Unveiling the land allocation puzzle: Government environmental attention and the industrial land transactions of polluting enterprises

Unveiling the land allocation puzzle: Government environmental attention and the industrial land transactions of polluting enterprises | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Unveiling the land allocation puzzle: Government environmental attention and the industrial land transactions of polluting enterprises Mengjie Li, Weijian Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6679793/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract As awareness of environmental protection increases, governments worldwide, especially those in developing countries, are increasingly concerned with environmental issues and focused on pursuing the coordinated progress of economic development and ecological balance in various countries. Focusing on China, the largest developing country in the world, this study systematically investigates the effects and internal mechanisms of governments’ environmental attention in regard to the industrial land transactions of polluting enterprises. This study demonstrates that when governments focus more on the environment, the likelihood and area of transactions involving polluting firms' industrial land decrease. The results of a robustness analysis, an endogenous analysis and a placebo test support the above conclusions. The discussion of internal mechanisms shows that local governments' environmental attention inhibits the industrial land transactions of polluting enterprises by increasing production costs, improving technical standards and crowding out available funds. Additionally, when governments pay attention to the environment, some polluting enterprises move closer to administrative boundaries or are directly located in the districts and counties of these boundaries, which leads to the transregional transfer of pollution. This study elucidates the procedure for allocating land resources under the constraints of the ecological environment and provides a scientific foundation for policy decisions by governments worldwide. Social science/Economics Social science/Environmental studies Figures Figure 1 Introduction With the rapid rise of industry, nations worldwide are facing many major environmental problems such as pollution and global warming. These issues threaten the ecosystem's equilibrium and long-term viability and negatively impact people's health and quality of life (Anjum, et al., 2025 , Jia and Lin, 2025 , McKay, et al., 2022 ). At the same time, industrial land is an important carrier of industrial production, and its trading activities directly pertain to the economic expansion and industrial structure of the surrounding area (Adamopoulos, et al., 2024 , Kjelsrud, et al., 2023 , You, et al., 2025 ). However, in the context of industrial land transactions, more attention is often given to economic factors, whereas less attention is given to environmental factors. This has led some polluting enterprises to damage the local environment substantially after obtaining industrial land. Achieving sustainable economic development is an important goal of social development worldwide, so environmental factors need to be fully considered in the apportionment and application of land resources to attain the unification of social, environmental, and economic advantages. Under the incentive of environmental protection assessment, will local governments reduce the number of approvals of industrial projects or inhibit market entry, especially for industries that are key to air pollution prevention and control, through administrative approval and other means? What are the internal mechanisms of these governments? To explore the above issues, on the basis of the multisource heterogeneous data of China's local government work reports and micro land transaction information, this study investigates the effects and internal mechanisms of governments’ environmental attention in regard to the industrial land transactions of polluting enterprises and further explores the heterogeneity effect and boundary layout impact of governments’ environmental attention. The following additions are made by this study in comparison to the literature. First, starting with government's environmental attention, this study focuses on the specific economic activity of polluting enterprises' industrial land transactions, links the macro policy considerations of governments to the micro land transaction behavior of enterprises, and establishes both global and micro research frameworks. Second, this work investigates the effects of local government attention on the industrial land transactions of polluting enterprises and further reveals the internal mechanism and heterogeneous effects of government attention in regard to the probability and area of industrial land transactions to provide more comprehensive and detailed research. Finally, a variety of big data technologies and microeconometric methods are comprehensively employed to process multisource heterogeneous data, such as local government work reports and micro land transaction information, by using web crawlers and text analysis. Additionally, the panel logit model, two-stage least squares method, Heckman selection model and subsample regression are used to improve the scientific accuracy of this research. This is how the remainder of the paper is structured. Literature review and research hypotheses constitute the second section. The research design, including the primary variables, benchmark model, and data sources used in this study, is covered in the third section. The findings of the empirical analyses, which contain benchmark, robustness, endogenous, and placebo analyses, are presented in the fourth section. Research on the connection between government environmental attention and industrial land transactions is further explored in the fifth section, which covers internal mechanisms, multidimensional heterogeneity, land transaction areas, and enterprise boundary layouts. The conclusions and policy implications are presented in the last part. Literature review and research hypotheses Environmental attention refers to the degree of attention given by individuals or society to environmental issues, environmental protection and ecological balance (Al Mamun, et al., 2025 , Blair, et al., 2013 , Du, et al., 2023 , Lee, et al., 2025 , Wang, et al., 2025 ). The literature mainly defines it in terms of three aspects: cognitive level, emotional dimension and behavioral intention. The literature that defines environmental attention at the cognitive level is based on ecology theory and suggests that the degree of this attention is determined by the understanding of environmental conditions, various environmental phenomena and problems and their causes and consequences on the basis of relevant theoretical knowledge to form the overall understanding of the environmental system (Brendel, et al., 2018 , Ma, et al., 2024 , Zhang, et al., 2025 ). The literature that defines this attention through the emotional dimension is based on emotional theory, which suggests that the degree of environmental attention is reflected in worry, anxiety, a sense of responsibility and other emotions caused by environmental damage and reflects people's inner attitudes and feelings related to the environment (Pagiaslis and Krontalis, 2014 , Wang and Wu, 2024 ). The literature that defines this attention through behavior intention is based on behavior theory and suggests that the degree of environmental attention is reflected in whether people are willing to take practical actions to protect the environment, such as participating in environmental protection activities and supporting environmental protection policies (Billah and Adnan, 2024 , Wang, et al., 2025 ). The effects of government focus on industrial land transactions has emerged as a key field of research because global environmental issues have become more severe and the idea of sustainable development has evolved (Lu and Tao, 2024 , Pu, et al., 2023 , Wappenhans, et al., 2024 , Zhang, et al., 2025 ). The government has been paying increasing attention to the environmental field by formulating and improving an array of environmental policies and regulations, including strict pollution discharge standards (Bakaki, et al., 2020 , Lu, et al., 2024 , Xie, et al., 2025 , Zhang, et al., 2024 ), environmental impact assessment systems (Chen, et al., 2024 , Li, et al., 2022 ), and green financial policies (Zhang, et al., 2024 ), to regulate the production and operation activities of enterprises. Some research works have revealed that the strict environmental regulation forces polluting enterprises to face more restrictions when they acquire industrial land, resulting in a reduction in their demand (Tang, et al., 2024 ). Additionally, the government's attention affects the price of industrial land. Research shows that in areas with strict environmental policies, enterprises' capacity to bid on industrial land is diminished because of environmental standards (Moffette, et al., 2024 , Zhang, et al., 2024 ). With the continuous improvement of the government's environmental attention, enterprises face many invisible pressures, which have gradually evolved into informal environmental regulations that affect enterprises' implementation of resource-saving and environmentally friendly strategies (Tam and Chan, 2017 , Zhang, et al., 2025 ). The initial research hypothesis is therefore put out. Hypothesis 1 Local governments' environmental attention restrains the industrial land transactions of polluting enterprises in an area by increasing the environmental standards of enterprises. According to the literature, an increase in the environmental attention of governments may improve the environmental standards of enterprises by affecting the cost effect, innovation effect and crowding out effect to reduce the number of industrial land transactions of polluting enterprises (Du and Li, 2021 , Song, et al., 2024 ). The first effect of the environmental attention is the cost effect. With improvements in the environmental attention of governments, their departments may intensify the execution of environmental regulations, reduce the expected price of enterprises' industrial land use after the transaction, and increase the pollution emission cost of enterprises in the region (Tu, et al., 2024 ). Some polluting firms that don't adhere to emission regulations may no longer be allowed to trade industrial land. The second effect of the environmental attention is the innovation effect. As environmental concerns of governments get more attention, to meet the production process and environmental standards of government departments, enterprises may engage in R&D and innovation, thus improving production technology standards following transactions of industrial land (Fang and Liu, 2024 , Guo and Jin, 2025 , Liu, et al., 2024 ). Some polluting enterprises that fail to meet technical production standards may no longer be allowed to trade industrial land (Ma, et al., 2025 ). The third effect of the environmental attention is the crowding out effect. With improvements in the environmental attention of governments, to meet governmental requirements for enterprises in terms of green products and green processes, enterprises need to invest more in environmental protection; this involves their operational income and profitable investments after they enter the market (Chen, et al., 2024 , Liu, et al., 2024 , Zhou, et al., 2025 ), causing some polluting enterprises to abandon industrial land transactions. Accordingly, Hypothesis 2 is proposed. Hypothesis 2 Local governments' environmental attention can reduce industrial land transactions of polluting enterprises by increasing pollution emission standards, leading to production technology innovation and the crowding out of available funds. Research design Econometric model. The panel logit model is employed as the benchmark regression to examine the impacts of environmental attention on the probability of industrial land transactions of polluting firms, given the binary nature of micro land transaction variables. The benchmark equation is shown in Eq. ( 1 ). The dependent variable LT it is the industrial land transaction choice of polluting enterprises, and GEA it is the variable of government environmental attention; X it is the control variable, which may affect the transaction probability of industrial land being selected; ν i and η t are used to control for individual effects and time effects, respectively; and ε it is the random perturbation term. β is the primary parameter to be assessed. If β < 0 and passes the significance test, the government's attention can reduce the probability of industrial land transactions by polluting enterprises and realize environmental governance from the source. Index establishment. The dependent variable is the industrial land transaction of polluting enterprises. This variable is constructed on the basis of the transaction of the industrial land of polluting enterprises in the sample. If an enterprise has industrial land transaction information in the current year, the industrial land transaction variable is 1; otherwise, it is 0. The core explanatory variable is government environmental attention. This variable is quantified by the proportion of the recurrence rate of low-carbon and environmental protection words in the work reports of Chinese local governments in the overall word count of the work reports. The control variables include enterprise scale ( ES ), capital intensity ( CI ), enterprise age ( EA ), production efficiency ( PE ) and enterprise ownership ( EO ). The headcount of staffers at the close of the year serves as a proxy for the enterprise scale. The proportion of an organization's average balance of fixed assets to its workforce serves as a measure of capital intensity. The gap between the enterprise's sample year and its founding year serves as a proxy for the enterprise's age. Productivity is determined by using a single labor productivity indicator, accounting for any deviations during the process of estimating total factor productivity (TFP). Enterprise type is derived from private enterprises, with the binary virtual variables of state-owned and foreign enterprises being established. Data sources. The macrolevel government environmental attention data were obtained by gathering 2874 government work reports from Chinese cities between 2003 and 2013. The recurrence of low-carbon and environmental protection words was retrieved from these reports, and their ratio of such words to the overall word count in the report was calculated. Microlevel enterprise data from 2003 to 2013 were derived from the databases of China Industrial Firm, China Firm Pollution Emission, and China Land Market Network. China National Bureau of Statistics created a database of industrial enterprises in China, using the enterprise legal person as the statistical unit. It mostly contains fundamental details and key financial