Investigate the Factors of Land Degradation in Chinese Provinces: An Application of Index Formation and Random Forest

Abstract

This study analyzes factors driving land degradation in China from 1996 to 2023, focusing on gross regional product, rural population, coal consumption, and wastewater generation. Using a quantile control random forest model, the findings reveal that economic growth significantly accelerates land degradation, while population, coal consumption, and wastewater management are critical but partially mitigable drivers. Environmental policies implemented post-2018, particularly following the Paris Agreement, have notably reduced degradation rates. This research provides a novel application of the quantile control random forest model to analyze long-term land degradation dynamics, offering precise insights into variable impacts across degradation levels. Additionally, it contributes to understanding the effectiveness of post-2018 environmental policies in mitigating degradation, aligning with global sustainability goals. The study highlights the need for sustainable land use, cleaner energy adoption, and enhanced wastewater management. Future research should explore localized data and broader variables for a deeper understanding of degradation dynamics.