Natural Hazards

, Volume 95, Issue 1–2, pp 7–23 | Cite as

Diffusion of China’s coal-fired power generation technologies: historical evolution and development trends

  • Meizhen Zhang
  • Tao LvEmail author
  • Xu Deng
  • Yuanxu Dai
  • Muhammad Sajid
Original Paper


With increasing environmental pressure and the promotion of structural reforms on the supply side, a trend of transformation and upgrading is inevitable in coal-fired power generation. This study aims to analyze the historical evolution and predict the development trends of subcritical (Sub-C), supercritical (SC) and ultra-supercritical (USC) coal-fired power generation technologies in China. Employing the hierarchical clustering method, we divided 29 Chinese Provinces into four clusters based on their resource endowment, economic development level, technological development and power supply structure. Then, with the Bass model, we analyzed the national- and provincial-level diffusion processes of these three technologies. The results show that currently, at the national level, Sub-C coal-fired power generation technology is in the mature stage, SC technology is in the late growth period, and USC technology is in the rapid growth phase. Further, the diffusion of these three technologies has different characteristics in different clusters of provinces, and it is being transferred from economically developed eastern provinces to economically underdeveloped central and western provinces where coal resources are relatively rich. This research is helpful to the government in making policies to optimize the technical and regional structures of coal-fired power generation.


Coal-fired power generation technologies Bass model Power generation structure Installed capacity 