information about enterprises. The Ministry of Ecological Environment of China produced a database of Chinese firms' pollution emissions, and yearly data were obtained from the main polluting enterprises' quarterly questionnaires. Among these, the main polluting firms were those in every district and county in which the pollution emissions comprise the top 85% of the overall emissions. The China Land Market Network, which was established by the Ministry of Land and Resources of China, is a platform supporting the dynamic monitoring system of China's land market. It integrates information release, monitoring analysis and sharing services and provides land supply information such as China's land supply plan, transfer announcement, and transfer results. The databases of China Industrial Firm and the China Firm Pollution Emission are combined on the basis of public fields such as firm name, organization code, firm zip code, and firm address information. Through matching, we identified the key polluting enterprises among all state-owned firms and nonstate-owned firms above a designated size. On this basis, we processed the enterprise name in the matched enterprise database and the land user variables in the land transfer data to correct the typos and remove the words that were not helpful for matching; thus, the final industrial land transaction data of polluting enterprises that were needed for this study were obtained. On this basis, we matched the macro government environmental attention data and the micro enterprise data on the basis of the name and code of the city in which a firm was situated. The matching data constitute the sample with the largest observed value among the available data, which can improve the generality and credibility of the research conclusions. Table 1 exhibits the descriptive statistical features of the primary variables. Table 1 Descriptive statistics of main variables. Main variables Observation Mean Standard deviation Min Max LT 541,530 0.0203 0.1411 0 1 GEA 453,465 43.03 20.56 0 228.72 ES 533,658 468.90 735.52 17 5027 CI 533,020 581.27 1158.13 19.73 8820 PE 528,953 49.91 109.63 0.0860 828.62 EA 541,323 12.98 11.22 2 57 SOE 541,530 0.1630 0.3693 0 1 FE 541,530 0.1315 0.3379 0 1 Empirical analysis results Benchmark analysis. The industrial land transactions of polluting enterprises are the explained variable in Table 2 , which also uses the environmental attention of local governments as the core explanatory variable. Enterprise scale, capital intensity, and production efficiency are introduced to control the production and operation characteristics of enterprises; enterprise age is introduced to control the survival characteristics of enterprises; and the variables of state-owned and foreign enterprises are introduced to control for ownership characteristics. These variables are based on the benchmark regression equation. Given the binary nature of the explained variables, the benchmark regression uses panel logit regression. Table 2 confirms Hypothesis 1 by demonstrating that the GEA variables are negative, which indicate that as governments become more environmentally conscious, the likelihood of industrial land transactions involving polluting industries decreases. A possible explanation for this is that as the government's environmental attention increases, the government may issue stricter environmental protection regulations, causing polluting enterprises to face higher environmental protection requirements and standards. This may reduce their willingness and ability to purchase industrial land and ultimately inhibit the industrial land transactions of polluting enterprises. The results of the control variable regression were consistent with the expectations. The coefficients of the variables ES , CI and PE are all significantly positive. It indicates that with increasing enterprise production scale, capital intensity and production efficiency, enterprises are more inclined to purchase new industrial land. The EA variable is negative, which indicates that the willingness of enterprises to purchase new industrial land decreases with increasing enterprise survival time. Taking private enterprises as the benchmark, the coefficients of the SOE and FE variables are significantly negative, indicating that, compared with private enterprises, state-owned enterprises and foreign enterprises are not as likely to engage in new industrial land transactions. Table 2 Benchmark estimation. (1) LT (2) LT (3) LT (4) LT (5) LT GEA -0.1214 *** -0.1332 *** -0.1595 *** -0.1706 *** -0.1324 *** (0.0284) (0.0287) (0.0286) (0.0288) (0.0286) ES 0.3030 *** 0.3149 *** 0.3170 *** 0.4339 *** (0.0105) (0.0090) (0.0091) (0.0098) CI 0.3281 *** 0.2943 *** 0.3328 *** (0.0078) (0.0108) (0.0110) PE 0.0424 *** 0.0614 *** (0.0094) (0.0093) EA -0.3245 *** (0.0188) SOE -0.5256 *** (0.0370) FE -0.7989 *** (0.0358) Constants -8.9407 *** -10.6668 *** -12.4038 *** -12.9850 *** -13.1714 *** (0.7109) (0.7141) (0.7148) (1.0046) (1.0053) Observation 453,465 445,862 445,825 439,456 439,371 Pseudo R 2 0.0629 0.0758 0.0937 0.0940 0.1075 Note : Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure. Robustness analysis. Because the impacts of government environmental attention on the industrial land transactions of polluting enterprises might not be fully apparent in the current period, Columns (1) and (2) of Table 3 test the impacts of lag one and lag two periods of government attention on the probability of industrial land transactions of polluting enterprises, respectively, to explore the time lag effect of the impact of environmental attention. The findings support the benchmark results by demonstrating that the coefficients of government environmental attention lag for one and two periods are significantly negative. This suggests that government attention has a long-term inhibitory effect on the probability of polluting enterprises engaging in new industrial land transactions. The benchmark regression measures the percentage of low-carbon and environmental protection terms in the work report of government as an objective way to gauge the government's attention to the environment. However, accounting for the interference of the overall word frequency in the government's work report, the number of low-carbon and environmental protection terms within the work report of government is utilized as an indicator of the local government's environmental attention, as displayed in Column (3) of Table 3 . The results show that after the variables of government environmental attention are replaced, the conclusion is in accordance with the benchmark analysis. With increasing government environmental attention, the likelihood of polluting enterprises engaging in industrial land transactions decreases significantly. Additionally, in Column (4) of Table 3 , the enterprises exiting the sample interval are removed to eliminate the interference of short-term enterprises on the analysis results. The findings demonstrate that, even when the interference of exiting enterprises is removed from the sample interval, the coefficient of the variable measuring the government's attention remains negative. This suggests that, as government's attention increases, the transaction probability of the industrial land of polluting enterprises in a region decreases, so the findings of the benchmark analysis are again confirmed. Table 3 Robustness analysis: Based on index replacement and sample screening. (1) (2) (3) (4) Lag one period Lag two period Word number Excluding exiting enterprises GEA -0.1318 *** -0.0799 ** -0.1185 *** -0.1319 *** (0.0342) (0.0394) (0.0235) (0.0295) Constants -12.2674 *** -13.4330 *** -13.2867 *** -13.0325 *** (0.7227) (1.0129) (1.0036) (1.0058) Control variable YES YES YES YES Observation 287,520 205,415 439,371 397,584 Pseudo R 2 0.0883 0.0791 0.1075 0.1103 Note : Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure. Endogenous analysis. In certain cases, endogeneity issues might result from the two-way causal link between variables and their omission, and instrumental variables, as a tool for controlling endogeneity problems, are widely used. Considering the correlation and exogenous requirements of instrumental variables, the air circulation coefficient is the instrumental variable in Column (1) of Table 4 . The air circulation coefficient is closely related to the distribution of pollutants in the environment and air quality, which can reflect changes in the regional environment. Thus, the air circulation coefficient is strongly correlated with local government environmental attention, meeting the correlation requirements of instrumental variables. The air circulation coefficient, which is mostly independent of enterprise choices regarding land transactions and may be considered an exogenous variable, is often impacted only by certain natural elements or building constructions. Column (2) of Table 4 uses the environmental attention of adjacent areas as the instrumental variable. The economic development of adjacent cities is often highly competitive, so the environmental attention of governments in adjacent areas is highly correlated with that of local governments; thus, the correlation requirements of instrumental variables are met. Additionally, the environmental attention of governments in neighboring regions is not easily affected by the land transaction behavior of enterprises in other regions, so exogenous requirements are met. The results in Table 4 demonstrate that when endogeneity is controlled, the government's environmental attention coefficients are significantly negative. This means that, as environmental concerns receive increased attention from governments, polluting firms are subject to stricter government regulations, and their transaction probability for new industrial land decreases. Weak instrumental variable tests and overidentification tests of the instrumental variables are conducted. The test results reject the original hypothesis and confirm the usefulness of the instrumental variables. Table 4 Endogenous analysis. (1)Air circulation coefficient (2)Environmental attention of adjacent areas GEA -0.1626 *** -0.0037 *** (0.0601) (0.0012) Constants 0.3701 ** -0.0692 *** (0.1660) (0.0039) Control variable YES YES Kleibergen-Paap rk LM statistics 64.09 98031.88 (0.0000) (0.0000) Kleibergen-Paap rk Wald F statistics 9.87 9.90 (0.0017) (0.0017) Observation 438,797 439,371 R 2 0.1591 0.1446 Note : Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure. Wald F statistics are used to determine if instrumental variables are weakly identified, LM statistics are used to identify instrumental variable insufficiency, and the p values of the statistics are displayed in brackets. Placebo analysis. The outcomes of the benchmark analysis demonstrate that local governments' environmental attention reduces the likelihood of polluting businesses buying industrial land in the area, and the conclusions of the benchmark analysis are corroborated by robustness and endogenous analyses. To ensure that the findings of the benchmark analysis are not the result of chance, Column (1) of Table 5 substitutes the nonindustrial land transaction variable of the polluting enterprise for the industrial land transaction variable in the benchmark analysis while maintaining the government's environmental attention as the primary independent variable. This is based on the land category in the enterprise land transaction information. Column (2) of Table 5 , which takes the industrial land transactions of polluting enterprises as the explanatory variable, replaces the environmental attention of governments with their attention to digitalization on the basis of the work reports of local governments. There is no evidence that government environmental attention can reduce the likelihood of nonindustrial land transactions, which have little correlation with enterprise production and emissions, as demonstrated by the results in column (1) of Table 5 . Nevertheless, government environmental attention can limit the trading of industrial land for polluting enterprises because industrial land is usually directly related to production activities, involving possible pollutant emissions, resource consumption and environmental damage. If an enterprise's environmental protection measures are not up to standard or cause great harm to the environment, the government may restrict or even prohibit its industrial land transactions to reduce potential environmental pollution. However, nonindustrial land, such as commercial land and residential land, has little correlation with the production and emissions of enterprises. The main purpose of these lands is not direct industrial production, and the impact on the environment is relatively small. Consequently, government environmental attention is not focused on this area, and there is no evidence that the government's attention can reduce the transaction likelihood of nonindustrial land for polluting enterprises. The findings in Column (2) of Table 5 demonstrate that the variable of government digital attention is not significant, which indicate that there is no proof that local government digital attention can lessen the likelihood of polluting enterprises purchasing industrial land. The government's attention is focused on the control and supervision of environmental pollution, ecological damage and other aspects. Because the utilization of industrial land is frequently closely linked to the production activities of enterprises, which may cause pollution problems, the government imposes stricter examinations and restrictions on the industrial land transactions of polluting enterprises when it attaches more importance to the environment. The focus of government digitalization is usually to enhance the efficiency of government services, optimize management processes, and promote information sharing. This may be related more to the digitalization of administrative affairs and the promotion of e-government, but it does not involve the direct assessment or limitation of the environmental impact of industrial land transactions of polluting enterprises. Therefore, a government's digital attention does not reduce the probability of polluting enterprises engaging in industrial land transactions. Table 5 Placebo analysis. (1)Non-industrial land transaction (2)Government digital attentions GEA -0.0794 (0.0915) GDA 0.0241 (0.0305) Constants -12.5249 *** -12.9305 *** (0.4723) (0.7151) Control variable YES YES Observation 364,254 433,525 Pseudo R 2 0.0899 0.1097 Note : Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure. Further discussion Discussion of internal mechanisms. To verify Hypothesis 2 , Table 6 explores three aspects of the internal mechanism by which local government environmental attention affects the industrial land transactions of polluting enterprises: the cost effect, the technology effect and the crowding out effect. To examine the mechanism by which the government's environmental attention impedes industrial land transactions by increasing the environmental costs of polluting enterprises, the