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June, 2014
Journal of Resources and Ecology
J. Resour. Ecol. 2014 5 (2) 97-104
DOI:10.5814/j.issn.1674-764x.2014.02.001
www.jorae.cn
Vol.5 No.2
Article
Regional Energy Efficiency in China Based on a Three-Stage
DEA Model
HUANG Dechun, DONG Yuyi, ZHANG Changzheng* and LIU Bingsheng
Business School of Hohai University, Nanjing 210098, China
Abstract: Energy is the material basis for social development and is closely related with the economy. Energy
shortage, a low utilization rate of energy and the deterioration of the environment have become the main restrictions
of economic development in China. Therefore, studying energy efficiency has a practical significance for developing
a harmonious and sustainable energy economy and building a conservation-minded and harmonious society. Here,
based on the three-stage DEA model we analyzed the energy efficiency of 29 provinces in China in 2009, set up an
evaluation index system of energy efficiency to compare differences in energy efficiency among regions and provide
regions with theoretical guidance to adjust energy consumption strategy and improve energy efficiency. We divided
technical efficiency into pure technical efficiency and scale efficiency, analyzed energy efficiency with its numerical
value and added environment variables to perfect the results. We found that scale efficiency is overestimated
before eliminating external factors and environment variables and pure technical efficiency is underestimated. To
solve this problem, regions should expand the scale of the enterprises and pay more attention to energy efficiency.
The scale returns of most provinces in the third stage are increasing (except Shandong province), which shows
that many enterprises are too small to reflect the economy scale. Therefore, all regions except Shandong should
increase their energy inputs to obtain the economy scale of energy utilization. From a regional perspective, eastern
energy efficiency is highest and western is the lowest. All regions should increase the size of enterprises to realize
the scale economy. Central and western regions in China should strengthen mutual cooperation, bring into play
their respective advantages, exploit new energy and new technology and improve the utilization ratio of energy.
Key words: regional energy efficiency; three-stage DEA model; technical efficiency; pure technical efficiency; scale
efficiency
1 Foreword
China achieved rapid economic growth through reform
and opening up over the last three decades. However, such
achievement was made with a cost of energy and to the
environment including natural resource shortages, energy
depletion and environmental deterioration. The Chinese
Government stated in the Eleventh Five-year Plan that “GDP
energy consumption shall be reduced by about 20% over
the end of the Tenth Five-year Plan period, while major
pollutants emission shall be reduced by 10% on the basis
of 2005”. Thus, in this context, studying and improving
regional energy efficiency will facilitate China’s overall
economic competitiveness as well as sustainable and healthy
economic development. The evaluation of energy efficiency
traditionally involves analyzing inter-provincial and interregional energy differences, or energy consumption intensity
of industrial sectors. Selected evaluation indicators are
mainly divided into single-factor and total-factor energy
efficiency indicators. However, the single-factor energy
efficiency indicator, only taking into account the effective
output of single energy input rather than other influencing
factors as capital and labor force, has huge defects. In recent
years, many have adopted total-factor indicators to evaluate
energy efficiency, while the method of Data Envelopment
Analysis (DEA) is generally applied. With regard to analysis
Received: 2013-07-19 Accepted: 2014-03-03
Foundation: this work was supported by National Social Science Fund of China (11BGL088), the Major Program of National Social Science Found
(11&ZD168), National Natural Science Foundation of China (G0213), and Science Foundation of the Chinese Education Commission (11JHQ024).
* Corresponding author: ZHANG Changzheng. Email: [email protected].
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of energy efficiency influencing factors, industrial structure
and technological progress are regarded as major factors;
however, due to differences in data selection time and
methods or in indicators selected, factors influencing
energy efficiency remain divided. Here, the author made
an attempt to overcome the defects of traditional research
methods and adopted nonparametric three-stage DEA
methods to evaluate energy efficiencies of 29 provinces in
China (excluding Tibet, and data of Chongqing is merged
into Sichuan). Energy efficiencies in different regions were
compared, providing theoretical guidance for adjusting
energy consumption strategies and improving energy
utilization in different regions.
