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China Economic Review
Article . 2014
License: taverne
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China Economic Review
Article . 2014 . Peer-reviewed
License: Elsevier TDM
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The drivers of energy intensity in China: A spatial panel data approach

A spatial panel data approach
Authors: Lei Jiang; Henk Folmer; Minhe Ji;

The drivers of energy intensity in China: A spatial panel data approach

Abstract

We use a panel of 29 Chinese provinces for the period 2003-2011 to estimate the drivers of energy intensity by means of a spatial Durbin error model. We find an inverted U-shaped relationship between energy intensity and income (energy intensity Kuznets curve). Ten provinces, notably the developed east coast provinces, have already passed the turning point of 29,673 RMB. The number of years for the other 19 provinces to reach the turning point ranges between 8.3 (Jilin) and 21.8 (Yunnan). The share of the secondary sector in the own province and in neighboring provinces causes an increase in energy intensity, the capital-labor ratio a decrease. Foreign direct investment (FDI) has a significant negative spatial spillover impact on energy intensity. To improve the sustainability of its energy resources and its environmental conditions, China needs to continue reducing its energy intensity by further developing modern industrial systems to counterbalance the negative effects of its economic growth and energy consumption. An adequate policy handle is investment in research and development and stimulation of their introduction into production processes. For that purpose, market mechanisms can be readily applied, particularly energy prices that adequately reflect energy scarcity and external effects. FDI is also an effective tool to transfer advanced technology to China.

Related Organizations
Keywords

China, Spatial Durbin error model, Energy intensity, Kuznets curve, Foreign direct investment

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
117
Top 1%
Top 10%
Top 10%
hybrid