He, RuiSandoval-reyes, MexitliScott, IanSemeano, RuiFerrão, PauloMatthews, ScottSmall, Mitchell J.2022-10-122022-10-122022-12-010959-6526PURE: 47068272PURE UUID: 091258b7-32bd-4b7b-ba99-5177664718b7crossref: 10.1016/j.jclepro.2022.134501Scopus: 85139594657WOS: 000875699500002ORCID: /0000-0001-9699-4473/work/151417549http://hdl.handle.net/10362/144663He, R., Sandoval-reyes, M., Scott, I., Semeano, R., Ferrão, P., Matthews, S., & Small, M. J. (2022). Global knowledge base for municipal solid waste management: Framework development and application in waste generation prediction. Journal of Cleaner Production, 377(December), 1-11. [134501]. https://doi.org/10.1016/j.jclepro.2022.134501--- %ABS2% ---This research is supported by the Mao Yisheng Fellowship of Carnegie Mellon University to Rui He, and through the CMU-Portugal project “Bee2Waste Crypto” (IDT-COP 45933). The authors would like to thank other members of the Bee2Waste Crypto project consortium for their support and inputs to this paper.Increasing municipal solid waste (MSW) generation has become not only a major sustainability challenge and a considerable financial burden for municipalities across the globe, but also an opportunity to promote a circular economy, provided adequate information is made available. Data and information on MSW generation, characterization, and management practices are prerequisites to studying and optimizing solid waste management systems (SWMS). However, such data and information are usually dispersed, unsystematized, and suffering from various availability and quality issues. This study aims to assemble and provide access to the current landscape of MSW data by establishing a comprehensive framework for understanding the interconnectedness of various sub-domains of MSW knowledge. Existing databases and governmental reports were reviewed to compile 1720 records of MSW generation, composition, management practices, and socioeconomic contexts for 219 countries and 410 cities. Multivariate linear regression and additive models were built to relate MSW generation, composition, and recovery rates to demographics, economic development, and climate patterns of cities and regions. These models generate new insights into the complex nature of SWMS and provide an evidence-based decision-making tool to future researchers and policy makers. Specifically, economic development (GDP), density factors (population, population density, and household size), sustainability initiatives, education, and regulation are all identified as positive drivers toward the targets of United Nations Sustainable Development Goal 12.11364013699652836065engMunicipal solid waste (MSW)Knowledge baseWaste dataWaste generation predictionRecyclingWaste managementUN SDGRenewable Energy, Sustainability and the EnvironmentBuilding and ConstructionGeneral Environmental ScienceStrategy and ManagementIndustrial and Manufacturing EngineeringSDG 11 - Sustainable Cities and CommunitiesSDG 12 - Responsible Consumption and ProductionSDG 13 - Climate ActionSDG 8 - Decent Work and Economic Growthjournal article10.1016/j.jclepro.2022.134501https://www.scopus.com/pages/publications/85139594657https://www.webofscience.com/wos/woscc/full-record/WOS:000875699500002https://linkinghub.elsevier.com/retrieve/pii/S0959652622040732