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, access via your institution.





References
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
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
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
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
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
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
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
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
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
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
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
IEA (2012) Technology roadmap: high-efficiency, low-emissions coal-fired power generation. International Energy Agency, Paris
IEA (2014) Emissions reduction through upgrade of coal-fired power plants: learning from Chinese experience. International Energy Agency, Paris
IEA (2015) World Energy Outlook 2015. International Energy Agency, Paris
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:264–323. https://doi.org/10.1145/331499.331504
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
Jiang K (2011) Green roadmap: China’s power sector’s pathway to lower carbon emissions. China Envionmental Science Press, Beijing
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-018-3524-4