Lagos, Nigeria, faces significant challenges in managing its municipal solid waste, with a rapidly growing population, insufficient infrastructure, and inadequate data collection and management practices. This study explores the potential of accurate waste data to drive artificial intelligence (AI) interventions and inform policy reforms to improve Lagos's waste management system. Through a qualitative research design, involving semi-structured interviews with key stakeholders, field observations, and document analysis, the study identifies the current challenges and opportunities in Lagos's waste management landscape.
The findings reveal the critical role of the informal sector in waste collection and recycling, the lack of reliable waste data due to infrastructure gaps and inconsistent reporting, and the potential for AI-driven solutions to optimize waste collection, transportation, and resource recovery. The study also highlights the importance of inclusive policies and governance structures that engage stakeholders, support the integration of informal waste workers, and promote a circular economy approach.
Drawing on the Socio-Technical Transitions Theory (STT) and the Advocacy Coalition Framework (ACF), the study analyzes the complex interplay of technological, social, and political factors shaping waste management transitions in Lagos.
The study proposes a set of actionable recommendations, including establishing a robust data management system, strengthening policy frameworks and governance, promoting inclusive waste management practices among others. These recommendations are grounded in best practices and successful examples from other cities and regions, adapted to the specific context of Lagos.
The findings and recommendations provide valuable insights for policymakers, waste management authorities, and researchers seeking to develop innovative, data-driven, and socially inclusive solutions to the waste management challenges in Lagos and beyond.
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