The influential factors of China's regional energy intensity and its spatial linkages: 1988–2007

Energy Policy 45 (2012) 583–593

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The influential factors of China’s regional energy intensity and its spatial linkages: 1988–2007 Huayi Yu n School of Public Administration, Renmin University of China, Beijing 100872, China

a r t i c l e i n f o

abstract

Article history: Received 8 November 2010 Accepted 5 March 2012 Available online 26 March 2012

A large amount of literature on China’s energy intensity seldom considers the regional differences of energy intensity inside China and the spatial effects. Based on spatial statistics methods, this paper explores the regional imbalance of China’s provincial energy intensity and the spatially correlation of energy intensity among provinces. Using spatial panel data models, this paper finds that GDP per capita, transportation infrastructure, the level of marketization, and scientific and technological input significantly reduce the energy intensity; the ratio of heavy industries to total industries and the ratio of coal consumption to total energy consumption significantly expand the energy intensity; meanwhile, the coefficient of the ratio of export to GDP is not significant. Then, the spillover and convergence of China’s regional energy intensity have been tested. The results indicate that the spillover effect between the eastern and western China is remarkable, and there exist absolute b-convergence of provincial energy intensity. Moreover, GDP per capita, transportation infrastructure, the level of marketization and scientific & technological input are conducive to conditional convergence after the spatial effects are controlled. According to the empirical results, this paper proposes some policy suggestions on reducing China’s energy intensity. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Energy intensity Spatial panel data model China

1. Introduction Since the reform and opening up in 1978, China’s economy has grown at a remarkable speed. However, this growth is gained on the basis of high energy consumption. Calculated by official exchange rates, China’s energy intensity in 2008 is 2.45 times the world average level; even when calculated by Purchasing Power Parity, China’s energy intensity in 2008 is still 1.54 times the world average.1 Although Chinese government has exerted itself to cut down energy consumption during recent years, there is still a long way to go in reducing China’s energy intensity. Therefore, to find out what factors are affecting China’s energy intensity and how to reduce the intensity effectively become crucial topics. Vast majority of the previous literature concerning the influential factors of China’s energy intensity is conducted from a macroscopic view, which does not take regional disparity of energy efficiency into consideration. Behind China’s high overall energy intensity, its provinces exhibit a dazzling diversity of energy intensity. The difference between high-energy intensity provinces and low-energy intensity ones is huge. From 1988 to n

Tel.: þ86 13466794619. E-mail address: [email protected] 1 The numerical values are calculated by the author according the data from OECD Factbook 2009 and the World Economic Outlook Database. 0301-4215/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2012.03.009

2007, five provinces with the lowest energy intensity are Hainan, Shanghai, Jiangsu, Zhejiang and Guangdong, whose average energy intensity are 3.13, 3.36, 3.37, 3.57, 3.72 tons of standard coal equivalent per 10000 CNY (tce/10000 CNY) respectively; on the contrary, five provinces with the highest energy intensity are Shanxi, Ningxia, Guizhou, Qinghai and Xinjiang, with the average energy intensity of 15.30, 14.37, 14.08, 13.09 and 11.33 tce/10000 CNY respectively. From Fig. 1, we can see that the geographical distribution of China’s energy intensity exhibits a characteristic of progressive increases from the eastern coastal provinces to the western provinces. Evidently, the effective reduction of energy intensity in the western provinces and the narrowing of the regional gap are of great practical importance for reducing China’s overall energy intensity. Hence, using the sub-province data provides us an effective visual angle to study the influential factors of China’s energy intensity. Only a few papers have discussed the influential factors through the analysis of sub-province data (Hu and Wang, 2006; Karl and Chen, 2010; Wei et al., 2009). However, in these papers, all regions are assumed to be cross-sectionally independent, and the spatial correlation of energy intensity is ignored. Anselin (1988) and LeSage and Pace (2009) point out that, when using the data related to location in regression, ignoring spatial correlation may lead to biased or inconsistent estimated results. Actually, at least two practical reasons can contribute to the spatial correlation of energy

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Fig. 1. The Quantile of China’s Provincial Energy Intensity.

intensity: (1) the energy transportation cost, which increases with distance increasing, is relatively high; (2) the factor endowment, industrial structure and residents’ habits of neighbor regions are more similar than the non-neighbor regions. Hence, spatial correlation of energy intensity should be considered in the regression model. Furthermore, what is the variation trend of the distribution of China’s regional energy intensity? What are the factors that lead to such variation trend? It is of vital importance to discuss these questions in order to explore a suitable way to reduce China’s overall energy intensity. However, existing studies have not given adequate answers to these questions. This paper tries to fill in the blanks. The following sections are structured as below: Section 2 gives an overview of relevant literature; Section 3 shows descriptive statistics of China’s regional energy intensity; Section 4 introduces the selection of variables and the specifications of the model; Section 5 estimates the influential factors of China’s regional energy intensity; Section 6 makes a calculation of the spill-over effects and the convergence trends of different regions in China; Section 7 is the conclusions and policy suggestion.

2. Relevant literature review A large body of studies have found that China’s overall energy intensity had continuously decreased during the 1980s and mostly 1990s (Zhao et al., 2010). Kambara (1992) and Lin and Polenske (1995) consider that the readjustment to industrial structure is the main factor to explain the decline of China’s overall energy intensity in the 1980s. Garbaccio et al. (1999) and Zhang (2003) conclude that the overwhelming contributor to the decline in late 1980s-1998 is the improvement of techniques and management. Shi (2002) finds that, before 1990, the readjustment to industrial structure reduces China’s energy intensity, however, after 1990, it increases the energy intensity. But the decreasing trend of China’s energy intensity has been reversed since 1998, and the past several years have witnessed an increasing energy intensity (Ma and Stern, 2008; Zhao et al., 2010). To analyze this change, Ma and Stern (2008) conclude that structural changes at the industry and sector level have decreased China’s overall energy intensity in the period 1980–2003, and the main factor of the increase in energy intensity is negative technological progresses; Zhao et al. (2010) finds that the most important impetus behind the increase during 1998– 2006 is the rapid development in energy-intensive industries, especially nonferrous metals processing, electric power, gas and hot water, nonmetal mineral products, and chemistry. Besides the industrial structure and technological progress, the impacts of some other factors (including foreign trade, energy price, FDI and R&D) on China’s overall energy intensity have also been

discussed (Shen, 2007; Shi and Polenske, 2005; Yin et al., 2008). Shen (2007) discusses the effect of foreign trade on China’s energy utilization efficiency, and finds that between 2002 and 2006, the improvement effect of foreign trade on the energy efficiency is gradually weakening. Zhang and Chen (2009) find that economic globalization (including integration efficiency investment, nonintegration of production and trade integration) has significantly reduced China’s overall energy intensity. Shi and Polenske (2005) and Hang and Tu (2007) find that energy prices plays an important role in the improvement of China’s energy utilization efficiency, as the rise of energy prices has reduced energy consumption and the energy intensities of different types of energy sources are subject to the relative price changes. Yin et al. (2008) find that FDI, human capital and technology R&D investment has significant influences on the reduction of China’s overall energy intensity. By using China’s provincial data, Hu and Wang (2006) and Shi (2007) compare the regional differences of the energy utilization efficiency, and Wei et al. (2009) and Karl and Chen (2010) study the influential factors of China’s energy utilization efficiency based on traditional panel data models. Although these studies have utilized the sub-regional data, the models they choose do not consider the spatial effects, which may lead the regression result to be biased and inconsistent (Anselin, 1988; LeSage and Pace, 2009). Zou and Lu (2005) take the spatial correlation of China’s regional energy intensity into consideration. However, their paper only uses the spatial cross-sectional data of 2003 and has only one independent variable -provincial GDP- in the model. This cannot give a convincing explanation for the change of regional energy intensity in China. Wondering whether there is a convergence of China’s energy utilization efficiency among different regions, Yang and Fang (2008) conclude that overall regional disparity of energy productivity does not show significant s convergence; different regions (eastern, central and western China) in different periods present different characteristics. In addition, the coefficients of absolute b convergence and conditions b convergence are significant, which indicates that China’s overall regional disparity of energy productivity is gradually becoming narrower. However, this study does not consider the spatial effect of provincial energy utilization efficiency.

