The Convergence Analysis on the Economic Growth and Energy Intensity Gap between Regional Sectors

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The Convergence Analysis on the Economic Growthand Energy Intensity Gap between Regional SectorsQi Shaozhou a & Kai Li aa Economics and Management School, Wuhan University , 430072 , Wuhan , Hubei , ChinaPublished online: 20 May 2013.

To cite this article: Qi Shaozhou & Kai Li (2011) The Convergence Analysis on the Economic Growth and EnergyIntensity Gap between Regional Sectors, Chinese Journal of Population Resources and Environment, 9:3, 33-46, DOI:10.1080/10042857.2011.10685037

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33

Chinese Journal of Population, Resources and Environment Vol.9 No.3 September 2011

The Convergence Analysis on the Economic Growth and Energy Intensity Gap between Regional Sectors

Qi Shaozhou, Li Kai

Economics and Management School of Wuhan University, Wuhan Hubei 430072, China

consumption of per unit of GDP or the energy consumption of per unit of added value of industrial sectors in the macro-economic field and in the energy consumption of per unit of output or the energy consumption of per unit of product in microeconomic field. The energy consumption of per unit of added value of industrial sectors reflects the different energy efficiency indicators of different industrial sectors, and it is a specific manifestation of the impact of the energy consumption level of industrial sectors on the energy con-sumption of per unit of GDP (Li, 2007). Most of the exist-ing literature is based on provinces (Here, “provinces” is the shortened form of provinces, autonomous regions, and municipalities directly under the Central Government.) or regions as a unit to study the relationship between regional economic growth and energy intensity. Taking provinces as the unit of analysis covers the changes between different sectors, which obviously have differences between each other. Therefore, this paper analyzes the convergence rela-tionship between economic growth and energy intensity at the industrial sector level, which will help us penetrate into the variation trend of the relationship between economic growth and energy intensity. At the same time, understand-ing the heterogeneity of sectors has strong policy implica-tions; it can help us make appropriate industrial policies for different sectors, reduce regional disparities by narrowing the gap between sectors, save energy and reduce emissions, and achieve sustainable development of the national econo-my.

Since the middle and late 1980s, the empirical researches on the convergence of economic growth have developed rapidly; many foreign scholars have done valuable explo-ration. Although different scholars have different point of views on whether economic growth is convergent or not, yet so far, most scholars agree that there is convergence of economic growth (including absolute convergence, condi-tional convergence, and club convergence); in this regard,

Abstract: In this paper, the authors have analyzed the relationship between energy intensity gap and GDP per worker gap of China’s western and eastern provinces over the period 1997–2006. Using panel data model with lag adjustment, taking the above provinces and six industrial sectors (agriculture, forestry, animal husbandry, and fisheries, industry, construction industry, transport, storage and post & telecommunications, wholesale and retail trades & catering industry, and other sectors of tertiary industry.) as the investigated subjects, the authors have conducted empirical study on the con-vergence of GDP per worker gap and the convergence of energy intensity gap with respect to the variation of GDP per worker gap, and have concluded that: First, the GDP per worker gap of the six industrial sectors and provinces are convergent, and of this, the convergence rate of GDP per worker gap of Construction Industry is the fastest, while that of Industry is the slowest. Second, the overall energy intensity gap between eastern and western prov-inces is convergent, that is, with the narrowing of GDP per worker gap between eastern and western provinces, the energy intensity gap converges, but its convergence rate is slower than that of GDP per worker gap. Third, energy intensity gap between various industrial sectors of the east and the west is either convergent or divergent, and there are differences. The energy intensity gap of agriculture, forestry, animal husbandry, and fisheries, industry, and construction industry is convergent, while that of the other three industrial sectors is divergent. Fourth, the convergence of the over-all energy intensity of the western provinces is not in conformity with the convergence of the various industrial sectors, and there are significant differences, indicating that the western provinces and autonomous regions should take measures to more effectively improve their overall energy utilization efficiency at the industrial sector level.Key words: convergence, industrial sector, energy intensity, panel data analysis

1 Introduction

Energy intensity usually finds expression in the energy

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Received 10 December 2010; accepted 12 April 2011

Corresponding author: Qi Shaozhou ([email protected])

4—qishaozhou likai.indd 33 2011-9-29 15:23:27

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some representative literature includes Baumol (1986), Barro and Sala–I–Martin (1992), Mankiw, Romer and Weil (1992), Bernard and Durlauf (1995), and Quah (1996). Oth-er scholars believe that economic growth has more diver-gent characteristics, and some representative literature in-cludes Delong (1988), Pagano (1993), Mauro and Godrecea (1994). As for the empirical analysis on the convergence of economic growth of China, it is studied mainly from the an-gles of provinces or the three areas, i.e. eastern, central and western area. Relevant literature includes Chen and Fleisher (1996), Wei (1997), Shen (1999), Cai and Du (2000), Liu (2001), Shen and Ma (2002), and He (2008). Peng (2005), from the sector point of view, analyzes the growth and differences of regional economy of China, and the results show that agriculture, industry, transport, storage, post and telecommunications industry, and other sectors of tertiary industry have weak divergence trend, while wholesale and retail trades and catering industry shows weak convergence trend. In short, because different scholars use different research methods, different models, different sample inter-vals, and different angles of view, therefore, their research results on the convergence of China’s economic growth are not the same, and some even are contradictory.

