This study investigates the influence of smart supply chain management on the Environmental, Social, and Governance (ESG) performance of Chinese manufacturing firms spanning from 2009 to 2022. Our findings reveal a positive association between smart supply chain management and enhanced ESG performance, a relationship consistently upheld across various analytical methodologies. Additionally, we uncover that smart supply chain practices stimulate corporate social responsibility (CSR) disclosure, contributing to heightened transparency and subsequently bolstering ESG metrics within firms. Furthermore, our analysis demonstrates that the positive effect of smart supply chain management on ESG outcomes is particularly pronounced among firms that are operating in less competitive and more environmentally impactful industries, receiving heightened media scrutiny, and influenced by Confucian principles. This research provides actionable insights for firms seeking to advance their ESG initiatives.

We conducted a nationwide field experiment with 4,800+ Chinese-listed companies, randomly raising ESG concerns to their management teams via high-visibility and high-stakes online platforms. Tracking the full impact-generating process, we find that companies respond to our concerns by providing high-quality answers, publishing ESG reports, and making commitments to investors. Over time, Environmental (E) inquiries boost stock valuations, while Governance (G) concerns prompt skepticism. Productive and opaque firms are more likely to respond, consistent with a signaling model where costly ESG actions signal firm quality under information asymmetry. Overall, ESG actions are likely driven by profit-oriented signaling rather than values-based motives.

The growing significance of sustainable development has underscored the importance of integrating corporate sustainability indicators into corporate strategies. As external stakeholders increasingly emphasize corporate environmential performance, social responsibility and governance (ESG), understanding its impact on corporate value becomes essential, especially in emerging markets like China. This research aims to bridge these knowledge gaps by empirically investigating the influence of ESG ratings on firms’ value among Chinese listed firms, with a special emphasis on the mediating roles played by financial distress and financing constraints. By analyzing data from listed companies of China over the period 2018 to 2022, this research explores the correlation between firms’ value and ESG ratings. The findings indicate a positive association between firms’ value and ESG ratings. Enhanced ESG ratings directly boost market valuation and indirectly elevate firm value by mitigating financing constraints and financial distress. Further analysis reveals the positive effects of ESG ratings are more noticeable in industries that are not heavily polluting and in state-owned enterprises. This research provides valuable insights for enterprise management by systematically examining how ESG ratings contribute to corporate value through the mitigation of financial distress and constraints, while also highlighting the variations in ESG strategy implementation across different types of enterprises.

With the increasing penetration of renewable energy (RE) in power systems, the electricity spot market has become increasingly uncertain, presenting significant challenges for generation companies (GenCos) in formulating effective bidding strategies. Most existing studies assume that GenCos act as perfectly rational decision makers, overlooking the impact of irrational bidding behaviors in uncertain market environments. To address this limitation, we incorporate prospect theory to model the decision-making process of bounded rational GenCos operating under risk. A bilevel stochastic model is developed to simulate strategic bidding in the spot market. In addition, a distributional re-inforcement learning algorithm is proposed to tackle the decision-making challenges faced by bounded rational GenCos with risk considerations. The proposed model and algorithm are validated through simulations using a 27-bus system from a region in eastern China. The results demonstrate that the algorithm effectively captures market uncertainties and learns the distribution of GenCo’s profits. Furthermore, simulated bidding strategies for various types of GenCos highlight the applicability of prospect theory to describe bounded rational decision-making behavior in electricity markets.

The role of institutional investors in the development of Environmental, Social, and Governance (ESG) criteria lacks consensus in the academic community. This study utilizes a quasi-natural experiment involving Chinese mutual funds that have signed the United Nations Principles for Responsible Investment (UNPRI) to investigate whether institutional Investors’ ESG investment commitments can significantly reduce ESG rating disagreement among the companies in their portfolios. We first find that companies held by ESG commitment institutional Investors exhibit less disagreement in ESG rating compared to those held by Non-ESG commitment institutional Investors. we then show that institutional Investor’ ESG investment commitment influence ESG rating disagreement by enhancing the quality of ESG disclosure and attracting external ESG attention. We further discover that institutional investors’ ESG investment commitments significantly mitigates the ESG rating disagreement among domestic ESG rating agencies and firms with a higher level of corporate governance.

