Rapid economic growth has resulted in a severe energy shortage and extensive environmental pollution in China. The question of how to save energy and reduce emissions has become a popular topic not only in economic but also in political and social circles. Most of the previous studies on the subject were conducted to evaluate the potential for energy savings and emission reductions. As an indispensable factor, each region's external environment has a strong impact on evaluating its potential for energy savings and emission reductions. However, the prior studies seldom considered each region's external environment, which resulted in research findings that are not rationally convincing. Hence, in this paper, a regional difference coefficient is introduced and calculated by applying technique for order preference by similarity to ideal solution to represent the external environment from two perspectives: (1) each region's economic and technical ability and (2) each region's resource reserves and supplies ability. Based on the regional difference coefficients, the original data envelopment analysis model was modified to calculate the potential for energy savings and emission reductions for 30 provincial administrative regions in China from the year 2001–2011. The results show that the gaps in the energy-saving and emission-reduction potential among these 30 regions are large and still increasing. Each region has its own causes for changes in energy savings and emission reductions. Thus, the important factors influencing regional energy savings and emission reductions are analyzed. The paper finds that technical progress and economic strength are the main factors impacting the regional energy-saving performance, especially in the underdeveloped provinces, while the industrial structure and energy mix are important factors influencing China's emission-reduction performance.

1.
National Bureau of Statistics (NBS)
, See http://www.stats.gov.cn/tjsj/ndsj/2012/indexch.htm for China Statistical Yearbook,
2012
.
2.
International Energy Agency (IEA)
, See http://www.iea.org/publications/freepublications/publication/name,27324,en.html for World Energy Outlook,
2010
.
3.
L.
Price
,
D. M.
Levine
,
N.
Zhou
,
D.
Fridley
,
N.
Aden
,
H. Y.
Lu
,
M.
McNeil
,
N. N.
Zheng
,
Y. N.
Qin
, and
P.
Yowargana
, “
Assessment of China's energy-saving and emission-reduction accomplishments and opportunities during the 11th Five Year Plan
,”
Energy Policy
39
,
2165
2178
(
2011
).
4.
A.
Meier
,
J.
Wright
, and
A. H.
Rosenfeld
,
Supplying Energy Through Greater Efficiency
(
University of California Press
,
Berkeley
,
1983
).
5.
X. D.
Guo
,
Z.
Lei
,
F.
Ying
, and
B. C.
Xie
, “
Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA
,”
Energy Policy
39
,
2352
2360
(
2011
).
6.
Y. W.
Bian
,
P.
He
, and
X.
Hao
, “
Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach
,”
Energy Policy
63
,
962
971
(
2013
).
7.
A.
Voltes-Dorta
,
J.
Perdiguero
, and
J. L.
Jiménez
, “
Are car manufacturers on the way to reduce CO2 emissions?: A DEA approach
,”
Energy Econ.
38
,
77
86
(
2013
).
8.
B.
Khoshnevisan
,
S.
Rafiee
,
M.
Omid
, and
M.
Mousazadeh
, “
Reduction of CO2 emission by improving energy use efficiency of greenhouse cucumber production using DEA approach
,”
Energy
55
,
676
682
(
2013
).
9.
O. A.
Olanrewaju
,
A. A.
Jimoh
, and
P. A.
Kholopane
, “
Assessing the energy potential in the South African industry: A combined IDA-ANN-DEA (index decomposition analysis-artificial neural network-data envelopment analysis) model
,”
Energy
63
,
225
232
(
2013
).
10.
A.
Mohammadi
,
S.
Rafiee
,
A.
Jafari
,
T.
Dalgaard
,
T. M.
Knudsen
,
A.
Keyhani
,
H. S.
Mousavi-Avval
, and
E. J.
Hermansen
, “
Potential greenhouse gas emission reductions in soybean farming: A combined use of life cycle assessment and data envelopment analysis
,”
J. Cleaner Prod.
54
,
89
100
(
2013
).
11.
K.
Wang
,
C.
Wang
,
X. D.
Lu
, and
J. N.
Chen
, “
Scenario analysis on CO2 emissions reduction potential in China's iron and steel industry
,”
Energy Policy
35
,
2320
2335
(
2007
).
12.
A.
Hasanbeigi
,
W.
Morrow
,
J.
Sathaye
,
E.
Masanet
, and
T. F.
Xu
, “
A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry
,”
Energy
50
,
315
325
(
2013
).
13.
B.
Zhu
,
W. J.
Zhou
,
S. Y.
Hu
,
Q.
Li
,
C.
Griffy-Brown
, and
Y.
Jin
, “
CO2 emissions and reduction potential in China's chemical industry
,”
Energy
35
,
4663
4670
(
2010
).
14.
J. P.
Tian
,
H.
Shi
,
X.
Li
, and
L. J.
Chen
, “
Measures and potentials of energy-saving in a Chinese fine chemical industrial park
,”
Energy
46
,
459
470
(
2012
).
