Measuring artificial intelligence's impact on sustainable energy transition: Empirical insights and policy implications

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Measuring artificial intelligence's impact on sustainable energy transition: Empirical insights and policy implications

Popis výsledku v původním jazyce

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&apos;s impact on the scale&apos;s energy transition is similar to the total factor productivity (TFP) effect, driving long-term sustainable energy transformation.

Název v anglickém jazyce

Measuring artificial intelligence's impact on sustainable energy transition: Empirical insights and policy implications

Popis výsledku anglicky

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&apos;s impact on the scale&apos;s energy transition is similar to the total factor productivity (TFP) effect, driving long-term sustainable energy transformation.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50201 - Economic Theory

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy Economics

ISSN

0140-9883

e-ISSN

1873-6181

Svazek periodika

2025

Číslo periodika v rámci svazku

150

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

22

Strana od-do

1-22

Kód UT WoS článku

001595271200001

EID výsledku v databázi Scopus

2-s2.0-105013885867