Particulate Matter (PM₂.₅) pollution in China has been a primary concern for public health in recent years, which requires banks to appropriately control their credit supply to industries with high pollution, high energy consumption, and surplus capacity. For this reason, this paper examines economic determinants of PM₂.₅ concentrations and incorporates the spatial spillover effect of bank credit by employing the spatial Durbin model (SDM) under the stochastic impacts by regression on population, affluence and technology framework. Using China's provincial dataset from 1998 to 2016, the main findings are as follows: First, there is evidence in support of spatial dependence of PM₂.₅ concentrations and their inverted U-shaped relationship with economic growth in China. Second, PM₂.₅ concentrations in a province tend to increase as the level of its own urbanization increases, but they decrease as its own human capital and bank credit increase. Meanwhile, the level of neighboring urbanization positively influences a province's PM₂.₅ concentrations, whereas neighboring population size, industrialization, trade openness, and bank credit present negative impacts. Third, indirect effects of the SDM indicate significant and negative spatial spillover effect of bank credit on PM₂.₅ concentrations. These findings implicate policies on reforming economic growth, urbanization, human capital and bank credit to tackle PM₂.₅ pollution in China from a cross-provincial collaboration perspective.

This paper constructs the calculation method of total factor efficiency and low-carbon technology progress indicators based on the undesirable SBM model and Malmquist-Luenberger productivity index model, and this empirically analyzes the relationship between green credit, environmental regulation, and total factor efficiency or low-carbon technology progress by using the panel data of 30 regions in China from 2009 to 2018. The study results show that the carbon emission efficiency evaluation results of each region vary greatly; the largest gap reaches 400%. China’s overall carbon emission efficiency has 35–40% room for improvement, and it is increasing year by year at the rate of 0.96% mainly depends on the improvement of technological progress. The improvement of China’s carbon emission efficiency should focus on the central and western areas. The relationship between the green credit level and the total factor efficiency and low-carbon technical progress is significant “U-shaped” curve, while the relationship between the environmental regulation intensity and the total factor efficiency and low-carbon technical progress is significant “inverted U-shaped” curve. The proportion of tertiary industry, ownership structure and average scale of industrial enterprises have significant positive, negative and negative effects on regional total factor efficiency and low-carbon technical progress.

At present, China’s economy is transforming from rapid development to high-quality development, and the prominent problems in the industrial structure need to be resolved urgently. The implementation of the green credit policy restricts loans to industries with high energy consumption, high pollution, and overcapacity to prevent excessive expansion and consumption of resources, thereby achieving the goals of industrial restructuring and environmental protection. Technological innovation is an important means to cope with environmental changes and adjust the industrial structure. Can technological innovation under green credit achieve the upgrading of the industrial structure? This paper combines green credit, technological innovation, and industrial structure upgrading to conduct research, and theoretically analyzes the impact of green credit and technological innovation on industrial structure upgrading, and analyzes how green credit affects industrial structure upgrading through technological innovation. An empirical test using panel data from 30 provinces in China from 2005 to 2017 revealed that green credit can promote the upgrading of the industrial structure. Econometric analysis based on the mediation effect model found that green credit not only directly promotes the upgrading of the industrial structure, but also has the mediation effect that affects the upgrading of the industrial structure through technological innovation. In the sub-regional regression analysis, technological innovation affects the upgrading of the industrial structure. The mediation effect is still significant. Based on the research in this article, it is recommended to actively implement green credit policies, expand the scale of green credit, firmly implement the innovation-driven development strategy, and at the same time vigorously develop clean energy and reduce energy consumption. The research significance of the article is to explore the impact of green credit on industrial structure upgrading, which has implications for the implementation and promotion of subsequent green credit policies. At the same time, it enriches the research on green credit.

By applying the ARDL (autoregressive distributed lag) bounds testing method, this study examines the short- and long-term dynamic relationship between carbon dioxide (CO2) emissions, economic growth (gross domestic product), industrialization, trade, and urban population in Tanzania from 1990 to 2020. The study found that economic growth, trade, industrialization, and the urban population all contributed to the increase in environmental degradation (i.e., carbon dioxide emissions). However, we found that financial credit (i.e., domestic credit to the private sector) reduces carbon dioxide emissions, and its effects are significant in EKC (environmental Kuznets curve) model. Our findings revealed that economic growth (i.e., income) was responsible for both short- and long-term increases in carbon dioxide emissions in Tanzania. Economic growth is harmful to the environmental quality above a threshold value of 6.23%. Furthermore, the environmental Kuznets curve hypothesis is confirmed for Tanzania. Our findings suggest that policymakers should monitor and use the threshold levels to manage carbon dioxide emissions and to protect the environmental quality. Further, a strong focus should be placed on formulating environmental policies (i.e., carbon tax policy) as industrialization, urban population, economic growth, and trade continue to grow in future, restricting carbon dioxide emissions and safeguarding the environment.

