Digital and Computational Innovations in Physical Sciences: A Tool for Sustainable Development in Nigeria

Nigeria’s sustainable development faces critical challenges arising from climate variability, energy insecurity, and
environmental degradation, necessitating robust applications of the physical sciences. Conventional analytical
methods have been limited in addressing the nonlinear and data-intensive nature of environmental and energy
systems, particularly within tropical contexts. Digital and computational innovations, including artificial
intelligence, machine learning, and computational optimization, have emerged as effective tools for enhancing
predictive accuracy, system optimization, and evidence-based decision-making. This study examines the
application of digital and computational innovations in advancing physical sciences research in Nigeria, with
focus on climate modeling, rainfall prediction, and renewable energy system optimization using a systematic
literature review. Machine learning and adaptive neuro-fuzzy models demonstrate improved rainfall prediction
accuracy, enhancing disaster preparedness and climate planning. Computational optimization applied to hybrid
renewable energy system integrates solar, wind and storage solutions. It is cost efficient, reliable, and reduces
carbon emissions, thereby supporting sustainable energy transitions. The study finds that digital and
computational innovations are crucial for societal impact in supporting climate resilience, energy security, and
long-term socio-economic growth, also strengthening the contribution of physical sciences to Nigeria’s
sustainable development agenda.

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