This study contrasts Artificial Intelligence (AI) and Human Intelligence (HI) in addressing sustainability issues that require a trade-off between technical efficiency, ethical judgement and contextual interpretation.
This study uses triangular fuzzy numbers, fused Analytic Hierarchy Process (AHP)-entropy weights and fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to compare AI and HI across five sustainability-governance scenarios. Sensitivity analysis is applied to identify the technical-weight thresholds where AI or HI becomes more suitable.
AI outperforms HI in structured, information-rich tasks such as renewable energy dispatch and circular waste logistics. HI outperforms AI in unstructured, context-rich, ethically dense tasks such as climate-smart agriculture and Environmental, Social and Governance (ESG)-oriented public procurement. The average closeness coefficient is 0.432 for AI and 0.568 for HI.
The study compares AI and HI optimisation modelling strategies for sustainability governance in a mathematically explicit, uncertainty-aware manner. It contributes to diverse areas of risk management research and provides a decision-making framework for industries seeking solutions to address sustainability governance challenges.
The study employs benchmark numerical scenarios rather than field observations; however, it provides a transparent comparative framework for future empirical testing.
Public institutions need to avoid one-size-fits-all automation and instead deploy AI where meaningful human oversight is provided.
Citation: Nijalingappa, Y., Almakki, M., Vasudevan, A. and Kumar, A. (2026): A Comparative Numerical Study of Artificial Intelligence and Human Intelligence in Solving Sustainability Problems. In Ahmed, A. (Ed.): World Sustainable Development Outlook 2026, Vol. 22, pp. 7-25. WASD: London, United Kingdom.