removal of sulfur dioxide (SO 2 ) and chemical oxygen demand (COD) at the enterprise level are introduced in Columns (1) and (2) of Table 6 , respectively, as a measure of the cost effect of enterprises. The pertinent data on the pollutant removal of enterprises are obtained from the pollution emission database of Chinese enterprises, which provides details on the production, removal and emission of major pollutants by Chinese enterprises. The results in Columns (1) and (2) of Table 6 demonstrate that the coefficients of local governments' environmental attention variables are positive, meaning that environmental attention increases the rate at which major pollutants, including SO 2 and COD, are removed from enterprises and improves their emission standards. This, in turn, increases businesses' production costs and reduces their ability to trade industrial land. Columns (3) and (4) of Table 6 present enterprise-level patent and green patent applications, respectively, as gauges of the enterprise innovation effect and investigate the internal mechanism by which government environmental attention inhibits industrial land transactions by requiring polluting firms to increase R&D innovation. The pertinent data of firm patent applications are from the Database of China Firm Patent, which provides the relevant information of all Chinese enterprises' patent applications after 1985, including patent name, patent type, patent details, application year and other information. The findings in Columns (3) and (4) of Table 6 indicate that local governments' environmental attention variables have significantly positive coefficients, meaning that environmental attention increases the likelihood of enterprise and green patent applications and improves the production technology standards of polluting enterprises. In other words, governments' increasing attention to the environment pressures polluting firms to improve technology and make them more active in technological innovation, while polluting enterprises face greater difficulties in this environment and have difficulty obtaining opportunities for industrial land transactions. In Columns (5) and (6) of Table 6 , the investment and financing constraints of governance equipment at the enterprise level are introduced as measures of the crowding out effect, and the internal mechanism by which government environmental attention inhibits industrial land transactions by crowding out the available funds of polluting enterprises is investigated. The pertinent data on investment in enterprise equipment treatment are obtained from the pollution emission database of Chinese enterprises. To assess the enterprise financing constraint variable, the ratio of interest expenses to sales revenues is made use of. The pertinent data are obtained from the database of Chinese industrial enterprises. The results in Columns (5) and (6) of Table 6 demonstrate that the coefficients of the variables pertaining to local governments' attention are positive. It indicates that governments' attention increases capital factor investment requirements and the investment and financing constraints of enterprises' pollution control. In other words, as local governments' environmental attention increases, more of the funds that these enterprises have available are taken up, which reduces the transaction probability of industrial land. Table 6 Internal mechanism: Based on Cost Effect, Technology Effect and Crowding Out Effect. Cost Effect Innovation Effect Crowding Out Effect (1) (2) (3) (4) (5) (6) SO2 removal COD removal Patent Green patent Equipment Financing constraints GEA 0.0066 *** 0.0439 *** 0.0256 ** 0.1240 ** 0.0499 ** 0.0019 *** (0.0012) (0.0015) (0.0103) (0.0601) (0.0209) (0.0002) Constants -0.1588 *** -0.0399 *** -0.4398 *** -7.7897 *** -2.8697 *** -0.0280 *** (0.0059) (0.0069) (0.0649) (0.3584) (0.1095) (0.0010) Control variable YES YES YES YES YES YES Observation 218,176 280,670 52,910 52,910 61,690 34,9248 R 2 /Pseudo R 2 0.1529 0.1107 0.3517 0.1268 0.2236 0.0575 Note : Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure. Discussion on multidimensional heterogeneity. Environmental protection measures, including energy consumption per GDP unit and total emissions of two major pollutants, SO 2 and COD, were established as mandatory metrics for the performance evaluation of municipal authorities in China for the first time in 2006. The central government uses the assessment results as a crucial foundation for making decisions about the political appointment and dismissal of local top cadres at all levels. The interaction between enterprise industrial land transactions and government attention may be impacted by period variance. The impact of government environmental attention on the industrial land transactions of polluting firms is examined, accounting for the period's peculiarities. The upper left of Fig. 1 reports the analysis findings. The results indicate that the of government environmental attention is not significant prior to the policy's implementation but becomes significantly negative in the sample following it. In other words, the implementation of the environmental goal constraint policy gradually leads to local governments’ environmental attention inhibiting the industrial land transactions of polluting enterprises. A possible explanation for this is that the implementation of an environmental objective constraint policy is accompanied by the strengthening of supervision and accountability mechanisms. To achieve the environmental objectives stipulated in the policy and avoid being held accountable by superiors for environmental problems, local governments more actively use policy tools to curb the industrial land transactions of polluting enterprises. Because China's regions differ greatly in terms of their state of development, it is advantageous to create control policies that are tailored to the particular conditions of each region to examine how government environmental initiatives affect the transaction of industrial land to polluting enterprises. To investigate the heterogeneity of the effect of government attention on the transaction probability of the industrial land of polluting enterprises in different regions, on the basis of the location characteristics, enterprises in the eastern region and those in the central and western regions are what the sample is divided into. The upper right of Fig. 1 reports the analysis findings. A comparison of the sample firms' environmental attention coefficients across various regions clearly reveals that with increasing local government environmental attention, the transaction probability of the industrial land of enterprises of the central and western regions is more restrained. The impact on polluting enterprises of the central and western regions may be more evident in the process of strengthening supervision, whereas the environmental supervision system in the eastern region may be relatively complete, such that polluting enterprises are subject to more restrictions on land use, which has restrained their expansion of industrial land to some extent. Furthermore, some polluting enterprises can relocate to the west and center areas from the east. The land transactions of polluting firms are hindered in the process of undertaking industries in the central and western regions, as governments increase the attention to environmental issues. The phased characteristics of China's economic development indicate that there may be significant differences in resource endowment and production technology across different industries. In the face of increasing environmental attention, production cost changes and technology adjustment willingness differ between heavy industry and light industry. To probe into the heterogeneous effects of government attention on the probability of land transactions by enterprises in different industries, the sample is grouped into heavy and light industrial firms on the basis of their industry characteristics. The lower left of Fig. 1 reports the analysis findings. The likelihood of heavy industrial enterprises engaging in industrial land transactions becomes more constrained as local government environmental attention increases, according to a comparison of the variables of government attention across sample enterprises in various industries. A possible reason for this is that the production scale of heavy industrial enterprises is large, and the requirement for land resources is also greater. The land transactions in this industry are more likely to attract attention, so restrictions increase accordingly. Additionally, the cost of environmental governance for heavy industrial enterprises is high, and the improvement of the environmental attention also makes them more cautious in land use. The industrial land transactions of various enterprise types exhibit some heterogeneity due to variations in enterprise ownership characteristics. In addition, the effects of government attention on various enterprise types may differ. The impact of government attention on the allocation of land to state-owned, foreign, and private firms is examined in relation to the features of enterprise ownership. The lower right of Fig. 1 report the findings. When local government environmental attention increases, state-owned enterprises' ability to engage in industrial land transactions is inhibited less than that of private enterprises, and foreign enterprises' ability to do so is most strongly inhibited. This is evident when the coefficients of government environmental attention among enterprises with varying property rights are compared. Possible reasons for this are as follows: state-owned enterprises usually have strong resource advantages and policy support and may be subject to relatively few external restrictions on industrial land transactions. Moreover, private enterprises are in a relatively weak position in market competition, facing more pressure from capital, resources and other aspects, resulting in strong inhibition of the scale of industrial land transactions by environmental attention. In addition to pressure on capital, resources and other aspects, foreign enterprises face more uncertainties in terms of policies, regulations, the market environment and other aspects, so they also face the strongest inhibitory impact of the government's attention on their industrial land transactions. Note An invalid line is the vertical line with an abscissa scale of 0. The figure's two-sided lines and diamonds are used to show the related variable's 95% confidence interval (CI) and effect size. When the CI horizontal line crosses the invalid vertical line, the coefficient of the variable is not statistically significant at the 5% level. Discussion of the land transaction scale and enterprise boundary layout. The previous section analyzed how the government attention affects the trading probability of the industrial land of polluting enterprises. However, environmental attention may also affect the trading scale of the industrial land of polluting enterprises. Next, we further investigate the influence of government environmental attention on the trading area of the industrial land of polluting firms. Considering that the probability of a polluting enterprise purchasing land and the transaction area of land are actually interdependent processes, if the two equations are estimated separately, the problem of sample self-selection may occur, affecting the accuracy of the estimation results. Therefore, the Heckman selection model ( HSM ) proposed by Heckman ( 1979 ) is used to correct for possible selectivity bias. The regression findings are presented in Columns (1) and (2) of Table 7 . The findings demonstrate that the coefficients of the environmental attention variables are significantly negative, suggesting that government environmental attention decreases both the transaction area and the transaction probability of polluting enterprises' industrial land. The government increases the requirements for industrial land's environmental evaluation if it begins to prioritize the environment. When applying for land, polluting enterprises often find it difficult to meet these strict standards, resulting in a reduction in the approved land area. Additionally, enterprises around administrative regions can spread pollutants to adjacent administrative regions to reduce pollution emissions in their regions. Does increasing attention to the environment from the local government lead to some enterprises moving to the administrative boundaries? To answer this question, Columns (3) and (4) of Table 7 are also based on the Heckman selection model to investigate whether the government's environmental attention affects whether polluting enterprises choose to layout to the administrative boundary and how the distance between the enterprises that choose to layout the administrative boundary and the boundary changes. The findings in Column (3) display that the environmental attention coefficient is considerably positive, suggesting that environmental attention increases the likelihood that polluting firms will be distributed to counties and administrative border districts. The findings in Column (4) display that the government's attention is negative, indicating that environmental attention will further reduce the distance between polluting enterprises with boundary layouts and boundaries and encourage some polluting enterprises to be closer to administrative boundaries. The government's environmental attention places pressure on and constraints on polluting enterprises. When this pressure increases, some polluting enterprises may attempt to find areas with relatively weak supervision due to cost, supervision and other factors. Thus, being close to the administrative boundary is a viable option. Table 7 Extended discussion: Based on land transaction scale and boundary layout. (1) (2) (3) (4) Probability Area Border districts and counties Distance from boundary GEA -0.0632 *** -0.1788 *** 0.1150 *** -0.0175 *** (0.0121) (0.0321) (0.0056) (0.0034) Constants -5.3931 *** -1.8608 *** 1.5278 *** 3.6148 *** (0.2523) (0.4534) (0.0250) (0.0185) Control variable YES YES YES YES Inverse mills ratio 0.1382 *** -0.4692 *** (0.0139) (0.0501) Observation 439,371 439,371 439,371 439,371 Pseudo R 2 0.1080 0.0126 Note : Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure. The inverse Mills ratio is used to measure whether the situation of sample selection bias occurs. If it is significant, the situation of a significant sample selection bias occurs, and the Heckman binary selection model is applicable. Conclusions and policy implications At the macro level, governments worldwide have increasingly emphasized the synergy between economic development and environmental conservation. At the microscopic level, as an important part of economic activities, the mode and decision-making of enterprise land transactions are strongly affected by policy and the market environment. On the basis of the multisource heterogeneous data of macro government work reports