Managing municipal solid waste (MSW) amid global environmental and public health concerns is increasingly challenging with population growth and urbanization. Developing tailored MSW management strategies that target environmental co-benefits within diverse national development and waste composition contexts is complex and urgently required. We combine amachine learning-derived MSW generation database with life cycle inventories of full technical modules to model a process-, component-, and technology-specific MSW management system. The human health, ecosystem quality and resource scarcity co-benefits in 171 countries by 2050 are assessed in 11 scenarios integrating hierarchical management intensities with Shared Socioeconomic Pathways. Results highlight that 63.9% of health damages can be mitigated; ecosystem damages can be completely offset and reversed to net benefits; and resource benefits can increase by 137.5%, during 2020-2050 under the ideal sustainability-focused scenario. Prioritizing lower- and middle-income countries (such as India), which could cumulatively contribute 40.5%, 37.8%, and 27.3% of heath, ecosystem, and resource benefits, respectively over 30 years, is crucial to averting prolonged damage peak and neutralization by accelerating transformation of their MSW management systems.

Municipal solid waste,waste management,Machine learning,Shared Socioeconomic Pathways,Life Cycle Assessment