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Front. Environ. Sci. Eng.    0, Vol. Issue () : 506-521    https://doi.org/10.1007/s11783-014-0656-y
RESEARCH ARTICLE
Regional characteristics of industrial energy efficiency in China: application of stochastic frontier analysis method
Tao HUANG1,*(),Akio ONISHI2,Feng SHI3,Masafumi MORISUGI4,Mjo Lwin CHERRY3
1. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
2. Graduate School of Engineering, Toyama Prefectural University, Toyama 9390398, Japan
3. Graduate School of Environmental Studies, Nagoya University, Nagoya 4648601, Japan
4. Graduate School of Urban Science, Meijo University, Gifu 5090292, Japan
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Abstract

This paper analyzed regional industrial energy efficiency in China with Total-Factor Energy Efficiency (TFEE). The East region has the best energy efficiency and the Central and the West regions stand as the second and the third respectively. However, it is found that industrial energy efficiency of all regions increased from 1998 to 2006. This result is consistent with level of economic development of every region. The industries of all provinces in China are not yet at the frontier efficiency position, therefore, to the frontier as target, their technology levels and production processes should be adjusted accordingly. Compared with the conventional energy efficiency, the inverse of energy intensity, which is defined as the ratio of actual output to energy input, is regarded as Single-Factor Energy Efficiency (SFEE) index. Although TFEE ranks are not changed for each region, they are different for each province. The comparative result also shows that the substitution among inputs (labor, capital stock, and energy) to produce the output is significant. The SFEE scores could be over-estimated if energy is taken as the single input in the production. Finally, we identified determining factors affecting industrial energy efficiency using Tobit model. The results indicate that an increase of per capita Gross Domestic Product (GDP), the percentage of output value of industry invested by Hong Kong, Macao, Taiwan and abroad, energy price and investment of scientific and technological activities for industry could be possible contributors and drivers to the industrial energy efficiency. However, increasing of heavy industry will lead to worse industrial energy efficiency.
Keywords industrial energy efficiency      stochastic frontier analysis      total-factor energy efficiency      single factor energy efficiency     
Corresponding Author(s): Tao HUANG   
Online First Date: 21 February 2014    Issue Date: 30 April 2015
 Cite this article:   
Tao HUANG,Akio ONISHI,Feng SHI, et al. Regional characteristics of industrial energy efficiency in China: application of stochastic frontier analysis method[J]. Front. Environ. Sci. Eng., 0, (): 506-521.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-014-0656-y
