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.

Green finance is one of the most important ways to help companies achieve green transformation and development. We construct a quasi-natural experiment with the “Green Credit Guidelines” and establish a difference-in-differences model to empirically test the implementation effect of the green credit policy in China. The results show that after the implementation of China’s green credit policy, the debt financing scale of listed companies in heavily polluting industries has decreased significantly, the debt financing cost has increased significantly, and the debt financing maturity has been shortened significantly, indicating that the green credit policy has inhibited the debt financing of heavily polluting enterprises. We further find that this inhibition has also been affected by the nature of controlling shareholders, environmental information disclosure levels, regional environmental regulations and regional financial development levels. China’s green credit policy has played a role in guiding listed companies to go green through the redistribution of debt financing.

The transformation of the traditional high-carbon economy to a low-carbon economy and the change in the economic development mode urgently require the transformation and development of traditional finance to green finance. This study examines the impact of green finance on the transition to a low-carbon economy in 30 Chinese provinces from 2001 to 2019 and further explores the role of low-carbon technological innovation in this facilitation process. We use the Global Malmquist- Luenberger index to measure low-carbon total factor productivity using gross regional product as the desired output, CO2 emissions as the undesired output, and capital stock, employment, and total energy consumption as input indicators to represent the low-carbon economic transition. We select seven indicators in four dimensions of green credit, green securities, green insurance, and green investment to construct a comprehensive green finance evaluation system, and then apply the entropy value method to calculate green finance indicators. The number of patents granted for low-carbon innovation is used to measure low-carbon technology innovation. Foreign direct investment, industrialization level, economic development level, and urbanization level are selected as control variables. Through panel data model, mediating effect model and 2SLS, we find that green finance can significantly contribute to the transformation of low-carbon economy, but this contribution decreases with the intervention of low-carbon technology innovation. The implications of our empirical results can help China to improve the development of green finance and thus promote the transformation and upgrading of a low-carbon economy.

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.

This study determines the impacts of gross domestic product, domestic bank credits given to private sector, and military expenditures on carbon emissions based on 1990–2019 time period. The panel quantile regression approach is applied for the Visegrád group countries. Our empirical results reveal that domestic bank credit given to private sector has a positive and meaningful impact on carbon emissions at medium and high quantile levels. On the other hand, it has been determined that gross domestic product has a reducing impact on carbon emissions, but military expenditures have an increasing impact on carbon emissions. Besides, as consequences of such tests, the difference between the quantiles, that is, the heterogeneous structure was revealed. A separate model was created with a different panel quantile approach for robustness control, and the results were compared by giving different values to penalty term. These results provide strong evidence for decision-makers and implementers.

Whether green credit policy is conducive to improving the green innovation efficiency of heavy polluting industries is of great significance for China’s sustainable economic development and the construction of ecological civilization. This paper uses China’s Green Credit Guidelines to conduct a quasi-natural experiment based on relevant panel data of industries from 2007 to 2018. Specifically, it employs the Super-SBM model including non-expected output to measure the green innovation efficiency of 35 industries in China, and constructs the propensity score matching difference-in-difference model to explore how green credit policy impact on the green innovation efficiency of heavy polluting industries. The results show that the coefficient of difference-in-difference ([Formula: see text]) was 0.262, which was significant at the 1% level; the coefficient of [Formula: see text] was not significant; the coefficient of [Formula: see text] was 0.490, which was significant at the 1% level; and the coefficient of [Formula: see text] was 0.173, which was significant at the 1% level, indicating that green credit policy significantly contributes to the green innovation efficiency of heavy polluting industries, though with a lag effect. Further study finds that green credit policy pushes heavy polluting industries to improve green innovation efficiency by increasing financing cost and R&D investment; meanwhile, the heterogeneity test shows that the higher the state-owned share of the industry, the greater the positive effect of green credit policy on its green innovation efficiency. Finally, in order to accelerate the implementation of green credit policy and promote the green innovation efficiency of heavy polluting industries, banks can guide heavy polluting industrial enterprises through credit to carry out green technological transformation, heavy polluting industries should raise awareness of green innovation, and government should encourage heavy polluting industrial enterprises to carry out green innovation, and guide and supervise the state-owned enterprises in particular, so that they can improve cleanliness and achieve green economic development.

