Cost outlook of coal power with CCS and BECCS based on a component learning curve incorporating efficiency upgrades: a case study of China

[Geoengineering News]

https://www.sciencedirect.com/science/article/abs/pii/S2213138826001360

Authors: Delu Wang, Fan Chen, Chunxiao Li, Lawrence Loh

23 March 2026

https://doi.org/10.1016/j.seta.2026.104950

Highlights

•A learning curve model with efficiency upgrades assessed CCS and BECCS cost trends.

•Cost trends and economic-environmental benefits of CCS and BECCS in China were obtained.

•Efficiency upgrades contribute 30% − 67% to the cost reduction of CCS and BECCS.

•Capital and fuel cost changes primarily drive COE reduction in CCS and BECCS.

Abstract

Grasping the cost outlook of CCS and BECSS is crucial for guiding coal power-dependent nations in technological strategy planning and investment decision-making during the low-carbon transition. Given the practical characteristics of technological learning in the coal power sector and the limitations of existing literature in forecasting technology costs, this study adopts a learning rate estimation method that incorporates efficiency upgrade based on the component learning curve approach. Taking China as a case study, it analyzes the future cost trends and economic-environmental benefits of CCS and BECCS from a systematic perspective. The case study results indicate that CCS and BECCS in China exhibit promising cost prospects. Their deployment enhances the overall learning rate of the power generation system, leading to a potential reduction in the cost of electricity (COE) of approximately $23.10 ∼ $55.75/MWh. As technological learning effects accumulate, the economic-environmental benefits of CCS and BECCS in China are expected to improve by more than 50%, with the advantage of CCS and BECCS-equipped technologies becoming increasingly pronounced. Moreover, further analysis reveal that efficiency upgrades play a supporting role, accounting for 30% ∼ 67% of cost reductions, while capital and fuel costs are the primary drivers of COE reduction in CCS and BECCS, jointly contributing 60% ∼ 86% of total cost reductions. The pre-learning value, defined as the threshold at which learning effects begin to materialize, constitutes a critical source of uncertainty influencing the pace of technological cost reduction, while fuel price levels exhibit a positive correlation with the extent to which learning effects are realized. This study provides forward-looking information for coal power system technology strategy planning in China, and offers scientific insights for coal power-dependent nations to accelerate the cost reduction potential of CCS and BECCS.

Source: ScienceDirect