https://academic.hep.com.cn/fese/EN/Y0/V/I/506
Fig.1  Energy consumption of each industry in China in 2006
Fig.2  Gross ouput value (a), energy consumtion (b), energy productivity (c) for industry and percentage of indicators affecting in industrial production (d) for different region in 2006

PHI: Percentage of heavy industry in each region

PEESTI: Percentage of employees engaged in scientific and technological activities of industry in the whole country

PESTI: Percentage of expenditures on scientific and technological institutions of industry in the whole country

PIA: Percentage of output value of industry invested by Hong Kong, Macao, Taiwan and abroad in the whole country

Fig.3  Production frontier in output, Y, and energy input, E, space
Fig.4  Administrative provinces and three major regions in China
variable SFA Tobit analysis
dependent variable gross output value of industry(100 million CNY) energy efficiency
independent variable per capita GDP (10000 CNY·capita-1)
percentage of heavy industry (%)
net value of fixed asset (100 million CNY) percentage of output value of industry invested by Hong Kong, Macao, Taiwan and abroad (%)
labour employment (10000 persons) energy price index
energy consumption (10000 tce) employees engaged in scientific and technological activities of industry (10000 people)
expenditures of scientific and technological institutions of industry (10000 CNY)
Tab.1  Data used in SFA and Tobit analysis
variable OLS SFA
coefficient coefficient standard error
constant 3.258(8.103)*** 2.56(8.866)*** 0.101
lnX1 0.236(3.964)*** 0.278(4.344)*** 0.096
lnX2 0.113(3.632)*** 0.149(3.534)*** 0.069
lnE 0.215(2.326)** 0.204(2.606)** 0.056
lnX1·lnX1 0.413(1.596)* 0.381(2.391)** 0.012
lnX2·lnX2 0.362(5.716)*** 0.411(5.813)*** 0.047
lnE·lnE 0.236(1.434)* 0.253(1.619)* 0.062
lnX1·lnX2 0.403(5.326)*** 0.429(4.895)*** 0.091
lnX1·lnE 0.526(2.418)** 0.536(1.640)* 0.016
lnX2·lnE 0.172(4.105)*** 0.166(3.306)*** 0.089
σ2 0.084(6.766)*** 0.012
γ 0.542(9.071)*** 0.059
h 0.056(3.126)*** 0.018
R2 0.939
log-likelihood ratio -44.716 -43.707
number of observations 270 270
Tab.2  Results of estimations of SFA
rank region 1998 1999 2000 2001 2002 2003 2004 2005 2006 mean
1 E Tianjin 0.842 0.850 0.857 0.864 0.871 0.878 0.884 0.890 0.895 0.870
2 E Fujian 0.823 0.832 0.840 0.848 0.856 0.863 0.870 0.876 0.883 0.855
3 E Zhejiang 0.740 0.753 0.764 0.776 0.786 0.797 0.807 0.816 0.825 0.785
4 E Beijing 0.702 0.716 0.729 0.742 0.754 0.766 0.777 0.788 0.798 0.752
5 E Hainan 0.618 0.635 0.651 0.666 0.681 0.696 0.709 0.723 0.736 0.679
6 E Guangdong 0.616 0.635 0.654 0.662 0.680 0.687 0.694 0.700 0.706 0.671
7 E Shanghai 0.592 0.609 0.626 0.642 0.658 0.673 0.688 0.702 0.715 0.656
8 E Liaoning 0.510 0.530 0.551 0.572 0.592 0.613 0.633 0.645 0.674 0.591
9 E Shandong 0.499 0.519 0.538 0.556 0.574 0.592 0.609 0.626 0.642 0.573