and micro enterprise land transactions, this study examines the effects and potential mechanisms of government attention on the land transactions of polluting enterprises. The primary conclusions are as follows. First, with governments paying more attention to the environment, the transaction probability and transaction area of industrial land for polluting enterprises decreases. Second, local governments' environmental attention restrains the industrial land transactions of polluting enterprises by increasing production costs, improving technical standards and crowding out available funds, that is, the cost effect, the technology effect and the crowding out effect. Third, local governments' attention has heterogeneous effects on the industrial land transactions of polluting enterprises. Compared with other firms, environmental attention has more potent inhibitory effects on the industrial land transactions of firms during the implementation period of environmental objectives, firms within the central and western areas, heavy industrial firms and foreign firms. Fourth, environmental attention prompts some polluting enterprises to be directly located in districts and counties at administrative boundaries, leading to the transregional transfer of pollution. The primary policy implications derived from the aforementioned results are as follows. First, governments should increasingly pay more attention to the environment. The comprehensive environmental protection assessment of local governments should be thoroughly strengthened, the improvement of environmental quality and the land transaction control of polluting enterprises should be included as important indicators, the continuous focus of local governments on environmental issues should be actively encouraged, and the extent of land usage of polluting enterprises from the source should be regulated. Second, the financial policy regulatory system should be reinforced, the environmental protection technology support system should be optimized, and the environmental cost sharing mechanism should be enhanced. Fair policies should be created, social capital should be encouraged to contribute to investments in environmental protection, and the financial burden that environmental protection places on polluting enterprises should be lessened. Investment in the R&D of environmental protection technology should be increased, enterprises should be provided with more low-cost and efficient environmental protection technology solutions, and the increased costs of enterprises should be reduced because of improvements in their technical standards. Financial institutions should be guided to allocate funds reasonably, excessive lending to polluting enterprises should be avoided, and more financial support to environmentally friendly enterprises should be provided. Third, differentiated environmental supervision strategies should be formulated. Special regional and industrial support, environmental protection technology training and special fund support for firms within the central and western regions and heavy industry enterprises should be provided, and the ability of these enterprises to protect the environment should be improved. The green development of foreign enterprises should be guided, the publicity and interpretation of environmental protection legislation and regulations for foreign enterprises should be strengthened, and advanced green production technology and management experience should be encouraged. Finally, a cross-regional environmental protection cooperation mechanism should be established, and regional monitoring of administrative boundaries should be strengthened. Environmental protection cooperation agreements are signed between adjacent administrative regions to clarify the responsibilities and obligations of all parties, and environmental monitoring stations are added in administrative border areas to jointly combat the transregional transfer of polluting enterprises. Declarations Author Contribution Mengjie Li: Formal analysis, Writing - Original Draft, Funding acquisitionWeijian Du: Conceptualization, Methodology, Software, Validation, Funding acquisition Acknowledgement This research is sponsored by the National Natural Science Foundation of China (72274112), National Social Science Fund of China (24BJY120), Taishan Scholars Project Funding (tsqn202211237, tsqn202306273), Shandong Provincial Natural Science Foundation (ZR2024MG040). Data Availability Data is provided within the manuscript. References Adamopoulos T, Brandt L, Chen CR et al (2024) Land security and mobility frictions. Quart J Econ 139:1941–1987. http://10.1093/qje/qjae010 Al Mamun A, Yang M, Hayat N et al (2025) The nexus of environmental values, beliefs, norms and green consumption intention. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Sep, 2025 Reviews received at journal 23 Jun, 2025 Reviews received at journal 20 Jun, 2025 Reviews received at journal 18 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers agreed at journal 14 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers invited by journal 11 Jun, 2025 Editor invited by journal 30 May, 2025 Editor assigned by journal 26 May, 2025 Submission checks completed at journal 26 May, 2025 First submitted to journal 16 May, 2025 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. 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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-6679793","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":471357246,"identity":"8458dfcd-c3c6-48ca-9cd7-e818d30ab12f","order_by":0,"name":"Mengjie Li","email":"","orcid":"","institution":"Shandong Technology and Business University","correspondingAuthor":false,"prefix":"","firstName":"Mengjie","middleName":"","lastName":"Li","suffix":""},{"id":471357247,"identity":"af976291-58c2-4334-a91f-d93e42ffc0da","order_by":1,"name":"Weijian Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDCCA0CcwCMBYjIzfABiAxCTh1gtjDMYmCWI0wIFzMw8xGjhu5Fj+OCBjIW8OQPzYWObP9Z15hIJjA/etjEARbADyRs5xgZAhxnubGBLTs5tS5ewnJHAbDi3jQEogl2LwY3cbRJALYwbDvAYH85tOCxhcCOBTZq3jSHB4ABOLdt/ALXYg7VY/AFrYf9NQMs2UIglgrQkM7BBbGHGp0XyzPvPIIclbzjAlmzY25YuubPnYbPknHMShhtwaOE7npb48WdPne2GA8yHJX78seY3Z08++OFNmY08LlvAgLEHSMg/gHMbgIQEHvUg8IOA/CgYBaNgFIxsAAD1JlkciUglggAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong Technology and Business University","correspondingAuthor":true,"prefix":"","firstName":"Weijian","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2025-05-16 10:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6679793/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6679793/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84752699,"identity":"22f79f65-4b9f-47bb-b7b4-e97df1c2affe","added_by":"auto","created_at":"2025-06-17 03:30:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26728,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental attention and industrial land transactions: Multidimensional heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: An invalid line is the vertical line with an abscissa scale of 0. The figure's two-sided lines and diamonds are used to show the related variable's 95% confidence interval (CI) and effect size. When the CI horizontal line crosses the invalid vertical line, the coefficient of the variable is not statistically significant at the 5% level.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6679793/v1/267c2af666c291fc0691f6e3.png"},{"id":84755006,"identity":"bcdabe55-b440-46f0-927c-e57562dd62a6","added_by":"auto","created_at":"2025-06-17 04:02:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":999514,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6679793/v1/94796fc1-b66f-43cc-af55-0c4f440631c6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling the land allocation puzzle: Government environmental attention and the industrial land transactions of polluting enterprises","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the rapid rise of industry, nations worldwide are facing many major environmental problems such as pollution and global warming. These issues threaten the ecosystem's equilibrium and long-term viability and negatively impact people's health and quality of life (Anjum, et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Jia and Lin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, McKay, et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At the same time, industrial land is an important carrier of industrial production, and its trading activities directly pertain to the economic expansion and industrial structure of the surrounding area (Adamopoulos, et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Kjelsrud, et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, You, et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, in the context of industrial land transactions, more attention is often given to economic factors, whereas less attention is given to environmental factors. This has led some polluting enterprises to damage the local environment substantially after obtaining industrial land. Achieving sustainable economic development is an important goal of social development worldwide, so environmental factors need to be fully considered in the apportionment and application of land resources to attain the unification of social, environmental, and economic advantages.\u003c/p\u003e \u003cp\u003eUnder the incentive of environmental protection assessment, will local governments reduce the number of approvals of industrial projects or inhibit market entry, especially for industries that are key to air pollution prevention and control, through administrative approval and other means? What are the internal mechanisms of these governments? To explore the above issues, on the basis of the multisource heterogeneous data of China's local government work reports and micro land transaction information, this study investigates the effects and internal mechanisms of governments\u0026rsquo; environmental attention in regard to the industrial land transactions of polluting enterprises and further explores the heterogeneity effect and boundary layout impact of governments\u0026rsquo; environmental attention.\u003c/p\u003e \u003cp\u003eThe following additions are made by this study in comparison to the literature. First, starting with government's environmental attention, this study focuses on the specific economic activity of polluting enterprises' industrial land transactions, links the macro policy considerations of governments to the micro land transaction behavior of enterprises, and establishes both global and micro research frameworks. Second, this work investigates the effects of local government attention on the industrial land transactions of polluting enterprises and further reveals the internal mechanism and heterogeneous effects of government attention in regard to the probability and area of industrial land transactions to provide more comprehensive and detailed research. Finally, a variety of big data technologies and microeconometric methods are comprehensively employed to process multisource heterogeneous data, such as local government work reports and micro land transaction information, by using web crawlers and text analysis. Additionally, the panel \u003cem\u003elogit\u003c/em\u003e model, two-stage least squares method, \u003cem\u003eHeckman\u003c/em\u003e selection model and subsample regression are used to improve the scientific accuracy of this research.\u003c/p\u003e \u003cp\u003eThis is how the remainder of the paper is structured. Literature review and research hypotheses constitute the second section. The research design, including the primary variables, benchmark model, and data sources used in this study, is covered in the third section. The findings of the empirical analyses, which contain benchmark, robustness, endogenous, and placebo analyses, are presented in the fourth section. Research on the connection between government environmental attention and industrial land transactions is further explored in the fifth section, which covers internal mechanisms, multidimensional heterogeneity, land transaction areas, and enterprise boundary layouts. The conclusions and policy implications are presented in the last part.\u003c/p\u003e"},{"header":"Literature review and research hypotheses","content":"\u003cp\u003eEnvironmental attention refers to the degree of attention given by individuals or society to environmental issues, environmental protection and ecological balance (Al Mamun, et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Blair, et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Du, et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Lee, et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Wang, et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The literature mainly defines it in terms of three aspects: cognitive level, emotional dimension and behavioral intention. The literature that defines environmental attention at the cognitive level is based on ecology theory and suggests that the degree of this attention is determined by the understanding of environmental conditions, various environmental phenomena and problems and their causes and consequences on the basis of relevant theoretical knowledge to form the overall understanding of the environmental system (Brendel, et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Ma, et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Zhang, et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The literature that defines this attention through the emotional dimension is based on emotional theory, which suggests that the degree of environmental attention is reflected in worry, anxiety, a sense of responsibility and other emotions caused by environmental damage and reflects people's inner attitudes and feelings related to the environment (Pagiaslis and Krontalis, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Wang and Wu, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The literature that defines this attention through behavior intention is based on behavior theory and suggests that the degree of environmental attention is reflected in whether people are willing to take practical actions to protect the environment, such as participating in environmental protection activities and supporting environmental protection policies (Billah and Adnan, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Wang, et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe effects of government focus on industrial land transactions has emerged as a key field of research because global environmental issues have become more severe and the idea of sustainable development has evolved (Lu and Tao, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Pu, et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Wappenhans, et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Zhang, et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The government has been paying increasing attention to the environmental field by formulating and improving an array of environmental policies and regulations, including strict pollution discharge standards (Bakaki, et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Lu, et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Xie, et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Zhang, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), environmental impact assessment systems (Chen, et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Li, et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and green financial policies (Zhang, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), to regulate the production and operation activities of enterprises. Some research works have revealed that the strict environmental regulation forces polluting enterprises to face more restrictions when they acquire industrial land, resulting in a reduction in their demand (Tang, et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, the government's attention affects the price of industrial land. Research shows that in areas with strict environmental policies, enterprises' capacity to bid on industrial land is diminished because of environmental standards (Moffette, et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Zhang, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). With the continuous improvement of the government's environmental attention, enterprises face many invisible pressures, which have gradually evolved into informal environmental regulations that affect enterprises' implementation of resource-saving and environmentally friendly strategies (Tam and Chan, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Zhang, et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The initial research hypothesis is therefore put out.