Table 1
Summary of the previous study on Chinese energy and environment efficiency
Paper
|
Input
|
Output
|
Methodology
|
(Ouyang and Yang, 2020)
|
Capital, Labor, Energy
Land
|
GDP
CO2
|
SBM-DEA
|
(Zhou et al., 2006)
|
Energy, Population
|
GDP
CO2
|
SBM-DEA
|
(Zhou and Ang, 2008)
|
Capital, Labor, Energy
|
GDP
CO2
|
BCC-DEA
|
(Long et al., 2015)
|
Capital, Labor, Energy
|
GDP
CO2
|
SBM-DEA and Malmquist Index
|
(Chang, 2013)
|
Capital, Labor, Energy
|
Cargo and Vessel
CO2
|
SBM-DEA
|
(Liu and Xin, 2019)
|
Capital, Labor, Energy
|
GDP
SO2, COD
|
SBM-GML
|
(Wang et al., 2020)
|
Size, Flight Shifts, Hours
|
Income and Transportation Turnover
CO2
|
GSBM-DEA and GML
|
(An et al., 2019)
|
Capital, Labor, Energy
|
GDP
Water Pollution
|
SBM-DEA and GML
|
(Shang et al., 2020)
|
Capital, Labor, Energy
|
GDP
CO2, SO2
|
SBM-DEA
|
(Yu et al., 2019)
|
Capital, Labor, Energy
|
GDP
CO2,
|
Super SBM-DEA
|
(Emrouznejad and Yang, 2016b)
|
Capital, Labor, Energy
|
Gross Industrial Output Value
CO2
|
DDF-GML
|
(H. Liu et al., 2020)
|
Capital, Labor, Coal, Petroleum, Natural Gas
|
GDP
Pollution Index
|
BCC-DEA
|
(Wang et al., 2021)
|
Capital, Labor, LPG, Electricity, Natural Gas
|
GDP, Retail Sales, General Budget Revenues
|
Global Malmquist
|
(Zhou et al., 2019)
|
Capital, Labor, Energy
|
Industrial Economic Output
CO2
|
Super SBM-DEA
|
Table 2
Summary of the selection of the input-output indicators.
|
Variables
|
Units
|
Output variables
|
TOV
|
108 yuan
|
|
CO2 emission
|
104 tons
|
Inputs variables
|
Total sown area of crops
|
103 hm2
|
|
Rural fixed assets investment
|
108 yuan
|
|
Total power of agricultural machinery
|
104 kW
|
|
TNE
|
104 persons
|
|
Standard coal consumption
|
104 tce
|
Table 3
Descriptive statistical characteristics of input and output variables from 2008 to 2018.
|
Variables
|
Average
|
Max
|
Min
|
S.D.
|
Output variables
|
TOV
|
3024.991
|
9549.6
|
153
|
2160.605
|
|
CO2 emission
|
1037.49
|
4167.78
|
122.40
|
709.78
|
Inputs variables
|
Total sown area of crops
|
5430.84
|
14783.40
|
103.80
|
3672.31
|
|
Rural fixed assets investment
|
1562.75
|
5203.58
|
48.13
|
1177.01
|
|
Total power of agricultural machinery
|
3265.31
|
13353.00
|
94.00
|
2899.80
|
|
TNE
|
933.76
|
2847.31
|
37.09
|
656.82
|
|
Standard coal consumption
|
432.70
|
1611.95
|
42.14
|
283.33
|
Table 4
Divide eight comprehensive economic zones according to 11th Five-Year Plan.
Areas
|
Regions
|
Northern coastal
|
Beijing, Tianjin, Hebei, Shandong
|
Northeast
|
Liaoning, Jilin, Heilongjiang
|
Eastern coastal
|
Shanghai, Jiangsu, Zhejiang
|
Southern coastal
|
Fujian, Guangdong, Hainan
|
Middle Yangtze River
|
Hubei, Hunan, Jiangxi, Anhui
|
Southwest
|
Chongqing, Sichuan, Guizhou, Yunnan, Guangxi
|
Middle Yellow River
|
Shanxi, Inner Mongolia, Henan, Shaanxi
|
Northwest
|
Gansu, Qinghai, Ningxia ,Xinjiang
|
Table 5
The rural energy efficiency value of 2008-2018.