Currently, there are not many researches on the conver-gence of energy efficiency of China; the existing ones are mainly from the national level and regional level to study. Stan (2006) assumes that China’s energy efficiency has conditional convergence, and on this basis, she calculates the energy saving potential of each province; but she only investigates the coefficient of variation of China’s overall energy efficiency, and doesn’t investigate the convergence of inter–regional energy efficiency. Shi and Zhang (2008) investigate the development trend of China’s energy ef-ficiency at the regional level, and they find that the west shows the characteristics of divergence, the east is conver-gent, and the central is convergent to the east. Li and Huo (2009) use Data Envelopment Analysis (DEA) method and analyze the total factor energy efficiency of all provinces, three major regions and the whole country. Their research finds that the regional total factor energy efficiency pres-ents an upward step distribution from the west to the east and from the north to the south. The energy efficiency of the whole country, the east, and the central area all shows a steady convergence trend, while the energy efficiency of the west does not show a significant convergence trend. As for at the sector level, some scholars mainly study the impact of sector energy efficiency on the overall energy efficiency,

and haven’t studied the variation trend of energy efficiency of the sector itself. Wu and Cheng (2006) have studied how and to what extent the structural change and energy inten-sity change of the six sectors can impact China’s energy intensity decrease, and their results show that the technical progress of industrial sectors is the dominant factor affect-ing energy intensity. Shi (2002) investigates the reasons that China’s energy consumption slows down under the condi-tions of rapid economic growth, and she believes that since China’s reform and opening up, the improvement of energy efficiency is very significant, and the improvement of in-dustrial energy utilization efficiency to some extent offsets the negative effect of changes in industrial structure.

Literature that is associated with the convergence of the relationship between economic growth and energy intensity is even less. Most of the literature focuses on the long–term equilibrium relationship between economic growth and energy consumption (e.g. Stern, 2000; Chontanawat et al., 2006) or on the “U” shaped relationship between economic growth and energy efficiency (Dong, 2008), and the litera-ture hasn’t conducted convergence analysis on the variation trend of the two sides. However, foreign scholars Markan-dya et al. (2004) have investigated the economic impact of the different energy endowments of different member states of the EU after its eastward expansion, and they find that the economic growth gap between the 12 Eastern European countries and Western European countries is convergent, and if the income per capita gap between them decreases by 1%, their energy consumption intensity gap will corre-spondingly decrease by 0.7%. Moreover, the convergence rates of different Eastern European countries are different, which depends on two parameters: one is the elasticity coefficient of energy consumption intensity change rate gap with respect to the GDP per capita change rate gap; the other is the speed of adjustment from actual energy consumption intensity to the desired energy intensity, i.e. lag time adjustment factor. Domestic scholars Qi and Luo (2007), Qi et al. (2009) have carried out empirical study on the convergence of the relationship between economic growth and energy consumption intensity between China’s eastern and western areas and between China and devel-oped countries. These studies mentioned above provide this paper with inspiration on research methods.

Because there is convergence of economic growth in China [Cai and Du (2000), Liu (2001), Shen and Ma (2002), etc.], then does the economic growth gap between differ-ent sectors tend to converge? If the economic growth gap


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Martin, 1992). We appropriately improve the traditional β convergence model and get the convergence model of GDP per worker of this paper:

ln(yi,t + 1/yit)=α+β ln (yit)+μit (1)

where, yit is GDP per worker; α is intercept; β is the esti-mated coefficient; μit is the error term. If β<0, then there is β convergence, which indicates that the GDP per worker growth rate and the GDP per worker of the previous session are negatively correlated, indicating that the less developed western areas develops faster than the developed eastern areas, and over time, the GDP per worker of the west will converge to that of the east.

2.1.2 The Convergence Model of the Relationship be-tween Energy Intensity and GDP per Worker

We refer to the research methods of Markandya et al. (2004), Qi and Luo (2007) and Qi et al. (2009) and con-struct the following model:

and

where, (i) refers to the various provinces; (t) represents the year; (yit) is the GDP per worker of (No. i) province in (t) period; (εit) is the energy intensity of (No. i) province in (t) period; (yft) is the average GDP per worker of all eastern provinces in (t) period; (εft) is the average energy intensity of all eastern provinces in (t) period; (A) is a constant; (η) is the elasticity coefficient of the change rate of energy inten-sity gap with respect to the change rate of GDP per worker gap, indicating that if the change rate of GDP per worker gap between the western and eastern areas decreases by 1%, the change rate of energy intensity gap between them will correspondingly increase or decrease by η%; (μ) is the lag time adjustment factor; (ε*

ft) is the energy intensity variable with time lag influence, while (εit) is the energy intensity variable without time lag influence. Taking into account the fact that panel data analysis has the influence of time series factors, therefore, in this model we add a one–session lag variable, to make forecasts more accurate. Taking natural logarithm to the above two equations and straightening out, we can get the convergence model of energy intensity gap with respect to the variation of GDP per worker:

lnεit/εi,t-1=B+C(lnεft/εi,t-1)+Dln∆yt+υit (2)

where, B=μlnA, C=μ, D=μη, ∆yt=yft/yit , and υit are error terms.

between different sectors is convergent, does the energy intensity gap between different sectors tend to converge? Does the convergence of energy intensity gap between dif-ferent sectors have the same characteristics? What about the variation trend of the relationship between economic growth and energy intensity of industrial sectors? These are the issues that this paper intends to study. However, in China, there is little literature investigating the convergence relationship between economic growth and energy intensity from the level of industrial sector. In order to more fully analyze the variation trend of the relationship between the two sides, this paper refers to the research methods of Markandya et al. (2004), Qi and Luo (2007), and Qi et al. (2009), and appropriately improves these methods ac-cording to the real conditions. This paper has collected the annual panel data of six industrial sectors (This paper classifies the overall economy of provinces into 6 industrial sectors, including agriculture, forestry, animal husbandry, and fisheries, industry, construction industry, transport, storage and post & telecommunications, wholesale and retail trades & catering industry, and other sectors of ter-tiary industry.) of 30 provinces (excluding China’s Tibet, Hong Kong, Macao, and Taiwan.) over the period 1997–2006. The structure of the following part of this paper is as follows: The second part is about theoretical models and sample description; in the third part, from the angles of provinces and sectors respectively, the authors use panel technology to empirically investigate the convergence of GDP per worker (This paper does not use the added value (GDP) of industrial sectors to reflect the industrial growth, but use the result of dividing the added value of industrial sectors with the total number of employees as the indicator of industrial growth, i.e. GDP per worker or labor produc-tivity.) and the convergence of energy intensity gap with respect to the variation of GDP per worker; the fifth part is about basic conclusions.