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e\u003cem\u003eHypothesis\u003c/em\u003e 1\u003c/strong\u003e \u003cp\u003eLocal governments' environmental attention restrains the industrial land transactions of polluting enterprises in an area by increasing the environmental standards of enterprises.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAccording to the literature, an increase in the environmental attention of governments may improve the environmental standards of enterprises by affecting the cost effect, innovation effect and crowding out effect to reduce the number of industrial land transactions of polluting enterprises (Du and Li, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Song, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The first effect of the environmental attention is the cost effect. With improvements in the environmental attention of governments, their departments may intensify the execution of environmental regulations, reduce the expected price of enterprises' industrial land use after the transaction, and increase the pollution emission cost of enterprises in the region (Tu, et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Some polluting firms that don't adhere to emission regulations may no longer be allowed to trade industrial land. The second effect of the environmental attention is the innovation effect. As environmental concerns of governments get more attention, to meet the production process and environmental standards of government departments, enterprises may engage in R\u0026amp;D and innovation, thus improving production technology standards following transactions of industrial land (Fang and Liu, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Guo and Jin, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Liu, et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Some polluting enterprises that fail to meet technical production standards may no longer be allowed to trade industrial land (Ma, et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The third effect of the environmental attention is the crowding out effect. With improvements in the environmental attention of governments, to meet governmental requirements for enterprises in terms of green products and green processes, enterprises need to invest more in environmental protection; this involves their operational income and profitable investments after they enter the market (Chen, et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Liu, et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Zhou, et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), causing some polluting enterprises to abandon industrial land transactions. Accordingly, Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is proposed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e\u003cem\u003eHypothesis\u003c/em\u003e 2\u003c/strong\u003e \u003cp\u003eLocal governments' environmental attention can reduce industrial land transactions of polluting enterprises by increasing pollution emission standards, leading to production technology innovation and the crowding out of available funds.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch design\u003c/h2\u003e \u003cp\u003e \u003cb\u003eEconometric model.\u003c/b\u003e The panel \u003cem\u003elogit\u003c/em\u003e model is employed as the benchmark regression to examine the impacts of environmental attention on the probability of industrial land transactions of polluting firms, given the binary nature of micro land transaction variables. The benchmark equation is shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"534\" height=\"25\"\u003e\u003c/p\u003e\n\u003cp\u003eThe dependent variable \u003cem\u003eLT\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the industrial land transaction choice of polluting enterprises, and \u003cem\u003eGEA\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the variable of government environmental attention; \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the control variable, which may affect the transaction probability of industrial land being selected; \u003cem\u003eν\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e are used to control for individual effects and time effects, respectively; and \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the random perturbation term. \u003cem\u003eβ\u003c/em\u003e is the primary parameter to be assessed. If \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0 and passes the significance test, the government's attention can reduce the probability of industrial land transactions by polluting enterprises and realize environmental governance from the source.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndex establishment.\u003c/b\u003e The dependent variable is the industrial land transaction of polluting enterprises. This variable is constructed on the basis of the transaction of the industrial land of polluting enterprises in the sample. If an enterprise has industrial land transaction information in the current year, the industrial land transaction variable is 1; otherwise, it is 0. The core explanatory variable is government environmental attention. This variable is quantified by the proportion of the recurrence rate of low-carbon and environmental protection words in the work reports of Chinese local governments in the overall word count of the work reports. The control variables include enterprise scale (\u003cem\u003eES\u003c/em\u003e), capital intensity (\u003cem\u003eCI\u003c/em\u003e), enterprise age (\u003cem\u003eEA\u003c/em\u003e), production efficiency (\u003cem\u003ePE\u003c/em\u003e) and enterprise ownership (\u003cem\u003eEO\u003c/em\u003e). The headcount of staffers at the close of the year serves as a proxy for the enterprise scale. The proportion of an organization's average balance of fixed assets to its workforce serves as a measure of capital intensity. The gap between the enterprise's sample year and its founding year serves as a proxy for the enterprise's age. Productivity is determined by using a single labor productivity indicator, accounting for any deviations during the process of estimating total factor productivity (TFP). Enterprise type is derived from private enterprises, with the binary virtual variables of state-owned and foreign enterprises being established.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData sources.\u003c/b\u003e The macrolevel government environmental attention data were obtained by gathering 2874 government work reports from Chinese cities between 2003 and 2013. The recurrence of low-carbon and environmental protection words was retrieved from these reports, and their ratio of such words to the overall word count in the report was calculated.\u003c/p\u003e \u003cp\u003eMicrolevel enterprise data from 2003 to 2013 were derived from the databases of China Industrial Firm, China Firm Pollution Emission, and China Land Market Network. China National Bureau of Statistics created a database of industrial enterprises in China, using the enterprise legal person as the statistical unit. It mostly contains fundamental details and key financial information about enterprises. The Ministry of Ecological Environment of China produced a database of Chinese firms' pollution emissions, and yearly data were obtained from the main polluting enterprises' quarterly questionnaires. Among these, the main polluting firms were those in every district and county in which the pollution emissions comprise the top 85% of the overall emissions. The China Land Market Network, which was established by the Ministry of Land and Resources of China, is a platform supporting the dynamic monitoring system of China's land market. It integrates information release, monitoring analysis and sharing services and provides land supply information such as China's land supply plan, transfer announcement, and transfer results.\u003c/p\u003e \u003cp\u003eThe databases of China Industrial Firm and the China Firm Pollution Emission are combined on the basis of public fields such as firm name, organization code, firm zip code, and firm address information. Through matching, we identified the key polluting enterprises among all state-owned firms and nonstate-owned firms above a designated size. On this basis, we processed the enterprise name in the matched enterprise database and the land user variables in the land transfer data to correct the typos and remove the words that were not helpful for matching; thus, the final industrial land transaction data of polluting enterprises that were needed for this study were obtained. On this basis, we matched the macro government environmental attention data and the micro enterprise data on the basis of the name and code of the city in which a firm was situated. The matching data constitute the sample with the largest observed value among the available data, which can improve the generality and credibility of the research conclusions. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e exhibits the descriptive statistical features of the primary variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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 of main variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation\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\u003eStandard deviation\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\u003cem\u003eLT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e541,530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1411\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\u003cem\u003eGEA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e453,465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.56\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\u003e228.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eES\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e533,658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e468.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e735.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e533,020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e581.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1158.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e528,953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e828.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e541,323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSOE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e541,530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3693\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\u003cem\u003eFE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e541,530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3379\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Empirical analysis results","content":"\u003cp\u003e \u003cb\u003eBenchmark analysis.\u003c/b\u003e The industrial land transactions of polluting enterprises are the explained variable in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which also uses the environmental attention of local governments as the core explanatory variable. Enterprise scale, capital intensity, and production efficiency are introduced to control the production and operation characteristics of enterprises; enterprise age is introduced to control the survival characteristics of enterprises; and the variables of state-owned and foreign enterprises are introduced to control for ownership characteristics. These variables are based on the benchmark regression equation. Given the binary nature of the explained variables, the benchmark regression uses panel \u003cem\u003elogit\u003c/em\u003e regression. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e confirms Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e by demonstrating that the \u003cem\u003eGEA\u003c/em\u003e variables are negative, which indicate that as governments become more environmentally conscious, the likelihood of industrial land transactions involving polluting industries decreases. A possible explanation for this is that as the government's environmental attention increases, the government may issue stricter environmental protection regulations, causing polluting enterprises to face higher environmental protection requirements and standards. This may reduce their willingness and ability to purchase industrial land and ultimately inhibit the industrial land transactions of polluting enterprises.