In recent years, with the continuous advancement of the dual-carbon goals, sustainable development has become a crucial issue in the global economy and corporate governance. Environmental, Social, and Governance (ESG) ratings have emerged as a focal point for both practitioners and academics. However, discrepancies in ESG ratings pose challenges for investor decision-making and significantly impact corporate governance efficiency and long-term sustainable development strategies. Using a sample of A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2015 to 2022, this study examines the impact and underlying mechanisms of ESG rating discrepancies on stock liquidity. The findings reveal that greater ESG rating discrepancies lead to lower stock liquidity, and this conclusion remains robust after a series of endogeneity and robustness tests. Mechanism tests indicate that ESG rating discrepancies exacerbate corporate information asymmetry, increase business risk, and heighten financing constraints, thereby reducing stock liquidity. Heterogeneity analysis shows that the impact of ESG rating discrepancies on stock liquidity is more pronounced among non-state-owned enterprises, firms operating in regions with a higher degree of marketization, and heavily polluting industries. This study not only enriches research on the relationship between ESG rating discrepancies and capital market performance but also provides empirical evidence for investors, regulatory agencies, and corporate managers to better understand the economic consequences of ESG rating discrepancies.

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With the growing global importance of Environmental, Social, and Governance (ESG) standards, corporate ESG performance has become a key indicator in assessing sustainable development capabilities. Governments utilize policy tools such as tax incentives to encourage corporate investment in green innovation, social responsibility, and corporate governance. However, a gap often exists between a company’s actual ESG performance and its reported disclosures, with some firms merely complying in form without implementing substantive sustainable practices. Against this backdrop, this study examines Chinese A-share listed companies from 2009 to 2023, adopting a dual principal framework encompassing both government and market perspectives. It investigates how tax incentives, by lowering the costs associated with green innovation and social responsibility investments, promote genuine ESG performance. Empirical findings indicate: (1) Tax incentives significantly enhance corporate ESG performance, confirming their positive effect on ESG-related actions; (2) Excessive short-term performance focus in capital markets undermines the long-term effectiveness of tax incentives on ESG investment, supporting the hypothesis that capital market attention negatively moderates the relationship between tax incentives and ESG behavior; (3) Ownership concentration positively moderates the relationship between tax incentives and ESG performance—firms with higher ownership concentration allow major shareholders to more effectively coordinate internal resources and promote strategic implementation, thereby amplifying the effect of tax incentives on ESG outcomes. Moreover, heterogeneity analysis shows that tax incentives have a significantly stronger positive effect on the ESG performance of non-state-owned enterprises, while the effect is weaker in state-owned firms, indicating that ownership structure plays a crucial role in the effectiveness of tax incentives. Further analysis reveals a marginal increasing effect of tax incentives on ESG performance—i.e., as the level of tax incentives rises, firms’ ESG performance improves accordingly. This study provides empirical support for governments in formulating more effective tax incentive policies and offers investors a fresh perspective in ESG-related investment decision-making.

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With the changes in the global economic environment, economic policy uncertainty (EPU) has had a profound impact on corporate operations and investment decisions. This study explores the impact of EPU on corporate Environmental, Social, and Governance (ESG) scores. Based on data from Chinese listed companies from 2000 to 2020, empirical analysis is conducted using a panel data regression model. The results show that economic policy uncertainty significantly increases corporate ESG scores, especially in the environmental and social dimensions. The study finds that policy uncertainty encourages companies to increase investments in these areas to enhance their social image and long-term competitiveness. Further analysis reveals that company size and industry characteristics play a moderating role in this impact. When facing economic policy uncertainty, companies optimize resource allocation by adjusting their financial structures (reducing financial leverage) to improve ESG performance. Policy recommendations include encouraging companies to continue enhancing their ESG performance in response to policy uncertainty in order to seize the opportunities for improvement brought about by this uncertainty.

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With the proliferation of social media, its role in information dissemination and sentiment transmission in capital markets has increasingly highlighted its impact on securities analysts' forecast behavior. This study, based on data from Chinese A-share listed companies from 2008-2023 and social media data from the East Money stock forum, systematically explores the mechanism by which social media attention affects analyst forecast quality. It also introduces an analysis of the moderating effects of investor sentiment and firm size. By constructing multiple regression models, conducting mediation effect tests, and employing subgroup regression methods, the study finds: (1) Social media interaction has a nonlinear effect on analyst forecast quality. A moderate volume of posts, readership, and comments can improve forecast accuracy by reducing information asymmetry (e.g., the coefficient of Post on forecast error is -0.042^(***) ), but excessive interaction triggers information noise, significantly increasing forecast dispersion (e.g., the coefficient of Post on forecast dispersion is 0.090***); (2) The transmission of investor sentiment is asymmetric. Negative sentiment fully mediates the positive impact of social media on forecast dispersion (accounting for 62.3% of the mediation effect), while positive sentiment does not pass significant tests due to analysts' "good news immunity"; (3) Firm size has a significant moderating effect. Small-cap firms, which rely more on social media due to information substitution effects, experience a stronger suppression of the impact of comment volume on forecast error (coefficient -0.033** vs. large firms' -0.015), but they are also more susceptible to negative sentiment interference (with an elasticity coefficient 2.3 times that of large firms). Large firms, due to business complexity, face an "information overload trap," where fragmented information exacerbates forecast disagreement. Theoretically, this study innovatively constructs an "information diffusion-sentiment transmission-size heterogeneity" chain framework, revealing the nonlinear mechanisms and boundary conditions of social media’s impact on analyst behavior. Practically, it proposes regulatory suggestions such as "sentiment circuit breakers" and "precision targeted disclosures," providing empirical evidence for optimizing capital market information governance. The findings deepen the theoretical understanding of information intermediary behavior in the new media environment and offer important references for preventing sentiment-driven market risks.

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