15.
X. Y.
Liu
,
D. J.
Chen
,
W. J.
Zhang
,
W. Z.
Qin
,
W. J.
Zhou
,
T.
Qiu
, and
B.
Zhu
, “
An assessment of the energy-saving potential in China's petroleum refining industry from a technical perspective
,”
Energy
59
,
38
49
(
2013
).
16.
B. Q.
Lin
and
M.
Moubarak
, “
Estimation of energy saving potential in China's paper industry
,”
Energy
65
,
182
189
(
2014
).
17.
J. L.
Hu
and
C. H.
Kao
, “
Efficient energy-saving targets for APEC economics
,”
Energy Policy
35
(
1
),
373
382
(
2007
).
18.
Q.
Cui
,
H. B.
Kuang
,
C. Y.
Wu
, and
Y.
Li
, “
The changing trend and influencing factors of energy efficiency: The case of nine countries
,”
Energy
64
,
1026
1034
(
2014
).
19.
J.
Lin
, “
Energy conservation investments: A comparison between China and the US
,”
Energy Policy
35
,
916
924
(
2007
).
20.
J.
Urban
and
M.
Scasny
, “
Exploring domestic energy-saving: The role of environmental concern and background variables
,”
Energy Policy
47
,
69
80
(
2012
).
21.
A.
Omri
, “
CO2 emissions, energy consumption and economic growth nexus in MENA countries: Evidence from simultaneous equations models
,”
Energy Econ.
40
,
657
664
(
2013
).
22.
V. G. R. C.
Govindaraju
and
C. F.
Tang
, “
The dynamic links between CO2 emissions, economic growth and coal consumption in China and India
,”
Appl. Energy
104
,
310
318
(
2013
).
23.
B. P. Y.
Loo
,
L.
Li
,
V.
Psaraki
, and
I.
Pagoni
, “
CO2 emissions associated with hubbing activities in air transport: An international comparison
,”
J. Transp. Geogr.
34
,
185
193
(
2014
).
24.
S.
Paul
and
R. N.
Bhattacharya
, “
CO2 emission from energy use in India: A decomposition analysis
,”
Energy Policy
32
,
585
593
(
2004
).
25.
M.
Zhang
,
H. L.
Mu
, and
Y. C.
Song
, “
Decomposition of energy-related CO2 emission in China: 1991–2006
,”
Ecol. Econ.
68
,
2122
2128
(
2009
).
26.
W. X.
Wei
and
F.
Yang
, “
Impact of technology advance on carbon dioxide emission in China
,”
Stat. Res.
27
(
7
),
36
44
(
2010
) [in Chinese].
27.
Y.
Zhang
,
J. Y.
Zhang
,
Z. F.
Yang
, and
S. S.
Li
, “
Regional differences in the factors that influence China's energy-related carbon emissions, and potential mitigation strategies
,”
Energy Policy
39
,
7712
7718
(
2011
).
28.
D.
Wang
,
R.
Nie
, and
H. Y.
Shi
, “
Analysis of regional differences in energy consumption and energy saving potential for Yangtze River Delta
,”
Energy Procedia
5
,
690
694
(
2011
).
29.
H. N.
Li
,
H. L.
Mu
,
M.
Zhang
, and
S. S.
Gui
, “
Analysis of regional difference on impact factors of China's energy-related CO2 emissions
,”
Energy
39
,
319
326
(
2012
).
30.
S. S.
Sharma
, “
Determinants of carbon dioxide emission: empirical evidence from 69 countries
,”
Appl. Energy
88
,
376
382
(
2011
).
31.
Z. W.
Wang
,
F. C.
Yin
,
Y. X.
Zhang
, and
X.
Zhang
, “
An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China
,”
Appl. Energy
100
,
277
284
(
2012
).
32.
P.
Wang
,
W. H.
Wu
,
B. Z.
Zhu
, and
Y. M.
Wei
, “
Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China
,”
Appl. Energy
106
,
65
71
(
2013
).
33.
M. L.
Song
,
Y. Q.
Song
,
H. Y.
Yu
, and
Z. Y.
Wang
, “
Calculation of China's environmental efficiency and relevant hierarchical cluster analysis from the perspective of regional differences
,”
Math. Comput. Modell.
58
,
1084
1094
(
2013
).
34.
L.
Jiang
and
M. H.
Ji
, “
Energy intensity and its spatial heterogeneity in China—A perspective of resource endowment, industrial structure, technological progress and market mechanism
,”
Ind. Econ. Res.
52
,
61
70
(
2011
) [in Chinese].
35.
F. S.
Meng
and
M. Y.
Li
, “
Research on influencing factors evaluation of energy supply in China
,”
Sci. Res. Manage.
35
(
9
),
50
57
(
2014
) [in Chinese].
36.
X. C.
Tan
,
D. X.
Chen
,
B. H.
Gu
, and
B.
Che
, “
Study on China's regional carbon emission factors: The case of Chongqing city
,”
Energy Procedia
61
,
2885
2889
(
2014
).