2 Theoretical model and sample description

2.1 Model

2.1.1 Convergence model of GDP per worker

β convergence shows that under the effect of the law of diminishing marginal returns of capital, the growth rate of income per capita and the initial income per capita of dif-ferent regions are negatively correlated (Barro and Sala–i–

* ftit ft

it

yA

y

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i t


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Equations (1) and (2), respectively, is the convergence of GDP per worker and the convergence of energy intensity gap with respect to the variation of GDP per worker of Chi-na’s eastern and western areas. If the empirical test results of equation (1) prove the existence of β convergence, it indicates that the GDP per worker of the east and the GDP per worker of the west have convergence relationship, and the economic growth trend of the west is faster than that of the east. On this basis, for the results of equation (2), we conduct the following discussion.

(1) If η<0, then if the gap of GDP per worker between China’s eastern and western provinces decreases by 1%, the energy intensity gap between them will correspondingly de-crease by η %, indicating that the variation trend of energy intensity gap between eastern and western provinces is con-vergent;

(2) If η<0, then if the gap of GDP per worker between China’s eastern and western provinces decreases by 1%, the energy intensity gap between them will correspondingly increase by η %, indicating that the variation trend of en-ergy intensity gap between eastern and western provinces is divergent.

2.2 Sample description

2.2.1 Division of sectors and regions

The traditional “tertiary industries” is a big concept. If we analyze in terms of the tertiary industries, the function of more detailed level of industries within the tertiary in-dustries will be covered. Therefore, it is necessary to make a more detailed division of the tertiary industries. Some scholars have studied the convergence of economic growth or convergence of energy intensity at a more detailed sec-tor–level, such as Bernard and Jones (1996), Master and Zhang (2008). But for the convergence relationship between economic growth and energy intensity of China’s provinces, few scholars have conducted such analysis at detailed sec-tor level. Studying the convergence relationship between economic growth and energy intensity of China’s provinces at detailed sector level is helpful for us to penetrate into the variation trend of energy intensity in the process of regional economic growth in China. Therefore, with industrial sec-tors as the unit and based on the available data, this paper classifies the overall economy of provinces into 6 industrial sectors, including Agriculture, Forestry, Animal Husbandry, and Fisheries (called “Agriculture” for short in Tables and Figures), Industry, Construction Industry, Transport, Stor-

age and Post & Telecommunications (called “Transport Industry” for short in Tables and Figures), Wholesale and Retail Trades & Catering Industry(called “Wholesale and Retail” for short in Tables and Figures), and Other Sectors of Tertiary Industry (called “Other Tertiary Industry” for short in Tables and Figures). What we want to answer is: First, whether the GDP per worker of all industrial sectors is convergent or not? Second, if there is convergence of GDP per worker of industrial sectors, will the energy inten-sity of industrial sectors be convergent?

How to classify the investigated provinces is another problem that we face. Traditionally, there are two kinds of classification: one is to classify all provinces into three cate-gories, i.e. the eastern area, the central area, and the western area; another one is to geographically classify all provinces into six regions, i.e. North China, East China, Northeast, Northwest, Southwest and Central South. Whether the first or the second division method, they are not suitable for this paper to conduct convergence analysis on the relationship between GDP per worker and energy intensity. For ex-ample, in the central area of China, the energy intensity of Shanxi Province is much higher than that of some provinces in the west, and the GDP per worker of Xijiang in the west-ern area is much higher than that of some provinces in the eastern area. In addition, over–detailed division will cause that the differences in the GDP per worker or the energy intensity between different regions are not obvious, and on the other hand, the differences within a region may be very obvious.

Therefore, this paper chooses the year of 1997 as the base year, and by calculating the statistical characteristics of average real GDP per worker of each province over the period 1997–2006 and the overall statistical characteristics of average real GDP per worker of the 30 provinces over the same period, we classify China’s 30 provinces (not in-cluding China’s Tibet, Hong Kong, Macao and Taiwan) into two areas, i.e. “eastern provinces” and “western provinces”. Table 1 is the statistical description of real GDP per worker and energy intensity of China’s different regions over the period 1997–2006. Table 1 shows that the median of aver-age real GDP per worker of 30 provinces during 1997–2006 is 18,300 Yuan (RMB)/person, and most of the 15 provinces that are over the median belong to the traditional eastern area, and, in proper order, they are Shanghai, Beijing, Tian-jin, Hebei, Zhejiang, Guangdong, Jiangsu, Fujian, Liaoning, Shandong, Heilongjiang, Xinjiang, Jilin, Inner Mongolia and Hubei provinces; while most of the provinces that are


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below the median are concentrated in the traditional west-ern region, and, in proper order, they are Shanxi, Anhui, Jiangxi, Henan, Hunan, Guangxi, Hainan, Chongqing, Sich-uan, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Gui-zhou. Among them, the GDP per worker of Shanghai is the highest, 73,400 Yuan (RMB)/person; the GDP per worker of Guizhou is the lowest, 5,800 Yuan (RMB)/person. This result is in line with the actual situation of China; developed provinces are mainly in the eastern region of China, while the less developed provinces are mainly in China’s western region.

From Table 1 we can also see that the average real GDP per worker (with 1997 as the base year) of 15 eastern prov-inces over the period 1997–2006 is 30,300 Yuan (RMB)/person, while that of 15 western provinces is only 12,100 Yuan (RMB)/person. Similarly, the average energy intensity of 15 eastern provinces over the period 1997–2006 is 1.38 tons of standard coal/ten thousand Yuan (RMB), while that of 15 western provinces is 1.82 tons of standard coal/ten thousand Yuan (RMB), indicating that provinces that have lower real GDP per worker will have higher energy intensi-ty. In other words, the energy utilization efficiency of devel-oped eastern provinces is higher than that of less developed western provinces, and this result is also consistent with the actual situation of China.

2.2.2 Data description

In this paper, the model is constructed using the annual data of China’s 30 provinces over the 10 year period 1997–

2006. The data used in this paper are from the statistical databases of China Economic Information Network, China Statistical Yearbook (1998–2007), China Energy Statistical Yearbook (1998–2007), China Economic Yearbook (2007), China Labor Statistical Yearbook (2004–2007), China Compendium of Statistics 1949–2007, and the Statistical Yearbook of various regions of relevant years.

