Does Social Trust weaken the information rent-seeking of environmental responsibility?

Does Social Trust weaken the information rent-seeking of environmental responsibility? | 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 Research Article Does Social Trust weaken the information rent-seeking of environmental responsibility? Xiaoyu Chen, Xinyi Lv This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5962762/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Social trust is crucial for effective information transparency. Using the demonstration city for building a social credit system in China as a quasi-natural experiment, our results indicate that an improved social credit environment reduces the intensity of environmental information disclosure by enterprises while enhancing their substantive green innovation. This effect may be attributed to the reduced ability of enterprises to engage in environmental information rent-seeking for additional bank loans. JEL Classification code: F239；F27；F49 Social trust Environmental responsibility Information disclosure Innovation Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Social trust, as an informal institution (Acemoglu et al., 2001 ), serves as the cornerstone of market operations and is crucial for enhancing the transparency of information disclosure in the market. Investors often make decisions based on publicly available information from companies (Chen et al., 2016 ). Although this information is typically audited, companies with informational advantages often disclose positive environmental information to improve their environmental ratings, enhance their market image, increase their ESG investment attractiveness, and secure government-provided environmental subsidies. This public information frequently contains a high degree of redundancy, making it difficult to verify much of the descriptive information about their environmental responsibility performance. As a result, the claimed environmental responsibility of companies is often inflated and unreliable. This raises an important research question: Would a trust mechanism be established to eliminate the inflated information in corporate green reports and enhance the quality of corporate disclosures? Previous literature has primarily examined social trust in relation to audit efficiency (Chen et al., 2018 ), corporate innovation (Kong et al., 2021 ), and company performance (Lu et al., 2018 ). However, literature has not further defined the issues of inflated claims and the value of information content within corporate social responsibility (CSR). We implement the pilot construction of China’s social credit system as a quasi-natural experiment, to investigate the impact of this system on corporate environmental responsibility information disclosure. Specifically, we explore whether the construction of social trust can effectively reduce the degree in environmental responsibility disclosures. Moreover, we also investigate how the reduction in bank loan shares following the establishment of the social credit system contributes to the decrease in corporate environmental information disclosure. This reduction in loan availability simultaneously compels companies to engage in higher levels of green patent research and development, aiming to enhance their eligibility for bank loans through improved environmental performance. 2. Policy background and research hypothesis 2.1. Policy background In 2014, the State Council of China issued the Outline of the Plan for the Construction of the Social Credit System, emphasizing the creation of an honest and trustworthy social environment. Although not specifically targeting corporate environmental protection, the policy promotes social integrity in various fields, including environmental protection and energy conservation. It mandates stricter measures for environmental monitoring, information disclosure, and pollution supervision, including improving the responsibility mechanism for enterprises' environmental assessment documents, establishing integrity databases for assessment agencies and experts, and strengthening credit assessment management of environmental project experts. These measures provide policy guidance for enterprises to disclose environmental protection information. After that, A series of specific measures for building social trust have been proposed. In 2015, the State Council and the People's Bank of China designated 11 cities as the first batch of national demonstration cities for the social credit system construction. In 2016, 32 additional cities were identified for demonstration work 1 . Enterprises in these pilot cities adopted more stringent information disclosure measures and increased the quality of information disclosure. This social credit system helps mitigate dishonest information disclosure, reduces information asymmetry, and limits rent-seeking behaviors related to environmental responsibility. 2.2. Research hypothesis Environmental information disclosure is a legal obligation for companies and a core regulatory requirement. From the perspective of the companies, environmental information disclosure may be driven by the desire to fulfill social responsibility, practice environmental stewardship, and attract ESG investments. Alternatively, it may be motivated by the need to reduce regulatory pressure, shape public image, and engage in information rent-seeking to secure more support from government and financial institutions (Krueger et al., 2021 ). Companies with higher ESG ratings and stronger environmental responsibility are often more likely to receive policy and financial support, although their tendency to "talk more and act less" increases the risk of information disclosure violations. In a weak regulatory environment, such violations are less frequently detected, incentivizing companies to embellish their environmental actions, thereby reducing the efficiency of information transmission and asset pricing (Berg et al., 2022 ). Thus, it is necessary to impose strict constraints on environmental information disclosure to minimize dishonest and misleading behaviors (Duflo et al., 2013 ). While other forms of environmental monitoring have potential, government-led initiatives and the establishment of formal or informal institutions have proven more effective in this domain (Buntaine et al., 2021; Burgess et al., 2017). Although the social credit system is currently an informal institution and does not yet involve detailed auditing and disclosure requirements, the dual objectives of institutional constraints and enhanced market competitiveness motivate companies to reduce ineffective environmental disclosures and adopt more substantial environmental measures to improve their ESG core competitiveness. Based on the above analysis and the content in Fig. 1 , we propose the following hypothesis: Hypothesis The construction of the social credit system can reduce inflated corporate environmental information disclosures and enhance green innovation in corporate environmental responsibility. 