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CO2 emissions change in Tianjin: The driving factors and the role of CCS

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  • Miao, Yuang
  • Lu, Huixia
  • Cui, Shizhang
  • Zhang, Xu
  • Zhang, Yusheng
  • Song, Xinwang
  • Cheng, Haiying

Abstract

The environmental damage caused by the greenhouse effect is intensified by the continuous rise of carbon dioxide emissions. As a major industrial base in northern China, Tianjin has been emitting more carbon and thus faces a greater challenge to reduce its emissions. To meet the dual carbon goals of Tianjin, it is critical to identify influencing factors of carbon emissions and to find suitable technologies to effectively reduce emissions. In this study, the energy consumption data of Tianjin from 2005 to 2020 were analyzed via the Logarithmic Mean Divisia Index (LMDI). The carbon emission driving factors were decomposed and the contributions of four primary factors, including energy structure, energy intensity, gross domestic product (GDP) per capita, and population to CO2 emissions of this region, were evaluated. The ideal characteristics of the oil reservoirs with low permeability and the suitable carbon dioxide sinks make carbon capture and storage(CCS) technology implementation feasible in Tianjin. Despite this, the effect of CCS technology on CO2 emissions in the future is still uncertain. Thus, the Bass model was firstly introduced to assess the future development trend of CCS technology and its capacity of CO2 emission reduction. The results revealed that energy intensity was the primary contribution to reducing carbon emissions, while GDP per capita was the main contributor to the overall increase of CO2 emissions in 2005–2020. The CCS technology diffusion process is fast, with rapid growth starting in 2030 and reaching growth saturation in 2040. According to the scenario analysis, CCS could make a substantial contribution of 2674.78× 104 tons per year after 2040 to the carbon emission reduction of Tianjin. Therefore, the implementation of energy conservation and emission reduction policies as well as the expansion of CCS projects are essential to ensure that Tianjin achieves its carbon neutrality goal by 2060. This study offers helpful guidance for other cities to forecast their carbon emission reduction trajectories and establish emission reduction strategies.

Suggested Citation

  • Miao, Yuang & Lu, Huixia & Cui, Shizhang & Zhang, Xu & Zhang, Yusheng & Song, Xinwang & Cheng, Haiying, 2024. "CO2 emissions change in Tianjin: The driving factors and the role of CCS," Applied Energy, Elsevier, vol. 353(PA).
  • Handle: RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014861
    DOI: 10.1016/j.apenergy.2023.122122
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    as
    1. Paltsev, Sergey & Morris, Jennifer & Kheshgi, Haroon & Herzog, Howard, 2021. "Hard-to-Abate Sectors: The role of industrial carbon capture and storage (CCS) in emission mitigation," Applied Energy, Elsevier, vol. 300(C).
    2. Lin, Chiun-Sin & Liou, Fen-May & Huang, Chih-Pin, 2011. "Grey forecasting model for CO2 emissions: A Taiwan study," Applied Energy, Elsevier, vol. 88(11), pages 3816-3820.
    3. Weijiang Liu & Tingting Liu & Yangyang Li & Min Liu, 2021. "Recycling Carbon Tax under Different Energy Efficiency Improvements: A CGE Analysis of China," Sustainability, MDPI, vol. 13(9), pages 1-17, April.
    4. Xu, Ning & Ding, Song & Gong, Yande & Bai, Ju, 2019. "Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model," Energy, Elsevier, vol. 175(C), pages 218-227.
    5. Meizhen Zhang & Tao Lv & Xu Deng & Yuanxu Dai & Muhammad Sajid, 2019. "Diffusion of China’s coal-fired power generation technologies: historical evolution and development trends," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 7-23, January.
    6. Yong Yang & Junsong Jia & Chundi Chen, 2020. "Residential Energy-Related CO 2 Emissions in China’s Less Developed Regions: A Case Study of Jiangxi," Sustainability, MDPI, vol. 12(5), pages 1-28, March.
    7. Zhang, Jingxiao & Jin, Weixing & Yang, Guo-liang & Li, Hui & Ke, Yongjian & Philbin, Simon Patrick, 2021. "Optimizing regional allocation of CO2 emissions considering output under overall efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    8. Barbara Koelbl & Machteld Broek & André Faaij & Detlef Vuuren, 2014. "Uncertainty in Carbon Capture and Storage (CCS) deployment projections: a cross-model comparison exercise," Climatic Change, Springer, vol. 123(3), pages 461-476, April.
    9. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    10. Wang, H. & Ang, B.W., 2018. "Assessing the role of international trade in global CO2 emissions: An index decomposition analysis approach," Applied Energy, Elsevier, vol. 218(C), pages 146-158.
    11. Wang, Yafei & Zhao, Hongyan & Li, Liying & Liu, Zhu & Liang, Sai, 2013. "Carbon dioxide emission drivers for a typical metropolis using input–output structural decomposition analysis," Energy Policy, Elsevier, vol. 58(C), pages 312-318.
    12. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    13. Lele Xin & Junsong Jia & Wenhui Hu & Huiqing Zeng & Chundi Chen & Bo Wu, 2021. "Decomposition and Decoupling Analysis of CO 2 Emissions Based on LMDI and Two-Dimensional Decoupling Model in Gansu Province, China," IJERPH, MDPI, vol. 18(11), pages 1-20, June.
    14. Ming, Zeng & Shaojie, Ouyang & Yingjie, Zhang & Hui, Shi, 2014. "CCS technology development in China: Status, problems and countermeasures—Based on SWOT analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 604-616.
    15. Xu, Shi-Chun & He, Zheng-Xia & Long, Ru-Yin, 2014. "Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI," Applied Energy, Elsevier, vol. 127(C), pages 182-193.
    16. Li, Kong & Xianzhong, Mu & Guangwen, Hu, 2021. "A decomposing analysis of productive and residential energy consumption in Beijing," Energy, Elsevier, vol. 226(C).
    Full references (including those not matched with items on IDEAS)

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