37.
C. L.
Hwang
and
K.
Yoon
,
Multiple Attribute Decision Making Methods and Applications
(
Springer
,
Berlin, Heidelberg, Germany
,
1981
).
38.
F.
Cavallaro
, “
Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems
,”
Appl. Energy
87
,
496
503
(
2010
).
39.
G. T.
Chi
,
D.
Lu
, and
X. F.
Sun
, “
The efficiency evaluation of China commercial bank based on city difference coefficient
,”
J. Ind. Eng./Eng. Manage.
21
(
3
),
29
55
(
2007
) [in Chinese].
40.
Q.
Cui
,
C. Y.
Wu
, and
H. B.
Kuang
, “
The evaluation of Chinese airport sustainable development capacity
,”
Sci. Res. Manage.
33
(
4
),
55
61
(
2012
) [in Chinese].
41.
X. F.
Pan
and
Y.
Yang
, “
Evaluation of industrial enterprise's innovation efficiency in China excluding the influence of environment variables
,”
Oper. Res. Manage. Sci.
23
(
6
),
244
251
(
2014
) [in Chinese].
42.
C. P.
Barros
and
P.
Wanke
, “
An analysis of African airlines efficiency with two-stage TOPSIS and neural networks
,”
J. Air Transp. Manage.
44–45
(
5–6
),
90
102
(
2015
).
43.
R.
Fare
,
S.
Grosskopf
,
C. A. K.
Lovell
, and
C.
Pasurka
, “
Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach
,”
Rev. Econ. Stat.
71
,
90
98
(
1989
).
44.
N. M.
Malana
and
H. M.
Malano
, “
Benchmarking productive efficiency of selected wheat areas in Pakistan and India—data envelopment analysis
,”
Irrig. Drain.
55
,
383
394
(
2006
).
45.
G. M.
Shi
,
J.
Bi
, and
J. N.
Wang
, “
Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs
,”
Energy Policy
38
,
6172
6179
(
2010
).
46.
C.
Shiyi
, “
The evaluation indicator of ecological development transition in China's regional economy
,”
Ecol. Indic.
51
,
42
52
(
2015
).
47.
J. L.
Hu
and
S. C.
Wang
, “
Total-factor energy efficiency of regions in China
,”
Energy Policy
34
,
3206
3217
(
2006
).
48.
C.
Wei
,
J.
Ni
, and
M.
Shen
, “
China's energy inefficiency: A cross-country comparison
,”
Soc. Sci. J.
48
,
478
488
(
2011
).
49.
Y. M.
Wei
,
H.
Liao
, and
Y.
Fan
, “
An empirical analysis of energy efficiency in China's iron and steel sector
,”
Energy
32
,
2262
2270
(
2007
).
50.
P.
Zhou
and
B. W.
Ang
, “
Linear programming models for measuring economy-wide energy efficiency performance
,”
Energy Policy
36
,
2911
2916
(
2008
).
51.
L. B.
Li
and
J. L.
Hu
, “
Ecological total-factor energy efficiency of regions in China
,”
Energy Policy
46
,
216
224
(
2012
).
52.
Q.
Wu
and
C. Y.
Wu
, “
Research on evaluation model of energy efficiency based on DEA
,”
J. Manage. Sci.
1
,
103
112
(
2009
) [in Chinese].
53.
X. L.
Yuan
,
B. S.
Zhang
, and
W. P.
Yang
, “
The total factor energy efficiency measurement of China based on environmental pollution
,”
China Ind. Econ.
2
,
76
86
(
2009
) [in Chinese].
54.
Y.
Dai
,
Y.
Zhu
,
Q.
Bai
,
X.
Hu
, and
S.
Yu
,
China's Low Carbon Development Pathways by 2050
(
Science Press
,
Beijing, China
,
2009
) [in Chinese].
55.
Ministry of Industry and Information
, See http://baike.baidu.com/view/7995109.htm for The 12th FYP for Industrial Energy-Saving,
2012
[in Chinese].
56.
The People's Government of Ningxia Hui Autonomous Region
, See http://www.nxetc.gov.cn/uploadfile/2013/1220/20131220032614744.doc for Ningxia Energy Saving Action Plan (
2014–2015
), 2013 [in Chinese].
57.
Shanghai Economic and Information Commission
, See http://wenku.baidu.com/view/0048db89e53a580216fcfe5b.html for The 12th FYP for Shanghai Industrial Energy Saving and Comprehensive Utilization (
2011–2015
), 2011 [in Chinese].
58.
The State Council
, See http://www.gov.cn/zwgk/2012-01/16/content_2045519.htm for Several opinions on further promoting sound and rapid economic and social development in Guizhou,
2012
.
59.
State Council of China
, See http://www.gov.cn/zwgk/2012-01/13/content_2043645.htm for The Work Program for Controlling GHG Emissions during 12th FYP Period, 2011 [in Chinese].
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