This paper classifies the overall economy of provinces into 6 industrial sectors, including agriculture, forestry, animal husbandry, and fisheries, industry, construction in-dustry, transport, storage and post & telecommunications, wholesale and retail trades & catering industry, and other sectors of tertiary industry. This division is on the basis of the division method under the column of Gross Regional Product (GRP) in China Statistical Yearbook to merge some economic sectors. For example, “wholesale and retail trades” and “hotels and catering industry” under the col-umn of Gross Regional Product (GRP) in China Statistical Yearbook are merged into wholesale and retail trades & ca-tering industry; all other sectors in the tertiary industry ex-cept transport, storage and post & telecommunications and wholesale and retail trades & catering industry are merged into “other tertiary industry” in this paper; and in the use of sector output data, these sectors are corresponding to the “Regional Balance Statistics of Energy (physical quantity)” in China Energy Statistics Yearbook and the “Employment by Sector and Region” in China Labor Statistical Year-book.

First, with 1997 as the base year, through GDP index,

Table 1 Statistical characteristics of real GDP per worker and energy intensity of China’s different regions over the period 1997–2006*

Variable Average value Median Max MinimumStandarddeviation

Sample vo l -ume

30 provinces

Real GDP per worker 2.1201 1.8258 7.3416 0.5799 1.3795 30

Energy intensity 1.5978 1.3370 7.0891 0.6290 0.8301 296**

15 eastern provinces


Energy intensity 1.3805 1.2605 3.0227 0.6290 0.4966 150

15 western provinces


Energy intensity 1.8210 1.3634 7.0891 0.7690 1.0245 146

Notes: * The unit of real GDP per worker and energy intensity are ten thousand yuan RMB/person and tons of standard coal/ten thousand Yuan RMB, respectively.

** The data of total energy consumption of Ningxia of 2000, 2001 and 2002 and Hainan of 2002 are unavailable.


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we obtain, respectively, the real GDP of the six industrial sectors of each province over the period 1997–2006, then divide it with the total number of employees (total employ-ment) of each sector and the corresponding result is the real GDP per worker of each sector (i.e. labor productivity). In general, the real GDP per capita reflects the level of income, while real GDP per worker reflects the technical efficiency. The data of employment by sector before 2003 is directly from China Statistical Yearbook; the data of employment by sector of all provinces over the period 2003–2006 are the result of summing up the “Number of Employed Persons in Urban Units by Sector” and the “Number of Engaged Persons in Urban Private Enterprises by Sector” in China Statistical Yearbook and the “Number of Rural Employment by Sector” in China Labor Statistical Yearbook.

According to the respective conversion coefficient of standard coal, we convert the consumption of raw coal, crude oil, electricity, natural gas of each sector of each province into standard coal, and in order to unify the com-putation, we adopt the national standard conversion coef-ficient, so the calculated results are not identical with the statistical data in regional Statistical Yearbook. Then, we divide the real total GDP with the actual total energy con-sumption and get the energy intensity of the six industrial sectors of each province over the period 1997–2006, and each industrial sector has a total of 296 valid data (The data of total energy consumption of Ningxia of 2000, 2001 and 2002 and Hainan of 2002 are unavailable.).

Similarly, the real GDP of each province is calculated in accordance with the price of 1997, and the total employ-ment and total energy consumption of each province are the results of summing up the total employment and total ener-gy consumption of all industrial sectors respectively. Then we divide the real total GDP with the total employment of the very province and get the real GDP per worker of each province respectively, i.e. the overall labor productivity of each province.

Fig. 1 shows the comparison of average labor productiv-ity of each sector; the average value is the result of averag-ing the data of the 30 sampled provinces. The real GDP per worker (the level of labor productivity) of each sector over the period 1997–2006 shows a significant difference: the level of labor productivity of industry has been the highest, while the level of labor productivity of agriculture has been lowest, and the gap between them is widening. In 1997, the level of labor productivity of Industry is 5.25 times of that of agriculture, and by 2006, the gap expands to 9.53 times.

In 1997, only the level of labor productivity of agriculture is below the average value (the GDP per worker by province), while in 2006, the level of labor productivity of construc-tion and wholesale and retail is also lower than the average level of labor productivity.

As we can see from Fig. 2, the energy intensity of differ-ent sectors also shows some differences. Although in recent years the energy intensity of industry shows a downward trend, yet it has been higher than that of other industrial sectors. Similarly, the energy intensity of transport industry always ranks the second in the six industrial sectors. Obvi-ously, in the six industrial sectors, industry and transport industry are the two most energy–wasting sectors, while the energy intensity gap between agriculture, construction, wholesale and retail, and other tertiary industry has less dif-ference.

01997 1998 1999 2000 2001 2002 2003 2004 2005 2006

0.5

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Construction ProvinceAgriculture Industry

Transport Industry

Wholesale and RetailOther Tertiary Industry

Fig. 2 Average energy intensity of each industrial sectorData Source: Working out according to the data of relevant China Statistical Yearbook and China Energy Statistical Yearbook.

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Fig. 1 Average labor productivity of each industrial sector Data source: Working out according to the data of China Statistical Yearbook and China Labor Statistical Yearbook of all provinces.

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3 Empirical analysis

3.1 Preliminary observations

Before the in–depth study of the proposition, it is neces-sary to use empirical data to conduct a preliminary statisti-cal observation, so that we can understand the convergence relationship of real GDP per worker between all sectors and provinces. We choose the data of real GDP per worker of the six industrial sectors of 30 provinces and that of all provinces as the sample, and map out the corresponding diagrams by sectors and province respectively over the

The

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The real GDP per worker of all provinces of 1997

Fig. 3 Agriculture

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Fig. 4 Industry

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Fig. 5 Construction industryThe real GDP per worker of all provinces of 1997

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Fig. 9 Provinces


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Fig. 8 Other tertiary industry


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Fig. 7 Wholesale and retail


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Fig. 6 Transport industry

Data Source: Working out according to the data of relevant China Statistical Yearbook and China Labor Statistical Yearbook of all provinces.