3. Research design 3.1. Methodology We implement a Difference-in-Differences (DID) approach, using the establishment of demonstration cities for social credit system construction as an exogenous shock, to examine the impact on Chinese listed companies. Specifically, the model is specified as follows: $$\:{Y}_{ict}=\alpha\:+{\beta\:}_{1}{DID}_{ict}+{X}_{ict}\varGamma\:+{\tau\:}_{t}+{id}_{i}+{\epsilon\:}_{ict}$$ 1 Where i, c and t represent the company, city, and year respectively. The variable \(\:{DID}_{ict}\) denotes the policy variable for the establishment of social credit system demonstration cities. This variable takes a value of 1 for cities after being designated as demonstration cities and 0 otherwise. \(\:{Y}_{ict}\) is the outcome variable measuring corporate environmental disclosure and green innovation, \(\:{X}_{ict}\) is a vector of city-level and firm-level control variables, \(\:{\tau\:}_{t}\) and \(\:{id}_{i}\) denote time fixed effects and firm fixed effects respectively, and is the error term. The coefficient \(\:{\beta\:}_{1}\) on the \(\:{DID}_{ict}\) represents the core result of the social trust construction. 3.2. Event study method Consistent with the standard DID model, the staggered Difference-in-Differences (DID) approach requires the parallel trends assumption. This means that before the policy intervention, the outcome variable should exhibit no significant differences between the treatment and control groups. The dynamic effects test model in this paper is specified as follows: $$\:{Y}_{it}={{\alpha\:}}_{0}+\sum\:_{\begin{array}{c}k=-3\\\:k\ne\:-1\end{array}}^{5}{\beta\:}_{k}I(t-{T}_{j}=k)+{X}_{it}\varGamma\:+{\tau\:}_{t}+{id}_{i}+{\epsilon\:}_{it}\:\:\:$$ 2 Where \(\:{T}_{j}\) represents the date when the pilot city initiated the policy, and \(\:t\) represents the current year of the data. When \(\:t-{T}_{j}=k\) , the dummy variable \(\:I(t-{T}_{j}=k)=1\) ; otherwise, it equals 0. In this setup, \(\:k=-1\) serves as the base period, and periods greater than 5 and less than − 3 are truncated to the last and first periods, respectively, for the dynamic effects test. If the coefficients for \(\:k<0\) are not significant, the parallel trends assumption is satisfied. Furthermore, considering that the social credit system pilot cities were established in two batches in 2015 and 2016, this paper also incorporates the group-period average treatment effects calculation method (De Chaisemartin and D'Haultfoeuille, 2024 ; Sun and Abraham, 2021 ; Callaway and Sant'Anna, 2021) in the parallel trends model to ensure robustness. 3.3. Variable selection and data sources We implement data from 2011 to 2020 to avoid the impacts of COVID-19 and economic cycles. The data sources include the CSMAR database, CNRDS database, China City Statistical Yearbook, and the annual reports of listed companies in China. Financial data for listed companies, used as control variables, are obtained from the CSMAR database, while city-level variables are sourced from the China City Statistical Yearbook. Environmental disclosure indicators are constructed from the annual report texts of listed companies, and patent data are organized from the CNRDS database. The core identification variables of this paper are primarily constructed from textual data as discussed in Fig. 2 . We extract the environmental responsibility disclosure information from the social responsibility reports and the Management Discussion and Analysis (MD&A) sections of corporate annual reports. Using text analysis methods, we construct indicators for environmental responsibility fulfillment. First, we identify the MD&A sections in the annual reports and use an environmental protection dictionary, which includes terms like "environmental protection," "environment," "energy consumption," "emission reduction," "pollution discharge," "carbon dioxide," "chemical oxygen demand," "low carbon," and "green." This dictionary is incorporated into the word segmentation module as a predefined noun dictionary. We then count the occurrences of these environmental protection terms in the MD&A sections of the annual reports, obtaining their precise word frequency, sentence frequency, expanded word frequency, and expanded sentence frequency. The sum of the precise word frequency and expanded word frequency is used as the core dependent variable of this paper. Table 1 presents the descriptive statistics and variable descriptions for all variables. Table 1 Descriptive statistics Variable N Definition Mean SD p5 p50 p95 Explained Variables envpro_se 29693 Frequency of expanded sentences related to environmental protection in the Management Discussion and Analysis section of listed companies' annual reports 4.163 7.039 0 1 18 envpro_sp 29693 Frequency of precise sentences related to environmental protection in the Management Discussion and Analysis section of listed companies' annual reports 3.020 5.213 0 1 13 envpro_we 29693 Frequency of expanded words related to environmental protection in the Management Discussion and Analysis section of listed companies' annual reports 5.761 10.21 0 2 26 envpro_wp 29693 Frequency of precise words related to environmental protection in the Management Discussion and Analysis section of listed companies' annual reports 3.633 6.650 0 1 17 green_patent_l 29693 Number of green patents applied by the company in the current year (log + 1) 0.317 0.785 0 0 2.079 Core Variable did 29693 policy shock 0.340 0.474 0 0 1 Control Variables size 29693 Total assets of the company (log) 22.21 1.515 20.30 21.99 24.94 top1 29693 Shareholding ratio of the largest shareholder 34.12 15.09 13.01 31.85 61.89 lnage 29693 Listing year of the company (log) 2.090 0.899 0 2.303 3.178 dta 29693 Debt-to-asset ratio (company debt/company assets) 0.458 1.174 0.107 0.424 0.826 prop1 29693 The proportion of primary industry (%) 4.390 4.882 0.09 2.98 14.84 prop2 29693 The proportion of secondary industry (%) 41.32 11.08 19.26 42.57 56.76 Mechanism variable lnloan_cash 29693 Bank loans obtained by the company in the current year (log) 16.58 7.944 0 19.77 23.22 4. Empirical results 4.1. Basic regression results As previously discussed, the core question of this paper is whether the construction of the social credit system can effectively eliminate the inflation in corporate environmental information disclosures, increase the informational content of environmental reports, and accurately reflect companies' actual performance in environmental responsibility. To address this, we analyzed the impact of the social credit system construction in the regions where companies are located on the frequency of environmental responsibility words and sentences in corporate disclosures. The results are presented in Table 2 . The results in Table 2 show that after implementing the social credit system pilot, the level of corporate environmental information disclosure has decreased. For example, after controlling for fixed effects and other variables, the social credit system reform led to an average reduction of 0.905 in the frequency of extended sentences related to environmental responsibility in corporate social responsibility reports. Similar statistical and economic implications are observed for other indicators, suggesting that companies may have previously exaggerated their environmental performance. The construction of the social credit system has effectively reduced the inflation of information in environmental disclosures and limited the opportunities for companies to engage in rent-seeking behaviors through social responsibility claims. Table 2 Basic Regression results (1) (2) (3) (4) VARIABLES envpro_se envpro_sp envpro_we envpro_wp did -0.905*** -0.639*** -1.234*** -0.757*** (0.208) (0.143) (0.283) (0.181) Control Yes Yes Yes Yes Year FE Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Observations 29,693 29,693 29,693 29,693 Adjusted R-squared 0.656 0.627 0.656 0.621 Note: We control size, top1, lnage, dta, prop1, prop2, Year fixed effect and Firm fixed effect in Table 2 and Table 3 to avoid omitted variable problem. Robust standard errors clusters at the city level are shown in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. 