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经济学人智库:2025绿色智能:绘制可持续人工智能的未来研究报告(中译版)(24页).pdf

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1、Greening intelligence:Charting the future of sustainable AISupported byEconomist Impact 2025Greening intelligence:Charting the future of sustainable AI2Contents3 About the report4 Executive summary6 Introduction8 Opportunities12 Stakeholder perceptions13 Challenges18 Moving forward20 Key takeaways21

2、 Appendix:Survey demographicsEconomist Impact 2025Greening intelligence:Charting the future of sustainable AI3Greening intelligence:Charting the future of sustainable AI is a report from Economist Impact,supported by Delta Electronics,that examines the challenges and opportunities that organisations

3、 worldwide face with Artificial Intelligence(AI)and sustainability.The report is based on a survey of 608 senior executives conducted between November 2024 and January 2025;all of the survey respondents are familiar with their firms plans for adoption and use of AI or providing AI services and produ

4、cts.The largest proportion of respondents come from the Asia-Pacific region(40.6%),followed by North America(29.4%),Europe(28.5%),the Middle East(0.8%)and Latin America(0.7%).Four in ten survey respondents(41%)are C-level executives or board members.Almost three-quarters(73%)of executives represent

5、organisations with annual global revenue above US$500m.Respondents come from a mix of AI suppliers and AI users,with the latter having a wide range of industries represented.Complete demographics can be found in the appendix.In addition,in-depth desk research and interviews with business leaders and

6、 experts were conducted between February and March 2025.Our thanks are due to the following interviewees for their time and insights:Prerit Mishra,head of data&analytics,DHL Asia-Pacific Josh Parker,head of sustainability and senior director of corporate sustainability,Nvidia Amitabh Sakar,vice-pres

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本文是《经济学人》影响(Economist Impact)发布的《绿色智能:绘制可持续人工智能的未来》报告,由Delta Electronics支持。报告基于2024年11月至2025年1月对608名高级执行官的调查,探讨了全球企业在人工智能(AI)和可持续性方面面临的挑战和机遇。 关键点包括: 1. AI的可持续性日益重要,企业认识到需要平衡AI创新与能源需求激增带来的环境问题。 2. 组织采取多种策略应对AI可持续性的复杂挑战,包括能源审计、与效率组织合作和优化硬件性能。 3. 成本不是AI可持续性的主要障碍,组织优先考虑生命周期评估和实时能源监控。 4. 区域间在AI可持续性方法上存在差异,需要全球合作和标准化方法。 5. AI用户和供应商在可持续性期望上存在差距,用户越来越期望供应商有可持续性目标。 6. AI创新对推动可持续性具有巨大潜力,但行业障碍继续减缓其进展。 7. 未来三年,AI可持续性将继续取得效率提升和更广泛的应用。
人工智能如何平衡创新与可持续发展? 企业如何应对人工智能的能源挑战? 人工智能在物流行业的应用有哪些创新?
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