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period 1997–2006, with the annual growth rate (expressed by formula: ln(η2006 / η1997)/9) of real GDP per worker (ex-pressed by “y”) as the ordinate axis, and with the initial real GDP per worker (expressed by ln(η1997) as the lateral axis.

As we can see from Fig. 3 to Fig. 9, the scatter trend line of Industry, Construction Industry, Transport Industry, Wholesale and Retail, and Other Tertiary Industry, and Provinces is downward sloping, primarily indicating that the growth rate of real GDP per worker and the initial real GDP per worker are negatively correlated, that is, they are inversely proportional; the labor productivity of less devel-oped provinces is greater than that of developed provinces, so that less developed provinces can finally catch up with developed provinces. The scatter trend line of agriculture first has an upward trend, then gradually decreases, indicat-ing that there is the possibility of convergence of real GDP per worker.

3.2 Empirical test and analysis on the convergence model of real GDP per worker

Then, we use panel data model to conduct empirical estimate on equation (1), and the corresponding panel data model is:

ln(yit / yi,t-1)=α+βln(yi,t-1)+hi+kt+φit (3)

where, hi=∑ δiHi(i=1,2……30) is the Provinces dummy variable, reflecting the persistent inter–provincial differenc-es, such as differences in resource endowments and indus-trial structure. kt=∑γt Kt (t=1997,1998,……2006) is the Year dummy variable, controlling the factors, except economic growth, which are varying with respect to the change of time, for example the change of technical efficiency, etc.. φit is a random disturbance factor that has nothing to do with the time and region. Our aim is to observe the sign of β co-efficient in the above equation, so that we can see whether there is absolute β convergence.

In order to facilitate testing, the equation (3) is rewritten as:

Y1=α+βX1+hi+kt+φit (4)

where, Y1=ln(yit/yi,t-1), and X1=ln(yi,t-1).If hi and kt of equation (4) are parameters to be estimated

and the residuals follow a normal distribution, then equation (4) is a Two Way Fixed Effects Model; if they are random, then the equation (4) is a Two Way Random Effects Model. In addition, because the panel data used in this paper are from 10 particular years and 30 provinces, and the cross–

section unit is much larger than the sequential unit, there-fore, we can believe that differences mainly are reflected in the different cross–sections, i.e. variable–intercept model whose model selection parameters do not change with re-spect to the variation of time. We will use Redundant Fixed Effects Test of Eviews6.0 program to test whether there are fixed time effects and fixed individual effects.

We use equation (4) to test the absolute β convergence hypothesis of Provinces and sectors and analyze the con-vergence of real GDP per worker of China’s Provinces and the six industrial sectors over the sampled period. We first conduct Redundant Fixed Effects Test, and find that the corresponding probability of F statistics and chi–square sta-tistics of each industrial sector and provinces is less than 1% significant level, indicating that the “effect is redundant” null hypothesis is rejected and it is better to choose the model with fixed cross–section and time; the corresponding estimate results are shown in Table 2.

We can see that with time the individual fixed effects model to estimate equation (4), China’s real GDP per worker of Provinces is significantly β convergent, and its convergence coefficient (the coefficient of variable X1) is–0.1836. Viewing from the sector level, all the six industrial sectors have shown a convergence, all the β convergence coefficients are negative, and all the t statistics are signifi-cant at the 1% level, indicating that the real GDP per work-er gap between sectors of the eastern provinces and west-ern provinces has absolute β convergence, that is, although the overall growth of labor productivity of sectors of west-ern provinces has fallen behind that of the eastern provinc-es, yet their growth trend is faster than that of the eastern provinces. Viewing from the degree of convergence, the convergence rate of real GDP per worker of Construction Industry is the fasted in the six industrial sectors, and its β convergence coefficient is –0.4327; Industry is the slowest, and its β convergence coefficient is –0.1299, which indi-cates that the labor productivity of China’s Industry sector has always been the highest, but the convergence trend of its labor productivity is slower than that of other industrial sectors.

3.3 Empirical test and analysis on the Convergence Model of the relationship between the Real GDP per Worker and Energy Intensity

Similarly, we use panel data model to conduct empirical estimate on equation (2), and the corresponding panel data model is:


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ln(εit/εi,t-1) = B + C ln(εft/εi,t-1) + D ln∆yt + wi + kt + υit (5)

where, wi = ∑ θiWi (i=16,17,…,30) is the western provinces dummy variable, reflecting the differences in energy con-sumption patterns, the differences in regulations, and dif-ferences in preferences brought about by the differences in resource endowments. kt = ∑ tKt (t=1997,…,2006) is the year dummy variable, controlling the factors, except eco-nomic growth, which are varying with respect to the change of time, for example the change of technical efficiency and the change of energy prices, etc.. Similarly, υit is a random disturbance factor that has nothing to do with the time and region. Our purpose here is to observe the size and sign of coefficients B, C and D in the above equation, so that we can test the convergence of energy intensity gap with re-spect to the variation of real GDP per worker.

In order to facilitate testing, the equation (5) is rewritten as:

Y2 = B + CX2 + DX3 + wi + kt + υit (6)

where, Y2 = ln(εit / εi,t-1), X2 = ln(εft / εi,t-1), X3 = ln∆yt.We use equation (6) to test the convergence of energy in-

tensity gap of provinces and sectors with respect to the vari-ation of real GDP per worker, and use the Redundant Fixed Effects Test of Eviews6.0 program to test whether there are fixed time effects and individual fixed effects; taking into account the fact that the residuals may have contemporane-ous correlation and cross–section heteroscedasticity, and in order to get more robust estimated value of coefficient covariance matrix, we use White Cross–section Weighting to eliminate the impact of cross–section contemporaneous

correlation and heteroscedasticity. The regression results are shown in Table 3.

We use the equation (6) to run a regression and get the results of coefficients B, C and D, and then work out the key variables through equation η lni

DB

(A) , i.e. the elas-

ticity coefficient of energy intensity gap with respect to the variation of real GDP per worker between western provinces and eastern provinces. The η value of agriculture, indus-try, construction, transport industry, wholesale and retail, and other tertiary industry sectors are 0.73, 0.39, 0.83, −0.24, −0.71 and −0.75,respectively. the η value of provinces is 0.28.

