IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v99y2016icp280-288.html
   My bibliography  Save this article

Learning curves for harnessing biomass power: What could explain the reduction of its cost during the expansion of China?

Author

Listed:
  • Lin, Boqiang
  • He, Jiaxin

Abstract

To explain the factors influencing the cost of biomass power in China, we applied an improved model of learning curves in this paper. The impact of cumulative installed capacity of biomass power and other factors on the cost of biomass technology were investigated. The results showed that installed capacity expansion has led to significant cost reduction. Meanwhile, the effect of economies of scale was observed in the analysis of generation cost. The ownership structure of the firm and the size of the developer had no influence on the learning effects and stronger policy support may, ironically, produce negative incentive effects on technology improvement.

Suggested Citation

  • Lin, Boqiang & He, Jiaxin, 2016. "Learning curves for harnessing biomass power: What could explain the reduction of its cost during the expansion of China?," Renewable Energy, Elsevier, vol. 99(C), pages 280-288.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:280-288
    DOI: 10.1016/j.renene.2016.07.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116306036
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2016.07.007?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. Kim, Dong Wook & Chang, Hyun Joon, 2012. "Experience curve analysis on South Korean nuclear technology and comparative analysis with South Korean renewable technologies," Energy Policy, Elsevier, vol. 40(C), pages 361-373.
    2. Wand, Robert & Leuthold, Florian, 2011. "Feed-in tariffs for photovoltaics: Learning by doing in Germany?," Applied Energy, Elsevier, vol. 88(12), pages 4387-4399.
    3. Zhang, Qin & Zhou, Dequn & Fang, Xiaomeng, 2014. "Analysis on the policies of biomass power generation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 926-935.
    4. Papineau, Maya, 2006. "An economic perspective on experience curves and dynamic economies in renewable energy technologies," Energy Policy, Elsevier, vol. 34(4), pages 422-432, March.
    5. Winkler, Harald & Hughes, Alison & Haw, Mary, 2009. "Technology learning for renewable energy: Implications for South Africa's long-term mitigation scenarios," Energy Policy, Elsevier, vol. 37(11), pages 4987-4996, November.
    6. Xingang, Zhao & Jieyu, Wang & Xiaomeng, Liu & Pingkuo, Liu, 2012. "China’s wind, biomass and solar power generation: What the situation tells us?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 6173-6182.
    7. Zhou, Zhaoqiu & Yin, Xiuli & Xu, Jie & Ma, Longlong, 2012. "The development situation of biomass gasification power generation in China," Energy Policy, Elsevier, vol. 51(C), pages 52-57.
    8. Miketa, Asami & Schrattenholzer, Leo, 2004. "Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes; first results," Energy Policy, Elsevier, vol. 32(15), pages 1679-1692, October.
    9. Boubakri, Narjess & Cosset, Jean-Claude & Saffar, Walid, 2008. "Political connections of newly privatized firms," Journal of Corporate Finance, Elsevier, vol. 14(5), pages 654-673, December.
    10. Gerssen-Gondelach, S.J. & Saygin, D. & Wicke, B. & Patel, M.K. & Faaij, A.P.C., 2014. "Competing uses of biomass: Assessment and comparison of the performance of bio-based heat, power, fuels and materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 964-998.
    11. Qiu, Yueming & Anadon, Laura D., 2012. "The price of wind power in China during its expansion: Technology adoption, learning-by-doing, economies of scale, and manufacturing localization," Energy Economics, Elsevier, vol. 34(3), pages 772-785.
    12. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    13. Ek, Kristina & Söderholm, Patrik, 2010. "Technology learning in the presence of public R&D: The case of European wind power," Ecological Economics, Elsevier, vol. 69(12), pages 2356-2362, October.
    14. Tooraj Jamasb, 2007. "Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 51-72.
    15. Liu, Jicheng & Wang, Sijia & Wei, Qiushuang & Yan, Suli, 2014. "Present situation, problems and solutions of China׳s biomass power generation industry," Energy Policy, Elsevier, vol. 70(C), pages 144-151.
    16. Amore, Mario Daniele & Bennedsen, Morten, 2013. "The value of local political connections in a low-corruption environment," Journal of Financial Economics, Elsevier, vol. 110(2), pages 387-402.
    17. Kobos, Peter H. & Erickson, Jon D. & Drennen, Thomas E., 2006. "Technological learning and renewable energy costs: implications for US renewable energy policy," Energy Policy, Elsevier, vol. 34(13), pages 1645-1658, September.
