Silenced? Does Social-Media Real-Name Registration Policy Facilitate Firm Misconduct? Evidence from Greenwashing in China

Silenced? Does Social-Media Real-Name Registration Policy Facilitate Firm Misconduct? Evidence from Greenwashing in China | 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 Silenced? Does Social-Media Real-Name Registration Policy Facilitate Firm Misconduct? Evidence from Greenwashing in China Chao Yuan, Junming Li, Youze Zhang, Weixing Cai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7649177/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Removing anonymity in social media may stimulate firm misconduct as it reduces social media’s monitoring effects. This paper employs China’s 2015 social media real-name registration policy as a quasi-natural experiment that requires internet users to register their real ID with the government. Results indicate that the policy caused a robust and economically meaningful increase in corporate greenwashing which is a classical misconduct. Results are robust with various robustness checks. Further analysis demonstrates that removing anonymity attenuates social media’s monitoring role in corporate greenwashing by mitigating three channels: reputation cost, market pressure from investors and regulation pressure that social media exerts on firms. Theoretically, our findings identify user anonymity as a key institutional condition enabling social media to function as an informal governance mechanism. The results also highlight a regulatory trade-off: identity-verification rules may curb online harm but can inadvertently weaken external oversight of firm misconduct. Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing Social science/Science technology and society social media real-name registration firm misconduct corporate greenwahsing Figures Figure 1 Figure 2 Figure 3 1. Introduction External stakeholders process and disseminate firm-related information on social media, thereby influencing firm decision making. Consequently, social platforms have become an important channel through which external stakeholders monitor firm misconduct such as greenwashing (Dyck et al., 2008; Lyon & Montgomery, 2013). However, if external stakeholders are deterred from speaking out about firm malfeasance on social media, can the platform’s monitoring function be maintained? The answer depends critically on whether users can post anonymously (Lapidot-Lefler & Barak, 2012). In other words, anonymity on social media can foster moral courage among users and enable social platforms to perform an informal institution monitoring role (Pan et al., 2023). If, however, a policy preserves anonymity vis-à-vis fellow users but removes anonymity vis-à-vis platform providers and public-security authorities, will users remain equally willing to voice criticisms? Early social media environments were characterized by anonymity, which allowed users to share information and views freely without fear of retaliation from firms, peers, or regulators; this environment fostered the willingness and capacity of users to monitor firm misconducts (Christopherson, 2007; Jardine, 2018). At the same time, anonymity enabled extreme opinions, fake news, and rumors (Eastwick & Gardner, 2009), prompting policymakers and scholars to advocate for real-name registration policy to curb such harms. China implemented a social-media real-name registration policy in 2015, requiring users to provide officially authenticated personal information to platform providers; these records are directly or indirectly linked with public-security databases (Gao et al., 2022; Huang et al., 2023). This policy change thus provides a useful quasi-natural experiment to examine how de-anonymization affects social media’s external monitoring role. Using China’s 2015 real-name registration policy and corporate greenwashing as the empirical setting, this paper investigates whether and how real-name requirements weaken social media monitoring of firm misconduct. The 2015 policy offers three empirical advantages. First, numerous studies document that social media is a salient venue for stakeholder monitoring of firm misconducts in China (Zhou et al., 2021; Liu et al., 2022; Long et al., 2025), so it is an important informational institution for corporate external governance. Second, the policy’s universal requirement that users complete identity verification supports the policy’s exogeneity for empirical identification. Third, Chinese internet users exhibit a pronounced tendency to avoid topics that may invite retaliation from others, a tendency that became more salient after the social media real-name registration policy and substantially compressed social-media oversight capacity (Chen et al., 2023). Greenwashing, a classical decoupling form of misconduct, has been the focus of extensive international research and is therefore an appropriate research object (Lyon & Montgomery, 2013; Yang et al., 2020; Pizzetti et al., 2021; Long et al., 2025; Jiang et al., 2025). Methodologically, we implement a difference-in-differences (DID) design with the 2015 policy as the intervention. Leveraging Guba — the largest online forum used by Chinese stakeholders to disseminate firm information and opinions — we identify firms that were heavily discussed by external stakeholders prior to the policy. Firms that attracted greater stakeholder attention face larger reputational cost, regulatory pressure, and market pressure from misconduct (Dyck et al., 2008; Baloria & Heese, 2018; Heese & Pacelli, 2024). After the social media real-name registration policy, the cost of disclosure and denunciation for informed stakeholders rises substantially, reducing their willingness to act as monitors. Hence, firms that were highly discussed on social media before the policy should experience the largest decline in social-media pressure and therefore the largest increase in opportunities to greenwashing. We define the treatment group as firms whose ratio of Guba posts to total assets in 2014 exceeds the sample median. To ensure robustness, we conduct a serious of robustness checks — including parallel-trend tests, placebo tests, alternative treatment group constructions, and an intensity DID specification — all of which yield consistent results. We further investigate the mechanisms through which the social media real-name registration policy affects firm behavior. Theoretically, social media constrains firm misconduct via three principal channels. Firstly, the reputational channel: by publicizing evidence of firm misconducts, social media inflict reputational costs on firms and their managers, thereby reducing the expected net benefit of engaging in greenwashing (Dyck et al., 2008). Secondly, the market pressure channel: stakeholders who obtain adverse information through social media may respond by trading on that information (for example, selling shares), exerting immediate valuation pressure that disciplines greenwashing (Joe et al., 2009; Tetlock, 2007). Thirdly, the regulatory pressure channel: information disseminated on social media can draw the attention of regulators, increasing the probability of detection and sanction and thus raising the expected enforcement cost of greenwashing (Lyon & Montgomery, 2013; Heese & Pacelli, 2024). The implementation of a real-name registration policy undermines these channels by raising the personal and legal costs borne by stakeholders who might otherwise disclose or denounce firm malfeasance. Our empirical analysis corroborates this mechanism-based prediction. After the 2015 social media real name registration policy, we document a statistically and economically significant weakening of all three channels, thereby facilitating an increase in corporate greenwashing. This paper makes three contributions. Firstly, our research extends the growing literature on social media as an external monitor by highlighting a previously underexplored institutional condition: user anonymity. Prior research has documented the effectiveness of social media as a social-monitoring institution and has articulated conditions under which that effectiveness obtains (Heese & Pacelli, 2024). We enrich this literature by demonstrating that the presence or removal of user anonymity—the distinction between anonymous and real-name registration regimes—materially conditions social media’s capacity to discipline firms. Secondly, our study contributes to the literature on external monitoring and firm misconduct. Social media has been recognized as an important external informal institution affecting firm misconduct (Li et al., 2022). This paper shows that de-anonymization reduces stakeholders’ willingness to disclose and denounce firms—because potential whistleblowers face greater hidden costs under real-name regimes—thereby weakening social media’s external oversight and increasing firms’ propensity to engage in misconduct. This finding offers a novel perspective on cross-regional heterogeneity in the effectiveness of media-based monitoring. Thirdly, this paper provides new empirical evidence on economic consequences of social media real-name registration policies. The debate over removing anonymity of social media has focused on a range of social and economic trade-offs, but empirical evidence remains mixed. By documenting how China’s 2015 social media real-name registration policy affected corporate greenwashing, we reveal a concrete economic consequence of de-anonymization and supply empirical guidance for ongoing policy debates about the costs and benefits of real-name schemes. The remainder of the paper is organized as follows. Section 2 reviews the establishment of China’s social media real-name policy and describes user verification modalities. Section 3 develops the conceptual framework and hypotheses. Section 4 presents the data and empirical design. Section 5 reports the main results. Section 6 examines the mechanisms, and Section 7 concludes. 2. Institutional background From 2012 onward, a phased real-name registration policy for social media was introduced by the Chinese government. This regulatory intervention serves as an exogenous quasi-experimental setting for studying the causal impact of social media real-name registration on corporate greenwashing. The following content of this section outlines (a) the phased establishment of the social media real-name registration policy and (b) the modalities of user real-name verification. 2.1 The phased establishment of the social media real-name registration policy The implementation of social media real-name registration policy consists of three steps in China. The first step was from the Standing Committee of the National People's Congress (SCNPC) in 28th December 2012, which announced that the internet services providers were required to collect clients’ real identity information by user agreements. This announcement represents the beginning of the internet real-name registration in China. However, the first policy did not set unified rules for internet services provides. Then in 16th July 2013, more unified rules were implemented by the Ministry of Industry and Information Technology (MIIT) which stipulates mobile phone users should link their phone number with their national identification number. The Ministry of Public Security manages users’ ID numbers and ensures users’ personal information safety. The final step was implemented by the Cyberspace Administration of China in 1st March 2015. The internet user accounts real-name registration was completed since then. This policy required internet users to provide their personal information including real names to Internet information service providers when registering. However, internet users’ account nicknames and online names are not restricted. This setting implies that the principle of "real-name registration at the back end and voluntary use at the front end", which provides protection for users. But the Ministry of Public Security can recall and trace users’ personal information to deal with crime events, which poses pressure to internet users. 2.2 The modalities of user real-name verification Since 2012, users accessing social media in China can be authenticated via three principal real-name verification modalities, any of which permit rapid identification and tracking by Public Security Bureau (PSB). The first modality is ID-card verification (see Figure 1): users submit their name and national ID number to the platform, which queries the PSB database; identity matching is completed by the PSB—often complemented by biometric checks such as face recognition—while privacy safeguards are applied as required by law. The second modality is mobile-phone verification (see Figure 1), which relies on the triple combination of name, mobile number, and ID number. In practice, some authentication service providers obtain identity and mobile data from carriers and leverage SMS/telecom verification to confirm users’ identities. Under the regulatory requirements issued on July 16, 2013, mobile carriers’ subscriber records have been linked with PSB identity system, effectively enabling mobile carriers to act as intermediaries for identity matching. The third modality is bank-card verification (see Figure 1), which incorporates banking data (procured, in some cases, from China UnionPay) to perform stricter identity checks—a procedure commonly used by service providers operating in finance-related sectors. As with mobile data, banking identity records are already reconciled with PSB files and fall under public security management, enabling authorities to rapidly retrieve user information and take actions where online criminality is suspected. Insert Figure 1 here However, ambiguous triggering conditions for PSB arouse the concern of misfeasance, which undermines the enthusiasm of stakeholders with advanced information to share the information. For example, on August 27, 2025, an employee of listed company Shandong Longertek Technology Co., Ltd. disclosed the company’s potential regulatory violations to other investors before the stock market closed, helping them to reduce losses. Unexpectedly, with the real-name registration institution, this employee was investigated in 7 hours by the public security authorities in terms of leakage[1]. This event highlighted the inefficiency in whistleblowers protection in China. [1] Resource: website 3. Conceptual Framework and Hypothesis Development 3.1 External monitoring role of social media Social media, as a salient informal institution, provides an online platform in which stakeholders—investors, employees, and customers—can share firm-related information and opinions. Collective-intelligence theory posits that when numerous informed individuals, motivated to disclose their own knowledge, contribute content to social platforms, these platforms aggregate dispersed signals and value-relevant information that can substantively shape corporate governance (Ang et al., 2021). Existing research show that such user-generated information often reduces information asymmetries between firms and investors and improves investors’ forecasts of firm fundamentals (Chen et al., 2014; Tang, 2018; Yoon et al., 2018). The validity of collective intelligence is therefore a key prerequisite for social media to function as an effective external monitor. The external monitoring function of social media operates by imposing additional external costs on firms (Heese & Pacelli, 2024). When firms contemplate engaging in misconduct, these external costs—reputational, market, and regulatory —enter the firm’s cost-benefit calculus. If the expected external cost exceeds the anticipated returns from misconduct, firms will refrain from such behavior (Becker, 1968). The reputation cost channel works through public condemnation on social platforms: exposure of firm misconduct on social media damages a firm’s reputation and can generate economically meaningful consequences for both executives and shareholders (Dyck et al., 2008). For executives, reputational loss reduces future employment prospects and compensation (Fama & Jensen, 1983); for firms, lower reputation raises financing costs and undermines access to external capital, ultimately impairing profitability (Gomes, 2000). Thus, both firms and managers have incentives to avoid leaving a negative public impression. The market pressure channel operates via investor behavior: negative discourse on social media exerts downward pressure on stock prices (Tetlock, 2007). Once stakeholders learn of adverse information, they may vocalize grievances online and, if discussion intensity is high, retail investors may sell shares (Das et al., 2005). Because equity value is a core performance metric for executives, investor-driven selloffs create managerial incentives to improve governance and reduce misconduct in order to protect minority shareholders (Gorton, 2014). In sum, by surfacing misconduct and mobilizing stakeholders, social media performs an external governance function that can deter firm misconduct (Joe et al., 2009; Baloria & Heese, 2018). The regulatory pressure channel increases the probability that regulators will scrutinize and sanction errant firms. Intense social-media attention to a firm’s misconduct tends to attract regulatory scrutiny and raises the likelihood of enforcement (Lyon & Montgomery, 2013). Regulators and enforcement agencies increasingly incorporate information disseminated on social platforms into their monitoring toolkits, thereby enhancing oversight capacity and further elevating the cost of noncompliance (Heese & Pacelli, 2024). Therefore, both firms and financial markets are sensitive to social media as it processes and disseminates various firm information to the public and regulators (Baloria and Heese, 2018). Gradually, social media becomes an important external monitor which actively reduces firm misconduct (Lyon and Montgomery, 2013). 