η is the elasticity coefficient of energy intensity gap with respect to the variation of real GDP per worker, indicating that if the real GDP per worker gap between western prov-inces and eastern provinces decreases by 1%, the energy in-tensity gap between them will be influenced by η percentage points. We further classify the value of η : η = 1 indicates that the convergence rate of energy intensity gap and that of real GDP per worker are equivalent; η>1 indicates that the convergence rate of energy intensity gap is faster than that of real GDP per worker; 0<η<1 indicates that the conver-gence rate of energy intensity gap is slower than that of real GDP per worker. –1<η<0 indicates that the divergence rate of energy intensity gap is slower than the convergence rate of real GDP per worker; η<–1 indicates that the divergence rate of energy intensity gap is faster than the convergence rate of real GDP per worker.

Overall, the energy intensity gap between China’s west-

Table 2 Regression results of two–way fixed effects of the convergence model of real GDP per worker of industrial sectors and provinces1

Agriculture Industry ConstructionTransportindustry

Wholesale and retail

Other tertiary industry

Provinces

Intercept–0.165 9*** 0.319 9*** 0.373 4*** 0.244 1*** 0.233 8*** 0.247 6*** 0.1849***

(–6.30) (5.68) (9.56) (5.06) (9.20) (8.96) (8.15)

X1 –0.380 5*** –0.129 9*** –0.432 7*** –0.131 3*** –0.275 0*** –0.286 4*** –0.1836***

(–8.31) (3.59) (–8.00) (–3.29) (–7.35) (–6.40) (–4.22)

R2 0.435 3 0.600 6 0.405 9 0.487 0 0.505 5 0.302 4 0.3186

Adjust R2

F Value

0.342 4 0.534 9 0.308 1 0.402 6 0.424 2 0.187 6 0.2065

4.69*** 9.14*** 4.15*** 5.77*** 6.22*** 2.63*** 2.84***

FixedEffects

Year Include Include Include Include Include Include Include

Provinces Include Include Include Include Include Include Include

Notes: 1. Figure in brackets are the t test value of corresponding coefficient; *** indicates 1% significance level; fixed effects include time fixed effects and indi-vidual fixed effects, and in this paper, we use Year dummy variable and Provinces dummy variable.

2. Here, we only consider the overall convergence of real GDP per worker of sectors and Provinces, and therefore, we haven’t given the estimate results of dummy variable coefficient of Year and Provinces.


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ern and eastern provinces is convergent (the η value is 0.28), and the convergence rate of energy intensity gap between western and eastern provinces is slower than that of the real GDP per worker between them, that is, if the real GDP per worker between them decreases by 1%, the energy intensity gap between them will decrease by 0.28%. Viewing from the six industrial sectors, the η value of the three sectors of the tertiary industry is negative, and of this, the η value of transport Industry is –0.24, the η value of wholesale and retail is –0.71, and the η value of other tertiary industry is

–0.75, which indicates that the energy intensity gap of trans-port industry, wholesale and retail, and other tertiary indus-try between eastern and western provinces is divergent, that is, if their real GDP per worker gap decreases by 1%, their energy intensity gap will increase by 0.24%, 0.71%, and 0.75% respectively. The reasons may lie in the fact that in eastern provinces, the international trade is more developed, capital flows relatively freely, the vigorous development of the tertiary industry upgrades the industrial structure, and energy–saving technologies are actively introduced; while

Table 3 Regression results of the fixed effects of convergence model of the relationship between energy intensity and real GDP per worker

Agriculture Industry ConstructionTransportindustry

Wholesaleand retail


Provinces

Intercept –0.4141*** 0.0555 –0.3810*** 0.2724*** 0.0729* 0.1230 –0.0144

(–3.19) (1.53) (–4.82) (4.67) (–1.96) (0.41) (–0.69)

X2 0.4399*** 0.3818*** 0.3952*** 0.4398*** 0.4278*** 0.7446*** 0.4635***

(5.94) (6.62) (6.04) (7.83) (5.65) (7.46) (6.53)

X3 0.3213* 0.1480* 0.3269*** –0.1063*** –0.3054 –0.5545* 0.1279***

(1.97) (1.80) (2.73) (–3.08) (–1.15) (–1.57) (4.83)

R2 0.3101 0.3465 0.3429 0.4088 0.4051 0.3975 0.3993

Adjust R2 0.2115 0.2564 0.2522 0.3243 0.2678 0.2585 0.3135

F Value 3.15*** 3.84*** 3.78*** 4.84*** 2.95*** 2.86*** 4.65***

FixedEffects

Year Exclude Exclude Exclude Exclude Include Include Exclude

Provinces Include Include Include Include Include Include Include

Wi coefficient of dummy variable of western provinces

Shanxi 0.5549 0.1456 0.3619 0.0035 0.2675 0.1663 0.3469

Anhui –0.2525 –0.1637 –0.1929 –0.3182 –0.1092 –0.2676 –0.1451

Jiangxi –0.0077 –0.2271 –0.5929 –0.1732 –0.4109 –0.5809 –0.2234

Henan –0.0799 –0.1508 –0.2146 –0.2869 –0.2285 –0.3292 –0.1057

Hunan 0.0538 –0.1688 –0.1249 –0.1274 –0.2553 –0.3641 –0.1629

Guangxi –0.4775 –0.0755 –0.3785 0.0158 –0.4385 0.1021 –0.1721

Hainan 0.0273 –0.0607 0.1803 0.1259 –0.1247 0.0685 –0.2341

Chongqing 0.2513 –0.0472 0.0991 0.0749 –0.1352 –0.7789 –0.0249

Sichuan –0.3494 –0.1770 –0.1021 0.0913 –0.0081 –0.0602 –0.1877

Guizhou 0.1989 0.2282 0.3174 0.4860 0.7083 1.6427 0.3087

Yunnan –0.1588 0.0084 0.1278 0.1251 –0.4838 –0.3301 –0.0620

Shaanxi –0.0344 –0.1375 0.2024 –0.0775 0.5765 –0.0374 –0.0508

Gansu 0.3603 0.1432 0.2051 0.2125 –0.0982 0.3513 0.1684

Qinghai –0.2413 0.2859 0.2767 –0.1669 0.3929 0.5189 0.2743

Ningxia 0.2899 0.3834 –0.1247 0.0773 0.5326 –0.1814 0.3934

Note: The estimation of coefficient covariance matrix uses White Cross–section Weighting to eliminate the impact of cross–section contemporaneous correlation and

heteroscedasticity; figure in brackets are the t test value of corresponding coefficient; *,** and *** indicate 10%, 5% and 1% significance level.


