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Real-time analytics and waste intelligence have become transformative solutions for tackling the escalating global challenge of waste management and generation. While much of the current discussion centres on quantitative modelling and technological efficiency, less focus is given to the socio-cultural, institutional, and contextual factors that affect adoption, particularly in South Asia. This chapter adopts a qualitative approach to explore the drivers, barriers, and implications of incorporating waste intelligence and analytics into municipal and industrial waste management systems. Based on 15 semi-structured interviews with municipal, community, technology, and policy makers across India, Sri Lanka, Bangladesh, and the Maldives, the study identifies four thematic areas: (1) operational efficiency, transparency, enhanced recycling; (2) technological reliability, financial hurdles, community resistance; (3) smart cities, public–private partnerships, global sustainability pressures; (4) regional differences in adoption, shaped by governance and cultural factors. The findings suggest that waste intelligence is more than a technical measure; it is also a socio-institutional process that relies on trust, participation, and coherent policy. The chapter concludes with region-specific recommendations for enhancing waste analytics to promote sustainable urban and industrial development.

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Waste Intelligence and Analytics for Real-Time Insights into Waste Generation—a Qualitative Exploration

  • 章节

  • 发表日期：

    2026年

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  • Mananage Shanika Hansini Rathnasiri,
  • Ali Najeeb,
  • Amit Joshi,
  • Muhandiramge Vasana Maduwanthi Rodrigo

摘要

Real-time analytics and waste intelligence have become transformative solutions for tackling the escalating global challenge of waste management and generation. While much of the current discussion centres on quantitative modelling and technological efficiency, less focus is given to the socio-cultural, institutional, and contextual factors that affect adoption, particularly in South Asia. This chapter adopts a qualitative approach to explore the drivers, barriers, and implications of incorporating waste intelligence and analytics into municipal and industrial waste management systems. Based on 15 semi-structured interviews with municipal, community, technology, and policy makers across India, Sri Lanka, Bangladesh, and the Maldives, the study identifies four thematic areas: (1) operational efficiency, transparency, enhanced recycling; (2) technological reliability, financial hurdles, community resistance; (3) smart cities, public–private partnerships, global sustainability pressures; (4) regional differences in adoption, shaped by governance and cultural factors. The findings suggest that waste intelligence is more than a technical measure; it is also a socio-institutional process that relies on trust, participation, and coherent policy. The chapter concludes with region-specific recommendations for enhancing waste analytics to promote sustainable urban and industrial development.