RENEWABLE ENERGY AND GREEN TECHNOLOGY TOWARDS SUSTAINABLE DEVELOPMENT IN NIGERIA: AN ECONOMETRIC DATA ANALYTICS APPROACH

This study examined the challenges affecting renewable energy and green technology towards sustainable development in Nigeria using a data analytics approach. The study was motivated by increasing environmental degradation, rising carbon emissions, unstable energy supply, and the slow pace of renewable energy development despite Nigeria's enormous renewable energy potential. Specifically, the study evaluated the relationship between renewable energy development and sustainable development, examined the role of green technology in environmental sustainability, and explored how data analytics can support evidence-based sustainability planning in Nigeria. The study is anchored on the Environmental Kuznets Curve (EKC) Theory, supported by the Sustainable Development Theory and the Green Growth Theory. Annual time series data covering 1990–2021, sourced from the World Bank, were analysed using descriptive statistics, correlation analysis, unit root testing, the ARDL bounds test, and the Error Correction Model, with the Human Development Index (HDI) as the proxy for sustainable development. Findings revealed a valid long-run equilibrium relationship among carbon emissions, government expenditure, renewable energy consumption, renewable electricity output, and human development, with a correctly signed and significant error correction term, though indicating a slow speed of adjustment. However, none of the individual explanatory variables showed a statistically significant long-run effect on human development, reflecting structural challenges including the dominance of traditional biomass, the marginal scale of modern renewable electricity output, and inconsistent government expenditure patterns. The study concludes that renewable energy and green technology hold genuine long-run potential for sustainable development in Nigeria, though constrained by infrastructural, institutional, and policy-related challenges, and recommends targeted investment, energy transition planning, and the institutionalization of data analytics in sustainability policymaking.