On the basis of the original camel rating system, this study added the green indicator and formed the G-CAMELS evaluation system (An improved rating system based on the CAMELS rating system to evaluate the business operation of financial institutions more comprehensively.) to comprehensively evaluate the competitiveness of commercial banks. It followed China’s current requirements for the sustainable development of commercial banks. In this paper, factor analysis, entropy methods, and dynamic evaluation models are used to obtain the ranking of competitiveness. In addition, according to the same steps as above, the comprehensive ranking based on the CAMELS evaluation system (A comprehensive rating system which is standardized, institutionalized and indexed for business operations of commercial banks and other financial institutions.) was obtained. The two ranking systems were compared. It is found that with the entropy weight method, in the G-CAMELS system, the weight of the green index is quite large, so it magnifies the impact of the financial industry on the environment. Compared with the original CAMELS system, the newly formed system will increase the ranking of state-owned banks and there is no significant change in the ranking of joint-stock banks. In order to improve the competitiveness of banks, state-owned banks should innovate their banking business and continue to implement the green credit policy; joint-stock banks should continue to seize the opportunity of green credit and expand profitability while paying attention to safety. In addition, the government could consider relaxing green credit standards for city commercial banks to ease pressure on banks.

This study uses data from seven countries with high energy consumption levels in 1997–2016 (i.e., the US, China, Japan, Canada, South Korea, Germany, and France) to establish a panel threshold model and analyze the multiple threshold effects of new energy consumption transformation on economic growth. Research results show the non-linear impact of new energy consumption transformation on economic growth. On the one hand, the transformation of new energy consumption will occasionally bring economic costs, thereby resulting in a negative impact on economic growth. On the other hand, economic cost occasionally disappears, thereby resulting in the positive impact of the transformation of new energy consumption on economic growth. This study proposes that economic cost is affected by the levels of research and development (R&D), economic development, and traditional energy dependence, therefore, we use these three variables as threshold variables. Threshold variable is essential in a panel threshold model. The behavioral varies of model can be predicted when threshold variable is at different ranges of levels. In other words, the behavior of panel threshold model may change as the level of threshold variable changes. In particular, when the R&D level is used as a threshold variable, the impact of new energy consumption transformation on economic growth will change from negative to positive as the level of R&D increases. We present a similar conclusion when the level of economic development is used as a threshold variable. However, when the level of traditional energy dependence is used as the threshold variable, the impact of new energy consumption transformation on economic growth will change from positive to negative as the level of traditional energy dependence increases.

Commercial banks follow sustainable development strategies to provide green credit for companies. As the main target for banks to issue green credit, environmental protection companies carry out research and development (R&D) activities for the purposes of protecting the environment and sustainable development. Therefore, the R&D level of environmental protection companies is a measure of whether the green credit policy meets its purpose. Therefore, it is particularly important to study green credit from a company perspective. This paper used panel data from 24 environmental protection companies listed on the Shanghai and Shenzhen Stock Exchange from 2012 to 2017 and adopted a threshold model to examine the relationship between green credit and company R&D level. The results indicate that there is a positive but nonlinear relationship between green credit and company R&D levels. The empirical analysis through the threshold model shows that a threshold effect remains on the relationship and this threshold effect is caused by the levels of firm size, bank lending, and government subsidies.

A prominent claim within the literature is that corporate social responsibility-disclosured firms are fundamentally more resilient to financial shocks, relative to firms that take no corporate social responsibility action. To test this, we examine the impact of corporate social responsibility (CSR) information disclosure on financial constraints (FC). Our sample is composed of A-share publicly listed firms from Shanghai and Shenzhen in China during 2013–2017. We find that CSR disclosure influences negatively financial constraints. The quantile regression results also indicate that the influences would more obvious when a company faces stronger financial constraints. Further, CSR disclosure influences negatively financial constraints in financially opaque firms, and the effect of financial opaque on the relationship strengthens when the company faces great financial constraints. After considering the problems of missing variables and endogenous, changing the level of CSR and FC measurement, using 2SLS and two-step GMM methods, the conclusion is still robust. However, the results should not be generalized, since the sample was based on 434 A-share publicly listed firms for 2013–2017. From the perspective of FC, this study contributes to the literature in the field of CSR and expands the empirical research on the economic consequences of CSR. It also can encourage enterprises to voluntarily disclose social responsibility information and it is of great significance to promote the stable development of the capital market and society.

Corporate social responsibility (CSR) and environmental, social, and governance (ESG) disclosures are critical for sustainable value creation. However, traditional evaluation methods struggle to quantify authentic performance and detect disclosure biases. In response, this study proposes an automated CSR polarity dictionary construction method that innovatively combines natural-language-processing technology and the multinomial inverse regression (MNIR) method. This method analyzes the correlations between corporate CSR reports and CSR ratings and constructs a dictionary that best reflects the CSR level of listed companies. We also used the CSR dictionary to construct a CSR disclosure level index for listed companies’ annual reports. This study reveals that CSR disclosure levels in annual reports expose manipulative disclosure practices and image management. However, this behavior has been proven to fail in generating excess returns for the company in the stock market. This phenomenon provides novel insights into corporate stock market performance management. In addition, the CSR disclosure level index is shown to effectively reflect the CSR level of enterprises in different industries and provides a theoretical reference for the social responsibility management of companies with different pollution levels. These findings facilitate efficient information release and strengthen ESG assessment frameworks through data-driven standardization.

Against the backdrop of the construction of an ecological civilization and the “Healthy China 2030” initiative, access to clean fuels is crucial for achieving optimal health and wellbeing, as well as sustainable social development. The purpose of this study is to investigate the effect of household clean fuel combustion (HCFC) on multiple dimensions of health among older adults while shedding light on the potential mechanisms. We performed a cross-sectional study of the data from the 2018 China Health and Retirement Longitudinal Survey, and we surmounted the underlying issues of endogeneity with the application of propensity score matching and the instrumental variable strategy. The results revealed that HCFC has positive effects on the health of older adults, particularly by improving their psychological wellbeing. The adoption of clean fuels among the elderly was associated with a significant increase in SRH by 3.06% to 3.42% and a decrease in CES-D by 7.96% to 8.28%. These positive environmental health effects became stronger among the elderly under the age of 75, as well as among those who were educated and had chronic diseases. Moreover, the results demonstrated that HCFC significantly alleviated chronic pain and increased social interaction among older adults, highlighting two potential pathways for promoting their wellbeing. Given that a significant number of rural households in China rely on polluting fuels, targeted strategies are crucial for promoting the use of clean fuels, particularly for vulnerable populations.