The Economic Impacts of Air Pollution and Greenhouse Gas Emissions: A Machine Learning Approach from a Global and Regional Perspective

Abstract

This study uses machine learning methods to comprehensively analyze the multifaceted impacts of PM2.5 and greenhouse gas emissions on economic development from a global and regional perspective. Using data sets from 2008-2023 for 145 countries, we examine the nonlinear relationship and regional heterogeneity between environmental pollution and economic variables, illustrating how geographic and policy factors regulate the interactions between environmental pollutants and economic variables. By constructing global and regional models, this study highlights the unique economic impacts of pollution in different contexts, while addressing gaps in existing econometric research. These findings advance the theoretical framework of environmental economics and provide actionable, data-driven insights for designing pollution control and sustainable economic strategies that address regional differences. This interdisciplinary integration highlights the transformative potential of combining machine learning with environmental policy analysis to achieve the global Sustainable Development Goals.