Qiang Wang , Yufang Li , Ugur Korkut Pata , Rongrong Li
{"title":"人工智能与全球碳不平等:应对可持续发展目标10、12和13的挑战和机遇","authors":"Qiang Wang , Yufang Li , Ugur Korkut Pata , Rongrong Li","doi":"10.1016/j.gsf.2025.102072","DOIUrl":null,"url":null,"abstract":"<div><div>This study examines the impact of artificial intelligence (AI) on carbon inequality (CI) in 67 countries from 1995 to 2019. The results suggest that (i) AI significantly amplifies CI both between and within countries due to its energy requirements and uneven deployment; (ii) trade openness and global value chain (GVC) positioning mitigate AI’s effect on inter-country CI, while robust governance—marked by larger government size and institutional transparency—curtails intra-country disparities; (iii) specific thresholds (trade openness > 4.74, GVC position > −1.07, government size > 2.90, transparency > −0.22) shift the impact of AI from exacerbating to reducing CI. The adverse effects of AI can be reversed through enhanced trade, GVC integration, and strong governance. Key policy implications: Policymakers must prioritize exceeding these thresholds to leverage AI for sustainable and equitable outcomes. This requires (a) promoting trade liberalization to spread the benefits of AI globally, reducing inter-country CI; (b) strengthening GVC participation to offset the carbon-intensive use of AI; (c) building government capacity and transparency to ensure fair adoption of AI domestically; and (d) embedding these strategies in climate policies to align AI with the long-term goals of environmental justice and the SDGs, particularly climate action (SDG 13) and reducing inequalities (SDG 10).</div></div>","PeriodicalId":12711,"journal":{"name":"Geoscience frontiers","volume":"16 4","pages":"Article 102072"},"PeriodicalIF":8.5000,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence and global carbon inequality: Addressing the challenges and opportunities for SDG 10, SDG 12, and SDG 13\",\"authors\":\"Qiang Wang , Yufang Li , Ugur Korkut Pata , Rongrong Li\",\"doi\":\"10.1016/j.gsf.2025.102072\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study examines the impact of artificial intelligence (AI) on carbon inequality (CI) in 67 countries from 1995 to 2019. The results suggest that (i) AI significantly amplifies CI both between and within countries due to its energy requirements and uneven deployment; (ii) trade openness and global value chain (GVC) positioning mitigate AI’s effect on inter-country CI, while robust governance—marked by larger government size and institutional transparency—curtails intra-country disparities; (iii) specific thresholds (trade openness > 4.74, GVC position > −1.07, government size > 2.90, transparency > −0.22) shift the impact of AI from exacerbating to reducing CI. The adverse effects of AI can be reversed through enhanced trade, GVC integration, and strong governance. Key policy implications: Policymakers must prioritize exceeding these thresholds to leverage AI for sustainable and equitable outcomes. This requires (a) promoting trade liberalization to spread the benefits of AI globally, reducing inter-country CI; (b) strengthening GVC participation to offset the carbon-intensive use of AI; (c) building government capacity and transparency to ensure fair adoption of AI domestically; and (d) embedding these strategies in climate policies to align AI with the long-term goals of environmental justice and the SDGs, particularly climate action (SDG 13) and reducing inequalities (SDG 10).</div></div>\",\"PeriodicalId\":12711,\"journal\":{\"name\":\"Geoscience frontiers\",\"volume\":\"16 4\",\"pages\":\"Article 102072\"},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2025-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoscience frontiers\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1674987125000775\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscience frontiers","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1674987125000775","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Artificial intelligence and global carbon inequality: Addressing the challenges and opportunities for SDG 10, SDG 12, and SDG 13
This study examines the impact of artificial intelligence (AI) on carbon inequality (CI) in 67 countries from 1995 to 2019. The results suggest that (i) AI significantly amplifies CI both between and within countries due to its energy requirements and uneven deployment; (ii) trade openness and global value chain (GVC) positioning mitigate AI’s effect on inter-country CI, while robust governance—marked by larger government size and institutional transparency—curtails intra-country disparities; (iii) specific thresholds (trade openness > 4.74, GVC position > −1.07, government size > 2.90, transparency > −0.22) shift the impact of AI from exacerbating to reducing CI. The adverse effects of AI can be reversed through enhanced trade, GVC integration, and strong governance. Key policy implications: Policymakers must prioritize exceeding these thresholds to leverage AI for sustainable and equitable outcomes. This requires (a) promoting trade liberalization to spread the benefits of AI globally, reducing inter-country CI; (b) strengthening GVC participation to offset the carbon-intensive use of AI; (c) building government capacity and transparency to ensure fair adoption of AI domestically; and (d) embedding these strategies in climate policies to align AI with the long-term goals of environmental justice and the SDGs, particularly climate action (SDG 13) and reducing inequalities (SDG 10).
Geoscience frontiersEarth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
17.80
自引率
3.40%
发文量
147
审稿时长
35 days
期刊介绍:
Geoscience Frontiers (GSF) is the Journal of China University of Geosciences (Beijing) and Peking University. It publishes peer-reviewed research articles and reviews in interdisciplinary fields of Earth and Planetary Sciences. GSF covers various research areas including petrology and geochemistry, lithospheric architecture and mantle dynamics, global tectonics, economic geology and fuel exploration, geophysics, stratigraphy and paleontology, environmental and engineering geology, astrogeology, and the nexus of resources-energy-emissions-climate under Sustainable Development Goals. The journal aims to bridge innovative, provocative, and challenging concepts and models in these fields, providing insights on correlations and evolution.