“Green economy and sustainable development” has become the focus of contemporary world economic development. As an important part of green financial instruments, green credit has become a hot topic. This paper investigates whether the Green Credit Policy has had any impact. Does it have a binding effect on the debt financing of heavily polluting enterprises? Using the Green Credit Guidelines as the starting point for the implementation of the Green Credit Policy, this paper takes Chinese A-share listed enterprises from 2004 to 2020 as the research sample, and applies the propensity score matching combined with difference-in-difference (PSM-DID) method to analyze the impact of green credit policies on the long- and short-term financing scale of heavily polluting enterprises. The study found that the implementation of the Green Credit Policy significantly suppressed the long-term financing of heavily polluting enterprises, but allowed for the expansion of short-term financing for heavily polluting enterprises. Compared with the state-owned enterprises, the Green Credit Policy has a more significant impact on non-state-owned enterprises in terms of suppressing long-term financing and increasing short-term financing, suggesting that the Green Credit Policy is affected by the “credit discrimination” of non-state-owned enterprises. Therefore, the Green Credit Policy still needs to be improved. This study provides empirical evidence of the effectiveness of green credit policies in China, and offers suggestions for further green credit policies in the future.

Background: The progress of green credit in China is accelerating, but its development is uneven and insufficient in different regions. And whether the issuance of green credit can effectively promote the improvement of the environment and economy is not well understood. Objective: Previous research has found that green credit promotes economic growth through improvement of the industrial structure and green technological innovation. However, these studies have not considered the positive externality of environmental improvement even though environmental improvement and economic growth are requirements of the sustainable development concept. Methods: We use the chain-mediated model to estimate the impact of green credit issuance on the economic growth of different provinces since the large-scale implementation of green credit in China with data from 2008 to 2016. Results and conclusion: This paper shows that the issuance of green credit can improve labor supply rather than labor productivity through the improvement of air quality to achieve regional economic growth. Such a chain-mediated path is different from the economic growth caused by industrial structural adjustment and green technology innovation. At the national level, every 1% increase in green credit issuance relative to industrial loans will increase the per capita gross domestic product (GDP) by approximately 4.6 yuan, or 0.012%, through air quality and labor supply, accounting for 2.875% of the total effect. Heterogeneity analysis indicates that due to regional industrial structure differences and diminishing marginal effects, the impact of green credit is stronger in the western region than in the eastern and central regions. For every 1% increase in the proportion of green credit issuance relative to industrial loans, the per capita GDP growth achieved through the chain-mediated path is approximately 30.17 yuan in the western region, approximately 6.6 times greater than that at the national level. Within a 95% confidence interval of 5000 bootstrap samples, this path is found to be true, and the chain-mediated effect accounts for approximately 12.96% of the total indirect effect. Limitations: The limitation of this paper is the measurement of green credit. Although green credit has a large volume, it remains underdeveloped, and there is a lack of perfect indicators. Most existing studies have adopted only alternative or reverse indicators to measure the issuance of green credit. For example, this paper takes the interest expenditure of six high-energy-consuming enterprises as the reverse indicator, which may to a certain extent lead to the overestimation of the issuance of green credit and its impact on the environment and economy. Future research can accurately explore the performance of green credit on the basis of its mature development.

Promoting economic growth while achieving sustainable development is the original purpose of green credit. Based on panel data of 30 Chinese provinces from 2008 to 2020, we systematically evaluate the economic impact of green credit by exploring the influencing factors, regional differences, and underlying mechanisms. The results show that: (1) Green credit is more prevalent in provinces with higher foreign direct investment (FDI), research and development expenditures (R&D), financial development, urbanization rates, and lower government fiscal spending. At the regional level, green credit issuance gradually decreases as the region moves towards the west. (2) From a national perspective, green credit issuance has a significant impact on local economies. Local GDP increases by 0.482% for every 1% increase in green credit. (3) The underlying mechanisms are the rationalization of intra-industry structure and the improvement of green total factor productivity. These findings are robust against alternative measures of economic growth, quantile regression estimation, and the construction of instrumental variables. Finally, we propose policy recommendations for China’s continued implementation of green credit policies.

Based on the theory of entrepreneurial cognition, we examined the relationship between entrepreneurial passion and opportunity identification, and the moderating roles of environmental complexity and mediating role of entrepreneurial contextual knowledge in this relationship. Results from 246 entrepreneurs supported the hypothesis that both their entrepreneurial passion and their entrepreneurial contextual knowledge would be positively related to opportunity identification, and these relationships would be moderated by environmental complexity. Further, environmental complexity enhanced the relationship between entrepreneurial passion and opportunity identification, and also amplified the relationship between contextual knowledge and opportunity identification. The results suggest that the effect of entrepreneurs’ passion on opportunity identification depends on the level of the individual's contextual knowledge and environmental complexity.

[This corrects the article DOI: 10.1371/journal.pone.0320080.].

Global warming presents urgent challenges for tilapia aquaculture. This study introduces a tailored loss function to assess long-term impacts under extreme drought, using historical drought data in China as a baseline. The TaiESM1 climate model within the CMIP6 framework is applied to project future conditions under SSP245, SSP370, and SSP585 scenarios, focusing on Guangdong Province (2024–2100). The results indicate a general decline in the frequency of extreme droughts across all scenarios. Under SSP245, technological advancements combined with reduced drought risk may boost yields of tilapia aquaculture of Guangdong to 2.369–2.418 million tons by 2100. In SSP370 and SSP585, while humidity increases, drought risk reduction is less pronounced, resulting in marginally lower yields (2.285–2.408 and 2.300–2.416 million tons, respectively). When a unified loss parameter is applied, projected yields exhibit a U-shaped trend across all SSP scenarios, reaching a minimum under SSP370 in the mid-century period before recovering toward the end of the century, driven by scenario-dependent marginal responses of production to changes in extreme drought risk, highlighting the nonlinearity of climate impacts driven by complex climatic factors and socioeconomic interactions. These findings are subject to uncertainties associated with the use of a single climate model and the simplified representation of drought impacts in the loss-function framework.