An integrated framework for dual-scale assessment of recyclable waste resource potential and environmental–economic benefits in a Chinese megacity
Artikel i vetenskaplig tidskrift, 2026

Municipal solid waste management is a key pathway for urban carbon mitigation and resource efficiency, yet recyclable waste generation across different urban sources remains insufficiently quantified. This study develops a dual-scale analytical framework to assess recyclable waste resource potential and environmental–economic benefits in a Chinese megacity. The framework integrates household-source measurement with municipal life-source recyclable generation prediction to characterize recyclable resource potential from complementary accounting perspectives. Household-source recyclables are quantified using two rounds of household surveys conducted in summer and winter 2025, involving 448 households, combined with Monte Carlo simulation, while municipal life-source recyclables are predicted using machine-learning models with multi-source socioeconomic variables. Scenario-based life cycle assessment is further applied to evaluate carbon and economic performance under virgin and recycling pathways. The results demonstrate substantial differences between the two accounting scales. Household-source recyclable generation was estimated at 251,800, 966,500, and 1,103,500 tons for waste glass, plastics, and paper, respectively, whereas municipal life-source recyclable generation reached 614,760, 3,100,000, and 3,109,600 tons, corresponding to 2.44-, 3.21-, and 2.82-fold higher estimates. These differences reflect the broader system boundary of municipal life-source accounting, which incorporates recyclable flows beyond household sources, rather than indicating a direct inconsistency between the two accounting scales. The environmental and economic assessments further reveal strong material-dependent heterogeneity. Waste plastics and paper exhibited more stable environmental–economic co-benefits, whereas waste glass showed greater sensitivity to collection efficiency, transportation distance, and market conditions. The proposed framework provides a complementary multi-scale approach for robust recyclable waste accounting and supports data-driven urban low-carbon management.

Monte Carlo simulation

Waste resource recovery

Household-source recyclables

Machine learning models

Carbon reduction and economic benefits

Författare

Jinfeng Tang

Guangzhou University

Chalmers, Kemi och kemiteknik, Energi och material

Jiajun Lin

Guangzhou University

Minhua Su

Guangzhou University

Yun Zhu

Guangzhou City Management Technology Research Center

Biming Chen

Guangzhou City Management Technology Research Center

Benhua Liao

Guangzhou City Management Technology Research Center

Binglan Wu

South China Normal University

Kaimin Shih

The University of Hong Kong

Yongjun Wei

Zhengzhou University

Hongguo Zhang

Guangzhou University

Junhua Xu

PureTech Industry

Geologian tutkimuskeskus (GTK)

Journal of Cleaner Production

0959-6526 (ISSN) 18791786 (eISSN)

Vol. 579 149604

Ämneskategorier (SSIF 2025)

Miljövetenskap

Energiteknik

DOI

10.1016/j.jclepro.2026.149604

Mer information

Senast uppdaterat

2026-10-16