This study was supported by the Humanities and Social Science Foundation (Grant No. 16YJA790037), Jiangsu Province Graduate Research and Innovation Project (Grant No. KYLX16_0516) and the Center for International Energy Policy Research (Grant No. 6J147125).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Bai H, Qiao S, Liu T, Zhang Y, Xu H (2016) An inquiry into inter-provincial carbon emission difference in China: aiming to differentiated KPIs for provincial low carbon development. Ecol Indic 60:754–765. CrossRefGoogle Scholar
  2. Bass FM (1969) A new product growth for model consumer durables. Manag Sci 15:215–227. Accessed 30 Sep 2017
  3. Bezdek RH, Wendling RM (2013) The return on investment of the clean coal technology program in the USA. Energy Policy 54(3):104–112. CrossRefGoogle Scholar
  4. China Electricity Council (2013) Notice on the publicity of energy efficiency benchmarking management and competition data of national 600 MW coal-fired power plants in 2012. Accessed 30 July 2017
  5. China Electricity Council (2016) Notice on the publicity of energy efficiency benchmarking management and competition data of national 300 MW coal-fired power plants in 2015. Accessed 30 July 2017
  6. Ciulla G, Brano VL, D’Amico A (2016) Modelling relationship among energy demand, climate and office building features: a cluster analysis at European level. Appl Energy 183:1021–1034. CrossRefGoogle Scholar
  7. Fisher JC, Pry RH (1971) A simple substitution model of technological change. Technol Forecast Soc Change 3:75–88. CrossRefGoogle Scholar
  8. Hao Y, Zhang ZY, Liao H, Wei YM (2015) China’s farewell to coal: a forecast of coal consumption through 2020. Energy Policy 86:444–455. CrossRefGoogle Scholar
  9. Harijan K, Uqaili MA, Memon M, Mirza UK (2011) Forecasting the diffusion of wind power in Pakistan. Energy 36:6068–6073. CrossRefGoogle Scholar
  10. Hong J, Koo H, Kim T (2016) Easy, reliable method for mid-term demand forecasting based on the Bass model: a hybrid approach of NLS and OLS. Eur J Oper Res 248:681–690. CrossRefGoogle Scholar
  11. Horbach J, Chen Q, Rennings K, Vögele S (2014) Do lead markets for clean coal technology follow market demand? a case study for China, Germany, Japan and the US. Environ Innov Soc Transit 10:42–58CrossRefGoogle Scholar
  12. IEA (2012) Technology roadmap: high-efficiency, low-emissions coal-fired power generation. International Energy Agency, ParisGoogle Scholar
  13. IEA (2014) Emissions reduction through upgrade of coal-fired power plants: learning from Chinese experience. International Energy Agency, ParisGoogle Scholar
  14. IEA (2015) World Energy Outlook 2015. International Energy Agency, ParisGoogle Scholar
  15. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:264–323. CrossRefGoogle Scholar
  16. Jeong Y, Lee K, Yoon B, Phaal R (2015) Development of a patent roadmap through the generative topographic mapping and Bass diffusion model. J Eng Technol Manag 38:53–70. CrossRefGoogle Scholar
  17. Jiang K (2011) Green roadmap: China’s power sector’s pathway to lower carbon emissions. China Envionmental Science Press, BeijingGoogle Scholar
  18. Jiang J, Ye B, Xie D, Tang J (2017) Provincial-level carbon emission drivers and emission reduction strategies in China: combining multi-layer LMDI decomposition with hierarchical clustering. J Clean Prod. CrossRefGoogle Scholar
  19. Kucharavy D, De Guio R (2011) Logistic substitution model and technological forecasting. Procedia Eng 9:402–416. CrossRefGoogle Scholar
  20. Kumar R, Agarwala A (2016) Renewable energy technology diffusion model for techno-economics feasibility. Renew Sustain Energy Rev 54:1515–1524. CrossRefGoogle Scholar
  21. Li Y (2012) Dynamics of clean coal-fired power generation development in China. Energy Policy 51:138–142. CrossRefGoogle Scholar
  22. Li A, Chen Z, Liao Y, Liu Y (2017) A synthetical evaluation of developing low-carbonized coal-fired power technologies in China. Int J Hydrog Energy 42:20857–20867. CrossRefGoogle Scholar
  23. Liang X, Wang Z, Zhou Z, Huang Z, Zhou J, Cen K (2013) Up-to-date life cycle assessment and comparison study of clean coal power generation technologies in China. J Clean Prod 39:24–31. CrossRefGoogle Scholar
  24. Liu CY, Wang JC (2010) Forecasting the development of the biped robot walking technique in Japan through S-curve model analysis. Scientometrics 82:21–36. CrossRefGoogle Scholar
  25. Marshall JP (2016) Disordering fantasies of coal and technology: carbon capture and storage in Australia. Energy Policy 99:288–298. CrossRefGoogle Scholar
  26. Meng M, Jing K, Mander S (2017) Scenario analysis of CO2 emissions from China’s electric power industry. J Clean Prod 142:3101–3108. CrossRefGoogle Scholar
  27. MEP (Ministry of Environmental protection of the people’s republic of China) (2014) Notice on publicity of the list of desulfurization and denitrification facilities for national coal-fired power plants and other main air pollution reduction projects. Accessed 30 July 2017
  28. MEP (Ministry of Environmental Protection of the People’s Republic of China), NDRC (National Development and Reform Commission), NEA (National Energy Administration) (2015) Full implementation of ultra-low emission and energy saving transformation of coal-fired power plants. Accessed 30 July 2017
  29. Mi Z, Wei YM, Wang B, Meng J, Liu Z, Shan Y, Liu J, Guan D (2017) Socioeconomic impact assessment of China’s CO2 emissions peak prior to 2030. J Clean Prod 142:2227–2236. CrossRefGoogle Scholar
  30. Mishra MK, Khare N, Agrawal AB (2015) Scenario analysis of the CO2 emissions reduction potential through clean coal technology in India’s power sector: 2014–2050. Energy Strateg Rev 7:29–38. CrossRefGoogle Scholar
  31. Na C, Yuan J, Xu Y, Hu Z (2015) Penetration of clean coal technology and its impact on China’s power industry. Energy Strateg Rev 7:1–8. CrossRefGoogle Scholar
  32. NDRC (National Development and Reform Commission), NEA (National Energy Administration) (2016) The 13th FYP plan for power development. Accessed 30 July 2017
  33. Nortan JA, Bass FM (1987) A diffusion theory model of adoption and substitution for successive generations of high-technology products. Manag Sci 33:1069–1087. CrossRefGoogle Scholar
  34. Rao KU, Kishore VVN (2009) Wind power technology diffusion analysis in selected states of India. Renew Energy 34:983–988. CrossRefGoogle Scholar
  35. Song M, Wang J, Zhao J (2016) Coal endowment, resource curse, and high coal-consuming industries location: analysis based on large-scale data. Resour Conserv Recycl 129:333–344CrossRefGoogle Scholar
  36. Tang X, Snowden S, McLellan BC, Höök M (2015) Clean coal use in China: challenges and policy implications. Energy Policy 87:517–523. CrossRefGoogle Scholar
  37. Tang BJ, Li R, Li XY, Chen H (2017) An optimal production planning model of coal-fired power industry in China: considering the process of closing down inefficient units and developing CCS technologies. Appl Energy 206:519–530. CrossRefGoogle Scholar
  38. Valle AD, Furlan C (2011) Forecasting accuracy of wind power technology diffusion models across countries. Int J Forecast 27:592–601. CrossRefGoogle Scholar
  39. Wang Z, Zhu Y, Zhu Y, Shi Y (2016) Energy structure change and carbon emission trends in China. Energy 115:369–377. CrossRefGoogle Scholar
  40. Winsor CP (1932) The Gompertz curve as a growth curve. Proc Natl Acad Sci USA 18:1–8. CrossRefGoogle Scholar
  41. Xu J, Li L, Zheng B (2016) Wind energy generation technological paradigm diffusion. Renew Sustain Energy Rev 59:436–449. CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Meizhen Zhang
    • 1
  • Tao Lv
    • 1
    Email author
  • Xu Deng
    • 1
  • Yuanxu Dai
    • 1
  • Muhammad Sajid
    • 1
  1. 1.Jiangsu Energy Economy and Management Research Base, School of ManagementChina University of Mining and TechnologyXuzhouChina

Personalised recommendations