Under the goal of carbon peaking and carbon neutrality, taking environment, social responsibility, and corporate governance (ESG) as the important investment factor has become an action guide and standard for capital market participants. The practice of the ESG concept is not only a new way for enterprises to form new asset advantages and realize green and low-carbon transformation, but also important access for promoting high-quality and sustainable development. Based on Chinese-listed companies within the period of 2009 to 2015, we investigate the impact of ESG responsibility performance on export performance as well as its mechanism. We theorize and find out show that ESG responsibility performance can significantly and stably promote enterprises’ export performance. Mechanism analysis shows that ESG can improve export performance by reducing financing costs and easing financing constraints, and the green technology innovation effect is also an important channel for ESG to affect export performance. Therefore, government should strengthen the supervision and incentive of ESG performance, encourage enterprises to improve their environmental, social and governance performance in order to adapt to the goal of carbon peak and carbon neutrality and promote the high-quality development of export trade. Future research may consider combining ESG accountability with other factors such as supply chain management, intermediate imports, and transnational spillovers to more fully understand its impact on export performance, so as to create more value for society.

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.

The frequent occurrence of extreme weather events not only poses serious challenges to global economic growth and financial stability but also affects firms negatively across multiple dimensions. Using a sample of Chinese A-share listed firms from 2006-2016, this study aims to explore the effect of climate risk on firm export sophistication. The findings show that climate risk inhibits firm export sophistication, with the results varying depending on firm and industry types. Specifically, climate risk (i) inhibits export sophistication for firms with low government subsidies more than for firms with high government subsidies; (ii) restraints export sophistication for firms in high-tech industries rather than for low-and medium-tech industries; and (iii) reduces export sophistication for firms in low-marketization regions more than for firms in high-marketization regions. In addition, channel analysis shows that climate risk inhibits firm export sophistication by increasing financial constraints and reducing human capital.

China faces significant environmental challenges, including reducing pollutants, improving environmental quality, and peaking carbon emissions. Industrial restructuring is key to achieving both emission reductions and economic transformation. This study uses the Environmentally Extended Multi-Regional Input-Output model and multi-objective optimization to analyze pathways for China’s industrial transformation to synergistically reduce emissions. Our findings indicate that under a compromise scenario, China’s carbon emissions could stabilize at around 10.9 billion tonnes by 2030, with energy consumption controlled at approximately 5 billion tonnes. The Papermaking sector in Guangdong and the Chemicals sector in Shandong are expected to flourish, while the Coal Mining sector in Shanxi and the Communication Equipment sector in Jiangsu will see reductions. The synergy strength between carbon emission reduction and energy conservation is highest at 11%, followed by a 7% synergy between carbon emission and nitrogen oxide reduction. However, significant trade-offs are observed between carbon emission reduction and chemical oxygen demand, and ammonia nitrogen reduction targets at -9%. This comprehensive analysis at regional and sectoral levels provides valuable insights for advancing China’s carbon reduction and pollution control goals.

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.

The growing emphasis on Environmental, Social, and Governance (ESG) principles, particularly in corporate sectors, shapes investment trends and operational strategies, whose shift is supported by the increasing role of media in monitoring and influencing corporate ESG performance, thereby driving the energy innovation. Therefore, based on reported events from Baidu News and patent text information of Chinese A-share listed companies from 2012 to 2022, this study innovatively applied machine learning and text analysis to measure ESG news sentiment and corporate energy innovation indicators. Combing with reputation, stakeholder, and agency theories, we find that a good reputation conveyed by positive ESG textual sentiments in the media significantly promotes corporate energy innovation, and the effect is mainly realized through alleviating financing constraints and agency problems and promoting green investment. Further analysis shows that ESG news sentiment promotes corporate energy innovation mainly among private firms, non-growth-stage firms, high-energy-consuming firms, and regions with better green finance development and higher ESG governance intensity. From the perspective of ESG news content and information content, greater ESG news attention can also exert an energy innovation incentive effect, in which the incentive effect exerted by positive media sentiment in the environmental (E) and social (S) dimensions, as well as excellent attention, is more robust. This study provides new insights for promoting green and low-carbon development and understanding the external governance role of media in corporate ESG development.

Environmental disclosure in emerging markets remains poorly understood, despite its critical role in sustainability governance. Here, we analyze 42,129 firm-year environmental disclosures from 4,571 Chinese listed firms (2008-2022) using machine learning techniques to characterize disclosure patterns and regulatory responses. We show that increased disclosure volume primarily comprises boilerplate content rather than material information. Cross-sectional analyses reveal systematic variations across industries, with manufacturing and high-pollution sectors exhibiting more comprehensive disclosures than consumer and technology sectors. Notably, regional rankings in environmental disclosure volume do not align with local economic development levels. Through examination of staggered regulatory implementation, we demonstrate that market-based mechanisms generate more substantive disclosures compared to command-and-control approaches. These results provide empirical evidence that firms strategically manage environmental disclosures in response to institutional pressures. Our findings have important implications for regulatory design in emerging markets and advance understanding of voluntary disclosure mechanisms in sustainability governance.

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.

The disparities in green transition have led to the call for a ‘just transition’. However, the large differences in energy efficiency across different regions have been identified as a primary hazard to the just transition. This study examines whether transferring low-carbon technology can improve the efficiency of energy, enhancing the overall energy efficiency, and marketing a sustainable and equitable energy future. In this paper, we utilize the Undesirable-SE-SBM model to estimate the energy efficiency of China's 30 provinces during 2012 to 2022, and empirically tested the impact of low-carbon technology transfer on the convergence of total-factor energy efficiency by convergence analysis. The results showed that: (1) There is evidence of σ convergence and absolute β convergence in the eastern and western regions, but not in the central region. (2) Low-carbon technology transfer can accelerate the convergence of total factor energy efficiency. Lagging regions that adopt low-carbon technologies can catch up with the advanced regions' level of total-factor energy efficiency. (3) There is regional heterogeneity in the effect of low-carbon technology transfer on the accelerating convergence of total factor energy efficiency. The western region experiences the most significant acceleration, followed by the eastern and central regions.