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in western provinces, they are mainly dependent on their own resources to seek development, give more priority to the development of heavy industry, focus on upgrading industrial energy efficiency, and overlook the energy ef-ficiency of the tertiary industry. These differences gradually widened the energy intensity gap of sectors of the tertiary industry between eastern and western provinces.

The energy intensity gap of the other three industrial sectors (agriculture, industry, and construction industry) between eastern and western provinces is convergent. Of this, the η value of construction industry is the greatest (0.83), and if the real GDP per worker gap of Industry sec-tor between eastern and western provinces decreases by 1%, their energy intensity gap will correspondingly decrease by 0.39%. Since 1990, the energy consumption of Industry al-ways accounts for about 70% of the total energy consump-tion. As we can see from Fig. 2, industry is the largest sec-tor of energy consumption in China, which not only reflects the fact that the energy efficiency of industry is rather low, but also indicates that reducing the energy consumption of Industry is the key to enhance the overall energy efficiency. Our government has taken enhancing energy efficiency as the important content of industrial sustainable development, and it is also a long–term basic strategy for our economic and social development. In recent years, China’s energy ef-ficiency has been greatly improved (Shi, 2002), and many western provinces have formulated relevant industrial poli-

cies to improve industrial energy efficiency. However, com-pared with other industrial sectors, the energy efficiency of Industry is still relatively low, and there is considerable potential for improvement.

From the above analysis we can see that the convergence of Provinces does not mean that the industrial sectors of all provinces are also convergent. The industrial sectors have a considerable degree of heterogeneity; therefore, the government should formulate appropriate industrial energy policies for different sectors, and narrowing the regional gap through reducing the gap between industrial sectors has important practical significance.

Using the computational formula of η value, we can work out the η value of the 15 western provinces respec-tively. The process is as follows: we have run a regression on equation (6) and get the value of B, C and D, and what’s more, we have got the dummy variable coefficient θi of fixed effects of the 15 western provinces, so that we can work out lnA=B/C and the corresponding Bi value of western

provinces, and thus we can use equation

lnii

DB

(A)

to work out the elastic coefficient of energy intensity gap between each western province and eastern provinces with respect to the variation of real GDP per worker. The results are shown in Table 4.

From Table 4 we can visually see the variation trend of energy intensity of each western province over the ten

Table 4 The η value of western provinces

Provinces Agriculture Industry ConstructionTransportindustry



Provinces

Shanxi –2.148 1 0.107 0 16.500 2 –0.238 6 –0.152 9 –0.316 6 –0.012 0

Anhui 0.453 7 –0.198 8 0.549 1 1.437 5 1.433 7 0.633 5 0.024 9

Jiangxi 0.717 1 –0.125 4 0.323 6 –0.663 7 0.154 0 0.200 0 0.016 7

Henan 0.612 3 –0.225 7 0.529 1 4.540 6 0.334 5 0.444 2 0.033 1

Hunan 0.839 5 –0.189 9 0.623 0 –0.454 1 0.285 3 0.379 9 0.022 4

Guangxi 0.339 2 –1.075 7 0.414 9 –0.228 5 0.142 3 –0.406 9 0.021 3

Hainan 0.781 9 –4.137 3 1.570 3 –0.165 3 1.004 7 –0.478 3 0.016 0

Chongqing 1.857 8 2.592 0 1.118 0 –0.189 6 0.835 3 0.139 7 0.101 1

Sichuan 0.396 1 –0.177 1 0.652 4 –0.181 0 –0.803 1 –1.458 6 0.019 7

Guizhou 1.405 5 0.075 8 4.955 3 –0.086 8 –0.066 6 –0.051 9 –0.013 5

Yunnan 0.527 9 0.336 7 1.244 7 –0.165 6 0.126 7 0.442 3 0.052 0

Shaanxi 0.674 4 –0.262 4 1.764 6 –0.337 8 –0.080 1 –1.070 1 0.060 9

Gansu 5.621 9 0.108 3 1.791 7 –0.135 8 2.057 0 –0.193 1 –0.025 8

Qinghai 0.461 5 0.063 0 3.021 6 –0.624 1 –0.111 7 –0.142 7 –0.015 3

Ningxia 2.435 2 0.049 0 0.623 2 –0.188 3 –0.085 9 1.568 5 –0.010 5


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year period 1997–2006. Of this, the value of each western province reflects the convergence or divergence of energy intensity gap between each western province and eastern provinces. If the η value a western province is positive, it means that if the real GDP per worker gap between the western province and eastern provinces decreases by 1%, the energy intensity gap between them will be correspond-ingly influenced by η percentage points, indicating that when the real GDP per worker of the western province is increasing, and its real GDP per worker gap with eastern provinces is narrowed, the province also has improved its energy efficiency; if the η value is negative, it is on the contrary.

If the energy intensity gap of a certain sector between eastern and western provinces is convergent, we can not conclude that the energy intensity gap of this sector of all western provinces is convergent, that is, in the process of narrowing the economic gap with eastern provinces, the variation trend of energy utilization efficiency of different western provinces is different. For Industry sector, as we can see from Table 4, its η value is 0.39, and the energy intensity gap of Industry sector between eastern and west-ern provinces is convergent, but the energy utilization ef-ficiency of some provinces is increased, including Shanxi, Chongqing, Guizhou, Yunnan, Gansu, Qinghai, and Ningx-

ia; the energy utilization efficiency of some provinces is de-creased, including Anhui, Jiangxi, Henan, Guangxi, Hunan, Hainan, Sichuan and Shaanxi.

