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

Abstract

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  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. https://doi.org/10.1016/j.ecolind.2015.08.029

    Article  Google Scholar 

  2. Bass FM (1969) A new product growth for model consumer durables. Manag Sci 15:215–227. https://www.jstor.org/stable/2628128. 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. https://doi.org/10.1016/j.enpol.2012.10.076

    Article  Google 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. https://kjfw.cec.org.cn/kejifuwu/2013-04-07/99877.html/. 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. https://kjfw.cec.org.cn/kejifuwu/2016-07-12/155498.html/. 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. https://doi.org/10.1016/j.apenergy.2016.09.046

    Article  Google Scholar 

  7. Fisher JC, Pry RH (1971) A simple substitution model of technological change. Technol Forecast Soc Change 3:75–88. https://doi.org/10.1016/S0040-1625(71)80005-7

    Article  Google 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. https://doi.org/10.1016/j.enpol.2015.07.023

    Article  Google Scholar 

  9. Harijan K, Uqaili MA, Memon M, Mirza UK (2011) Forecasting the diffusion of wind power in Pakistan. Energy 36:6068–6073. https://doi.org/10.1016/j.energy.2011.08.009

    Article  Google 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. https://doi.org/10.1016/j.ejor.2015.07.034

    Article  Google 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 10:42–58

    Article  Google Scholar 

  12. IEA (2012) Technology roadmap: high-efficiency, low-emissions coal-fired power generation. International Energy Agency, Paris

    Google Scholar 

  13. IEA (2014) Emissions reduction through upgrade of coal-fired power plants: learning from Chinese experience. International Energy Agency, Paris

    Google Scholar 

  14. IEA (2015) World Energy Outlook 2015. International Energy Agency, Paris

    Google Scholar 

  15. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:264–323. https://doi.org/10.1145/331499.331504

    Article  Google 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. https://doi.org/10.1016/j.jengtecman.2015.08.006

    Article  Google Scholar 

  17. Jiang K (2011) Green roadmap: China’s power sector’s pathway to lower carbon emissions. China Envionmental Science Press, Beijing

    Google 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. https://doi.org/10.1016/j.jclepro.2017.03.189

    Article  Google Scholar 

  19. Kucharavy D, De Guio R (2011) Logistic substitution model and technological forecasting. Procedia Eng 9:402–416. https://doi.org/10.1016/j.proeng.2011.03.129

    Article  Google Scholar 

  20. Kumar R, Agarwala A (2016) Renewable energy technology diffusion model for techno-economics feasibility. Renew Sustain Energy Rev 54:1515–1524. https://doi.org/10.1016/j.rser.2015.10.109

    Article  Google Scholar 

  21. Li Y (2012) Dynamics of clean coal-fired power generation development in China. Energy Policy 51:138–142. https://doi.org/10.1016/j.enpol.2011.06.012

    Article  Google 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. https://doi.org/10.1016/j.ijhydene.2017.06.099

    Article  Google 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. https://doi.org/10.1016/j.jclepro.2012.08.003

    Article  Google 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. https://doi.org/10.1007/s11192-009-0055-5

    Article  Google Scholar 

  25. Marshall JP (2016) Disordering fantasies of coal and technology: carbon capture and storage in Australia. Energy Policy 99:288–298. https://doi.org/10.1016/j.enpol.2016.05.044

    Article  Google 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. https://doi.org/10.1016/j.jclepro.2016.10.157

    Article  Google 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. http://www.mep.gov.cn/gkml/hbb/bgg/201407/t20140711278584.htm/. 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. http://www.mep.gov.cn/gkml/hbb/bwj/201512/t20151215_319170.htm/. 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. https://doi.org/10.1016/j.jclepro.2016.11.055

    Article  Google 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. https://doi.org/10.1016/j.esr.2015.03.001

    Article  Google 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. https://doi.org/10.1016/j.esr.2014.11.004

    Article  Google Scholar 

  32. NDRC (National Development and Reform Commission), NEA (National Energy Administration) (2016) The 13th FYP plan for power development. http://ghs.ndrc.gov.cn/ghwb/gjjgh/201706/t20170605_849993.html/. 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. https://doi.org/10.1287/mnsc.33.9.1069

    Article  Google Scholar 

  34. Rao KU, Kishore VVN (2009) Wind power technology diffusion analysis in selected states of India. Renew Energy 34:983–988. https://doi.org/10.1016/j.renene.2008.08.013

    Article  Google 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 129:333–344

    Article  Google 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. https://doi.org/10.1016/j.enpol.2015.09.041

    Article  Google 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. https://doi.org/10.1016/j.apenergy.2017.08.215

    Article  Google Scholar 

  38. Valle AD, Furlan C (2011) Forecasting accuracy of wind power technology diffusion models across countries. Int J Forecast 27:592–601. https://doi.org/10.1016/j.ijforecast.2010.05.018

    Article  Google Scholar 

  39. Wang Z, Zhu Y, Zhu Y, Shi Y (2016) Energy structure change and carbon emission trends in China. Energy 115:369–377. https://doi.org/10.1016/j.energy.2016.08.066

    Article  Google Scholar 

  40. Winsor CP (1932) The Gompertz curve as a growth curve. Proc Natl Acad Sci USA 18:1–8. https://doi.org/10.1073/pnas.18.1.1

    Article  Google Scholar 

  41. Xu J, Li L, Zheng B (2016) Wind energy generation technological paradigm diffusion. Renew Sustain Energy Rev 59:436–449. https://doi.org/10.1016/j.rser.2015.12.271

    Article  Google Scholar 

Download references

Acknowledgements

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).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Tao Lv.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, M., Lv, T., Deng, X. et al. Diffusion of China’s coal-fired power generation technologies: historical evolution and development trends. Nat Hazards 95, 7–23 (2019). https://doi.org/10.1007/s11069-018-3524-4

Download citation

Keywords

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