    18. Wang, Xingwei & Cai, Yanpeng & Dai, Chao, 2014. "Evaluating China's biomass power production investment based on a policy benefit real options model," Energy, Elsevier, vol. 73(C), pages 751-761.
    19. Partridge, Ian, 2013. "Renewable electricity generation in India—A learning rate analysis," Energy Policy, Elsevier, vol. 60(C), pages 906-915.
    20. Zhao, Zhen-yu & Yan, Hong, 2012. "Assessment of the biomass power generation industry in China," Renewable Energy, Elsevier, vol. 37(1), pages 53-60.
    21. Sun, Yanwei & Wang, Run & Liu, Jian & Xiao, Lishan & Lin, Yanjie & Kao, William, 2013. "Spatial planning framework for biomass resources for power production at regional level: A case study for Fujian Province, China," Applied Energy, Elsevier, vol. 106(C), pages 391-406.
    22. Sener, Can & Fthenakis, Vasilis, 2014. "Energy policy and financing options to achieve solar energy grid penetration targets: Accounting for external costs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 854-868.
    23. Xingang, Zhao & Jieyu, Wang & Xiaomeng, Liu & Tiantian, Feng & Pingkuo, Liu, 2012. "Focus on situation and policies for biomass power generation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3722-3729.
    24. Zhang, Huiming & Li, Lianshui & Zhou, Dequn & Zhou, Peng, 2014. "Political connections, government subsidies and firm financial performance: Evidence from renewable energy manufacturing in China," Renewable Energy, Elsevier, vol. 63(C), pages 330-336.
    25. Ouyang, Xiaoling & Lin, Boqiang, 2014. "Levelized cost of electricity (LCOE) of renewable energies and required subsidies in China," Energy Policy, Elsevier, vol. 70(C), pages 64-73.
    26. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    27. Wright, Daniel G. & Dey, Prasanta K. & Brammer, John, 2014. "A barrier and techno-economic analysis of small-scale bCHP (biomass combined heat and power) schemes in the UK," Energy, Elsevier, vol. 71(C), pages 332-345.
    28. 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.
    29. Söderholm, Patrik & Sundqvist, Thomas, 2007. "Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies," Renewable Energy, Elsevier, vol. 32(15), pages 2559-2578.
    30. Nikolaos Kouvaritakis & Antonio Soria & Stephane Isoard, 2000. "Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 104-115.
    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. Lin, Boqiang & Tan, Ruipeng, 2017. "Are people willing to pay more for new energy bus fares?," Energy, Elsevier, vol. 130(C), pages 365-372.
    2. Xiao, Jin & Li, Guohao & Xie, Ling & Wang, Shouyang & Yu, Lean, 2021. "Decarbonizing China's power sector by 2030 with consideration of technological progress and cross-regional power transmission," Energy Policy, Elsevier, vol. 150(C).
    3. Alemzero, David & Acheampong, Theophilus & Huaping, Sun, 2021. "Prospects of wind energy deployment in Africa: Technical and economic analysis," Renewable Energy, Elsevier, vol. 179(C), pages 652-666.
    4. Zhao, Zhen-Yu & Chen, Yu-Long & Li, Heng, 2019. "What affects the development of renewable energy power generation projects in China: ISM analysis," Renewable Energy, Elsevier, vol. 131(C), pages 506-517.
    5. 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.
    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. Yu-zhuo, Zhang & Xin-gang, Zhao & Ling-zhi, Ren & Ji, Liang & Ping-kuo, Liu, 2017. "The development of China's biomass power industry under feed-in tariff and renewable portfolio standard: A system dynamics analysis," Energy, Elsevier, vol. 139(C), pages 947-961.
    8. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    9. Abdul Ghani Olabi & Tabbi Wilberforce & Khaled Elsaid & Enas Taha Sayed & Tareq Salameh & Mohammad Ali Abdelkareem & Ahmad Baroutaji, 2021. "A Review on Failure Modes of Wind Turbine Components," Energies, MDPI, vol. 14(17), pages 1-44, August.
    10. He, Jiaxin & Liu, Ying & Lin, Boqiang, 2018. "Should China support the development of biomass power generation?," Energy, Elsevier, vol. 163(C), pages 416-425.
    11. Zhao Xin-gang & Wang Wei & Hu Shuran & Liu Xuan, 2023. "Impacts of Government Policies on the Adoption of Biomass Power: A System Dynamic Perspective," Sustainability, MDPI, vol. 15(2), pages 1-11, January.
    12. Elia, A. & Taylor, M. & Ó Gallachóir, B. & Rogan, F., 2020. "Wind turbine cost reduction: A detailed bottom-up analysis of innovation drivers," Energy Policy, Elsevier, vol. 147(C).
    13. Lin, Boqiang & Kuang, Yunming, 2020. "Household heterogeneity impact of removing energy subsidies in China: Direct and indirect effect," Energy Policy, Elsevier, vol. 147(C).
    14. Zheng-Xia He & Shi-Chun Xu & Qin-Bin Li & Bin Zhao, 2018. "Factors That Influence Renewable Energy Technological Innovation in China: A Dynamic Panel Approach," Sustainability, MDPI, vol. 10(1), pages 1-30, January.

