Skip to main content

Advertisement

SpringerLink
Log in

Rebound effects for residential electricity use in urban China: an aggregation analysis based E-I-O and scenario simulation

  • S.I.: Energy and Climate Policy Modeling
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Technological progress is considered an important means of decreasing energy consumption. However, rebound effects caused by energy efficiency improvements directly affect the realization of energy savings and emission reduction. This paper focuses on the main theory and methodology of direct and indirect rebound effects. Using 30 sets of provincial panel data and national input–output data for China from 2007, this paper builds a co-integrating equation, a panel error correction model, and an 8-sector energy-input–output model. We subsequently estimate the direct and indirect rebound effects of urban residential electricity use. The results indicate that in the long term the direct plus indirect partial rebound effect is 0.79; in the short term it is 0.78. Thus, the majority of the expected electricity reduction in Chinese urban residential energy consumption arising from efficiency improvement may be offset. These rebound effects impair the functioning of energy efficiency policies. Therefore, the Chinese government should not improve energy efficiency alone—they must also take into consideration the relevant energy-pricing reforms when formulating energy policies.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Notes

  1. \(\eta _{\varepsilon } (E)=\frac{{\partial E}/E}{{\partial \varepsilon }/\varepsilon }=\frac{\partial \ln E}{\partial \ln \varepsilon }=\frac{\partial \ln (S/\varepsilon )}{\partial \ln \varepsilon }=\frac{\partial \ln S}{\partial \ln \varepsilon }-\frac{\partial \ln \varepsilon }{\partial \ln \varepsilon }=\frac{\partial \ln S}{\partial \ln \varepsilon }-1=\eta _\varepsilon (S)-1\).

  2. \(\eta _{\varepsilon } (S)=\frac{{\partial S}/S}{{\partial \varepsilon }/\varepsilon }=\frac{\partial \ln S}{\partial \ln \varepsilon }=\frac{\partial \ln S}{\partial \ln P_{S} }\frac{\partial \ln P_{S} }{\partial \ln \varepsilon }=\eta _{P_{S} } (S).\frac{\partial \ln (P_{E} /\varepsilon )}{\partial \ln \varepsilon } =\eta _{P_{S} } (S)\left( {\frac{\partial \ln P_{E} }{\partial \ln \varepsilon }-\frac{\partial \ln \varepsilon }{\partial \ln \varepsilon }} \right) =\eta _{P_{S} } (S)\left( {\frac{\partial \ln P_{E} }{\partial \ln \varepsilon }-1} \right) \)

    If energy price change cannot affect energy efficiency, then \({\partial \ln P_{E} }/{\partial \ln \varepsilon }=0\).

  3. \(\eta _{P_{E} } (E)=\frac{\partial \ln E}{\partial \ln P_{E} }=\frac{\partial \ln E}{\partial \ln P_S }.\frac{\partial \ln P_S }{\partial \ln P_{E} }=\frac{\partial \ln (S/\varepsilon )}{\partial \ln P_{S} }.\frac{\partial \ln (P_E /\varepsilon )}{\partial \ln P_{E} } =\left[ {\eta _{P_S } (S)-\frac{\partial \ln \varepsilon }{\partial \ln P_S }} \right] \left( {1-\frac{\partial \ln \varepsilon }{\partial \ln P_E }} \right) \)

    If \(\varepsilon \) is a constant variable, \({\partial \ln \varepsilon }/{\partial \ln P_S }=0\) and \({\partial \ln \varepsilon }/{\partial \ln P_E }=0\), and so \(\eta _{P_E } (E)=\eta _{P_S } (S)\).

  4. The direct changes in price have a greater effect on the improvement of production efficiency. When the time series are stationary, the decline in energy prices will not affect the energy efficiency. However, the rising energy prices will promote the technology progress (Wang et al. 2014).

  5. http://news.sohu.com/s2011/dianjia/. On June 14, 2012, the multistep residential tariff pricing reform draft had implement in China’s 29 provinces.

References

  • Akinobu, M., Yasuhiko, K., Mu, H., & Zhou, W. (2008). Electricity demand in the Chinese urban household-sector. Applied Energy, 85, 1113–1125.

    Article  Google Scholar 

  • Alfredsson, E. (2004). Green consumption: No solution for climate change. Energy, 29(4), 513–524.

    Article  Google Scholar 

  • Bentzen, J. (2004). Estimating the rebound effect in U.S manufacturing energy consumption. Energy Economics, 26(1), 123–134.

    Article  Google Scholar 

  • Berkhout, H. G., Muskens, J. C., & Velthuijsen, J. W. (2000). Defining the rebound effect. Energy Policy, 28, 425–432.