This study is the first attempt to examine the effects of changing climate and financial development on rice cultivation in the context of agrarian economy like Thailand from 1969 to 2016. The current study also uses other important variables, such as cultivated area, organic fertilizers usage, and rural labor to determine the long-term connection amid variables. In this study, we applied several econometric techniques, for instance the autoregressive distributive lag–error correction model (ARDL–ECM), vector error correction model (VECM), impulse response functions (IMFs), and variance decomposition (VARD) method to estimate the most reliable and robust outcomes. The empirical results showed that in the long- and short-run, there is a reduction in rice cultivation as temperature increase. The carbon dioxide (CO₂) positively affects rice cultivation in the long-run, while this association is negative in the short-run. The findings further revealed that in the long- and short-run, domestic credit provided by the financial sector significantly positive improved rice cultivation, while domestic credit to private sector by banks negatively affects rice cultivation. The important input factors, including cultivated area, organic fertilizers usage, and rural labor significantly positive contributed toward rice cultivation in the long- and short-run. The calculated long-run causal connection of all the studied variables with rice cultivation is validated. The estimated short-run causal relationship is unidirectional among temperature, CO₂ emissions, financial development, rural labor, and rice cultivation. In addition, our outcomes are robust and also verified by IMFs and VARD method. The study offers some important policy suggestions to increase rice production with the help of sound and well-developed financial systems and climate controls.

This paper examines the impact of earthquake risk on the lending behavior of local Chinese banks. The results show that when facing earthquake risk, banks will adjust their lending behavior, including decreasing the amount of loans and mortgage loans and increasing credit loans and loan loss provision, but the effect only exists for banks without inter-provincial branches or that are located in cities with a higher population density. In general, earthquake risk cannot significantly affect the performance of banks, but it can deteriorate the performance of banks that have not adjusted their lending behavior.

In the face of mounting climate change challenges, reducing emissions has emerged as a key driver of environmental sustainability and sustainable growth. Despite the fact that research has been conducted on the environmental Kuznets curve (EKC), few researchers have analyzed this in the light of economic complexity. Thus, the current research assesses the effect of economic complexity on CO₂ emissions in the MINT nations while taking into account the role of financial development, economic growth, and energy consumption for the period between 1990 and 2018. Using the novel method of moments quantile regression (MMQR) with fixed effects, an inverted U-shape interrelationship is found between economic growth and CO₂ emissions, thus validating the EKC hypothesis. Energy consumption and economic complexity increase CO₂ emissions significantly from the 1st to 9th quantiles. Furthermore, there is no significant interconnection between financial development and CO₂ emissions across all quantiles (1st to 9th). The outcomes of the causality test reveal a feedback causal connection between economic growth and CO₂, while a unidirectional causality is established from economic complexity and energy use to CO₂ emissions in the MINT nations. Based on the findings, we believe that governments should stimulate the financial sector to provide domestic credit facilities to industrialists, investors, and other business enterprises on more favorable terms so that innovative technologies for environmental protection can be implemented with other policy recommendations.

With urgent desire of promoting sustainable development, this study explored the impact and affecting mechanism of green credit on green economy efficiency through spatial lag model. Panel data from 2008 to 2019 of 30 selected provinces and cities in China were collected to portray the green economy efficiency with application of Sup-SBM DEA including unexpected outputs. The results show that (1) green credit had significant promoting effect on green economy efficiency, while time–space inconsistency existed. (2) During the promoting process, level of marketization and environmental regulation generated threshold effects, which were positively correlated with regional green economy efficiency. (3) Industrial structure upgrading and environmental investment played positive intermediary roles between green credit and green economy efficiency. (4) Considering endogenous issues, the positive effect still existed though the promotion effect was weakened. This study further enriches the literature and provides deep insights into the policy design when formulating the green economy path.

Green credit plays a crucial role in reducing energy consumption and environmental degradation in China. Using data on China’s new energy listed companies from 2007 to 2018, this study explores the impact of green credit on new energy firms’ value, as well as the mediating effects of financing constraints and external supervision on the relationship between green credit and new energy companies’ economic benefits. Our results suggest that green credit significantly improved new energy firms’ value, and this positive impact can last over the long term. The above result is robust to using alternative measures, replacement of fixed effects, exclusion of abnormal samples, and placebo test. Additional tests reveal that green credit improves new energy companies’ value by alleviating financing constraints and strengthening external supervision. Finally, green credit’s value-enhancement effect is heterogeneous, depending on corporate property rights, business life cycle, implementation of Green Credit Guideline policy, and the firms’ geographical location. Our conclusions suggest that government should not only pay attention to the continuity of green credit commitment but also the mitigation of financing constraints and improvement of external supervision for new energy companies. Moreover, heterogeneous factors should be considered to formulate and calibrate related policy rather than a one-size-fits-all policy.

The link between CO2 emission and its possible determinant, namely financial development, foreign direct investment, economic growth, and energy consumption, gives a significant environmental research place. This study attempts to determine the nonlinear link between CO2 emissions and its determinants in China for the period 1985–2019. Based on the theoretic links and foundation, we develop an asymmetric autoregressive distributed lag (ARDL) model using annual data in this study. The findings of the nonlinear ARDL suggest that domestic credit from the banks (DCB) has a negative effect on environmental pollution in the short and long run, while domestic credit to the private sector (DCP) has an insignificant negative role in environmental pollution in China. The results show that positive change in DCB causes a decrease in CO2 emissions, while negative change increases it in the short and long term. The coefficient for a positive shock in DCP is a negative, and negative shock in DCP has a significant positive impact on environmental pollution in the short and long run. This means that nonlinear ARDL financial development effects on environmental pollution have deviated from the linear ARDL estimates. Empirical findings also support the presence of asymmetric long-run cointegration among the indicators. The findings also show that energy consumption and real GDP have a significant positive impact on CO2 emissions in the long-run, while foreign direct investment (FDI) improves the environment. Based on the empirical findings, the study endorses the suitable channelization of the finance market towards clean energy projects without compromising economic growth.