4.2. Parallel trend test We test the parallel trends assumption in our Difference-in-Differences (DID) setup and account for the group-time average treatment effects. First, we calculate the average treatment effects for specific groups at specific times, then reasonably weight and aggregate these effects across groups and time periods. This approach avoids using earlier treated groups as poor control groups, thereby preventing estimation bias. The results shown in Fig. 3 indicate that the parallel trends assumption is largely satisfied, demonstrating that considering heterogeneous treatment effects does not substantially impact the estimation results of this paper. 4.3. Placebo test To verify the robustness of our conclusions, we employ a placebo method by randomly selecting the treatment group for validation. This involves simulating the impact of the pilot city policy on companies randomly, with the program generating random policy treatment group individuals. We then estimate the regression coefficients of the "hypothetical interaction terms" based on the original model. As illustrated in Fig. 3 , the regression coefficients of the interaction terms for the four variables, obtained through the random generation of the treatment group, exhibit a bell-shaped distribution. The mean of the randomly generated estimated coefficients is close to zero, indicating no omitted error term affecting the asymptotic DID interaction term. This demonstrates that the coefficient estimation results of the asymptotic DID are not significantly different from zero due to an ignored error term's influence on the dependent variable. Additionally, the original interaction term regression coefficient is significantly different from zero, suggesting that the identified regression coefficient stands out as an "extreme value" when incorporated into the results of the randomly generated treatment group. Even if this result were coincidental, its probability of occurrence is minimal. 4.4. Mechanism analysis The previous sections have shown that the construction of the social credit system significantly increased local governments' focus on social credit and significantly reduced the over-embellishment of corporate environmental responsibility disclosures. But why does the construction of the social credit system effectively eliminate the inflation in corporate environmental information, enhance the stringency of government regulation, and make companies' actual actions more transparent and authentic? Moreover, if the inflated corporate environmental information is reduced, do companies shift towards genuine environmental actions, such as green patent development? To address these questions, this paper explores the relationship between the social credit system and corporate environmental responsibility in Table 3 . Columns (1) and (2) of Table 3 show the impact of environmental word frequency disclosures in the management discussion and analysis section on corporate bank loans when the policy pilot variable DID is 0 and 1, respectively. Column (3) presents the impact of the social credit system construction on green patent development, and Column (4) shows the effect of green patent development on corporate loan acquisition post-social credit system construction. The results indicate that before the initiation of the social credit system pilot, the impact of environmental word frequency on bank loans was positive but not significant, suggesting a weak positive influence of environmental word frequency on corporate loan acquisition. However, after the launch of the social credit system pilot, the impact of environmental word frequency on corporate loan acquisition became significantly negative. This implies that inflated environmental disclosures had a negative effect on companies' ability to secure funding, reducing their motivation for such disclosures under the social credit system. Consequently, this shift promoted more genuine environmental actions. Furthermore, the construction of the social credit system significantly encouraged green patent development, a tangible environmental action, which in turn positively influenced corporate loan acquisition. Table 3 Mechanism analysis (1) (2) (3) (4) VARIABLES lnloan_cash lnloan_cash green_patent_total_l lnloan_cash envpro_se 0.039*** -0.006 (0.011) (0.009) did 0.091*** 0.737*** (0.029) (0.236) green_patent_total_l 0.233*** (0.079) Control Yes Yes Yes Yes Year FE Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Observations 19,496 10,080 29,693 29,693 Adjusted R-squared 0.630 0.697 0.564 0.621 Note: Robust standard errors clusters at the city level ໿are shown in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. 5. Conclusion Credit is the cornerstone of market institutions, and the construction of a social credit system has a significant impact on corporate environmental behavior and transparency. This paper utilizes the implementation of China's social credit system pilot policy and employs a Difference-in-Differences (DID) approach to examine the effects of this institutional reform on the environmental responsibility information disclosure and ESG performance of listed companies. By conducting a textual analysis of environmental word frequency and sentence frequency in the annual reports of companies listed on the Shanghai and Shenzhen stock exchanges, our empirical results reveal that the construction of the social credit system significantly reduces the inflation in information disclosures, improves the quality and transparency of corporate environmental information disclosures, and enhances actual corporate performance in environmental protection expenditures. Mechanism analysis results indicate that the social credit system pilot prevents companies from using environmental responsibility disclosures to secure loan support. Additionally, due to increased regulation of these disclosures, the social credit system pilot effectively reduces the false content in information disclosures, prompting companies to engage in genuine environmental actions, such as green innovation, to obtain more loan support. Our findings support the notion that enhancing social credit levels improves the efficiency of corporate information disclosure and aids in the implementation of policies that provide corresponding policy and financial support to companies based on reliable information. Declarations Author Contribution Xiaoyu Chen wrote the main manuscript text and data programming, and Xinyi Lv mainly worked for the research design and data programming. Data