Existing research literature on this topic involves two
aspects of energy efficiency: energy efficiency evaluation
and analysis on energy efficiency influencing factors. Both
single-factor and total-factor energy efficiency indicators
have been used to evaluate energy efficiency. For totalfactor evaluation methods, this paper summarizes the
dimensions of input indicator, output indicator, research
method and research time (Table 1).
Gao and Wang (2006), adopting clustering methodology,
thought economic development level, industrial structure,
investment and energy price were factors influencing
energy efficiency. Qiu and Shen (2008), adopting clustering
methodology and the Theil method, conducted quantitative
analysis on energy efficiency influencing factors using
panel data. Li and Wang (2008), through analyzing data
from 30 provinces and cities from 1995 to 2005 with a
generalized Fisher index, thought regional structural factor
was the major factor energy intensity variation. Qu (2009)
constructed the panel model of regional energy efficiency
with the variables of energy efficiency, technological
progress, energy price, industrial structure, industry
structure and institutional factor and thought these factors
had positive effects in improving energy efficiency in the
east but small influence on central and western China.
Yang (2009) used multivariate regression analysis on panel
data from 29 provinces from 1986 to 2005 and found that
structural factors had the greatest influence on energy
efficiency, while capital deepening and opening up both had
a negative impact on energy efficiency.
Energy efficiency evaluation analyses were mainly
conducted on total-factor energy efficiency indicators,
which offset the defect of a single-factor energy efficiency
indicator; however, most studies have adopted DEA or
super-efficiency DEA models which could not avoid the
influence of environment and errors on the efficiency
value. Therefore, analysis with data from different times
and regions might be concluded with deviated results. No
unified standard system had been formed for analyzing
energy efficiency influencing factors. Here, the author
adopted the three-stage DEA model proposed by Fried et
al. (2002) to evaluate regional energy efficiency in China
with the purpose of obtaining more accurate results and
proposing more reasonable suggestions.
2 Principles of three-stage DEA model
2.1 The first stage: traditional DEA model
Banker et al. (1984) first built the DEA-CCR model in
1978; however, CCR analyzed input and output and
calculated efficiency value under the condition of returns
to scale remaining constant, which was contrary to the
practical situation. Coelli et al. (1998) introduced the DEABCC model which decomposed the technical efficiency (TE)
in the CCR model into pure technical efficiency (PTE) and
scale efficiency (SE), i.e. TE=PTE×SE. This more accurately
reflects operations and the management level of decisionmaking units. The BCC model may be divided into input
oriented and output oriented models. Where, the former is
to reduce resource input to the greatest extent to improve
efficiency under the condition that the output remains
unchanged, while the latter is to increase output efficiency
evaluation under the condition that the input factors remain
unchanged. As for energy efficiency evaluation, it is easy to
control input, but controlling output is relatively difficult.
Therefore, an input oriented BCC model is adopted in this
paper.
Suppose there are n decision making units (DMU), each
with m input and s output; xik (i=1, 2, …, m) is the ith input
variable of the kth decision making unit; yjk (j=1, 2, …, s) is
the jth output variable of the kth decision making unit. Then,
calculation of total efficiency of the pth decision-making unit
is converted into a linear programming problem:
Table 1 Literature analysis of energy efficiency evaluation.
Author-Year
Wu and Wu 2009
Data period
2006
Yan and Tang 2009
Wei and Shen 2007
Cai and Xiao 2010
1996–2006
1995–2004
1998–2007
Lin and Xu 2010
1997–2008
Input indicator
Total employees, total energy consumption and
depreciation of fixed assets
Energy, labour force and capital stock
Capital stock, labour force and energy
Capital stock, labour force, coal consumption
and oil consumption
Labour force, capital stock and total energy
consumption
Output indicator
Regional GDP
Research method
DEA
GDP
GDP
GDP and total industrial
waste gas emission
Economic output
DEA
DEA
Super-efficiency
DEA
DEA
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HUANG Dechun, et al.: Regional Energy Efficiency in China Based on a Three-Stage DEA Model
min θ
{
X ni = X ni +  max Z i β
∗

i
n
}− Z β
i
n
 +  max {Vni } − Vni 
  i

n=1, 2, … N, i=1, 2, …, I
(3)
*
s.t
(1)
where, X1=(x11, x22, …, xm1), Y1=(y11, y21, …, ys1) and the
model is called a BCC model. θ is the total efficiency value
of investigated decision-making unit, and 0≤θ≤1. When
θ=1, the investigated decision-making unit is a point on
efficient frontier plane, hence it is effective. For ineffective
unit of θ<1, 1–θ is the proportion of redundant input by
investigated decision-making unit.