At the same time, we can also get the overall η value of each of the western provinces, and the overall η value of each of the western provinces can be used to compare with the η value of each of the industrial sectors of each of the western provinces, so that we can visually see the variation trend of the overall energy utilization efficiency of each western province over the period 1997–2006 and the varia-tion trend of the energy utilization efficiency of each indus-trial sectors over the same period. Then we combine the η value of industrial sectors and the η value of provinces, if η>0, we mark with “ + ”; if η<0, we mark with “-”. The re-sults are shown in Table 5.

From Table 5, we can directly see the condition of over-all energy utilization efficiency of each province and the condition of energy utilization efficiency of each industrial sector in the process of western provinces narrowing their labor productivity gap with eastern provinces. For example, the overall η value of Shanxi is negative, indicating that when Shanxi narrows its economic gap with eastern prov-inces, its energy utilization efficiency is decreasing; from the perspective of six industrial sectors, the energy utiliza-tion efficiency of industry and construction is increasing,

Table 5 The sign of the η value of western provinces

Provinces Agriculture Industry ConstructionTransportindustry



Provinces

Shanxi – – + + – – –

Anhui + + – + + + +

Jiangxi + + – + – + +

Henan + + – + + + +

Hunan + + – + – + +

Guangxi + + – + – + –

Hainan + + – + – + –

Chongqing + + + + – + +

Sichuan + + – + – – –

Guizhou – + + + – – –

Yunnan + + + + – + +

Shaanxi + + – + – – –

Gansu – + + + – + –

Qinghai – + + + – – –

Ningxia – + + + – – +


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while the energy utilization efficiency of agriculture, trans-port industry, and wholesale and retail is decreasing; an-other obvious fact is that most of the η value of agriculture and Construction of western provinces is positive, and only the η value of agriculture of Shanxi is negative, indicating that when the two sectors of most western provinces narrow their labor productivity gap with eastern provinces, their en-ergy utilization efficiency is increasing. While for industry and other tertiary industry, the signs of η value of western provinces are obviously different.

Overall, the energy utilization efficiency of eastern prov-inces is higher, and compared with eastern provinces, the energy utilization efficiency of western provinces is rather low. When western provinces are improving their energy utilization efficiency, they should develop more effective industrial development plans and industrial energy policies. However, at the current level of economic development, whether it is the East or the West, they all have the potential and possibilities to further improve their energy utilization efficiency.

4 Basic conclusions

In this paper, the authors have analyzed the convergence relationship between energy intensity gap and GDP per worker gap of China’s western and eastern provinces over the period 1997–2006. On the basis of using panel data model with lag adjustment to conduct empirical estimate, the authors have conducted empirical test on the conver-gence of real GDP per worker gap and the convergence of energy intensity gap with respect to the variation of real GDP per worker gap of provinces and industrial sectors re-spectively, and have concluded that:

First, overall, the real GDP per worker gap between China’s eastern provinces and western provinces has β con-vergence. Viewing from the industrial sectors, six industrial sectors have shown β convergence, and of this, the conver-gence rate of Construction Industry is the fastest in the six industrial sectors, and that of Industry is the slowest.

Second, with the convergence of real GDP per worker gap between China’s eastern provinces and western prov-inces, the energy intensity gap between them is also con-vergent, but the rate of the latter is slower that that of the former. If the real GDP per worker gap between China’s eastern provinces and western provinces decreases by 1%, the energy intensity gap between them will correspondingly decrease by 0.28%. In the process of economic growth, the

variation trends of energy utilization efficiency of differ-ent western provinces are different, i.e. the sign of their η value is different, and of this, the η value of Anhui, Jiangxi, Henan, Hunan, Guangxi, Hainan, Chongqing, Sichuan, Yunnan, and Shaanxi is positive, indicating that the energy utilizition efficiency of these provinces is increasing in the process of economic growth and in the process of their nar-rowing the real GDP per worker gap with eastern provinces; however, the η value of Shanxi, Guizhou, Gansu, Qinghai, Ningxia is negative, indicating that when the real GDP per worker of these provinces is increasing, their energy utiliza-tion efficiency is declining.

Third, from the perspective of six industrial sectors, the η value of three sectors of tertiary industry is negative, that is, the energy intensity gap of transport industry, whole-sale and retail, and other tertiary industry between eastern provinces and western provinces is divergent, i.e. if the real GDP per worker gap of the three sectors decreases by 1%, the energy intensity gap of them between the East and the West will correspondingly increase by 0.24%, 0.71%, and 0.75%, respectively. On the other hand, the energy intensity gap of agriculture, construction industry and industry be-tween the East and the West is convergent.

Fourth, when each of the western provinces narrows their labor productivity gap with eastern provinces, the variation trends of their overall energy utilization efficiency are different, and so are the variation trends of energy uti-lization efficiency of their industrial sectors. Most of the η value of agriculture and construction of western provinces are positive, and only the η value of agriculture of Shanxi is negative, indicating that when most western provinces narrow their labor productivity gap with eastern provinces, their energy utilization efficiency is increasing. While for industry and other tertiary industry, the signs of η value of western provinces are obviously different.

The comparison of energy efficiency at sector level can help us clearly understand the level of energy utilization efficiency and the energy saving potential of the western provinces. When western provinces are improving their energy utilization efficiency, they should develop more ef-fective industrial development plans and industrial energy policies; according to the variation trend of energy intensity of different industrial sectors, they should take different measures at the sector level in order to enhance the overall energy utilization efficiency. Meanwhile, when the govern-ment develops regional economic development strategy, they should take full advantage of the differences in energy


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endowments and the gap of energy utilization efficiency of different provinces and cooperate with each other, and blaze a road of energy-saving, sustainable and regional equilib-rium development road.

Acknowledgement: We thank the anonymous reviewers for their constructive comments and suggestions. This paper is sponsored by “Project Fund of Humanities and Social Sciences of Ministry of Edu-cation” (Grant No.: 09YJA790157) and “Proprietary Research Project of Humanities and Social Sciences of Wuhan University” (Grant No.:

09ZZKY032).

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The Convergence Analysis on the Economic Growth and Energy Intensity Gap between Regional Sectors