3. The spatial statistics of China’s provincial energy intensity 3.1. Data sources and data processing This paper uses the panel data of China’s 30 provinces from 1988 to 2007.2 The original data source of GDP, transportation infrastructure, the ratio of export to GDP, the level of marketization, the ratio of coal consumption to total energy consumption, and the ratio of heavy industries to total industries are derived from China Compendium of Statistics 1949–2008. The original data of energy consumption and the energy abundance degree are derived from yearly China Energy Statistical Yearbook. The data of scientific & technological input are derived from yearly China Statistical Yearbook of Science and Technology. Partial data of Chongqing before 1997 are derived from the yearly Chongqing Statistical Yearbook. China’s provincial geographic information data are derived from China’s 1:4,000,000 GIS Database of National Fundamental Geographic Information System (http://nfgis.nsdi. gov.cn). All the empirical variables have been deflated by the GDP deflator. Like Barro (1997) and Xu and Li (2006), we divide the 20-year sample into 5 periods and use the 4-year average value of each variable in the regression so as to eliminate interference from business cycle fluctuations. Since energy intensity is calculated as units of energy per unit of GDP, different energy sources should be unified to equivalent 2

As there is not sufficient data on Tibet, the sample does not include Tibet.

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Table 1 Data Description. Variable

Mean

Std. Dev.

Min

Max

Obs

Energy Intensity (EI). overall 7.485 3.953 2.239 21.474 N ¼ 150 between 3.437 3.128 15.287 n¼ 30 within 2.033 3.342 14.198 T¼ 5 Per capital GDP (PGDP). overall 4146.464 2477.381 550.619 29914.324 N ¼ 150 between 2133.673 674.653 24557.873 n¼ 30 within 1056.487 976.993 20863.118 T¼ 5 Transportation Infrastructure (INFRA) overall 0.340 0.251 0.017 1.510 N ¼ 150 between 0.195 0.033 0.834 n¼ 30 within 0.161 0.021 1.016 T¼ 5 The Ratio of Export to GDP (EXPORT) overall 1258.306 1402.491 153.699 8105.493 N ¼ 150 between 1288.686 206.465 7616.941 n¼ 30 within 514.288 188.010 5778.142 T¼ 5 The Level of Marketization (N3P) overall 0.369 0.084 0.223 0.894 N ¼ 150 between 0.064 0.299 0.652 n¼ 30 within 0.056 0.247 0.611 T¼ 5 The Ratio of Heavy Industries to Total Industries (HIND) overall 0.631 0.146 0.286 0.940 N ¼ 150 between 0.116 0.415 0.829 n¼ 30 within 0.091 0.378 0.857 T¼ 5 The Ratio of Coal Consumption to Total Energy Consumption (RCOAL) overall 0.714 0.125 0.408 0.905 N ¼ 150 between 0.099 0.518 0.857 n¼ 30 within 0.075 0.634 0.828 T¼ 5 Scientific and Technological Input (STBUGET) overall 191.464 186.854 34.9701 1211.761 N ¼ 150 between 182.791 49.822 1043.330 n ¼ 30 within 48.977 37.215 385.615 T¼ 5

unit according to their calorific values. In the energy statistical system of National Bureau of Statistics of China, the energy consumption contains the consumption of four types of energy sources: (1) coal (include coal-fired power), (2) petroleum, (3) natural gas, (4) hydroelectric power, nuclear power and wind energy, etc. In national average level, the coal (include coal-fire power) consumption consists the overwhelming majority of the total energy consumption,3 but the share of coal is declining slowly. In different provinces, the shares of the four energy sources in total energy consumption differentiate, but the differences are not distinct.4 Table 1 presents the descriptive statistic results of all the variables of this paper. 3.2. The overall distribution of China’s provincial energy intensity Fig. 2 displays China’s provincial energy intensity distribution. From 1988 to 2003, the energy intensity of each province decreases, while from 2004 to 2007, the energy intensities of some eastern and western provinces increase slightly. For the accumulative decline range of energy intensity between 1988 and 2007, Beijing, Tianjin, Hunan, Henan, Shanxi, and three northeastern provinces (Heilongjiang, Jilin, Liaoning) have a relatively sharp drop, with the decline range exceeding 50%. The decline range of energy intensities of some underdeveloped western provinces such as Yunnan, Guizhou, Ningxia, Qinghai, is very limited. In addition, due to relatively low initial energy intensities, the energy intensities of some relatively well-developed south-eastern coastal provinces, such as Shanghai, Guangdong, Zhejiang, Fujian, and Hainan, do 3

The ratios of coal consumption to total energy consumption are more than 65% in the sample interval. 4 The average of the ratio of coal consumption to total energy consumption is 0.714, and the standard deviation is 0.125.

Fig. 2. Distribution of China’s Provincial Energy Intensity (1988–2007).

not show a large decrease. The changes of the energy intensity show a certain trend of spatial agglomeration. The disparity of energy intensity in the eastern provinces is gradually narrowing, which presents an agglomeration of low-energy intensity provinces. Some western provinces, which are contiguous to eastern provinces, show a trend of narrowing the energy intensity gap with eastern provinces, while for other western provinces locating far from the eastern provinces, this trend is not obvious. 3.3. The spatial correlation of China’s provincial energy intensity 3.3.1. The choice of spatial weight matrix Before using the spatial econometric model, the spatial weight matrix which reflects the geographic relationship among different regions needs to be defined. In the past literature, there are two categories of spatial weight matrix: the binary contiguity matrix and the distance function matrix. In the binary contiguity matrix, if the two regions i and j are neighbors, then the matrix elements wij ¼1; if the two regions are non-neighbors, then wij ¼0. Usually, the neighborhood relation in the binary contiguity matrix is determined by observing whether the regions border one another, by setting the distance range, or by setting the k-nearest neighbor standard.5 In the distance function matrix, the element is no longer binary but a distant function wij ¼ f(dij). The distance dij refers to the distance between the geometric centers (centroid) or capitals of region i and region j. In previous literature on spatial econometrics, the binary contiguity matrix is the common used weight matrix (Garrett et al., 2007). The limitation of this matrix is that all the neighboring regions are assumed to have equal influence and other spatial correlations beyond neighbors are ignored. Also, the standard for choosing neighbors is relatively subjective. Hence, in this paper, the distance function matrix is used as the spatial weight matrix so as to accurately portray the different impacts of provinces with different distances from a certain province. As the provincial capital is generally the economic center and transportation hub of the province, I choose the geographical distance between the urban centers of the capitals as the provincial distance dij. In this way, the distant can reflect the economic distance between different regions rather than pure geographic distance. The 5 If the distance between the two regions is more than a given value, wij ¼0, contrary, wij ¼1. Setting up a binary neighbor matrix using k-nearest neighbor method, each region is assumed to have the same number (k) of neighbors. The k-neighbors are determined by minimum distances. If a pair of two regions are neighbors, wij ¼1; otherwise, wij ¼0.