Availability Data will be made available on request. Please contact Xiaoyu Chen at [email protected] for data. References Acemoglu, D., Johnson, S., & Robinson, J. A. (2001). The colonial origins of comparative development: An empirical investigation. American economic review, 91(5), 1369-1401. Berg, F., Koelbel, J. F., & Rigobon, R. (2022). Aggregate confusion: The divergence of ESG ratings. Review of Finance, 26(6), 1315-1344. Buntaine, M. T., Greenstone, M., He, G., Liu, M., Wang, S., & Zhang, B. (2024). Does the Squeaky Wheel Get More Grease? The Direct and Indirect Effects of Citizen Participation on Environmental Governance in China. American Economic Review, 114(3), 815-850. Burgess, R., Hansen, M., Olken, B. A., Potapov, P., & Sieber, S. (2012). The political economy of deforestation in the tropics. The Quarterly journal of economics, 127(4), 1707-1754. Callaway, B., & Sant’Anna, P. H. (2021). Difference-in-differences with multiple time periods. Journal of econometrics, 225(2), 200-230. Chen, B., Liu, S., & Zhang, Q. (2016). Can public information promote market stability?. Economics letters, 143, 103-106. Chen, D., Li, L., Liu, X., & Lobo, G. J. (2018). Social trust and auditor reporting conservatism. Journal of Business Ethics, 153, 1083-1108. De Chaisemartin, C., & d'Haultfoeuille, X. (2024). Difference-in-differences estimators of intertemporal treatment effects. Review of Economics and Statistics, 1-45. Duflo, E., Greenstone, M., Pande, R., & Ryan, N. (2013). Truth-telling by third-party auditors and the response of polluting firms: Experimental evidence from India. The Quarterly Journal of Economics, 128(4), 1499-1545. Garrett, J., Hoitash, R., & Prawitt, D. F. (2014). Trust and financial reporting quality. Journal of Accounting Research, 52(5), 1087-1125. Kong, D., Zhao, Y., & Liu, S. (2021). Trust and innovation: Evidence from CEOs' early-life experience. Journal of Corporate Finance, 69, 101984. Krueger, P., Sautner, Z., Tang, D. Y., & Zhong, R. (2021). The effects of mandatory ESG disclosure around the world. Journal of Accounting Research. Lu, J. W., Song, Y., & Shan, M. (2018). Social trust in subnational regions and foreign subsidiary performance: Evidence from foreign investments in China. Journal of International Business Studies, 49, 761-773. Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of econometrics, 225(2), 175-199. Footnotes We construct our core quasi-natural experimental independent variables using these pilot cities. Due to space limitations, the specific list of pilot cities is available upon request. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5962762","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":412111372,"identity":"5a283fd4-d662-4abe-b1be-98cc68ac4a7f","order_by":0,"name":"Xiaoyu Chen","email":"data:image/png;base64,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","orcid":"","institution":"Tsinghua University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Chen","suffix":""},{"id":412111373,"identity":"43b38e6c-2263-4072-b7ee-b0c9555c3adc","order_by":1,"name":"Xinyi Lv","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Lv","suffix":""}],"badges":[],"createdAt":"2025-02-05 06:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5962762/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5962762/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75869055,"identity":"cd848d03-f0ec-48aa-9be9-0d40055303b6","added_by":"auto","created_at":"2025-02-10 06:43:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62761,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesis development\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5962762/v1/d420949e2aceaee2c21e4519.png"},{"id":75869056,"identity":"950c3138-7f8d-473e-ba1c-485b47bd09ba","added_by":"auto","created_at":"2025-02-10 06:43:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58370,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental disclosure process with textual analysis\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5962762/v1/050c51fa6ebebb5f0b80e9e5.png"},{"id":75869058,"identity":"b4bd34b9-8fe8-49f9-93b8-19dd27363b27","added_by":"auto","created_at":"2025-02-10 06:43:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59546,"visible":true,"origin":"","legend":"\u003cp\u003eParallel trend\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5962762/v1/ffe75d1e9bf37bdbef73cc83.png"},{"id":75869317,"identity":"e0236729-7b8c-414d-9713-a0148a77438f","added_by":"auto","created_at":"2025-02-10 06:51:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50525,"visible":true,"origin":"","legend":"\u003cp\u003ePlacebo test\u003c/p\u003e\n\u003cp\u003eNotes: Fig.4 represents the estimation of the regression coefficients of the \"hypothetical interaction terms\" based on formula (1). The estimations are repeated 500 times, and we generate kernel density frequency estimation plots. The estimated regression coefficients and p-values are shown in Fig.4. The vertical line represents the coefficient estimate from the original regression results, and the horizontal line represents p=0.1.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5962762/v1/c8c1df44bb8b7cd0d0b05c52.png"},{"id":76426187,"identity":"56d0235c-f26c-4bc9-9ac3-ff735317c49a","added_by":"auto","created_at":"2025-02-17 05:39:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":827765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5962762/v1/1ecfcfca-7bca-496f-aeb6-ed7cce062284.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Does Social Trust weaken the information rent-seeking of environmental responsibility?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSocial trust, as an informal institution (Acemoglu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), serves as the cornerstone of market operations and is crucial for enhancing the transparency of information disclosure in the market. Investors often make decisions based on publicly available information from companies (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although this information is typically audited, companies with informational advantages often disclose positive environmental information to improve their environmental ratings, enhance their market image, increase their ESG investment attractiveness, and secure government-provided environmental subsidies. This public information frequently contains a high degree of redundancy, making it difficult to verify much of the descriptive information about their environmental responsibility performance. As a result, the claimed environmental responsibility of companies is often inflated and unreliable.