10 E Jiangsu 0.428 0.448 0.469 0.488 0.508 0.527 0.546 0.564 0.582 0.507
11 E Guangxi 0.490 0.509 0.528 0.547 0.565 0.583 0.601 0.618 0.634 0.564
12 E Hebei 0.385 0.405 0.426 0.446 0.466 0.486 0.506 0.525 0.544 0.465
1 C Anhui 0.511 0.531 0.549 0.568 0.585 0.603 0.620 0.636 0.652 0.584
2 C Jiangxi 0.522 0.538 0.554 0.570 0.584 0.599 0.613 0.626 0.639 0.583
3 C Jilin 0.509 0.528 0.547 0.565 0.583 0.600 0.617 0.634 0.650 0.581
4 C Hunan 0.505 0.524 0.543 0.562 0.580 0.597 0.614 0.631 0.647 0.578
5 C Heilongjiang 0.422 0.442 0.462 0.482 0.502 0.521 0.540 0.559 0.577 0.501
6 C Hubei 0.405 0.425 0.446 0.466 0.486 0.505 0.524 0.543 0.562 0.485
7 C Henan 0.387 0.408 0.428 0.448 0.469 0.488 0.508 0.527 0.546 0.468
8 C Inner Mongolia 0.360 0.381 0.401 0.422 0.442 0.463 0.483 0.502 0.522 0.442
9 C Shanxi 0.325 0.345 0.366 0.387 0.408 0.428 0.448 0.469 0.488 0.407
1 W Chongqing 0.522 0.541 0.560 0.578 0.595 0.612 0.629 0.645 0.661 0.594
2 W Yunnan 0.493 0.512 0.531 0.550 0.568 0.586 0.604 0.620 0.637 0.567
3 W Gansu 0.440 0.461 0.481 0.500 0.520 0.539 0.557 0.575 0.593 0.518
4 W Shaanxi 0.423 0.444 0.464 0.484 0.503 0.523 0.542 0.560 0.578 0.502
5 W Ningxia 0.414 0.434 0.455 0.475 0.494 0.514 0.533 0.552 0.570 0.493
6 W Guizhou 0.411 0.432 0.452 0.472 0.492 0.511 0.531 0.549 0.568 0.491
7 W Sichuan 0.408 0.428 0.449 0.469 0.489 0.508 0.527 0.546 0.565 0.488
8 W Xinjiang 0.362 0.383 0.404 0.424 0.445 0.465 0.485 0.504 0.523 0.444
9 W Qinghai 0.319 0.339 0.360 0.381 0.401 0.422 0.442 0.463 0.482 0.401
1 east 0.604 0.620 0.636 0.651 0.666 0.680 0.694 0.706 0.720 0.664
2 central 0.438 0.458 0.477 0.497 0.515 0.534 0.552 0.570 0.587 0.514
3 west 0.409 0.429 0.449 0.469 0.489 0.508 0.528 0.546 0.564 0.488
total 0.499 0.518 0.536 0.554 0.571 0.588 0.605 0.620 0.636 0.570
Tab.3  Technical Efficiency of industry by SFA
rank region 1998 1999 2000 2001 2002 2003 2004 2005 2006 mean
1 E Tianjin 0.794 0.794 0.803 0.807 0.816 0.823 0.828 0.830 0.834 0.814
2 E Fujian 0.774 0.783 0.788 0.791 0.799 0.807 0.811 0.825 0.827 0.800
3 E Beijing 0.709 0.717 0.726 0.747 0.755 0.766 0.771 0.759 0.760 0.745
4 E Guangdong 0.699 0.711 0.728 0.736 0.759 0.762 0.754 0.765 0.773 0.743
5 E Zhejiang 0.715 0.720 0.729 0.732 0.740 0.743 0.743 0.748 0.747 0.735
6 E Shanghai 0.666 0.699 0.692 0.702 0.715 0.733 0.746 0.749 0.761 0.718
7 E Hainan 0.649 0.671 0.682 0.696 0.718 0.736 0.760 0.756 0.761 0.714
8 E Jiangsu 0.624 0.635 0.658 0.661 0.680 0.701 0.732 0.731 0.733 0.684
9 E Guangxi 0.587 0.604 0.612 0.629 0.643 0.670 0.687 0.694 0.703 0.648
10 E Liaoning 0.585 0.591 0.616 0.626 0.632 0.642 0.663 0.672 0.680 0.634
11 E Hebei 0.509 0.521 0.542 0.558 0.579 0.604 0.621 0.637 0.647 0.580
12 E Shandong 0.516 0.523 0.536 0.561 0.568 0.587 0.591 0.619 0.621 0.569
1 C Jiangxi 0.662 0.669 0.680 0.699 0.712 0.725 0.740 0.741 0.748 0.708
2 C Anhui 0.630 0.638 0.656 0.660 0.663 0.674 0.677 0.681 0.693 0.663