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elements of the spatial weight matrix are defined as follows: ( 0, if i ¼ j wij ¼ 1=dij , if i a j

ð1Þ

In order to normalize the outside influence upon each region, the spatial weight matrix is in row-standardized. That is, the elements wij in each row sum to 1. 3.3.2. The global spatial correlation of regional energy intensity The spatial correlation of China’s overall energy intensity can be measured by the global Moran’s I index. In a given period, the formula for calculating global Moran’s I index is: PP n i j wij zi zj It ¼ P ð2Þ S i z2i where, zi and zj represent the energy intensity deviation of province i and j at period t respectively. wij refers to the element in the spatial weight matrix, and S is the sum of all the elements of the weight matrix. As S¼n in the row-standardized spatial weights matrix, the formula can be simplified as: PP z0 Wzt i j wij zi zj It ¼ P 2 ¼ t0 ð3Þ zt zt z i i where zt is the deviation vector of all provincial energy intensities at period t, and W is the row-standardized spatial weight matrix. The range of Global Moran’s I index is [  1, 1]. This index, with the variable greater than 0, presents the overall positive spatial correlation; with one less than 0, it indicates a negative correlation; and with 0, it means irrelevance. The significance of Global Moran’s I index can be tested by Z statistics. Table 2 shows the global Moran’s I index of China’s regional energy intensity in various periods from 1988 to 2007. In each period, China’s regional energy intensity exhibits positive spatial correlation at the 5% significance level. This indicates that there is a phenomenon of the energy intensity amalgamation across various regions, that is, provinces with relatively high energy intensities crowd together, while those with relatively low energy intensities aggregate. The results also show that, as time goes on, there is a remarkable increase trend of global Moran’s I index, as well as a growth of its significance. That is to say, with China’s economic development and the market-oriented reform, the communication and cooperation between different regions are gradually strengthening, and the spatial correlation of the energy intensity is also growing. 3.3.3. Local spatial autocorrelation Although the global Moran’s I index could demonstrate that there is a spatial amalgamation characteristic of China’s regional energy intensity, it cannot show where the spatial amalgamation is strong or weak, as the Global Moran’s I index is simply an overall statistic. In order to figure out where the local spatial amalgamation is high or low, in other words, which regions have made more contribution to the global spatial autocorrelation, the Moran’s scatter plot and Moran’s local index should be used (Anselin, 1995, 1996). In Moran’s scatter plot, the abscissa axis refers to the deviation of provincial energy intensity, while the ordinate axis refers to Table 2 China’s Global Moran’s I Index of Energy Intensity and Its Significance.

Moran’s I Z(I) P-value

1988–1991

1992–1995

1996–1999

2000–2003

2004–2007

0.133 2.033 0.021

0.158 2.166 0.015

0.204 2.252 0.012

0.228 2.469 0.007

0.312 2.907 0.002

Fig. 3. LISA Clustering Maps of China’s Provincial Energy Intensity (1988–2007).

spatial lags of the deviation of energy intensity. In this way, the four quadrants correspond to the four different local spatial correlation: the quadrant I is the HH clustering, which means provinces with high energy intensities are also contiguous to neighboring provinces with high energy intensities; the quadrant II is the HL clustering, which means provinces with high energy intensities are contiguous to neighboring provinces with low energy intensities; the quadrant III is the LL clustering, and the quadrant IV is the LH clustering. Although the Moran scatter plot gives types of clustering relationships between a single region and its adjacent areas, it cannot show the significance. In order to acquiring the significance, the Local Moran index should to be calculated. The local Moran index can be written as: P X nzi j wij zj Ii ¼ P 2 ¼ z0i wij z0j ð4Þ i zi j This index describes the spatial amalgamation degree of energy intensity of each province with its surrounding provinces in a certain period. A positive value indicates that the energy intensity of the province has spatial amalgamations with similar values as the surrounding provinces, namely, HH clustering or LL clustering; a negative value indicates amalgamation of a nonsimilar value, namely, LH, or HL clustering. The significance of the local Moran index can be tested by Z statistic. Combining the Moran scatter plot with the local Moran index, the clustering LISA maps shall be depicted (Fig. 3), which can visually demonstrate the clustering types and the level of significance. From Fig. 3, we can see that China’s energy intensity presents a clear east and west blocked spatial structure, and its boundary is generally similar to China’s geographical demographic boundaries of east and west. Most of the eastern provinces fall into the category of LL clustering, especially southeastern provinces, such as Guangdong, Fujian, Zhejiang, Shanghai, Jiangsu and Jiangxi, and the LL clustering of these provinces is significant in each period. This indicates that there is a distinct spatial amalgamation region with low energy intensity in eastern China. Most of the western provinces fall into HH clustering category, such as Xinjiang, Ningxia, Qinghai, Inner Mongolia and other provinces, and the HH clustering of these provinces are significant in each period. This indicates that the

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western regions show a trend of spatial amalgamation of high energy intensity. The transitional provinces, which are sandwiched between the significant LL clustering eastern provinces and significant HH clustering western provinces, show a staggered distribution of HL or LH clustering. However, these HL and LH clustering regions hardly show statistical significance. Only the HL clustering of Guizhou is significant in the various periods, which has become the pitch of high energy intensity. From 1988 to 1991, China’s western region presented a certain staggered distribution of clustering types of energy intensity, which means the high-energy-intensity provinces were contiguous to low-energy-intensity ones, and vice versa. For instance, Ningxia is significant typical HL clustering, while the clustering type of its neighbors, Gansu and Shaanxi, is the LH clustering. From 1992 to 1995, the western provinces with HH clustering created a block of HH clustering provinces, and after this period, the clustering type has remained basically unchanged. With the expansion of the LL concentrated regions, Liaoning and Jilin in northeast China are gradually changing from LH clustering to LL clustering, while Henan and Hebei have changed from HH clustering into LH clustering. This indicates that the energy utilization efficiency of the coastal areas in southeastern China has had spillover effects on surrounding areas, reducing the energy intensity of its surrounding provinces, especially the adjacent provinces. According to the analysis above we can conclude that, during the past two decades (1988–2007), China’s eastern and western regions have formed a significantly continuous low energy intensity zone and a significantly high energy intensity zone respectively. Thus, the total spatial correlation of China’s energy intensity mainly comes from the local spatial correlation of the eastern and western regions interior. Generally speaking, the spatial correlation of energy intensity comes from two aspects. One is that the transportation cost of energy will increase as the distance increases, which will make the energy intensity of neighbor regions more similar to no-neighbor ones. The other lies in the fact that the factor endowment, industrial structure and residents’ habits of neighbor regions have more similarities than the non-neighbor regions do. Anselin (1988) point out, when there is spatial dependence or spatial heterogeneity in regional data, traditional regression method will lead to regression bias. As seen above, there are obvious characteristics of spatial correlation of China’s energy intensity, and ignoring spatial effects may lead to significant bias when studying the influential factors of energy intensity and its convergence.

