Yogeesh Nijalingappa
Government First Grade College
India
ORCID: 0000-0001-8080-7821
Mohammed Almakki
Amity University Dubai
UAE
ORCID: 0000-0002-9348-4651
Asokan Vasudevan
INTI International University
Malaysia
ORCID: 0000-0002-9866-4045
Anuj Kumar
Al-Quds University
Palestine
ORCID: 0000-0002-1205-2794

Type of Paper: Research
Received: 31 December 2026 / Revised: 23 July 2026 / Accepted: 22 September 2026 / Published: 17 October 2026
DOI: 10.47556/B.OUTLOOK2026.24.1

Purpose: 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.
Design/Methodology/Approach: 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.
Findings: 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.
Originality/Value: 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.
Research Limitations: The study employs benchmark numerical scenarios rather than field observations; however, it provides a transparent comparative framework for future empirical testing.
Practical Implications: Public institutions need to avoid one-size-fits-all automation and instead deploy AI where meaningful human oversight is provided.
Keywords: Artificial Intelligence; Sustainability Governance; Human Intelligence; Environmental, Social and Governance; Optimisation Modelling Strategies; Fuzzy TOPSIS.
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. xx-xx. WASD: London, United Kingdom.