    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. 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.
    2. 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.
    3. 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).
    4. Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    5. Hong, Sungjun & Chung, Yanghon & Woo, Chungwon, 2015. "Scenario analysis for estimating the learning rate of photovoltaic power generation based on learning curve theory in South Korea," Energy, Elsevier, vol. 79(C), pages 80-89.
    6. Sascha Samadi, 2016. "A Review of Factors Influencing the Cost Development of Electricity Generation Technologies," Energies, MDPI, vol. 9(11), pages 1-25, November.
    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. Gan, Peck Yean & Li, ZhiDong, 2015. "Quantitative study on long term global solar photovoltaic market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 88-99.
    9. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    10. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    11. Schauf, Magnus & Schwenen, Sebastian, 2021. "Mills of progress grind slowly? Estimating learning rates for onshore wind energy," Energy Economics, Elsevier, vol. 104(C).
    12. 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.
    13. 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.
    14. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng, 2022. "Effects of learning curve models on onshore wind and solar PV cost developments in the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    15. Williams, Eric & Hittinger, Eric & Carvalho, Rexon & Williams, Ryan, 2017. "Wind power costs expected to decrease due to technological progress," Energy Policy, Elsevier, vol. 106(C), pages 427-435.
    16. Wiser, Ryan & Millstein, Dev, 2020. "Evaluating the economic return to public wind energy research and development in the United States," Applied Energy, Elsevier, vol. 261(C).
    17. Choi, Donghyun & Kim, Yeong Jae, 2023. "Local and global experience curves for lumpy and granular energy technologies," Energy Policy, Elsevier, vol. 174(C).
    18. Kahouli-Brahmi, Sondes, 2009. "Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach," Ecological Economics, Elsevier, vol. 68(4), pages 1195-1212, February.
    19. Kahouli, Sondès, 2011. "Effects of technological learning and uranium price on nuclear cost: Preliminary insights from a multiple factors learning curve and uranium market modeling," Energy Economics, Elsevier, vol. 33(5), pages 840-852, September.
    20. Lohwasser, Richard & Madlener, Reinhard, 2013. "Relating R&D and investment policies to CCS market diffusion through two-factor learning," Energy Policy, Elsevier, vol. 52(C), pages 439-452.

    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:renene:v:99:y:2016:i:c:p:280-288. 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/renewable-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.