4. Variables and model setting 4.1. The setting of basic model In this paper, the spatial panel data econometric models are used to analyze the influential factors on China’s provincial energy intensity. Unlike the traditional panel data model, the spatial panel data model adds spatial effects to the model so as to determine the influence of spatial factors on the dependent variable. Following different types of spatial effects, this paper has considered two different spatial panel data models: the spatial lag (SAR) panel data model and the spatial error (SEM) panel data model. The SAR panel data model can be expressed as: X X yit ¼ rwij yjt þ xðkÞ bk þ mi þ gt þ vit 9r9 o1 ð5Þ it iaj

k

Subscript i and t denote province and year respectively; yit is a dependent variable; xit is a vector of independent variables with dimension k  1; wij is the spatial weight elements between province i and j; vit is a random disturbance, satisfying vit  N (0, s2); r is the spatial autocorrelation coefficient. mi and gt are, respectively,

587

the individual effect and the time effect, which must be controlled by the traditional panel data model. Using matrix form, formula (5) can be written as: y ¼ rðIT  W N Þyþ X b þ m þ g þ v

9r9 o 1

ð6Þ

where, y is of dimension NT  1; X is of dimension NT  K; m, g, and v are all of dimension NT  1. The SAR panel data model includes the spatial lag items of dependent variables, which can reflect the spatial autocorrelation effect. That is, it contains the effect that the energy intensity of a typical province is affected by energy intensities of other regions. The SEM panel data model can be expressed as: y ¼ Xb þ e

e ¼ lðIT  W N Þe þ m þ g þ v 9l9 o 1

ð7Þ

where l is the spatial autoregressive coefficient of regression residuals; the remaining variables have the same meaning as the SAR panel data model. The spatial error panel data model can reflect the spatial heterogeneity of the error term. The formula can be transformed into: y ¼ X b þ ð1lðIT  W N ÞÞ1 ðm þ g þ vÞ

9l9 o 1

ð8Þ

From the formula above, we can see each province’s stochastic impact affects not only itself, but also other provinces through spatial weight matrix. In other words, in the SEM panel data model, if the energy intensity of one province encounters a certain shock, that shock will impact the current energy intensity of other provinces, and such impact will last for the next few periods. Since spatial panel data models contain the spatial effect items, which makes dependent variables correlated with the error term, the traditional panel data OLS methods will cause the estimated coefficients to be biased and inconsistent. Hence, the maximum likelihood estimation (MLEs) framework is generally adopted to estimate the coefficients of spatial panel data model. Elhorst (2003, 2009) has generalized the maximum likelihood functions and MLEs methods for the fixed effects and random effects SAR panel data model, as well as the fixed effects and random effects SEM panel data model. However, Lee and Yu (2010) prove that, in the fixed effect SAR panel data model, the MLEs estimator of variance parameter is inconsistent when T is small. In order to achieve consistent estimators, Lee and Yu (2010) suggest using a data transformation from 0 0 (IN ð1=NÞlN lN ) with another transformation from (IT ð1=TÞlT lT ) to eliminate both the individual and time fixed effects, and then using the quasi-maximum likelihood (QML) approach to eliminate the transformed equation.6 Hence, we will use Lee and Yu (2010)’s method to estimate the fixed effect SAR panel model.7 4.2. The explanatory variables Based on the existing theoretical and empirical studies on the influential factors of China’s energy utilization efficiency and the available data, this paper mainly considers the influential factors on China’s energy intensity from the aspects of economic performance, transportation infrastructure, foreign trade, market-orientation, industrial structure, energy consumption structure, and science

6

lT is the vector of ones. Professor Elhorst of University of Groningen has provided Matlab routines at his website www.regroningen.nl/elhorst for both the fixed effects and random effects SAR model, as well as the fixed effects and random effects SEM model. The Matlab routines include the bias correction procedure proposed by Lee and Yu (2010) if the spatial panel data model contains spatial and/or time-period fixed effects. 7

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and technology investment. The specific definitions of chosen variables are as below: 1) Per capital GDP (PGDP): measured by the real GDP divided by domestic population. Economic growth is regarded as an impact factor on energy consumption (Kraft and Kraft, 1978). Economic growth may also exert influence on energy efficiency. Markandya et al. (2006) consider that economic growth is the driving force for the improvement of energy consumption, that is to say, the expansion of economy will lead to a lower level of energy intensity. The empirical result of Markandya et al. (2006) indicates that 1% decrease in the per capita income gap between western European developed countries and 12 eastern European transition economies leads to the decrease of the energy intensity growth rate in the transition countries by 1.02%. Qi et al. (2011) also indicate that the per capita GDP gap between China and eight developed countries decreases by 1%, the energy intensity gap between them will correspondingly decrease by 1.55%. However, previous studies mainly consider the influence of economic growth on energy efficiency at national level. Whether the economic growth decrease the energy intensity in China’s subnational level will be tested in this empirical model. I expect that the provincial per capita GDP will reduce the energy intensity. 2) Transportation infrastructure (INFRA): using road network density which is calculated by dividing road mileage by the size of the region as the proxy. Theoretically, transportation infrastructure has positive external effects. From enterprise perspective, good transportation infrastructure can reduce production costs and help enterprises to optimize a combination of different inputs to improve production efficiency. From macroeconomic perspective, good transportation infrastructure can optimize the spatial allocation of energy resources, which enables the energy transported to the place to gain higher energy efficiency. Thus, improvement of transportation infrastructure is expected to be conducive to reducing energy intensity. 3) The ratio of export to GDP (EXPORT): measured by the proportion of total exports in GDP.8 An economy’s energy consumption has a close connection with its foreign trade. On the one hand, since the reform and opening up, China has given up the national strategy of giving priority to the development of heavy industry. Promoting the development of foreign trade relying on the comparative advantage of low labor cost has become China’s national strategy. Shi (2002) as well as Zhang and Chen (2009) consider China’s rapid growth of foreign trade to be one of the most important reasons for the improvement of energy efficiency. On the other hand, the foreign trade process is accompanied by the transference of energy consumption. Shen (2007) indicates that, in the past several decades, western developed countries have gradually transferred their energy-intensive industries to developing countries. In recent years, China’s export of energy-intensive products has risen rapidly, which counteracted the effort of Chinese government’s effort on reducing the energy intensity. Therefore, an empirical study is needed to determine the impact of export on China’s regional energy intensity. 4) The level of marketization (N3P): using the proportion of tertiary industry in GDP as the proxy. The increase in the level of marketization can lead to optimization of resource allocation, and at the same time, market competition will also promote enterprises to improve efficiency while control costs, which can improve energy efficiency. Meanwhile, the proportion of

8 Since the gross provincial export data are predominately in U.S. dollars, we converted this data to RMB-denominate data by using the yearly average exchange rate of RMB to U.S. dollars.

