Forecasting Municipal Solid Waste Removal Volume Based on Socioeconomic Indicators for Carbon Reduction Strategy in Beijing’s Waste Management.

Yaxin Cui; Min Yee Chin; Hong Sheng Loh; Chew Tin Lee; Pei Ying Ong; Yee Van Fan; Kok Sin Woon

Municipal solid waste (MSW) management poses a significant challenge amidst global population growth and urbanization. With Beijing as a focal point due to its substantial contribution to MSW generation and greenhouse gas (GHG) emissions, this study employs two-stage Bayesian-optimized Artificial Neural Network models to forecast MSW removal volume and evaluate associated GHG emissions in Beijing. The analysis integrates socioeconomic indicators, including population and GDP, to elucidate the complex relationship between MSW generation and economic development. Various MSW treatment scenarios are assessed by alternating the configuration of sanitary landfills, incineration, and composting. Results indicate a projected MSW removal volume of approximately 14 Mt by 2060, a 63.16 % reduction compared to 2023. Scenario 2 (50 % incineration and 50 % composting) demonstrates the potential to reduce GHG emissions by approximately 4.11 Mt of CO2e compared to the current practice. The findings underscore the need for comprehensive waste management strategies integrating waste segregation, incineration, and composting to achieve sustainable MSW treatment.

INTEGRATED waste management; SUSTAINABILITY; ARTIFICIAL neural networks; WASTE management; SANITARY landfills; INCINERATION

CET Journal - Chemical Engineering Transactions, 2024, Vol 114, p451

1974-9791

Academic Journal

10.3303/CET24114076