\u003c/p\u003e \u003cp\u003eThis raises an important research question: Would a trust mechanism be established to eliminate the inflated information in corporate green reports and enhance the quality of corporate disclosures? Previous literature has primarily examined social trust in relation to audit efficiency (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), corporate innovation (Kong et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and company performance (Lu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, literature has not further defined the issues of inflated claims and the value of information content within corporate social responsibility (CSR). We implement the pilot construction of China\u0026rsquo;s social credit system as a quasi-natural experiment, to investigate the impact of this system on corporate environmental responsibility information disclosure. Specifically, we explore whether the construction of social trust can effectively reduce the degree in environmental responsibility disclosures. Moreover, we also investigate how the reduction in bank loan shares following the establishment of the social credit system contributes to the decrease in corporate environmental information disclosure. This reduction in loan availability simultaneously compels companies to engage in higher levels of green patent research and development, aiming to enhance their eligibility for bank loans through improved environmental performance.\u003c/p\u003e"},{"header":"2. Policy background and research hypothesis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Policy background\u003c/h2\u003e \u003cp\u003eIn 2014, the State Council of China issued the Outline of the Plan for the Construction of the Social Credit System, emphasizing the creation of an honest and trustworthy social environment. Although not specifically targeting corporate environmental protection, the policy promotes social integrity in various fields, including environmental protection and energy conservation. It mandates stricter measures for environmental monitoring, information disclosure, and pollution supervision, including improving the responsibility mechanism for enterprises' environmental assessment documents, establishing integrity databases for assessment agencies and experts, and strengthening credit assessment management of environmental project experts. These measures provide policy guidance for enterprises to disclose environmental protection information.\u003c/p\u003e \u003cp\u003eAfter that, A series of specific measures for building social trust have been proposed. In 2015, the State Council and the People's Bank of China designated 11 cities as the first batch of national demonstration cities for the social credit system construction. In 2016, 32 additional cities were identified for demonstration work\u003csup\u003e1\u003c/sup\u003e. Enterprises in these pilot cities adopted more stringent information disclosure measures and increased the quality of information disclosure. This social credit system helps mitigate dishonest information disclosure, reduces information asymmetry, and limits rent-seeking behaviors related to environmental responsibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Research hypothesis\u003c/h2\u003e \u003cp\u003eEnvironmental information disclosure is a legal obligation for companies and a core regulatory requirement. From the perspective of the companies, environmental information disclosure may be driven by the desire to fulfill social responsibility, practice environmental stewardship, and attract ESG investments. Alternatively, it may be motivated by the need to reduce regulatory pressure, shape public image, and engage in information rent-seeking to secure more support from government and financial institutions (Krueger et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Companies with higher ESG ratings and stronger environmental responsibility are often more likely to receive policy and financial support, although their tendency to \"talk more and act less\" increases the risk of information disclosure violations. In a weak regulatory environment, such violations are less frequently detected, incentivizing companies to embellish their environmental actions, thereby reducing the efficiency of information transmission and asset pricing (Berg et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, it is necessary to impose strict constraints on environmental information disclosure to minimize dishonest and misleading behaviors (Duflo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While other forms of environmental monitoring have potential, government-led initiatives and the establishment of formal or informal institutions have proven more effective in this domain (Buntaine et al., 2021; Burgess et al., 2017). Although the social credit system is currently an informal institution and does not yet involve detailed auditing and disclosure requirements, the dual objectives of institutional constraints and enhanced market competitiveness motivate companies to reduce ineffective environmental disclosures and adopt more substantial environmental measures to improve their ESG core competitiveness.\u003c/p\u003e\u003cp\u003eBased on the above analysis and the content in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we propose the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003eThe construction of the social credit system can reduce inflated corporate environmental information disclosures and enhance green innovation in corporate environmental responsibility.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research design","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Methodology\u003c/h2\u003e \u003cp\u003eWe implement a Difference-in-Differences (DID) approach, using the establishment of demonstration cities for social credit system construction as an exogenous shock, to examine the impact on Chinese listed companies. Specifically, the model is specified as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{ict}=\\alpha\\:+{\\beta\\:}_{1}{DID}_{ict}+{X}_{ict}\\varGamma\\:+{\\tau\\:}_{t}+{id}_{i}+{\\epsilon\\:}_{ict}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere i, c and t represent the company, city, and year respectively. The variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{DID}_{ict}\\)\u003c/span\u003e\u003c/span\u003e denotes the policy variable for the establishment of social credit system demonstration cities. This variable takes a value of 1 for cities after being designated as demonstration cities and 0 otherwise. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{ict}\\)\u003c/span\u003e\u003c/span\u003e is the outcome variable measuring corporate environmental disclosure and green innovation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{ict}\\)\u003c/span\u003e\u003c/span\u003e is a vector of city-level and firm-level control variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{id}_{i}\\)\u003c/span\u003e\u003c/span\u003e denote time fixed effects and firm fixed effects respectively, and is the error term. The coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e on the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{DID}_{ict}\\)\u003c/span\u003e\u003c/span\u003e represents the core result of the social trust construction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Event study method\u003c/h2\u003e \u003cp\u003eConsistent with the standard DID model, the staggered Difference-in-Differences (DID) approach requires the parallel trends assumption. This means that before the policy intervention, the outcome variable should exhibit no significant differences between the treatment and control groups. The dynamic effects test model in this paper is specified