2.2 The second stage: adjusting input indicator variable
The slack variables of input and output analyzed in the first
stage will be influenced by external environmental factors,
random error and internal management factors. Traditional
DEA models, instead of accurately reflecting whether the
influence on efficiency comes from internal management
or external environment and random error, attributes all
influencing factors to internal management. Therefore,
Timmer (1971) introduced stochastic frontier analysis (SFA)
to consider the influence of external environmental factors
on relative efficiency. Suppose the nth input value of the ith
DMU is Xni and the slack variable is Sni, then Sni=Xni–Xnλ>0.
According to Batese and Coelli (1995), the relation model
between slack variable and environment variable is:
Sni = f(Zi, βn)+Vni+Uni
n=1, 2, …, N, i=1, 2, …, I (2)
where, S ni is the slack variable of the n th input of the
i th decision-making unit, Z 1=(z 1i, z 2i, …, z ki) presents k
environmental variables; f(Zi,βn) represents the influence
of environmental variable on input slack variable S ni;
generally, f(Zi, βn)=Zi, βn; Vni+Uni is the combined error term;
suppose Vni−N(0, σ2vn) reflects random error item, Uni reflects
management inefficiency and obeys a truncated normal
distribution, i.e. Uni−N(μ, σ2vn) and Vni is independent from
and uncorrelated to Uni. When γ=σ2vn/(σ2vn+σi2) is close to
1, the influence of management factor predominates; when
γ=σ2vn/(σ2vn+σ2vn) is close to 0, the influence of random error
predominates.
Through adjusting input variable data of the nth decisionmaking unit with the result of SFA model regression
and eliminating the influences of environmental factor
and random error, an efficiency value purely reflecting
management level can be calculated. The adjustment
formula is as follows:
where, X ni is input after adjustment; Xni is original input
value. The first square bracket in Formula (3) indicates
all decision-making units are adjusted to be under the
same external environment and the second square bracket
indicates random errors of all decision-making units are
adjusted to the same to make all decision-making units
under the same external environment and fortune.
2.3 The third stage: DEA model after adjustment
In this stage, input the input data after adjustment in
the second stage and original output data into the DEA
model to calculate the relative efficiency value; then the
result is the efficiency value reflecting management level
after eliminating the influence of environmental factor
and random factor and reflects efficiency value of the
management level.
3 Variable selection and data source
3.1 Selection of input and output variables
Energy, labour force and capital were selected in this
paper as three input factors. Wherein, energy input factor
is represented by energy consumption, which has been
converted into 10 000 ton standard coal; labour force input
is mainly represented by employees at the end of current
year; and capital input is represented by capital stock,
which is generally estimated with the perpetual inventory
method at the end of each year in case the data of capital
stock cannot be obtained directly. This paper follows Zhang
et al. (2004), wherein, the economic depreciation rate is
9.6% of total fixes assets of each province is adopted as the
depreciation rate. Calculation method: Ki,t=Ii,t +(1–δi)Ki.t-1,
where Ki,t is the capital stock in the tth year of region i, Ii,t
is the investment of region i in the tth year, δi is fixed assets
depreciation rate of region i.
The selection of output variable takes into account
that energy input results in rapid economic and industrial
development. Therefore, in this paper, total industrial output
value and regional GDP are selected as output variables.
3.2 Selection of environmental variable
Generally, environmental variable means the factor
which may influence energy utilization efficiency but is
beyond the scope of subjective control. In consideration
of the factors influencing energy utilization, in this paper,
technological progress and industrial structure are adopted
as environmental variables.
Technological progress becomes increasingly influential
to energy utilization efficiency. Advanced energy conversion
technologies may reduce energy waste and energy-saving
technologies may directly reduce energy consumption per
unit product. Under the current condition with increasingly
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Journal of Resources and Ecology Vol.5 No.2, 2014
less traditional energy, every country increases the input in
scientific and technological research and development to
find new energy and technologies. This paper adopts R&D
funding as the measurement indicator of technological
progress.