3.2 Social media anonymity and corporate greenwashing Massive literature documents that firms frequently present themselves as more environmentally responsible than they actually are through symbolic gestures and deceptive disclosure—commonly referred to as “greenwashing”(Laufer, 2003). Conceptually, greenwashing is a form of decoupling and therefore a type of misconduct: managers exploit informational asymmetries vis-à-vis investors to mislead stakeholders and conceal their inaction on ESG dimensions (e.g., Lyon & Montgomery, 2013). Absent credible external sanctions, firms have incentives to continue polishing their environmental image despite substantive inaction (Lyon & Maxwell, 2011). As Lyon and Montgomery (2013) note, if firms face little or no external scrutiny, they have incentives to greenwash irrespective of their true environmental performance. By the logic developed in Section 3.1, attention and discussion on social media should, in principle, curb greenwashing by increasing the expected external costs of such behavior. Campbell (2007), however, cautions that social-media scrutiny will only be effective when stakeholders are sufficiently strong and organized to countervail corporate power. The effectiveness of this monitoring therefore rests on whether informed users are willing and able to disclose salient information on public platforms. Campbell’s (2007) concern was thereby realized with the introduction of China’s 2015 social media real-name registration policy. A strategic interaction between informed stakeholders and firms was created: to avoid identification and tracking, the willingness of many informed stakeholders to share information on social media was curtailed. When informed stakeholders publicly denounce a firm’s greenwashing behavior on social media, such denunciations are expected to generate reputational, regulatory, and market pressures for the firm. Upon detection of online criticism, firms have been observed to—prior to escalation—deploy ties to regulators, government agencies, or public-security organs to identify and contact complainants, and inducements or legal threats (e.g., cease-and-desist letters) have been used to secure retractions or deletions of hostile posts. Anticipating such reprisals, informed stakeholders are induced to weigh the benefits of public exposure (e.g., visibility, image-building, intrinsic satisfaction) against the heightened personal risks of posting (Antweiler & Frank, 2004; Chen & Hwang, 2022; Toubia & Stephen, 2013). Given the substantial power asymmetry between ordinary netizens and firms in China, and the removing in anonymity resulting from real-name registration, the expected net payoff from disclosure is reduced and the volume of informative posts is diminished (Huang et al., 2023). The resulting contraction in credible disclosures weakens the reputational, regulatory, and market channels through which social media’s external pressure is exerted, thereby attenuating its monitoring efficacy and producing an increased propensity for firms to engage in greenwashing. Thus, we propose our research hypothesis (H1): Hypothesis 1 : Following implementation of the social media real-name registration policy, firms’ propensity to engage in greenwashing is increased. 4. Data and methodology 4.1 Sample selection and data source Our empirical analysis relies on firms listed in China’s A-share market over 2010–2019, with firm-level data obtained from the Wind and CSMAR databases. We exclude firms under “special treatment” (ST), financial firms, and firms listed for fewer than two years prior to 2015, when the social media real-name registration policy was implemented. Guba platform data are drawn from Chinese Research Data Service (CNRDS) database, while market and accounting variables are from Wind. The final panel comprises 7,045 firm-year observations across 865 firms. All continuous variables are winsorized at the 1% and 99% levels to mitigate the effects of extreme values. 4.2 Research design We use the following difference-in-difference (DID) model to examine the relation between the social media real-name registration policy and corporate greenwashing: $$\:\begin{array}{cc}{\:Gws\:}_{i,j,t}&\:={\beta\:}_{0}+{\beta\:}_{1}{\:Post\:}_{t}\times\:{\:Treat\:}_{i}+{\beta\:}_{k}{\:Control\:}_{i,t}+{\delta\:}_{i}+{\tau\:}_{j,t}+{\epsilon\:}_{i,j,t}\end{array}$$ 1 where, \(\:i\) , \(\:j\) , and \(\:t\) represent firm, industry, and year, respectively. \(\:{\:Gws\:}_{i,j,t}\) represents the degree of greenwashing. Followed by the research of Zhang (2022), GWS is derived from the normalized difference between the ESG ratings provided by Bloomberg and HuaZheng. \(\:{\:Post\:}_{t}\) is a time dummy variable. Specifically, \(\:{\:Post\:}_{t}\) equals 0 for the years preceding the policy's implementation (2010–2014), and equals 1 for the year of the policy's introduction and the subsequent years (2015–2019). \(\:{\:Treat\:}_{i}\) is a dummy variable for the treatment group, which is identified based on the ratio of the number of Guba posts to total assets in the year preceding the event (i.e., 2014), where \(\:{\:Treat\:}_{i}\) equals 1 if above the mdian, and 0 otherwise. \(\:{Control\:}_{i,t}\) denotes the set of firm-specific covariates. \(\:{\delta\:}_{i}\) and \(\:{\tau\:}_{j,t}\) capture firm and industry-year fixed effects, and \(\:\:{\epsilon\:}_{i,j,t}\) is the disturbance term. We include a set of firm characteristics as control variables that may affect the extent of corporate greenwashing. Specifically, firm size ( SIZE ) is measured by the natural logarithm of total assets at the end of year t, and firm age ( AGE ) is measured by the number of years since the firm’s IPO. In addition, we control several financial indicators, including leverage ( LEV , defined as total liabilities over total assets), return on equity ( ROE ), operating cash flow ratio ( CFLOW ), revenue growth ( GROWTH ), and the ratio of net fixed assets to total assets ( FIXED ). We also incorporate corporate governance measures, including the proportion of independent directors ( INDEP ), the ownership share of the largest shareholder ( TOP ), and board size ( BOARD ). Definitions and measurement details of all variables are reported in Table 1 . Table 1 Variables definitions Variable type Variable symbol Description Dependent variable Gws The difference between a firm’s normalized position is relative to peers in ESG disclosure (Huazheng E score) and in ESG performance (Bloomberg E score). Independent variable Treat The number of Guba message board in 2014/firm total assets (ten thousand yuan); equals 1 if above the median, and 0 otherwise. Post After the promulgation of the Internet User Account Name Management Regulations by the Cyberspace Administration of China (after March 2015), the value is 1, otherwise it is 0. Posts_Asset The number of Guba message boards in 2014/firm total asset (ten thousand yuan) Control variable Size the logarithm of total asset Age Number of years since the firm’s IPO Lev liability/total asset Roe profit/net asset Cflow Net cash flows from operating activities/total asset Growth The growth rate of revenue Fixed Net fixed asset / total asset Indep The number of independent directors / The total number of directors Top1 the shareholding ratio of the largest shareholder Board the logarithm of number of directors Insert Table 1 here 4.3 Descriptive statistics Table 2 reports summary statistics for the main sample of firm–year observations from 2010 to 2019. The variable Gws which captures the extent of greenwashing, has a mean of − 0.003 and a variance of 1.208, indicating substantial cross-firm heterogeneity. The averages of the Treat and the Post are 0.478 and 0.527, respectively, suggesting a relatively balanced distribution of observations before and after the policy implementation. Finally, the summarized statistics of control variables are consistent with prior studies. Table 2 Descriptive statistics Variables Obs. Mean SD Min p25 p50 p75 Max Gws 7045 -0.003 1.208 -3.576 -0.738 0.115 0.848 2.449 Treat 7045 0.478 0.500 0.000 0.000 0.000 1.000 1.000 Post 7045 0.527 0.499 0.000 0.000 1.000 1.000 1.000 Posts_Asset 7045 0.012 0.019 0.000 0.003 0.007 0.014 0.206 Size 7045 23.150 1.367 20.370 22.170 23.030 23.980 27.040 Age 7045 12.950 6.569 0.000 8.000 13.000 18.000 26.000 Lev 7045 0.486 0.199 0.067 0.334 0.500 0.641 0.881 Roe 7045 0.080 0.108 -0.491 0.038 0.083 0.132 0.339 Cflow 7045 0.056 0.068 -0.142 0.015 0.054 0.096 0.246 Growth 7045 0.292 0.582 -0.401 -0.023 0.126 0.391 3.278 Fixed 7045 0.242 0.183 0.003 0.095 0.199 0.355 0.751 Indep 7045 0.377 0.072 0.250 0.333 0.364 0.429 0.600 Top1 7045 0.384 0.162 0.088 0.253 0.375 0.505 0.774 Board 7045 2.196 0.200 1.609 2.079 2.197 2.303 2.708 Insert Table 2 here 5. Results 5.1 main results Table 3 reports the estimation results of Eq. ( 1 ), which examines the relation between the social media real-name registration policy and corporate greenwashing. Column (1) includes only the interaction term ( Treat×Post ) and the dependent variable ( GWS ), controlling for firm and year fixed effects. Columns ( 2 ) and ( 3 ) sequentially introduce firm-level control variables and industry-by-year fixed effects. Across all specifications, the coefficient on Treat×Post is consistently positive and statistically significant. In column ( 3 ), the coefficient is 0.166 and significant at the 5% level. The magnitude of the estimate indicates that the implementation of the social media real-name registration policy is associated with an economically meaningful increase of 0.166 in corporate greenwashing. The difference between treatment and control firms suggests that firms in the treatment group are more inclined to overstate their environmental contributions or obscure their lack of action when the monitoring role of social media is weakened by the social media real-name registration requirement. Coefficients on the control variables, where significant, are in line with prior research. Table 3 Baseline results: real-name registration policy and corporate greenwashing Gws ( 1 ) ( 2 ) ( 3 ) Treat×Post 0.178*** 0.133** 0.166** (0.047) (0.055) (0.070) Size 0.189*** 0.206*** (0.057) (0.059) Age -0.017* 0.057 (0.010) (0.147) Lev -0.641*** -0.646*** (0.190) (0.188) Roe 0.045 0.029 (0.150) (0.154) Cflow -0.633** -0.623** (0.281) (0.284) Growth -0.034 -0.030 (0.026) (0.026) Fixed -0.292 -0.256 (0.248) (0.248) Indep 0.239 0.320 (0.226) (0.235) Top1 -0.191 -0.227 (0.299) (0.318) Board -0.413*** -0.368** (0.150) (0.150) Constant -0.048*** -2.878** -4.364* (0.012) (1.248) (2.305) Firm FE Y Y Y Industry×Year FE N N Y Observations 7045 7045 7045 Adjusted R-squared 0.476 0.482 0.480 Notes: The results reported are from an OLS estimation. The models include firm as well as industry-by-year fixed effects. Standard errors are clustered at firm level to capture variation in standard errors across firms, which are displayed in parentheses below the coefficient estimates. *, **, *** represent significance at the 10%, 5%, and 1% level, respectively. Insert Table 3 here 5.2 Robustness test We conduct a series of additional robustness tests to enhance the reliance of our empirical results and conclusions. 5.2.1 Parallel trend test Following Fang et al. (2023), we test whether the greenwashing behavior of treatment and control firms satisfies the parallel trend assumption prior to the 2015 implementation of the social media real-name registration policy, and we further examine its dynamic effects thereafter. Figure 2 plots the parallel trend tests. The estimates reveal a stable and statistically indistinguishable pattern between the two groups before the policy’s implementation, indicating that treatment and control firms followed similar pre-trends in greenwashing. However, a significant divergence emerges after 2015, with treatment firms exhibiting a marked increase in greenwashing relative to control firms. These findings reinforce our conclusion that the implementation of the social media real-name registration policy attenuates the monitoring role of social media and thereby facilitates firms’ engagement in greenwashing activities. Insert Fig. 2 here 5.2.2 Intensity DID Following Duchin et al. (2010), who employ an intensity difference-in-differences (DID) framework, we replace the binary treatment variable Treat with Posts_Asset , a continuous variable defined as the ratio of the number of Guba posts to total assets. The intensity DID extends the standard DID by accommodating treatments that vary in strength or “dose,” rather than assuming a purely binary intervention. In our context, the effect of the social media real-name registration policy is not strictly dichotomous; therefore, employing an intensity DID allows the analysis to more precisely capture the gradient nature of the policy’s impact, rather than restricting inference to a single average treatment effect. Table 4 reports the results from the intensity DID specification. Column ( 1 ) presents the coefficient estimate on the interaction term Posts_Asset× Post controlling for firm and year fixed effects. Columns ( 2 ) and ( 3 ) progressively incorporate firm-level controls and industry-by-year fixed effects. Across all specifications, the coefficients on Posts_Asset×Post remain positive and statistically significant at the 1% level, consistent with the baseline results reported in Table 3 . These findings corroborate our earlier evidence, confirming that the implementation of the real-name registration policy in 2015 significantly increased firms’ propensity to engage in greenwashing activities. Table 4 Robustness test: Intensity DID Gws ( 1 ) ( 2 ) ( 3 ) Posts_Asset×Post 0.079*** 0.065*** 0.075*** (0.019) (0.022) (0.026) Size 0.188*** 0.205*** (0.057) (0.059) Age -0.018* 0.052 (0.009) (0.146) Lev -0.655*** -0.662*** (0.190) (0.186) Roe 0.033 0.007 (0.150) (0.154) Cflow -0.634** -0.626** (0.281) (0.284) Growth -0.035 -0.032 (0.026) (0.026) Fixed -0.286 -0.251 (0.247) (0.246) Indep 0.253 0.331 (0.226) (0.235) Top1 -0.183 -0.228 (0.299) (0.316) Board -0.411*** -0.368** (0.149) (0.149) Constant -0.051*** -2.843** -4.272* (0.011) (1.248) (2.296) Firm FE Y Y Y Industry×Year FE N N Y Observations 7045 7045 7045 Adjusted R-squared 0.477 0.482 0.480 Notes: The independent variable is Posts_Asset×Post where Postss_Asset is measured by the ratio of the number of Guba posts to total assets. Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively. Insert Table 4 here 5.2.3 Alternative construction of treat and control firms In the baseline regression, the 2014 posts-to-total-assets ratio on Guba is used as the grouping variable. Given the volatility of annual post counts and the potential idiosyncrasies of single-year grouping, Treat2 is defined as the average posts-to-assets ratio over 2010–2014, with observations above the sample median coded as Treat2 = 1 and all others as 0. The difference-in-differences (DID) specification is then estimated using the interaction term Treat2×Post . The results, reported in Table 5 , indicate that following the implementation of the social media real-name registration policy, firms experiencing more intensive discussion on Guba exhibited significantly higher levels of greenwashing. These findings are consistent with the baseline regression results, further supporting the robustness of our main conclusions. Table 5 Robustness test: alternative treat and control firms Gws ( 1 ) ( 2 ) ( 3 ) Treat_mean×Post 0.169*** 0.118** 0.141** (0.046) (0.054) (0.070) Size 0.181*** 0.196*** (0.057) (0.059) Age -0.015 0.056 (0.010) (0.147) Lev -0.645*** -0.650*** (0.190) (0.186) Roe 0.045 0.028 (0.150) (0.154) Cflow -0.655** -0.649** (0.283) (0.286) Growth -0.033 -0.030 (0.026) (0.026) Fixed -0.304 -0.271 (0.245) (0.243) Indep 0.244 0.323 (0.226) (0.235) Top1 -0.189 -0.226 (0.298) (0.318) Board -0.412*** -0.368** (0.150) (0.150) Constant -0.049*** -2.711** -4.102* (0.012) (1.262) (2.299) Firm FE Y Y Y Industry×Year FE N N Y Observations 7045 7045 7045 Adjusted R-squared 0.476 0.481 0.480 Notes: this table presents results with firms divided by Treat_mean. Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively. Insert Table 5 here 5.2.4 Placebo test Generally, corporate greenwashing behavior may be influenced by a variety of policy factors (Zhang, 2023), which could potentially confound the baseline empirical results. To address this concern, a placebo (randomization) test is conducted. Specifically, Eq. ( 1 ) is repeatedly re-estimated using a random sampling procedure: the DID regression is run 1000 times on randomly drawn subsamples, and the t-statistics of the estimated coefficients are recorded. The resulting distribution of t-statistics, presented in Fig. 3 , is approximately normal with a mean of zero and largely centered around zero. These placebo test results indicate that random policy shocks do not generate significant effects, supporting the interpretation that the observed increase in corporate greenwashing is attributable to the implementation of the social media real-name registration policy rather than to other idiosyncratic policy factors. Insert Fig. 3 here 5.2.5 Alternative dependent variables Following Hu et al. (2023), we use an alternative method to measure corporate greenwashing. Firstly, we extract a set of core keywords—such as “environment”, “ecology”, “sustainability”, “carbon”, “green”, “clean”, “energy” and “governance”—from the Management Discussion and Analysis (MD&A) sections of annual reports. Using the jieba segmentation tool in Python, we expanded the list to 135 candidate terms. After manual screening, 100 terms that reliably capture firms’ green and environmental disclosure are retained (see Appendix A for the detailed frequency distribution). Based on these terms, we construct two dummy variables: Oral and Actual. Oral is defined as the ratio of the number of green-related keywords disclosed by a firm each year to its total assets, thereby adjusting for firm size. It equals 1 if this ratio exceeds the industry-year median and 0 otherwise. Actual takes the value of 1 if a firm is subject to an environmental penalty in that year, and 0 otherwise, with data obtained from the CCER database. Finally, we define the interaction term Gws2 as the product of Oral and Actual. As a robustness check, Gws2 serves as an alternative dependent variable to the baseline measure Gws , ensuring that our findings are not driven by a particular specification of greenwashing. Empirical results are presented in Table 6 . All coefficients are consistent with findings in baseline results. Table 6 Robustness test: alternative independent variables Gws2 ( 1 ) ( 2 ) ( 3 ) Post×Post 0.103*** 0.042*** 0.045*** (0.012) (0.014) (0.016) Size -0.063*** -0.064*** (0.013) (0.013) Age 0.022*** -0.020 (0.002) (0.030) Lev -0.024 -0.024 (0.050) (0.050) Roe 0.035 0.028 (0.050) (0.052) Cflow -0.005 0.011 (0.065) (0.066) Growth 0.006 0.002 (0.006) (0.006) Fixed -0.101* -0.126** (0.058) (0.058) Indep -0.008 -0.001 (0.061) (0.060) Top1 0.019 0.030 (0.071) (0.069) Board -0.032 -0.026 (0.035) (0.036) Constant 0.057*** 1.352*** 1.895*** (0.003) (0.288) (0.500) Firm FE Y Y Y Industry×Year FE N N Y Observations 6940 6940 6938 Adjusted R-squared 0.197 0.218 0.222 Notes: The dependent variable in all models is Gws2 . Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively. Insert Table 6 here 6. Mechanism analysis As discussed in Chap. 3, social media generally constrains firm greenwashing and other forms of misconduct through three primary channels: reputational costs, market pressure, and regulation pressure. The implementation of the social media real-name registration policy, however, is expected to attenuate the effectiveness of these channels. Accordingly, we conduct further empirical analyses to examine the impact of the policy on each of these mechanisms. 6.1 Mechanism analysis: reputation cost This study examines whether the social media real-name registration policy promotes corporate greenwashing by attenuating the reputational cost channel, using a grouped regression approach. Firms are grouped based on two criteria: analyst coverage and the proportion of negative media reports. Specifically, analyst coverage is measured by the natural logarithm of the number of securities analysts following a listed firm, while the negative media report ratio is defined as the number of negative reports divided by the total number of reports (negative plus positive) for a given firm (data source: Junming, to be supplemented). Conceptually, similar to social media monitoring, firms with low analyst coverage or a low proportion of negative reports are subject to weaker external informal monitoring, implying lower expected reputational costs associated with engaging in misconduct behaviors such greenwashing. Once the monitoring role of social media is weakened by the social media real-name registration policy, these firms are expected to be more likely to engage in greenwashing. Table 7 presents empirical results. Panel A reports the results based on analyst coverage. In the low analyst coverage group (Column 2), the coefficient on Treat×Post is 0.240 and statistically significant at the 5% level. By contrast, in the high analyst coverage group (Column 1), the coefficient is -0.006 and not significant. The difference between the two coefficients is confirmed by the p-value from an equality test. These results suggest that firms with lower analyst coverage increase their greenwashing activities following the implementation of the social media real-name registration policy due to insufficient external monitoring. Similarly, Panel B reports results based on the proportion of negative media reports. In the low negative-report group, the coefficient on Treat×Post is 0.301 and significant at the 1% level, whereas in the high negative-report group, the coefficient is 0.127 and not significant. The coefficient difference is validated through the p-value for equality testing. These findings indicate that firms with weaker external monitoring, as proxied by lower negative media coverage, increase their greenwashing activities after social media monitoring is weakened by the social media real-name registration policy. Overall, the results in Panels A and B confirm that the social media real-name registration policy stimulates corporate greenwashing behavior through the reputational cost channel. Table 7 Mechanism analysis: reputational cost Panel A: Financial analyst attention Gws High financial analyst attention Low financial analyst attention ( 1 ) ( 2 ) Treat×Post -0.006 0.240** (0.115) (0.095) p-value for equality test 0.030** Controls Y Y Firm FE Y Y Industry×Year FE Y Y Observations 3,343 3,502 Adjusted R-squared 0.525 0.471 Panel B: N egative reports Gws High Negative reports Low Negative reports ( 1 ) ( 2 ) Treat×Post 0.127 0.301*** (0.107) (0.101) p-value for equality test 0.080* Controls Y Y Firm FE Y Y Industry×Year FE Y Y Observations 3,530 3,329 Adjusted R-squared 0.476 0.530 Notes: The analyst data in Panel A derived from the CSMAR database. The media coverage data in Panel B comes from the CNRDS database. The financial analyst attention is measured by the natural logarithm of the number of securities analysts following a listed firm, while the negative media report ratio is defined as the number of negative reports divided by the total number of reports. Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively. Insert Table 7 here 6.2 Mechanism analysis: market pressure The second channel examined is the market pressure mechanism. Following research of El Ghoul et al. (2023) and Xu et al. (2024), firms are grouped based on two indicators: the proportion of institutional shareholding ratio and Tobin’s Q. Conceptually, firms with higher institutional shareholding ratio face stronger market power from institutional investors, implying greater potential stock price declines if engaging in greenwashing. Similarly, a higher Tobin’s Q reflects elevated firm valuation, which also increases the potential market value loss associated with greenwashing. However, once social media monitoring is weakened by the social media real-name registration policy, the market pressure exerted via social media on these firms is reduced, thereby increasing their incentives to engage in greenwashing. Consequently, following the policy implementation, we expect that the promoting effect of the policy on greenwashing is stronger among firms with high institutional shareholding ratio and high Tobin’s Q. Table 6.2 reports the empirical results. Panel C presents results based on institutional shareholding ratio. In the high institutional shareholding ratio group (Column 1), the coefficient on Treat×Post is 0.187 and statistically significant, whereas in the low institutional shareholding ratio group (Column 2), the coefficient is 0.003 and not significant. The difference between the two coefficients is confirmed by the p-value from the equality test. These findings suggest that after the social media real-name registration policy, firms with higher institutional shareholding ratio anticipate a reduction in market pressure associated with social media scrutiny and thus exhibit greater greenwashing behavior. Similarly, Panel D reports results based on Tobin’s Q. In the low Tobin’s Q group, the coefficient on Treat×Post is -0.017 and not significant, whereas in the high Tobin’s Q group, the coefficient is 0.213 and statistically significant at the 1% level. The coefficient difference is validated by the p-value for equality testing. These results indicate that, following the policy implementation, firms with higher Tobin’s Q anticipate reduced market pressure from social media exposure and are therefore more inclined to increase greenwashing activities. Overall, the results in Panels C and D provide supporting evidence that the social media real-name registration policy facilitates corporate greenwashing by weakening the market pressure channel of social media monitoring. Insert Table 8 here Table 8 Mechanism analysis: market pressure Panel C: Institutional investor ratio Gws ( 1 ) ( 2 ) High institutional investor ratio Low institutional investor ratio Treat×Post 0.187* 0.003 (0.112) (0.105) p-value for equality test 0.040** Controls Y Y Firm FE Y Y Industry×Year FE Y Y Observations 3,452 3,424 Adjusted R-squared 0.510 0.487 Panel D: TobinQ Gws High Tobin Q Low Tobin Q ( 1 ) ( 2 ) Treat×Post 0.213** -0.017 (0.107) (0.115) p-value for equality test 0.020** Controls Y Y Firm FE Y Y Industry×Year FE Y Y Observations 3,406 9,320 Adjusted R-squared 0.476 0.586 Notes: Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively. 6.3 Mechanism analysis: regulation pressure The third channel examined is the regulation pressure mechanism. Following the implementation of the social media real-name registration policy, the effectiveness of social media as a monitoring tool is weakened, reducing both the proactive and reactive likelihood that regulators identify firm misconduct, such as greenwashing, through information disseminated on these platforms. Consequently, regulation pressure on firms is alleviated. Under this context, we hypothesize that, after the introduction of the social media real-name registration policy, firms are less likely to face environmental penalties, thereby increasing their incentives to engage in greenwashing. To test this mechanism, data on regulatory penalties imposed on firms for environmental violations were collected from the China Certified Emission Reduction (CCER) database, including both the annual number of penalties and the monetary amounts. We further construct four sub-indicators: PA, measured as the natural logarithm of penalty monetary amounts; PN, measured as the natural logarithm of penalty counts; PAR, measured as the ratio of penalty monetary amounts to total assets (log-transformed); and PNR, measured as the ratio of penalty counts to total assets (log-transformed). Table 6.3 reports the empirical results. In Columns ( 1 )–( 4 ), the coefficients on Treat×Post are − 0.460, -0.233, -0.351, and − 0.029, respectively, all statistically significant at the 1% level. These findings indicate that, following the social media real-name registration policy, both the monetary amounts and frequency of environmental penalties imposed on firms decreased significantly. This suggests that regulators’ ability to detect environmental violations through social media—either proactively or reactively—was reduced. Overall, the results in Table 6.3 provide robust evidence that the social media real-name registration policy promotes corporate greenwashing by weakening the regulation pressure channel of social media monitoring. Insert Table 9 here Table 9 Mechanism analysis: regulation pressure ( 1 ) ( 2 ) ( 3 ) ( 4 ) PA PN PAR PNR Treat×Post -0.460*** -0.233*** -0.351*** -0.029*** (0.076) (0.035) (0.065) (0.004) Controls Yes Yes Yes Yes Firm FE Yes Yes Yes Yes Industry×Year FE Yes Yes Yes Yes Observations 7045 7045 7045 7045 Adjusted R-squared 0.413 0.492 0.290 0.485 Notes: Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively. 