The attribution problem of a seemingly intangible industry

Q2 Environmental Science Environmental Challenges Pub Date : 2024-08-01 DOI:10.1016/j.envc.2024.101003
{"title":"The attribution problem of a seemingly intangible industry","authors":"","doi":"10.1016/j.envc.2024.101003","DOIUrl":null,"url":null,"abstract":"<div><p>We promote a more comprehensive understanding of the environmental costs associated with AI and “clouds” by raising awareness about the resource and emission-intensive nature of Artificial Intelligence through the use of data centres. While acknowledging the potential sustainability benefits of a green digital transformation, we challenge perceptions of a virtual and dematerialised industry often reinforced through the cloud metaphor. Using the planetary boundary (PB) framework, we go beyond conventional assessments of carbon emissions during a data centre's operational state to train AI and explore their multiple impacts on different Earth systems throughout the entire hardware lifecycle. For each stage of AI's hardware lifecycle, it is necessary to ascertain whether the environmental impacts associated with that stage can be quantified in a manner that allows for comparison with the control variables defined in the PB framework. In a second phase of the study, we examine the geographical distribution of individual lifecycle stages in order to assess the relative vulnerability of countries to the environmental harms associated with AI development stages. By mapping the lifecycle stages around the world, it becomes evident that the extraction, manufacturing, and disposal of AI hardware have a significant detrimental impact on the environment and populations in the Global South (the Majority World), while the benefits of AI development and use are largely concentrated in the West (the Minority World). Our findings underscore the need to scrutinize the benefits and costs associated with AI. To address the issue, it is proposed that more robust environmental policy obligations be introduced for electronic products throughout their entire lifecycle.</p></div>","PeriodicalId":34794,"journal":{"name":"Environmental Challenges","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667010024001690/pdfft?md5=776da69c7767463573112524f864dc11&pid=1-s2.0-S2667010024001690-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Challenges","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667010024001690","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0

Abstract

We promote a more comprehensive understanding of the environmental costs associated with AI and “clouds” by raising awareness about the resource and emission-intensive nature of Artificial Intelligence through the use of data centres. While acknowledging the potential sustainability benefits of a green digital transformation, we challenge perceptions of a virtual and dematerialised industry often reinforced through the cloud metaphor. Using the planetary boundary (PB) framework, we go beyond conventional assessments of carbon emissions during a data centre's operational state to train AI and explore their multiple impacts on different Earth systems throughout the entire hardware lifecycle. For each stage of AI's hardware lifecycle, it is necessary to ascertain whether the environmental impacts associated with that stage can be quantified in a manner that allows for comparison with the control variables defined in the PB framework. In a second phase of the study, we examine the geographical distribution of individual lifecycle stages in order to assess the relative vulnerability of countries to the environmental harms associated with AI development stages. By mapping the lifecycle stages around the world, it becomes evident that the extraction, manufacturing, and disposal of AI hardware have a significant detrimental impact on the environment and populations in the Global South (the Majority World), while the benefits of AI development and use are largely concentrated in the West (the Minority World). Our findings underscore the need to scrutinize the benefits and costs associated with AI. To address the issue, it is proposed that more robust environmental policy obligations be introduced for electronic products throughout their entire lifecycle.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
看似无形的产业的归属问题
我们通过数据中心的使用,提高人们对人工智能的资源和排放密集型性质的认识,从而促进人们更全面地了解与人工智能和 "云 "相关的环境成本。在承认绿色数字化转型可能带来的可持续发展利益的同时,我们也对经常通过云隐喻强化的虚拟和非物质化产业的观念提出了质疑。利用行星边界(PB)框架,我们超越了对数据中心运行状态下碳排放的传统评估,对人工智能进行了训练,并探索了它们在整个硬件生命周期中对不同地球系统的多重影响。对于人工智能硬件生命周期的每个阶段,有必要确定与该阶段相关的环境影响是否可以量化,以便与 PB 框架中定义的控制变量进行比较。在研究的第二阶段,我们研究了各个生命周期阶段的地理分布,以评估各国在人工智能发展阶段相关环境危害面前的相对脆弱性。通过绘制全球各生命周期阶段的地图,我们发现,人工智能硬件的提取、制造和处置对全球南部(多数世界)的环境和人口造成了严重的有害影响,而人工智能开发和使用所带来的益处则主要集中在西方(少数世界)。我们的研究结果突出表明,有必要仔细审查与人工智能相关的收益和成本。为解决这一问题,建议对电子产品的整个生命周期引入更有力的环境政策义务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Environmental Challenges
Environmental Challenges Environmental Science-Environmental Engineering
CiteScore
8.00
自引率
0.00%
发文量
249
审稿时长
8 weeks
期刊最新文献
Long-term monitoring, predicting and connection between built-up land and urban heat island patterns based on remote sensing data Overcoming barriers to proactive plastic recycling toward a sustainable future Development of a brand value measurement model with a corporate social responsibility perspective. A comparative analysis of consumer perception of energy providers in Spain and Colombia Application of meta-heuristic hybrid models in estimating the average air temperature of Caspian sea coast of Iran Global change drives potential niche contraction and range shift of globally threatened African vulture
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1