Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways

Grant Faber*, Andrew Ruttinger, Till Strunge, Tim Langhorst, Arno Zimmermann, Mitchell van der Hulst, Farid Bensebaa, Sheikh Moni, Ling Tao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
258 Downloads (Pure)


Comparisons of emerging carbon capture and utilization (CCU) technologies with equivalent incumbent technologies are necessary to support technology developers and to help policy-makers design appropriate long-term incentives to mitigate climate change through the deployment of CCU. In particular, early-stage CCU technologies must prove their economic viability and environmental reduction potential compared to already-deployed technologies. These comparisons can be misleading, as emerging technologies typically experience a drastic increase in performance and decrease in cost and greenhouse gas emissions as they develop from research to mass-market deployment due to various forms of learning. These changes complicate the interpretation of early techno-economic assessments (TEAs) and life cycle assessments (LCAs) of emerging CCU technologies. The effects of learning over time or cumulative production themselves can be quantitatively described using technology learning curves (TLCs). While learning curve approaches have been developed for various technologies, a harmonized methodology for using TLCs in TEA and LCA for CCU in particular is required. To address this, we describe a methodology that incorporates TLCs into TEA and LCA to forecast the environmental and economic performance of emerging CCU technologies. This methodology is based on both an evaluation of the state of the art of learning curve assessment and a literature review of TLC approaches developed in various manufacturing and energy generation sectors. Additionally, we demonstrate how to implement this methodology using a case study on a CO2 mineralization pathway. Finally, commentary is provided on how researchers, technology developers, and LCA and TEA practitioners can advance the use of TLCs to allow for consistent, high-resolution modeling of technological learning for CCU going forward and enable holistic assessments and fairer comparisons with other climate technologies.

Original languageEnglish
Article number820261
JournalFrontiers in Climate
Publication statusPublished - 14 Apr 2022


  • carbon capture and utilization
  • CO mineralization
  • experience curves
  • learning rates
  • life cycle assessment
  • prospective assessment
  • techno-economic assessment
  • technology learning curves

ASJC Scopus subject areas

  • Global and Planetary Change
  • Management, Monitoring, Policy and Law
  • Pollution
  • Environmental Science (miscellaneous)
  • Atmospheric Science


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