Cost effectiveness of carbon capture and storage based on probability estimation of social cost of carbon

Carbon capture and storage (CCS) technology can assist climate change mitigation by addressing residual emissions. Its cost effectiveness, which has been questioned, can be assessed by comparing CO2 avoidance costs with the social cost of carbon (SCC), the damage resulting from increased greenhouse gas emissions. However, SCC estimation is laced with uncertainties derived from the parameters included in integrated assessment models (IAMs). This study assesses the cost effectiveness of CCS technology by comparing CO2 avoidance costs with SCC probability ranges derived from an automated generalized IAM-based machine learning approach. This methodology offers a significant advancement by providing a rapid evaluation of SCC probability distribution across various scenarios, thereby capturing uncertainties within the SCC and aiding the assessment of the economic benefits of CCS expansion. The findings indicate that average SCC values decrease by approximately 23.8 % by 2100 with additional CCS expansion. In addition, the CO2 avoidance cost is below the SCC, highlighting the potential social benefits of using CCS to mitigate carbon-related damage. The broad spectrum of SCC proba- bilities yielded by this study equips policymakers with valuable insights for evaluating not only the SCC value itself but also the economic rationale for future CCS expansion.