Influencing Factors Analysis on Provincial Difference of Rural Energy Efficiency in China Employing Super Efficiency SBM Model and Global Malmquist-Luenberger Index

DOI: https://doi.org/10.21203/rs.3.rs-610581/v1

Abstract

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.

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Tables

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

10tons

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

image

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

10 

GMLEC  GMLTC

Middle Yangtze River

GMLTC

Southwest

GMLEC  GMLTC

Middle Yellow River

3

6

7

GMLEC  GMLTC

Northwest

4

6

6

GMLTC