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Frontiers of Earth Science

Front. Earth Sci.    2015, Vol. 9 Issue (2) : 355-368     DOI: 10.1007/s11707-014-0469-0
RESEARCH ARTICLE |
Characterizing China’s energy consumption with selective economic factors and energy-resource endowment: a spatial econometric approach
Lei JIANG1,2,Minhe JI1,*(),Ling BAI1
1. The Key Laboratory of Geographic Information Science (Ministry of Education of China), East China Normal University, Shanghai 200241, China
2. Faculty of Spatial Sciences, University of Groningen, Groningen 9700AV, the Netherlands
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Abstract

Coupled with intricate regional interactions, the provincial disparity of energy-resource endowment and other economic conditions in China have created spatially complex energy consumption patterns that require analyses beyond the traditional ones. To distill the spatial effect out of the resource and economic factors on China’s energy consumption, this study recast the traditional econometric model in a spatial context. Several analytic steps were taken to reveal different aspects of the issue. Per capita energy consumption (AVEC) at the provincial level was first mapped to reveal spatial clusters of high energy consumption being located in either well developed or energy resourceful regions. This visual spatial autocorrelation pattern of AVEC was quantitatively tested to confirm its existence among Chinese provinces. A Moran scatterplot was employed to further display a relatively centralized trend occurring in those provinces that had parallel AVEC, revealing a spatial structure with attraction among high-high or low-low regions and repellency among high-low or low-high regions. By a comparison between the ordinary least square (OLS) model and its spatial econometric counterparts, a spatial error model (SEM) was selected to analyze the impact of major economic determinants on AVEC. While the analytic results revealed a significant positive correlation between AVEC and economic development, other determinants showed some intricate influential patterns. The provinces endowed with rich energy reserves were inclined to consume much more energy than those otherwise, whereas changing the economic structure by increasing the proportion of secondary and tertiary industries also tended to consume more energy. Both situations seem to underpin the fact that these provinces were largely trapped in the economies that were supported by technologies of low energy efficiency during the period, while other parts of the country were rapidly modernized by adopting advanced technologies and more efficient industries. On the other hand, institutional change (i.e., marketization) and innovation (i.e., technological progress) exerted positive impacts on AVEC improvement, as always expected in this and other studies. Finally, the model comparison indicated that SEM was capable of separating spatial effect from the error term of OLS, so as to improve goodness-of-fit and the significance level of individual determinants.

Keywords per capita energy consumption      economic growth      energy endowment      spatial autocorrelation      spatial econometric model     
Corresponding Authors: Minhe JI   
Online First Date: 25 September 2014    Issue Date: 30 April 2015
 Cite this article:   
Lei JIANG,Minhe JI,Ling BAI. Characterizing China’s energy consumption with selective economic factors and energy-resource endowment: a spatial econometric approach[J]. Front. Earth Sci., 2015, 9(2): 355-368.
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http://journal.hep.com.cn/fesci/EN/10.1007/s11707-014-0469-0
http://journal.hep.com.cn/fesci/EN/Y2015/V9/I2/355
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Lei JIANG
Minhe JI
Ling BAI
Fig.1  Spatial distribution of per capita energy consumption (ton/per person) by province (2008)
Fig.2  Spatial distribution of per capita energy reserves (ton/per person) by province (2008)
Fig.3  Spatial distribution of per capita GDP (CNY/per person) by province (2008).
EnergyAverage low calorie valueConversion factor/(kgce/kg)
Raw coal20,908 kJ /(5,000 kcal)/kg0.7143
Crude oil41,816 kJ /(10,000 kcal)/ kg1.4286
Natural gas38,931 kJ /(9,310 kcal)/ kg1.3300
Tab.1  Conversion factors from the physical unit of other energy forms to its coal equivalent
VariableMoran’s Ip-valueMeanSd
LnAVEC0.41700.0007-0.03880.1243
LnAVGDP0.48220.0001-0.03450.1225
LnAVES0.59130.0001-0.03490.1228
Tab.2  Moran’s I statistic tests
Fig.4  Moran scatter plot for per capita energy consumption (Log) by province in China for 2008
VariableCoefficientStd. errort-statisticProbabilityToleranceVIF
Constant-15.19882.1570-7.04630.0000
LnAVGDP0.89820.13876.47610.00000.20174.9583
LnAVES0.10410.02993.47680.00210.27813.5952
LnSI0.81790.35292.31750.03020.22734.3991
LnTI1.21290.39073.10450.00520.21994.5470
LnMKT-0.68160.3230-2.11020.04640.15546.4330
LnRD-0.56720.2404-2.35910.02760.81261.2306
LnPS-0.20510.0598-3.43250.00240.38322.6097
Adjusted R20.8549
F-statistic25.4169
p (F-statistic)0.0000
Log LKHD15.3509
AIC-0.4901
SC-0.1164
Tab.3  Results of OLS regressive estimation
VariableSpatial error modelSpatial lag model
CoefficientStd. errorProbabilityCoefficientStd. errorProbability
Constant-15.83601.58710.0000-14.99381.85280.0000
LnAVGDP0.85870.09180.00000.88100.12350.0000
LnAVES0.11180.02130.00000.09880.02670.0002
LnSI0.97190.25720.00020.78890.30230.0091
LnTI1.34370.29470.00001.21160.33370.0003
LnMKT-0.68920.22510.0022-0.67450.27570.0144
LnRD-0.51340.18580.0057-0.58540.20800.0049
LnPS-0.16260.04640.0005-0.21520.05290.0000
λ-0.67540.24510.0059
ρ0.06730.12080.5777
R20.91130.8909
Log likelihood17.055915.4696
AIC-18.1117-12.9393
SC-6.9022-0.3285
LM-err1.20310.2727
Robust LM-err2.71540.0994
LM-lag0.48170.4877
Robust LM-lag1.99400.1579
Tab.4  Spatial error model and spatial lag model via Maximum Likelihood
VariableCoefficientStd. errorProbability
Constant-34.38809.82960.0015
LnAVGDP0.73620.15970.0000
LnAVES0.04790.03920.1065
LnSI1.35900.57770.0115
LnTI2.11280.74150.0040
LnMKT-0.83750.38770.0175
LnRD-0.50620.24550.0215
LnPS-0.26720.06840.0010
W*LnAVGDP0.21590.39860.2915
W*LnAVES0.20680.12840.0485
W*LnSI1.80330.88630.0225
W*LnTI2.81501.41400.0225
W*LnMKT-0.15680.75920.4115
W*LnRD1.65071.16320.0735
W*LnPS-0.19220.30080.2535
ρ-0.27200.29780.1825
R20.9384
Tab.5  Bayesian spatial Durbin model results
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