Predictive Environmental Governance: Role of IoT and Artificial Intelligence in Environmental Monitoring in the Niger Delta

Abstract

Nigerian environmental governance, particularly in the Niger Delta region, has been characterized by reactivity as an approach which is very stubborn as if using manual inspection, lengthy laboratory processes, and report generation, none of which are aligned to the dynamics of pollution generation. This research presents an analysis and synthesis of literature that has been published between 2020 and 2025 about the interplay of IoT, artificial intelligence (AI), machine learning (ML), and cloudbased analytics in advancing the development and deployment of predictive environmental management in response to the existing reactive model. By utilizing well over sixty scholarly sources that have been conducted primarily for the air, water, and soil monitoring in both developed and developing countries, the paper traces the evolution of the process of environmental monitoring from manual processes and early-stage sensor solutions to predictive designs while considering various machine learning models, sensor networks, and decision-making procedures. The results generated by those technologies include early warning, compliance monitoring, enhanced community protection, and improved accountability, despite the complexities associated with implementation. Nevertheless, the paper also mentions a number of impediments that hinder the use of predictive environmental governance in resource-constrained and oil-producing settings such as gaps in infrastructure, problems of data governance, cybersecurity threats, and institutional readiness challenges. Lastly, the paper provides recommendations for implementing a transition to predictive environmental governance in Nigeria in phases.