectively assess changes in resource allocation efficiency and their influencing factors. The calculation formula for the Malmquist index is as follows: $$\:{M}_{t,t+1}=\frac{{Y}_{t+1}}{{Y}_{t}}\times\:\frac{{X}_{t+1}}{{X}_{t}}$$ Where: \(\:{M}_{t,t+1}\) ​ represents the Malmquist index from time t to time t + 1; \(\:{Y}_{t}\) and \(\:{Y}_{t+1}\) represent the actual GDP at time t and t + 1 (as the output side); \(\:{X}_{t}\) and \(\:{X}_{t+1}\) represent the input quantities at time t and t + 1 (including fixed capital stock and number of employees). (3) Productive services industry agglomeration ( \(\:Psia\) ).This study follows the approach of Guo and Huang ( 2020 ) and uses the location entropy index to measure \(\:Psia\) . The formula for calculating the location entropy index is as follows: $$\:{Psia}_{it}=\frac{{B}_{it}/{E}_{it}}{{\sum\:}_{i=1}^{n}{B}_{it}/{\sum\:}_{i=1}^{n}{E}_{it}}$$ Where \(\:{B}_{it}\) is the number of employees in productive services in city (i) in year (t), and \(\:{E}_{it}\) is the total number of employees in city (i) in year (t). This index reflects the concentration of the productive service industry in the total employment of a city. A higher location entropy value indicates a stronger agglomeration of the productive service industry. Control variables. (1) Economic development ( \(\:pgdp\) ):This is represented by the logarithm of regional GDP per capita. According to the Environmental Kuznets Curve (EKC) theory, there is an inverted U-shaped relationship between economic development level and environmental pollution, meaning that early-stage economic growth may lead to increased environmental pollution, but after reaching a certain level of economic development, pollution may decrease with technological progress and the implementation of environmental protection policies. Therefore, \(\:pgdp\) ² is also included in the model to capture this inverted U-shaped relationship.(2) Financial development ( \(\:finance\) ): This is represented by the logarithm of the balance of deposits and loans of financial institutions. Financial development can promote the flow of capital and technological innovation, thereby influencing environmental governance and pollution levels. The degree of development of the financial system may indirectly affect urban air quality by providing support for green investments and environmental protection technologies. (3) Industrial structure ( \(\:indus\) ): This is represented by the proportion of the secondary industry added value to GDP. The industrial structure, particularly the proportion of the secondary industry, is closely related to pollution levels. Cities with a high degree of industrialization tend to face higher levels of pollution emissions, thus it is necessary to control the effect of industrial structure on air quality. (4) Government intervention ( \(\:gov\) ): This is represented by the proportion of local government fiscal expenditure to GDP. Government can significantly influence urban environmental quality through fiscal expenditures that regulate environmental protection policies, infrastructure construction, and other areas. Regions with higher fiscal expenditures are likely to implement more environmental protection measures, thereby improving air quality. (5) Population density ( \(\:pop\) ): This is represented by the logarithm of the population per square kilometer. Population density is closely related to the demand for transportation, energy consumption, and other factors. Densely populated cities are likely to generate higher levels of pollution emissions, so it is necessary to consider the effect of this factor on air quality. (6) Urban technological innovation ( \(\:tech\) ): This is represented by the logarithm of the number of invention patents per 10,000 people. Technological innovation is a key factor in promoting green development and reducing pollution. Innovative cities may reduce industrial pollution and improve environmental quality through the development of new technologies. Therefore, controlling for this variable helps to more accurately assess the effect of CBEC on air quality. This study uses panel data from 284 cities in China from 2009 to 2022 to examine the effect of CBEC on urban air quality. The data is sourced from the "China Urban Statistical Yearbook", "China Environmental Statistical Yearbook", some city annual reports, and the EPS database. Descriptive statistics of the relevant variables are shown in Table 1 . Table 1 Descriptive statistics. Variables Obs Mean SD Min Max \(\:ln\text{s}{o}_{2}\) \(\:lnp\text{s}{o}_{2}\) 3962 3962 9.739 3.900 1.368 1.338 1.099 0.034 13.281 7.796 \(\:Policy\) 3962 0.102 0.302 0 1 \(\:pgdp\) 3962 10.688 0.630 4.595 13.056 \(\:pgdp\) ² 3962 114.632 13.396 21.115 170.451 \(\:finance\) 3962 2.512 1.272 0.588 21.301 \(\:indus\) 3962 0.457 0.111 0.107 0.897 \(\:gov\) 3962 0.200 0.116 0.044 2.223 \(\:pop\) 3962 5.897 0.680 3.433 8.137 \(\:tech\) 3962 0.017 0.017 0.001 0.207 Empirical results and robustness tests Baseline regression. The baseline regression results of the effect of the establishment of CBEC pilot zones on urban air quality are shown in Table 2 . In models (1) and (3), only city fixed effects and year fixed effects are controlled. The regression results indicate that the establishment of CBEC pilot zones has a significantly negative effect on both \(\:ln\text{s}{o}_{2}\) and \(\:lnp\text{s}{o}_{2}\) . This suggests that the establishment of CBEC pilot zones has a noticeable effect in reducing air pollution in the respective areas, thereby increasing environmental welfare in the region. In models (2) and (4), after including control variables, the policy effect remains significantly negative. Therefore, the establishment of CBEC pilot zones can significantly improve urban air quality, supporting hypothesis H 1 . Table 2 Baseline regression results of the effect of CBEC pilot zones on urban air quality. Variables \(\:\varvec{l}\varvec{n}\mathbf{s}{\varvec{o}}_{2}\) \(\:\varvec{l}\varvec{n}\varvec{p}\mathbf{s}{\varvec{o}}_{2}\) (1) (2) (3) (4) \(\:Policy\) -0.171 ** (-2.57) -0.154 ** (-2.39) -0.210 *** (-3.41) -0.183 *** (-2.88) \(\:pgdp\) 0.833(0.86) 0.912(0.85) \(\:pgdp\) ² -0.040(-0.88) -0.038(-0.76) \(\:finance\) -0.004(-0.17) 0.002(0.08) \(\:indus\) 0.799 * (1.77) 0.479(1.17) \(\:gov\) -0.268 (-0.79) -0.143(-0.40) \(\:pop\) -0.162(-0.64) -0.217(-1.13) \(\:tech\) -1.519(-0.88) -2.632(-1.61) Constant 9.756 *** (1434.34) 6.118(1.13) 3.921 *** (624.22) -1.578(-0.27) City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Obs 3962 3962 3962 3962 R 2 0.875 0.885 0.890 0.900 Note: *, **, and *** indicate significance at the 10%, 5%, and 1% significance levels, respectively. The standard errors are clustered at the city level. Parallel trend and dynamic effect tests A crucial prerequisite for the difference-in-differences (DID) method is the parallel trend assumption. This assumption requires that before the policy shock of the establishment of CBEC pilot zones, the air pollution levels in the treatment and control cities exhibit similar time-varying trends. Following Wang et al. ( 2020 ), we use an event study approach to construct the following model: $$\:{Uaq}_{it}={{\alpha\:}}_{0}+{\sum\:}_{k=-5,k\ne\:-1}^{4}{\alpha\:}_{k}{Policy}_{i,{t}_{0}+k}+\eta\:{Controls}_{it}+{\delta\:}_{i}+{\mu\:}_{t}+{\epsilon\:}_{it}$$ 2 In this context, \(\:{t}_{0}\) represents the time point when the CBEC pilot zone was established. \(\:{Policy}_{i,{t}_{0}+k}\) is a dummy variable representing the situation \(\:{\:k}_{th}\) years before or after the implementation of the CBEC pilot zone policy. If the treatment city \(\:i\) is in the \(\:{\:({t}_{0}+k)}_{th}\:\) year, the variable is assigned a value of 1, otherwise, it is 0. Setting of k: Since the year of the establishment of the CBEC pilot zone is considered as the 0th period, and the first batch was established in 2015, 2022 marks the 7th year. Therefore, the maximum value of k in formula (2) is 7. Considering that there is limited data for the 5 years before and 4 years after the policy implementation, this study consolidates the data for the 5 years before the policy into the − 5th period, and the data for the 4 years after the policy into the 4th period. The year prior to the establishment of CBEC pilot zone is used as the base year, meaning that the virtual variable for k = − 1 is not included in formula (2). The estimated coefficient \(\:{\alpha\:}_{k}\) ​ of \(\:{Policy}_{i,{t}_{0}+k}\:\) is shown in Fig. 1 . According to the results in Fig. 1 , the estimated coefficients for all periods prior to the implementation of the CBEC pilot zone policy are not significant, indicating no substantial differences between the treatment and control groups. This confirms that the research sample passes the parallel trend test. After the implementation of the CBEC pilot zone policy, the coefficients for most years show a significant negative effect, consistent with the baseline regression results. This suggests that the DID method can be used to examine the effect of CBEC pilot zones on urban air quality. At the same time, it verifies the main conclusion of this paper, namely that the CBEC pilot zone policy has a significant long-term effect on improving urban air quality. Robustness test Placebo test. To rule out the possibility that omitted variables or unobservable factors may affect the estimation results, a placebo test is conducted, following the method of Li et al. ( 2024 ). Specifically, a fictitious policy shock is created to serve as a placebo test. This involves randomly selecting a group of cities equal in number to the cities with CBEC pilot zones as the treatment group, and generating a fictitious policy dummy variable to include in the baseline model. Theoretically, if the baseline regression results are not influenced by omitted variables or other random factors, the estimated coefficient for the fictitious policy dummy variable should not significantly differ from 0. This implies that randomly established CBEC pilot zones would not have a significant effect on urban air quality. The placebo test was repeated 500 times, and Fig. 2 shows the distribution of estimated coefficients for the 500 regressions with fictitious policy dummy variables. The results indicate that the estimated coefficients are very close to 0 and follow a normal distribution. In contrast, the estimated coefficient for the policy variable in the baseline regression is significantly outside this distribution. This suggests that the estimation results are robust, and the observed effects of the establishment of CBEC pilot zones on urban air quality are not driven by omitted variables or random factors. Instrumental variable estimation. To address potential endogeneity issues between the establishment of CBEC pilot zones and urban air quality, and to correct for possible omitted variable bias, an instrumental variable (IV) approach is used. This approach helps mitigate concerns about reverse causality and inconsistencies in the estimation results. Following Nunn and Qian ( 2014 ), two instrumental variables are chosen: (1)IV1: interaction between the number of internet users in China from the previous year and the number of post offices per 100 people in 1984; (2)IV2: interaction between the number of post offices per 100 people in 1984 and the number of telephones per 100 people in 1984. Two-stage least squares (2SLS) regression analysis is employed, with results presented in Table 3 . The first stage regression results are shown in columns (1) and (2). The regression results indicate that regardless of whether control variables are included, there is a significant positive correlation between the instrumental variables (IV1 and IV2) and the policy variable for the establishment of CBEC pilot zones. The validity of the instrumental variables in this study was verified through weak instrumental variable testing. Columns (3) and (4) show the regression results of \(\:ln\text{s}{o}_{2\:}\) in the second stage. The results indicate that regardless of whether control variables are included, the coefficients of the core explanatory variable (Policy) are significantly negative; Columns (5) and (6) show the regression results of \(\:lnp\text{s}{o}_{2}\) in the second stage. The results show that regardless of whether control variables are included, the coefficient of the core explanatory variable (Policy) is significantly negative, indicating that the establishment of CBEC pilot zones significantly suppressed urban industrial sulfur dioxide emissions (including \(\:ln\text{s}{o}_{2\:}\) and \(\:lnp\text{s}{o}_{2\:}\) ), which is consistent with the baseline results and proves the reliability of the previous research conclusions. Table 3 2SLS estimation results. Variables \(\:Policy\) \(\:ln\text{s}{o}_{2}\) \(\:lnp\text{s}{o}_{2}\) (1) (2) (3) (4) (5) (6) IV1 0.253 *** (10.41) 0.191 *** (13.66) IV2 0.251 *** (9.81) 0.180 *** (6.48) \(\:Policy\) -1.125 *** (-3.52) -1.446 *** (-7.84) -0.903 *** (-4.10) -0.890 *** (-2.36) \(\:Controls\) No Yes No Yes No Yes City FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Kleibergen-Paap LM 44.760 *** 190.379 *** Kleibergen-Paap Wald F 108.318[16.38] 186.614[16.38] R 2 0.287 0.466 0.194 0.177 Obs 2902 2902 2902 2902 2902 2902 Note : Standard errors clustered at the city level are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. PSM-DID test. To further control the effect of other differences of CBEC pilot zones and non CBEC pilot zones on air quality, this study once again used propensity score matching (PSM) to pair the control group for regression analysis. Firstly, estimate the propensity score using the Logit model. Specifically, whether to establish a policy for CBEC pilot zones is considered as the dependent variable, while economic development level ( \(\:pgdp\) / \(\:pgdp\) ²), financial development level( \(\:finance\) ), industrial structure ( \(\:indus\) ), government intervention ( \(\:gov\) ), population density ( \(\:pop\) ), and technological innovation level ( \(\:tech\) ) are considered as matching variables. Secondly, based on the estimated score, a 1:1 K -nearest neighbor matching was performed to obtain the control group sample that is most similar to the treatment group sample. At the same time, a comparative analysis of the mean feature variables between the treatment group and the control group sample was conducted, and it was found that there was no significant difference in the basic characteristics between the two groups. Finally, the matched samples were used to regress model (1), and the regression results are shown in columns (1) and (2) of Table 4 . The coefficient of the interaction term is still significantly negative, consistent with the benchmark regression results using the entire sample in the previous section. This fully demonstrates that the selection bias of the sample has no effect on the research conclusions. Table 4 PSM-DID test results. Variables \(\:ln\text{s}{o}_{2}\) \(\:lnp\text{s}{o}_{2}\) (1) (2) \(\:Policy\) -0.150 ** (-2.36) -0.130 ** (-2.22) \(\:Controls\) Yes Yes City FE Yes Yes Year FE Yes Yes R 2 0.876 0.903 Obs 3960 3960 Excluding confounding policies. Given that various similar or related policies may be implemented simultaneously or intersect, potentially leading to policy overlap effects, it is essential to control for the effect of other significant policies that could influence the results. This paper addresses potential confounding factors by controlling for policies closely related to the development of CBEC pilot zones, such as innovation city pilot programs (Yang, 2023 ), low-carbon city construction (Song et al., 2019 ), broadband city initiatives (Ju, 2023 ).To account for these overlapping policies, the dummy variables for the aforementioned policies are added to the baseline regression model. Table 5 reports the above regression results. Columns (1)–(3) and (5)–(7) show the regression results with the addition of other policy dummy variables respectively, while columns (4) and (8) show the results with the addition of other policy dummy variables to the regression equation simultaneously. All results show that after controlling for other policy shocks, the coefficient of policy remained significantly negative, and the coefficient size does not change significantly compared to the benchmark results, indicating that other policy shocks did not affect the causal relationship between the establishment of CBEC pilot zones and urban air quality. Thus, the conclusions of the previous analysis remain valid. Table 5 The results of excluding confounding policies. Variables \(\:ln\text{s}{o}_{2}\) \(\:lnp\text{s}{o}_{2}\) (1) (2) (3) (4) (5) (6) (7) (8) \(\:Policy\) -0.154 ** -0.168 *** -0.144 ** -0.155 ** -0.134 ** -0.147 ** -0.126 ** -0.135 ** (-2.40) (-2.64) (-2.28) (-2.49) (-2.28) (-2.51) (-2.17) (-2.37) Constant 6.096 5.738 6.329 5.947 5.145 4.799 5.304 5.101 (1.11) (1.01) (1.21) (1.07) (0.89) (0.80) (0.94) (0.85) innocity Yes Yes Yes Yes lowcartoncity Yes Yes Yes Yes broadbandcity Yes Yes Yes Yes \(\:Controls\) Yes Yes Yes Yes Yes Yes Yes Yes City FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes R 2 0.876 0.876 0.876 0.876 0.893 0.894 0.894 0.894 Obs 3962 3948 3948 3948 3962 3948 3948 3948 Other robustness tests (1) Changing the dependent variable This article is based on the approach of Guo and Shi ( 2017 ), using the annual average concentration of PM2.5 as a proxy variable for urban air pollution level. The reason may be that PM2.5 is generated in industrial production and residential life processes, and it is considered the main culprit causing haze pollution. In view of this, this article replaces the dependent variable in the benchmark regression with the annual average concentration of PM2.5 for robustness testing. Due to the fact that the latest PM2.5 data available in this article is up to 2020 (data source: Columbia University Center for Socioeconomic Data and Applications), the sample size for regression has been reduced.The specific regression results are shown in columns (1) and (2) of Table 6 . It can be seen that regardless of whether control variables are included, the regression coefficient of the policy for the establishment of CBEC pilot zones is significantly negative, which also indicates the relative robustness of the previous results. (2) Reanalyzing the difference variables When defining the policy dummy variable in the previous text, the value of 1 is taken for the year of policy implementation and subsequent years. Given that the policy announcement dates could mean the policy was not fully implemented within the year, a revised approach is adopted. Following Lu et al. ( 2017 ), adjustments are made for the year of policy implementation based on specific cohorts: First batch (March, 2015): Post = 5/6 for 2015, then 1. Second batch (January, 2016): Post = 1 for the implementation year and subsequent years. Third batch (July, 2018): Post = 1/2 for the implementation year, then 1. Fourth batch(December, 2019): Post = 1/12 for the implementation year, then 1. Fifth batch(April, 2020): Post = 7/12 for the implementation year, then 1. In addition, as the sixth and seventh batches were established in 2022, considering that the policy effects have not yet been fully released, the sixth and seventh batches will be regarded as the control group. These adjusted post variables are incorporated into the model. Results, as shown in Table 6 , columns (3) and (4), are consistent with baseline findings. (3) Considering policy anticipation effects To account for potential pre-policy anticipation effects, an interaction term between the policy dummy and a dummy variable for the year prior to implementation is added to the model (1). Results are reported in Table 6 , columns (5) and (6). The interaction term is not significant, indicating no significant anticipatory effects and affirming the exogeneity of the policy of the establishment of CBEC pilot zones. (4) Considering lagged policy effects Recognizing that the effect of the policy of the establishment of CBEC pilot zones on air quality may not be immediate, the core explanatory variable ( Policy ) is lagged by one year, with all control variables also lagged to avoid simultaneity bias. Results are presented in Table 6 , columns (7) and (8). The coefficient for Policy remains significantly negative, supporting the previous conclusions about the positive effect of the policy on air quality. (5) Excluding central cities Given the potential differences in administrative behavior and resources between central cities (e.g., provincial capitals, sub-provincial cities, and municipalities) and ordinary prefecture-level cities, these central cities are excluded from the sample. Results show that even after excluding central cities, the policy still significantly reduces air pollution (coefficients for \(\:ln\text{s}{o}_{2}\) and \(\:lnp\text{s}{o}_{2}\) are − 0.103 and − 0.092, respectively, with p < 0.1). This further validates the robustness of the previous findings. Table 6 Results of other robustness tests. Variables Dependent variable replaced Reanalyzing differential variables Policy expected effects Policy lag effects PM2.5 \(\:\left(1\right)\) PM2.5 \(\:\left(2\right)\) \(\:ln\text{s}{o}_{2}\left(3\right)\) \(\:lnp\text{s}{o}_{2}\left(4\right)\) \(\:ln\text{s}{o}_{2}\left(5\right)\) \(\:lnp\text{s}{o}_{2}\left(6\right)\) \(\:ln\text{s}{o}_{2}\left(7\right)\) \(\:lnp\text{s}{o}_{2}\left(8\right)\) \(\:Policy\) -0.024 ** -0.017 ** -0.140 ** -0.123 ** -0.137 ** -0.122 ** -0.187 *** -0.169 *** (-2.59) (-2.09) (-2.31) (-2.19) (-2.07) (-2.00) (-2.99) (-2.94) Pilot cities \(\:\times\:\) One year prior to policy 0.019 0.001 (0.34) (0.04) \(\:Controls\) No Yes Yes Yes Yes Yes Yes Yes City FE Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes R 2 0.951 0.952 0.876 0.893 0.876 0.893 0.889 0.897 Obs 3405 3405 3962 3962 3962 3962 3678 3678 Heterogeneity tests Geographic location heterogeneity. To further explore the geographic heterogeneity of the effect of the establishment of CBEC pilot zones on urban air quality, the sample cities are divided into eastern and central-western regions based on the classification by China's National Bureau of Statistics. Eastern cities include 11 provinces (or municipalities, autonomous regions) such as Beijing, Shanghai, and Zhejiang, while central-western cities include 20 provinces (or municipalities, autonomous regions) such as Shanxi and Jilin. As shown in Table 7 , columns (1) to (4), there is a clear geographic heterogeneity in the effects of the policy. The policy's coefficient is significantly negative in eastern cities, but not significant in central-western cities. This indicates that the ecological and environmental benefits of the policy are more pronounced in eastern cities. The possible reasons are as follows: First, the GDP of eastern cities is significantly higher than that of central-western regions, with more concentrated economic activities, leading to greater energy consumption and pollution emissions. The establishment of CBEC pilot zones has not only promoted the expansion of logistics, transportation, and warehousing but also driven the application of green technologies (such as green packaging and logistics technologies) and pollution control measures. Second, due to economic and industrial agglomeration, eastern cities face more pollution sources, such as construction dust and emissions from the catering industry. The policies of CBEC pilot zones can significantly improve environmental quality by promoting green technology innovation and raising environmental awareness. In addition, governments in eastern regions have strong implementation capabilities, effectively driving policy execution, particularly in environmental protection and sustainable development. In contrast, governments in central-western cities have weaker execution power and insufficient investment in green development, leading to less noticeable environmental effects of the CBEC pilot zone policies. Digital infrastructure heterogeneity. Well-developed digital infrastructure provides better internet services for CBEC businesses, which may affect the policy’s effect on urban air quality. Using internet penetration rate as a proxy for digital infrastructure, the sample is divided into high and low digital infrastructure groups based on the median internet penetration rate of cities in the initial year. The results are shown in Table 7 , columns (5) to (8). In cities with high digital infrastructure, the policy’s effect on air quality is significantly negative. In contrast, in cities with lower digital infrastructure, the relationship is not significant. This suggests that the environmental benefits of the policy are more pronounced in cities with better digital infrastructure, as better infrastructure facilitates CBEC activities and increases the likelihood of firms engaging in these activities, thereby enhancing the policy’s effect on reducing air pollution. The reason for this is that regions with more developed digital infrastructure make CBEC activities more convenient, allowing businesses to more easily engage in CBEC. Particularly after the approval of CBEC pilot zones, businesses in these regions can quickly seize policy opportunities, enjoy institutional benefits, and promote industrial upgrading and innovation. As CBEC activities continue to increase, e-commerce platforms, logistics systems, and related industrial chains in these regions will continue to grow, thereby driving the application of green technologies and strengthening environmental protection measures. Ultimately, the air pollution reduction effects of the CBEC pilot zone policies in these regions will gradually emerge and be enhanced, as the promotion of green technologies and green logistics helps reduce carbon emissions and other pollutants, driving regional sustainable development. Environmental regulation intensity heterogeneity. Environmental regulation, aimed at protecting the environment, can directly improve regional environmental quality and also influence CBEC firms’ behavior, which in turn affects the environment. Regulations such as environmental taxes and laws may increase operational costs for e-commerce firms, prompting them to adopt eco-friendly technologies and improve environmental quality. The intensity of environmental regulations may therefore affect the policy’s effect on urban air quality. Following Chen and Chen ( 2018 ), the frequency and proportion of environmental terms in government work reports are used as proxies for environmental regulation intensity. Assuming that the environmentally related terms in the government work report include "environmental protection," "pollution control," "energy conservation and emission reduction," "carbon emissions," "SO 2 emissions," and other related terms, the calculation of environmental regulation intensity is as follows: ERI= \(\:\:\frac{{\sum\:}_{i=1}^{n}{\text{F}\text{r}\text{e}\text{q}\text{u}\text{e}\text{n}\text{c}\text{y}\:\text{o}\text{f}\:\text{e}\text{n}\text{v}\text{i}\text{r}\text{o}\text{n}\text{m}\text{e}\text{n}\text{t}\text{a}\text{l}\text{l}\text{y}\:\text{r}\text{e}\text{l}\text{a}\text{t}\text{e}\text{d}\:\text{t}\text{e}\text{r}\text{m}\text{s}}_{i}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{w}\text{o}\text{r}\text{d}\text{s}\:\text{i}\text{n}\:\text{t}\text{h}\text{e}\:\text{r}\text{e}\text{p}\text{o}\text{r}\text{t}\text{s}}\times\:100\) \(\:\:\frac{{\sum\:}_{i=1}^{n}{\text{F}\text{r}\text{e}\text{q}\text{u}\text{e}\text{n}\text{c}\text{y}\:\text{o}\text{f}\:\text{e}\text{n}\text{v}\text{i}\text{r}\text{o}\text{n}\text{m}\text{e}\text{n}\text{t}\text{a}\text{l}\text{l}\text{y}\:\text{r}\text{e}\text{l}\text{a}\text{t}\text{e}\text{d}\:\text{t}\text{e}\text{r}\text{m}\text{s}}_{i}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{w}\text{o}\text{r}\text{d}\text{s}\:\text{i}\text{n}\:\text{t}\text{h}\text{e}\:\text{r}\text{e}\text{p}\text{o}\text{r}\text{t}\text{s}}\times\:100\) Where: \(\:{\sum\:}_{i=1}^{n}{\text{F}\text{r}\text{e}\text{q}\text{u}\text{e}\text{n}\text{c}\text{y}\:\text{o}\text{f}\:\text{e}\text{n}\text{v}\text{i}\text{r}\text{o}\text{n}\text{m}\text{e}\text{n}\text{t}\text{a}\text{l}\text{l}\text{y}\:\text{r}\text{e}\text{l}\text{a}\text{t}\text{e}\text{d}\:\text{t}\text{e}\text{r}\text{m}\text{s}}_{i}\) represents the total frequency of all environmentally related terms appearing in the government work reports. The sample is divided into high and low environmental regulation intensity groups based on the median. Results are presented in Table 7 , columns (9) to (12). In cities with low environmental regulation intensity, the policy’s effect on air quality is more significant. In contrast, in cities with high environmental regulation intensity, the policy’s effect is less pronounced. This suggests that the policy's environmental benefits are more evident in cities with weaker environmental regulations, where CBEC policy can have a more pronounced effect on reducing air pollution. The main reason for this may be that in regions with higher environmental regulation, the government has already implemented strict environmental protection policies and measures, such as pollution emission standards and the promotion of green technologies. As a result, businesses are more adaptive to environmental protection requirements, pollution sources are effectively controlled, and environmental governance has reached a high level. Therefore, the environmental effects brought about by the CBEC pilot zone policies are relatively limited. In contrast, in regions with weaker environmental regulations, due to lenient policies or ineffective implementation, businesses may not have adopted sufficient green measures. In such cases, the implementation of the CBEC pilot zone policies may become an opportunity to drive business transformation and the application of green technologies, leading to more significant environmental protection effects. Furthermore, in regions with high environmental regulation intensity, the government’s strong execution capacity enables efficient implementation of environmental policies, which limits the further role of the CBEC pilot zone policies in environmental improvement. On the other hand, in regions with weaker regulations, the government relies more on the CBEC pilot zone policies to strengthen environmental governance and promote the application of green technologies and environmental protection measures, thereby enhancing the ecological effects of the policies. Table 7 Heterogeneity test results. Variables Geographical location Digital infrastructure Environmental regulations eastern cities midwest cities high low high low \(\:ln\text{s}{o}_{2}\) \(\:lnp\text{s}{o}_{2}\) \(\:ln\text{s}{o}_{2}\) \(\:lnp\text{s}{o}_{2}\) \(\:ln\text{s}{o}_{2}\) \(\:lnp\text{s}{o}_{2}\) \(\:ln\text{s}{o}_{2}\) \(\:lnp\text{s}{o}_{2}\) \(\:ln\text{s}{o}_{2}\) \(\:lnp\text{s}{o}_{2}\) \(\:ln\text{s}{o}_{2}\) \(\:lnp\text{s}{o}_{2}\) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) \(\:Policy\) − .213 ** − .185 ** − .005 − .001 − .130 ** − .093 * − .216 − .175 − .114 * − .095 − .188 ** − .174 ** (-2.48) (-2.34) (-0.06) (-0.02) (-2.14) (-1.68) (-0.81) (-0.69) (-1.66) (-1.54) (-2.02) (-2.08) \(\:Controls\) Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes City FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes R 2 0.904 0.913 0.865 0.888 0.903 0.922 0.856 0.881 0.897 0.904 0.881 0.897 Obs 1357 1357 2605 2605 2114 2114 1834 1834 1918 1918 1899 1899 Mechanism testing Regarding the selection of a mechanism testing method, given the ongoing debate over the applicability of mediation effect models in economic research and the potential issues they may cause, such as endogeneity bias and ambiguity in identifying causal channels (Jiang, 2022 ), this paper adopts the approach of Liu and Mao (2019). Specifically, it examines the effect of the establishment of CBEC pilot zones on mechanism variables to test the mechanisms at play. The theoretical analysis suggests that CBEC enhances a city's green technology innovation capability, productive services industry agglomeration, and resource allocation optimization, which, in turn, improves air quality. First, the improvement of green technology innovation helps cities transform traditional production methods, increase energy efficiency, reduce energy consumption, accelerate the development and use of clean energy, and optimize the energy consumption structure, thereby improving air quality. Second, productive service industry agglomeration provides high value-added, high-tech, low-energy, and low-pollution services to manufacturing industries, promoting their green transformation. This agglomeration also fosters production specialization, expands capital- and knowledge-intensive production, and creates economies of scale and resource-sharing effects, which together enhance the productivity of production factors, reduce unit energy consumption and pollution emissions, and contribute to better air quality. Finally, resource allocation optimization not only accelerates the flow of traditional factors across regions but also promotes the integration of traditional and data factors, enhancing the efficiency of factor production and reducing unwanted outputs in the manufacturing process, thus effectively reducing air pollution. Therefore, CBEC improves urban air quality primarily through three channels: green technology innovation capacity, productive service industry agglomeration, and resource allocation optimizing. To verify whether these channels are valid, this paper constructs the following econometric model: $$\:{Mech}_{it}={\beta\:}_{0}+{\beta\:}_{1}{Policy}_{it}+{\beta\:}_{2}{Controls}_{it}+{\delta\:}_{i}+{\mu\:}_{t}+{\epsilon\:}_{it}$$ 3 In Eq. ( 3 ), \(\:{Mech}_{it}\) is the mechanism variable, which includes green technology innovation ( \(\:Greentec\) ), resource allocation ( \(\:Ra\) ), and productive service industry agglomeration ( \(\:Psia\) ). Due to the lack of employment in the calculation of productive service industry agglomeration after 2020, there may be a decrease in the observed values. The specific regression results are shown in Table 8 . Columns (1) and (2) demonstrate that the establishment of CBCE pilot zones has a significant positive effect on urban green technology innovation. By promoting the research, development, and application of green technologies, these pilot zones help cities make progress in reducing energy consumption, improving energy efficiency, and optimizing energy consumption structures (Bilal et al., 2021 ). These innovations not only facilitate the green transformation of the economy but also directly improve urban air quality (Hu et al., 2021 ). Specifically, green technology innovations make energy consumption more efficient, reduce pollutant emissions, and effectively lower the concentration of air pollutants, thereby validating hypothesis H 2 . The regression results in columns (3) and (4) show that the establishment of CBEC pilot zones also significantly improves resource allocation efficiency. On the one hand, this optimization makes the allocation of production factors more rational, which can effectively improve factor productivity (Chen and Wu, 2024 ). On the other hand, the improvement in resource allocation efficiency makes the production processes of businesses and industries more efficient, reducing the generation of undesirable outputs (Sun et al., 2020 ). The increase in production efficiency and the reduction of undesirable outputs help mitigate air pollution and improve air quality (Sun et al., 2020 ), thus validating hypothesis H 3 . According to the regression results in columns (5) and (6), the establishment of CBEC pilot zones