tertiary industry in GDP is also an important indicator that can reflect a region’s economic structure. Through studying the Eastern European and post-Soviet countries, Cornillie and Fankhauser (2004) find that, in the transition process from planned economy to market economy, the energy intensity of these countries has obviously decreased. Hence, the development of a low-power consumption tertiary industry is expected to contribute to reducing energy intensity. 5) The ratio of heavy industries to total industries (HIND): measured by the proportion of heavy industrial output in the gross industrial output value. A large amount of studies have taken into account the impact of industrial structure on energy utilization efficiency. When energy flows from a high energy consumption sector to a low one, it promotes the improvement of overall energy efficiency. Thus, a decline in the ratio of heavy industries to total industries is expected to reduce energy intensity. 6) The ratio of coal consumption to total energy consumption (RCOAL): measured by the coal consumption (include coal-fired power) divided by the total energy consumption. Compared with petroleum, natural gas and nuclear, the power transfer efficiency of coal is relatively low. At the national level, the high rate of coal consumption to total energy consumption has become a key factor for China’s low energy efficiency (Zhang and Wang, 2008). According to Zhang and Wang (2008)’s estimation, when the ratio of coal consumption to total energy consumption increases by 1%, China’s energy efficiency will decrease by 6.06%. I expect that reducing the ratio of coal consumption to total energy consumption will lead to the decrease of China’s provincial energy intensity. 7) Scientific & Technological Input (STBUGET): measured by the proportion of technology expenditures in GDP. Some previous literature has found that R&D investment has a significant influence on the improvement of energy efficiency (Popp, 2001; Yin et al., 2008). But R&D funding does not include the financial input of technology import, upgrading and exchange from enterprise level. Thus, this paper selects the proportion of scientific & technological expenses9 in GDP as the scientific & technological input variable. The improvement of science and technology investment is expected to reduce energy intensity. 5. The estimation results of influential factors of China’s regional energy intensity 5.1. The full sample estimation results Before regression analysis, I first conduct a Hausman test to choose the panel data specifications between fixed effect and random effect. The Hausman test results show that the calculated w2 (7) is 14.32 with corresponding p value 0.014, which indicates that the estimated parameters will biased and inconsistent under the specification of random effect. Hence, fixed effect model is the appropriate model. Furthermore, I have tested the applicability of the SAR panel data model and the SEM panel data model through LM-lag and LM-err statistics (Anselin, 1988; Anselin et al., 1996). The results show that, the calculated LM-lag is 29.71 (p value is 0.000), and the calculated LM-err is 20.57 (p value is 0.000), which indicates that both the spatial lag and the spatial error effect exist. Hence, the two specifications of the spatial panel data models should be estimated. Table 3 gives the full sample estimation results of the influential factors on China’s provincial energy intensity from 1988 to 2007 under different specifications. 9 Science and technology expense subjects include large and medium-sized enterprises, research institutes, universities and other institutions.

H. Yu / Energy Policy 45 (2012) 583–593

Table 3 Full Sample Estimation of the Influence Factors of China’s Provincial Energy Intensity (1988–2007).

Ln(PGDP) Ln(INFRA) Ln(EXPORT) Ln(N3P) Ln(HIND) Ln(RCOAL) Ln(STBUGET) const

Fixed Effect

SAR

SEM

0.044 (0.845)  0.031nn (  1.493) 0.005 (0.858) 0.355 (0.448) 0.583nnn (1.922) 0.139nn (2.321)  0.375 (  0.388) 2.611 (0.508)

 0.067nn (  1.409)  0.053nn (  1.602) 0.051 (0.788)  0.166nnn (  3.011) 0.377nnn (5.440) 0.106nn (1.435)  0.050 (  0.823)

 0.083nn (  1.321)  0.055nnn (  1.882) 0.071 (0.611)  0.109nn (  1.411) 0.233nnn (4.099) 0.146nnn (3.553)  0.051n (  1.043)

r

0.355nnn (6.009)

l Sample adj-R2 Log-likelihood

150 0.547

150 0.901  122.326

0.389nnn (5.871) 150 0.920  115.852

Note: The dependent variable is the natural logarithm of energy intensity. The number in the parentheses is t statistic; nnn, nn and n indicate rejection of the null hypothesis at 1%, 5% and 10%, respectively. The range of the Variance Inflation Factor (VIF) of the independent variables is (0.732, 2.311), which can be seen as an evidence that there is no multicollinearity among the independent variables.

The first column of Table 3 is the estimated result of the specified fixed effects, assuming there is no spatial effect (assume r ¼ l ¼0). This fixed effect regression result does not match with previous theoretical expectations. In addition, the adjusted goodness-of-fit of fixed effect regression results is relatively low (the explanatory power of independent variables on the dependent variable is only 54.7%), implying that the model may contain spatial effect. The second and third columns of Table 3 are the estimated results of SAR and SEM panel data model respectively. The estimated coefficients of these two specifications are similar, and each adjusted goodness-of-fit exceeds 90%. It can be seen that the explanatory power of independent variables has been greatly enhanced after taking spatial effect into account. The coefficient r in the second column is positive and very significant, indicating the existence of a spatial autoregressive effect, which means that the energy intensity of one province is positively affected by energy intensities of surrounding provinces. The coefficient l in the third column is also positive and quite significant, indicating there is a spatial correlation of error terms of spatial error model, which means the error term of one province’s energy intensity has positive spillover effects on its surrounding areas. As seen from the estimated results of the SAR and SEM panel data model, the coefficients of independent variables are basically consistent with theoretical expectations. The differences of the coefficients of these two models are small. When other conditions are constant, 1% increase of per capital GDP, transportation infrastructure and the level of marketization are associated with 0.067%, 0.053% 0.166% drop of energy intensity in SAR model and 0.083%, 0.055% and 0.109% drop of energy intensity in SEM model; while 1% increase of the ratio of heavy industries to total industries and the ratio of coal consumption to total energy consumption are associated with 0.377% and 0.106% increase of energy intensity in SAR model and 0.233% and 0.146% increase of energy intensity in SEM model. The coefficient of the ratio of export to GDP is not significant in both SAR and SEM panel data models, while the coefficient of scientific & technological inputs is not significant in SAR panel data model.

589

This indicates that improving the economic performance, transportation infrastructure and the level of marketization will lead to the decrease of the energy intensity; while increasing the ratio of heavy industries to total industries and the ratio of coal consumption to total energy consumption will lead to the increase of energy intensity. It is worth noting that, in each regression result, the absolute value of the coefficients of the ratio of heavy industries to total industries is the maximum of all coefficients and is very significant. Hence, reducing the proportion of heavy industry in economy and speeding up the development of service sector are the most direct and efficient choices for China’s local governments to decrease the energy intensity. The significant positive coefficient of the ratio of coal consumption to total energy consumption indicates that encouraging replacing the consumption of coal by non-coal energy will play a conspicuous role in decreasing energy intensity. Unlike Zhang and Chen (2009)’s conclusion that economic globalization has significantly decreased China’s overall energy intensity, this paper finds that foreign trade has a very weak influence on China’s provincial energy intensity. With the deepening of economic globalization, some energy intensive industries gradually shifted from western countries to China. But China lacks legislations to restrict the development of energy-intensive industries. Some local governments even encourage the foreign investment on energy-intensive industries in order to boost the local economy. Hence, the improvement of energy efficiency brought by China’s opening up has been offset by the international energy intensive industries transfer. From a perspective of policy-making, purely expanding foreign trade might not improve energy efficiency. The Chinese government should pay close attention to the impact of the foreign trade structure on regional energy efficiency, and take advantage of policy tools to adjust the export structure so as to achieve energy-saving goals. As can be seen in Table 3, the effects of scientific & technological inputs on reducing energy intensity are not obvious. On the one hand, China’s scientific & technological projects are almost government-oriented, so a lot of scientific and technical achievements have not been converted into productive forces that can reduce energy consumption; on the other hand, scientific and technical achievements have trans-regional spillovers effect, which may also reduce the effects of scientific & technological inputs on the improvement of local energy efficiency.