7 Conclusion This paper documents that China’s 2015 social media real-name registration policy materially weakened social media’s capacity to discipline firms and thereby increased corporate greenwashing. Exploiting the policy as a quasi-natural experiment and applying a suite of robustness checks, we find consistent evidence that firms received more attention on social platforms prior to the reform displayed larger post-policy increases in greenwashing. Additional sensitivity analyses corroborate the baseline results. Mechanism tests reveal that the effect is transmitted through three channels: the policy reduces expected reputational costs associated with public exposure, mutes investor-driven market pressure, and lowers the likelihood that regulators detect violations via social-media signals. By demonstrating that de-anonymization diminishes stakeholders’ willingness to disclose adverse information, the paper advances theory on media-based governance by identifying anonymity as a critical institutional precondition for effective external monitoring. From a policy perspective, the findings point to a nontrivial trade-off. While real-name requirements can help suppress abusive or false online content, they may also erode a valuable, low-cost source of external oversight over firm behavior. Policymakers should therefore consider complementary designs—such as protected or third-party anonymous reporting channels, strengthened formal enforcement capacities, or calibrated identity-verification regimes—that mitigate online harm without unduly impairing whistleblowing and crowd-sourced monitoring. Limitations include the single-country setting, focus on greenwashing as one form of misconduct, and reliance on available social-media and penalty records; future work could test generalizability across institutional contexts, examine other misconduct domains (e.g., financial misreporting), and combine experimental or survey methods to unpack the psychological versus institutional drivers of disclosure under anonymity constraints. Declarations Author Contribution Chao Yuan is responsible for conceptualization, methodology, emprical tests, formal analysis, investigation and original draft writing.Junming Li is responsible for methodology, data curation, empirical tests, formal anslysis and investigaiton.Youze Zhang is responsible for conceptualization, methodology and original draft writing.Weixing Cai is responsible for conceptualization and original draft writing. Data Availability Data will be made available on request by email: [email protected] References Ang JS, Hsu C, Tang D, Wu C (2021) The role of social media in corporate governance. J Acc Econ 96(2):1–32 Antweiler W, Frank MZ (2004) Is all that talk just noise? The information content of internet stock message boards. J Financ 59(3):1259–1294 Baloria VP, Heese J (2018) The effects of media slant on firm behavior. J Financ Econ 129(1):184–202 Becker GS (1968) Crime and punishment: An economic approach. 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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-7649177","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":539738364,"identity":"a5219417-bd18-43ea-8fae-edac6c4f88d0","order_by":0,"name":"Chao Yuan","email":"","orcid":"","institution":"Guangdong University of Finance \u0026 Economics","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Yuan","suffix":""},{"id":539738365,"identity":"fbcf9374-ddc4-4f18-b73f-195b522d7681","order_by":1,"name":"Junming Li","email":"","orcid":"","institution":"Guangdong University of Finance \u0026 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1","display":"","copyAsset":false,"role":"figure","size":69545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThree real-name verification modalities in China\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7649177/v1/cf2d987703e603262ea8809c.png"},{"id":95181740,"identity":"7ed7a16b-057f-427f-b850-9c258b3e9b78","added_by":"auto","created_at":"2025-11-05 08:32:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTest for parallel trends\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7649177/v1/63ffcdcf9417de5b5c8a1ed3.png"},{"id":95228070,"identity":"d3d64fe5-dbaf-4d6a-b304-28c270261833","added_by":"auto","created_at":"2025-11-05 16:33:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38555,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlacebo test results\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7649177/v1/5b859bdbd4c4b37ec22f69a0.png"},{"id":95312640,"identity":"5a93658d-f297-4346-b2dd-7e18ca42a520","added_by":"auto","created_at":"2025-11-06 15:49:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1588045,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7649177/v1/ddbacef7-b578-441a-8347-1d9d117b0f75.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Silenced? Does Social-Media Real-Name Registration Policy Facilitate Firm Misconduct? Evidence from Greenwashing in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eExternal stakeholders process and disseminate firm-related information on social media, thereby influencing firm decision making. Consequently, social platforms have become an important channel through which external stakeholders monitor firm misconduct such as greenwashing (Dyck et al., 2008; Lyon \u0026amp; Montgomery, 2013). However, if external stakeholders are deterred from speaking out about firm malfeasance on social media, can the platform\u0026rsquo;s monitoring function be maintained? The answer depends critically on whether users can post anonymously (Lapidot-Lefler \u0026amp; Barak, 2012). In other words, anonymity on social media can foster moral courage among users and enable social platforms to perform an informal institution monitoring role (Pan et al., 2023). If, however, a policy preserves anonymity vis-\u0026agrave;-vis fellow users but removes anonymity vis-\u0026agrave;-vis platform providers and public-security authorities, will users remain equally willing to voice criticisms?\u003c/p\u003e\n\u003cp\u003eEarly social media environments were characterized by anonymity, which allowed users to share information and views freely without fear of retaliation from firms, peers, or regulators; this environment fostered the willingness and capacity of users to monitor firm misconducts (Christopherson, 2007; Jardine, 2018). At the same time, anonymity enabled extreme opinions, fake news, and rumors (Eastwick \u0026amp; Gardner, 2009), prompting policymakers and scholars to advocate for real-name registration policy to curb such harms. China implemented a social-media real-name registration policy in 2015, requiring users to provide officially authenticated personal information to platform providers; these records are directly or indirectly linked with public-security databases (Gao et al., 2022; Huang et al., 2023). This policy change thus provides a useful quasi-natural experiment to examine how de-anonymization affects social media\u0026rsquo;s external monitoring role.\u003c/p\u003e\n\u003cp\u003eUsing China\u0026rsquo;s 2015 real-name registration policy and corporate greenwashing as the empirical setting, this paper investigates whether and how real-name requirements weaken social media monitoring of firm misconduct. The 2015 policy offers three empirical advantages. First, numerous studies document that social media is a salient venue for stakeholder monitoring of firm misconducts in China (Zhou et al., 2021; Liu et al., 2022; Long et al., 2025), so it is an important informational institution for corporate external governance. Second, the policy\u0026rsquo;s universal requirement that users complete identity verification supports the policy\u0026rsquo;s exogeneity for empirical identification. Third, Chinese internet users exhibit a pronounced tendency to avoid topics that may invite retaliation from others, a tendency that became more salient after the social media real-name registration policy and substantially compressed social-media oversight capacity (Chen et al., 2023). Greenwashing, a classical decoupling form of misconduct, has been the focus of extensive international research and is therefore an appropriate research object (Lyon \u0026amp; Montgomery, 2013; Yang et al., 2020; Pizzetti et al., 2021; Long et al., 2025; Jiang et al., 2025).\u003c/p\u003e\n\u003cp\u003eMethodologically, we implement a difference-in-differences (DID) design with the 2015 policy as the intervention. Leveraging \u003cem\u003eGuba\u0026nbsp;\u003c/em\u003e\u0026mdash; the largest online forum used by Chinese stakeholders to disseminate firm information and opinions \u0026mdash; we identify firms that were heavily discussed by external stakeholders prior to the policy. Firms that attracted greater stakeholder attention face larger reputational cost, regulatory pressure, and market pressure from misconduct (Dyck et al., 2008; Baloria \u0026amp; Heese, 2018; Heese \u0026amp; Pacelli, 2024). After the social media real-name registration policy, the cost of disclosure and denunciation for informed stakeholders rises substantially, reducing their willingness to act as monitors. Hence, firms that were highly discussed on social media before the policy should experience the largest decline in social-media pressure and therefore the largest increase in opportunities to greenwashing. We define the treatment group as firms whose ratio of \u003cem\u003eGuba\u0026nbsp;\u003c/em\u003eposts to total assets in 2014 exceeds the sample median. To ensure robustness, we conduct a serious of robustness checks \u0026mdash; including parallel-trend tests, placebo tests, alternative treatment group constructions, and an intensity DID specification \u0026mdash; all of which yield consistent results.\u003c/p\u003e\n\u003cp\u003eWe further investigate the mechanisms through which the social media real-name registration policy affects firm behavior. Theoretically, social media constrains firm misconduct via three principal channels. Firstly, the reputational channel: by publicizing evidence of firm misconducts, social media inflict reputational costs on firms and their managers, thereby reducing the expected net benefit of engaging in greenwashing (Dyck et al., 2008). Secondly, the market pressure channel: stakeholders who obtain adverse information through social media may respond by trading on that information (for example, selling shares), exerting immediate valuation pressure that disciplines greenwashing (Joe et al., 2009; Tetlock, 2007). Thirdly, the regulatory pressure channel: information disseminated on social media can draw the attention of regulators, increasing the probability of detection and sanction and thus raising the expected enforcement cost of greenwashing (Lyon \u0026amp; Montgomery, 2013; Heese \u0026amp; Pacelli, 2024). The implementation of a real-name registration policy undermines these channels by raising the personal and legal costs borne by stakeholders who might otherwise disclose or denounce firm malfeasance. Our empirical analysis corroborates this mechanism-based prediction. After the 2015 social media real name registration policy, we document a statistically and economically significant weakening of all three channels, thereby facilitating an increase in corporate greenwashing.\u003c/p\u003e\n\u003cp\u003eThis paper makes three contributions. Firstly, our research extends the growing literature on social media as an external monitor by highlighting a previously underexplored institutional condition: user anonymity. Prior research has documented the effectiveness of social media as a social-monitoring institution and has articulated conditions under which that effectiveness obtains (Heese \u0026amp; Pacelli, 2024). We enrich this literature by demonstrating that the presence or removal of user anonymity\u0026mdash;the distinction between anonymous and real-name registration regimes\u0026mdash;materially conditions social media\u0026rsquo;s capacity to discipline firms. Secondly, our study contributes to the literature on external monitoring and firm misconduct. Social media has been recognized as an important external informal institution affecting firm misconduct (Li et al., 2022). This paper shows that de-anonymization reduces stakeholders\u0026rsquo; willingness to disclose and denounce firms\u0026mdash;because potential whistleblowers face greater hidden costs under real-name regimes\u0026mdash;thereby weakening social media\u0026rsquo;s external oversight and increasing firms\u0026rsquo; propensity to engage in misconduct. This finding offers a novel perspective on cross-regional heterogeneity in the effectiveness of media-based monitoring. Thirdly, this paper provides new empirical evidence on economic consequences of social media real-name registration policies. The debate over removing anonymity of social media has focused on a range of social and economic trade-offs, but empirical evidence remains mixed. By documenting how China\u0026rsquo;s 2015 social media real-name registration policy affected corporate greenwashing, we reveal a concrete economic consequence of de-anonymization and supply empirical guidance for ongoing policy debates about the costs and benefits of real-name schemes.\u003c/p\u003e\n\u003cp\u003eThe remainder of the paper is organized as follows. Section 2 reviews the establishment of China\u0026rsquo;s social media real-name policy and describes user verification modalities. Section 3 develops the conceptual framework and hypotheses. Section 4 presents the data and empirical design. Section 5 reports the main results. Section 6 examines the mechanisms, and Section 7 concludes.\u003c/p\u003e"},{"header":"2. Institutional background","content":"\u003cp\u003eFrom 2012 onward, a phased real-name registration policy for social media was introduced by the Chinese government. This regulatory intervention serves as an exogenous quasi-experimental setting for studying the causal impact of social media real-name registration on corporate greenwashing. The following content of this section outlines (a) the phased establishment of the social media real-name registration policy and (b) the modalities of user real-name verification.\u003c/p\u003e\n\u003ch2\u003e2.1 The phased establishment of the social media real-name registration policy\u003c/h2\u003e\n\u003cp\u003eThe implementation of social media real-name registration policy consists of three steps in China. The first step was from the Standing Committee of the National People\u0026apos;s Congress (SCNPC) in 28th December 2012, which announced that the internet services providers were required to collect clients\u0026rsquo; real identity information by user agreements. This announcement represents the beginning of the internet real-name registration in China. However, the first policy did not set unified rules for internet services provides. Then in 16th July 2013, more unified rules were implemented by the Ministry of Industry and Information Technology (MIIT) which stipulates mobile phone users should link their phone number with their national identification number. The Ministry of Public Security manages users\u0026rsquo; ID numbers and ensures users\u0026rsquo; personal information safety.\u003c/p\u003e\n\u003cp\u003eThe final step was implemented by the Cyberspace Administration of China in 1st March 2015. The internet user accounts real-name registration was completed since then. This policy required internet users to provide their personal information including real names to Internet information service providers when registering. However, internet users\u0026rsquo; account nicknames and online names are not restricted. This setting implies that the principle of \u0026quot;real-name registration at the back end and voluntary use at the front end\u0026quot;, which provides protection for users. But the Ministry of Public Security can recall and trace users\u0026rsquo; personal information to deal with crime events, which poses pressure to internet users.