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.

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.

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.

Given the alarming deterioration of the environment, the present analysis investigates the role of eco-innovation, natural resources and financial development in influencing the environmental degradation of China. Applying the novel method of quantile ARDL, the current research is beneficial in portraying the dependence patterns of the variables with special emphasis on the nexus of eco-innovation and ecological footprint across numerous quantiles of the distribution which has not been examined so far in the literature. The empirical findings reveal that in the long run, eco-innovation reduces the level of ecological deterioration in China across all quantiles. On the other hand, the results suggest that the increase in credit to the private sector and natural resource rents augments environmental degradation. The outcomes imply that the over-dependence on natural resources and financial development can worsen the goals of sustainable development in China if the strategies of conservation and management are ignored. Moreover, witnessing the favorable role of eco-innovation, competent policies and regulations can be made toward sustainable efficient technologies and eco-friendly energy sources to halt global warming.

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.

The climate variability in Pakistan adversely affects rice crops and undermines the food security and livelihoods of millions of rural households whose survival depends directly on rice farming. This study examines farmers’ risk perception, adaptation responses, and adaptation impact on rice productivity. We employed a multi-stage sampling method for selecting 480 farmers from the rice production zone of Punjab province, a region that produces more than 60% of the total rice in the country and faces significant production decline due to climate change. We used the risk matrix method to determine farmers’ perception of climate change-induced risk and used the propensity score matching (PSM) technique to analyze the impact of adaptation measures on rice yield and crop returns. Results show that farmers had high perceptions and were concerned about biological and financial risks, followed by biophysical, atmospheric, and social risks. Farmers applied supplementary irrigation, changed rice cultivation dates, changed rice varieties, resized farms, and altered irrigation application times as adaptation measures to cope with changing climate effects. Probit regression analysis showed that the adaptation measures had been largely affected by farmers’ socioeconomic attributes and risk perceptions. The PSM estimates showed that all adaptation measures had a positive impact on rice yield and crop return. Specifically, the cultivation of alternative rice varieties, farm resizing, and supplementary irrigation were the most effective strategies, followed by the adjustment in cultivation dates and irrigation time. Having implications beyond Pakistan, this study suggests improving farmers’ access to irrigation water, credit, and farm advisory services to facilitate the extent of adaptation.

To better understand Africa’s banks and the environment, this study investigates the impact of financial performance on environmental performance in Africa. We examined financial performance, environmental performance, and some control indicators dated from 2000 to 2016 by applying panel quantile regression and panel vector autoregressive techniques. Our results indicate that (i) in North African countries, carbon emission had a significant negative impact on financial performance on the 25th quantile and (ii) in the South, carbon emission had a statistically positive impact on financial performance on the 25th and 50th quantiles with the marginal effect increases from the lower quantile to the highest quantile. Also, bank deposits statistically negatively impacted financial performance on the 25th and 50th quantiles for both North and South economies. The dynamic panel quantile results show dissimilar effects at different quantiles. Also, the panel vector autoregressive results show that in North Africa carbon emission had a positive impact. Our results validate the stability test of the panel vector autoregressive model. The granger causality results in the North show a bilateral causal link between carbon emission and bank credit, carbon emission, and bank deposit. Since sustainability has become one of our era’s most thorny issues, this paper provides extensive policy directives to assist African nations in boosting a greener future.

This study is hinged on analyzing factors such as agriculture and globalization (de jure trade and financial) that threaten a sustainable environment using two proxies of ecological footprint: carbon and noncarbon ecological footprint in the Philippines while controlling for the influence of fossil to GDP, economic growth, urban population, and financial development using the autoregressive distributed lag (ARDL) framework. The result provides evidence of long‐run stable state among the variables. The result validates inverted U‐shaped pattern of EKC involving relationship between agricultural development and ecological footprint for the Philippines indicating that initially, ecological footprint increases as the agriculture develops and then declines as the agriculture matures to generate efficiency and low carbon. In addition, this study explores elasticities of the variables using ARDL, FMOLS, DOLS, and CCR procedure and found that de jure financial globalization exerts positive influence on ecological footprint in the long run. De jure trade globalization is found to be negative and significant in the long run. It is also found that agricultural level operates below the threshold level required to maximize the growth benefits of agricultural system towards mitigating environmental sustainability. Further empirical result shows a positive relationship between economic growth, fossil fuel, urban-population growth, and ecological footprint, and negative insignificant relationship between credit to private sector and ecological footprint. The government should optimize the use of agricultural land through well-articulated economic integration strategy fashioned to pave way for cleaner and low-carbon technologies sources like solar, geothermal, biomass, biogas, tidal power, photovoltaic, and wind energy in the agricultural production to avoid further deterioration of the environment.

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.