Skip to main content

Advertisement

Log in

Determinants of technical inefficiency in China’s coal-fired power plants and policy recommendations for CO2 mitigation

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

This study applies data envelopment analysis (DEA) to estimate the technical efficiency (TE) and CO2 emission reduction potential of 1270 coal-fired power plants in 28 Chinese provinces and municipalities. The large dataset used in the study includes 727 combined heat and power (CHP) plants and 543 thermal power plants. Results show an average TE score of 0.57 for the CHP power plants and 0.58 for the thermal power plants, suggesting a significant potential to reduce coal consumption in both types of coal-fired plants. Total CO2 emission reduction potential was estimated to be 953 Mt-CO2, or 19% of the total CO2 emissions of China’s electricity and heat producing sectors, indicating that China’s coal-fired power plants have a significant potential to mitigate CO2 emissions through technological improvement. In the second stage of the study, a Tobit regression analysis was conducted to identify the determinants of TE. Factors such as the plant’s annual operation rate and capacity utilization rate were found to be significant influences. Based on our results, we propose that the Chinese government create a power distribution structure that generates electricity using technologically efficient equipment in areas rich in coal resources and distributes the generated electricity to other areas of the country.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The data were digitized by the authors from 2014 Power Industry Statistics. The dataset is not free and not available online.

Notes

  1. Meta-frontier DEA analysis (O'Donnell et al., 2008) is another method that differs from the combined use of DEA analysis and regression analysis to identify the sources of technical inefficiency. For example, Eguchi et al. (2021) applied meta-frontier DEA analysis to three years of input-output data for a number of Chinese power plants (567 plants in 2009, 569 plants in 2010, and 507 plants in 2011) and decomposed the sources of technical inefficiency into regional inefficiency and scale inefficiency. However, meta-frontier DEA analysis is not suitable for identifying multiple determinants, as in this study, as it may lose the robustness of the DEA results due to a decrease in sample size (Eguchi et al. 2021). The combined approach of DEA analysis and regression analysis allows us to test the statistical significance of the analysis results.

  2. Although the produced heat from CHP power plants should be converted to its electricity equivalent, heat supply data for each CHP plant were unavailable. To overcome this problem, Zhou et al. (2012) suggests that CHP and thermal power plants should be evaluated at separate production frontiers.

  3. The TE scores of Hunan and Sichuan provinces in the CHP model, as well as Jiangxi province in the thermal model, should be interpreted with caution due to the small dataset.

  4. In Lam and Shiu (2004), UTILIZATION, an independent variable similar to HOUR in this study, was used in the regression model. UTILIZATION is defined as the ratio of the average annual utilization hours of the thermal power plants in each province to the total hours in a year.

  5. In Lam and Shiu (2001), CAPACITY, an independent variable similar to LOAD in this study, was used in the regression model. CAPACITY is defined as the average load of thermal power plants in each province divided by the average installed capacity in each province.

References

  • Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci 30:1078–1092

    Article  Google Scholar 

  • Bi GB, Song W, Zhou P, Liang L (2014) Does environmental regulation affect energy efficiency in China’s thermal power generationα Empirical evidence from a slacks-based DEA model. Energy Policy 66:537–546

    Article  Google Scholar 

  • Central Compilation & Translation Press in China (2016) The 13th Five-Year Plan for economic and social development of the People's Republic of China (2016–2012). http://en.ndrc.gov.cn/newsrelease/201612/P020161207645765233498.pdf. Accessed 16 May 2021

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444

    Article  Google Scholar 

  • China Electricity Council (2015) 2014 Statistical data compilation of the electric power industry. SDX Joint Publishing Company

  • China Electricity Yearbook committee (2012) China Electricity Yearbook 2011. China Statistics Press, Beijing

    Google Scholar 

  • China Electricity Yearbook committee (2018) China Electricity Yearbook 2017. China Statistics Press, Beijing

    Google Scholar 

  • Chung YH, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: A directional distance function approach. J Environ Manag 51:229–240

    Article  Google Scholar 

  • Cook, WD, Zhu J (2013) Data Envelopment Analysis: Balanced Benchmarking

  • Du L, Mao J (2015) Estimating the environmental efficiency and marginal CO2 abatement cost of coal-fired power plants in China. Energy Policy 85:347–356

    Article  CAS  Google Scholar 

  • Du L, Hanley A, Zhang N (2016) Environmental technical efficiency, technology gap and shadow price of coal-fuelled power plants in China: A parametric meta-frontier analysis. Resour. Energy Econ. 43:14–32

    Article  CAS  Google Scholar 

  • Duan N, Guo JP, Xie BC (2016) Is there a difference between the energy and CO2 emission performance for China’s thermal power industry? A bootstrapped directional distance function approach. Appl Energy 162:1552–1563

