Skip to main content

Advertisement

Log in

Can emission trading help to improve energy efficiency in China?

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

This paper investigates whether the operation of emission trading scheme (ETS) helps to improve energy efficiency in China. Stochastic frontier analysis is used to estimate energy efficiency at province level and sector levels, and the difference-in-differences technique is applied to assess the effectiveness of emission trading. The main findings are as follows: (1) energy efficiency may be underestimated if control variables are taken no account in efficiency estimation; (2) the diversity of energy efficiency is rather high across different provinces or sectors, and (3) ETS is not as effective for energy efficiency improvement as supposed, but its stimulation effect on provincial energy efficiency is prominently positive. It is suggested that it might be more appropriate to implement ETS program in industry or transport sector than in various sectors throughout the country.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Due to data availability of the control variables, five provinces are removed from our samples in model 2 and model 3, i.e., Shanxi, Inner Mongolia, Guizhou, Shaanxi, and Ningxia, respectively. Therefore, the comparison of estimation results contains 25 provinces in Figure 1.

  2. Due to data availability of variables, three provinces are removed from our samples in construction sector, i.e., Heilongjiang, Guangxi and Hainan, respectively. Therefore, the estimation results of energy efficiency in construction sector contain 27 provinces.

  3. Due to data availability of variables, two provinces are removed from our samples in transport sector, i.e., Henan and Hainan, respectively. Therefore, the estimation results of energy efficiency in transport sector contain 28 provinces.

References

  • Ang, B. W. (2006). Monitoring changes in economy-wide energy efficiency: From energy–GDP ratio to composite efficiency index. Energy Policy, 34, 574–582.

    Article  Google Scholar 

  • Coelli, T. J. (1996). A guide to FRONTIER 4.1: A computer program for stochastic frontier production and cost function estimation. CPEA working papers (No. 7/96). Armidale: Department of Econometrics, University of New England.

    Google Scholar 

  • Cui, L. B., Fan, Y., Zhu, L., & Bi, Q. H. (2014). How will the emissions trading scheme save cost for achieving China’s 2020 carbon intensity reduction target? Applied Energy, 9, 1043–1052.

    Article  Google Scholar 

  • Cui, Q., Wei, Y.-M., & Li, Y. (2016). Exploring the impacts of the EU ETS emission limits on airline performance via the dynamic environmental DEA approach. Applied Energy, 183, 984–994.

    Article  Google Scholar 

  • Duan, M., Pang, T., & Zhang, X. (2014). Review of carbon emissions trading pilots in China. Energy & Environment, 25, 527–550.

    Article  Google Scholar 

  • EC (2011). Energy Efficiency Plan 2011. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, COM (2011) 109 final.

  • EC (2012). The state of the European carbon market in 2012. Report from the commission to the European parliament and the council, COM (2012) 652 final.

  • Filippini, M., & Hunt, L. C. (2010). Energy demand and energy efficiency in the OECD countries: A stochastic demand frontier approach. Surrey Energy Economics Centre School of Economics Discussion Papers 32, 59–80.

  • Filippini, M., & Hunt, L. C. (2015a). Measurement of energy efficiency based on economic foundations. Energy Economics, 52, s5–s16.

    Article  Google Scholar 

  • Filippini, M., & Hunt, L. C. (2015b). Measuring persistent and transient energy efficiency in the US. Energy Efficiency, 9, 663–675.

    Article  Google Scholar 

  • Filippini, M., Hunt, L. C., & Zorić, J. (2014). Impact of energy policy instruments on the estimated level of underlying energy efficiency in the EU residential sector. Energy Policy, 69, 73–81.

    Article  Google Scholar 

  • Ghosh, R., & Kathuria, V. (2016). The effect of regulatory governance on efficiency of thermal power generation in India: A stochastic frontier analysis. Energy Policy, 89, 11–24.

    Article  Google Scholar 

  • Goldsmith, R. W. (1951). A perpetual inventory of national wealth. National Bureau of Economic Research, 12, 5–74.

    Google Scholar 

  • Holt, C. A., & Shobe, W. M. (2016). Price and quantity collars for stabilizing emission allowance prices: Laboratory experiments on the EU ETS market stability reserve. Journal of Environmental Economics and Management, 80, 69–86.

    Article  Google Scholar 

  • Hübler, M., Voigt, S., & Löschel, A. (2014). Designing an emissions trading scheme for China—An up-to-date climate policy assessment. Energy Policy, 75, 57–72.

    Article  Google Scholar 

  • Jiang, J., Xie, D., Ye, B., Shen, B., & Chen, Z. (2016). Research on China’s cap-and-trade carbon emission trading scheme: Overview and outlook. Applied Energy, 178, 902–917.

