TY - JOUR
T1 - Unraveling the role of artificial intelligence, eco-innovation efficiency, and stringent environmental policies in environmental sustainability
T2 - Is the load capacity curve hypothesis true in G7 economies?
AU - Hamid, Salman
AU - Wang, Ke
AU - Zhang, Xiang
N1 - Publisher Copyright:
© 2025 Elsevier Ltd.
PY - 2026/6
Y1 - 2026/6
N2 - In recent times, the global environmental repercussions have intensified the imminent threat of global warming and climate change. In response, implementing innovative approaches and sustainable practices for ecological preservation remains a considerable challenge even for developed nations, such as G7. It is therefore inevitable to identify key factors driving the progress of environmental sustainability. Motivated by this, the current research is an earliest attempt which delve the impact of artificial intelligence (AI), eco-innovation efficiency (EIE), environmental policy stringency (EPS), and green growth (GG) on load capacity factor (LCF) under the load capacity curve (LCC) framework to achieve environmental sustainability in G7 countries. In this regard, innovative approaches of Driscoll-Kraay standard errors (DKSE) and panel-corrected standard errors (PCSE) are employed to investigate the long-run relationships, using the data from 1990 to 2020. The findings highlight that: (i) eco-innovation efficiency primarily promotes environmental sustainability by improving load capacity factor, which is advantageous for G7 countries; (ii) artificial intelligence, environmental policy stringency, and green growth inhabits environmental sustainability by decreasing load capacity factor, which are detrimental for G7 countries; (iii) the LCC hypothesis is invalid in G7 countries illustrating an inverted “U-shaped” relationship between income and LCF. This implies that economic growth initially improves environmental sustainability but later deteriorates the environment after reaching a certain threshold. These findings emphasize that decision-makers should restructure energy and environmental policies for G7 countries by prioritizing AI technologies, augmenting stringent environmental policies, implementing clean energy initiatives, and decoupling economic growth and resource consumption along with further strengthening ecologically efficient technologies.
AB - In recent times, the global environmental repercussions have intensified the imminent threat of global warming and climate change. In response, implementing innovative approaches and sustainable practices for ecological preservation remains a considerable challenge even for developed nations, such as G7. It is therefore inevitable to identify key factors driving the progress of environmental sustainability. Motivated by this, the current research is an earliest attempt which delve the impact of artificial intelligence (AI), eco-innovation efficiency (EIE), environmental policy stringency (EPS), and green growth (GG) on load capacity factor (LCF) under the load capacity curve (LCC) framework to achieve environmental sustainability in G7 countries. In this regard, innovative approaches of Driscoll-Kraay standard errors (DKSE) and panel-corrected standard errors (PCSE) are employed to investigate the long-run relationships, using the data from 1990 to 2020. The findings highlight that: (i) eco-innovation efficiency primarily promotes environmental sustainability by improving load capacity factor, which is advantageous for G7 countries; (ii) artificial intelligence, environmental policy stringency, and green growth inhabits environmental sustainability by decreasing load capacity factor, which are detrimental for G7 countries; (iii) the LCC hypothesis is invalid in G7 countries illustrating an inverted “U-shaped” relationship between income and LCF. This implies that economic growth initially improves environmental sustainability but later deteriorates the environment after reaching a certain threshold. These findings emphasize that decision-makers should restructure energy and environmental policies for G7 countries by prioritizing AI technologies, augmenting stringent environmental policies, implementing clean energy initiatives, and decoupling economic growth and resource consumption along with further strengthening ecologically efficient technologies.
KW - Artificial intelligence
KW - Eco-innovation efficiency
KW - Environmental sustainability
KW - G7
KW - LCC hypothesis
KW - Stringent environmental policies
UR - https://www.scopus.com/pages/publications/105024354283
U2 - 10.1016/j.techsoc.2025.103187
DO - 10.1016/j.techsoc.2025.103187
M3 - Article
AN - SCOPUS:105024354283
SN - 0160-791X
VL - 85
JO - Technology in Society
JF - Technology in Society
M1 - 103187
ER -