Zero-Waste City Planning in Nigeria Using AI-Driven Spatioeconomic Scenario Modelling

Uncontrolled urban expansion, infrastructural inadequacies, and weak waste governance continue to pose significant environmental and public health risks in Nigerian cities. This study presents a high-resolution, AI-driven spatioeconomic modelling framework for zero-waste city planning, applied to Lagos, Abuja, and Port Harcourt. Using Long Short-Term Memory (LSTM) neural networks integrated with spatial datasets and socioeconomic indicators, the study forecasts municipal solid waste (MSW) generation and evaluates the impact of three strategic scenarios: Baseline, Policy-Enforced, and Innovation-Driven. The modelling system demonstrated high predictive performance, with R² values exceeding 0.90 and error margins consistently below 10%. Results indicate that the Innovation-Driven scenario—featuring smart bins, decentralized composting hubs, real-time waste routing, and incentive-based citizen engagement—produced the most sustainable outcomes, reducing waste volumes by up to 35% while expanding spatial service coverage. The Policy-Enforced scenario improved collection visibility but lacked systemic adaptability, while the Baseline trajectory projected unsustainable accumulation of waste in urban corridors. Spatial analysis revealed pronounced inequalities in service access, with underserved clusters concentrated in informal settlements and peri-urban zones. These disparities were substantially mitigated under the Innovation-Driven scenario, demonstrating the capacity of intelligent infrastructure to rebalance urban waste flows. The findings underscore the necessity of integrating AI, geospatial intelligence, and circular economy strategies into national and municipal planning systems. This research provides a replicable framework for data-informed, technology-enabled zero-waste transitions in Africa’s rapidly urbanizing contexts. It offers practical guidance for governments, urban planners, and development agencies working toward inclusive and resilient city futures.