Among the three industries, the secondary industry has
high energy consumption. Therefore, the secondary industry
development in each region is directly relevant to energy
utilization and industrial structure is represented by the
proportion of the secondary industry in GDP.
3.3 Data source
In consideration of data integrity and availability, this
paper selects data for 29 provinces, municipalities and
autonomous regions in 2009 (Tibet was not selected due to
the incompleteness and data of Chongqing is merged into
that of Sichuan). Except the R&D fund from the China
Statistical Yearbook on Science and Technology in 2010, all
others are from the China Statistical Yearbook in 2010.
4 Empirical analysis
4.1 Traditional DEA empirical result in the first stage
Adopting the input oriented BCC model, technical
efficiency (TE), pure technical efficiency (PTE) and scale
efficiency (SE) are obtained in the first stage (Table 2). In
addition, the difference value, i.e. slack value, between
the ideal value and actual value of input variable can be
obtained, which will be applied in the next stage.
According to Table 2, taking no account of the influences
of environmental factor and random error, the average
technical efficiency, average pure technical efficiency
and average scale efficiency of these provinces and cities
in China are 0.863, 0.917 and 0.943 respectively. Five
provinces have technical efficiencies reaching 1, i.e. the
technology frontier, which are Beijing, Tianjin, Shanghai,
Guangdong and Gansu; however, the other 24 provinces
are under an inefficient state with large demand. Most
provinces have scale efficiencies larger than pure technical
efficiencies, which means that the technical inefficiencies
of most provinces have resulted from pure technical
inefficiency but not scale inefficiency. According to results
obtained in the first stage, pure technical inefficiency is the
major factor restricting the energy utilization rate; however,
we still have to further analyze if pure technical efficiency
is underestimated or scale efficiency overestimated without
considering the influences of the external environment and
random error.
4.2 SFA regression result in the second stage
Let slack value of each input variable analyzed in the
first stage be the dependent variable and environmental
variable R&D funds and number of industrial enterprises be
independent variables to analyze if external environmental
variable influences the difference between ideal and actual
input variables. If the analysis shows that environmental
variables will influence input variable difference, Formula
(3) will be adopted to eliminate external environmental
factors, thereby to obtain the input variable X *ni after
eliminating external environmental variables. Frontier 4.1
was used to obtain regression results (Table 3).
According to Table 3, the slack variables of R&D
funding and proportion of the secondary industry in GDP to
capital stock and energy consumption passes the test with
1% significance and the slack variable of proportion of the
Table 2 Comparison of energy efficiency between 29 regions in China in 2009.
Region
Beijing
Tianjin
Hebei
Shanxi
Inner Mongolia
Liaoning
Jilin
Heilongjiang
Shanghai
Jiangsu
Zhejiang
Anhui
Fujian
Jiangxi
Shandong
TE
1.000
1.000
0.773
0.982
0.931
0.881
0.812
0.856
1.000
0.920
0.849
0.941
0.858
0.830
0.841
PTE
1.000
1.000
0.779
1.000
0.956
1.000
0.815
0.861
1.000
1.000
0.862
0.950
0.872
0.880
1.000
SE
1.000
1.000
0.992
0.982
0.974
0.881
0.996
0.994
1.000
0.920
0.985
0.991
0.983
0.943
0.841
RTS
–
–
drs
drs
irs
drs
irs
irs
–
drs
drs
drs
irs
irs
drs
Region
Henan
Hubei
Hunan
Guangdong
Guangxi
Hainan
Sichuan
Guizhou
Yunnan
Shaanxi
Gansu
Qinghai
Ningxia
Xinjiang
Average value
TE
0.795
0.946
0.899
1.000
0.853
0.801
0.779
0.798
0.743
0.896
1.000
0.683
0.672
0.675
0.863
PTE
0.801
0.991
0.925
1.000
0.897
1.000
0.803
0.861
0.763
0.901
1.000
1.000
0.957
0.709
0.917
SE
0.993
0.955
0.972
1.000
0.951
0.801
0.971
0.928
0.974
0.994
1.000
0.683
0.702
0.952
0.943
RTS
drs
drs
irs
–
irs
irs
drs
irs
irs
drs
–
irs
irs
irs
Notes: TE is technical efficiency; PTE is pure technical efficiency; SE is scale efficiency; TE=PTE×SE; RTS is returns to scale; ‘irs’ is increasing returns
to scale; ‘drs’ is decreasing returns to scale; and ‘–‘ means the returns to scale remain unchanged.