6. Spatial spillover and convergence mechanism As it can be seen before, there is a distinct spatial effect in China’s provincial energy intensity, and this spatial effect presents an obvious block structure of China’s western and eastern regions. Hence, the first question is whether there are any spillover effects of energy intensity between these two blocks? If the answer is affirmative, it indicates that the energy intensities of different regions can influence each other, and the regional difference may be lessened. Then another interesting question arises: is there a potential energy intensity convergence trend that the provinces with high energy intensity gradually catch up with the provinces with low energy intensity? As there is large intensity difference between eastern and western provinces, the key to decreasing China’s overall energy intensity is to reduce such difference. Discussing these two questions can help us explore an effective way to reduce China’s overall energy intensity. 6.1. The spatial spillover effects of energy intensity In order to study the spillover effects of energy intensity in different regions, I follow Ledyaeva (2009)’s approach of studying the spatial spillover effects between groups of different regions,

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H. Yu / Energy Policy 45 (2012) 583–593

Table 4 The Estimation Results of China’s Eastern-western and Costal-inland Spillover Effects of Energy Intensity (1988–2007). Eastern-western Spillover Effects

Ln(PGDP) Ln(INFRA) Ln(EXPORT) Ln(N3P) Ln(HIND) Ln(RCOAL) Ln(STBUGET)

r

SAR

SEM

SAR

SEM

 0.050nnn (  2.945)  0.178 (  0.677) 0.031 (0.838)  0.158nnn ( 3.122) 0.310nnn (5.984) 0.149nnn (3.489) 0.053 (1.112) 0.245nnn (3.202)

 0.122 ( 0.876)  0.193 ( 0.585) 0.081 (1.013)  0.107nn ( 1.460) 0.296nnn (4.987) 0.131nnn (3.853)  0.044 (  0.421)

 0.076nn ( 2.233)  0.210 (  0.717)  0.023 (  0.209)  0.134nnn ( 3.238) 0.341nnn (5.987) 0.122nnn (4.056)  0.043n (1.157) 0.356nnn (5.132)

 0.096nnn (  3.118)  0.135 (  0.523) 0.034 (0.578) 0.141nnn (4.006) 0.309nnn (5.147) 0.152nnn (4. 147)  0.039 (0.871)

l Sample adj-R2 Log-likelihood

Costal-inland Spillover Effects

150 0.909  104.438

0.158nnn (2.998) 150 0.922  110.839

0.223nnn (3.153) 150 0.909  118.704

150 0.886  122.562

Note: The dependent variable is the natural logarithm of energy intensity. The number in the parentheses is t statistic; nnn, nn and n indicate rejection of the null hypothesis at 1%, 5% and 10%, respectively.

and make a corresponding adjustment to the spatial weight matrix W. The principle of dealing with the weight matrix when studying the spatial spillover effects can be illustrated through a simplified spatial weight matrix of four provinces: if the 1st and 3rd provinces are in the east, and the 2nd and 4th provinces are in the west, then the elements which span the eastern and western regions are the functions of the distance between corresponding provinces, while the elements which do not span the eastern and western regions are 0. So the weight matrix turns out to be: 2 3 0 w12 0 w14 6 7 0 w23 0 7 6 w21 7 W ¼6 ð9Þ 6 0 0 w34 7 w32 4 5 w41 0 w43 0 Using the adjusted spatial weight matrix, we can estimate China’s eastern-western spillover effects, as well as costal-inland spillover effects. Table 4 gives the regression results. It can be seen from Table 4 that, in the regression of easternwestern spillover effects and costal-inland spillover effects respectively, the spatial effect variables (r, l) are significant at the level of 5%. There is an obvious positive spatial spillover both between eastern and western regions and between coastal and inland areas as the spatial lag coefficient r is positive and significant. The spillover effect between coastal and inland provinces is significantly higher than that between eastern and western regions. This indicates that there exists a mechanism of gradient spillover in China’s provincial energy intensity, that is to say, the eastern coastal provinces with the highest energy efficiency firstly spillover their energy efficiency to other eastern, non-coastal provinces, and then the energy efficiency spills over to western provinces. Similarly, the spatial error coefficient l also revealed that the coastal-inland model is larger than the easternwestern model. The spillover effects may hint a certain convergence mechanism of China’s provincial energy intensity. 6.2. The convergence of China’s provincial energy intensity In this section, I tested whether there is a convergence of China’s provincial energy efficiency through the b convergence framework according to the neo-classical growth theory. Theoretically speaking, under the condition that essential factors of production can flow freely, the energy efficiency improving speed of the provinces with low energy efficiency will be faster than those with high energy efficiency, and each province’s energy efficiency will ultimately converge to create a long-term equilibrium. As China’s provincial energy intensity is highly spatially correlated, it is necessary to relax the assumption in Barro and

Sala-i-Martin (1992)’s b convergence model that different regions are homogeneous and independent. Hence, in this paper, the spatial effects are added into the b convergence model as Garrett et al. (2007)’s suggestions. The absolute b convergence models with spatial effect are as follows: X SAR : lnðEIi,t þ 1 =EIit Þ ¼ a þ rwij lnðEIi,t þ 1 =EIit Þ þ b lnðEIit Þ þ uit iaj

uit ¼ mi þ gt þ vit

9r9 o 1

SEM : lnðEIi,t þ 1 =EIit Þ ¼ a þ blnðEIit Þ þ uit uit ¼ lwij uit þ mi þ gt þ vit

9r9 o1

ð10Þ

where EIit is the energy intensity; mi and gt, respectively, are the individual effect and time effect; r and l, respectively, are spatial lag coefficient and spatial error coefficient; vit is a random disturbance term. After regression, if the coefficient b is negative, it indicates that the initial value of energy intensity of each province has an inverse relationship with the growth rate, and there is an absolute b convergence of China’s provincial energy intensity; if the coefficient b is positive, it indicates that the initial value of the energy intensity of each province has a positive correlation with the growth rate, provincial energy intensity is divergent, and the absolute b convergence does not exist. In addition to the absolute b convergence, we also need to test the existence of conditional convergence for the provincial energy intensity. Absolute convergence assumes that except for the initial value of energy intensity, the remaining conditions of each region are the same. The conditional b convergence relaxes such a restriction and adds a number of control variables to the model, which makes the various regions possess different convergence paths. If the regression coefficient b is still negative after adding the control variables, there is a conditional b convergence. The conditional b convergence models with a spatial effect can be written as: SAR : lnðEIi,t þ 1 =EIit Þ ¼ a þ

