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未来资源研究所(RFF):2026工业数据体系改进:现状与未来方向研究报告(英文版)(28页).pdf

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1、Improving Industrial Data:Status and Future DirectionsAImproving Industrial Data:Status and Future Directions Marc Hafstead and Aaron Bergman Report 26-01 January 2026Resources for the FutureiAbout the AuthorsMarc Hafstead is a Resources for the Future(RFF)fellow and director of the Carbon Pricing I

2、nitiative and the Climate Finance and Financial Risk Initiative.His research has primarily focused on the evaluation and design of federal and state-level climate and energy policies using sophisticated multi-sector models of the US economy.With Stanford Professor and RFF University Fellow Lawrence

3、H.Goulder,he wrote Confronting the Climate Challenge:US Policy Options(Columbia University Press)to evaluate the environmental and economic impacts of federal carbon taxes,cap-and-trade programs,clean energy standards,and gasoline.His research has also analyzed the distributional and employment impa

4、cts of carbon pricing and the design of tax adjustment mechanisms to reduce the emissions uncertainty of carbon tax policies.Aaron Bergman is a fellow at RFF.Prior to joining RFF,he was the Lead for Macroeconomics and Emissions at the Energy Information Administration(EIA),managing EIAs modeling in

5、those areas.Before working at EIA,Bergman spent almost a decade in the policy office at the Department of Energy,working on a broad array of climate and environmental policies.Bergman has worked in the White House at the Office of Science and Technology Policy,managing the Quadrennial Energy Review

6、and handling the methane measurement portfolio,and at the Council on Environmental Quality,working on carbon regulation.Bergman entered the federal government in 2009 as a Science and Technology Policy Fellow with the American Association for the Advancement of Science,after working in high energy p

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1. **工业数据现状与挑战**:美国工业制造数据存在来源分散、标准不一、聚合度高、关键数据缺失(如设备特性、技术成本)等问题,阻碍了投资决策、经济分析和政策制定。 2. **数据改进方向**:需优先收集三类数据: - 现有设施及设备特性(如产能、能耗); - 生产、能源、材料使用及国内商品流动数据; - 现有与新兴技术的成本和性能评估(含工艺流程细节)。 3. **工业数据“共享库”建设**:通过建立利益相关者社区、确定数据优先级、制定统一标准、优化原始数据及组织访问机制,提升数据一致性和可用性,支持产能扩张、经济建模和工艺分析。 4. **潜在效益**:改善数据可提升分析精度,优化工业投资与策略,增强美国制造业竞争力(如供应链韧性、技术创新)。
工业数据如何提升? 数据 commons 有何价值? 技术成本如何评估?
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