IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v191y2020ics0360544219321577.html
   My bibliography  Save this article

Analysis of China’s olefin industry using a system optimization model considering technological learning and energy consumption reduction

Author

Listed:
  • Xu, Zhongming
  • Fang, Chenhao
  • Ma, Tieju

Abstract

Recently, China has been increasingly producing olefins from alternative resources, especially coal. Technological learning, energy consumption reduction, and environmental policies/regulations will have a great impact on the economic and environmental values of coal-to-olefin (CTO) projects. How should China configure its future olefin industry considering these factors? Little work has been performed to explore this question. This study develops a system optimization model to analyze the optimal configuration of China’s olefin industry under different scenarios of technological learning, energy consumption reduction, and environmental policies/regulations. Our results show that in all scenarios, the oil-to-olefin process will remain dominant in China’s olefin industry in the next two decades, and with technological learning, the CTO process is competitive in China’s olefin industry, especially when CO2 emissions are not controlled. To control the CO2 emissions of China’s olefin industry, our study indicates that requiring CTO implementation along with carbon capture and storage (CCS) would have both economic and environmental value compared with imposing a carbon tax (assume 20$/t CO2 from the year 2021). However, policymakers should be cautioned about the uncertainties and risks of CCS. This study also provides some insights for those who are considering investing in China’s olefin industry.