significantly promotes productive service industry agglomeration. Productive service industry agglomeration not only brings economies of scale but also promotes resource sharing, which helps improve the overall productivity of production factors (Peng et al., 2023 ). Additionally, as the productive service industry agglomerates, it can effectively enhance energy utilization efficiency, reduce energy consumption per unit, and decrease pollutant emissions (Ma and Yao, 2022 ). These agglomeration effects improve urban air quality by strengthening the collaborative effects within the industrial chain (Wang et al., 2023 ), thus validating hypothesis H 4 . These regression results indicate that the establishment of CBEC pilot zones indirectly promotes the improvement of urban air quality through their effects on green technology innovation, resource allocation, and productive service industry agglomeration. This phenomenon reflects the multiple driving roles of CBEC as a policy tool in urban economic transformation and green development. The CBEC pilot zones not only provide businesses with more market opportunities but also offer a good platform for technological innovation, resource optimization, and industrial agglomeration. However, it is worth noting that employment data related to productive service industry agglomeration is missing after 2020, which may have led to a reduction in the sample size for the regression results, affecting the stability and representativeness of the analysis. Therefore, future research could consider more comprehensive datasets to further validate the universality and long-term effects of these mechanisms. Overall, the implementation of CBEC pilot zone policies provides Chinese cities with a multi-dimensional path for economic and environmental optimization, particularly in improving air quality. This offers valuable practical experience for policymakers and promotes the coordinated development of high-quality economic growth and environmental sustainability. Table 8 Mechanism test results. Variables \(\:Greentec\) \(\:Ra\) \(\:Psia\) (1) (2) (3) (4) (5) (6) \(\:Policy\) 0.260 *** 0.186 *** 0.007 * 0.009 * 0.071*** 0.084 *** (9.01) (6.74) (1.46) (1.68) (3.15) (3.84) \(\:Controls\) No Yes No Yes No Yes City FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes R 2 0.871 0.893 0.781 0.769 0.845 0.848 Obs 3962 3962 3880 3880 3115 3115 Conclusion and recommendations This paper utilizes panel data from 284 Chinese cities from 2009 to 2022, treating the establishment of CBEC pilot zones as a quasi-natural experiment. It empirically examines the emission reduction effects of CBEC on urban air pollution and explores the transmission mechanisms and heterogeneity of these effects. The research findings indicate that: (1) CBEC significantly improves urban air quality, with a particularly notable effect in reducing per capita industrial sulfur dioxide emissions. This conclusion is validated through various robustness tests, including parallel trend, dynamic effect tests, placebo tests, and the PSM-DID method. (2) Heterogeneity analysis shows that the emission reduction effect of CBEC is more pronounced in eastern cities compared to central and western regions; cities with higher levels of digital infrastructure experience stronger emission reduction effects from CBEC; and cities with weaker environmental regulations tend to amplify the emission reduction effect. (3) Mechanism analysis reveals that CBEC effectively reduces urban air pollution and improves air quality by green technology innovation, productive service industry agglomeration, and resource allocation optimizing. Based on these findings, the paper proposes several policy recommendations to further enhance the positive role of CBEC in promoting urban green development and improving air quality. First, China should build on the development practices of CBEC pilot zones to further accelerate the rapid growth of CBEC. The challenge of balancing economic development and environmental improvement has long been a difficult issue, with few studies effectively integrating the two. However, this study demonstrates that CBEC not only promotes economic growth but also contributes to environmental improvement. Therefore, CBEC should be viewed as a key tool for driving high-quality development, with comprehensive reforms and accelerated construction of CBEC pilot zones. On one hand, local regions should leverage their unique industrial advantages and use CBEC pilot zones to foster CBEC-enabled industrial belt development models, facilitating industrial upgrading and transformation. On the other hand, the resource integration capabilities of CBEC should be utilized to optimize the allocation of traditional production factors, improve resource utilization efficiency, and achieve a win-win situation for both economic and environmental benefits. Second, given the differences in resource endowments across cities, policies should be tailored to local conditions to maximize the economic and social welfare gains from CBEC. The heterogeneity analysis in this study offers valuable insights for advancing CBEC development. The findings reveal significant regional differences in the environmental effects of CBEC. Therefore, the government should formulate targeted policies for the development of CBEC pilot zones based on the resource endowments and actual conditions of each region, enhancing the inclusivity and flexibility of these policies while avoiding blind replication of other regions' experiences. Only in this way can CBEC fully realize its environmental and economic benefits in different cities. Third, improving information infrastructure and accelerating the spread of the internet are critical to promoting the development of CBEC. The growth of CBEC and the establishment of CBEC pilot zones are highly dependent on the internet and other modern information and communication infrastructure. The heterogeneity analysis also shows that in cities with more advanced internet development, the effect of CBEC on air quality improvement is more significant. Therefore, the government should thoroughly implement the strategy of building a strong cyber infrastructure, accelerate the development of new types of infrastructure, especially internet infrastructure, promote the interconnectivity of urban and rural broadband networks, enhance service quality, and further increase the speed and efficiency of information transmission, providing strong support for the environmental benefits of CBEC. Declarations Author Contribution Author contributions: Conceptualization: Shiwen Luo and Hongsheng Zhang;Data collection and processing: Shiwen Luo; Methodology: Shiwen Luo; Software: Hongsheng Zhang; Writing—original draft: Shiwen Luo; Writing—review and editing: Hongsheng Zhang. All authors have read and approved the fnal manuscript. Data Availability Data will be made available on request. 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Journal of cleaner production 264:121698. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6420572","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":456251207,"identity":"8845bb52-0576-4441-a1e7-82b7ed5e93f3","order_by":0,"name":"shiwen Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACPhDB+M9Gjp+Z+eADorSwQcg0Y8l2tmQDUrQcStxwnsdMgDgt/GcMHxfwHEjcfJjBjIGhxiaasBaJHGPjGRJ3jLcdZkh7wHAsLbeBsBbebdI8Bs9kgVqOGzA2HCZCC//Z7b95Eg4zbm5mbJMgTgtD7jZmngOHFTcwM7MRqUUi/7M0b0OascRhNmaDBGL8ws9/LPEzbwMwKvvPf3zwocaGsBZUkECa8lEwCkbBKBgFuAAATsc5+4JKbjkAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang Financial College","correspondingAuthor":true,"prefix":"","firstName":"shiwen","middleName":"","lastName":"Luo","suffix":""},{"id":456251208,"identity":"479a1c53-f60d-44dc-b738-2927162e900f","order_by":1,"name":"Hongsheng Zhang","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Hongsheng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-04-10 13:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6420572/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6420572/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82896728,"identity":"80255c3e-3262-4076-9a07-91dccd2a006a","added_by":"auto","created_at":"2025-05-16 13:01:16","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":145959,"visible":true,"origin":"","legend":"\u003cp\u003eParallel trend and dynamic effect tests.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6420572/v1/4ef4942211732d2dda4b3086.jpeg"},{"id":82896726,"identity":"5075fa64-0525-43a9-9d02-e65e5ca46e41","added_by":"auto","created_at":"2025-05-16 13:01:16","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":211687,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of estimated coefficients for Placebo policy dummy variables.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6420572/v1/602f96975058bf761b43a10a.jpeg"},{"id":95221715,"identity":"a15cbda8-ccc5-4771-83a3-1f590a3cb6f3","added_by":"auto","created_at":"2025-11-05 16:19:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1625374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6420572/v1/5ff714b8-0c9b-40e2-bd0e-01fd7c578a3c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Does cross-border e-commerce contribute to urban air quality improvement? Evidence from China’s pilot zones","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the reform and opening up, China's economy has achieved remarkable achievements that have caught the world's attention. However, this success has also brought about ecological problems such as environmental pollution and excessive resource exploitation. The \"2023 China Ecological Environment Bulletin\" pointed out that among 339 prefecture-level and above cities nationwide, 136 cities still have air quality (primarily including PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, CO, and O\u003csub\u003e3\u003c/sub\u003e) that exceeds the standard, accounting for 40.1%. Air pollution shows a spatial characteristic of \"heavier in the east and north, lighter in the west and south,\" with the most severely polluted cities mainly distributed in the Beijing-Tianjin-Hebei region and the Shandong Peninsula (Lin and Wang, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Air pollution not only poses a health crisis for the public and a trust crisis for the government, but also reduces residents' happiness and affects sustainable social development (Sicard et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To improve urban air quality, and achieve the goal of “carbon peaking” before 2030, Chinese government has prioritized ecological and environmental construction. It has clearly stated the need to establish and improve the environmental governance system, adopting precise, scientific, legal, and systematic pollution control, and promoting pollution and carbon reduction in a coordinated manner to continuously improve air quality.\u003c/p\u003e \u003cp\u003eEcological environmental issues can essentially be attributed to problems with production and lifestyle. As a new driving force and model for foreign trade development, CBEC not only promotes the global allocation of resources but also accelerates the innovation of green technologies and the adoption of low-carbon production methods, thereby profoundly influencing changes in production and lifestyle. From the production side, CBEC has facilitated a shift in production methods, making companies more focused on green production and resource conservation (Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With the support of CBEC platforms, companies optimize production processes through the adoption of digital tools and intelligent technologies, achieving precise management, thereby improving production efficiency, reducing waste emissions, and lowering energy consumption. At the same time, CBEC has heightened companies' awareness of the growing global demand for green products, compelling them to adjust their production methods and actively transition towards green technologies and sustainable materials in order to meet market demands for environmental protection and sustainable development. On the distribution side, CBEC reduces carbon emissions during the transportation of goods by optimizing logistics and supply chain management (Wang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Through technologies such as big data, artificial intelligence, and the Internet of Things, CBEC platforms have significantly improved logistics scheduling efficiency and optimized transportation plans, thereby reducing unnecessary carbon emissions. At the same time, the platforms adopt a model of centralized procurement and decentralized distribution, effectively reducing inventory accumulation and resource waste during the circulation process. Additionally, CBEC platforms encourage companies to adopt green logistics technologies, further reducing energy consumption and carbon emissions during transportation. On the consumption side, CBEC promotes green consumption, enhancing consumers' awareness and choice of sustainable products (Zhang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). CBEC platforms often have powerful information dissemination capabilities, allowing consumers to learn about green consumption concepts through platform content pushes, thereby encouraging green consumption behaviors. Moreover, the global nature of CBEC enables consumers to access the latest trends and practices in green consumption from different countries, further enhancing their awareness of green consumption. At the same time, the product information and user reviews provided by CBEC platforms help consumers make more rational choices regarding environmentally friendly products, effectively reducing the environmental burden of consumption activities. In summary, CBEC demonstrates a multi-dimensional green effect across the entire supply chain by promoting the optimization of production methods, logistics models, and consumption patterns. Therefore, we speculate that there is likely an inherent logical relationship between CBEC and urban air quality. If CBEC can improve urban air quality, what are the underlying mechanisms? Are there heterogeneous effects? Exploring the environmental welfare effects of CBEC not only contributes to the sustainable development of the industry but also provides important practical references for the formulation of environmental governance policies.\u003c/p\u003e \u003cp\u003eRegarding the effect of economic activities on the ecological environment, early research primarily focused on the relationship between economic growth and environmental protection. Grossman and Krueger (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) proposed the famous EKC theory, which posits an inverted U-shaped relationship between pollution levels and per capita income, suggesting that pollution intensifies in the early stages of economic development but begins to decrease once per capita income reaches a certain level. In contrast, some scholars have proposed a U-shaped relationship hypothesis, arguing that economic growth may lead to a continued deterioration of pollution (Smulders et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As globalization and economic integration have accelerated, the research focus has gradually shifted to the relationship between trade and environment, introducing the \"Pollution Haven Hypothesis\" (Levinson and Taylor, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and the \"Pollution Halo Hypothesis\" (Li and Lu, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which explore the transfer and improvement of environmental pollution under different trade patterns. In recent years, with the development of the digital economy, digital trade has emerged as a new mode of economic activity that has a unique effect on the ecological environment. Studies have shown that digital trade can effectively reduce waste and lower carbon emissions by improving resource allocation efficiency and information transparency, thereby alleviating ecological pressure (Ji et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On one hand, digital trade promotes the construction of green supply chains and low-carbon logistics through paperless processes and reduced logistics demands (Saengchai \u0026amp; Jermsittiparsert, 2019); on the other hand, forms of digital trade, such as CBEC, encourage the transformation of consumption patterns and actively guide green consumption (Li and Deng, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The environmental effects of digital trade are considered a promising area for research, especially in exploring how to leverage digital technology to achieve a balance between green development and economic growth (Fu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs an important manifestation of digital trade, CBEC demonstrates significant potential in promoting industrial transformation and upgrading, as well as green development, and has received widespread attention in recent years. Related studies initially focused on CBEC as a complementary mode to traditional foreign trade, and later gradually expanded to explore its multiple roles in global resource allocation, technology innovation-driven growth, industrial upgrading, and economic development (Chen and Luo, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang and Liu, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among them, most studies primarily focus on the economic welfare effects of CBEC, such as its ability to effectively reduce trade costs and cross-border distances (Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), promote trade facilitation and global market expansion (Liang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), drive regional economic growth (Yang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), optimize industrial structure (Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), enhance enterprise innovation capabilities (Chen and Luo, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), improve consumer welfare (Zhang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and social welfare (Niu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as well as accelerate the application of green technologies and the efficient allocation of resources (Zhang and Liu, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Overall, CBEC plays a significant role in the multidimensional development of the economy and society. However, current research on the relationship between CBEC and ecological environment remains limited. Existing literature has primarily explored the effect of CBEC on carbon emission efficiency at the enterprise level (Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), or has only preliminarily verified the effect of cross-border e-commerce on the reduction of SO\u003csub\u003e2\u003c/sub\u003e emissions in cities based on panel data from 106 Chinese cities between 2010 and 2018 (Ma and Zhang, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e),which is still a lack of robustness tests, mechanism analysis, and in-depth heterogeneity analysis.