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Investigate the Factors of Land Degradation in Chinese Provinces

An Application of Index Formation and Random Forest

Dados Bibliográficos

ID21648747
AutoresZhihui Tu (School of Economics Fujian Open University Fuzhou China), Z P Tu (0009-0008-3579-648X, Education Department of Fujian Province), Qingan Huang (0000-0001-9172-9187, Office of Academic Research Fujian Open University Fuzhou China), Qing‐An Huang (0000-0002-0646-7635, Hong Kong Metropolitan University), Xin Zhao (0000-0002-6232-9835, School of Statistics and Applied Mathematics Anhui University of Finance & Economics Bengbu China, autor correspondente), Kamel Si Mohammed (0000-0002-4359-9151, University Belhadj Bouchaib Ain Temouchent Algeria), Abdelmohsen A Nassani (0000-0001-9658-8299, Department of Management, College of Business Administration King Saud University Riyadh Saudi Arabia)
Ano2025
Volume36
Fascículo8
Páginas2571-2591
Data de publicação2025-05-15
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoLand Degradation and Development (JOURNAL)
Identificadores do periódicoISSN: 1085-3278 • E-ISSN: 1099-145X
EditoraWiley (PUBLISHER • GB)
DOI10.1002/ldr.5517
OpenAlexW4407718720
IdiomaEN
Referências citadas59

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

Agriculture · Archaeology · Forest degradation · Geography · Land degradation · Physical geography · Random forest · World Wide Web · Computer Science · Environmental Changes in China · Environmental Science · Land Use and Ecosystem Services · Remote Sensing and Land Use · Artificial Intelligence · Forestry

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