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HUANG Dechun, et al.: Regional Energy Efficiency in China Based on a Three-Stage DEA Model
Table 3 SFA regression results.
Employee slack variable
Constant value
R&D fund
Proportion of the secondary
industry in GDP
Sigma-squared
Gamma
Log likelihood
Coefficient value
–1.38E+03
–5.66E–06
2.15E+03
2.04E+06
1.00E+00
–2.29E+02
T test value
–1.38E+03***
–1.78E–01
2.51E+03***
Total energy consumption slack
variable
Coefficient value T test value Coefficient value
T test value
–6.92E+03*** –6.92E+03***
–4.68E+03
–4.68E+03***
–6.756E–05
–1.75E+01***
1.85E–04
1.36E+01
1.28E+04
1.28E+04***
7.03E+03
7.03E+03***
2.04E+06***
2.98E+03***
Capital stock slack variable
2.32E+07
1.00E+00
–2.65E+02
2.32E+07***
3.51E+07***
1.77E+07
1.00E+00
–2.60E+02
1.77E+07***
1.43E+06***
Notes: T is the indicator to test if explaining variable has significant impact on explained variable; *** represents the test with 1% significance level;
** represents the test with 5% significance level; * represents the test with 10% significance level.
second industry in GDP also passes the test, indicating that
environmental factors have a significant influence on input
redundancy. Formula (3) shall be used to eliminate external
environment variables and random factor and finally make
all provinces the same external environment characteristic
in the third stage.
Input slack variable means possible reduced input
through improving operation and management levels;
therefore, if the environmental variable is positively
correlated with input slack variable, increasing the
environmental variable input will go against improving
energy utilization rate. It can be seen from Table 3 that
regression coefficients of R&D funding input to two slack
variables of employees and capital stock are both negative
and both pass the test at a 1% significance level, indicating
that increasing R&D funding input will increase the energy
utilization rate. Similarly, the secondary industries are
mostly high energy-consuming or extensive industries,
which also have huge energy consumption and result in
serious pollution, hence increasing the proportion of the
secondary industry in GDP will restrict improving the
energy utilization rate, which is also consistent with the
actual situation. Reasonably adjusting industrial structure
and increasing scientific and technological R&D funding
input to develop new technologies are perfect ways to
improve energy utilization.
4.3 DEA empirical results after adjusting input in the
third stage
After adjusting input variables of energy efficiencies of 29
provinces and municipalities in China in 2009, input the
adjusted variable (this variable is the value obtained after
eliminating environmental variable and random factor
with Formula (3)) and the original output variable into
DEAP2.1, then the technical efficiency taking no account of
the external environmental factor and random error can be
obtained (Table 4).
Through comparing results of the first stage, it can be
seen that the energy efficiency values before and after
adjustment are different to some extent. The average
technical efficiency is reduced from 0.863 to 0.801 and
scale efficiency value declines from 0.943 to 0.802 and
pure technical efficiency shows great growth from 0.917
to 0.998. According to further research, scale efficiencies
of all provinces and regions all reduce before and after
adjustment, which indicates that diseconomy of scale is the
cause of low energy efficiency, unlike the result of the first
stage that indicated that low energy efficiency resulted from
pure technical inefficiency. After eliminating environmental
and random factors, there are 15 provinces witnessing a
decrease in technical efficiency value, indicating that they
were overestimated because of their better environmental
factor or fortune; 11 provinces witness reducing efficiency
values because of their poorer external environment or
fortune but not low technical level.
Without eliminating the external environmental factor
and random factor, all provinces have overestimated scale
efficiencies and underestimated pure technical efficiency
and the overestimation extent is higher than underestimation
extent, resulting in overestimation of the technical efficiency
value.
4.4 Analysis on overall and regional differences of
energy efficiencies in provinces and municipalities
in China
Eliminating the influence of external environmental
factor and random factor, analysis in the third stage may
reflect practical operating conditions in energy utilization.
