The driving forces of the change in China's energy intensity: An empirical research using DEA-Malmquist and spatial panel estimations
Junbing Huang,
Dan Du and
Yu Hao
Economic Modelling, 2017, vol. 65, issue C, 41-50
Abstract:
Energy shortage and environmental degradation have become significant hurdles for China's sustainable development nowadays. One of the most efficient and effective ways to ease energy shortage is to sufficiently reduce energy intensity. In the extant literature on the influential factors of China's energy intensity, the regional imbalance and spatial spillover effects were basically ignored, which may yield to biased and unreasonable results. As a result, in this paper, the driving forces of China's provincial energy intensity were for the first time investigated by combining the Data Envelopment Analysis (DEA)-Malmquist and spatial panel approaches for the period between 2000 and 2014. The results indicate that technological progress plays a dominant role in decreasing China's overall energy intensity. In both the Eastern and Central regions, the technological progress and its components can decrease energy intensity, while this effect doesnot significantly exist in the Western region. Rapid industrialization should be responsible for China's currently high energy intensity, while energy price hiking is conducive to the decrease in energy intensity. Moreover, there is also clear evidence that these factors influence on energy intensity partly through the spatial spillover effects.
Keywords: DEA-Malmquist; Energy intensity; Spatial spillover effects; China (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (41)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0264999316308458
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:65:y:2017:i:c:p:41-50
DOI: 10.1016/j.econmod.2017.04.027
Access Statistics for this article
Economic Modelling is currently edited by S. Hall and P. Pauly
More articles in Economic Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().