Journal of Economic, Finance Research and Review (JEFRR)

Behavioural Science and AI-Driven Interventions in Energy Conservation: A Systematic Literature Review

Sudarmaji, Eka

27 February 2026 · Vol. 2, Issue 2, pp. 130-137

DOI: 10.65150/EP-jefrr/V2E2/2026-08

Abstract

This systematic literature review synthesises findings from 45 peer-reviewed studies (2016–January 2026) on behavioural interventions for energy conservation. Key results show that social comparison nudges lower electricity use by 1.5% to 2.9%; about 52% of savings persist post-intervention via technology. Intervention efficacy is 2–4 times higher among liberals versus conservatives and is context-dependent. AI-personalised interventions outperform static nudges, reducing decay effects by up to half. Boosts foster sustained performance, with 26.6% greater long-term effects than nudges. Notable challenges include moral licensing (5–8% rebound), boomerang effects among low users, and publication bias that inflates impact estimates. There are gaps in cross-cultural validation, long-term persistence, transparency, trust, and integration in energy planning. This review provides actionable policy recommendations for designing ethical and effective behavioural programs that support technology-driven climate action.

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Sudarmaji, & Eka (2026). Behavioural Science and AI-Driven Interventions in Energy Conservation: A Systematic Literature Review. Journal of Economic, Finance Research and Review, 2(2), 130-137. https://doi.org/10.65150/EP-jefrr/V2E2/2026-08

@article{Sudarmaji2026,
  title   = {Behavioural Science and AI-Driven Interventions in Energy Conservation: A Systematic Literature Review},
  author  = {Sudarmaji and Eka},
  journal = {Journal of Economic, Finance Research and Review},
  year    = {2026},
  volume  = {2},
  number  = {2},
  pages   = {130-137},
  doi     = {10.65150/EP-jefrr/V2E2/2026-08},
  url     = {https://doi.org/10.65150/EP-jefrr/V2E2/2026-08}
}

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