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.
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Notes
\(\eta _{\varepsilon } (E)=\frac{{\partial E}/E}{{\partial \varepsilon }/\varepsilon }=\frac{\partial \ln E}{\partial \ln \varepsilon }=\frac{\partial \ln (S/\varepsilon )}{\partial \ln \varepsilon }=\frac{\partial \ln S}{\partial \ln \varepsilon }-\frac{\partial \ln \varepsilon }{\partial \ln \varepsilon }=\frac{\partial \ln S}{\partial \ln \varepsilon }-1=\eta _\varepsilon (S)-1\).
\(\eta _{\varepsilon } (S)=\frac{{\partial S}/S}{{\partial \varepsilon }/\varepsilon }=\frac{\partial \ln S}{\partial \ln \varepsilon }=\frac{\partial \ln S}{\partial \ln P_{S} }\frac{\partial \ln P_{S} }{\partial \ln \varepsilon }=\eta _{P_{S} } (S).\frac{\partial \ln (P_{E} /\varepsilon )}{\partial \ln \varepsilon } =\eta _{P_{S} } (S)\left( {\frac{\partial \ln P_{E} }{\partial \ln \varepsilon }-\frac{\partial \ln \varepsilon }{\partial \ln \varepsilon }} \right) =\eta _{P_{S} } (S)\left( {\frac{\partial \ln P_{E} }{\partial \ln \varepsilon }-1} \right) \)
If energy price change cannot affect energy efficiency, then \({\partial \ln P_{E} }/{\partial \ln \varepsilon }=0\).
\(\eta _{P_{E} } (E)=\frac{\partial \ln E}{\partial \ln P_{E} }=\frac{\partial \ln E}{\partial \ln P_S }.\frac{\partial \ln P_S }{\partial \ln P_{E} }=\frac{\partial \ln (S/\varepsilon )}{\partial \ln P_{S} }.\frac{\partial \ln (P_E /\varepsilon )}{\partial \ln P_{E} } =\left[ {\eta _{P_S } (S)-\frac{\partial \ln \varepsilon }{\partial \ln P_S }} \right] \left( {1-\frac{\partial \ln \varepsilon }{\partial \ln P_E }} \right) \)
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 \(\eta _{P_E } (E)=\eta _{P_S } (S)\).
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).
http://news.sohu.com/s2011/dianjia/. On June 14, 2012, the multistep residential tariff pricing reform draft had implement in China’s 29 provinces.
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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.
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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
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DOI: https://doi.org/10.1007/s10479-016-2153-0