TY - JOUR
T1 - Adapting Technology Learning Curves for Prospective Techno-Economic and Life Cycle Assessments of Emerging Carbon Capture and Utilization Pathways
AU - Faber, Grant
AU - Ruttinger, Andrew
AU - Strunge, Till
AU - Langhorst, Tim
AU - Zimmermann, Arno
AU - van der Hulst, Mitchell
AU - Bensebaa, Farid
AU - Moni, Sheikh
AU - Tao, Ling
N1 - Funding Information:
This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
Funding Information:
The authors would like to acknowledge the support and feedback from the entire International CCU Assessment Harmonization Group, which includes representatives from the National Energy Technology Laboratory, the National Renewable Energy Laboratory, Argonne National Laboratory, the National Research Council of Canada, IASS Potsdam, TU Berlin, RWTH Aachen, The University of Sheffield, the Global CO Initiative at the University of Michigan, and EIT Climate-KIC. Volker Sick and Tim Skone offered expert oversight of this project along with all the other teams in the group. 2
Publisher Copyright:
Copyright © 2022 Faber, Ruttinger, Strunge, Langhorst, Zimmermann, van der Hulst, Bensebaa, Moni and Tao.
PY - 2022/4/14
Y1 - 2022/4/14
N2 - 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.
AB - 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.
KW - carbon capture and utilization
KW - CO mineralization
KW - experience curves
KW - learning rates
KW - life cycle assessment
KW - prospective assessment
KW - techno-economic assessment
KW - technology learning curves
UR - http://www.scopus.com/inward/record.url?scp=85128887180&partnerID=8YFLogxK
U2 - 10.3389/fclim.2022.820261
DO - 10.3389/fclim.2022.820261
M3 - Article
AN - SCOPUS:85128887180
SN - 2624-9553
VL - 4
JO - Frontiers in Climate
JF - Frontiers in Climate
M1 - 820261
ER -