Therefore, integrating analysis results in the third stage with
the practical condition allows for deeper analysis.
4.4.1 Overall analysis
According to analysis results in the third stage, the
comprehensive technical efficiency value is 0.801 with a
low overall level and pure technical efficiency value of 0.998
with high level and good performance, indicating that most
enterprises have matured decision-making and management
levels regarding energy utilization. On the other hand, low
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Journal of Resources and Ecology Vol.5 No.2, 2014
Table 4 Comparison of energy efficiency between 29 regions in China in 2009 after adjusting input in the third stage.
Region
Beijing
Tianjin
Hebei
Shanxi
Inner Mongolia
Liaoning
Jilin
Heilongjiang
Shanghai
Jiangsu
Zhejiang
Anhui
Fujian
Jiangxi
Shandong
TE
0.808
0.956
0.965
0.974
0.767
1.000
0.885
0.858
1.000
0.990
0.835
0.926
0.704
0.657
0.966
PTE
1.000
1.000
1.000
1.000
0.997
1.000
0.999
1.000
1.000
1.000
1.000
1.000
0.999
0.998
1.000
SE
0.808
0.956
0.965
0.974
0.769
1.000
0.886
0.858
1.000
0.990
0.835
0.926
0.705
0.658
0.966
RTS
irs
irs
irs
irs
irs
irs
irs
irs
–
irs
irs
irs
irs
irs
drs
Region
Henan
Hubei
Hunan
Guangdong
Guangxi
Hainan
Sichuan
Guizhou
Yunnan
Shaanxi
Gansu
Qinghai
Ningxia
Xinjiang
Average value
TE
0.956
0.960
0.803
1.000
0.695
0.222
0.981
0.592
0.735
0.937
0.813
0.248
0.278
0.718
0.801
PTE
1.000
1.000
0.995
1.000
0.990
1.000
1.000
0.987
0.994
1.000
1.000
1.000
1.000
0.990
0.998
SE
0.956
0.960
0.806
1.000
0.702
0.222
0.981
0.600
0.739
0.937
0.813
0.248
0.278
0.725
0.802
RTS
irs
irs
irs
–
irs
irs
irs
irs
irs
irs
–
irs
irs
irs
Notes: TE is technical efficiency; PTE is pure technical efficiency; SE is scale efficiency; TE=PTE×SE; RTS is returns to scale; ‘irs’ is increasing returns
to scale; ‘drs’ is decreasing returns to scale; and ‘–‘ means the returns to scale remain unchanged.
comprehensive energy efficiency has mainly resulted from
low scale efficiency in each province or municipality, which
in reality is mainly reflected in insufficient importance
attached by most enterprises to energy utilization as well
as small scale and low status in decision-making of the
enterprises. However, with overdevelopment of energy
resources, less and less energy resources are available with
higher and higher energy utilization costs, and low energy
utilization efficiency becomes an increasingly important
factor restricting enterprise development. To improve
comprehensive energy utilization efficiency, the scale
energy utilization rate must be enhanced.
4.4.2 Regional analysis
According to traditional regional classification methods
we divide China into three regions: east (Beijing, Tianjin,
Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian,
Shandong and Guangdong), central (Shanxi, Jilin,
Heilongjiang, Anhui, Jiangxi, Henan, Hunan, Hubei and
Hainan) and west (Inner Mongolia, Sichuan, Guizhou,
Yunnan, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang).
According to respective statistical data for these regions in
the first and third stage, regardless of adjustment, the east
always has the highest technical efficiency, central China
occupies second place and the west has the lowest. As the
efficiency values calculated in the third stage are more
accurate, regional efficiency differences are analyzed based
on data of the third stage. As for comprehensive technical
efficiency, the east and the west are 0.922 and 0.676, the
highest and the lowest respectively. However, the values of
pure technical efficiencies in all regions are all quite high
with small difference; the difference of scale efficiency is
the same with that of comprehensive technical efficiency. Of
course, exceptions exist, e.g. Hainan in the central group has
only an efficiency of 0.222, which is quite low, while the
ones of Sichuan and Shaanxi in the west are 0.981 and 0.937
respectively. The above data show that scale efficiencies
in these regions are relatively smaller than pure technical
efficiency values; therefore, the enterprises should improve
scale economical efficiency of energy utilization and attach
importance to energy utilization. The central region and
east should enhance cooperation to narrow the gap. High
efficiency value in the east indicates that, supported by the
developed economy and with powerful policy support and
abundant talent, the east does a better job in development
of new energy and new technologies than inland regions.
