Comparative Study of Agricultural Value Added in China and India on CO2 Emissions: Based on K-nearest Neighbor Algorithm

Abstract

This study contributes to the current discourse on the relationship between environmental sustainability and economic growth by examining the connections between poverty, carbon emissions, and the value generated in the agricultural sector. We assess the agricultural sectors of China and India, analyzing their roles in global carbon emissions through empirical data, econometric models, and machine learning techniques. We find a nonlinear relationship between economic growth and emissions; initially, development elevates carbon output, while subsequent progress encourages sustainability through technological innovations and policy initiatives. Among the machine learning methods employed, the K-Nearest Neighbors (KNN) algorithm demonstrated the highest predictive accuracy. The results indicate that China’s agricultural sector is the dominant contributor to carbon emissions, accounting for 78.2%, while India’s sector contributes 21.8%. These findings underscore the disparities in agricultural carbon footprints between the two nations and highlight the necessity of integrated strategies that balance economic growth with environmental protection. Policy implications involve implementing focused poverty reduction initiatives, renewable energy strategies, and sustainable farming methods to lessen the effects of climate change while promoting economic growth. The study highlights how artificial intelligence and machine learning can improve predictive accuracy and guide effective climate policies.