\u003c/p\u003e \u003cp\u003eIn summary, existing literature on CBEC primarily focuses on its economic welfare effects, providing valuable references for analyzing the relationship between CBEC and the ecological environment. However, as a significant driving force in global trade, understanding how CBEC leverages its advantages to optimize resource allocation, accelerate green technology innovation, and enhance the agglomeration of productive services to improve urban air quality holds substantial practical significance for high-quality economic development. Therefore, it is essential to conduct an in-depth exploration of this topic. Based on this, this paper utilizes the quasi-natural experiment of CBEC pilot zones, employing panel data from 284 cities in China from 2009 to 2022. The study systematically analyzes the improvement effect of CBEC on urban air quality using a multi-period Difference-in-Differences (DID) method, while also investigating its transmission mechanisms and heterogeneous effects.\u003c/p\u003e \u003cp\u003eThe marginal contributions of this research are primarily reflected in three aspects: First, from the unique perspective of the establishment of CBEC pilot zones, this study systematically evaluates the effect of CBEC on urban air quality, providing new theoretical perspectives and empirical evidence for research on the relationship between CBEC and environment. Second, from the perspectives of agglomeration, technological, and resource allocation effects, this study deeply analyzes the mechanisms through which CBEC influences urban air quality, enriching the theoretical framework of the environmental impact pathways of CBEC. Finally, from dimensions such as geographic location, digital infrastructure, and environmental regulation, this study examines the heterogeneity of CBEC in improving urban air quality, revealing the diversified effect of CBEC on urban air quality under different environmental conditions.\u003c/p\u003e\n\u003ch3\u003ePolicy background and research hypotheses\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003ePolicy background.\u003c/b\u003e As an innovative trade model combining \"Internet + international trade,\" CBEC has played an increasingly significant role in expanding foreign trade and related areas. In recent years, China’s CBEC has achieved rapid growth. According to statistics from China Customs, the import and export value has increased from CNY 36\u0026nbsp;billion in 2015 to CNY 2.37 trillion in 2023, with an average annual growth rate of 19.38%.This rapid growth has been largely driven by policy support. To promote the regulated development of CBEC, the Chinese government has consistently included it in its annual government work reports for ten consecutive years. Since 2012, the government has introduced a series of policy documents, including the “Notice on Promoting Healthy and Rapid Development of E-Commerce,” the “Notice on Pilot Programs for CBEC Foreign Exchange Payment by Payment Institutions,” and the “Opinions on Promoting Stable Growth and Structural Adjustment of Imports and Exports.” These policy measures, such as the facilitation of financial services, integration of tax administration, and standardization of market supervision, have created a high-quality business environment conducive to the prosperity of CBEC.\u003c/p\u003e \u003cp\u003eTo address the deep-rooted contradictions and institutional challenges in the development of CBEC and to improve management systems that adapt to and lead global CBEC development, the Chinese government introduced the policy of CBEC pilot zones. In 2015, Hangzhou became the first pilot city in the country. The Hangzhou CBEC pilot zones achieved significant success in areas such as industrial clusters, brand promotion, and ecosystem development, and established the “Hangzhou Experience” with its core \"Six Systems and Two Platforms\" model. Building on this, since 2016, the State Council has approved the establishment of 165 CBEC Comprehensive Pilot Zones in seven batches by the end of 2022, covering 31 provinces. From a policy perspective, these CBEC pilot zones have made significant progress in building the “Six Systems and Two Platforms,” effectively reducing trade costs, optimizing the business environment, creating a complete industrial chain and ecosystem, and promoting the clustering of CBEC industries. This has injected strong momentum into the development of high-quality trade. The key to high-quality development lies in supply-side structural reform and green sustainable development. Under the new circumstances, China's CBEC must not only achieve a transition from traditional to new growth drivers in trade but also play an active role in environmental protection.\u003c/p\u003e "},{"header":"Research hypotheses","content":"\u003ch2\u003eThe effect of CBEC on urban air quality\u003c/h2\u003e\u003cp\u003eIn the process of promoting the transformation and upgrading of foreign trade, CBEC not only emphasizes the convenience of import and export trade but also increasingly focuses on the green and environmentally friendly characteristics of import and export goods. By strengthening cross-border industrial cooperation and forming industrial alliances, CBEC enterprises jointly promote green sustainable development across the entire industry, sharing green technologies and successful experiences to reduce environmental effect (Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). CBEC enterprises not only actively promote the construction of green supply chains but also take on greater social responsibility. They give back to society and enhance their public image by participating in public welfare activities and implementing environmental protection projects, gradually establishing a brand image associated with green and environmental protection (Tu and Shangguan, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These initiatives effectively reduce environmental pollution in areas such as production, transportation, and packaging, contributing to improved urban environmental quality. At the policy level, governments and industry organizations have also taken a series of measures to promote the green transformation of CBEC. For example, local governments encourage green development in the industry by promoting green packaging, green warehousing, and environmentally friendly transportation. The newly revised national standard for \"Express Packaging Materials\" further improves the original standards from the perspectives of \"reduction,\" \"greening,\" and \"recyclability,\" aiming to reduce excessive packaging and advance the greening process in the express delivery sector. With the greening transformation of the express delivery industry, the widespread use of environmentally friendly transportation tools, such as electric vehicles and bicycles, has effectively reduced exhaust emissions and noise pollution, significantly improving urban air quality. Moreover, the establishment of CBEC pilot zones is regarded as an important policy tool for promoting green development in CBEC. Specifically regarding urban environments, Ma and Zhang (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have preliminarily verified that CBEC has a significant positive effect on reducing SO\u003csub\u003e2\u003c/sub\u003e emissions in cities, with a more pronounced effect in economically developed cities and those with a higher proportion of the secondary industry.This indicates that CBEC is not only an important force for economic development but can also play a positive role in green transformation and urban environmental governance. Based on the above analysis, the following hypothesis is proposed:\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eH\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eCBEC can improve urban air quality.\u003c/p\u003e\u003ch3\u003eCBEC, green technology innovation, and urban air quality\u003c/h3\u003e\u003cp\u003eIn the past, local governments in China commonly exhibited a phenomenon of \"race to the bottom\" in economic development, where short-term economic growth was pursued at the expense of environmental protection. This resulted in a misallocation of environmental resources, leading to a serious imbalance between economic development and environmental protection, further exacerbating air pollution (Liu and Xu, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The establishment of CBEC pilot zones aims to eliminate policy barriers, create a fair competitive market environment, and reduce excessive government intervention in the market, allowing the market to play a decisive role in resource allocation (Liu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As the degree of marketization continues to increase, previously misallocated environmental resources will be optimized, gradually guiding enterprises to adjust their production behaviors and promoting green technology innovation. To support the construction of these CBEC pilot zones, various favorable policies have been implemented, aiming to create a conducive policy environment for the development of CBEC and related industries. These policy measures not only attract the agglomeration of production factors such as labor and capital but also create a \"policy gap effect,\" further driving the development of the CBEC industry (Lu and Wang, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The agglomeration of production factors provides ample technical talent and intermediate inputs for green technology innovation, while also intensifying market competition pressure, prompting enterprises to gain competitive advantages through green technology innovation (Zeng et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). When engaging in green technology innovation activities, transaction costs are one of the important constraints faced by enterprises. The development of CBEC effectively reduces the spatial and temporal barriers to information exchange and material exchange both between and within enterprises, significantly lowering external market transaction costs and internal management costs. This reduction in costs releases more resources for enterprises, which can be further invested in green technology innovation (Yang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), thereby enhancing the level of green technology innovation in cities. Research indicates that green technology innovation not only promotes economic growth but also effectively reduces atmospheric pollutant emissions and improves environmental conditions (Luo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li and Bai, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The widespread application of green technology helps enterprises optimize production methods, enhance energy utilization efficiency, and reduce energy consumption (Javed et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, green technology innovation accelerates the development and utilization of new energy sources, further optimizing the energy consumption structure (Zhang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), thus improving urban air quality. Therefore, green technology innovation can contribute to the improvement of urban air pollution conditions. Based on the above analysis, the following hypothesis is proposed:\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eH\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eCBEC can improve urban air quality by the pathway of green technology innovation.\u003c/p\u003e\u003ch3\u003eCBEC, resource allocation, and urban air quality\u003c/h3\u003e\u003cp\u003eThe main causes of air pollution lie in extensive production methods and inefficient resource utilization, while improving resource allocation efficiency can effectively reduce undesirable outputs in production, thereby improving air quality (Guo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As a green, sustainable, and high-quality foreign trade model, CBEC helps optimize resource allocation, enhance overall production efficiency, and reduce negative effects on the environment. First, CBEC promotes the deep development of data elements and international cooperation, facilitating the integrated development of global e-commerce supply chains. This not only achieves effective integration of global resources but also enhances resource utilization efficiency by optimizing resource allocation in international markets (Wang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Second, supported by digital technology, CBEC platforms integrate various resources such as suppliers, manufacturers, and logistics service providers. By utilizing technologies like big data and cloud computing, these platforms achieve precise matching of resources, thus increasing the overall utilization rate of resources. As reforms in CBEC deepen, the construction of CBEC pilot zones relies on an integrated innovation model of trial and exploration. This not only accelerates the interregional flow of traditional production factors but also expands the supply scale of data elements, optimizes their allocation structure, and enhances the capacity for resource reallocation. On one hand, the improved intelligence level of CBEC pilot zones enables many processes in cross-border trade to be replaced by artificial intelligence technologies, with intelligent systems significantly outperforming traditional mechanical automation in substituting low-skilled labor (Koomey et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This acceleration of technological substitution effectively optimizes the structure of resource factor allocation and enhances overall factor allocation efficiency. On the other hand, the high-quality business environment created by CBEC pilot zones provides fertile ground for innovations in digital information technology. The demand for new digital jobs brought about by this technological innovation, especially the surge in knowledge-intensive labor, further enhances the potential for high-quality factor allocation. Moreover, CBEC pilot zones promote the deep integration of data elements with traditional factors by expanding the application scenarios of digital elements (Bingbing et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This integration not only optimizes resource allocation efficiency but also provides strong support for promoting green development and a low-carbon economy. Based on the above analysis, the following hypothesis is proposed:\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eH\u003csub\u003e3\u003c/sub\u003e\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eCBEC can improve urban air quality by the pathway of resource allocation optimizing.\u003c/p\u003e\u003ch3\u003eCBEC, productive service industry agglomeration, and urban air quality\u003c/h3\u003e\u003cp\u003eThe agglomeration of the productive service industry has been proven to be an effective pathway for reducing environmental pollution and improving environmental quality (Zhuang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The productive service industry consists of supporting service industries closely related to manufacturing, covering various fields such as research and development, logistics and transportation, information services, and financial services. To better serve the manufacturing sector and share production information, technology, and natural resources, the productive service industry often naturally concentrates in specific regions, resulting in agglomeration effects. This concentration significantly enhances the production efficiency of manufacturing by providing high value-added, high-tech, low-energy, and low-pollution services, thereby reducing resource consumption and facilitating green transformation, which effectively decreases pollution emissions (Bartolomeo et al., 2003). Productive service industry agglomeration also promotes production specialization, driving the development of capital and knowledge-intensive production. This agglomeration effect helps improve the utilization efficiency of production factors, reduce unnecessary emissions during the production process, and strengthen environmental pollution control (Yang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the economies of scale and resource-sharing effects brought about by the agglomeration of the productive service industry contribute to enhancing total factor productivity, reducing unit energy consumption, and improving energy utilization efficiency (Hosoe and Naito, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The combined effects of these factors not only reduce pollution emissions and ecological damage but also significantly improve urban air quality. In practice, the development of CBEC often supports and attracts leading enterprises, drawing in logistics, customs clearance, information security, legal services, and other supporting productive service companies to settle in CBEC pilot zones, thereby promoting the agglomeration of the productive service industry. Relying on its institutional advantages and location benefits, CBEC pilot zones combine with local characteristic industrial clusters to focus on building the CBEC industry chain and ecosystem, further promoting the agglomeration of related enterprises in production, operations, logistics, and warehousing, thereby forming CBEC industrial clusters (Dai and Min, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This agglomeration effect not only improves the collaborative efficiency of the industry chain but also promotes green and low-carbon development by efficiently integrating various factor resources. CBEC pilot zones also leverage efficient government services and investment trade facilitation measures to utilize the cumulative advantages of other policies, gathering various high-quality factor resources. Through innovative institutional guarantees and policy support, these regions can quickly attract a large number of productive service enterprises, forming an industry ecosystem of collaborative development (Lu and Wang, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Based on the above analysis, the following hypothesis is proposed:\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eH\u003csub\u003e4\u003c/sub\u003e\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eCBEC can improve urban air quality by the pathway of productive service industry agglomeration.