With the exponential development of an ecological and sustainable economy and society, the concept and practice of environmental, social, and governance (ESG) investments are being popularized in the capital market of China. ESG disclosure is an important supplement to financial disclosure and plays an increasingly significant role in asset pricing. In this paper, we selected corporate bond data in China’s secondary bond market from 2015 to 2020, and introduced the Nelson–Siegel model to study the influence of ESG disclosure on corporate bond credit spreads in the secondary market. This model passed robustness tests when we used alternative data fitted by the modified Nelson–Siegel model. Results show that ESG disclosure significantly reduces credit spreads on corporate bonds in the secondary market. State ownership and industry play significant roles in moderating the impact of ESG disclosure on corporate bond credit spreads. Specifically, the ESG disclosure of non-state-owned companies and companies in non-high-pollution and -energy-consumption industries has a greater impact on reducing corporate bond credit spreads. Therefore, we urge regulatory departments to establish a sound ESG disclosure evaluation system, and the issue companies to improve the quality of their ESG disclosure, especially non-state-owned companies, and those in non-high-pollution and -energy-consumption industries. Corporate bond investors would benefit from integrating ESG information into their investment decision-making process.

Capturing determinants of bond default risks has aroused heated discussions ever since the “rigid payment” system collapsed in China. Within this context; this paper aims to clarify the relation between an issuer’s environmental; social; and corporate (ESG) performance and its bond default rate. We developed an ESG factors-embedded Logistic Regression model to empirically examine Chinese default bonds and outstanding industrial bonds from 2014 to 2019. Results indicate that the bond default rate is positively correlated with the company’s energy consumption and negatively correlated with its attention to social responsibilities; and corporate governance; in addition to its financial performances. In conclusion; to fully take ESG factors into consideration during the decision-making process and daily operations might improve stability and credibility of corporations in modern Chinese national context.

This paper systematically investigates the properties of six kinds of entropy-based risk measures: Information Entropy and Cumulative Residual Entropy in the probability space, Fuzzy Entropy, Credibility Entropy and Sine Entropy in the fuzzy space, and Hybrid Entropy in the hybridized uncertainty of both fuzziness and randomness. We discover that none of the risk measures satisfy all six of the following properties, which various scholars have associated with effective risk measures: Monotonicity, Translation Invariance, Sub-additivity, Positive Homogeneity, Consistency and Convexity. Measures based on Fuzzy Entropy, Credibility Entropy, and Sine Entropy all exhibit the same properties: Sub-additivity, Positive Homogeneity, Consistency, and Convexity. These measures based on Information Entropy and Hybrid Entropy, meanwhile, only exhibit Sub-additivity and Consistency. Cumulative Residual Entropy satisfies just Sub-additivity, Positive Homogeneity, and Convexity. After identifying these properties, we develop seven portfolio models based on different risk measures and made empirical comparisons using samples from both the Shenzhen Stock Exchange of China and the New York Stock Exchange of America. The comparisons show that the Mean Fuzzy Entropy Model performs the best among the seven models with respect to both daily returns and relative cumulative returns. Overall, these results could provide an important reference for both constructing effective risk measures and rationally selecting the appropriate risk measure under different portfolio selection conditions.

From the perspective of financing cost, this article investigates the benefits of green bonds to the issuer. Based on 227 green bonds and 405 conventional bonds selected from China’s bond market, we find that (1) green bonds can decrease financing cost by at least 15 bps in the primary market, which is more significant than the effect in the secondary market; (2) third-party certification can strengthen the ‘greenium’ of green bonds in both the primary and secondary markets; and (3) there is no ‘greenium’ effect for financial green bonds in either primary or secondary markets in China, even for green bonds with third-party certification.

China’s economy has experienced a period of remarkable growth and entered a stage of high-quality development, necessitating the implementation of innovative eco-friendly practices involving green technology innovation. The capital market environment plays a direct role in influencing the sources and scale of external financing for businesses, thus affecting their green technology innovation activities. The Shanghai–Hong Kong Stock Connect (SHSC) mechanism in China is the first official two-way opening of China’s capital market. Therefore, this paper regards the SHSC policy as a quasi-natural experiment platform for the opening of the capital market and employs a difference-in-difference methodology to investigate the impact of the SHSC policy on enterprise green technology innovation and its influence mechanisms. The overall sample covers Chinese A-share listed enterprises from 2012 to 2017. The findings demonstrate that the SHSC policy significantly enhances the level of green technology innovation by enhancing corporate governance and alleviating financing constraints. Non-state-owned enterprises are more affected than state-owned ones, and there is no significant difference in the impact between heavily polluting industries and non-heavily polluting ones.

In recent decades, sustainable development and environmental protection, especially air quality, have become key concerns for governments as well as scholars. As a typical regulation for air quality monitoring, the Ambient Air Quality Standard (AAQS) (2012) is a good attempt at balancing economic growth and environmental protection in development exploration. Therefore, this paper takes the AAQS (2012) as a quasi-natural experiment to detect its influence on the total factor productivity (TFP) of polluting firms. The results of a DID model suggest that the AAQS (2012) significantly improves the TFP of polluting firms in China even after several robust tests. Further, this paper also finds that firms in high urbanization and with over-investment experience are more sensitive to the new standard. As for the channels, the results show that air quality monitoring affects firms’ TFP by adjusting the industry concentration level and innovation capabilities. This research offers a novel perspective for decision-makers, highlighting the importance of harmonizing economic growth with environmental sustainability.