TY - JOUR
T1 - Can artificial intelligence contribute to sustainable development by reducing the impact of energy supply on CO2 emissions?
AU - Zambrano-Monserrate, Manuel A.
AU - Hernández Soto, Gonzalo
AU - Subramaniam, Yogeeswari
N1 - Publisher Copyright:
© 2025 Taylor & Francis Group, LLC.
PY - 2025
Y1 - 2025
N2 - This paper analyzed the moderating role of artificial intelligence (AI) in the relationship between CO2 emissions and energy supply in 64 developed and developing countries over the period 2000–2019. To our knowledge, this is the first study to specifically address this question. Our findings show that while both total energy supply and AI usage individually contribute to higher CO2 emissions, their interaction has a mitigating effect, reducing emissions when AI adoption increases alongside energy supply. However, this moderating effect is not uniform across all contexts. The quantile analysis shows that AI’s capacity to reduce emissions is more pronounced in high-emission countries, while in lower-emission economies, its impact is weaker. Additionally, income-level analysis indicates that AI is more effective in curbing emissions in high-income countries due to better infrastructure and technological integration, whereas its effect is negligible in lower-middle-income nations. These results highlight the importance of tailored and adaptive policy approaches that consider both economic and environmental contexts to maximize AI’s potential in emission reduction strategies.
AB - This paper analyzed the moderating role of artificial intelligence (AI) in the relationship between CO2 emissions and energy supply in 64 developed and developing countries over the period 2000–2019. To our knowledge, this is the first study to specifically address this question. Our findings show that while both total energy supply and AI usage individually contribute to higher CO2 emissions, their interaction has a mitigating effect, reducing emissions when AI adoption increases alongside energy supply. However, this moderating effect is not uniform across all contexts. The quantile analysis shows that AI’s capacity to reduce emissions is more pronounced in high-emission countries, while in lower-emission economies, its impact is weaker. Additionally, income-level analysis indicates that AI is more effective in curbing emissions in high-income countries due to better infrastructure and technological integration, whereas its effect is negligible in lower-middle-income nations. These results highlight the importance of tailored and adaptive policy approaches that consider both economic and environmental contexts to maximize AI’s potential in emission reduction strategies.
KW - Artificial intelligence
KW - CO2 emissions
KW - energy supply
KW - moderating effect
KW - panel data
UR - https://www.scopus.com/pages/publications/105015417980
U2 - 10.1080/15567249.2025.2558529
DO - 10.1080/15567249.2025.2558529
M3 - Artículo
AN - SCOPUS:105015417980
SN - 1556-7249
VL - 20
JO - Energy Sources, Part B: Economics, Planning and Policy
JF - Energy Sources, Part B: Economics, Planning and Policy
IS - 1
M1 - 2558529
ER -