    Article  Google Scholar 

  • Liao, Z., Zhu, X., & Shi, J. (2014). Case study on initial allocation of Shanghai carbon emission trading based on Shapley value. Journal of Cleaner Production, 103, 338–344.

    Article  Google Scholar 

  • Lundgren, T., Marklund, P.-O., & Zhang, S. (2014). Energy efficiency in Swedish industry - a stochastic frontier approach. CERE Working Paper.

  • Lundgren, T., Marklund, P.-O., & Zhang, S. (2016). Industrial energy demand and energy efficiency—Evidence from Sweden. Resource and Energy Economics, 43, 130–152.

    Article  Google Scholar 

  • Meng, S., Siriwardana, M., Mcneill, J., et al. (2018). The impact of an ETS on the Australian energy sector: An integrated CGE and electricity modelling approach. Energy Economics, 69, 213–224.

    Article  Google Scholar 

  • Mo, J.-L., Agnolucci, P., Jiang, M.-R., & Fan, Y. (2016). The impact of Chinese carbon emission trading scheme (ETS) on low carbon energy (LCE) investment. Energy Policy, 89, 271–283.

    Article  Google Scholar 

  • Perino, G., & Willner, M. (2016). Procrastinating reform: The impact of the market stability reserve on the EU ETS. Journal of Environmental Economics and Management, 80, 37–52.

    Article  Google Scholar 

  • Porter, M. E., & Linde, C. V. D. (1995). Toward a new conception of the environment-competitiveness relationship. Journal of Economic Perspectives, 9, 97–118.

    Article  Google Scholar 

  • Schmidt, L. P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6, 21–37.

    Article  MathSciNet  MATH  Google Scholar 

  • Tang, L., Wu, J., Yu, L., & Bao, Q. (2015). Carbon emissions trading scheme exploration in China: A multi-agent-based model. Energy Policy, 81, 152–169.

    Article  Google Scholar 

  • Tang, L., Shi, J., & Bao, Q. (2016). Designing an emissions trading scheme for China with a dynamic computable general equilibrium model. Energy Policy, 97, 507–520.

    Article  Google Scholar 

  • Venmans, F. M. J. (2016). The effect of allocation above emissions and price uncertainty on abatement investments under the EU ETS. Journal of Cleaner Production, 126, 595–606.

    Article  Google Scholar 

  • Yang, L., Li, F., & Zhang, X. (2016). Chinese companies’ awareness and perceptions of the emissions trading scheme (ETS): Evidence from a national survey in China. Energy Policy, 98, 254–265.

    Article  Google Scholar 

  • Ye, B., Jiang, J., Miao, L., Li, J., & Peng, Y. (2015). Innovative carbon allowance allocation policy for the Shenzhen emission trading scheme in China. Sustainability, 8, 3.

    Article  Google Scholar 

  • Zhang, Y. J., Wang, A. D., & Tan, W. (2015). The impact of China's carbon allowance allocation rules on the product prices and emission reduction behaviors of ETS-covered enterprises. Energy Policy, 86, 176–185.

    Article  Google Scholar 

  • Zhang, C., Wang, Q., Shi, D., Li, P., & Cai, W. (2016). Scenario-based potential effects of carbon trading in China: An integrated approach. Applied Energy, 182, 177–190.

    Article  Google Scholar 

  • Zhou, P., & Wang, M. (2016). Carbon dioxide emissions allocation: A review. Ecological Economics, 125, 47–59.

    Article  Google Scholar 

  • Zhou, P., Ang, B. W., & Zhou, D. Q. (2012). Measuring economy-wide energy efficiency performance: A parametric frontier approach. Applied Energy, 90, 196–200.

    Article  Google Scholar 

  • Zhou, X., Fan, L. W., & Zhou, P. (2015). Marginal CO2 abatement costs: Findings from alternative shadow price estimates for Shanghai industrial sectors. Energy Policy, 77, 109–117.

    Article  Google Scholar 

  • Zhu, L., Zhang, X. B., Li, Y., Wang, X., & Guo, J. (2017). Can an emission trading scheme promote the withdrawal of outdated capacity in energy-intensive sectors? A case study on China's iron and steel industry. Energy Economics, 63, 332–347.

    Article  Google Scholar 

Download references

Acknowledgements

This study is financially supported by the National Social Science Fund of China (no. 17BGL251).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Li-Wei Fan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Fan, LW. Can emission trading help to improve energy efficiency in China?. Energy Efficiency 12, 979–991 (2019). https://doi.org/10.1007/s12053-018-9735-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-018-9735-4

Keywords

Navigation