as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{it}={{\\alpha\\:}}_{0}+\\sum\\:_{\\begin{array}{c}k=-3\\\\\\:k\\ne\\:-1\\end{array}}^{5}{\\beta\\:}_{k}I(t-{T}_{j}=k)+{X}_{it}\\varGamma\\:+{\\tau\\:}_{t}+{id}_{i}+{\\epsilon\\:}_{it}\\:\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{j}\\)\u003c/span\u003e\u003c/span\u003e represents the date when the pilot city initiated the policy, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e represents the current year of the data. When \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t-{T}_{j}=k\\)\u003c/span\u003e\u003c/span\u003e, the dummy variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I(t-{T}_{j}=k)=1\\)\u003c/span\u003e\u003c/span\u003e; otherwise, it equals 0. In this setup, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k=-1\\)\u003c/span\u003e\u003c/span\u003e serves as the base period, and periods greater than 5 and less than \u0026minus;\u0026thinsp;3 are truncated to the last and first periods, respectively, for the dynamic effects test. If the coefficients for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\u0026lt;0\\)\u003c/span\u003e\u003c/span\u003e are not significant, the parallel trends assumption is satisfied.\u003c/p\u003e \u003cp\u003eFurthermore, considering that the social credit system pilot cities were established in two batches in 2015 and 2016, this paper also incorporates the group-period average treatment effects calculation method (De Chaisemartin and D'Haultfoeuille, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sun and Abraham, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Callaway and Sant'Anna, 2021) in the parallel trends model to ensure robustness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Variable selection and data sources\u003c/h2\u003e \u003cp\u003eWe implement data from 2011 to 2020 to avoid the impacts of COVID-19 and economic cycles. The data sources include the CSMAR database, CNRDS database, China City Statistical Yearbook, and the annual reports of listed companies in China. Financial data for listed companies, used as control variables, are obtained from the CSMAR database, while city-level variables are sourced from the China City Statistical Yearbook. Environmental disclosure indicators are constructed from the annual report texts of listed companies, and patent data are organized from the CNRDS database.\u003c/p\u003e \u003cp\u003eThe core identification variables of this paper are primarily constructed from textual data as discussed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. We extract the environmental responsibility disclosure information from the social responsibility reports and the Management Discussion and Analysis (MD\u0026amp;A) sections of corporate annual reports. Using text analysis methods, we construct indicators for environmental responsibility fulfillment. First, we identify the MD\u0026amp;A sections in the annual reports and use an environmental protection dictionary, which includes terms like \"environmental protection,\" \"environment,\" \"energy consumption,\" \"emission reduction,\" \"pollution discharge,\" \"carbon dioxide,\" \"chemical oxygen demand,\" \"low carbon,\" and \"green.\" This dictionary is incorporated into the word segmentation module as a predefined noun dictionary. We then count the occurrences of these environmental protection terms in the MD\u0026amp;A sections of the annual reports, obtaining their precise word frequency, sentence frequency, expanded word frequency, and expanded sentence frequency. The sum of the precise word frequency and expanded word frequency is used as the core dependent variable of this paper. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics and variable descriptions for all variables.\u003c/p\u003e \u003cp\u003e \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\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep95\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eExplained Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eenvpro_se\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency of expanded sentences related to environmental protection in the Management Discussion and Analysis section of listed companies' annual reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eenvpro_sp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency of precise sentences related to environmental protection in the Management Discussion and Analysis section of listed companies' annual reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eenvpro_we\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency of expanded words related to environmental protection in the Management Discussion and Analysis section of listed companies' annual reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eenvpro_wp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency of precise words related to environmental protection in the Management Discussion and Analysis section of listed companies' annual reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egreen_patent_l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of green patents applied by the company in the current year (log\u0026thinsp;+\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCore Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epolicy shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal assets of the company (log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etop1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShareholding ratio of the largest shareholder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elnage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eListing year of the company (log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDebt-to-asset ratio (company debt/company assets)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprop1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe proportion of primary industry (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprop2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe proportion of secondary industry (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanism variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elnloan_cash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBank loans obtained by the company in the current year (log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.22\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":"4. Empirical results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Basic regression results\u003c/h2\u003e \u003cp\u003eAs previously discussed, the core question of this paper is whether the construction of the social credit system can effectively eliminate the inflation in corporate environmental information disclosures, increase the informational content of environmental reports, and accurately reflect companies' actual performance in environmental responsibility. To address this, we analyzed the impact of the social credit system construction in the regions where companies are located on the frequency of environmental responsibility words and sentences in corporate disclosures. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show that after implementing the social credit system pilot, the level of corporate environmental information disclosure has decreased. For example, after controlling for fixed effects and other variables, the social credit system reform led to an average reduction of 0.905 in the frequency of extended sentences related to environmental responsibility in corporate social responsibility reports. Similar statistical and economic implications are observed for other indicators, suggesting that companies may have previously exaggerated their environmental performance. The construction of the social credit system has effectively reduced the inflation of information in environmental disclosures and limited the opportunities for companies to engage in rent-seeking behaviors through social responsibility claims.\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\u003eBasic Regression results\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 \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eenvpro_se\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eenvpro_sp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eenvpro_we\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eenvpro_wp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.905***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.639***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.234***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.757***\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(0.208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.283)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.181)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29,693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29,693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29,693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29,693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: We control size, top1, lnage, dta, prop1, prop2, Year fixed effect and Firm fixed effect in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e to avoid omitted variable problem. Robust standard errors clusters at the city level are shown in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e4.2. Parallel trend test\u003c/p\u003e \u003cp\u003eWe test the parallel trends assumption in our Difference-in-Differences (DID) setup and account for the group-time average treatment effects. First, we calculate the average treatment effects for specific groups at specific times, then reasonably weight and aggregate these effects across groups and time periods. This approach avoids using earlier treated groups as poor control groups, thereby preventing estimation bias.\u003c/p\u003e \u003cp\u003eThe results shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicate that the parallel trends assumption is largely satisfied, demonstrating that considering heterogeneous treatment effects does not substantially impact the estimation results of this paper.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e4.3. Placebo test\u003c/p\u003e \u003cp\u003eTo verify the robustness of our conclusions, we employ a placebo method by randomly selecting the treatment group for validation. This involves simulating the impact of the pilot city policy on companies randomly, with the program generating random policy treatment group individuals. We then estimate the regression coefficients of the \"hypothetical interaction terms\" based on the original model. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the regression coefficients of the interaction terms for the four variables, obtained through the random generation of the treatment group, exhibit a bell-shaped distribution. The mean of the randomly generated estimated coefficients is close to zero, indicating no omitted error term affecting the asymptotic DID interaction term. This demonstrates that the coefficient estimation results of the asymptotic DID are not significantly different from zero due to an ignored error term's influence on the dependent variable. Additionally, the original interaction term regression coefficient is significantly different from zero, suggesting that the identified regression coefficient stands out as an \"extreme value\" when incorporated into the results of the randomly generated treatment group. Even if this result were coincidental, its probability of occurrence is minimal.\u003c/p\u003e\u003cp\u003e4.4. Mechanism analysis\u003c/p\u003e \u003cp\u003eThe previous sections have shown that the construction of the social credit system significantly increased local governments' focus on social credit and significantly reduced the over-embellishment of corporate environmental responsibility disclosures. But why does the construction of the social credit system effectively eliminate the inflation in corporate environmental information, enhance the stringency of government regulation, and make companies' actual actions more transparent and authentic? Moreover, if the inflated corporate environmental information is reduced, do companies shift towards genuine environmental actions, such as green patent development?\u003c/p\u003e \u003cp\u003eTo address these questions, this paper explores the relationship between the social credit system and corporate environmental responsibility in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show the impact of environmental word frequency disclosures in the management discussion and analysis section on corporate bank loans when the policy pilot variable DID is 0 and 1, respectively. Column (3) presents the impact of the social credit system construction on green patent development, and Column (4) shows the effect of green patent development on corporate loan acquisition post-social credit system construction.\u003c/p\u003e \u003cp\u003eThe results indicate that before the initiation of the social credit system pilot, the impact of environmental word frequency on bank loans was positive but not significant, suggesting a weak positive influence of environmental word frequency on corporate loan acquisition. However, after the launch of the social credit system pilot, the impact of environmental word frequency on corporate loan acquisition became significantly negative. This implies that inflated environmental disclosures had a negative effect on companies' ability to secure funding, reducing their motivation for such disclosures under the social credit system. Consequently, this shift promoted more genuine environmental actions. Furthermore, the construction of the social credit system significantly encouraged green patent development, a tangible environmental action, which in turn positively influenced corporate loan acquisition.