Artificial Intelligence, Environmental Economics, and Sustainable Development: Oil Spill Monitoring and Management in Niger Delta

Abstract

The frequent oil spills in Niger Delta have continued to create serious environmental, economic, and social problems, despite several regulations and environmental management over the years. This study focuses on oil spill monitoring and management in the Niger Delta region of Nigeria through the application of artificial intelligence from the perspective of environmental economics and sustainable development. A mixed-methods research design using quantitative and qualitative data collected from environmental agencies, oil company staff, community leaders, environmental activists, intellectuals, and policymakers from Rivers, Bayelsa, Delta, and Akwa Ibom States were adopted for the study. Sustainable development theory, environmental Kuznets curve theory, and technology acceptance model were the study bases. Findings indicate that AI-driven technologies significantly improve the speed, accuracy, and efficiency of oil spill detection, environmental risk assessment, and remediation planning. It also finds that AI adoption positively impacts the environment, reduces environmental degradation and improves community livelihoods. However, challenges relating to data infrastructure, institutional capacity, funding, and policy implementation remain major obstacles. Study findings reveal that AI-based environmental monitoring systems can contribute to promoting sustainable environmental development and enhancing environmental accountability in the Niger Delta region of Nigeria. Policy recommendations are offered to governments, oil companies, environmental stakeholders, etc.