Contingent upon the empirical work done, the current study seeks to investigate the environmental load capacity factor (LCF) consequences of financial development in three different ways for 48 Asian economies. We used the two-step system generalized method of moments (GMM) technique to analyze the data from 1996 to 2020. Initially, we investigated the environmental consequences of financial development by considering six dimensions of financial development. Then, we modified the original environmental Kuznets curve (EKC) into the financial market-based EKC (FM-EKC) to compare short- and long-run environmental consequences of financial development. Ultimately, the study explores the intersecting marginal effects of financial development and institutional quality on environmental quality. Our results show that foreign direct investment (FDI), financial development, economic growth, and environmental quality (LCF) exhibit statistically significant long-run co-integrating relationships in the studied economies. This study demonstrated how FDI, financial development, and economic expansion contribute to environmental deterioration in 48 Asian countries. The nexus between finance and sustainability is moderated by the institutional quality and the regulatory environment, resulting in the FM-EKC idea. The key findings of system GMM analysis confirmed that Asian countries have an inverted U-shaped FM-EKC, which we attempt to explain with three different justifications. This study showed that the strong institutional structure in an economy guarantees the favorable environmental consequences of financial development in the long run. It also suggested that a healthier education structure of an economy can help improve the environmental quality of an economy.

Controlling environmental contamination requires the use of environmental regulation. The growth of green finance depends on digital finance. The objectives of the study are threefold: first, to explore the impact of digital financial inclusion in deriving climate change; second, to trace the shape of the financial inclusion-based environmental Kuznets curve; and third, to investigate the intersecting effect of digital financial inclusion and institutional quality on environmental quality. Using panel data from 48 Asian economies between 1996 and 2020, heterogeneity, non-stationarity, and cross-sectional dependence are addressed using an econometric method called “dynamic common correlated effects (DCCE).” The empirical evidence confirms a significant relationship between environmental performance and financial inclusiveness. Furthermore, the findings also validated the inverted U-shape environmental Kuznets curve based on financial inclusiveness. Our research suggests that a strong institutional framework has the potential to mitigate the long-term negative consequences of financial inclusion on the environment. To establish coordinated control of environmental quality, the government fully utilizes the environmental regulation and digital inclusive finance environmental governance. Consequently, to achieve environmental sustainability, policymakers in Asian countries should develop policies that enhance financial inclusion and institutional quality.

Current environmental indicators assess environmental quality, but no single indicator measures the overall environmental performance of a country, state, or region in an easy and intuitive methodology. This paper provides a simple but informative indicator known as the Comprehensive Environmental Performance Index (CEPI) for 48 Asian countries for the period from 1996 to 2020. The CEPI represents a step toward clarity by combining six different indicators (Ecological Footprint, Environmental performance, environmental vulnerability, environmental sustainability, adjusted net savings, and pressure on nature) data into one indicator. Contrary to other indices, the CEPI does not use complex mathematical procedures but is designed for simplicity, which facilitates understanding and applying economics to professionals and laymen. We adopt PCA (Principal Component Analysis) to maximize ease of understanding. In addition to Raw CEPI, which gives equal weightings to its components, we build weighted CEPI and show that the two indices behave similarly to the Asian data.