\u003c/p\u003e\n\u003ch2\u003e2.2 The modalities of user real-name verification\u003c/h2\u003e\n\u003cp\u003eSince 2012, users accessing social media in China can be authenticated via three principal real-name verification modalities, any of which permit rapid identification and tracking by Public Security Bureau (PSB). The first modality is ID-card verification (see Figure 1): users submit their name and national ID number to the platform, which queries the PSB database; identity matching is completed by the PSB\u0026mdash;often complemented by biometric checks such as face recognition\u0026mdash;while privacy safeguards are applied as required by law. The second modality is mobile-phone verification (see Figure 1), which relies on the triple combination of name, mobile number, and ID number. In practice, some authentication service providers obtain identity and mobile data from carriers and leverage SMS/telecom verification to confirm users\u0026rsquo; identities. Under the regulatory requirements issued on July 16, 2013, mobile carriers\u0026rsquo; subscriber records have been linked with PSB identity system, effectively enabling mobile carriers to act as intermediaries for identity matching. The third modality is bank-card verification (see Figure 1), which incorporates banking data (procured, in some cases, from China UnionPay) to perform stricter identity checks\u0026mdash;a procedure commonly used by service providers operating in finance-related sectors. As with mobile data, banking identity records are already reconciled with PSB files and fall under public security management, enabling authorities to rapidly retrieve user information and take actions where online criminality is suspected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Figure 1 here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHowever, ambiguous triggering conditions for PSB arouse the concern of misfeasance, which undermines the enthusiasm of stakeholders with advanced information to share the information. For example, on August 27, 2025, an employee of listed company Shandong Longertek Technology Co., Ltd. disclosed the company\u0026rsquo;s potential regulatory violations to other investors before the stock market closed, helping them to reduce losses. Unexpectedly, with the real-name registration institution, this employee was investigated in 7 hours by the public security authorities in terms of leakage[1]. This event highlighted the inefficiency in whistleblowers protection in China.\u003c/p\u003e\n\u003cp\u003e[1] Resource: website\u0026nbsp;\u003c/p\u003e"},{"header":"3. Conceptual Framework and Hypothesis Development","content":"\u003ch2\u003e3.1 External monitoring role of social media\u003c/h2\u003e\n\u003cp\u003eSocial media, as a salient informal institution, provides an online platform in which stakeholders\u0026mdash;investors, employees, and customers\u0026mdash;can share firm-related information and opinions. Collective-intelligence theory posits that when numerous informed individuals, motivated to disclose their own knowledge, contribute content to social platforms, these platforms aggregate dispersed signals and value-relevant information that can substantively shape corporate governance (Ang et al., 2021). Existing research show that such user-generated information often reduces information asymmetries between firms and investors and improves investors\u0026rsquo; forecasts of firm fundamentals (Chen et al., 2014; Tang, 2018; Yoon et al., 2018). The validity of collective intelligence is therefore a key prerequisite for social media to function as an effective external monitor.\u003c/p\u003e\n\u003cp\u003eThe external monitoring function of social media operates by imposing additional external costs on firms (Heese \u0026amp; Pacelli, 2024). When firms contemplate engaging in misconduct, these external costs\u0026mdash;reputational, market, and regulatory \u0026mdash;enter the firm\u0026rsquo;s cost-benefit calculus. If the expected external cost exceeds the anticipated returns from misconduct, firms will refrain from such behavior (Becker, 1968).\u003c/p\u003e\n\u003cp\u003eThe reputation cost channel works through public condemnation on social platforms: exposure of firm misconduct on social media damages a firm\u0026rsquo;s reputation and can generate economically meaningful consequences for both executives and shareholders (Dyck et al., 2008). For executives, reputational loss reduces future employment prospects and compensation (Fama \u0026amp; Jensen, 1983); for firms, lower reputation raises financing costs and undermines access to external capital, ultimately impairing profitability (Gomes, 2000). Thus, both firms and managers have incentives to avoid leaving a negative public impression.\u003c/p\u003e\n\u003cp\u003eThe market pressure channel operates via investor behavior: negative discourse on social media exerts downward pressure on stock prices (Tetlock, 2007). Once stakeholders learn of adverse information, they may vocalize grievances online and, if discussion intensity is high, retail investors may sell shares (Das et al., 2005). Because equity value is a core performance metric for executives, investor-driven selloffs create managerial incentives to improve governance and reduce misconduct in order to protect minority shareholders (Gorton, 2014). In sum, by surfacing misconduct and mobilizing stakeholders, social media performs an external governance function that can deter firm misconduct (Joe et al., 2009; Baloria \u0026amp; Heese, 2018).\u003c/p\u003e\n\u003cp\u003eThe regulatory pressure channel increases the probability that regulators will scrutinize and sanction errant firms. Intense social-media attention to a firm\u0026rsquo;s misconduct tends to attract regulatory scrutiny and raises the likelihood of enforcement (Lyon \u0026amp; Montgomery, 2013). Regulators and enforcement agencies increasingly incorporate information disseminated on social platforms into their monitoring toolkits, thereby enhancing oversight capacity and further elevating the cost of noncompliance (Heese \u0026amp; Pacelli, 2024).\u003c/p\u003e\n\u003cp\u003eTherefore, both firms and financial markets are sensitive to social media as it processes and disseminates various firm information to the public and regulators (Baloria and Heese, 2018). Gradually, social media becomes an important external monitor which actively reduces firm misconduct (Lyon and Montgomery, 2013).\u003c/p\u003e\n\u003ch2\u003e3.2 Social media anonymity and corporate greenwashing\u003c/h2\u003e\n\u003cp\u003eMassive literature documents that firms frequently present themselves as more environmentally responsible than they actually are through symbolic gestures and deceptive disclosure\u0026mdash;commonly referred to as \u0026ldquo;greenwashing\u0026rdquo;(Laufer, 2003). Conceptually, greenwashing is a form of decoupling and therefore a type of misconduct: managers exploit informational asymmetries vis-\u0026agrave;-vis investors to mislead stakeholders and conceal their inaction on ESG dimensions (e.g., Lyon \u0026amp; Montgomery, 2013). Absent credible external sanctions, firms have incentives to continue polishing their environmental image despite substantive inaction (Lyon \u0026amp; Maxwell, 2011). As Lyon and Montgomery (2013) note, if firms face little or no external scrutiny, they have incentives to greenwash irrespective of their true environmental performance.\u003c/p\u003e\n\u003cp\u003eBy the logic developed in Section 3.1, attention and discussion on social media should, in principle, curb greenwashing by increasing the expected external costs of such behavior. Campbell (2007), however, cautions that social-media scrutiny will only be effective when stakeholders are sufficiently strong and organized to countervail corporate power. The effectiveness of this monitoring therefore rests on whether informed users are willing and able to disclose salient information on public platforms.\u003c/p\u003e\n\u003cp\u003eCampbell\u0026rsquo;s (2007) concern was thereby realized with the introduction of China\u0026rsquo;s 2015 social media real-name registration policy. A strategic interaction between informed stakeholders and firms was created: to avoid identification and tracking, the willingness of many informed stakeholders to share information on social media was curtailed. When informed stakeholders publicly denounce a firm\u0026rsquo;s greenwashing behavior on social media, such denunciations are expected to generate reputational, regulatory, and market pressures for the firm. Upon detection of online criticism, firms have been observed to\u0026mdash;prior to escalation\u0026mdash;deploy ties to regulators, government agencies, or public-security organs to identify and contact complainants, and inducements or legal threats (e.g., cease-and-desist letters) have been used to secure retractions or deletions of hostile posts. Anticipating such reprisals, informed stakeholders are induced to weigh the benefits of public exposure (e.g., visibility, image-building, intrinsic satisfaction) against the heightened personal risks of posting (Antweiler \u0026amp; Frank, 2004; Chen \u0026amp; Hwang, 2022; Toubia \u0026amp; Stephen, 2013). Given the substantial power asymmetry between ordinary netizens and firms in China, and the removing in anonymity resulting from real-name registration, the expected net payoff from disclosure is reduced and the volume of informative posts is diminished (Huang et al., 2023). The resulting contraction in credible disclosures weakens the reputational, regulatory, and market channels through which social media\u0026rsquo;s external pressure is exerted, thereby attenuating its monitoring efficacy and producing an increased propensity for firms to engage in greenwashing.\u003c/p\u003e\n\u003cp\u003eThus, we propose our research hypothesis (H1):\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e: Following implementation of the social media real-name registration policy, firms\u0026rsquo; propensity to engage in greenwashing is increased.\u003c/p\u003e"},{"header":"4. Data and methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Sample selection and data source\u003c/h2\u003e\u003cp\u003eOur empirical analysis relies on firms listed in China\u0026rsquo;s A-share market over 2010\u0026ndash;2019, with firm-level data obtained from the Wind and CSMAR databases. We exclude firms under \u0026ldquo;special treatment\u0026rdquo; (ST), financial firms, and firms listed for fewer than two years prior to 2015, when the social media real-name registration policy was implemented. Guba platform data are drawn from Chinese Research Data Service (CNRDS) database, while market and accounting variables are from Wind. The final panel comprises 7,045 firm-year observations across 865 firms. All continuous variables are winsorized at the 1% and 99% levels to mitigate the effects of extreme values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Research design\u003c/h2\u003e\u003cp\u003eWe use the following difference-in-difference (DID) model to examine the relation between the social media real-name registration policy and corporate greenwashing:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{cc}{\\:Gws\\:}_{i,j,t}\u0026amp;\\:={\\beta\\:}_{0}+{\\beta\\:}_{1}{\\:Post\\:}_{t}\\times\\:{\\:Treat\\:}_{i}+{\\beta\\:}_{k}{\\:Control\\:}_{i,t}+{\\delta\\:}_{i}+{\\tau\\:}_{j,t}+{\\epsilon\\:}_{i,j,t}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e represent firm, industry, and year, respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:Gws\\:}_{i,j,t}\\)\u003c/span\u003e\u003c/span\u003e represents the degree of greenwashing. Followed by the research of Zhang (2022), \u003cem\u003eGWS\u003c/em\u003e is derived from the normalized difference between the ESG ratings provided by Bloomberg and HuaZheng. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:Post\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e is a time dummy variable. Specifically, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:Post\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e equals 0 for the years preceding the policy's implementation (2010\u0026ndash;2014), and equals 1 for the year of the policy's introduction and the subsequent years (2015\u0026ndash;2019). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:Treat\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a dummy variable for the treatment group, which is identified based on the ratio of the number of Guba posts to total assets in the year preceding the event (i.e., 2014), where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:Treat\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e equals 1 if above the mdian, and 0 otherwise. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Control\\:}_{i,t}\\)\u003c/span\u003e\u003c/span\u003e denotes the set of firm-specific covariates. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\delta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{j,t}\\)\u003c/span\u003e\u003c/span\u003e capture firm and industry-year fixed effects, and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\epsilon\\:}_{i,j,t}\\)\u003c/span\u003e\u003c/span\u003e is the disturbance term.\u003c/p\u003e\u003cp\u003eWe include a set of firm characteristics as control variables that may affect the extent of corporate greenwashing. Specifically, firm size (\u003cem\u003eSIZE\u003c/em\u003e) is measured by the natural logarithm of total assets at the end of year t, and firm age (\u003cem\u003eAGE\u003c/em\u003e) is measured by the number of years since the firm\u0026rsquo;s IPO. In addition, we control several financial indicators, including leverage (\u003cem\u003eLEV\u003c/em\u003e, defined as total liabilities over total assets), return on equity (\u003cem\u003eROE\u003c/em\u003e), operating cash flow ratio (\u003cem\u003eCFLOW\u003c/em\u003e), revenue growth (\u003cem\u003eGROWTH\u003c/em\u003e), and the ratio of net fixed assets to total assets (\u003cem\u003eFIXED\u003c/em\u003e). We also incorporate corporate governance measures, including the proportion of independent directors (\u003cem\u003eINDEP\u003c/em\u003e), the ownership share of the largest shareholder (\u003cem\u003eTOP\u003c/em\u003e), and board size (\u003cem\u003eBOARD\u003c/em\u003e). Definitions and measurement details of all variables are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eVariables definitions\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\u003cp\u003eVariable type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable symbol\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDependent variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGws\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe difference between a firm\u0026rsquo;s normalized position is relative to peers in ESG disclosure (Huazheng E score) and in ESG performance (Bloomberg E score).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIndependent variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTreat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe number of Guba message board in 2014/firm total assets (ten thousand yuan); equals 1 if above the median, and 0 otherwise.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAfter the promulgation of the Internet User Account Name Management Regulations by the Cyberspace Administration of China (after March 2015), the value is 1, otherwise it is 0.