Modeling the effects of environmental degradation, renewable energy, and technological advancement on food security in Sierra Leone: Does institutional quality matter?

Background Achieving food security in Sierra Leone is increasingly constrained by environmental degradation, climate variability, and weak institutional capacity. Despite growing challenges, empirical evidence on the combined influence of environmental stressors, renewable energy use, technological adoption, and institutional quality on food security remains scarce. Food security in this study is assessed through a multidimensional lens, focusing on the production of key staple and export crops. Methods The study analyzes both direct and mediated effects using time series data from 1990 to 2023. Dynamic Autoregressive Distributed Lag (DYNARDL) simulations and Fully Modified Ordinary Least Squares (FMOLS) are employed to capture short- and long-term dynamics, nonlinearities, and asymmetric responses to shocks. Results Renewable energy adoption and technological progress significantly enhance agricultural output, with institutional quality serving as a partial mediator. Conversely, air pollution and temperature variability consistently reduce crop yields, underscoring agriculture’s vulnerability to ecological and climatic stress. Nonlinear estimates indicate diminishing returns when energy and technology inputs exceed optimal levels, while dynamic simulations reveal asymmetric effects between positive and negative shocks. Robustness checks confirm that clean energy and modern inputs bolster production of rice, maize, cassava, cocoa, vegetables, and fruits, whereas environmental degradation uniformly depresses output. Conclusion The findings highlight the urgent need for integrated strategies that combine clean energy transition, sustainable technological innovation, environmental stewardship, and institutional reform. The study offers policy-relevant insights for building climate-resilient food systems in Sierra Leone and other low-income, environmentally vulnerable regions.