TY - JOUR
T1 - The Prospects of Carbon Capture and Storage in China's Power Sector under the 2 °C Target
T2 - A Component-based Learning Curve Approach
AU - Kang, Jia Ning
AU - Wei, Yi Ming
AU - Liu, Lancui
AU - Han, Rong
AU - Chen, Hao
AU - Li, Jiaquan
AU - Wang, Jin Wei
AU - Yu, Bi Ying
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/10
Y1 - 2020/10
N2 - Carbon capture and storage (CCS) is indispensable in achieving the well below 2 °C warming target, especially for China with coal-dominated energy structures. However, high investment makes the development of CCS far from the global warming target. Concerns about the high cost have led to a spread of pessimistic attitude towards the future of CCS to some extent. Therefore, this study provides the first instance to determine the unit CO2 avoided cost learning curves of various CCS pathways for China's thermal power generation based on a component-based approach, and establishes a non-linear technology optimization model with endogenous technological progress. Model developed by this study aims to explore the economically optimal CCS deployment strategy for achieving the necessary emission reductions in line with 2 °C target in China and the amount of investment required. Results show that the nominal learning rates of CO2 avoided costs are in the range of 0.018 to 0.151 for four CCS pathways. The strategic priority should be given to the integrated gasification combined cycle (IGCC) pathway and the supercritical pulverized coal (SPC) pathway, considering that they have greater cost reduction potential than that of oxy-combustion and gas-fired pathway. To achieve the 2 °C target, the total abatement cost will be at least 330 billion US dollars with a CCS capacity of 499 GW for power sector by 2050. The preferred time of large-scale deployment of CCS in China's power sector is around 2030. Postponing CCS commercialization will surge the abatement cost to $626 billion by 2050. In addition, this study provides the long term break-even CO2 prices for implementing CCS, ranging from approximately $46/ton CO2 to $62/ton CO2, which is informative for the future subsidy setting and carbon trading market.
AB - Carbon capture and storage (CCS) is indispensable in achieving the well below 2 °C warming target, especially for China with coal-dominated energy structures. However, high investment makes the development of CCS far from the global warming target. Concerns about the high cost have led to a spread of pessimistic attitude towards the future of CCS to some extent. Therefore, this study provides the first instance to determine the unit CO2 avoided cost learning curves of various CCS pathways for China's thermal power generation based on a component-based approach, and establishes a non-linear technology optimization model with endogenous technological progress. Model developed by this study aims to explore the economically optimal CCS deployment strategy for achieving the necessary emission reductions in line with 2 °C target in China and the amount of investment required. Results show that the nominal learning rates of CO2 avoided costs are in the range of 0.018 to 0.151 for four CCS pathways. The strategic priority should be given to the integrated gasification combined cycle (IGCC) pathway and the supercritical pulverized coal (SPC) pathway, considering that they have greater cost reduction potential than that of oxy-combustion and gas-fired pathway. To achieve the 2 °C target, the total abatement cost will be at least 330 billion US dollars with a CCS capacity of 499 GW for power sector by 2050. The preferred time of large-scale deployment of CCS in China's power sector is around 2030. Postponing CCS commercialization will surge the abatement cost to $626 billion by 2050. In addition, this study provides the long term break-even CO2 prices for implementing CCS, ranging from approximately $46/ton CO2 to $62/ton CO2, which is informative for the future subsidy setting and carbon trading market.
KW - Carbon capture and storage
KW - China's thermal power sector
KW - Component-based learning curve
KW - Technological progress
UR - http://www.scopus.com/inward/record.url?scp=85090711210&partnerID=8YFLogxK
U2 - 10.1016/j.ijggc.2020.103149
DO - 10.1016/j.ijggc.2020.103149
M3 - Article
AN - SCOPUS:85090711210
SN - 1750-5836
VL - 101
JO - International Journal of Greenhouse Gas Control
JF - International Journal of Greenhouse Gas Control
M1 - 103149
ER -