    Article  CAS  Google Scholar 

  • Eguchi S, Kagawa S, Okamoto S (2015) Environmental and economic performance of a biodiesel plant using waste cooking oil. J Clean Prod 101:245–250

    Article  CAS  Google Scholar 

  • Eguchi S, Takayabu H, Lin C (2021) Sources of inefficient power generation by coal-fired thermal power plants in China: A metafrontier DEA decomposition approach. Renew Sustain Energy Rev 138:110562

    Article  Google Scholar 

  • Fukuyama H, Yoshida Y, Managi S (2011) Modal choice between air and rail: a social efficiency benchmarking analysis that considers CO2 emissions. Environmental Economics and Policy Studies 89:89–102

    Article  Google Scholar 

  • Greene WH (2002) ECONOMETRICS ANALYSIS FIFTH EDITION. Prentice Hall

  • Horii N (2007) China Industrial Handbook 2007-2008 Edition, Chapter 4 Electric Power Industry (In Japanese)

  • Hu J, Wang S (2006) Total-factor energy efficiency of regions in China. Energy Policy 34:3206–3217

    Article  Google Scholar 

  • International Energy Agency (2020) Data and statistics (https://www.iea.org/data-and-statistics?country=WORLD&fuel=Energy%20supply&indicator=TPESbySource, accessed 10.21.20).

  • International Energy Agency Clean Coal Centre (IEACCC) (2016) An overview of HELE technology deployment in the coal power plant fleets of China, EU, Japan and USA. (https://www.iea-coal.org/an-overview-of-hele-technology-deployment-in-the-coal-power-plant-fleets-of-china-eu-japan-and-usa-ccc-273/, accessed 04.29.21)

  • IPCC (2006) IPCC Guidelines for national greenhouse gas inventories, Institute for Global Environmental Strategies (IGES)

  • Kaneko S, Fujii H, Sawazu N, Fujikura R (2010) Financial allocation strategy for the regional pollution abatement cost of reducing sulfur dioxide emissions in the thermal power sector in China. Energy Policy 38:2131–2141

    Article  Google Scholar 

  • Lam P, Shiu A (2001) A data envelopment analysis of the efficiency of China's thermal power generation. Util. Policy 10:75–83

    Article  Google Scholar 

  • Lam P, Shiu A (2004) Efficiency and Productivity of China's Thermal Power Generation. Int J Ind Organ 24:75–83

    Google Scholar 

  • Lin B, Yang L (2014) Efficiency effect of changing investment structure on China’s power industry. Renew Sustain Energy Rev 39:403–411

    Article  Google Scholar 

  • Liu CH, Lin SJ, Lewis C (2010) Evaluation of thermal power plant operational performance in Taiwan by data envelopment analysis. Energy Policy 38:1049–1058

    Article  Google Scholar 

  • Long X, Zhao X, Cheng F (2015) The comparison analysis of total factor productivity and eco-efficiency in China’s cement manufactures. Energy Policy 81:61–66

    Article  Google Scholar 

  • Long X, Sun M, Cheng F, Zhang J (2017) Convergence analysis of eco-efficiency of China’s cement manufacturers through unit root test of panel data. Energy 134:709–717

    Article  Google Scholar 

  • Long X, Chen B, Park B (2018a) Effect of 2008’s Beijing Olympic Games on environmental efficiency of 268 China’s cities. J Clean Prod 172:1423–1432

    Article  Google Scholar 

  • Long X, Wu C, Zhang J, Zhang J (2018b) Environmental efficiency for 192 thermal power plants in the Yangtze River Delta considering heterogeneity: A metafrontier directional slacks-based measure approach. Renew Sustain Energy Rev 82:3962–3971

    Article  Google Scholar 

  • Nakaishi T (2021) Developing effective CO2 and SO2 mitigation strategy based on marginal abatement costs of coal-fired power plants in China. Appl Energy 294:116978. https://doi.org/10.1016/j.apenergy.2021.116978

  • O’Donnell CJ, Rao DSP, Battese GE (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Econ 34:231–255

    Article  Google Scholar 

  • Shan Y, Guan D, Zheng H, Ou J, Li Y, Meng J et al (2018) Data Descriptor: China CO2 emission accounts. Sci Data 5:170201

    Article  CAS  Google Scholar 

  • Song C, Li M, Zhang F, He YL, Tao WQ (2015) A data envelopment analysis for energy efficiency of coal-fired power units in China. Energy Convers Manag 102:121–130

    Article  Google Scholar 

  • Sun C, Liu X, Li A (2018) Measuring unified efficiency of Chinese fossil fuel power plants: Intermediate approach combined with group heterogeneity and window analysis. Energy Policy 123:8–18

