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Role of carbon capture and utilization (CCU) for decarbonization of industrial sector: A case study of Japan

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  • Kawai, Eiji
  • Ozawa, Akito
  • Leibowicz, Benjamin D.

Abstract

Carbon capture and utilization (CCU), which is a process used to captured CO2 and convert it into other substances via chemical reactions with hydrogen, is considered to be the most effective carbon–neutral technology for heavy industries. Therefore, comprehensive model-based studies on the economic viability of CCU for decarbonizing the industrial sector can provide effective solutions for controlling carbon emissions from petrochemical industry. In this study, we investigated 1) the roles that CCU could play in Japan’s decarbonization pathways to meet the country’s 2050 carbon emission goals, 2) factors that influence CCU deployment, and 3) how the market penetration of zero-emission vehicles (ZEVs) could affect CCU deployment. Notably, we applied the MARKet ALlocation (MARKAL) model and extended it to integrate CCU technologies and represent chemical production processes, including those required to manufacture basic petrochemical products. Furthermore, the total optimal (minimized) system cost was determined, while considering climate policies and technological assumptions, by analyzing scenarios based on various parameters associated with CO2 emissions and CCU costs. Our study indicates that CCU has the potential to reduce the use of fossil fuel-based energy required by the petrochemical industry by 32%, thus, substantially contributing to Japan’s 2050 CO2 emissions target. Notably, CCU technology can play a key role in near-future decarbonization efforts, especially in cases where ZEV penetration is not as fast as expected.

Suggested Citation

  • Kawai, Eiji & Ozawa, Akito & Leibowicz, Benjamin D., 2022. "Role of carbon capture and utilization (CCU) for decarbonization of industrial sector: A case study of Japan," Applied Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:appene:v:328:y:2022:i:c:s0306261922014404
    DOI: 10.1016/j.apenergy.2022.120183
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    4. Yan, Bicheng & Gudala, Manojkumar & Hoteit, Hussein & Sun, Shuyu & Wang, Wendong & Jiang, Liangliang, 2024. "Physics-informed machine learning for noniterative optimization in geothermal energy recovery," Applied Energy, Elsevier, vol. 365(C).
    5. Li, Hang & Ma, Hongling & Liu, Jiang & Zhu, Shijie & Zhao, Kai & Zheng, Zhuyan & Zeng, Zhen & Yang, Chunhe, 2023. "Large-scale CAES in bedded rock salt: A case study in Jiangsu Province, China," Energy, Elsevier, vol. 281(C).

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