Measuring artificial intelligence's impact on sustainable energy transition: Empirical insights and policy implications
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28120%2F25%3A63591758" target="_blank" >RIV/70883521:28120/25:63591758 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0140988325006528?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0140988325006528?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.eneco.2025.108825" target="_blank" >10.1016/j.eneco.2025.108825</a>
Alternative languages
Result language
angličtina
Original language name
Measuring artificial intelligence's impact on sustainable energy transition: Empirical insights and policy implications
Original language description
Artificial intelligence (AI) has emerged as a transformative technology with significant potential for accelerating the transition to sustainable energy systems. This study provides novel empirical insights into the effect of AI on energy efficiency and renewable energy integration. Using econometric techniques, such as cross-sectionally augmented error correction models (CS-ECMs) and pooled mean group (PMG) models, we analyzed data from 30 countries (1995–2020). The results indicate that AI patents reduce energy intensity by 0.84 tons of oil equivalent (toe) per 1000 USD and that AI-related research increases the sustainable energy transition index by 10 points in the long term. AI-driven optimization techniques and predictive maintenance have substantial longterm effects on energy sustainability. This study also discusses the implications of AI-driven innovation on energy policies and sustainable economic growth. These findings fill a critical gap in the literature by providing robust empirical evidence of the long-term impact of AI on sustainable energy transitions and offers valuable insight for policymakers and stakeholders aiming to achieve a future with sustainable energy. These findings underscore the importance of sustained investment in AI technologies and interdisciplinary collaboration in achieving global energy sustainability goals. For now, AI's impact on the scale's energy transition is similar to the total factor productivity (TFP) effect, driving long-term sustainable energy transformation.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50201 - Economic Theory
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Energy Economics
ISSN
0140-9883
e-ISSN
1873-6181
Volume of the periodical
2025
Issue of the periodical within the volume
150
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
22
Pages from-to
1-22
UT code for WoS article
001595271200001
EID of the result in the Scopus database
2-s2.0-105013885867