    Article  Google Scholar 

  • Takayabu H (2020) CO2 mitigation potentials in manufacturing sectors of 26 countries. Energy Econ 86:104634

    Article  Google Scholar 

  • Takayabu H, Kagawa S, Fujii H et al (2019) Impacts of productive efficiency improvement in the global metal industry on CO2 emissions. J Environ Manage 248:109261

    Article  CAS  Google Scholar 

  • Thakur T, Deshmukh SG, Kaushik SC (2006) Efficiency evaluation of the state owned electric utilities in India. Energy Policy 34:2788–2804

    Article  Google Scholar 

  • Tong D, Zhang Q, Davis SJ et al (2018) Targeted emission reductions from global super-polluting power plant units. Nat Sustain 1:59–68

    Article  Google Scholar 

  • Tsutsui M (2001) Analysis of the efficiency taking into account the environmental performance of electrical industry: application of DEA. Central Res. Inst. Electr. Power Ind. Rep Y00017:1–12 (in Japanese)

    Google Scholar 

  • Wang C, Cao X, Mao J, Qin P (2019) The changes in coal intensity of electricity generation in Chinese coal-fired power plants. Energy Econ 80:491–501

    Article  Google Scholar 

  • Wei X, Zhang N (2020) The shadow prices of CO2 and SO2 for Chinese Coal-fired Power Plants: A partial frontier approach. Energy Econ 85:104576

    Article  Google Scholar 

  • Wei C, Löschel A, Liu B (2013) An empirical analysis of the CO2 shadow price in Chinese thermal power enterprises. Energy Econ 40:22–31

    Article  CAS  Google Scholar 

  • Wu C, Oh K, Long X, Zhang J (2019) Effect of installed capacity size on environmental efficiency across 528 thermal power stations in North China. Environ Sci Pollut Res 26:29822–29833

    Article  Google Scholar 

  • Yan D, Lei Y, Li L, Song W (2017) Carbon emission efficiency and spatial clustering analyses in China’s thermal power industry: Evidence from the provincial level. J Clean Prod 156:518–527

    Article  CAS  Google Scholar 

  • Yang H, Pollitt M (2009) Incorporating both undesirable outputs and uncontrollable variables into DEA: The performance of Chinese coal-fired power plants. Eur J Oper Res 197:1095–1105

    Article  Google Scholar 

  • Yang H, Pollitt M (2010) The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants. Energy Policy 38:4440–4444

    Article  Google Scholar 

  • Zhang N, Kong F, Choi Y, Zhou P (2014) The effect of size-control policy on unified energy and carbon efficiency for Chinese fossil fuel power plants. Energy Policy 70:193–200

    Article  Google Scholar 

  • Zhao X, Ma C (2013) Deregulation, vertical unbundling and the performance of China’s large coal-fired power plants. Energy Econ 40:474–483

    Article  Google Scholar 

  • Zhou P, Ang BW, Wang H (2012) Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach. Eur J Oper Res 221:625–635

    Article  Google Scholar 

  • Zhou Y, Xing X, Fang K, Liang D, Xu C (2013) Environmental efficiency analysis of power industry in China based on an entropy SBM model. Energy Policy 57:68–75

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to the editor and anonymous referees for their helpful comments and suggestions. We accept full responsibility for any errors in the manuscript.

Funding

This research was supported by JSPS KAKENHI Grant Number JP20H00081.

Author information

Authors and Affiliations

Authors

Contributions

Tomoaki Nakaishi: conceptualization; formal analysis; investigation; methodology; project administration; software; validation; visualization; writing—original draft; writing—review & editing. Shigemi Kagawa: conceptualization; funding acquisition; formal analysis; investigation; methodology; software; validation; writing—original draft; writing—review & editing. Hirotaka Takayabu: conceptualization; formal analysis; investigation; methodology; software; validation; writing—original draft; writing—review & editing. Chen Lin: data curation; resources; validation; writing—review & editing.

Corresponding author

Correspondence to Tomoaki Nakaishi.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of interest

The authors declare no competing interests.

Additional information

Responsible Editor: Ilhan Ozturk

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(CSV 92 kb)

Appendix

Appendix

Table 7 The 33 most efficient CHP power plants in China
Table 8 The 18 most efficient thermal power plants in China
Table 9 The 10 CHP power plants with the highest CO2 emissions reduction potential
Table 10 The 10 thermal power plants with the highest CO2 emissions reduction potential

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nakaishi, T., Kagawa, S., Takayabu, H. et al. Determinants of technical inefficiency in China’s coal-fired power plants and policy recommendations for CO2 mitigation. Environ Sci Pollut Res 28, 52064–52081 (2021). https://doi.org/10.1007/s11356-021-14394-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-021-14394-4

Keywords

Navigation