|
Province
|
2008
|
2009
|
2010
|
2011
|
2012
|
2013
|
2014
|
2015
|
2016
|
2017
|
2018
|
|
Northern Coastal
|
Beijing
|
1.057
|
1.068
|
1.074
|
1.085
|
1.128
|
1.199
|
1.263
|
1.259
|
1.241
|
1.430
|
1.637
|
1.222
|
|
Tianjin
|
1.185
|
1.118
|
1.113
|
1.101
|
1.066
|
1.125
|
1.089
|
1.044
|
1.037
|
1.010
|
1.023
|
1.083
|
|
Hebei
|
0.619
|
0.644
|
0.562
|
0.496
|
0.475
|
0.428
|
0.421
|
0.372
|
0.374
|
0.330
|
0.339
|
0.460
|
|
Shandong
|
1.160
|
1.166
|
1.140
|
1.130
|
1.135
|
1.135
|
1.136
|
1.131
|
1.108
|
1.108
|
1.114
|
1.133
|
|
Average
|
1.005
|
0.999
|
0.972
|
0.953
|
0.951
|
0.972
|
0.977
|
0.952
|
0.940
|
0.969
|
1.028
|
0.974
|
Northeast
|
Liaoning
|
1.083
|
1.104
|
1.086
|
1.068
|
1.062
|
1.054
|
1.037
|
1.021
|
0.777
|
0.630
|
0.677
|
0.963
|
|
Heilongjiang
|
0.686
|
0.612
|
0.588
|
0.595
|
0.643
|
1.012
|
1.019
|
1.006
|
1.006
|
1.025
|
1.020
|
0.837
|
|
Jilin
|
1.048
|
1.057
|
1.027
|
1.034
|
1.091
|
0.529
|
0.484
|
0.472
|
0.395
|
0.304
|
0.301
|
0.704
|
|
Average
|
0.939
|
0.924
|
0.900
|
0.899
|
0.932
|
0.865
|
0.846
|
0.833
|
0.726
|
0.653
|
0.666
|
0.835
|
Eastern Coastal
|
Shanghai
|
2.040
|
2.109
|
2.340
|
2.455
|
2.314
|
2.283
|
2.321
|
2.357
|
2.268
|
2.289
|
2.406
|
2.289
|
|
Jiangsu
|
1.099
|
1.095
|
1.094
|
1.110
|
1.099
|
1.119
|
1.118
|
1.141
|
1.157
|
1.168
|
1.149
|
1.123
|
|
Zhejiang
|
0.700
|
0.691
|
0.710
|
0.701
|
0.693
|
0.585
|
0.469
|
0.454
|
0.459
|
0.534
|
0.459
|
0.587
|
|
Average
|
1.280
|
1.298
|
1.381
|
1.422
|
1.369
|
1.329
|
1.303
|
1.318
|
1.294
|
1.330
|
1.338
|
1.333
|
Southern Coastal
|
Fujian
|
1.085
|
1.076
|
1.086
|
1.088
|
1.107
|
1.168
|
1.185
|
1.189
|
1.194
|
1.223
|
1.238
|
1.149
|
|
Guangdong
|
1.110
|
1.090
|
1.080
|
1.076
|
1.053
|
1.052
|
1.056
|
1.047
|
1.083
|
1.085
|
1.091
|
1.075
|
|
Hainan
|
1.913
|
1.860
|
1.701
|
1.624
|
1.514
|
1.501
|
1.655
|
1.612
|
1.512
|
1.548
|
1.511
|
1.632
|
|
Average
|
1.369
|
1.342
|
1.289
|
1.263
|
1.225
|
1.240
|
1.299
|
1.282
|
1.263
|
1.285
|
1.280
|
1.285
|
Middle Yangtze River
|
Hunan
|
0.679
|
0.543
|
0.598
|
0.562
|
0.561
|
0.475
|
0.469
|
0.438
|
0.449
|
0.370
|
0.356
|
0.500
|
|
Hubei
|
1.033
|
1.019
|
1.024
|
1.041
|
1.029
|
1.016
|
1.007
|
0.579
|
