Energy Economics, Journal Year: 2024, Volume and Issue: 131, P. 107355 - 107355
Published: Feb. 1, 2024
Language: Английский
Energy Economics, Journal Year: 2024, Volume and Issue: 131, P. 107355 - 107355
Published: Feb. 1, 2024
Language: Английский
Energy Economics, Journal Year: 2022, Volume and Issue: 112, P. 106161 - 106161
Published: June 30, 2022
Language: Английский
Citations
130Resources Policy, Journal Year: 2022, Volume and Issue: 79, P. 102974 - 102974
Published: Sept. 5, 2022
Language: Английский
Citations
118Resources Policy, Journal Year: 2022, Volume and Issue: 79, P. 103006 - 103006
Published: Sept. 20, 2022
Language: Английский
Citations
115Urban Climate, Journal Year: 2022, Volume and Issue: 46, P. 101342 - 101342
Published: Nov. 13, 2022
Language: Английский
Citations
114Information Economics and Policy, Journal Year: 2022, Volume and Issue: 61, P. 101007 - 101007
Published: Oct. 23, 2022
Language: Английский
Citations
108Sustainable Production and Consumption, Journal Year: 2022, Volume and Issue: 35, P. 431 - 443
Published: Dec. 1, 2022
Language: Английский
Citations
108Energy Economics, Journal Year: 2022, Volume and Issue: 115, P. 106343 - 106343
Published: Oct. 10, 2022
Language: Английский
Citations
105Renewable Energy, Journal Year: 2022, Volume and Issue: 195, P. 670 - 680
Published: June 15, 2022
Language: Английский
Citations
81Economic Analysis and Policy, Journal Year: 2022, Volume and Issue: 76, P. 502 - 521
Published: Sept. 12, 2022
Language: Английский
Citations
79Humanities and Social Sciences Communications, Journal Year: 2024, Volume and Issue: 11(1)
Published: Aug. 14, 2024
Abstract This study examines the multifaceted impact of artificial intelligence (AI) on environmental sustainability, specifically targeting ecological footprints, carbon emissions, and energy transitions. Utilizing panel data from 67 countries, we employ System Generalized Method Moments (SYS-GMM) Dynamic Panel Threshold Models (DPTM) to analyze complex interactions between AI development key metrics. The estimated coefficients benchmark model show that significantly reduces footprints emissions while promoting transitions, with most substantial observed in followed by footprint reduction reduction. Nonlinear analysis indicates several insights: (i) a higher proportion industrial sector diminishes inhibitory effect but enhances its positive transitions; (ii) increased trade openness amplifies AI’s ability reduce promote (iii) benefits are more pronounced at levels development, enhancing (iv) as transition process deepens, effectiveness reducing increases, role further transitions decreases. enriches existing literature providing nuanced understanding offers robust scientific foundation for global policymakers develop sustainable management frameworks.
Language: Английский
Citations
79