    Article  Google Scholar 

  • Binswanger, M. (2001). Technological progress and sustainable development: What about the rebound effect? Ecological Economics, 36(1), 119–132.

    Article  Google Scholar 

  • Brandt, L., & Holz, C. A. (2006). Spatial price differences in China: Estimates and implications. Economic Development and Cultural Change, 55(1), 43–86.

    Article  Google Scholar 

  • Brannlund, R., Ghalwash, T., & Nordstom, J. (2007). Increased energy efficiency and the rebound effect: Effects on consumption and emissions. Energy Economics, 29(1), 1–17.

    Article  Google Scholar 

  • Brookes, L. G. (1978). Energy policy, the energy price fallacy and the role of nuclear energy in the U.K. Energy Policy, 6, 94–106.

    Article  Google Scholar 

  • Brookes, L. G. (1990). The fifth fuel and the greenhouse effect. Energy World, 18(2), 199–201.

    Google Scholar 

  • Dargay, J. M., & Gately, D. (1995). The imperfect price irreversibility of non-transportation of all demand in the OECD. Energy Economics, 17(1), 59–71.

    Article  Google Scholar 

  • Druckman, A., Chitnis, M., Sorrell, S., & Jackson, T. (2011). Missing carbon reductions? Exploring rebound and backfire effects in UK households. Energy Policy, 39(6), 3572–3581.

    Article  Google Scholar 

  • Freire-González, J. (2010). Empirical evidence of direct rebound effect in catalonia. Energy Policy, 38(5), 2309–2314.

    Article  Google Scholar 

  • Freire-González, J. (2011). Methods to empirically estimate direct and indirect rebound effect of energy-saving technological changes in households. Ecological Modeling, 223(1), 32–40.

    Article  Google Scholar 

  • Frondel, M., Peters, J., & Vance, C. (2008). Identifying the rebound: Evidence from a German household panel. Energy Journal, 29(4), 154–163.

    Article  Google Scholar 

  • Gately, D. (1993). The imperfect price reversibility of world oil demand. Energy Journal, 14(4), 163–182.

    Article  Google Scholar 

  • Greening, L. A., Greene, D. L., & Difiglio, C. (2000). Energy efficiency and consumption-the rebound effect-a survey. Energy Policy, 28, 389–401.

    Article  Google Scholar 

  • Grubb, M. J. (1990). Energy efficiency and economic fallacies. Energy Policy, 18(8), 783–785.

    Article  Google Scholar 

  • Haas, R., & Biermayr, P. (2000). The rebound effect for space heating: Empirical evidence from Austria. Energy Policy, 28, 403–410.

    Article  Google Scholar 

  • Hymel, K. M., Small, K. A., & Dender, K. V. (2010). Induced demand and rebound effects in road transport. Transportation Research Part B: Methodological, 44(10), 1220–1241.

    Article  Google Scholar 

  • Jevons, W. S. (1865). The coal question: Can Britain survive? In A. W. Flux (Ed.), The coal question: An inquiry concerning the progress of the nation and the probable exhaustion of our coal-mines. New York: Augustus M. Kelley.

    Google Scholar 

  • Jin, S. (2007). The effectiveness of energy efficiency improvement in a developing country: Rebound effect of residential electricity use in South Korea. Energy Policy, 35(11), 5622–5629.

    Article  Google Scholar 

  • Khazzoom, J. D. (1980). Economic implications of mandated efficiency in standards for household appliances. Energy Journal, 1(4), 21–40.

    Google Scholar 

  • Kok, R., Benders, R. M., & Moll, H. C. (2006). Measuring the environmental load of household consumption using some methods based on input–output energy analysis: A comparison of methods and a discussion of results. Energy Policy, 34(17), 2744–2761.

    Article  Google Scholar 

  • Leontief, W., & Ford, D. (1970). Environmental repercussions and the economic structure, an input–output approach. Review of Economics and Statistics, 52(3), 262–271.

    Article  Google Scholar 

  • Liao, H., Du, J., & Wei, Y. M. (2013). Energy conservation in China: Key provincial sectors at two-digit level. Applied Energy, 104, 457–465.

    Article  Google Scholar 

  • Liao, H., & Wei, Y. M. (2012). Will the aggregation approach affect energy efficiency performance assessment? Renewable & Sustainable Energy Reviews, 16, 4537–4542.

    Article  Google Scholar 

  • Lin, B. Q., & Liu, X. (2013). Electricity tariff reform and rebound effect of residential electricity consumption in China. Energy, 59, 240–247.

    Article  Google Scholar 

  • Mizobuchi, K. (2008). An empirical study on the rebound effect considering capital costs. Energy Economics, 30(5), 2486–2516.

