Analysis of Environmental Protection and Economic Sustainability by Integrating Machine Learning Algorithms

This paper investigates the interplay between environmental protection and sustainable economic growth using machine learning algorithms to analyze the correlation between PM2.5 levels and carbon emissions. It draws upon key theoretical models such as the “triple bottom line,” the “circular economy,” and the OECD's “green growth” theory, which advocate for a balance between environmental sustainability and economic growth. Empirical analysis focuses on the negative correlation between PM2.5 and subsequent per capita carbon emissions, suggesting that better air quality today may lead to higher carbon emissions tomorrow due to reduced environmental pressures and a subsequent focus on economic development. Additionally, good air quality may lower public urgency on environmental issues, potentially leading to higher carbon lifestyles and increased economic activities that contribute to carbon emissions. The study's findings underscore the complexity of environmental-economic dynamics, highlighting the need for multifaceted policy approaches that consider various factors including policy responses, public perception, and regional interactions. The research contributes a nuanced perspective to policymakers and scholars, emphasizing the intricate relationship between environmental governance and economic sustainability.