Therefore, central and western China should learn advanced
management experience and technologies from the east and
introduce innovative thinking methods and energy-saving
and net energy technologies to improve energy efficiency,
thereby realizing coordinated development of the regional
economy. High pure technical efficiencies in the table do
not really imply high pure technical efficiencies in these
regions, because DEA measures relative efficiency and
it only indicates convergent pure technical efficiencies
of these regions. Therefore, these regions still need to
improve operation and management levels and strengthen
competitiveness.
5 Conclusions
After eliminating random error and external environmental
factor with the SFA model, comprehensive efficiency
values, pure technical efficiency values and scale efficiency
values of provinces and municipalities all increased or
HUANG Dechun, et al.: Regional Energy Efficiency in China Based on a Three-Stage DEA Model
reduced to some extent. Underestimated pure technical
efficiency and overestimated scale efficiency indicate that
external environmental factor, having great influence on
energy efficiency, should be eliminated by analyses.
Analysis in the third stage shows that all regions are
witnessing progressive increasing in returns to scale (except
Shandong where returns to scale is decreasing progressively,
indicating that Shandong with large energy utilization scale,
has realized economical efficiency of scale); therefore,
all regions but Shandong should increase energy input for
the purpose of economical efficiency of scale in energy
utilization.
Regionally, energy efficiencies in the central, east and
west are significantly different. The east has the highest
energy efficiency, the central region takes second place and
the west has the lowest. This pattern indicates that supported
by a developed economy and powerful policy support and
advanced talent, the east does a better job in development of
new energy and new technologies than other regions.
Recommendation 1: Enlarge enterprise scale to realize
economical efficiency of scale. According to the analysis
result in the third stage, low scale efficiency is the major
factor resulting in low technical efficiency. Combining
with reality, there are numerous small and mediumsized enterprises in China. Many small enterprises
only attach importance to the result rather than energy
utilization efficiency in the primary stage, resulting in
scale diseconomies of energy utilization. Therefore, small
and medium-sized enterprises should pay attention to this
problem.
Recommendation 2: Continuously promote technological
progress to enhance energy utilization rate. The east should
take full advantage of abundant funds and advanced
technologies to develop new energy technologies, e.g. solar
energy, hydrogen energy, nuclear energy, electrochemical
power source and biomass energy. The central region should
give full play to the advantages to selectively cultivate high
and new technology industry and continuously improve
energy-saving and cleaning technologies. The west should
learn advanced management experience and technologies
from central China and the east to improve energy
efficiency.
Recommendation 3: Devote to developing high-tech,
high value-added, low-energy and low-pollution industries
and properly reduce production scales of high-energy
consumption products, e.g. the secondary industries mostly
103
have high energy consumption and large proportion of the
secondary industry in GDP is also found in the statistics.
Therefore, the ratio of the primary, secondary and tertiary
industries should be coordinated to improve energy
utilization.
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Journal of Resources and Ecology Vol.5 No.2, 2014
基于三阶段DEA模型中国区域能源效率研究
黄德春，董宇怡，张长征，刘炳胜
河海大学商学院，南京 210098
摘 要：能源短缺、利用率低、环境恶化已经成为制约我国经济发展的重要环节。本文运用三阶段DEA模型对中国29个省
市2009年的能源效率进行了分析。文章将技术效率分为纯技术效率和规模效率，并利用其数值来分析能源效率，同时，加入环
境变量来分析完善上述计算结果。结果表明，在剔除外部因素和环境变量以前规模效率被高估，纯技术效率被低估。大部分省
在第三阶段计算出的规模收益是递增的，这说明很多企业规模较小不能体现出规模经济性。从区域上来看则是东部地区的能源
效率最高，中部次之，西部最低。针对这一结果，本文给出几点建议：中西部地区应加强合作，发挥各自优势，开发新技术新
能源，提高能源利用率，促进经济健康发展。
关键词：区域能源效率；三阶段DEA；技术效率；纯技术效率；规模效率