    Article  Google Scholar 

  • Ouyang, J. L., Long, E. S., & Hokao, K. (2010). Rebound effect in Chinese household energy efficiency and solution for mitigating it. Energy, 35, 5269–5276.

    Article  Google Scholar 

  • Roy, J. (2000). The rebound effect: Some empirical evidence from India. Energy Policy, 28, 433–438.

    Article  Google Scholar 

  • Saunders, H. D. (1992). The Khazzoom-Brookes postulate and neoclassical growth. The Energy Journal, 13(4), 131–148.

    Article  Google Scholar 

  • Saunders, H. D. (2000). A view from the macro-side: Rebound, backfire and Khazzoom-Brookes. Energy Policy, 28, 439–449.

    Article  Google Scholar 

  • Saunders, H. D. (2000). Does predicted rebound depend on distinguishing between energy and energy service? Energy Policy, 28, 497–500.

    Article  Google Scholar 

  • Saunders, H. D. (2008). Fuel conserving (and using) production functions. Energy Economics, 30, 2184–2235.

    Article  Google Scholar 

  • Shao, S., Huang, T., & Yang, L. L. (2014). Using latent variable approach to estimate China’s economy-wide energy rebound effect over 1954–2010. Energy Policy, 72, 235–248.

    Article  Google Scholar 

  • Shao, S., Yang, L. L., & Huang, T. (2013). Theoretical model and experience from China of energy rebound effect. Economic Research Journal (China), 2, 96–109.

    Google Scholar 

  • Small, K. A., & Van-Dender, K. (2007). Fuel efficiency and motor vehicle travel: The declining rebound effect. Energy Journal, 28(1), 25–51.

    Article  Google Scholar 

  • Sorrell, S., & Dimitropoulos, J. (2008). The rebound effect: Microeconomic definitions, limitations and extensions. Ecological Economics, 65(3), 636–649.

    Article  Google Scholar 

  • Thomas, B. A., & Azevedo, I. L. (2013). Estimating direct and indirect rebound effects for U.S. households with input–output analysis Part 1: Theoretical framework. Ecological Economics, 86, 199–210.

    Article  Google Scholar 

  • Thomas, B. A., & Azevedo, I. L. (2013). Estimating direct and indirect rebound effects for U.S. households with input–output analysis Part 2: Simulation. Ecological Economics, 86, 188–198.

    Article  Google Scholar 

  • Wang, Z. H., & Lu, M. L. (2014). An empirical study of direct rebound effect for road freight transport in China. Applied Energy, 133, 274–281.

    Article  Google Scholar 

  • Wang, Z. H., Lu, M. L., & Wang, J. C. (2014). Direct rebound effect on urban residential electricity use: An empirical study in China. Renewable and Sustainable Energy Reviews, 30, 124–132.

    Article  Google Scholar 

  • Wang, K., Wei, Y. M., & Zhang, X. (2013). Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis. Applied Energy, 104, 105–116.

    Article  Google Scholar 

  • Wang, Z. H., Yang, Z. M., & Zhang, Y. X. (2012). Relationships between energy technology patents and CO2 emissions in China: An empirical study. Journal of Renewable and Sustainable Energy, 4(3), 45–54.

    Google Scholar 

  • Wang, K., Yu, S. W., & Zhang, W. (2013). China’s regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation. Mathematical and Computer Modelling, 58, 1117–1127.

    Article  Google Scholar 

  • Wang, Z. H., Zhang, B., & Zhang, Y. X. (2011). Determinants and policy implications for household electricity-saving behavior: Evidence from Beijing, China. Energy Policy, 39, 3550–3557.

    Article  Google Scholar 

Download references

Acknowledgments

This study is supported by the National Nature Science Foundation of China (Reference Nos. 71173017, 71573016, 71521002) and State Key Development Program of Basic Research of China (Reference No. 2012CB95570003). The authors also want to thank Prof. Yiming Wei for his comment and suggestion.

Author information

Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

    Milin Lu & Zhaohua Wang

  2. School of Economics and Management, Beijing Institute of Petrochemical Technology, Beijing, 102617, China

    Milin Lu

  3. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China

    Zhaohua Wang

  4. Collaborative Innovation Centre of Electric Vehicles in Beijing, Beijing, 100081, China

    Zhaohua Wang

Authors
  1. Milin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaohua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaohua Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, M., Wang, Z. Rebound effects for residential electricity use in urban China: an aggregation analysis based E-I-O and scenario simulation. Ann Oper Res 255, 525–546 (2017). https://doi.org/10.1007/s10479-016-2153-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-016-2153-0

Keywords

Search

Navigation