\u003c/p\u003e"},{"header":"Research design","content":"\u003cp\u003e \u003cb\u003eModel.\u003c/b\u003e To address the institutional barriers and deep-rooted contradictions faced in the development of cross-border e-commerce, the Chinese government has successively established CBEC pilot zones since 2015. By November 2022, 165 CBEC pilot zones had been established across 31 provinces. Considering the continuity of the sample interval data, this paper selects the cities from the first five batches of CBEC pilot zones as the sample. This paper considers the establishment of CBEC pilot zones as a policy shock, and based on theoretical analysis, employs the DID method to evaluate its impact on urban air quality. The specific model setup is as follows:\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Uaq}_{it}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}{Policy}_{it}+{\\alpha\\:}_{2}{Controls}_{it}+{\\delta\\:}_{i}+{\\mu\\:}_{t}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:t\\:\\)\u003c/span\u003e\u003c/span\u003edenote cities and years respectively, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Uaq\\)\u003c/span\u003e\u003c/span\u003e represents urban air quality, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{0}\\:\\)\u003c/span\u003e\u003c/span\u003eis the intercept term. The explanatory variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Policy}_{it}\\)\u003c/span\u003e\u003c/span\u003e is composed of the interaction term of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Treat}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Post}_{t}\\)\u003c/span\u003e\u003c/span\u003e, representing the policy effect of the establishment of CBEC pilot zones. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Treat}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a policy group dummy variable, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Treat=1\\)\u003c/span\u003e\u003c/span\u003e for cities with pilot zones and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Treat=0\\)\u003c/span\u003e\u003c/span\u003e for those without. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Post\\)\u003c/span\u003e\u003c/span\u003e is a time dummy variable, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Post=0\\)\u003c/span\u003e\u003c/span\u003e before the establishment and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Post=1\\)\u003c/span\u003e\u003c/span\u003e during and after the establishment year. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Controls}_{it}\\)\u003c/span\u003e\u003c/span\u003e is a set of city-level control variables. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003edenotes city fixed effects, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{t}\\:\\)\u003c/span\u003e\u003c/span\u003edenotes time fixed effects, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{it}\\:\\)\u003c/span\u003e\u003c/span\u003e is the random disturbance term.\u003c/p\u003e\u003ch3\u003eVariable settings\u003c/h3\u003e\u003cp\u003e \u003cem\u003eDependent variable (\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Uaq\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e).\u003c/em\u003eThe dependent variable is urban air quality. Based on the availability of urban-level air pollution data, this study follows Guo et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and uses the total industrial sulfur dioxide emissions (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e) and per capita industrial sulfur dioxide emissions (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e) to measure urban air quality.\u003c/p\u003e\u003cp\u003e \u003cem\u003eExplanatory variable (\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Policy\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e).\u003c/em\u003eThe explanatory variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Policy\\)\u003c/span\u003e\u003c/span\u003e represents the policy of the establishment of CBEC pilot zones, defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Treat}_{i}\\times\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Post}_{t}\\)\u003c/span\u003e\u003c/span\u003e. The policy dummy variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Treat}_{i}\\)\u003c/span\u003e\u003c/span\u003e is generated based on whether the sample city becomes a pilot zone, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Post}_{t}\\)\u003c/span\u003e\u003c/span\u003e is generated based on the establishment time. The interaction term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Treat}_{i}\\times\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Post}_{t}\\)\u003c/span\u003e\u003c/span\u003e indicates the policy effect.\u003c/p\u003e\u003ch3\u003eMechanism variables\u003c/h3\u003e\u003cp\u003e(1) \u003cem\u003eGreen technology innovation\u003c/em\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Greentec\\)\u003c/span\u003e\u003c/span\u003e).The measurement of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Greentec\\)\u003c/span\u003e\u003c/span\u003e primarily relies on data integrated from the National Intellectual Property Office's patent database, along with further identification of green patents using the \"Green Patent List\" published by the World Intellectual Property Organization (WIPO) in 2010. Specifically, patents that meet the definition and classification criteria for green technology innovation are selected from the database, and the number of green patent applications per 10,000 people in each city is calculated to assess the level of green technology innovation. The related measurement methods reference the studies by Zhang et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Bilal et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), using the number of green patents as the core indicator of green technology innovation.\u003c/p\u003e\u003cp\u003e(2) \u003cem\u003eResource allocation\u003c/em\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Ra\\)\u003c/span\u003e\u003c/span\u003e).\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:Ra\\)\u003c/span\u003e\u003c/span\u003e is measured using the total factor productivity of cities, primarily calculated using the \u003cem\u003eMalmquist\u003c/em\u003e index. The \u003cem\u003eMalmquist\u003c/em\u003e index has been widely applied in the field of productivity measurement, particularly for efficiency change analysis in multi-period data (Fare et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). By conducting a comparative analysis of productivity changes across different periods, it can effectively assess changes in resource allocation efficiency and their influencing factors. The calculation formula for the \u003cem\u003eMalmquist\u003c/em\u003e index is as follows:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{M}_{t,t+1}=\\frac{{Y}_{t+1}}{{Y}_{t}}\\times\\:\\frac{{X}_{t+1}}{{X}_{t}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{t,t+1}\\)\u003c/span\u003e\u003c/span\u003e​ represents the Malmquist index from time t to time t + 1; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{t}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{t+1}\\)\u003c/span\u003e\u003c/span\u003erepresent the actual GDP at time t and t + 1 (as the output side); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{t}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{t+1}\\)\u003c/span\u003e\u003c/span\u003e represent the input quantities at time t and t + 1 (including fixed capital stock and number of employees).\u003c/p\u003e\u003cp\u003e(3) \u003cem\u003eProductive services industry agglomeration\u003c/em\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Psia\\)\u003c/span\u003e\u003c/span\u003e).This study follows the approach of Guo and Huang (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and uses the location entropy index to measure \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Psia\\)\u003c/span\u003e\u003c/span\u003e. The formula for calculating the location entropy index is as follows:\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{Psia}_{it}=\\frac{{B}_{it}/{E}_{it}}{{\\sum\\:}_{i=1}^{n}{B}_{it}/{\\sum\\:}_{i=1}^{n}{E}_{it}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{B}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the number of employees in productive services in city (i) in year (t), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the total number of employees in city (i) in year (t). This index reflects the concentration of the productive service industry in the total employment of a city. A higher location entropy value indicates a stronger agglomeration of the productive service industry.\u003c/p\u003e\u003cp\u003e \u003cem\u003eControl variables.\u003c/em\u003e(1) Economic development (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pgdp\\)\u003c/span\u003e\u003c/span\u003e):This is represented by the logarithm of regional GDP per capita. According to the Environmental Kuznets Curve (EKC) theory, there is an inverted U-shaped relationship between economic development level and environmental pollution, meaning that early-stage economic growth may lead to increased environmental pollution, but after reaching a certain level of economic development, pollution may decrease with technological progress and the implementation of environmental protection policies. Therefore, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pgdp\\)\u003c/span\u003e\u003c/span\u003e² is also included in the model to capture this inverted U-shaped relationship.(2) Financial development (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:finance\\)\u003c/span\u003e\u003c/span\u003e): This is represented by the logarithm of the balance of deposits and loans of financial institutions. Financial development can promote the flow of capital and technological innovation, thereby influencing environmental governance and pollution levels. The degree of development of the financial system may indirectly affect urban air quality by providing support for green investments and environmental protection technologies. (3) Industrial structure (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:indus\\)\u003c/span\u003e\u003c/span\u003e): This is represented by the proportion of the secondary industry added value to GDP. The industrial structure, particularly the proportion of the secondary industry, is closely related to pollution levels. Cities with a high degree of industrialization tend to face higher levels of pollution emissions, thus it is necessary to control the effect of industrial structure on air quality. (4) Government intervention (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:gov\\)\u003c/span\u003e\u003c/span\u003e): This is represented by the proportion of local government fiscal expenditure to GDP. Government can significantly influence urban environmental quality through fiscal expenditures that regulate environmental protection policies, infrastructure construction, and other areas. Regions with higher fiscal expenditures are likely to implement more environmental protection measures, thereby improving air quality. (5) Population density (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pop\\)\u003c/span\u003e\u003c/span\u003e): This is represented by the logarithm of the population per square kilometer. Population density is closely related to the demand for transportation, energy consumption, and other factors. Densely populated cities are likely to generate higher levels of pollution emissions, so it is necessary to consider the effect of this factor on air quality. (6) Urban technological innovation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:tech\\)\u003c/span\u003e\u003c/span\u003e): This is represented by the logarithm of the number of invention patents per 10,000 people. Technological innovation is a key factor in promoting green development and reducing pollution. Innovative cities may reduce industrial pollution and improve environmental quality through the development of new technologies. Therefore, controlling for this variable helps to more accurately assess the effect of CBEC on air quality.\u003c/p\u003e\u003cp\u003eThis study uses panel data from 284 cities in China from 2009 to 2022 to examine the effect of CBEC on urban air quality. The data is sourced from the \"China Urban Statistical Yearbook\", \"China Environmental Statistical Yearbook\", some city annual reports, and the EPS database. Descriptive statistics of the relevant variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.739\u003c/p\u003e \u003cp\u003e3.900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.368\u003c/p\u003e \u003cp\u003e1.338\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.099\u003c/p\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.281\u003c/p\u003e \u003cp\u003e7.796\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Policy\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pgdp\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.688\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.595\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.056\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pgdp\\)\u003c/span\u003e\u003c/span\u003e²\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.632\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.396\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.115\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e170.451\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:finance\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.512\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.272\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.301\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:indus\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:gov\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.223\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pop\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.897\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.433\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.137\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:tech\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Empirical results and robustness tests","content":"\u003cp\u003e \u003cb\u003eBaseline regression.\u003c/b\u003e The baseline regression results of the effect of the establishment of CBEC pilot zones on urban air quality are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In models (1) and (3), only city fixed effects and year fixed effects are controlled. The regression results indicate that the establishment of CBEC pilot zones has a significantly negative effect on both \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e. This suggests that the establishment of CBEC pilot zones has a noticeable effect in reducing air pollution in the respective areas, thereby increasing environmental welfare in the region. In models (2) and (4), after including control variables, the policy effect remains significantly negative. Therefore, the establishment of CBEC pilot zones can significantly improve urban air quality, supporting hypothesis H\u003csub\u003e1\u003c/sub\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline regression results of the effect of CBEC pilot zones on urban air quality.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{l}\\varvec{n}\\mathbf{s}{\\varvec{o}}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{l}\\varvec{n}\\varvec{p}\\mathbf{s}{\\varvec{o}}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Policy\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.171\u003csup\u003e**\u003c/sup\u003e(-2.57)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.154\u003csup\u003e**\u003c/sup\u003e(-2.39)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.210\u003csup\u003e***\u003c/sup\u003e(-3.