Waste management is a growing challenge globally, with major impacts on social health, greenhouse gas emissions, and sustainable development. This paper provides an in-depth analysis on the potential of emerging technologies like the Internet of Things (IoT), blockchain platforms, big data analytics and artificial intelligence to enable more intelligent, sustainable waste management systems. A robust methodology of literature review, real-world case analysis, deduction and critical reasoning was utilized. The key findings are: (1) logistics optimization through machine learning driven dynamic routing and load optimization, reducing costs by 25-40%, (2) GHG emission reductions above 15% from optimized transportation, (3) 40%+ improvements in recycling rates and landfill diversion through waste stream automation and citizen engagement apps, and (4) over 50% reduction in waste contamination enabled by automated waste characterization using image recognition. However, barriers like infrastructure costs, lack of capabilities, and change management constrain adoption. Targeted pilots, open data sharing, and partnerships can drive implementation. Intelligent waste systems are critical for cities to cost-effectively tackle the growing waste challenge while meeting sustainability goals.
International Conference on Computer Science Electronics and Information (ICCSEI 2023), Yogyakarta, Indonesia, Edited by Wibawa, A.P.; Pranolo, A.; Drezewski, R.; Hernandez, L.; Ismail, A.R.; Haviluddin, ; Saleh, A.Y.; Ghosh, A.; Kurniawan, F.; Konate, S.; Abdalla, M.A.A.; E3S Web of Conferences, Volume 501, id.01010
https://ui.adsabs.harvard.edu/link_gateway/2024E3SWC.50101010G/doi:10.1051/e3sconf/202450101010
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