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Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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The Future of Behavioral Economics and AI in Sustainable Development

Authors: Durgeshwary Kolhe; Arshad Bhat;

The Future of Behavioral Economics and AI in Sustainable Development

Abstract

Abstract This study examines how behavioral economics and artificial intelligence can be used together to address the problem of sustainable development with a transformative potential. Combining the knowledge of human decision-making and the predictive and analytical abilities of AI, other approaches to developing effective interventions that facilitate sustainable practices in the realms of personality, organization, and policy were proposed. The study addresses the question on how AI-based systems can enhance behavior nudges, maximize economic incentives and overcome cognitive biases that hinder sustainable decision-making. This is based on the use of case studies and theoretical models, emphasis on ethical aspects, limitations on implementation, and the future of this multidisciplinary field. This analysis demonstrates that such a combination can accelerate the progress of achievement of the United Nations Sustainable Development Goals as well as offer equitable and clear solutions that respect human autonomy.

Keywords

Behavioral economics, artificial intelligence, sustainable development, predictive modeling, cognitive biases, green consumption, environmental decisionmaking, SDGs.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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