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePosts_Asset\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe number of Guba message boards in 2014/firm total asset (ten thousand yuan)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eControl variable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ethe logarithm of total asset\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of years since the firm\u0026rsquo;s IPO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLev\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eliability/total asset\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRoe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eprofit/net asset\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCflow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNet cash flows from operating activities/total asset\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrowth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe growth rate of revenue\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNet fixed asset / total asset\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe number of independent directors / The total number of directors\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=\"left\" colname=\"c3\"\u003e\u003cp\u003ethe shareholding ratio of the largest shareholder\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBoard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ethe logarithm of number of directors\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Descriptive statistics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports summary statistics for the main sample of firm\u0026ndash;year observations from 2010 to 2019. The variable \u003cem\u003eGws\u003c/em\u003e which captures the extent of greenwashing, has a mean of \u0026minus;\u0026thinsp;0.003 and a variance of 1.208, indicating substantial cross-firm heterogeneity. The averages of the Treat and the Post are 0.478 and 0.527, respectively, suggesting a relatively balanced distribution of observations before and after the policy implementation. Finally, the summarized statistics of control variables are consistent with prior studies.\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\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=\"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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep25\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep50\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep75\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\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\u003eGws\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.576\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.738\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.449\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTreat\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePost\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePosts_Asset\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e23.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e27.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e18.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e26.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoe\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.491\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCflow\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.142\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.246\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.401\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFixed\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTop1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBoard\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.708\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e5.1 main results\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the estimation results of Eq.\u0026nbsp;(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), which examines the relation between the social media real-name registration policy and corporate greenwashing. Column (1) includes only the interaction term (\u003cem\u003eTreat\u0026times;Post\u003c/em\u003e) and the dependent variable (\u003cem\u003eGWS\u003c/em\u003e), controlling for firm and year fixed effects. Columns (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) sequentially introduce firm-level control variables and industry-by-year fixed effects. Across all specifications, the coefficient on \u003cem\u003eTreat\u0026times;Post\u003c/em\u003e is consistently positive and statistically significant. In column (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), the coefficient is 0.166 and significant at the 5% level. The magnitude of the estimate indicates that the implementation of the social media real-name registration policy is associated with an economically meaningful increase of 0.166 in corporate greenwashing. The difference between treatment and control firms suggests that firms in the treatment group are more inclined to overstate their environmental contributions or obscure their lack of action when the monitoring role of social media is weakened by the social media real-name registration requirement. Coefficients on the control variables, where significant, are in line with prior research.\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\u003eBaseline results: real-name registration policy and corporate greenwashing\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGws\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(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTreat\u0026times;Post\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.178***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.133**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.166**\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(0.047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\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\u003e0.189***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.206***\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(0.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\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\u003e-0.017*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.057\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(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.147)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\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\u003e-0.641***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.646***\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(0.190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.188)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoe\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\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.029\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(0.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.154)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCflow\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\u003e-0.633**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.623**\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(0.281)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.284)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrowth\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\u003e-0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.030\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(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFixed\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\u003e-0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.256\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(0.248)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.248)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndep\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\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.320\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(0.226)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.235)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTop1\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\u003e-0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.227\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(0.299)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.318)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBoard\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\u003e-0.413***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.368**\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(0.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.150)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.048***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.878**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.364*\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(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.248)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.305)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u0026times;Year FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAdjusted R-squared\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.480\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: The results reported are from an OLS estimation. The models include firm as well as industry-by-year fixed effects. Standard errors are clustered at firm level to capture variation in standard errors across firms, which are displayed in parentheses below the coefficient estimates. *, **, *** represent significance at the 10%, 5%, and 1% level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Robustness test\u003c/h2\u003e\u003cp\u003eWe conduct a series of additional robustness tests to enhance the reliance of our empirical results and conclusions.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e5.2.1 Parallel trend test\u003c/h2\u003e\u003cp\u003eFollowing Fang et al. (2023), we test whether the greenwashing behavior of treatment and control firms satisfies the parallel trend assumption prior to the 2015 implementation of the social media real-name registration policy, and we further examine its dynamic effects thereafter. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e plots the parallel trend tests. The estimates reveal a stable and statistically indistinguishable pattern between the two groups before the policy\u0026rsquo;s implementation, indicating that treatment and control firms followed similar pre-trends in greenwashing. However, a significant divergence emerges after 2015, with treatment firms exhibiting a marked increase in greenwashing relative to control firms. These findings reinforce our conclusion that the implementation of the social media real-name registration policy attenuates the monitoring role of social media and thereby facilitates firms\u0026rsquo; engagement in greenwashing activities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e5.2.2 Intensity DID\u003c/h2\u003e\u003cp\u003eFollowing Duchin et al. (2010), who employ an intensity difference-in-differences (DID) framework, we replace the binary treatment variable \u003cem\u003eTreat\u003c/em\u003e with \u003cem\u003ePosts_Asset\u003c/em\u003e, a continuous variable defined as the ratio of the number of Guba posts to total assets. The intensity DID extends the standard DID by accommodating treatments that vary in strength or \u0026ldquo;dose,\u0026rdquo; rather than assuming a purely binary intervention. In our context, the effect of the social media real-name registration policy is not strictly dichotomous; therefore, employing an intensity DID allows the analysis to more precisely capture the gradient nature of the policy\u0026rsquo;s impact, rather than restricting inference to a single average treatment effect. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the results from the intensity DID specification. Column (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) presents the coefficient estimate on the interaction term \u003cem\u003ePosts_Asset\u0026times; Post\u003c/em\u003e controlling for firm and year fixed effects. Columns (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) progressively incorporate firm-level controls and industry-by-year fixed effects. Across all specifications, the coefficients on \u003cem\u003ePosts_Asset\u0026times;Post\u003c/em\u003e remain positive and statistically significant at the 1% level, consistent with the baseline results reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These findings corroborate our earlier evidence, confirming that the implementation of the real-name registration policy in 2015 significantly increased firms\u0026rsquo; propensity to engage in greenwashing activities.\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\u003eRobustness test: Intensity DID\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGws\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(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePosts_Asset\u0026times;Post\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.079***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.065***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.075***\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(0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\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\u003e0.188***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.205***\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(0.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\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\u003e-0.018*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.052\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(0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.146)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\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\u003e-0.655***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.662***\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(0.190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.186)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoe\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\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\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(0.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.154)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCflow\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\u003e-0.634**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.626**\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(0.281)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.284)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrowth\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\u003e-0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.032\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(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFixed\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\u003e-0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.251\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(0.247)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.246)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndep\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\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.331\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(0.226)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.235)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTop1\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\u003e-0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.228\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(0.299)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.316)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBoard\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\u003e-0.411***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.368**\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(0.149)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.149)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.051***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.843**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.272*\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(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.248)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.296)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u0026times;Year FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAdjusted R-squared\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.480\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: The independent variable is \u003cem\u003ePosts_Asset\u0026times;Post\u003c/em\u003e where \u003cem\u003ePostss_Asset\u003c/em\u003e is measured by the ratio of the number of Guba posts to total assets. Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e5.2.3 Alternative construction of treat and control firms\u003c/h2\u003e\u003cp\u003eIn the baseline regression, the 2014 posts-to-total-assets ratio on Guba is used as the grouping variable. Given the volatility of annual post counts and the potential idiosyncrasies of single-year grouping, \u003cem\u003eTreat2\u003c/em\u003e is defined as the average posts-to-assets ratio over 2010\u0026ndash;2014, with observations above the sample median coded as \u003cem\u003eTreat2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 and all others as 0. The difference-in-differences (DID) specification is then estimated using the interaction term \u003cem\u003eTreat2\u0026times;Post\u003c/em\u003e. The results, reported in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, indicate that following the implementation of the social media real-name registration policy, firms experiencing more intensive discussion on Guba exhibited significantly higher levels of greenwashing. These findings are consistent with the baseline regression results, further supporting the robustness of our main conclusions.\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\u003eRobustness test: alternative treat and control firms\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\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGws\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(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTreat_mean\u0026times;Post\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.169***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.118**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.141**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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(0.046)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\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\u003e0.181***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.196***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\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\u003e-0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.147)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\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\u003e-0.645***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.650***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.190)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.186)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoe\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\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCflow\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\u003e-0.655**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.649**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.283)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.286)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrowth\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\u003e-0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFixed\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\u003e-0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.245)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.243)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndep\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\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.226)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.235)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTop1\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\u003e-0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.318)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBoard\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\u003e-0.412***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.368**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.150)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.049***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.711**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.102*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"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(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.262)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.299)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u0026times;Year FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAdjusted R-squared\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: this table presents results with firms divided by Treat_mean. Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e5.2.4 Placebo test\u003c/h2\u003e\u003cp\u003eGenerally, corporate greenwashing behavior may be influenced by a variety of policy factors (Zhang, 2023), which could potentially confound the baseline empirical results. To address this concern, a placebo (randomization) test is conducted. Specifically, Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is repeatedly re-estimated using a random sampling procedure: the DID regression is run 1000 times on randomly drawn subsamples, and the t-statistics of the estimated coefficients are recorded. The resulting distribution of t-statistics, presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, is approximately normal with a mean of zero and largely centered around zero. These placebo test results indicate that random policy shocks do not generate significant effects, supporting the interpretation that the observed increase in corporate greenwashing is attributable to the implementation of the social media real-name registration policy rather than to other idiosyncratic policy factors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e5.2.5 Alternative dependent variables\u003c/h2\u003e\u003cp\u003eFollowing Hu et al. (2023), we use an alternative method to measure corporate greenwashing. Firstly, we extract a set of core keywords\u0026mdash;such as \u0026ldquo;environment\u0026rdquo;, \u0026ldquo;ecology\u0026rdquo;, \u0026ldquo;sustainability\u0026rdquo;, \u0026ldquo;carbon\u0026rdquo;, \u0026ldquo;green\u0026rdquo;, \u0026ldquo;clean\u0026rdquo;, \u0026ldquo;energy\u0026rdquo; and \u0026ldquo;governance\u0026rdquo;\u0026mdash;from the Management Discussion and Analysis (MD\u0026amp;A) sections of annual reports. Using the jieba segmentation tool in Python, we expanded the list to 135 candidate terms. After manual screening, 100 terms that reliably capture firms\u0026rsquo; green and environmental disclosure are retained (see Appendix A for the detailed frequency distribution). Based on these terms, we construct two dummy variables: Oral and Actual. Oral is defined as the ratio of the number of green-related keywords disclosed by a firm each year to its total assets, thereby adjusting for firm size. It equals 1 if this ratio exceeds the industry-year median and 0 otherwise. Actual takes the value of 1 if a firm is subject to an environmental penalty in that year, and 0 otherwise, with data obtained from the CCER database. Finally, we define the interaction term \u003cem\u003eGws2\u003c/em\u003e as the product of Oral and Actual. As a robustness check, \u003cem\u003eGws2\u003c/em\u003e serves as an alternative dependent variable to the baseline measure \u003cem\u003eGws\u003c/em\u003e, ensuring that our findings are not driven by a particular specification of greenwashing. Empirical results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. All coefficients are consistent with findings in baseline results.\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\u003eRobustness test: alternative independent variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGws2\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(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePost\u0026times;Post\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.103***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.042***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.045***\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(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSize\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\u003e-0.063***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.064***\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(0.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.013)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\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\u003e0.022***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.020\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(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLev\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\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.024\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(0.050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.050)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRoe\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\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\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(0.050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.052)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCflow\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\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.011\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(0.065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.066)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrowth\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\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\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(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFixed\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\u003e-0.101*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.126**\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(0.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.058)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndep\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\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001\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(0.061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTop1\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\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030\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(0.071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.069)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBoard\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\u003e-0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.026\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(0.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.057***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.352***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.895***\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(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.500)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u0026times;Year FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAdjusted R-squared\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: The dependent variable in all models is \u003cem\u003eGws2\u003c/em\u003e. Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"6. Mechanism analysis","content":"\u003cp\u003eAs discussed in Chap.\u0026nbsp;3, social media generally constrains firm greenwashing and other forms of misconduct through three primary channels: reputational costs, market pressure, and regulation pressure. The implementation of the social media real-name registration policy, however, is expected to attenuate the effectiveness of these channels. Accordingly, we conduct further empirical analyses to examine the impact of the policy on each of these mechanisms.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Mechanism analysis: reputation cost\u003c/h2\u003e\u003cp\u003eThis study examines whether the social media real-name registration policy promotes corporate greenwashing by attenuating the reputational cost channel, using a grouped regression approach. Firms are grouped based on two criteria: analyst coverage and the proportion of negative media reports. Specifically, analyst coverage is measured by the natural logarithm of the number of securities analysts following a listed firm, while the negative media report ratio is defined as the number of negative reports divided by the total number of reports (negative plus positive) for a given firm (data source: Junming, to be supplemented). Conceptually, similar to social media monitoring, firms with low analyst coverage or a low proportion of negative reports are subject to weaker external informal monitoring, implying lower expected reputational costs associated with engaging in misconduct behaviors such greenwashing. Once the monitoring role of social media is weakened by the social media real-name registration policy, these firms are expected to be more likely to engage in greenwashing.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents empirical results. Panel A reports the results based on analyst coverage. In the low analyst coverage group (Column 2), the coefficient on \u003cem\u003eTreat\u0026times;Post\u003c/em\u003e is 0.240 and statistically significant at the 5% level. By contrast, in the high analyst coverage group (Column 1), the coefficient is -0.006 and not significant. The difference between the two coefficients is confirmed by the p-value from an equality test. These results suggest that firms with lower analyst coverage increase their greenwashing activities following the implementation of the social media real-name registration policy due to insufficient external monitoring. Similarly, Panel B reports results based on the proportion of negative media reports. In the low negative-report group, the coefficient on \u003cem\u003eTreat\u0026times;Post\u003c/em\u003e is 0.301 and significant at the 1% level, whereas in the high negative-report group, the coefficient is 0.127 and not significant. The coefficient difference is validated through the p-value for equality testing. These findings indicate that firms with weaker external monitoring, as proxied by lower negative media coverage, increase their greenwashing activities after social media monitoring is weakened by the social media real-name registration policy. Overall, the results in Panels A and B confirm that the social media real-name registration policy stimulates corporate greenwashing behavior through the reputational cost channel.