Author(s)

Lirong Yao

Corresponding Author

Lirong Yao

Abstract

Rapid urbanization and population growth have intensified the challenges of waste management in China, where a uniform national approach often fails to address significant regional diversity in geography, culture, and economic development. This study proposes a regional adaptive waste management system that integrates advanced environmental technologies, drawing on international experiences from the United States and Bangladesh. The framework incorporates artificial intelligence (AI)-based classification, blockchain-enabled traceability, GIS-driven planning, and culturally sensitive models for ritual waste. Region-specific AI classifiers trained on southern organic-rich waste streams and northern industrial residues achieved classification accuracies of 92.6% and 89.8%, outperforming a nationwide model. A blockchain consortium pilot in suburban Beijing reduced illegal dumping incidents by 41% and improved corporate compliance to 94%, enhancing transparency and public trust. GIS-based planning in Yunnan, Inner Mongolia, and Tibet optimized waste collection, shortening transport routes by 17.3% and reducing landfill overload by 28%. In Xinjiang and Ningxia, culturally informed ritual waste models improved resident acceptance to 87%, demonstrating the importance of aligning technical solutions with local practices. The results confirm that combining technological innovation with region-specific customization can significantly enhance efficiency, transparency, and cultural adaptability in waste management. This study contributes a scalable and context-sensitive framework for China, offering a pathway toward sustainable, inclusive, and efficient waste governance, while also providing transferable insights for other countries facing heterogeneous waste management challenges.

Keywords

Regional waste management, Artificial intelligence classification, Blockchain traceability, GIS-based planning