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Sustainability 2016, 8(6), 544;

China’s Energy Intensity, Determinants and Spatial Effects

1,* and 2
School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China
The Research Center for East-West Cooperation in China, The Key Lab of GIScience of the Education Ministry PRC, East China Normal University, Shanghai 200062, China
Author to whom correspondence should be addressed.
Academic Editor: Vincenzo Torretta
Received: 1 April 2016 / Revised: 28 May 2016 / Accepted: 7 June 2016 / Published: 9 June 2016
(This article belongs to the Section Sustainable Use of the Environment and Resources)
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In the shadow of the energy crisis and environmental degradation, energy intensity is a hot topic in academic circles in China. The energy intensity distribution map of China indicates the fairly large geographic disparities globally and clustering locally in some areas, ascending from the southeast regions to the northwest provinces. Although energy intensity and its determinants vary from place to place, few studies have been made from the spatial perspective. Determinates of energy intensity and spatial spillover effects should be taken into consideration. Controlling for seven exogenous variables (per capita GDP; the share of the secondary sector; foreign direct investment; international trade, energy price, the share of coal, and transport sector) and their spatial lags, we apply a spatial Durbin model to test for spatial spillover effects among energy intensity and exogenous variables from a panel of 29 Chinese provinces over 1998 to 2014. We find that per capita GDP has an insignificant and negative direct and indirect effect, but has a significant and negative total effect on energy intensity. The share of the secondary sector and the share of coal are found to have significant and positive direct and indirect effects on energy intensity. Foreign Direct Investment (FDI) and Trade have significant and negative direct and indirect effects on energy intensity. The direct effect of energy price is found to be significantly positive while the indirect effect is negative. Only the direct effect of the Transport variable is significant and positive. The results of this study offer some theoretical evidence for differential localized policy making related to reduction in energy intensity. View Full-Text
Keywords: energy intensity; determinants; spatial Durbin model; panel data; China energy intensity; determinants; spatial Durbin model; panel data; China

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Jiang, L.; Ji, M. China’s Energy Intensity, Determinants and Spatial Effects. Sustainability 2016, 8, 544.

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