X

X

iaj

k

rwij lnðEIi,t þ 1 =EIit Þ þ

xðkÞ it bk þ b lnðEI it Þ þ uit

9r9 o 1 X ðkÞ xit bk þ b lnðEIit Þ þ uit SEM : lnðEIi,t þ 1 =EIit Þ ¼ a þ uit ¼ mi þ gt þ vit

k

uit ¼ lwij uit þ mi þ gt þ vit

9r9 o1

ð11Þ

where xit is the vector of a k  1 control variable, the other settings are the same to the absolute b convergence model. Before regressing, Hausman test also gives evidence that fixed effect specification is superior to random one. Therefore, panel data models with fixed individual and time effect are used. Table 5 gives the estimated results of an absolute b convergence as well as conditional b convergences. We can see that, all of the coefficients of spatial

H. Yu / Energy Policy 45 (2012) 583–593

591

Table 5 The Estimation Result of the b Convergence of China’s Provincial Energy Intensity.

Ln(EE)

Absolute

Conditional Convergence

FE

I

II

III

IV

V

VI

VII

VIII

 0.998nnn (  2.765)

 1.169nnn (  5.012)

 1.216nnn (  4.097)  0.105nnn (  2.238)

 1.115nnn ( 3.528)

 1.226nnn ( 3.313)

 1.107nnn (  3.077)

0.203 (1.033)

 0.049 (0.409)

 0.991nn ( 2.021)

Ln(PGDP)

 0.135nn ( 2.329)

Ln(INFRA) Ln(EXPORT)

0.031 (0.688)  0.196nn (  1.971)

Ln(N3P)

0.215nnn (2.878)

Ln(HIND)

0.154nnn (2.801)

Ln(COALP)

 0.084nn ( 1.900)

Ln(STBUGET)

r

 0.177nnn (  2.357)

 0.377nnn (  2.432)

 0.079nnn ( 3.332)

 0.120nnn ( 2.976)

l Sample adj-R2 Loglikelihood

150 0.443

150 0.629  113.128

150 0.553  117.539

150 0.477  104.163

150 0.478  115.314

 0.046nnn (  2.324) 0.339nn (2.017) 150 0.561  111.345

150 0.474  113.521

 0.129nn ( 2.123)

150 0.467  112.686

0.277nnn (3.122) 150 0.532  110.529

Note: This table only gives out the final results of choosing the form of spatial effect (SAR, SEM). In setting I and III, the spatial error term is also significant, but the coefficients of SAR and SEM are in essence the same. Hence, in this table, we just give the result of SAR. The number in the parentheses is t statistic; nnn, nn and n indicate rejection of the null hypothesis at 1%, 5% and 10%, respectively.

effects are significant, which indicate the existence of spatial effects.10 From the practical viewpoint, the convergence of China’s regional energy intensity has a close relationship with the following reasons. Firstly, China’s labor-intensive industries have gradually transferred from eastern region to western region. Because of the rising cost in eastern China and the expansion of domestic demand, the attraction of central and western China for laborintensive industries is growing, which relieves the central and western China from energy-intensive industries. Secondly, the regional gaps of technology are narrowing. In the past, compared with central and western China, eastern China has obvious advantages in technology. With the intensifying of China’s opening-up and the process of western region development, central and western China have gradually increased its exchanges and contacts with the outside world. Advanced technologies have gradually diffused to the central and western China. Thirdly, China’s infrastructures have been greatly improved. For example, many infrastructures of energy transportation, such as west-east electricity transmission and the west-east natural gas transmission projects, are invested by the central government in the past two decades. Hence, energy can be effectively transmitted to the regions with high energy efficiency rather than be wasted locally. Fourthly, with the process of market-oriented reform and the weakening of local protectionism, the nationwide energy market gradually formed, which optimized the allocation of energy. From the regression results of absolute convergence, we can see that the coefficient b in fixed effect model is 0.998 and the coefficient b in SAR model (setting I) is 1.172, both of which are

10 As the dependent variable in Table 5 is In(EEi,t þ 1/EEit) rather than EEit which is the dependent variable of Table 3, the sign of coefficient of spatial effects in Table 5 may be different from Table 3. The coefficient l is the spatial error term of In(EEi,t þ 1/EEit). If the independent variables have strong negative spatial correlations, the coefficient l may become somewhat negative, that is, negative spillover effects on error term of dependent variable. Hence, in setting V and VIII, the negative spatial coefficient l is possible.

significant. This indicates that there exists absolute convergence for China’s provincial energy intensity. The provinces with high energy intensity have potential to catch up with those with low energy intensity, which means the regional disparity of energy intensity is diminishing. This is consistent with former research conclusions about the spatial spillover effect of China’s energy intensity. Setting II–VII are the estimates of conditional convergence under various circumstances. In setting II, III and V and VIII, per capita GDP, transportation infrastructure, level of marketization, and scientific & technological input are added as the control variable respectively. The result shows that coefficient b is still significantly negative, and the coefficient of these three control variables is significantly negative respectively. This indicates that when controlling the provincial diversity of per capital GDP, transportation infrastructure, the level of marketization and scientific & technological input, the convergence mechanism of China’s provincial energy intensity still exists. Thus improving the economic performance, transportation infrastructure and level of marketization, and increasing scientific & technological inputs will be benefit to narrowing the gap of provincial energy intensity. In setting III, after adding the ratio of export to GDP into the convergence model as control variables, the coefficient b is still significant, but the coefficient of the control variable is not. This indicates that purely promoting foreign trade cannot directly reduce the regional diversity of the energy intensity, which is consistent with the previous conclusions of the influential factors on energy intensity. In setting VI and VII, the ratio of heavy industries to total industries and the ratio of coal consumption to total energy consumption are added as control variables respectively. However, in these two settings, the coefficient b becomes not significant and the coefficients of these two control variables are significantly positive. This means that the differences in both the ratio of heavy industries to total industries and the ratio of coal consumption to total energy consumption will result in regional divergence of energy intensity rather than convergence.

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H. Yu / Energy Policy 45 (2012) 583–593