The role of risk assessment and capital structure is vital for the sustainable growth of firms and increasing the shareholders’ wealth. This research explores the correlation between firm risk and capital structure using datasets from the sugar and cement sectors of Pakistan as a developing economy. This study is unique as it involved two firms of different nature (sugar firms operate seasonally while cement firms operate yearly) to view the real picture on the impact of risk and structure assessment on firms’ credibility and shareholders’ wealth. For this purpose, 15-year data (2000–2014) containing the financial statements of the target sectors were collected and the ANOVA analysis was applied with credit risk, liquidity risk, systematic risk, and firm size were used as the regressor variables, firm growth and dividend payout ratio as the control variables, and leverage as the regression variable. The findings showed that credit risk and liquidity risk are significantly correlated with leverage. This suggests that decision-makers pertaining to firms’ risk and efficiency must focus more on risk to pursue a stronger and sustainable increase in shareholder wealth.

The Ghanaian economy relies heavily on maize and soybean production. The entire maize and soybean production system is low-tech, making it extremely susceptible to environmental factors. As a result, climate change and variability have an influence on agricultural production, such as maize and soybean yields. Therefore, the study’s ultimate purpose was to analyze the influence of CO₂ emissions, precipitation, domestic credit, and fertilizer consumption on maize and soybean productivity in Ghana by utilizing the newly constructed dynamic simulated autoregressive distributed lag (ARDL) model for the period 1990 to 2020. The findings indicated that climate change enhances maize and soybean yields in Ghana in both the short run and long run. Also, the results from the frequency domain causality showed that climate change causes maize and soybean yield in the long-run. These outcomes were robust to the use of the ordinary least squares estimator and the impulse response technique. The findings show that crop and water management strategies, as well as information availability, should be considered in food production to improve resistance to climate change and adverse climatic circumstances.

Sustainable supply chain management (SSCM) has been a tough challenge for developing economies like Saudi Arabia. Implementation of SSCM practices in the manufacturing industry has been prone to multiple risk factors that need to be identified, evaluated, and prioritized especially considering the dynamics of the manufacturing industry in a developing economy. Moreover, it is also imperative to trace out feasible and sustainable strategies to overcome the risks to SSCM practices adoption. This study serves this purpose and identifies, evaluates, prioritizes the risk factors, sub-factors, and strategies to overcome these risk factors in the implementation of SSCM practices in the manufacturing industry in Saudi Arabia. An integrated multi-criteria decision analysis approach by combining fuzzy AHP and fuzzy WASPAS methods is employed for the analyses. The fuzzy AHP analysis results show that economic risks are dominant risks followed by the managerial policy risks and environmental risks in implementing SSCM. Industrial emissions are the leading risk factors in the overall ranking of the sustainable supply chain sub-risk factors followed by market dynamics, management policy failures, financial constraints, and credit uncertainty. While evaluating the sustainable supply chain strategies using fuzzy WASPAS, it is concluded that commitment and support of top, middle, and lower level management is the most pivotal strategy to deal with the risks to SSCM in Saudi Arabia followed by establishing environmental policies and goals to adopt SSCM, and provision of the financial resources and subsidies.

In thirst for economic growth, economies are engaged in anti-environmental activities that drive them towards climate change and CO₂ emissions. Extensive CO₂ emissions is a serious threat around the globe, especially in low-income countries that can prove detrimental to the environment. To prevent the worst impacts of carbon emission, it becomes necessary to explore the cause of CO₂ emissions. In this vein, this work is conducted to evaluate the determinants of CO₂ emissions in low-income countries spanning from 2000 to 2020. For estimation of models, panel data techniques are employed. The outcome of the study revealed that trade FDI, urbanization, and GDP per capita are the main contributing factors to environmental degradation. Trade openness has also impacted environmental degradation positively but insignificantly. In contrast, population density and domestic credit to private sector (DCPS) have negatively impacted low-income countries’ carbon emissions. The study extended important policy implications to low-income countries’ governments and environmental policymakers.