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.183\u003csup\u003e***\u003c/sup\u003e(-2.88)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pgdp\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.833(0.86)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.912(0.85)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pgdp\\)\u003c/span\u003e\u003c/span\u003e²\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.040(-0.88)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.038(-0.76)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:finance\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.004(-0.17)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002(0.08)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:indus\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.799\u003csup\u003e*\u003c/sup\u003e(1.77)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.479(1.17)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:gov\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.268 (-0.79)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.143(-0.40)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pop\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.162(-0.64)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.217(-1.13)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:tech\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.519(-0.88)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.632(-1.61)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.756\u003csup\u003e***\u003c/sup\u003e(1434.34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.118(1.13)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.921\u003csup\u003e***\u003c/sup\u003e(624.22)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.578(-0.27)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *, **, and *** indicate significance at the 10%, 5%, and 1% significance levels, respectively. The standard errors are clustered at the city level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eParallel trend and dynamic effect tests\u003c/h2\u003e\u003cp\u003eA crucial prerequisite for the difference-in-differences (DID) method is the parallel trend assumption. This assumption requires that before the policy shock of the establishment of CBEC pilot zones, the air pollution levels in the treatment and control cities exhibit similar time-varying trends. Following Wang et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we use an event study approach to construct the following model:\u003c/p\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{Uaq}_{it}={{\\alpha\\:}}_{0}+{\\sum\\:}_{k=-5,k\\ne\\:-1}^{4}{\\alpha\\:}_{k}{Policy}_{i,{t}_{0}+k}+\\eta\\:{Controls}_{it}+{\\delta\\:}_{i}+{\\mu\\:}_{t}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cp\u003eIn this context, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{0}\\)\u003c/span\u003e\u003c/span\u003e represents the time point when the CBEC pilot zone was established. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Policy}_{i,{t}_{0}+k}\\)\u003c/span\u003e\u003c/span\u003e is a dummy variable representing the situation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:k}_{th}\\)\u003c/span\u003e\u003c/span\u003e years before or after the implementation of the CBEC pilot zone policy. If the treatment city \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e is in the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:({t}_{0}+k)}_{th}\\:\\)\u003c/span\u003e\u003c/span\u003eyear, the variable is assigned a value of 1, otherwise, it is 0. Setting of k: Since the year of the establishment of the CBEC pilot zone is considered as the 0th period, and the first batch was established in 2015, 2022 marks the 7th year. Therefore, the maximum value of k in formula (2) is 7. Considering that there is limited data for the 5 years before and 4 years after the policy implementation, this study consolidates the data for the 5 years before the policy into the − 5th period, and the data for the 4 years after the policy into the 4th period. The year prior to the establishment of CBEC pilot zone is used as the base year, meaning that the virtual variable for k = − 1 is not included in formula (2).\u003c/p\u003e\u003cp\u003eThe estimated coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{k}\\)\u003c/span\u003e\u003c/span\u003e​ of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Policy}_{i,{t}_{0}+k}\\:\\)\u003c/span\u003e\u003c/span\u003eis shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. According to the results in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the estimated coefficients for all periods prior to the implementation of the CBEC pilot zone policy are not significant, indicating no substantial differences between the treatment and control groups. This confirms that the research sample passes the parallel trend test. After the implementation of the CBEC pilot zone policy, the coefficients for most years show a significant negative effect, consistent with the baseline regression results. This suggests that the DID method can be used to examine the effect of CBEC pilot zones on urban air quality. At the same time, it verifies the main conclusion of this paper, namely that the CBEC pilot zone policy has a significant long-term effect on improving urban air quality.\u003c/p\u003e\u003ch2\u003eRobustness test\u003c/h2\u003e\u003cp\u003e \u003cem\u003ePlacebo test.\u003c/em\u003eTo rule out the possibility that omitted variables or unobservable factors may affect the estimation results, a placebo test is conducted, following the method of Li et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Specifically, a fictitious policy shock is created to serve as a placebo test. This involves randomly selecting a group of cities equal in number to the cities with CBEC pilot zones as the treatment group, and generating a fictitious policy dummy variable to include in the baseline model. Theoretically, if the baseline regression results are not influenced by omitted variables or other random factors, the estimated coefficient for the fictitious policy dummy variable should not significantly differ from 0. This implies that randomly established CBEC pilot zones would not have a significant effect on urban air quality. The placebo test was repeated 500 times, and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the distribution of estimated coefficients for the 500 regressions with fictitious policy dummy variables. The results indicate that the estimated coefficients are very close to 0 and follow a normal distribution. In contrast, the estimated coefficient for the policy variable in the baseline regression is significantly outside this distribution. This suggests that the estimation results are robust, and the observed effects of the establishment of CBEC pilot zones on urban air quality are not driven by omitted variables or random factors.\u003c/p\u003e\u003cp\u003e \u003cem\u003eInstrumental variable estimation.\u003c/em\u003e To address potential endogeneity issues between the establishment of CBEC pilot zones and urban air quality, and to correct for possible omitted variable bias, an instrumental variable (IV) approach is used. This approach helps mitigate concerns about reverse causality and inconsistencies in the estimation results. Following Nunn and Qian (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), two instrumental variables are chosen: (1)IV1: interaction between the number of internet users in China from the previous year and the number of post offices per 100 people in 1984; (2)IV2: interaction between the number of post offices per 100 people in 1984 and the number of telephones per 100 people in 1984. Two-stage least squares (2SLS) regression analysis is employed, with results presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe first stage regression results are shown in columns (1) and (2). The regression results indicate that regardless of whether control variables are included, there is a significant positive correlation between the instrumental variables (IV1 and IV2) and the policy variable for the establishment of CBEC pilot zones. The validity of the instrumental variables in this study was verified through weak instrumental variable testing. Columns (3) and (4) show the regression results of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2\\:}\\)\u003c/span\u003e\u003c/span\u003ein the second stage. The results indicate that regardless of whether control variables are included, the coefficients of the core explanatory variable (Policy) are significantly negative; Columns (5) and (6) show the regression results of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e in the second stage. The results show that regardless of whether control variables are included, the coefficient of the core explanatory variable (Policy) is significantly negative, indicating that the establishment of CBEC pilot zones significantly suppressed urban industrial sulfur dioxide emissions (including \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2\\:}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2\\:}\\)\u003c/span\u003e\u003c/span\u003e), which is consistent with the baseline results and proves the reliability of the previous research conclusions.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e2SLS estimation results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Policy\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIV1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.253\u003csup\u003e***\u003c/sup\u003e(10.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.191\u003csup\u003e***\u003c/sup\u003e(13.66)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIV2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.251\u003csup\u003e***\u003c/sup\u003e(9.81)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.180\u003csup\u003e***\u003c/sup\u003e(6.48)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Policy\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.125\u003csup\u003e***\u003c/sup\u003e(-3.52)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.446\u003csup\u003e***\u003c/sup\u003e(-7.84)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.903\u003csup\u003e***\u003c/sup\u003e(-4.10)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.890\u003csup\u003e***\u003c/sup\u003e(-2.36)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Controls\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen-Paap LM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.760\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190.379\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen-Paap Wald F\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108.318[16.38]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186.614[16.38]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2902\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2902\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2902\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2902\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2902\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2902\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: Standard errors clustered at the city level are reported in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cem\u003ePSM-DID test.\u003c/em\u003eTo further control the effect of other differences of CBEC pilot zones and non CBEC pilot zones on air quality, this study once again used propensity score matching (PSM) to pair the control group for regression analysis. Firstly, estimate the propensity score using the Logit model. Specifically, whether to establish a policy for CBEC pilot zones is considered as the dependent variable, while economic development level (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pgdp\\)\u003c/span\u003e\u003c/span\u003e/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pgdp\\)\u003c/span\u003e\u003c/span\u003e²), financial development level(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:finance\\)\u003c/span\u003e\u003c/span\u003e), industrial structure (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:indus\\)\u003c/span\u003e\u003c/span\u003e), government intervention (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:gov\\)\u003c/span\u003e\u003c/span\u003e), population density (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:pop\\)\u003c/span\u003e\u003c/span\u003e), and technological innovation level (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:tech\\)\u003c/span\u003e\u003c/span\u003e) are considered as matching variables. Secondly, based on the estimated score, a 1:1 \u003cem\u003eK\u003c/em\u003e-nearest neighbor matching was performed to obtain the control group sample that is most similar to the treatment group sample. At the same time, a comparative analysis of the mean feature variables between the treatment group and the control group sample was conducted, and it was found that there was no significant difference in the basic characteristics between the two groups. Finally, the matched samples were used to regress model (1), and the regression results are shown in columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The coefficient of the interaction term is still significantly negative, consistent with the benchmark regression results using the entire sample in the previous section. This fully demonstrates that the selection bias of the sample has no effect on the research conclusions.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePSM-DID test results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Policy\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.150\u003csup\u003e**\u003c/sup\u003e(-2.36)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.130\u003csup\u003e**\u003c/sup\u003e(-2.22)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Controls\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3960\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3960\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cem\u003eExcluding confounding policies.\u003c/em\u003e Given that various similar or related policies may be implemented simultaneously or intersect, potentially leading to policy overlap effects, it is essential to control for the effect of other significant policies that could influence the results. This paper addresses potential confounding factors by controlling for policies closely related to the development of CBEC pilot zones, such as innovation city pilot programs (Yang, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), low-carbon city construction (Song et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), broadband city initiatives (Ju, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).To account for these overlapping policies, the dummy variables for the aforementioned policies are added to the baseline regression model. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports the above regression results. Columns (1)–(3) and (5)–(7) show the regression results with the addition of other policy dummy variables respectively, while columns (4) and (8) show the results with the addition of other policy dummy variables to the regression equation simultaneously. All results show that after controlling for other policy shocks, the coefficient of policy remained significantly negative, and the coefficient size does not change significantly compared to the benchmark results, indicating that other policy shocks did not affect the causal relationship between the establishment of CBEC pilot zones and urban air quality. Thus, the conclusions of the previous analysis remain valid.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of excluding confounding policies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Policy\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.154\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.168\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.144\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.155\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.134\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.147\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.126\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.135\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.40)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.64)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.28)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-2.49)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.28)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-2.51)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-2.17)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-2.37)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.096\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.738\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.329\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.947\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.145\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.799\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.304\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.101\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.01)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.21)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.07)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.89)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.80)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.94)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(0.85)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einnocity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elowcartoncity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebroadbandcity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Controls\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3948\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3948\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3948\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3948\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3948\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3948\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eOther robustness tests\u003c/h2\u003e\u003cp\u003e(1) Changing the dependent variable\u003c/p\u003e\u003cp\u003eThis article is based on the approach of Guo and Shi (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), using the annual average concentration of PM2.5 as a proxy variable for urban air pollution level. The reason may be that PM2.5 is generated in industrial production and residential life processes, and it is considered the main culprit causing haze pollution. In view of this, this article replaces the dependent variable in the benchmark regression with the annual average concentration of PM2.5 for robustness testing. Due to the fact that the latest PM2.5 data available in this article is up to 2020 (data source: Columbia University Center for Socioeconomic Data and Applications), the sample size for regression has been reduced.The specific regression results are shown in columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. It can be seen that regardless of whether control variables are included, the regression coefficient of the policy for the establishment of CBEC pilot zones is significantly negative, which also indicates the relative robustness of the previous results.