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This paper constructs an evaluation index system for the high-quality development of the marine economy based on the five dimensions of the new development paradigm. It employs entropy method, kernel density analysis, and Dagum Gini coefficient method to analyze the high-quality development level of China's marine economy and its spatial-temporal evolution from 2013 to 2022. The findings reveal that: (1) The comprehensive index for the high-quality development of China's marine economy exhibits an overall fluctuating upward trend; (2) The high-quality development levels of the marine economy in the eastern and southern marine economic circles are both above the national average, while that in the northern marine economic circle is below the national average; (3) The focus of high-quality development in China's marine economy is shifting towards economically developed regions along the southeast coast, demonstrating a trend of "higher in the south and lower in the north." Moreover, the gap in high-quality development of the marine economy among the three major marine economic circles is gradually narrowing, and the high-quality development of regional marine economies tends to become more coordinated.

In this paper, we present a rational expectation equilibrium model in which fundamental and ESG traders hold heterogeneous valuations towards the risky asset. Trading occurs based on private information and price signal which is determined by a weighted combination of these diverse valuations. Our findings indicate that higher level of ESG rating disagreement increases ESG information uncertainty, thereby reducing trading intensity among ESG traders and attenuating the price informativeness about ESG. We further discover that allowing fundamental traders access to ESG information increases the coordination possibilities in the financial market, leading to multiple equilibria exhibiting characteristics of strategic substitutability and complementarity. Additionally, through measuring the ESG rating disparities among four prominent agencies in China, we deduce that ESG rating disagreement negatively impacts price informativeness by decreasing stock illiquidity.

It has been widely recognized that firms’ environmental, social, and governance (ESG) performances are crucial for shaping their information environments. Nonetheless, the impact of ESG performances on important analyst report attributes still remains clear. Our study reveals that superior firm. ESG performances significantly enhance the analyst report readability. The mechanism analysis demonstrates that this effect is primarily driven by increased information accessibility (the information acquisition channel) and greater analysts’ research efforts (the analyst effort channel). As expected, this effect is more pronounced in firms operating in highly polluted industries, firms with opaque financial infomration and state-owned enterprises (SOEs). Finally, our findings reveal that the release of analyst reports triggers higher market reactions for firms with superior ESG performances. In overall, our study highlights the criticial role of firm ESG performances in boosting financial analysts’ information production process.

Green bonds, pivotal to green finance, draw growing attention from scholars and investors. Social media’s proliferation has amplified the influence of investor sentiment, necessitating robust analysis of its market impact. However, general sentiment lexicons often fail to capture domain-specific slang and nuanced expressions unique to China’s bond market, leading to inaccuracies in sentiment analysis. Thus, this study constructs a specialized sentiment lexicon for the green bond market, namely the COBC (Chinese online bond comments sentiment lexicon), to dissect bond market slang and investor remarks. Compared to three general lexicons (Textbook, SnowNLP, and VADER), it improves the average prediction accuracy by approximately 87.2% in sentiment analysis of Chinese online language within the green bond domain. Sentiment scores derived from COBC-based dictionary analysis are systematically integrated as predictive features into a two-stage hybrid predictive model is proposed integrating Support Vector Machine (SVM), Auto-Regressive Moving Average (ARMA), Bidirectional Long Short-Term Memory Networks (BiLSTM), and Attention Mechanisms to forecast China's green bond market, represented by the China Bond 45 Green Bond Index. First, ARMA-SVR is employed to extract residuals and statistical features from the green bond index. Then, the BiLSTM-Attention model is applied to assess the impact of investor sentiment on the index. Empirical results show that incorporating investor sentiment significantly enhances the predictive accuracy of the green bond index, achieving an average of 67.5% reduction in Mean Squared Error (MSE), and providing valuable insights for market participants and policymakers.