0.605
|
0.589
|
0.588
|
0.866
|
|
Jiangxi
|
0.513
|
0.537
|
0.536
|
0.539
|
0.579
|
0.482
|
0.430
|
0.419
|
0.411
|
0.445
|
0.406
|
0.482
|
|
Anhui
|
0.495
|
0.547
|
0.608
|
0.574
|
0.637
|
0.432
|
0.412
|
0.387
|
0.350
|
0.403
|
0.352
|
0.472
|
|
Average
|
0.680
|
0.662
|
0.691
|
0.679
|
0.701
|
0.601
|
0.579
|
0.456
|
0.454
|
0.452
|
0.426
|
0.580
|
Southwest
|
Sichuan
|
1.100
|
1.029
|
1.008
|
1.004
|
1.045
|
1.004
|
0.739
|
1.020
|
1.045
|
1.010
|
1.030
|
1.003
|
|
Chongqing
|
0.320
|
0.324
|
0.306
|
0.327
|
0.343
|
0.414
|
0.451
|
0.475
|
0.495
|
0.519
|
0.555
|
0.412
|
|
Guangxi
|
1.436
|
1.445
|
1.388
|
1.369
|
1.289
|
1.270
|
1.006
|
1.036
|
1.015
|
1.159
|
1.095
|
1.228
|
|
Yunnan
|
0.388
|
0.383
|
0.347
|
0.382
|
0.401
|
0.357
|
0.336
|
0.322
|
0.297
|
0.350
|
0.345
|
0.355
|
|
Guizhou
|
0.204
|
0.200
|
0.201
|
0.195
|
0.220
|
0.227
|
0.259
|
0.329
|
0.337
|
0.412
|
0.403
|
0.272
|
|
Average
|
0.690
|
0.676
|
0.650
|
0.656
|
0.659
|
0.655
|
0.558
|
0.636
|
0.638
|
0.690
|
0.686
|
0.654
|
Middle Yellow River
|
Henan
|
0.562
|
0.547
|
0.650
|
0.551
|
0.510
|
0.629
|
0.606
|
0.525
|
0.568
|
0.547
|
0.578
|
0.570
|
|
Shaanxi
|
0.458
|
0.429
|
0.452
|
0.481
|
0.498
|
0.438
|
0.416
|
0.413
|
0.392
|
0.461
|
0.438
|
0.443
|
|
Inner Mongolia
|
1.020
|
1.019
|
1.001
|
1.042
|
1.053
|
1.062
|
1.046
|
1.028
|
1.012
|
1.020
|
1.016
|
1.029
|
|
Shanxi
|
0.151
|
0.212
|
0.212
|
0.199
|
0.200
|
0.204
|
0.194
|
0.180
|
0.184
|
0.186
|
0.185
|
0.192
|
|
Average
|
0.548
|
0.552
|
0.579
|
0.568
|
0.565
|
0.583
|
0.565
|
0.537
|
0.539
|
0.553
|
0.554
|
0.559
|
Northwest
|
Qinghai
|
1.231
|
1.300
|
1.171
|
1.063
|
1.230
|
1.191
|
1.163
|
1.146
|
1.078
|
1.104
|
1.043
|
1.156
|
|
Ningxia
|
1.194
|
1.198
|
1.211
|
1.410
|
1.366
|
1.704
|
1.410
|
1.632
|
1.517
|
1.323
|
1.408
|
1.398
|
|
Gansu
|
0.253
|
0.256
|
0.271
|
0.253
|
0.285
|
0.305
|
0.288
|
0.290
|
0.281
|
0.245
|
0.248
|
0.271
|
|
Xinjiang
|
0.500
|
0.509
|
0.555
|
0.601
|
0.618
|
0.530
|
0.470
|
0.434
|
0.365
|
0.439
|
0.440
|
0.496
|
|
Average
|
0.794
|
0.816
|
0.802
|
0.832
|
0.875
|
0.933
|
0.833
|
0.875
|
0.810
|
0.778
|
0.785
|
0.830
|
Table 6
Dynamic analysis and decomposition of rural energy in China.