3 C Heilongjiang 0.509 0.529 0.524 0.533 0.552 0.577 0.598 0.612 0.627 0.562
4 C Hunan 0.599 0.589 0.598 0.626 0.644 0.655 0.673 0.701 0.706 0.643
5 C Jilin 0.577 0.584 0.600 0.617 0.627 0.652 0.670 0.688 0.698 0.635
6 C Inner Mongolia 0.536 0.556 0.570 0.582 0.598 0.608 0.618 0.617 0.624 0.590
7 C Hubei 0.492 0.506 0.533 0.536 0.557 0.548 0.580 0.586 0.598 0.549
8 C Henan 0.467 0.486 0.499 0.512 0.531 0.547 0.565 0.593 0.623 0.536
9 C Shanxi 0.454 0.473 0.496 0.503 0.526 0.548 0.559 0.574 0.585 0.524
1 W Chongqing 0.622 0.656 0.670 0.670 0.688 0.683 0.690 0.706 0.711 0.677
2 W Yunnan 0.590 0.593 0.602 0.621 0.643 0.665 0.691 0.704 0.708 0.646
3 W Guizhou 0.566 0.578 0.583 0.596 0.614 0.639 0.664 0.658 0.672 0.619
4 W Xinjiang 0.472 0.489 0.513 0.526 0.539 0.568 0.581 0.614 0.629 0.548
5 W Sichuan 0.499 0.508 0.513 0.521 0.538 0.568 0.581 0.581 0.595 0.545
6 W Gansu 0.485 0.490 0.516 0.527 0.537 0.549 0.578 0.601 0.613 0.544
7 W Shaanxi 0.509 0.482 0.503 0.530 0.555 0.557 0.567 0.600 0.596 0.544
8 W Ningxia 0.470 0.486 0.502 0.525 0.542 0.556 0.570 0.541 0.593 0.532
9 W Qinghai 0.440 0.473 0.485 0.480 0.507 0.540 0.559 0.567 0.595 0.516
1 east 0.652 0.664 0.676 0.687 0.700 0.715 0.726 0.732 0.737 0.699
2 central 0.547 0.559 0.573 0.585 0.601 0.615 0.631 0.644 0.656 0.601
3 west 0.517 0.528 0.543 0.555 0.574 0.592 0.609 0.619 0.635 0.575
total 0.580 0.592 0.605 0.617 0.633 0.648 0.662 0.672 0.682 0.632
Tab.4  Total factor energy efficiency of industry by SFA
Fig.5  Total factor energy efficiency of industry (a) and the gap between regions (b) in China
rank region 1998 1999 2000 2001 2002 2003 2004 2005 2006 Mean
1 E Zhejiang 9.608233 11.70069 12.20359 12.44768 12.36185 13.21273 12.01047 11.43448 12.06046 11.89335367
2 E Fujian 10.33863 10.78492 11.34738 12.66854 12.1001 12.26971 12.77375 12.746337 11.47625 11.83395744
3 E Guangdong 8.858568 9.406162 10.60308 11.07579 11.65386 10.91483 12.58147 12.30009 12.24416 11.07089
4 E Shanghai 6.535525 5.546228 7.107947 7.968528 8.959637 10.03819 11.08585 10.7893 10.80059 8.759088333
5 E Jiangsu 6.541688 7.392628 8.007527 9.112053 9.805904 10.1486 8.922549 8.518351 8.940406 8.598856222
6 E Shandong 5.866981 6.793318 7.496634 7.153854 8.131926 8.071871 8.246278 6.816168 7.240714 7.313082667
7 E Tianjin 4.685262 5.774593 5.640284 6.020863 6.056051 6.693381 7.613979 8.336718 8.638355 6.606609556
8 E Beijing 3.069998 3.544721 3.981127 3.649236 3.990662 4.374789 5.335323 5.783907 6.425046 4.461645444
9 E Hainan 4.010886 4.127595 4.254919 4.380468 4.466092 4.225297 4.330793 4.331046 4.252884 4.264442222
10 E Guangxi 3.639113 3.735861 3.950016 4.117489 4.702803 4.128344 4.121931 4.233536 4.624729 4.139313556
11 E Liaoning 2.935793 3.393278 3.32638 3.71712 3.826694 4.036752 4.449076 4.618152 4.666211 3.885495111
12 E Hebei 3.766789 4.033851 4.254032 4.068379 3.966521 3.75429 3.812271 3.38667 3.52091 3.840412556