\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\u003eMechanism analysis\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 \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elnloan_cash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elnloan_cash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003egreen_patent_total_l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elnloan_cash\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eenvpro_se\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.039***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edid\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\u003e0.091***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.737***\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(0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.236)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egreen_patent_total_l\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 \u003cp\u003e0.233***\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 \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirm FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29,693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29,693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Robust standard errors clusters at the city level ໿are shown in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eCredit is the cornerstone of market institutions, and the construction of a social credit system has a significant impact on corporate environmental behavior and transparency. This paper utilizes the implementation of China's social credit system pilot policy and employs a Difference-in-Differences (DID) approach to examine the effects of this institutional reform on the environmental responsibility information disclosure and ESG performance of listed companies. By conducting a textual analysis of environmental word frequency and sentence frequency in the annual reports of companies listed on the Shanghai and Shenzhen stock exchanges, our empirical results reveal that the construction of the social credit system significantly reduces the inflation in information disclosures, improves the quality and transparency of corporate environmental information disclosures, and enhances actual corporate performance in environmental protection expenditures. Mechanism analysis results indicate that the social credit system pilot prevents companies from using environmental responsibility disclosures to secure loan support. Additionally, due to increased regulation of these disclosures, the social credit system pilot effectively reduces the false content in information disclosures, prompting companies to engage in genuine environmental actions, such as green innovation, to obtain more loan support.\u003c/p\u003e \u003cp\u003eOur findings support the notion that enhancing social credit levels improves the efficiency of corporate information disclosure and aids in the implementation of policies that provide corresponding policy and financial support to companies based on reliable information.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXiaoyu Chen wrote the main manuscript text and data programming, and Xinyi Lv mainly worked for the research design and data programming.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request. Please contact Xiaoyu Chen at [email protected] for data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcemoglu, D., Johnson, S., \u0026amp; Robinson, J. A. (2001). The colonial origins of comparative development: An empirical investigation. American economic review, 91(5), 1369-1401.\u003c/li\u003e\n\u003cli\u003eBerg, F., Koelbel, J. F., \u0026amp; Rigobon, R. (2022). Aggregate confusion: The divergence of ESG ratings. Review of Finance, 26(6), 1315-1344.\u003c/li\u003e\n\u003cli\u003eBuntaine, M. T., Greenstone, M., He, G., Liu, M., Wang, S., \u0026amp; Zhang, B. (2024). Does the Squeaky Wheel Get More Grease? The Direct and Indirect Effects of Citizen Participation on Environmental Governance in China. American Economic Review, 114(3), 815-850.\u003c/li\u003e\n\u003cli\u003eBurgess, R., Hansen, M., Olken, B. A., Potapov, P., \u0026amp; Sieber, S. (2012). The political economy of deforestation in the tropics. The Quarterly journal of economics, 127(4), 1707-1754.\u003c/li\u003e\n\u003cli\u003eCallaway, B., \u0026amp; Sant\u0026rsquo;Anna, P. H. (2021). Difference-in-differences with multiple time periods. Journal of econometrics, 225(2), 200-230.\u003c/li\u003e\n\u003cli\u003eChen, B., Liu, S., \u0026amp; Zhang, Q. (2016). Can public information promote market stability?. Economics letters, 143, 103-106.\u003c/li\u003e\n\u003cli\u003eChen, D., Li, L., Liu, X., \u0026amp; Lobo, G. J. (2018). Social trust and auditor reporting conservatism. Journal of Business Ethics, 153, 1083-1108.\u003c/li\u003e\n\u003cli\u003eDe Chaisemartin, C., \u0026amp; d\u0026apos;Haultfoeuille, X. (2024). Difference-in-differences estimators of intertemporal treatment effects. Review of Economics and Statistics, 1-45.\u003c/li\u003e\n\u003cli\u003eDuflo, E., Greenstone, M., Pande, R., \u0026amp; Ryan, N. (2013). Truth-telling by third-party auditors and the response of polluting firms: Experimental evidence from India. The Quarterly Journal of Economics, 128(4), 1499-1545.\u003c/li\u003e\n\u003cli\u003eGarrett, J., Hoitash, R., \u0026amp; Prawitt, D. F. (2014). Trust and financial reporting quality. Journal of Accounting Research, 52(5), 1087-1125.\u003c/li\u003e\n\u003cli\u003eKong, D., Zhao, Y., \u0026amp; Liu, S. (2021). Trust and innovation: Evidence from CEOs\u0026apos; early-life experience. Journal of Corporate Finance, 69, 101984.\u003c/li\u003e\n\u003cli\u003eKrueger, P., Sautner, Z., Tang, D. Y., \u0026amp; Zhong, R. (2021). The effects of mandatory ESG disclosure around the world. Journal of Accounting Research.\u003c/li\u003e\n\u003cli\u003eLu, J. W., Song, Y., \u0026amp; Shan, M. (2018). Social trust in subnational regions and foreign subsidiary performance: Evidence from foreign investments in China. Journal of International Business Studies, 49, 761-773.\u003c/li\u003e\n\u003cli\u003eSun, L., \u0026amp; Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of econometrics, 225(2), 175-199.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e We construct our core quasi-natural experimental independent variables using these pilot cities. Due to space limitations, the specific list of pilot cities is available upon request.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Social trust, Environmental responsibility, Information disclosure, Innovation","lastPublishedDoi":"10.21203/rs.3.rs-5962762/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5962762/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSocial trust is crucial for effective information transparency. Using the demonstration city for building a social credit system in China as a quasi-natural experiment, our results indicate that an improved social credit environment reduces the intensity of environmental information disclosure by enterprises while enhancing their substantive green innovation. This effect may be attributed to the reduced ability of enterprises to engage in environmental information rent-seeking for additional bank loans.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification code: \u003c/strong\u003eF239；F27；F49\u003c/p\u003e","manuscriptTitle":"Does Social Trust weaken the information rent-seeking of environmental responsibility?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-10 06:42:55","doi":"10.21203/rs.3.rs-5962762/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a254ccf-f38b-443e-9bc3-414884f7f703","owner":[],"postedDate":"February 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T05:38:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-10 06:42:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5962762","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5962762","identity":"rs-5962762","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}