引领人工智能驱动的可再生能源供应链转型:数字化战略路线图

IF 4.4 2区 工程技术 Q2 ENERGY & FUELS
Iman Ghasemian Sahebi , Abolfazl Edalatipour , Mooud Dabaghiroodsari , Seyyed Mohammad Hossein Hasheminasab , Behzad Masoomi , Seyedeh Elham Kamali
{"title":"引领人工智能驱动的可再生能源供应链转型:数字化战略路线图","authors":"Iman Ghasemian Sahebi ,&nbsp;Abolfazl Edalatipour ,&nbsp;Mooud Dabaghiroodsari ,&nbsp;Seyyed Mohammad Hossein Hasheminasab ,&nbsp;Behzad Masoomi ,&nbsp;Seyedeh Elham Kamali","doi":"10.1016/j.esd.2025.101663","DOIUrl":null,"url":null,"abstract":"<div><div>The global transition toward renewable energy necessitates supply chains that are not only sustainable but also digitally transformed - a concept we term digitainability. In this regard, Artificial Intelligence (AI) technology has emerged as a promising tool for advancing the digitainability of the renewable energy supply chain. This study investigates the transformative role of AI in advancing the digitainability of renewable energy supply chains. Through an extensive, content-focused literature review, the researchers identified 11 distinct AI functions critical to RESC digitainability. To better understand how these functions interact and complement each other, the study applied the Interpretive Structural Modeling (ISM) method, drawing on insights from supply chain experts. By employing ISM, we uncover the interdependencies among these functions and develop a strategic roadmap for their sequential implementation. Unlike prior studies, which often adopt linear approaches, this research provides a systemic and holistic framework for integrating AI capabilities to enhance supply chain sustainability. The roadmap equips managers and stakeholders with actionable insights to prioritize investments, foster collaboration, and navigate the complexities of AI adoption in RESC. By bridging theoretical exploration with practical application, this study contributes to the global effort to achieve a sustainable and digital energy future.</div></div>","PeriodicalId":49209,"journal":{"name":"Energy for Sustainable Development","volume":"85 ","pages":"Article 101663"},"PeriodicalIF":4.4000,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Navigating the AI-powered transformation of renewable energy supply chains: A strategic roadmap to digitainability\",\"authors\":\"Iman Ghasemian Sahebi ,&nbsp;Abolfazl Edalatipour ,&nbsp;Mooud Dabaghiroodsari ,&nbsp;Seyyed Mohammad Hossein Hasheminasab ,&nbsp;Behzad Masoomi ,&nbsp;Seyedeh Elham Kamali\",\"doi\":\"10.1016/j.esd.2025.101663\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The global transition toward renewable energy necessitates supply chains that are not only sustainable but also digitally transformed - a concept we term digitainability. In this regard, Artificial Intelligence (AI) technology has emerged as a promising tool for advancing the digitainability of the renewable energy supply chain. This study investigates the transformative role of AI in advancing the digitainability of renewable energy supply chains. Through an extensive, content-focused literature review, the researchers identified 11 distinct AI functions critical to RESC digitainability. To better understand how these functions interact and complement each other, the study applied the Interpretive Structural Modeling (ISM) method, drawing on insights from supply chain experts. By employing ISM, we uncover the interdependencies among these functions and develop a strategic roadmap for their sequential implementation. Unlike prior studies, which often adopt linear approaches, this research provides a systemic and holistic framework for integrating AI capabilities to enhance supply chain sustainability. The roadmap equips managers and stakeholders with actionable insights to prioritize investments, foster collaboration, and navigate the complexities of AI adoption in RESC. By bridging theoretical exploration with practical application, this study contributes to the global effort to achieve a sustainable and digital energy future.</div></div>\",\"PeriodicalId\":49209,\"journal\":{\"name\":\"Energy for Sustainable Development\",\"volume\":\"85 \",\"pages\":\"Article 101663\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-02-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy for Sustainable Development\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0973082625000134\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy for Sustainable Development","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0973082625000134","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

摘要

全球向可再生能源的转型不仅需要可持续的供应链,还需要数字化转型——我们称之为数字化转型。在这方面,人工智能(AI)技术已成为推进可再生能源供应链数字化的有前途的工具。本研究探讨了人工智能在推进可再生能源供应链数字化方面的变革性作用。通过广泛的、以内容为中心的文献综述,研究人员确定了11种不同的人工智能功能,这些功能对RESC的数字化至关重要。为了更好地理解这些功能是如何相互作用和互补的,该研究应用了解释结构建模(ISM)方法,借鉴了供应链专家的见解。通过使用ISM,我们揭示了这些功能之间的相互依赖关系,并为它们的顺序实现制定了战略路线图。与以往通常采用线性方法的研究不同,本研究为整合人工智能能力以增强供应链可持续性提供了一个系统和整体的框架。该路线图为管理人员和利益相关者提供了可操作的见解,以确定投资的优先级,促进合作,并在RESC中应对人工智能采用的复杂性。通过将理论探索与实际应用相结合,本研究为实现可持续和数字化能源未来的全球努力做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Navigating the AI-powered transformation of renewable energy supply chains: A strategic roadmap to digitainability
The global transition toward renewable energy necessitates supply chains that are not only sustainable but also digitally transformed - a concept we term digitainability. In this regard, Artificial Intelligence (AI) technology has emerged as a promising tool for advancing the digitainability of the renewable energy supply chain. This study investigates the transformative role of AI in advancing the digitainability of renewable energy supply chains. Through an extensive, content-focused literature review, the researchers identified 11 distinct AI functions critical to RESC digitainability. To better understand how these functions interact and complement each other, the study applied the Interpretive Structural Modeling (ISM) method, drawing on insights from supply chain experts. By employing ISM, we uncover the interdependencies among these functions and develop a strategic roadmap for their sequential implementation. Unlike prior studies, which often adopt linear approaches, this research provides a systemic and holistic framework for integrating AI capabilities to enhance supply chain sustainability. The roadmap equips managers and stakeholders with actionable insights to prioritize investments, foster collaboration, and navigate the complexities of AI adoption in RESC. By bridging theoretical exploration with practical application, this study contributes to the global effort to achieve a sustainable and digital energy future.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Energy for Sustainable Development
Energy for Sustainable Development ENERGY & FUELS-ENERGY & FUELS
CiteScore
8.10
自引率
9.10%
发文量
187
审稿时长
6-12 weeks
期刊介绍: Published on behalf of the International Energy Initiative, Energy for Sustainable Development is the journal for decision makers, managers, consultants, policy makers, planners and researchers in both government and non-government organizations. It publishes original research and reviews about energy in developing countries, sustainable development, energy resources, technologies, policies and interactions.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信