Abstract:
Urbanization in Sri Lanka is accelerating rapidly, with nearly 47% of the population projected to
live in urban centers by 2035, up from 19% in 2000. This surge, coupled with rising consumerism,
has intensified municipal solid waste (MSW) challenges, particularly in cities like Colombo, which
generates over 1,200 tons of waste daily. Current waste management practices remain linear,
collection, disposal, and landfill, leading to weak recycling rates, poor infrastructure, and limited
citizen engagement, thereby worsening plastic pollution, biodiversity loss, and climate-linked
disasters. This study proposes RESCUE (Real-time Environmental Surveillance and CommunityUtilized Ecology), a novel socio-techno-ecological framework tailored for Sri Lankan urban
contexts. The study employed a conceptual systems design approach integrating a comprehensive
literature review, stakeholder mapping, cultural profiling, ecological assessment, and iterative
framework modelling. RESCUE integrates AI-driven predictive analytics, IoT-enabled
infrastructure, community engagement, and ecological risk profiling to deliver an adaptive,
inclusive, and resilient urban waste management system. The framework comprises four
interconnected layers: data infrastructure, analytics and decision-making, community engagement,
and governance feedback. Comparative analysis with global systems, such as those in Barcelona,
Seoul, Singapore, and INSEE’s Ecocycle, demonstrates RESCUE’s novelty in embedding social
inclusivity, ecological sensitivity, and technological integration. Pilot deployments, multistakeholder collaborations, and real-time monitoring strategies are recommended for validation.
This framework aligns with UN Sustainable Development Goals (SDGs), particularly SDG 11
(Sustainable Cities) and SDG 12 (Responsible Consumption). RESCUE offers a scalable, replicable
model for urban waste governance in Sri Lanka and other rapidly urbanizing South Asian cities.