\u003c/p\u003e\u003cp\u003e(2) Reanalyzing the difference variables\u003c/p\u003e\u003cp\u003eWhen defining the policy dummy variable in the previous text, the value of 1 is taken for the year of policy implementation and subsequent years. Given that the policy announcement dates could mean the policy was not fully implemented within the year, a revised approach is adopted. Following Lu et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), adjustments are made for the year of policy implementation based on specific cohorts:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eFirst batch (March, 2015): Post = 5/6 for 2015, then 1.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSecond batch (January, 2016): Post = 1 for the implementation year and subsequent years.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThird batch (July, 2018): Post = 1/2 for the implementation year, then 1.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFourth batch(December, 2019): Post = 1/12 for the implementation year, then 1.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFifth batch(April, 2020): Post = 7/12 for the implementation year, then 1.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eIn addition, as the sixth and seventh batches were established in 2022, considering that the policy effects have not yet been fully released, the sixth and seventh batches will be regarded as the control group. These adjusted post variables are incorporated into the model. Results, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, columns (3) and (4), are consistent with baseline findings.\u003c/p\u003e\u003cp\u003e(3) Considering policy anticipation effects\u003c/p\u003e\u003cp\u003eTo account for potential pre-policy anticipation effects, an interaction term between the policy dummy and a dummy variable for the year prior to implementation is added to the model (1). Results are reported in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, columns (5) and (6). The interaction term is not significant, indicating no significant anticipatory effects and affirming the exogeneity of the policy of the establishment of CBEC pilot zones.\u003c/p\u003e\u003cp\u003e(4) Considering lagged policy effects\u003c/p\u003e\u003cp\u003eRecognizing that the effect of the policy of the establishment of CBEC pilot zones on air quality may not be immediate, the core explanatory variable (\u003cem\u003ePolicy\u003c/em\u003e) is lagged by one year, with all control variables also lagged to avoid simultaneity bias. Results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, columns (7) and (8). The coefficient for \u003cem\u003ePolicy\u003c/em\u003e remains significantly negative, supporting the previous conclusions about the positive effect of the policy on air quality.\u003c/p\u003e\u003cp\u003e(5) Excluding central cities\u003c/p\u003e\u003cp\u003eGiven the potential differences in administrative behavior and resources between central cities (e.g., provincial capitals, sub-provincial cities, and municipalities) and ordinary prefecture-level cities, these central cities are excluded from the sample. Results show that even after excluding central cities, the policy still significantly reduces air pollution (coefficients for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e are − 0.103 and − 0.092, respectively, with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.1). This further validates the robustness of the previous findings.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of other robustness tests.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDependent variable replaced\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eReanalyzing differential variables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePolicy expected effects\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePolicy lag effects\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM2.5\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(1\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM2.5\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(2\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\left(3\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\left(4\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\left(5\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\left(6\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\left(7\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\left(8\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Policy\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.024\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.017\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.140\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.123\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.137\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.122\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.187\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.169\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.59)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.09)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.31)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-2.19)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.07)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-2.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-2.99)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-2.94)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePilot cities \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003eOne year prior to policy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.04)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Controls\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3405\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3405\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3678\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3678\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eHeterogeneity tests\u003c/h2\u003e\u003cp\u003e \u003cem\u003eGeographic location heterogeneity.\u003c/em\u003eTo further explore the geographic heterogeneity of the effect of the establishment of CBEC pilot zones on urban air quality, the sample cities are divided into eastern and central-western regions based on the classification by China's National Bureau of Statistics. Eastern cities include 11 provinces (or municipalities, autonomous regions) such as Beijing, Shanghai, and Zhejiang, while central-western cities include 20 provinces (or municipalities, autonomous regions) such as Shanxi and Jilin. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, columns (1) to (4), there is a clear geographic heterogeneity in the effects of the policy. The policy's coefficient is significantly negative in eastern cities, but not significant in central-western cities. This indicates that the ecological and environmental benefits of the policy are more pronounced in eastern cities.\u003c/p\u003e\u003cp\u003eThe possible reasons are as follows: First, the GDP of eastern cities is significantly higher than that of central-western regions, with more concentrated economic activities, leading to greater energy consumption and pollution emissions. The establishment of CBEC pilot zones has not only promoted the expansion of logistics, transportation, and warehousing but also driven the application of green technologies (such as green packaging and logistics technologies) and pollution control measures. Second, due to economic and industrial agglomeration, eastern cities face more pollution sources, such as construction dust and emissions from the catering industry. The policies of CBEC pilot zones can significantly improve environmental quality by promoting green technology innovation and raising environmental awareness. In addition, governments in eastern regions have strong implementation capabilities, effectively driving policy execution, particularly in environmental protection and sustainable development. In contrast, governments in central-western cities have weaker execution power and insufficient investment in green development, leading to less noticeable environmental effects of the CBEC pilot zone policies.\u003c/p\u003e\u003cp\u003e \u003cem\u003eDigital infrastructure heterogeneity.\u003c/em\u003eWell-developed digital infrastructure provides better internet services for CBEC businesses, which may affect the policy’s effect on urban air quality. Using internet penetration rate as a proxy for digital infrastructure, the sample is divided into high and low digital infrastructure groups based on the median internet penetration rate of cities in the initial year. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, columns (5) to (8). In cities with high digital infrastructure, the policy’s effect on air quality is significantly negative. In contrast, in cities with lower digital infrastructure, the relationship is not significant. This suggests that the environmental benefits of the policy are more pronounced in cities with better digital infrastructure, as better infrastructure facilitates CBEC activities and increases the likelihood of firms engaging in these activities, thereby enhancing the policy’s effect on reducing air pollution.\u003c/p\u003e\u003cp\u003eThe reason for this is that regions with more developed digital infrastructure make CBEC activities more convenient, allowing businesses to more easily engage in CBEC. Particularly after the approval of CBEC pilot zones, businesses in these regions can quickly seize policy opportunities, enjoy institutional benefits, and promote industrial upgrading and innovation. As CBEC activities continue to increase, e-commerce platforms, logistics systems, and related industrial chains in these regions will continue to grow, thereby driving the application of green technologies and strengthening environmental protection measures. Ultimately, the air pollution reduction effects of the CBEC pilot zone policies in these regions will gradually emerge and be enhanced, as the promotion of green technologies and green logistics helps reduce carbon emissions and other pollutants, driving regional sustainable development.\u003c/p\u003e\u003cp\u003e \u003cem\u003eEnvironmental regulation intensity heterogeneity.\u003c/em\u003e Environmental regulation, aimed at protecting the environment, can directly improve regional environmental quality and also influence CBEC firms’ behavior, which in turn affects the environment. Regulations such as environmental taxes and laws may increase operational costs for e-commerce firms, prompting them to adopt eco-friendly technologies and improve environmental quality. The intensity of environmental regulations may therefore affect the policy’s effect on urban air quality. Following Chen and Chen (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the frequency and proportion of environmental terms in government work reports are used as proxies for environmental regulation intensity.\u003c/p\u003e\u003cp\u003eAssuming that the environmentally related terms in the government work report include \"environmental protection,\" \"pollution control,\" \"energy conservation and emission reduction,\" \"carbon emissions,\" \"SO\u003csub\u003e2\u003c/sub\u003e emissions,\" and other related terms, the calculation of environmental regulation intensity is as follows:\u003c/p\u003e\u003ch2\u003eERI=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\frac{{\\sum\\:}_{i=1}^{n}{\\text{F}\\text{r}\\text{e}\\text{q}\\text{u}\\text{e}\\text{n}\\text{c}\\text{y}\\:\\text{o}\\text{f}\\:\\text{e}\\text{n}\\text{v}\\text{i}\\text{r}\\text{o}\\text{n}\\text{m}\\text{e}\\text{n}\\text{t}\\text{a}\\text{l}\\text{l}\\text{y}\\:\\text{r}\\text{e}\\text{l}\\text{a}\\text{t}\\text{e}\\text{d}\\:\\text{t}\\text{e}\\text{r}\\text{m}\\text{s}}_{i}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{w}\\text{o}\\text{r}\\text{d}\\text{s}\\:\\text{i}\\text{n}\\:\\text{t}\\text{h}\\text{e}\\:\\text{r}\\text{e}\\text{p}\\text{o}\\text{r}\\text{t}\\text{s}}\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\frac{{\\sum\\:}_{i=1}^{n}{\\text{F}\\text{r}\\text{e}\\text{q}\\text{u}\\text{e}\\text{n}\\text{c}\\text{y}\\:\\text{o}\\text{f}\\:\\text{e}\\text{n}\\text{v}\\text{i}\\text{r}\\text{o}\\text{n}\\text{m}\\text{e}\\text{n}\\text{t}\\text{a}\\text{l}\\text{l}\\text{y}\\:\\text{r}\\text{e}\\text{l}\\text{a}\\text{t}\\text{e}\\text{d}\\:\\text{t}\\text{e}\\text{r}\\text{m}\\text{s}}_{i}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{w}\\text{o}\\text{r}\\text{d}\\text{s}\\:\\text{i}\\text{n}\\:\\text{t}\\text{h}\\text{e}\\:\\text{r}\\text{e}\\text{p}\\text{o}\\text{r}\\text{t}\\text{s}}\\times\\:100\\)\u003c/span\u003e\u003cp\u003eWhere:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=1}^{n}{\\text{F}\\text{r}\\text{e}\\text{q}\\text{u}\\text{e}\\text{n}\\text{c}\\text{y}\\:\\text{o}\\text{f}\\:\\text{e}\\text{n}\\text{v}\\text{i}\\text{r}\\text{o}\\text{n}\\text{m}\\text{e}\\text{n}\\text{t}\\text{a}\\text{l}\\text{l}\\text{y}\\:\\text{r}\\text{e}\\text{l}\\text{a}\\text{t}\\text{e}\\text{d}\\:\\text{t}\\text{e}\\text{r}\\text{m}\\text{s}}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the total frequency of all environmentally related terms appearing in the government work reports.\u003c/p\u003e\u003cp\u003eThe sample is divided into high and low environmental regulation intensity groups based on the median. Results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, columns (9) to (12). In cities with low environmental regulation intensity, the policy’s effect on air quality is more significant. In contrast, in cities with high environmental regulation intensity, the policy’s effect is less pronounced. This suggests that the policy's environmental benefits are more evident in cities with weaker environmental regulations, where CBEC policy can have a more pronounced effect on reducing air pollution.\u003c/p\u003e\u003cp\u003eThe main reason for this may be that in regions with higher environmental regulation, the government has already implemented strict environmental protection policies and measures, such as pollution emission standards and the promotion of green technologies. As a result, businesses are more adaptive to environmental protection requirements, pollution sources are effectively controlled, and environmental governance has reached a high level. Therefore, the environmental effects brought about by the CBEC pilot zone policies are relatively limited. In contrast, in regions with weaker environmental regulations, due to lenient policies or ineffective implementation, businesses may not have adopted sufficient green measures. In such cases, the implementation of the CBEC pilot zone policies may become an opportunity to drive business transformation and the application of green technologies, leading to more significant environmental protection effects. Furthermore, in regions with high environmental regulation intensity, the government’s strong execution capacity enables efficient implementation of environmental policies, which limits the further role of the CBEC pilot zone policies in environmental improvement. On the other hand, in regions with weaker regulations, the government relies more on the CBEC pilot zone policies to strengthen environmental governance and promote the application of green technologies and environmental protection measures, thereby enhancing the ecological effects of the policies.