Leveraging machine learning for sustainable solid waste management: A global perspective - Ajaya Subedi; Sahil Shrestha; Anish Ghimire; Shukra Raj Paudel (Sustainable Futures, 2025)
A diverse array of socioeconomic, demographic, and environmental factors influences the quantity and composition of municipal solid waste (MSW). Yet, the paucity of consistent waste data continues to impede the formulation of effective solid waste management strategies globally. This gap poses significant barriers to achieving various Sustainable Development Goals (SDGs), particularly those related to responsible consumption and production, environmental sustainability, and promotion of circular economy. In light of these challenges, this study primarily accentuates the transformative potential of Machine Learning (ML) for sustainable waste management. ML models offer promising solutions for projecting waste composition and generation trends while optimizing resource distribution by analyzing key influential factors. Nevertheless, existing ML applications often lack standardization and generalizability across regions. Therefore, this paper objectively advocates for the necessity of robust global ML models to standardize metrics, facilitating reliable comparisons of region- or nation-wise solid waste generation and composition patterns. Additionally, this study aims to emphasize the broader utility of such models in, but not limited to, addressing leachate quantity, landfill composition, and groundwater contamination issues, ultimately promoting scalable, data-driven approaches for informed and sustainable waste management worldwide.