A comparative numerical study of artificial intelligence and human intelligence in solving sustainability problems, Yogeesh Nijalingappa, Mohammed Almakki, Asokan Vasudevan, Anuj Kumar

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

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.
Originality/value: This study contributes to diverse risk management research areas and provides a decision-making framework for industries seeking solutions to mitigate this phenomenon. We analyze five benchmark sustainability-governance scenarios.
Results: AI outperforms HI in structured, information-rich tasks such as renewable energy dispatch and waste logistics; HI outperforms AI in unstructured, context-rich, ethically dense tasks such as agriculture governance and ESG-oriented public procurement. In AI it is 0.432 and in HI it is 0.568 mean closeness coefficients respectively.
Research limitations/implications: The study employs benchmark numerical scenarios as opposed to field observations; however, it provides a transparent comparative framework that can be used in future empirical testing.
Practical implications: Public institutions need to avoid one-size-fits-all automation and rather deploy AI where a meaningful human oversight is provided.
Originality/value: The study compares AI and HI optimization modelling strategies for sustainability governance in a mathematically explicit, uncertainty aware manner.
Keywords: Artificial Intelligence; Sustainability Governance; Human Intelligence; Fuzzy TOPSIS; Decision-making; Numerical Modelling; Ethics; Renewable Energy.