7. Conclusions and policy suggestions Unlike most of the previous literature on China’s energy intensity issue from national perspective, this paper studies the regional differences and spatial correlation of China’s provincial energy intensity. In the sample interval, the energy intensity of western provinces is much higher than that of eastern ones. Furthermore, obviously there exists a spatial correlation of China’s provincial energy intensity distribution, and the spatial correlation presents an apparent block structure in eastern and western China. This spatial structure indicates that the key to reducing China’s overall energy intensity is narrowing the difference of China’s regional energy intensity. China’s western provinces have more potential in reducing energy intensity than eastern ones. However, in recent years, the Chinese government did not consider the regional variation when making energy saving policies. In China’s Eleventh Five-Year Plan period (2006–2010), the central government stipulated that all provinces should reduce the energy intensity at parallel percentage per year, which discouraged China’s overall energy saving potential. Hence, the Chinese government should change the compulsive energy saving policy. Based upon the theoretical analysis, this paper studies the influential factors on China’s energy intensity. In order to avoid the regression bias from ignoring spatial correlation, this paper adopts the spatial econometrics methods. From the regression results, per capita GDP, transportation infrastructure, and the level of marketization significantly reduce the energy intensity. This indicates that, promoting the local economic growth as well as improving the transportation infrastructure and level of marketization are the key factors in reducing the energy intensity. Both the ratio of heavy industries to total industries and the ratio of coal consumption to total energy consumption increase the energy intensity, which indicates that the Chinese government should promote the economic structural adjustment and encourage the development of new energy resources. This paper also finds out that foreign trade has a very weak influence on China’s provincial energy intensity. Along with promoting foreign trade, Chinese government should enact strict legislations on restricting the energy intensive industries transferred to China. The variation trend of China’s regional energy intensity has been discussed. We find that there exists a significant crossregional spillover effect of energy intensity from east to west, as well as a significant b-convergence trend. This indicates that the provincial disparity of energy intensity is gradually shrinking. This trend has a close link with labor-intensive industries’ shift from eastern to western region, the narrowing of regional technological gap, the improvement of transportation, and the weakness of local protectionism. Hence, the government should promote the information exchange between the eastern and western regions, and develop appropriate policies to strengthen the developed provinces aiding their counterparts in the western region, which includes introducing advanced technologies and management experiences to the west provinces and promoting industrial upgrading in the western region. Moreover, for western China, boosting the economic growth rate, improving the transportation infrastructures and enhancing level of marketization, as well as increasing scientific & technological inputs contribute to narrow the gap of energy utilization efficiency between the eastern and western regions. Therefore, the Chinese government should exert more efforts in these aspects.

University of China (no. 10XNF049) and the project of ’’985’’ in China. The author wants to thank the two anonymous reviewers for their comments and suggestions.

Acknowledgments This paper is funded by the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin

Fig. A1

H. Yu / Energy Policy 45 (2012) 583–593

593

Table B1 The Local Moran Index of China’s Provincial Energy Intensity (1988–2007). 1988–1991

Anhui Beijing Chongqing Fujian Gansu Guangdong Guangxi Guizhou Hainan Hebei Heilongjiang Henan Hubei Hunan Jiangsu

1992–1995

1996–1999

2000–2003

2004–2007

Local Moran Index

Z P Local Statistic value Moran Index

Z P Local Statistic value Moran Index

Z P Local Statistic value Moran Index

Z P Local Statistic value Moran Index

Z P Statistic value

0.154  0.059  0.020 0.331 0.188 0.287 0.132  0.260 0.305 0.177 0.128 0.052 0.046  0.023 0.302

1.196  0.116 0.112 3.286 1.570 2.963 1.303  1.792 2.545 1.389 0.792 0.822 0.649 0.101 1.965

1.096 0.001  0.261 3.455 1.528 2.933 0.991  2.844 2.245 0.816 0.594 0.466 0.926  0.095 2.089

1.298 0.220  0.484 3.375 1.139 2.370 0.505  3.785 1.402 0.339 0.184 0.343 1.314 0.824 2.294

1.151 0.285  0.394 2.982 1.005 1.949 0.356  3.033 0.988 0.255 0.140 0.267 1.342 1.126 2.235

1.675 0.509  0.374 2.125 0.323 1.286 0.289  1.709 0.659 0.251 0.231 0.355 1.055 0.556 2.052

0.096 0.454 0.455 0.001 0.045 0.002 0.096 0.037 0.005 0.082 0.214 0.205 0.258 0.460 0.025

0.138  0.034  0.068 0.347 0.214 0.280 0.092  0.390 0.264 0.090 0.087 0.014 0.079  0.045 0.323

0.137 0.167 0.500 0.012 0.397  0.097 0.000 0.341 0.063 0.151 0.002 0.222 0.161 0.030 0.002  0.510 0.012 0.153 0.207 0.017 0.276 0.003 0.321 0.002 0.177 0.121 0.462 0.062 0.018 0.358

Appendix A. The Moran Scatterplot of China’s Provincial Energy Intensity (1988–2007) See Fig. A1.

Appendix B See Table B1 here. References Anselin, L., 1988. Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. Anselin, L., 1995. Local Indicators of Spatial Association-LISA. Geographical Analysis 27 (2), 93–116. Anselin, L., 1996. The Moran Scatterplot as an ESDA Tool to Assess Local Instablility in Spatial Association. In: Fischer, M., Scholten, H.J., Unwin, D.J. (Eds.), Spatial analytical perspectives on GIS. Taylor & Francis, London. Anselin, L., Bera, A.K., Florax, R., Yoon, M.J., 1996. Simple Diagnostic Tests for Spatial Dependence. Regional Science and Urban Economics 26 (1), 77–104. Barro, R.J., 1997. Determinants of Economic Growth, A Cross-Country Empirical Study. MIT Press, Cambridge. Barro, R.J., Sala-i-Martin, X., 1992. Convergence. Journal of Political Economy 100, 223–251. Cornillie, J., Fankhauser, S., 2004. The energy intensity of transition countries. Energy Economics 26 (3), 283–295. Elhorst, J.P., 2003. Specification and Estimation of Spatial Panel Data Models. International Regional Science Review 26 (3), 244–268. Elhorst, J.P., 2009. Spatial Panel Data Models. In: Fischer, M., Getis, A. (Eds.), Handbook of Applied Spatial Analysis. Springer, Berlin. Garbaccio, R.F., Ho, M.S., Jorgenson, D.W., 1999. Why has the Energy-Output Ratio Fallen in China? The Energy Journal 20 (3), 63–91. Garrett, T.A., Wagner, G.A., Wheelock, D.C., 2007. Regional Disparities in the Spatial Correlation of State Income Growth, 1977–2002. Annals of Regional Science 41 (3), 601–618. Hang, L., Tu, M., 2007. The Impacts of Energy Prices on Energy Intensity: Evidence from China. Energy Policy 35 (5), 2978–2988. Hu, J., Wang, S., 2006. Total-factor Energy Efficiency of Regions in China. Energy Policy 34 (17), 3206–3217. Kambara, T., 1992. The Energy Situation in China. China Quarterly 131, 608–636. Karl, Y., Chen, Z., 2010. Government Expenditure and Energy Intensity in China. Energy Policy 38 (2), 691–694. Kraft, J., Kraft, A., 1978. On the relationship between energy and GNP. Energy Development 3, 401–403. Ledyaeva, S., 2009. Spatial Econometric Analysis of Foreign Direct Investment Determinants in Russian Regions. World Economy 32 (4), 643–666.

0.097 0.147 0.413 0.026 0.314  0.085 0.000 0.298 0.127 0.129 0.009 0.177 0.307 0.011 0.000  0.416 0.080 0.098 0.367 0.004 0.427  0.006 0.366 0.003 0.093 0.119 0.205 0.097 0.011 0.348

0.125 0.216 0.388 0.072 0.347  0.084 0.001 0.214 0.158 0.018 0.026 0.113 0.361 0.004 0.001  0.243 0.162 0.056 0.399 0.004 0.444 0.013 0.360 0.005 0.097 0.100 0.13 0.033 0.013 0.318

0.0458 0.305 0.354 0.017 0.374 0.099 0.386 0.044 0.255 0.401 0.409 0.361 0.146 0.289 0.020

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