 Improving Industrial Data: Status and Future DirectionsMarc Hafstead and
Aaron Bergman
No 26-01, RFF Reports from Resources for the Future Abstract: Investments to advance industrial manufacturing drive US productivity, innovation, global competitiveness, and supply chain robustness. Economic and energy data on industrial manufacturing are fundamental to inform business investment and operation decisions as well as public- and private-sector long-term strategies for economic growth, industrial competitiveness, and technology research and development. Improvements in data could improve these critical decisions about priorities for investment and unlock industrial manufacturing benefits. This can occur by improving three types of analysis: (1) analysis of capacity expansion — the construction and upgrading of industrial manufacturing, particularly the adoption of new, advanced technologies; (2) analysis of economic growth, including trade flows, labor needs, regional production, and investments; (3) analysis of technology innovation and optimization within facilities and manufacturing clusters. Improvements in industrial data and analysis could enhance business investment and operational decisions, long-term strategies for economic growth, industrial competitiveness, and technology research and development. While the myriad sources of economic and energy data on industrial manufacturing already support such decisions to an extent, this support is compromised by weaknesses and gaps. These data sources are often disconnected, and each user must invest time to process each data source and establish connections across numerous sources. Economic and energy researchers often build their own datasets at considerable expense, but mechanisms to leverage and combine those efforts are not readily available. In addition, significant data gaps in valuable information limit decisionmakers, such as industrial energy equipment characteristics (vintage, energy use, competitiveness) and the distribution of those characteristics across firms and/or regions.To address this, Resources for the Future and the National Laboratory of the Rockies convened a workshop in December 2024 that included sessions on data gaps, data requirements, and methods and opportunities for developing new data. The sessions explored the potential of an industrial data “commons” that could leverage and facilitate industrial energy and economic analysis. This document draws from the workshop but is not a report of workshop results, outcomes, or agreements. Instead, this report offers concepts for improving industrial data to further leverage existing data, set priorities for new and improved data, and advance shared industrial analysis capabilities through development of an industrial data commons. These concepts respond to the potential high value of coordination across a broad range of industrial data stakeholders. Although the precise definition of an industrial data commons remains to be developed, this report describes its building blocks:Creating a community of stakeholders, Determining priorities for data types, Establishing data standards and methods, Improving primary data; and,Organizing data access and collaboration mechanisms.An industrial data commons organized around these building blocks could create substantial opportunities for new industrial modeling and analysis, more informed decision-making, and consequent economic benefits. See Figure ES-1 for a schematic that summarizes the conceptual framework presented in this report. Improved industrial data supports more effective analysis and better policy decisions. The data that meets these needs include current facility data such as capital equipment characteristics and operational data; cost and performance data for incumbent and emerging technologies; historical production, trade, and commodity flows, and projected demands. This report identifies potential priorities and next steps to improve these datasets by developing an industrial data commons, in support of better decisions on industrial investments and strategies. Date: 2026-01-07 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:rff:report:rp-26-01 Access Statistics for this paper More papers in RFF Reports from Resources for the Future Contact information at EDIRC. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.