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity test results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eGeographical location\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eDigital infrastructure\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c14\" namest=\"c10\"\u003e \u003cp\u003eEnvironmental regulations\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eeastern cities\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003emidwest cities\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ln\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:lnp\\text{s}{o}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(12)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Policy\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e− .213\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e− .185\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e− .005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e− .001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e− .130\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e− .093\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e− .216\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e− .175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e− .114\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e− .095\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e− .188\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e− .174\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-2.48)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-2.34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.06)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.02)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.14)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(-1.68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(-0.81)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(-0.69)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(-1.66)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(-1.54)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(-2.02)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e(-2.08)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Controls\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1357\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1357\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2605\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2605\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2114\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2114\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1834\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1834\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1918\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1918\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1899\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1899\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eMechanism testing\u003c/h2\u003e\u003cp\u003eRegarding the selection of a mechanism testing method, given the ongoing debate over the applicability of mediation effect models in economic research and the potential issues they may cause, such as endogeneity bias and ambiguity in identifying causal channels (Jiang, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), this paper adopts the approach of Liu and Mao (2019). Specifically, it examines the effect of the establishment of CBEC pilot zones on mechanism variables to test the mechanisms at play. The theoretical analysis suggests that CBEC enhances a city's green technology innovation capability, productive services industry agglomeration, and resource allocation optimization, which, in turn, improves air quality. First, the improvement of green technology innovation helps cities transform traditional production methods, increase energy efficiency, reduce energy consumption, accelerate the development and use of clean energy, and optimize the energy consumption structure, thereby improving air quality. Second, productive service industry agglomeration provides high value-added, high-tech, low-energy, and low-pollution services to manufacturing industries, promoting their green transformation. This agglomeration also fosters production specialization, expands capital- and knowledge-intensive production, and creates economies of scale and resource-sharing effects, which together enhance the productivity of production factors, reduce unit energy consumption and pollution emissions, and contribute to better air quality. Finally, resource allocation optimization not only accelerates the flow of traditional factors across regions but also promotes the integration of traditional and data factors, enhancing the efficiency of factor production and reducing unwanted outputs in the manufacturing process, thus effectively reducing air pollution. Therefore, CBEC improves urban air quality primarily through three channels: green technology innovation capacity, productive service industry agglomeration, and resource allocation optimizing. To verify whether these channels are valid, this paper constructs the following econometric model:\u003c/p\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{Mech}_{it}={\\beta\\:}_{0}+{\\beta\\:}_{1}{Policy}_{it}+{\\beta\\:}_{2}{Controls}_{it}+{\\delta\\:}_{i}+{\\mu\\:}_{t}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Mech}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the mechanism variable, which includes green technology innovation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Greentec\\)\u003c/span\u003e\u003c/span\u003e), resource allocation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Ra\\)\u003c/span\u003e\u003c/span\u003e), and productive service industry agglomeration (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Psia\\)\u003c/span\u003e\u003c/span\u003e). Due to the lack of employment in the calculation of productive service industry agglomeration after 2020, there may be a decrease in the observed values.\u003c/p\u003e\u003cp\u003eThe specific regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Columns (1) and (2) demonstrate that the establishment of CBCE pilot zones has a significant positive effect on urban green technology innovation. By promoting the research, development, and application of green technologies, these pilot zones help cities make progress in reducing energy consumption, improving energy efficiency, and optimizing energy consumption structures (Bilal et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These innovations not only facilitate the green transformation of the economy but also directly improve urban air quality (Hu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Specifically, green technology innovations make energy consumption more efficient, reduce pollutant emissions, and effectively lower the concentration of air pollutants, thereby validating hypothesis H\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eThe regression results in columns (3) and (4) show that the establishment of CBEC pilot zones also significantly improves resource allocation efficiency. On the one hand, this optimization makes the allocation of production factors more rational, which can effectively improve factor productivity (Chen and Wu, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the other hand, the improvement in resource allocation efficiency makes the production processes of businesses and industries more efficient, reducing the generation of undesirable outputs (Sun et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The increase in production efficiency and the reduction of undesirable outputs help mitigate air pollution and improve air quality (Sun et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), thus validating hypothesis H\u003csub\u003e3\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eAccording to the regression results in columns (5) and (6), the establishment of CBEC pilot zones significantly promotes productive service industry agglomeration. Productive service industry agglomeration not only brings economies of scale but also promotes resource sharing, which helps improve the overall productivity of production factors (Peng et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, as the productive service industry agglomerates, it can effectively enhance energy utilization efficiency, reduce energy consumption per unit, and decrease pollutant emissions (Ma and Yao, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These agglomeration effects improve urban air quality by strengthening the collaborative effects within the industrial chain (Wang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), thus validating hypothesis H\u003csub\u003e4\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eThese regression results indicate that the establishment of CBEC pilot zones indirectly promotes the improvement of urban air quality through their effects on green technology innovation, resource allocation, and productive service industry agglomeration. This phenomenon reflects the multiple driving roles of CBEC as a policy tool in urban economic transformation and green development. The CBEC pilot zones not only provide businesses with more market opportunities but also offer a good platform for technological innovation, resource optimization, and industrial agglomeration. However, it is worth noting that employment data related to productive service industry agglomeration is missing after 2020, which may have led to a reduction in the sample size for the regression results, affecting the stability and representativeness of the analysis. Therefore, future research could consider more comprehensive datasets to further validate the universality and long-term effects of these mechanisms. Overall, the implementation of CBEC pilot zone policies provides Chinese cities with a multi-dimensional path for economic and environmental optimization, particularly in improving air quality. This offers valuable practical experience for policymakers and promotes the coordinated development of high-quality economic growth and environmental sustainability.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism test results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Greentec\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Ra\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Psia\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Policy\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.260\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.186\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.071***\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.084\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(9.01)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.74)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.46)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3.15)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3.84)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Controls\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e 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colname=\"c5\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3962\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3880\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3880\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3115\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3115\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Conclusion and recommendations","content":"\u003cp\u003eThis paper utilizes panel data from 284 Chinese cities from 2009 to 2022, treating the establishment of CBEC pilot zones as a quasi-natural experiment. It empirically examines the emission reduction effects of CBEC on urban air pollution and explores the transmission mechanisms and heterogeneity of these effects. The research findings indicate that: (1) CBEC significantly improves urban air quality, with a particularly notable effect in reducing per capita industrial sulfur dioxide emissions. This conclusion is validated through various robustness tests, including parallel trend, dynamic effect tests, placebo tests, and the PSM-DID method. (2) Heterogeneity analysis shows that the emission reduction effect of CBEC is more pronounced in eastern cities compared to central and western regions; cities with higher levels of digital infrastructure experience stronger emission reduction effects from CBEC; and cities with weaker environmental regulations tend to amplify the emission reduction effect. (3) Mechanism analysis reveals that CBEC effectively reduces urban air pollution and improves air quality by green technology innovation, productive service industry agglomeration, and resource allocation optimizing. Based on these findings, the paper proposes several policy recommendations to further enhance the positive role of CBEC in promoting urban green development and improving air quality.\u003c/p\u003e\u003cp\u003eFirst, China should build on the development practices of CBEC pilot zones to further accelerate the rapid growth of CBEC. The challenge of balancing economic development and environmental improvement has long been a difficult issue, with few studies effectively integrating the two. However, this study demonstrates that CBEC not only promotes economic growth but also contributes to environmental improvement. Therefore, CBEC should be viewed as a key tool for driving high-quality development, with comprehensive reforms and accelerated construction of CBEC pilot zones. On one hand, local regions should leverage their unique industrial advantages and use CBEC pilot zones to foster CBEC-enabled industrial belt development models, facilitating industrial upgrading and transformation. On the other hand, the resource integration capabilities of CBEC should be utilized to optimize the allocation of traditional production factors, improve resource utilization efficiency, and achieve a win-win situation for both economic and environmental benefits.\u003c/p\u003e\u003cp\u003eSecond, given the differences in resource endowments across cities, policies should be tailored to local conditions to maximize the economic and social welfare gains from CBEC. The heterogeneity analysis in this study offers valuable insights for advancing CBEC development. The findings reveal significant regional differences in the environmental effects of CBEC. Therefore, the government should formulate targeted policies for the development of CBEC pilot zones based on the resource endowments and actual conditions of each region, enhancing the inclusivity and flexibility of these policies while avoiding blind replication of other regions' experiences. Only in this way can CBEC fully realize its environmental and economic benefits in different cities.\u003c/p\u003e\u003cp\u003eThird, improving information infrastructure and accelerating the spread of the internet are critical to promoting the development of CBEC. The growth of CBEC and the establishment of CBEC pilot zones are highly dependent on the internet and other modern information and communication infrastructure. The heterogeneity analysis also shows that in cities with more advanced internet development, the effect of CBEC on air quality improvement is more significant. Therefore, the government should thoroughly implement the strategy of building a strong cyber infrastructure, accelerate the development of new types of infrastructure, especially internet infrastructure, promote the interconnectivity of urban and rural broadband networks, enhance service quality, and further increase the speed and efficiency of information transmission, providing strong support for the environmental benefits of CBEC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contributions: Conceptualization: Shiwen Luo and Hongsheng Zhang;Data collection and processing: Shiwen Luo; Methodology: Shiwen Luo; Software: Hongsheng Zhang; Writing\u0026mdash;original draft: Shiwen Luo; Writing\u0026mdash;review and editing: Hongsheng Zhang. All authors have read and approved the fnal manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBartolomeo DM, dal Maso D, De Jong P, Eder P, Groenewegen P, Hopkinson P, Zaring O (2003) Eco-efficient producer services\u0026mdash;what are they, how do they benefit customers and the environment and how likely are they to develop and be extensively utilised?. Journal of Cleaner Production 11(8):829-837.\u003c/li\u003e\n\u003cli\u003eBilal A, Li X, Zhu N, Sharma R, Jahanger A (2021) Green technology innovation, globalization, and CO2 emissions: recent insights from the OBOR economies. Sustainability, 14(1), 236.\u003c/li\u003e\n\u003cli\u003eBingbing Z, Yujia C, Jing Z, Zhijun Y. (2023) CBEC comprehensive pilot areas and regional coordinated development: window radiation or siphon effect. 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Journal of cleaner production 264:121698.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6420572/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6420572/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As China's rapid urbanization and industrialization accelerate, the issue of deteriorating air quality has gained widespread attention. Cross-border e-commerce (CBEC), as a representative of digital trade, provides a new solution for improving environmental governance performance. Based on panel data from 284 Chinese cities from 2009 to 2022, this paper utilizes the establishment of CBEC pilot zones as a quasi-natural experiment and employs a multi-period Difference-in-Differences method to assess the effect of CBEC on urban sulfur dioxide (SO2) emissions. The results indicate that CBEC significantly improves urban air quality. Heterogeneity analysis shows that this effect is particularly pronounced in eastern cities, especially those with higher levels of digital infrastructure and weaker environmental regulations. Mechanism tests further reveal that CBEC effectively enhances urban air quality by the pathways of green technology innovation, productive service industry agglomeration, and resource allocation optimizing. Therefore, it is essential to accelerate CBEC reforms, promote the construction of CBEC pilot zones, and strengthen policy implementation to fully leverage its potential in improving urban air quality.","manuscriptTitle":"Does cross-border e-commerce contribute to urban air quality improvement? Evidence from China’s pilot zones","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 13:01:12","doi":"10.21203/rs.3.rs-6420572/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"762f6025-cdf6-4886-aa35-2ae3d2df6f48","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48496137,"name":"Business and commerce/Economics"},{"id":48496138,"name":"Social science/Environmental studies"}],"tags":[],"updatedAt":"2025-11-03T14:23:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 13:01:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6420572","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6420572","identity":"rs-6420572","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}