Suggested Citation

  • Xu, Zhongming & Fang, Chenhao & Ma, Tieju, 2020. "Analysis of China’s olefin industry using a system optimization model considering technological learning and energy consumption reduction," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321577
    DOI: 10.1016/j.energy.2019.116462
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219321577
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.116462?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rubin, Edward S. & Yeh, Sonia & Antes, Matt & Berkenpas, Michael & Davison, John, 2007. "Use of experience curves to estimate the future cost of power plants with CO2 capture," Institute of Transportation Studies, Working Paper Series qt46x6h0n0, Institute of Transportation Studies, UC Davis.
    2. Saygin, D. & Patel, M.K. & Worrell, E. & Tam, C. & Gielen, D.J., 2011. "Potential of best practice technology to improve energy efficiency in the global chemical and petrochemical sector," Energy, Elsevier, vol. 36(9), pages 5779-5790.
    3. McFarland, J. R. & Reilly, J. M. & Herzog, H. J., 2004. "Representing energy technologies in top-down economic models using bottom-up information," Energy Economics, Elsevier, vol. 26(4), pages 685-707, July.
    4. Riahi, Keywan & Rubin, Edward S. & Schrattenholzer, Leo, 2004. "Prospects for carbon capture and sequestration technologies assuming their technological learning," Energy, Elsevier, vol. 29(9), pages 1309-1318.
    5. Fuminori Sano, Keigo Akimoto, Takashi Homma and Toshimasa Tomoda, 2006. "Analysis of Technological Portfolios for CO2 Stabilizations and Effects of Technological Changes," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 141-162.
    6. Zhang, Xiliang & Karplus, Valerie J. & Qi, Tianyu & Zhang, Da & He, Jiankun, 2016. "Carbon emissions in China: How far can new efforts bend the curve?," Energy Economics, Elsevier, vol. 54(C), pages 388-395.
    7. Xu, Zhongming & Zhang, Yaru & Fang, Chenhao & Yu, Yadong & Ma, Tieju, 2019. "Analysis of China's olefin industry with a system optimization model – With different scenarios of dynamic oil and coal prices," Energy Policy, Elsevier, vol. 135(C).
    8. Gielen, D. J. & Yagita, H., 2002. "The long-term impact of GHG reduction policies on global trade: A case study for the petrochemical industry," European Journal of Operational Research, Elsevier, vol. 139(3), pages 665-681, June.
    9. Ren, Tao & Patel, Martin K. & Blok, Kornelis, 2008. "Steam cracking and methane to olefins: Energy use, CO2 emissions and production costs," Energy, Elsevier, vol. 33(5), pages 817-833.
    10. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    11. Junginger, Martin & de Visser, Erika & Hjort-Gregersen, Kurt & Koornneef, Joris & Raven, Rob & Faaij, Andre & Turkenburg, Wim, 2006. "Technological learning in bioenergy systems," Energy Policy, Elsevier, vol. 34(18), pages 4024-4041, December.
    12. Yu, Yang & Li, Hong & Che, Yuyuan & Zheng, Qiongjie, 2017. "The price evolution of wind turbines in China: A study based on the modified multi-factor learning curve," Renewable Energy, Elsevier, vol. 103(C), pages 522-536.
    13. Xiang, Dong & Qian, Yu & Man, Yi & Yang, Siyu, 2014. "Techno-economic analysis of the coal-to-olefins process in comparison with the oil-to-olefins process," Applied Energy, Elsevier, vol. 113(C), pages 639-647.
    14. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    15. Boulamanti, Aikaterini & Moya, Jose A., 2017. "Production costs of the chemical industry in the EU and other countries: Ammonia, methanol and light olefins," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1205-1212.
    16. Badyda, Krzysztof & Krawczyk, Piotr & Pikoń, Krzysztof, 2016. "Relative environmental footprint of waste-based fuel burned in a power boiler in the context of end-of-waste criteria assigned to the fuel," Energy, Elsevier, vol. 100(C), pages 425-430.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Moglianesi, Andrea & Keppo, Ilkka & Lerede, Daniele & Savoldi, Laura, 2023. "Role of technology learning in the decarbonization of the iron and steel sector: An energy system approach using a global-scale optimization model," Energy, Elsevier, vol. 274(C).
    2. Wang, Yihan & Wen, Zongguo & Yao, Jianguo & Doh Dinga, Christian, 2020. "Multi-objective optimization of synergic energy conservation and CO2 emission reduction in China's iron and steel industry under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    3. Wan, Zhanghao & Yang, Shiliang & Hu, Jianhang & Bao, Guirong & Wang, Hua, 2022. "CFD study of the reactive gas-solid hydrodynamics in a large-scale catalytic methanol-to-olefin fluidized bed reactor," Energy, Elsevier, vol. 243(C).
    4. Ding, Bingqing & Makowski, Marek & Nahorski, Zbigniew & Ren, Hongtao & Ma, Tieju, 2022. "Optimizing the technology pathway of China's liquid fuel production considering uncertain oil prices: A robust programming model," Energy Economics, Elsevier, vol. 115(C).
    5. Zhao, Jinyang & Yu, Yadong & Ren, Hongtao & Makowski, Marek & Granat, Janusz & Nahorski, Zbigniew & Ma, Tieju, 2022. "How the power-to-liquid technology can contribute to reaching carbon neutrality of the China's transportation sector?," Energy, Elsevier, vol. 261(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    2. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    3. Ding, Bingqing & Makowski, Marek & Nahorski, Zbigniew & Ren, Hongtao & Ma, Tieju, 2022. "Optimizing the technology pathway of China's liquid fuel production considering uncertain oil prices: A robust programming model," Energy Economics, Elsevier, vol. 115(C).
    4. Xu, Zhongming & Zhang, Yaru & Fang, Chenhao & Yu, Yadong & Ma, Tieju, 2019. "Analysis of China's olefin industry with a system optimization model – With different scenarios of dynamic oil and coal prices," Energy Policy, Elsevier, vol. 135(C).
    5. Karali, Nihan & Park, Won Young & McNeil, Michael, 2017. "Modeling technological change and its impact on energy savings in the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 202(C), pages 447-458.
    6. Elia, A. & Kamidelivand, M. & Rogan, F. & Ó Gallachóir, B., 2021. "Impacts of innovation on renewable energy technology cost reductions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    7. Santhakumar, Srinivasan & Meerman, Hans & Faaij, André, 2021. "Improving the analytical framework for quantifying technological progress in energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    8. Wu, X.D. & Yang, Q. & Chen, G.Q. & Hayat, T. & Alsaedi, A., 2016. "Progress and prospect of CCS in China: Using learning curve to assess the cost-viability of a 2×600MW retrofitted oxyfuel power plant as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1274-1285.
    9. Lai, N.Y.G. & Yap, E.H. & Lee, C.W., 2011. "Viability of CCS: A broad-based assessment for Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3608-3616.
    10. Upstill, Garrett & Hall, Peter, 2018. "Estimating the learning rate of a technology with multiple variants: The case of carbon storage," Energy Policy, Elsevier, vol. 121(C), pages 498-505.
    11. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    12. Bibas, Ruben & Méjean, Aurélie & Hamdi-Cherif, Meriem, 2015. "Energy efficiency policies and the timing of action: An assessment of climate mitigation costs," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 137-152.
    13. Li, Sheng & Gao, Lin & Zhang, Xiaosong & Lin, Hu & Jin, Hongguang, 2012. "Evaluation of cost reduction potential for a coal based polygeneration system with CO2 capture," Energy, Elsevier, vol. 45(1), pages 101-106.
    14. Yang, Lin & Lv, Haodong & Wei, Ning & Li, Yiming & Zhang, Xian, 2023. "Dynamic optimization of carbon capture technology deployment targeting carbon neutrality, cost efficiency and water stress: Evidence from China's electric power sector," Energy Economics, Elsevier, vol. 125(C).
    15. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    16. Ranjit R. Desai & Eric Hittinger & Eric Williams, 2022. "Interaction of Consumer Heterogeneity and Technological Progress in the US Electric Vehicle Market," Energies, MDPI, vol. 15(13), pages 1-25, June.
    17. Zhao, Zhitong & Chong, Katie & Jiang, Jingyang & Wilson, Karen & Zhang, Xiaochen & Wang, Feng, 2018. "Low-carbon roadmap of chemical production: A case study of ethylene in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 580-591.
    18. Li, Sheng & Zhang, Xiaosong & Gao, Lin & Jin, Hongguang, 2012. "Learning rates and future cost curves for fossil fuel energy systems with CO2 capture: Methodology and case studies," Applied Energy, Elsevier, vol. 93(C), pages 348-356.
    19. Zhou, Wenji & Zhu, Bing & Chen, Dingjiang & Zhao, Fangxian & Fei, Weiyang, 2014. "How policy choice affects investment in low-carbon technology: The case of CO2 capture in indirect coal liquefaction in China," Energy, Elsevier, vol. 73(C), pages 670-679.
    20. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321577. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.