\u003c/p\u003e \u003cp\u003eThe results of the control variable regression were consistent with the expectations. The coefficients of the variables \u003cem\u003eES\u003c/em\u003e, \u003cem\u003eCI\u003c/em\u003e and \u003cem\u003ePE\u003c/em\u003e are all significantly positive. It indicates that with increasing enterprise production scale, capital intensity and production efficiency, enterprises are more inclined to purchase new industrial land. The \u003cem\u003eEA\u003c/em\u003e variable is negative, which indicates that the willingness of enterprises to purchase new industrial land decreases with increasing enterprise survival time. Taking private enterprises as the benchmark, the coefficients of the SOE and FE variables are significantly negative, indicating that, compared with private enterprises, state-owned enterprises and foreign enterprises are not as likely to engage in new industrial land transactions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eBenchmark estimation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003cem\u003eLT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003cem\u003eLT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003cem\u003eLT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003cem\u003eLT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003cem\u003eLT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e 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align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0286)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0288)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0286)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eES\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3030\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3149\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3170\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4339\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0091)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0098)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\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\u003e0.3281\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2943\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3328\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0110)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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colname=\"c6\"\u003e \u003cp\u003e(0.0093)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEA\u003c/em\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.3245\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\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(0.0188)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSOE\u003c/em\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.5256\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\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(0.0370)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFE\u003c/em\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.7989\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\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(0.0358)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstants\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.9407\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.6668\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.4038\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-12.9850\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-13.1714\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(0.7109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.7141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.7148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.0046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.0053)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e453,465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445,862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e445,825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e439,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e439,371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePseudo R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRobustness analysis.\u003c/b\u003e Because the impacts of government environmental attention on the industrial land transactions of polluting enterprises might not be fully apparent in the current period, Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e test the impacts of lag one and lag two periods of government attention on the probability of industrial land transactions of polluting enterprises, respectively, to explore the time lag effect of the impact of environmental attention. The findings support the benchmark results by demonstrating that the coefficients of government environmental attention lag for one and two periods are significantly negative. This suggests that government attention has a long-term inhibitory effect on the probability of polluting enterprises engaging in new industrial land transactions.\u003c/p\u003e \u003cp\u003eThe benchmark regression measures the percentage of low-carbon and environmental protection terms in the work report of government as an objective way to gauge the government's attention to the environment. However, accounting for the interference of the overall word frequency in the government's work report, the number of low-carbon and environmental protection terms within the work report of government is utilized as an indicator of the local government's environmental attention, as displayed in Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results show that after the variables of government environmental attention are replaced, the conclusion is in accordance with the benchmark analysis. With increasing government environmental attention, the likelihood of polluting enterprises engaging in industrial land transactions decreases significantly.\u003c/p\u003e \u003cp\u003eAdditionally, in Column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the enterprises exiting the sample interval are removed to eliminate the interference of short-term enterprises on the analysis results. The findings demonstrate that, even when the interference of exiting enterprises is removed from the sample interval, the coefficient of the variable measuring the government's attention remains negative. This suggests that, as government's attention increases, the transaction probability of the industrial land of polluting enterprises in a region decreases, so the findings of the benchmark analysis are again confirmed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eRobustness analysis: Based on index replacement and sample screening.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLag one period\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLag two period\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWord number\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eExcluding exiting enterprises\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGEA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1318\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0799\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1185\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1319\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(0.0342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0394)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0295)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstants\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-12.2674\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.4330\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.2867\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.0325\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(0.7227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.0129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.0036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.0058)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e287,520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205,415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e439,371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e397,584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePseudo R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e: Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEndogenous analysis.\u003c/b\u003e In certain cases, endogeneity issues might result from the two-way causal link between variables and their omission, and instrumental variables, as a tool for controlling endogeneity problems, are widely used. Considering the correlation and exogenous requirements of instrumental variables, the air circulation coefficient is the instrumental variable in Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The air circulation coefficient is closely related to the distribution of pollutants in the environment and air quality, which can reflect changes in the regional environment. Thus, the air circulation coefficient is strongly correlated with local government environmental attention, meeting the correlation requirements of instrumental variables. The air circulation coefficient, which is mostly independent of enterprise choices regarding land transactions and may be considered an exogenous variable, is often impacted only by certain natural elements or building constructions. Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e uses the environmental attention of adjacent areas as the instrumental variable. The economic development of adjacent cities is often highly competitive, so the environmental attention of governments in adjacent areas is highly correlated with that of local governments; thus, the correlation requirements of instrumental variables are met. Additionally, the environmental attention of governments in neighboring regions is not easily affected by the land transaction behavior of enterprises in other regions, so exogenous requirements are met.\u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrate that when endogeneity is controlled, the government's environmental attention coefficients are significantly negative. This means that, as environmental concerns receive increased attention from governments, polluting firms are subject to stricter government regulations, and their transaction probability for new industrial land decreases. Weak instrumental variable tests and overidentification tests of the instrumental variables are conducted. The test results reject the original hypothesis and confirm the usefulness of the instrumental variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eEndogenous analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)Air circulation coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)Environmental attention of adjacent areas\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\u003eGEA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1626\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0037\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(0.0601)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstants\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3701\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0692\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(0.1660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0039)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eKleibergen-Paap rk\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLM\u003c/em\u003e statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98031.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eKleibergen-Paap rk\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eWald F\u003c/em\u003e statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e438,797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e439,371\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\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote\u003c/em\u003e: Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure. \u003cem\u003eWald F\u003c/em\u003e statistics are used to determine if instrumental variables are weakly identified, \u003cem\u003eLM\u003c/em\u003e statistics are used to identify instrumental variable insufficiency, and the \u003cem\u003ep\u003c/em\u003e values of the statistics are displayed in brackets.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePlacebo analysis.\u003c/b\u003e The outcomes of the benchmark analysis demonstrate that local governments' environmental attention reduces the likelihood of polluting businesses buying industrial land in the area, and the conclusions of the benchmark analysis are corroborated by robustness and endogenous analyses. To ensure that the findings of the benchmark analysis are not the result of chance, Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e substitutes the nonindustrial land transaction variable of the polluting enterprise for the industrial land transaction variable in the benchmark analysis while maintaining the government's environmental attention as the primary independent variable. This is based on the land category in the enterprise land transaction information. Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which takes the industrial land transactions of polluting enterprises as the explanatory variable, replaces the environmental attention of governments with their attention to digitalization on the basis of the work reports of local governments.\u003c/p\u003e \u003cp\u003eThere is no evidence that government environmental attention can reduce the likelihood of nonindustrial land transactions, which have little correlation with enterprise production and emissions, as demonstrated by the results in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Nevertheless, government environmental attention can limit the trading of industrial land for polluting enterprises because industrial land is usually directly related to production activities, involving possible pollutant emissions, resource consumption and environmental damage. If an enterprise's environmental protection measures are not up to standard or cause great harm to the environment, the government may restrict or even prohibit its industrial land transactions to reduce potential environmental pollution. However, nonindustrial land, such as commercial land and residential land, has little correlation with the production and emissions of enterprises. The main purpose of these lands is not direct industrial production, and the impact on the environment is relatively small. Consequently, government environmental attention is not focused on this area, and there is no evidence that the government's attention can reduce the transaction likelihood of nonindustrial land for polluting enterprises.