DOI: https://doi.org/10.21203/rs.3.rs-610581/v1
Given the current circumstance of increasingly severe resource and environmental deterioration, the progress of Chinese rural energy efficiency has a remarkable impression on Chinese future high-quality development. Energy consumption in rural areas accounts for a considerable proportion, so it is imperative to make a specific and accurate assessment of rural energy efficiency. This paper abandons the traditional method of regional division and separates China into eight economic zones. First of all, this paper applies Super-SBM model to calculate the rural energy efficiency and constructs a Global Malmquist-Luenberger (GML) Index based on 2008–2018 panel data. Subsequently, GML is decomposed into technical efficiency change index (GMLEC) and technological progress change index (GMLTC) to analyze green total-factor productivity (GTFP) in rural areas. Eventually, the GML and its decomposition terms of eight economic zones are explained by practicing a cumulative multiplication method from 2008 to 2018. The consequences of employing panel data of region prove that: (1) There is a severe regional imbalance of Chinese rural energy efficiency. (2) The rural energy efficiency in the northwest and southwest (western region) is higher than the Middle Yangtze River and the Middle Yellow River (central region). (3) GMLTC has a significant impact on GTFP.
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 |
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 |
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 |