1 C Heilongjiang 4.735073 4.876973 5.555948 6.58561 7.000642 7.789393 8.238169 8.68376 8.410904 6.875163556
2 C Hubei 4.241197 4.424454 4.968306 5.846822 6.072557 6.130393 6.291539 6.459776 6.710577 5.682846778
3 C Henan 4.948393 5.233033 5.473281 5.632254 5.976346 6.279567 5.689842 5.264374 5.270224 5.529701556
4 C Hunan 3.940991 5.447012 6.255036 5.666209 5.735968 6.226663 5.865452 4.340895 4.605242 5.342607556
5 C Jiangxi 3.911224 4.20021 4.682049 4.591501 5.062789 5.309221 4.883165 5.488496 5.462782 4.843493
6 C Anhui 3.562085 3.622517 3.780409 3.946193 4.221732 4.410096 5.18196 5.703953 5.716031 4.460552889
7 C Jilin 2.603533 3.110248 3.296989 3.701708 3.9694 3.838402 3.884588 3.671168 3.66544 3.526830667
8 C Inner Mongolia 2.176832 2.125325 2.2108 2.247668 2.316357 2.389511 2.421186 3.032633 3.422664 2.482552889
9 C Shanxi 1.950403 2.164018 2.243054 1.932418 1.822197 1.877531 1.937685 2.111537 2.235718 2.030506778
1 W Sichuan 3.580775 4.236856 4.940036 5.520495 5.534154 5.110595 5.478289 6.551784 6.947 5.322220444
2 W Yunnan 4.450309 5.266315 5.639332 5.388132 4.980805 4.615035 4.065102 3.821009 4.239887 4.718436222
3 W Chongqing 2.462511 2.343646 2.364861 3.421831 3.446393 5.644857 6.316161 5.813584 6.874333 4.298686333
4 W Shaanxi 2.961 4.047708 4.43262 3.91387 3.863704 5.02299 4.46085 4.343645 5.090786 4.237463667
5 W Gansu 2.371055 2.496779 2.748299 3.087456 3.387468 3.763568 3.060757 3.011378 3.161784 3.009838222
6 W Xinjiang 2.46338 2.538768 2.770548 2.869463 3.039228 2.826531 2.82705 2.207971 2.219144 2.640231444
7 W Qinghai 2.108345 1.635633 1.976106 2.614372 2.843171 2.77849 2.727177 2.882949 2.442531 2.445419333
8 W Guizhou 2.023333 2.060836 2.311212 2.33793 2.42217 2.193748 2.014475 2.143512 2.138357 2.182841444
9 W Ningxia 1.933155 1.85723 1.642596 1.437557 1.340251 1.300433 1.400474 1.500905 1.604377 1.557442
1 east 5.821 6.353 6.848 7.198 7.502 7.656 7.940 7.775 7.908 7.222
2 central 3.563 3.912 4.274 4.461 4.686 4.917 4.933 4.973 5.056 4.530
3 west 2.705984778 2.942641222 3.202845556 3.399011778 3.428593778 3.695138556 3.594481667 3.586304111 3.857577667 3.379175457
total 4.209368667 4.5973802 4.9821466 5.237382967 5.435247733 5.645860267 5.734255367 5.6776028 5.836949867 5.261799385
Tab.5  SFEE – the inverse of energy intensity
variables coefficient standard error
constant 0.7579(24.3359)*** 0.0311
per capita GDP 0.0536(6.5579)*** 0.0082
percentage of heavy industry -0.2498(-6.6154)*** 0.0378
percentage of Industrial Funds from Hong Kong, Macao, Taiwan and abroad 0.0496(2.1264)** 0.0233
energy price 0.0013(1.0382)* 0.0001
employees engaged in scientific and technological activities of industry 0.0138(7.4857)*** 0.0018
expenditures of scientific and technological institutions of industry 0.0035(2.2323)** 0.0002
R2 0.4723
log-likelihood 332.4028
number of observations 270
Tab.6  Results of factor analysis for industrial energy efficiency
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