\u003c/p\u003e \u003cp\u003eThe findings in Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrate that the variable of government digital attention is not significant, which indicate that there is no proof that local government digital attention can lessen the likelihood of polluting enterprises purchasing industrial land. The government's attention is focused on the control and supervision of environmental pollution, ecological damage and other aspects. Because the utilization of industrial land is frequently closely linked to the production activities of enterprises, which may cause pollution problems, the government imposes stricter examinations and restrictions on the industrial land transactions of polluting enterprises when it attaches more importance to the environment. The focus of government digitalization is usually to enhance the efficiency of government services, optimize management processes, and promote information sharing. This may be related more to the digitalization of administrative affairs and the promotion of e-government, but it does not involve the direct assessment or limitation of the environmental impact of industrial land transactions of polluting enterprises. Therefore, a government's digital attention does not reduce the probability of polluting enterprises engaging in industrial land transactions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003ePlacebo analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)Non-industrial land transaction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)Government digital attentions\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\u003eGEA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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(0.0915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGDA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0241\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0305)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstants\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-12.5249\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.9305\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(0.4723)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.7151)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\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\u003e\u003cem\u003eObservation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e364,254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e433,525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePseudo R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote\u003c/em\u003e: Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Further discussion","content":"\u003cp\u003e \u003cb\u003eDiscussion of internal mechanisms.\u003c/b\u003e To verify Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e explores three aspects of the internal mechanism by which local government environmental attention affects the industrial land transactions of polluting enterprises: the cost effect, the technology effect and the crowding out effect. To examine the mechanism by which the government's environmental attention impedes industrial land transactions by increasing the environmental costs of polluting enterprises, the removal of sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e) and chemical oxygen demand (COD) at the enterprise level are introduced in Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, respectively, as a measure of the cost effect of enterprises. The pertinent data on the pollutant removal of enterprises are obtained from the pollution emission database of Chinese enterprises, which provides details on the production, removal and emission of major pollutants by Chinese enterprises. The results in Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrate that the coefficients of local governments' environmental attention variables are positive, meaning that environmental attention increases the rate at which major pollutants, including SO\u003csub\u003e2\u003c/sub\u003e and COD, are removed from enterprises and improves their emission standards. This, in turn, increases businesses' production costs and reduces their ability to trade industrial land.\u003c/p\u003e \u003cp\u003eColumns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e present enterprise-level patent and green patent applications, respectively, as gauges of the enterprise innovation effect and investigate the internal mechanism by which government environmental attention inhibits industrial land transactions by requiring polluting firms to increase R\u0026amp;D innovation. The pertinent data of firm patent applications are from the Database of China Firm Patent, which provides the relevant information of all Chinese enterprises' patent applications after 1985, including patent name, patent type, patent details, application year and other information. The findings in Columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicate that local governments' environmental attention variables have significantly positive coefficients, meaning that environmental attention increases the likelihood of enterprise and green patent applications and improves the production technology standards of polluting enterprises. In other words, governments' increasing attention to the environment pressures polluting firms to improve technology and make them more active in technological innovation, while polluting enterprises face greater difficulties in this environment and have difficulty obtaining opportunities for industrial land transactions.\u003c/p\u003e \u003cp\u003eIn Columns (5) and (6) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the investment and financing constraints of governance equipment at the enterprise level are introduced as measures of the crowding out effect, and the internal mechanism by which government environmental attention inhibits industrial land transactions by crowding out the available funds of polluting enterprises is investigated. The pertinent data on investment in enterprise equipment treatment are obtained from the pollution emission database of Chinese enterprises. To assess the enterprise financing constraint variable, the ratio of interest expenses to sales revenues is made use of. The pertinent data are obtained from the database of Chinese industrial enterprises. The results in Columns (5) and (6) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrate that the coefficients of the variables pertaining to local governments' attention are positive. It indicates that governments' attention increases capital factor investment requirements and the investment and financing constraints of enterprises' pollution control. In other words, as local governments' environmental attention increases, more of the funds that these enterprises have available are taken up, which reduces the transaction probability of industrial land.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eInternal mechanism: Based on Cost Effect, Technology Effect and Crowding Out Effect.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCost Effect\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003eInnovation Effect\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCrowding Out Effect\u003c/em\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSO2 removal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCOD removal\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePatent\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eGreen patent\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eEquipment\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eFinancing constraints\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGEA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0066\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0439\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0256\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1240\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0499\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0019\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(0.0012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0601)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0209)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstants\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1588\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0399\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4398\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.7897\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.8697\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0280\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(0.0059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.3584)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.1095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218,176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280,670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52,910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52,910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61,690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34,9248\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\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Pseudo R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote\u003c/em\u003e: Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDiscussion on multidimensional heterogeneity.\u003c/b\u003e Environmental protection measures, including energy consumption per GDP unit and total emissions of two major pollutants, SO\u003csub\u003e2\u003c/sub\u003e and COD, were established as mandatory metrics for the performance evaluation of municipal authorities in China for the first time in 2006. The central government uses the assessment results as a crucial foundation for making decisions about the political appointment and dismissal of local top cadres at all levels. The interaction between enterprise industrial land transactions and government attention may be impacted by period variance. The impact of government environmental attention on the industrial land transactions of polluting firms is examined, accounting for the period's peculiarities. The upper left of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reports the analysis findings. The results indicate that the of government environmental attention is not significant prior to the policy's implementation but becomes significantly negative in the sample following it. In other words, the implementation of the environmental goal constraint policy gradually leads to local governments\u0026rsquo; environmental attention inhibiting the industrial land transactions of polluting enterprises. A possible explanation for this is that the implementation of an environmental objective constraint policy is accompanied by the strengthening of supervision and accountability mechanisms. To achieve the environmental objectives stipulated in the policy and avoid being held accountable by superiors for environmental problems, local governments more actively use policy tools to curb the industrial land transactions of polluting enterprises.\u003c/p\u003e \u003cp\u003eBecause China's regions differ greatly in terms of their state of development, it is advantageous to create control policies that are tailored to the particular conditions of each region to examine how government environmental initiatives affect the transaction of industrial land to polluting enterprises. To investigate the heterogeneity of the effect of government attention on the transaction probability of the industrial land of polluting enterprises in different regions, on the basis of the location characteristics, enterprises in the eastern region and those in the central and western regions are what the sample is divided into. The upper right of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reports the analysis findings. A comparison of the sample firms' environmental attention coefficients across various regions clearly reveals that with increasing local government environmental attention, the transaction probability of the industrial land of enterprises of the central and western regions is more restrained. The impact on polluting enterprises of the central and western regions may be more evident in the process of strengthening supervision, whereas the environmental supervision system in the eastern region may be relatively complete, such that polluting enterprises are subject to more restrictions on land use, which has restrained their expansion of industrial land to some extent. Furthermore, some polluting enterprises can relocate to the west and center areas from the east. The land transactions of polluting firms are hindered in the process of undertaking industries in the central and western regions, as governments increase the attention to environmental issues.\u003c/p\u003e \u003cp\u003eThe phased characteristics of China's economic development indicate that there may be significant differences in resource endowment and production technology across different industries. In the face of increasing environmental attention, production cost changes and technology adjustment willingness differ between heavy industry and light industry. To probe into the heterogeneous effects of government attention on the probability of land transactions by enterprises in different industries, the sample is grouped into heavy and light industrial firms on the basis of their industry characteristics. The lower left of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reports the analysis findings. The likelihood of heavy industrial enterprises engaging in industrial land transactions becomes more constrained as local government environmental attention increases, according to a comparison of the variables of government attention across sample enterprises in various industries. A possible reason for this is that the production scale of heavy industrial enterprises is large, and the requirement for land resources is also greater. The land transactions in this industry are more likely to attract attention, so restrictions increase accordingly. Additionally, the cost of environmental governance for heavy industrial enterprises is high, and the improvement of the environmental attention also makes them more cautious in land use.\u003c/p\u003e \u003cp\u003eThe industrial land transactions of various enterprise types exhibit some heterogeneity due to variations in enterprise ownership characteristics. In addition, the effects of government attention on various enterprise types may differ. The impact of government attention on the allocation of land to state-owned, foreign, and private firms is examined in relation to the features of enterprise ownership. The lower right of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e report the findings. When local government environmental attention increases, state-owned enterprises' ability to engage in industrial land transactions is inhibited less than that of private enterprises, and foreign enterprises' ability to do so is most strongly inhibited. This is evident when the coefficients of government environmental attention among enterprises with varying property rights are compared. Possible reasons for this are as follows: state-owned enterprises usually have strong resource advantages and policy support and may be subject to relatively few external restrictions on industrial land transactions. Moreover, private enterprises are in a relatively weak position in market competition, facing more pressure from capital, resources and other aspects, resulting in strong inhibition of the scale of industrial land transactions by environmental attention. In addition to pressure on capital, resources and other aspects, foreign enterprises face more uncertainties in terms of policies, regulations, the market environment and other aspects, so they also face the strongest inhibitory impact of the government's attention on their industrial land transactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eAn invalid line is the vertical line with an abscissa scale of 0. The figure's two-sided lines and diamonds are used to show the related variable's 95% confidence interval (CI) and effect size. When the CI horizontal line crosses the invalid vertical line, the coefficient of the variable is not statistically significant at the 5% level.