Citation

Baffour Gyau, Emmanuel and Li, Yaya and Appiah, Michael and Gyamfi, Bright Akwasi and Onifade, Stephen Taiwo (2025) How does energy management AI technology innovation promote environmental mitigation? Energy Strategy Reviews, 61. p. 101873. ISSN 2211-467X

Text How does energy management AI technology innovation promote environmental mitigation_.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Given the escalating threat of climate change, the potential of artificial intelligence to revolutionize global sustainability efforts lies in its ability to optimize energy management and enhance energy efficiency, thereby paving the way for a greener future. This study investigates the impact of energy management artificial intel ligence technology innovations (EMAITI) on environmental degradation. It examines a panel of 19 countries from 2010 to 2020 by applying the panel correlated standard errors regression model, along with the instru mental variable generalized method of moments for robustness checks. Firstly, the results show that EMAITI reduces environmental degradation, and these findings remain consistent even under robustness tests. The mediation analysis reveals that EMAITI reduces environmental degradation by decreasing energy intensity levels. Additionally, the moderating effects of research and development, as well as globalization, further strengthen the impact of EMAITI in reducing environmental degradation. Financial development, industrialization, and renewable energy consumption are found to reduce environmental degradation, whereas economic growth is associated with increased environmental degradation. However, a heterogeneity analysis reveals variations in effects between developed and developing countries, emphasizing the need for tailored environmental policies. The study emphasizes the significant role of energy management AI technology innovations in lowering energy intensity while highlighting the influences of research and development and globalization to inform effective environmental policies across diverse economies.

Item Type:	Article
Uncontrolled Keywords:	Ecological
Subjects:	Q Science > QH Natural history
Divisions:	Faculty of Business (FOB)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 04:31
Last Modified:	05 Oct 2025 06:14
URII:	http://shdl.mmu.edu.my/id/eprint/14571

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