Integrating Artificial Intelligence into Resource Management for Sustainable Economic Growth in Ghana

This study examines how Artificial Intelligence (AI) integration within resource management (RM) systems fosters sustainable economic growth in Ghana. Amid persistent challenges of resource inefficiency and environmental degradation in resource-dependent economies, the research explores how AI-driven innovations enhance operational performance and ecological balance across key sectors — energy, agriculture, mining, and water. Drawing on annual time-series and panel data from 2000–2023, the study employs a mixed-econometric framework combining the Nonlinear Autoregressive Distributed Lag (NARDL) model with panel-based Fixed Effects (FE), Random Effects (RE), and System-GMM estimators. Core variables include GDP per capita, sectoral output, AI infrastructure, digital penetration, smart technology use, and RM indices, alongside controls for foreign direct investment, trade openness, human capital, and energy use. Stationarity, cointegration, and diagnostic tests confirm model validity. The results reveal a significant asymmetric impact of AI on economic growth — positive shocks yield persistent gains, while negative shocks constrain short-run performance. Resource management exerts a strong complementary effect, particularly when enhanced by AI interventions, and governance quality amplifies this synergy. Control variables such as Foreign Direct Investment (FDI) and human capital contribute positively, whereas excessive energy use undermines sustainability. The findings advance growth and innovation theory by demonstrating that AI-induced technological shocks generate nonlinear and asymmetric responses in developing contexts. By integrating AI and RM within a unified analytical model, this study provides new empirical evidence on how digital transformation can catalyze inclusive, sustainable development in resource-rich emerging economies and underscores the policy importance of institutional capacity, AI governance, and sectoral readiness.