Bidyut Kumar Ghosh

Type of the article: Research Article

Abstract
In the context of growing pressure on forest ecosystems arising from agricultural area expansion and intensification, expanding population, and climate variability, this study aims to identify and quantify the impacts of these changes on forest coverage in the South Asian region. Using a balanced panel dataset for 1990–2023, the analysis employs a regularized fixed-effects estimation to identify the key drivers of forest area change and assess variable importance.
The findings show that a 1% increase in agricultural value added is achieved at the cost of a 0.32% decrease in forest area, making it the most significant driver of forest loss. Use of inorganic fertilizer also exerts a strong negative influence, as forest cover is reduced by 0.18% for every additional percentage usage of fertilizer. Irrigation expansion has a similarly adverse effect, contributing to a 0.21% decline per 1% increase in irrigated area. Population density growth intensifies pressure on forests, with each additional 10 persons per km² corresponding to a 0.05% decrease in forest area. However, pasture share exhibits a positive association: a 1-percentage-point increase corresponds to a 0.14% rise in forest area, and cattle density also shows a modest but positive effect. The results indicate the presence of mixed livestock–forest systems and early forest-transition dynamics in some countries.
Overall, the findings demonstrate that the pattern of agricultural practices determines forest trajectories in South Asia, and achieving sustainability will require country-specific strategies that balance productivity growth with integrated land-use planning and long-term conservation goals.

Type of the article: Research Article

Abstract
The rapid expansion of the digital payment system has transformed financial inclusion in India; however, it has not eliminated persistent socioeconomic and spatial disparities in digital financial participation. The Unified Payments Interface (UPI) adoption has been marked by highly uneven adoption across socioeconomic groups, which limits its direct benefits. Using the World Bank’s Global Findex 2025 microdata, the machine learning-based causal forest model analyzes the heterogeneous effects of UPI readiness on individual-level digital payment adoption. The results show that UPI readiness has a positive average treatment effect of 0.161 on the digital payment index (0-1 scale). Substantial heterogeneity persists across urban–rural location, income, education, and gender, confirming entrenched digital divides. The most pronounced heterogeneity emerges along an inclusion-exclusion spectrum: individuals facing the greatest affordability and usability constraints experience treatment effects up to 3.7 times larger than the most digitally included groups. The findings point to the need to develop targeted digital inclusion policies for socially vulnerable population groups, aimed at strengthening their financial resilience and expanding their digital engagement. The study also provides novel empirical evidence on the effective use of machine learning methods to inform public finance policies aimed at fostering inclusive digital ecosystems.