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The evolution and driving forces of industrial aggregate energy intensity in China: An extended decomposition analysis

Juan Wang, Mingming Hu and João F.D. Rodrigues

Applied Energy, 2018, vol. 228, issue C, 2195-2206

Abstract: This study adopts the log-mean Divisial index (LMDI) method to decompose the changes in the industrial aggregate energy intensity (IAEI) of China into both macro and technological factors: sectoral energy intensity, industrial structure, research and development (R&D) efficiency, R&D intensity and investment intensity. Afterwards we determine the contributions of 36 industrial sub-sectors to IAEI through different factors using attribution analysis. The results show that the IAEI decreased by 38.26% from 2003 to 2015. This drop is predominantly caused by R&D efficiency (−76.01%). The sub-sectors of ferrous metals (−14.94%) and non-metallic mineral products (−13.36%) are the main contributors to the R&D efficiency effect. The sectoral energy intensity effect contributes −27.19%, mainly due to the sub-sectors of ferrous metals (−15.97%) and non-ferrous metals (−5.68%). The industrial structure effect also contributes to a decline of IAEI (−15.06%), of which, petroleum, coking and nuclear fuel (−5.57%) and ferrous metals (−4.73%) are the sub-sectors that contribute the most. Conversely, investment intensity (174.09%) and R&D intensity (52.06%) contribute to increase the IAEI, largely owing to sub-sectors of petroleum, coking and nuclear fuel processing, chemical materials and non-metallic mineral products. Our findings suggest that the combined effects of the policies implemented during the time frame of 2003 to 2015 led to a reduction in IAEI, with investment intensity being the focus of improvement. Nevertheless, different policies and measures should be put forward in different sub-sectors due to their varying degrees of adaptability and policy sensitivity.

Keywords: Industrial aggregate energy intensity; R&D expenditure; Investment; Index decomposition analysis; Attribution analysis (search for similar items in EconPapers)
Date: 2018
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