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDiscussion of the land transaction scale and enterprise boundary layout.\u003c/b\u003e The previous section analyzed how the government attention affects the trading probability of the industrial land of polluting enterprises. However, environmental attention may also affect the trading scale of the industrial land of polluting enterprises. Next, we further investigate the influence of government environmental attention on the trading area of the industrial land of polluting firms. Considering that the probability of a polluting enterprise purchasing land and the transaction area of land are actually interdependent processes, if the two equations are estimated separately, the problem of sample self-selection may occur, affecting the accuracy of the estimation results. Therefore, the Heckman selection model (\u003cem\u003eHSM\u003c/em\u003e) proposed by Heckman (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) is used to correct for possible selectivity bias. The regression findings are presented in Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The findings demonstrate that the coefficients of the environmental attention variables are significantly negative, suggesting that government environmental attention decreases both the transaction area and the transaction probability of polluting enterprises' industrial land. The government increases the requirements for industrial land's environmental evaluation if it begins to prioritize the environment. When applying for land, polluting enterprises often find it difficult to meet these strict standards, resulting in a reduction in the approved land area.\u003c/p\u003e \u003cp\u003eAdditionally, enterprises around administrative regions can spread pollutants to adjacent administrative regions to reduce pollution emissions in their regions. Does increasing attention to the environment from the local government lead to some enterprises moving to the administrative boundaries? To answer this question, Columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e are also based on the Heckman selection model to investigate whether the government's environmental attention affects whether polluting enterprises choose to layout to the administrative boundary and how the distance between the enterprises that choose to layout the administrative boundary and the boundary changes. The findings in Column (3) display that the environmental attention coefficient is considerably positive, suggesting that environmental attention increases the likelihood that polluting firms will be distributed to counties and administrative border districts. The findings in Column (4) display that the government's attention is negative, indicating that environmental attention will further reduce the distance between polluting enterprises with boundary layouts and boundaries and encourage some polluting enterprises to be closer to administrative boundaries. The government's environmental attention places pressure on and constraints on polluting enterprises. When this pressure increases, some polluting enterprises may attempt to find areas with relatively weak supervision due to cost, supervision and other factors. Thus, being close to the administrative boundary is a viable option.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eExtended discussion: Based on land transaction scale and boundary layout.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProbability\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eArea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eBorder districts and counties\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eDistance from boundary\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGEA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0632\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1788\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1150\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0175\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(0.0121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0034)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstants\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.3931\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.8608\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5278\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6148\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(0.2523)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.4534)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0185)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eControl variable\u003c/em\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eInverse mills ratio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.1382\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e-0.4692\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e(0.0139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(0.0501)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObservation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e439,371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e439,371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e439,371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e439,371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePseudo R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.1080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e: Statistical significance is indicated at the 10%, 5%, and 1% levels by the symbols *, **, and ***, respectively, and the clustering robust standard error is in brackets. The fixed effects of individual and year are controlled during the regression procedure. The inverse Mills ratio is used to measure whether the situation of sample selection bias occurs. If it is significant, the situation of a significant sample selection bias occurs, and the \u003cem\u003eHeckman\u003c/em\u003e binary selection model is applicable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusions and policy implications","content":"\u003cp\u003eAt the macro level, governments worldwide have increasingly emphasized the synergy between economic development and environmental conservation. At the microscopic level, as an important part of economic activities, the mode and decision-making of enterprise land transactions are strongly affected by policy and the market environment. On the basis of the multisource heterogeneous data of macro government work reports and micro enterprise land transactions, this study examines the effects and potential mechanisms of government attention on the land transactions of polluting enterprises. The primary conclusions are as follows. First, with governments paying more attention to the environment, the transaction probability and transaction area of industrial land for polluting enterprises decreases. Second, local governments' environmental attention restrains the industrial land transactions of polluting enterprises by increasing production costs, improving technical standards and crowding out available funds, that is, the cost effect, the technology effect and the crowding out effect. Third, local governments' attention has heterogeneous effects on the industrial land transactions of polluting enterprises. Compared with other firms, environmental attention has more potent inhibitory effects on the industrial land transactions of firms during the implementation period of environmental objectives, firms within the central and western areas, heavy industrial firms and foreign firms. Fourth, environmental attention prompts some polluting enterprises to be directly located in districts and counties at administrative boundaries, leading to the transregional transfer of pollution.\u003c/p\u003e \u003cp\u003eThe primary policy implications derived from the aforementioned results are as follows. First, governments should increasingly pay more attention to the environment. The comprehensive environmental protection assessment of local governments should be thoroughly strengthened, the improvement of environmental quality and the land transaction control of polluting enterprises should be included as important indicators, the continuous focus of local governments on environmental issues should be actively encouraged, and the extent of land usage of polluting enterprises from the source should be regulated. Second, the financial policy regulatory system should be reinforced, the environmental protection technology support system should be optimized, and the environmental cost sharing mechanism should be enhanced. Fair policies should be created, social capital should be encouraged to contribute to investments in environmental protection, and the financial burden that environmental protection places on polluting enterprises should be lessened. Investment in the R\u0026amp;D of environmental protection technology should be increased, enterprises should be provided with more low-cost and efficient environmental protection technology solutions, and the increased costs of enterprises should be reduced because of improvements in their technical standards. Financial institutions should be guided to allocate funds reasonably, excessive lending to polluting enterprises should be avoided, and more financial support to environmentally friendly enterprises should be provided. Third, differentiated environmental supervision strategies should be formulated. Special regional and industrial support, environmental protection technology training and special fund support for firms within the central and western regions and heavy industry enterprises should be provided, and the ability of these enterprises to protect the environment should be improved. The green development of foreign enterprises should be guided, the publicity and interpretation of environmental protection legislation and regulations for foreign enterprises should be strengthened, and advanced green production technology and management experience should be encouraged. Finally, a cross-regional environmental protection cooperation mechanism should be established, and regional monitoring of administrative boundaries should be strengthened. Environmental protection cooperation agreements are signed between adjacent administrative regions to clarify the responsibilities and obligations of all parties, and environmental monitoring stations are added in administrative border areas to jointly combat the transregional transfer of polluting enterprises.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMengjie Li: Formal analysis, Writing - Original Draft, Funding acquisitionWeijian Du: Conceptualization, Methodology, Software, Validation, Funding acquisition\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research is sponsored by the National Natural Science Foundation of China (72274112), National Social Science Fund of China (24BJY120), Taishan Scholars Project Funding (tsqn202211237, tsqn202306273), Shandong Provincial Natural Science Foundation (ZR2024MG040).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamopoulos T, Brandt L, Chen CR et al (2024) Land security and mobility frictions. 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Humanit Social Sci Commun 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://10.1057/s41599-025-04791-2\u003c/span\u003e\u003cspan address=\"http://10.1057/s41599-025-04791-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6679793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6679793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As awareness of environmental protection increases, governments worldwide, especially those in developing countries, are increasingly concerned with environmental issues and focused on pursuing the coordinated progress of economic development and ecological balance in various countries. Focusing on China, the largest developing country in the world, this study systematically investigates the effects and internal mechanisms of governments’ environmental attention in regard to the industrial land transactions of polluting enterprises. This study demonstrates that when governments focus more on the environment, the likelihood and area of transactions involving polluting firms' industrial land decrease. The results of a robustness analysis, an endogenous analysis and a placebo test support the above conclusions. The discussion of internal mechanisms shows that local governments' environmental attention inhibits the industrial land transactions of polluting enterprises by increasing production costs, improving technical standards and crowding out available funds. Additionally, when governments pay attention to the environment, some polluting enterprises move closer to administrative boundaries or are directly located in the districts and counties of these boundaries, which leads to the transregional transfer of pollution. This study elucidates the procedure for allocating land resources under the constraints of the ecological environment and provides a scientific foundation for policy decisions by governments worldwide.","manuscriptTitle":"Unveiling the land allocation puzzle: Government environmental attention and the industrial land transactions of polluting enterprises","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 03:30:01","doi":"10.21203/rs.3.rs-6679793/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-02T15:00:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T09:39:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-20T14:40:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-18T13:37:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108813554537039342916398437876677721230","date":"2025-06-18T00:40:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164227559991122781588127827977185615837","date":"2025-06-14T10:51:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264140651908173042718161874774909828886","date":"2025-06-12T09:46:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11438897282350068310741986129652086767","date":"2025-06-12T04:48:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-12T00:13:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-30T14:27:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-26T06:22:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-26T06:22:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-05-16T10:12:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0d7afeaa-d5fb-4172-8ee0-007c34a7e4b8","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":50056792,"name":"Social science/Economics"},{"id":50056793,"name":"Social science/Environmental studies"}],"tags":[],"updatedAt":"2026-02-10T11:25:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 03:30:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6679793","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6679793","identity":"rs-6679793","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}