Region
|
Value
|
2009
|
2010
|
2011
|
2012
|
2013
|
2014
|
2015
|
2016
|
2017
|
2018
|
Northern Coastal
|
GML
|
0.961
|
1.029
|
1.093
|
1.104
|
1.182
|
1.089
|
0.952
|
1.030
|
0.953
|
1.059
|
GMLEC
|
1.010
|
0.968
|
0.971
|
0.989
|
0.975
|
0.996
|
0.971
|
1.001
|
0.971
|
1.007
|
GMLTC
|
0.951
|
1.066
|
1.131
|
1.116
|
1.210
|
1.093
|
0.983
|
1.028
|
0.981
|
1.051
|
Northeast
|
GML
|
1.005
|
1.052
|
1.221
|
1.166
|
1.064
|
1.023
|
1.014
|
0.996
|
1.037
|
1.081
|
GMLEC
|
0.964
|
0.987
|
1.004
|
1.027
|
1.028
|
0.972
|
0.992
|
0.871
|
0.861
|
1.021
|
GMLTC
|
1.042
|
1.067
|
1.216
|
1.136
|
1.142
|
1.054
|
1.023
|
1.157
|
1.163
|
1.059
|
Eastern Coastal
|
GML
|
0.983
|
1.126
|
1.195
|
1.035
|
1.099
|
1.041
|
1.014
|
1.059
|
1.052
|
1.016
|
GMLEC
|
0.996
|
1.009
|
0.996
|
0.996
|
0.948
|
0.934
|
0.990
|
1.003
|
1.055
|
0.953
|
GMLTC
|
0.987
|
1.116
|
1.200
|
1.039
|
1.165
|
1.122
|
1.025
|
1.056
|
1.003
|
1.073
|
Southern Coastal
|
GML
|
0.970
|
1.073
|
1.126
|
1.027
|
1.170
|
1.123
|
1.038
|
1.297
|
0.890
|
1.133
|
GMLEC
|
1.000
|
1.000
|
1.000
|
1.000
|
1.000
|
1.000
|
1.000
|
1.000
|
1.000
|
1.000
|
GMLTC
|
0.970
|
1.073
|
1.126
|
1.027
|
1.170
|
1.123
|
1.038
|
1.297
|
0.890
|
1.133
|
Middle Yangtze River
|
GML
|
0.948
|
1.142
|
1.206
|
1.099
|
1.005
|
1.024
|
1.034
|
1.112
|
0.920
|
1.014
|
GMLEC
|
0.988
|
1.053
|
0.972
|
1.045
|
0.839
|
0.958
|
0.857
|
0.988
|
1.009
|
0.937
|
GMLTC
|
0.967
|
1.086
|
1.240
|
1.054
|
1.210
|
1.070
|
1.270
|
1.127
|
0.916
|
1.084
|
Southwest
|
GML
|
0.958
|
0.971
|
1.243
|
1.111
|
1.144
|
0.995
|
1.129
|
1.225
|
1.017
|
1.145
|
GMLEC
|
0.996
|
0.970
|
1.029
|
1.044
|
1.026
|
0.982
|
1.127
|
0.998
|
1.090
|
1.007
|
GMLTC
|
0.962
|
1.003
|
1.211
|
1.068
|
1.121
|
1.028
|
1.012
|
1.227
|
0.935
|
1.138
|
Middle Yellow River
|
GML
|
0.983
|
1.164
|
1.245
|
1.139
|
1.253
|
1.010
|
0.915
|
1.035
|
0.947
|
1.082
|
GMLEC
|
1.078
|
1.061
|
0.962
|
0.991
|
1.034
|
0.965
|
0.947
|
1.013
|
1.037
|
1.000
|
GMLTC
|
0.926
|
1.098
|
1.291
|
1.150
|
1.223
|
1.048
|
0.973
|
1.022
|
0.913
|
1.084
|
Northwest
|
GML
|
0.926
|
0.962
|
0.994
|
1.128
|
1.115
|
1.012
|
1.014
|
0.971
|
1.008
|
1.067
|
GMLEC
|
1.008
|
1.037
|
1.005
|
1.038
|
0.982
|
0.958
|
0.983
|
0.952
|
1.019
|
1.003
|
GMLTC
|
0.919
|
0.920
|
0.993
|
1.091
|
1.138
|
1.059
|
1.032
|
1.023
|
0.993
|
1.064
|
Table 7
Dynamic decomposition results in eight comprehensive economic zones.
Region
|
GML<1
|
GMLEC≥1
|
GMLTC≥1
|
Main factors
|
Northern coastal
|
3
|
3
|
7
|
GMLTC
|
Northeast
|
1
|
4
|
10
|
GMLTC
|
Eastern coastal
|
1
|
3
|
9
|
GMLTC
|
Southern coastal
|
2
|
10
|
8
|
GMLEC GMLTC
|
Middle Yangtze River
|
2
|
3
|
8
|
GMLTC
|
Southwest
|
3
|
6
|
8
|
GMLEC GMLTC
|
Middle Yellow River
|
3
|
6
|
7
|
GMLEC GMLTC
|
Northwest
|
4
|
6
|
6
|
GMLTC
|