The interplay between Municipal Solid Waste (MSW) Management and data science unveils a panorama of opportunities and challenges, set against the backdrop of rising global waste and evolving technological landscapes. This article threads through the multifaceted aspects of incorporating data science into MSW management, unearthing key findings, novel knowledge, and instigating a call to action for stakeholders (e.g. policymakers, local authorities, waste management professionals, technology developers, and the general public) across the spectrum. Predominant challenges like the enigmatic nature of "black-box" models and tangible knowledge gaps in the sector are scrutinized, ushering in a narrative that emphasizes transparent, stakeholder-inclusive, and policy-adaptive approaches. Notably, a conscious shift towards "white-box" and "grey-box" data science models has been spotlighted as a pivotal response to transparency issues. Furthermore, the discourse highlights the necessity of crafting data science solutions that are specifically moulded to the nuanced challenges of MSW management, and it underscores the importance of recalibrating existing policies to be reflexive to technological advancements. A resolute call echoes for stakeholders to not just adapt but immerse themselves in a continuous learning trajectory, championing transparency, and fostering collaborations that hinge on innovative, data-driven methodologies. Thus, as the realms of data science and MSW management entwine, the article sheds light on the potential transformation awaiting waste management paradigms, contingent on the nurtured amalgamation of technological advances, policy alignment, and collaborative synergy.
Keywords: Artificial intelligence; Circular economy; Machine learning; Municipal solid waste; Waste-to-energy processing.
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