This study delves into the impact of digital inclusive finance on environmental pollution, with a specific focus on air pollution. Utilizing data from 265 Chinese cities, advanced econometric methods such as the bi-directional fixed effects model, threshold model, spatial Durbin model, and multi-period difference-in-differences model are employed, incorporating a variety of control variables. The empirical findings indicate that digital inclusive finance significantly reduces air pollution. This mechanism chiefly operates through enhancing public environmental consciousness and fostering green technological innovation. The study also uncovers the spatial spillover effect and non-linear characteristics of digital inclusive finance on air pollution, along with its interactive effects with specific policies (e.g., smart city pilot policies and the "major protection, no major development" initiative). Moreover, heterogeneity analysis reveals regional variations in the environmental effects of digital inclusive finance. These insights provide a novel perspective on the relationship between financial technology and environmental protection and offer crucial guidance for policymaking.
Environmental degradation is at an alarming level in developing economies. The present paper examines the direct and indirect impacts of corruption on environmental deterioration using the panel data of 64 developing countries. Adopting the generalized method of moments (GMM) technique, the paper finds evidence that corruption exhibits a positive impact on pollution. Subsequently, there is also evidence indicating that the level of pollution tends to be higher in countries with a higher level of corruption, eliminating the effectiveness of income effect on environmental preservation. These results also suggest that environmental degradation is monotonically increasing with higher corruption and invalidate the presence of the EKC. Hence, a policy focuses that an anti-corruption particularly in the environmental and natural resources sector needs to be emphasized and enforced in order to reduce or possibly to totally eliminate the rent for corruption.
The main purpose of this work is to analyze the impact of environmental degradation proxied by CO2 emissions per capita along with some other explanatory variables namely energy use, trade, and human capital on economic growth in selected higher CO2 emissions economies namely China, the USA, India, and Japan. For empirical analysis, annual data over the period spanning between 1971 and 2013 are used. After using relevant and suitable tests for checking data properties, the panel fully modified ordinary least squares (FMOLS) method is employed as an analytical technique for parameter estimation. The panel group FMOLS results reveal that almost all variables are statistically significant, whereby test rejects the null hypotheses of non cointegration, demonstrating that all variables play an important role in affecting the economic growth role across countries. Where two regressors namely CO2 emissions and energy use show significantly negative impacts on economic growth, for trade and human capital, they tend to show the significantly positive impact on economic growth. However, for the individual analysis across countries, the panel estimate suggests that CO2 emissions have a significant positive relationship with economic growth for China, Japan, and the USA, while it is found significantly negative in case of India. The empirical findings of the study suggest that appropriate and prudent policies are required in order to control pollution emerging from areas other than liquefied fuel consumption. The ultimate impact of shrinking pollution will help in supporting sustainable economic growth and maturation as well as largely improve society welfare.