\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\u003eMechanism analysis: reputational cost\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\u003cp\u003ePanel A: Financial analyst attention\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGws\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh financial analyst attention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow financial analyst attention\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTreat\u0026times;Post\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.240**\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(0.115)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.095)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep-value for equality test\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.030**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u0026times;Year FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAdjusted R-squared\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePanel B: N\u003cb\u003eegative reports\u003c/b\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGws\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh Negative reports\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow Negative reports\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTreat\u0026times;Post\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.301***\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(0.107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.101)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep-value for equality test\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.080*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u0026times;Year FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,329\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAdjusted R-squared\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: The analyst data in Panel A derived from the CSMAR database. The media coverage data in Panel B comes from the CNRDS database. The financial analyst attention is measured by the natural logarithm of the number of securities analysts following a listed firm, while the negative media report ratio is defined as the number of negative reports divided by the total number of reports. Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Mechanism analysis: market pressure\u003c/h2\u003e\u003cp\u003eThe second channel examined is the market pressure mechanism. Following research of El Ghoul et al. (2023) and Xu et al. (2024), firms are grouped based on two indicators: the proportion of institutional shareholding ratio and Tobin\u0026rsquo;s Q. Conceptually, firms with higher institutional shareholding ratio face stronger market power from institutional investors, implying greater potential stock price declines if engaging in greenwashing. Similarly, a higher Tobin\u0026rsquo;s Q reflects elevated firm valuation, which also increases the potential market value loss associated with greenwashing. However, once social media monitoring is weakened by the social media real-name registration policy, the market pressure exerted via social media on these firms is reduced, thereby increasing their incentives to engage in greenwashing. Consequently, following the policy implementation, we expect that the promoting effect of the policy on greenwashing is stronger among firms with high institutional shareholding ratio and high Tobin\u0026rsquo;s Q.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;6.2 reports the empirical results. Panel C presents results based on institutional shareholding ratio. In the high institutional shareholding ratio group (Column 1), the coefficient on \u003cem\u003eTreat\u0026times;Post\u003c/em\u003e is 0.187 and statistically significant, whereas in the low institutional shareholding ratio group (Column 2), the coefficient is 0.003 and not significant. The difference between the two coefficients is confirmed by the p-value from the equality test. These findings suggest that after the social media real-name registration policy, firms with higher institutional shareholding ratio anticipate a reduction in market pressure associated with social media scrutiny and thus exhibit greater greenwashing behavior. Similarly, Panel D reports results based on Tobin\u0026rsquo;s Q. In the low Tobin\u0026rsquo;s Q group, the coefficient on \u003cem\u003eTreat\u0026times;Post\u003c/em\u003e is -0.017 and not significant, whereas in the high Tobin\u0026rsquo;s Q group, the coefficient is 0.213 and statistically significant at the 1% level. The coefficient difference is validated by the p-value for equality testing. These results indicate that, following the policy implementation, firms with higher Tobin\u0026rsquo;s Q anticipate reduced market pressure from social media exposure and are therefore more inclined to increase greenwashing activities. Overall, the results in Panels C and D provide supporting evidence that the social media real-name registration policy facilitates corporate greenwashing by weakening the market pressure channel of social media monitoring.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMechanism analysis: market pressure\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\u003cp\u003ePanel C: Institutional investor ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGws\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh institutional investor ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow institutional investor ratio\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTreat\u0026times;Post\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.187*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\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(0.112)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.105)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep-value for equality test\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.040**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u0026times;Year FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,424\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAdjusted R-squared\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePanel D: \u003cb\u003eTobinQ\u003c/b\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGws\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh Tobin Q\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow Tobin Q\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTreat\u0026times;Post\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.213**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.017\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(0.107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.115)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ep-value for equality test\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.020**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControls\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirm FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndustry\u0026times;Year FE\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9,320\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAdjusted R-squared\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes: Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e6.3 Mechanism analysis: regulation pressure\u003c/h2\u003e\u003cp\u003eThe third channel examined is the regulation pressure mechanism. Following the implementation of the social media real-name registration policy, the effectiveness of social media as a monitoring tool is weakened, reducing both the proactive and reactive likelihood that regulators identify firm misconduct, such as greenwashing, through information disseminated on these platforms. Consequently, regulation pressure on firms is alleviated. Under this context, we hypothesize that, after the introduction of the social media real-name registration policy, firms are less likely to face environmental penalties, thereby increasing their incentives to engage in greenwashing. To test this mechanism, data on regulatory penalties imposed on firms for environmental violations were collected from the China Certified Emission Reduction (CCER) database, including both the annual number of penalties and the monetary amounts. We further construct four sub-indicators: PA, measured as the natural logarithm of penalty monetary amounts; PN, measured as the natural logarithm of penalty counts; PAR, measured as the ratio of penalty monetary amounts to total assets (log-transformed); and PNR, measured as the ratio of penalty counts to total assets (log-transformed).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;6.3 reports the empirical results. In Columns (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u0026ndash;(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), the coefficients on \u003cem\u003eTreat\u0026times;Post\u003c/em\u003e are \u0026minus;\u0026thinsp;0.460, -0.233, -0.351, and \u0026minus;\u0026thinsp;0.029, respectively, all statistically significant at the 1% level. These findings indicate that, following the social media real-name registration policy, both the monetary amounts and frequency of environmental penalties imposed on firms decreased significantly. This suggests that regulators\u0026rsquo; ability to detect environmental violations through social media\u0026mdash;either proactively or reactively\u0026mdash;was reduced. Overall, the results in Table\u0026nbsp;6.3 provide robust evidence that the social media real-name registration policy promotes corporate greenwashing by weakening the regulation pressure channel of social media monitoring.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e \u003cb\u003ehere\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMechanism analysis: regulation pressure\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(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\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\u003ePA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePNR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTreat\u0026times;Post\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.460***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.233***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.351***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.029***\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(0.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControls\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\u003eFirm FE\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\u003eIndustry\u0026times;Year FE\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\u003eObservations\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAdjusted R-squared\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Standard errors are clustered at firm level which are displayed in parentheses. *, **, *** represent significance at the 10, 5, and 1 percent level, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"7 Conclusion","content":"\u003cp\u003eThis paper documents that China\u0026rsquo;s 2015 social media real-name registration policy materially weakened social media\u0026rsquo;s capacity to discipline firms and thereby increased corporate greenwashing. Exploiting the policy as a quasi-natural experiment and applying a suite of robustness checks, we find consistent evidence that firms received more attention on social platforms prior to the reform displayed larger post-policy increases in greenwashing. Additional sensitivity analyses corroborate the baseline results. Mechanism tests reveal that the effect is transmitted through three channels: the policy reduces expected reputational costs associated with public exposure, mutes investor-driven market pressure, and lowers the likelihood that regulators detect violations via social-media signals.\u003c/p\u003e\u003cp\u003eBy demonstrating that de-anonymization diminishes stakeholders\u0026rsquo; willingness to disclose adverse information, the paper advances theory on media-based governance by identifying anonymity as a critical institutional precondition for effective external monitoring. From a policy perspective, the findings point to a nontrivial trade-off. While real-name requirements can help suppress abusive or false online content, they may also erode a valuable, low-cost source of external oversight over firm behavior. Policymakers should therefore consider complementary designs\u0026mdash;such as protected or third-party anonymous reporting channels, strengthened formal enforcement capacities, or calibrated identity-verification regimes\u0026mdash;that mitigate online harm without unduly impairing whistleblowing and crowd-sourced monitoring. Limitations include the single-country setting, focus on greenwashing as one form of misconduct, and reliance on available social-media and penalty records; future work could test generalizability across institutional contexts, examine other misconduct domains (e.g., financial misreporting), and combine experimental or survey methods to unpack the psychological versus institutional drivers of disclosure under anonymity constraints.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eChao Yuan is responsible for conceptualization, methodology, emprical tests, formal analysis, investigation and original draft writing.Junming Li is responsible for methodology, data curation, empirical tests, formal anslysis and investigaiton.Youze Zhang is responsible for conceptualization, methodology and original draft writing.Weixing Cai is responsible for conceptualization and original draft writing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request by email: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAng JS, Hsu C, Tang D, Wu C (2021) The role of social media in corporate governance. 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Int Rev Finance 21(3):966\u0026ndash;988\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":"social media, real-name registration, firm misconduct, corporate greenwahsing","lastPublishedDoi":"10.21203/rs.3.rs-7649177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7649177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRemoving anonymity in social media may stimulate firm misconduct as it reduces social media\u0026rsquo;s monitoring effects. This paper employs China\u0026rsquo;s 2015 social media real-name registration policy as a quasi-natural experiment that requires internet users to register their real ID with the government. Results indicate that the policy caused a robust and economically meaningful increase in corporate greenwashing which is a classical misconduct. Results are robust with various robustness checks. Further analysis demonstrates that removing anonymity attenuates social media\u0026rsquo;s monitoring role in corporate greenwashing by mitigating three channels: reputation cost, market pressure from investors and regulation pressure that social media exerts on firms. Theoretically, our findings identify user anonymity as a key institutional condition enabling social media to function as an informal governance mechanism. The results also highlight a regulatory trade-off: identity-verification rules may curb online harm but can inadvertently weaken external oversight of firm misconduct.\u003c/p\u003e","manuscriptTitle":"Silenced? Does Social-Media Real-Name Registration Policy Facilitate Firm Misconduct? Evidence from Greenwashing in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 08:31:58","doi":"10.21203/rs.3.rs-7649177/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-16T20:32:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T09:19:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323475839251279804182270971981845534878","date":"2025-12-12T00:44:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T00:39:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38094624623424289083020832692955261683","date":"2025-11-04T10:30:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T03:24:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-27T03:19:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-25T03:31:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-09T00:31:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-10-09T00:28:37+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":"e1766351-45c3-491e-9e48-c62f835ae54f","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57404732,"name":"Business and commerce/Information systems and information technology"},{"id":57404733,"name":"Physical sciences/Mathematics and computing"},{"id":57404734,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-04-02T00:23:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 08:31:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7649177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7649177","identity":"rs-7649177","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}