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4.2. Measure results
By applying the models proposed in this study, we obtain measures
of eco-efficiency and its three sub-efficiencies
1
in 30 Chinese provinces
from 2001 to 2014. On the one hand, for more convenient comparison,
we calculate the annual average scores of these four efficiencies, which
can stand for the average levels of relevant efficiencies for each pro-
vince during the observation period, as reported in Table 2. On the
other hand, we rank these observed provinces, based on their annual
average scores of the four efficiencies respectively, and depict those
annual provincial efficiency scores in Fig. 2. Additionally, we obtain the
annual change rates of these four efficiencies calculated as geometrical
mean for each province during the study periods.
Firstly, China’s regional economic efficiency is shown to be higher
than energy and environmental efficiencies, in addition to eco-
efficiency in general. During 2001–2014, the annual economic effi-
ciency value of China is about 0.85, while the other three efficiency
values are less than 0.70, especially eco-efficiency lower than 0.55.
Under a total-factor framework, Wang and Feng (2015) also found that
China performed well in terms of economic e fficiency but poorly in
terms of energy and environmental efficiencies from 2002 to 2011. The
results reflect the severity of the ecological problem that China is cur-
rently facing, i.e., the rapid economic growth of China in these years
entails costs of high energy consumption and environmental destruc-
tion. Secondly, from Table 2 and Fig. 2, we can find that there is sig-
nificant heterogeneity among the eco-efficiency and its three sub effi-
ciencies of different provinces. For example, both Hebei and Shanxi
have high economic efficiency, while their energy and environmental
efficiencies are, relatively, far lower than the national average level. As
four efficiencies with diverse connotations, in addition to 30 provinces,
are involved, we conduct a comprehensive analysis in Section 4.3,
where development modes of China’s provinces are recognized based
on the performance of the composite eco-efficiency indicators. Thirdly,
Table 2 shows that the average annual change rate of 30 Chinese pro-
vinces’ eco-efficiency over 2001
–2014
is about −0.5%, meaning that
Table 1
Descriptive statistics, 2001–2014.
Variable Data Unit Mean Max Min Std.dev.
Inputs Capital Capital stock estimated by the perpetual inventory method Billion (CNY) 25991.530 140055.000 1085.710 24091.260
Labor Total number of employees Ten thousand persons 2451.109 6606.500 279.000 1637.957
Energy Total energy consumption Ten thousand tec 10644.460 39423.000 520.000 7527.542
Land The construction land area Square kilometers 1222.670 5398.000 97.890 918.816
Water Total water consumption Billion cubic meters 193.258 591.300 20.500 136.141
Desirable output GDP Real gross domestic product at 2000 constant prices Billion (CNY) 8549.583 49659.700 294.562 8224.670
Undesirable output PI Pollution index derived from six pollutants Normalized index, no unit 23.810 72.062 1.358 13.961
Table 2
Annual average scores of the eco-efficiency indicators, provincial rankings, and the annual change rates of corresponding indicators over 2001–2014.
Province Eco-efficiency Economic efficiency Energy efficiency Environmental efficiency
Score Ranking Change(%)
2001–2014
Score Ranking Change(%)
2001–2014
Score Ranking Change(%)
2001–2014
Score Ranking Change(%)
2001–2014
Beijing 0.811 5 −0.037 0.931 10 −0.021 0.936 5 −0.030 0.867 5 −0.029
Tianjin 1.011 1 −0.004 1.032 1 −0.014 1.008 1 −0.007 1 1 0.000
Hebei 0.393 22 −0.015 0.908 13 −0.013 0.325 28 −0.010 0.578 24 −0.007
Shanxi 0.397 20 0.011 0.927 11 0.000 0.274 30 −0.023 0.532 30 0.000
Inner Mongolia 0.384 24 0.017 0.969 6 0.001 0.334 27 0.022 0.557 27 0.008
Liaoning 0.531 11 −0.018 0.932 9 −0.005 0.457 19 −0.028 0.624 13 −0.008
Jilin 0.433 15 −0.001 0.72 26 0.010 0.532 15 −0.015 0.608 18 0.000
Heilongjiang 0.484 13 −0.017 0.855 16 −0.014 0.575 13 −0.035 0.656 11 −0.005
Shanghai 0.744 7 −0.052 0.988 4 −0.012 0.763 7 −0.035 0.779 7 −0.038
Jiangsu 0.566 9 −0.065 0.904 14 −0.010 0.681 9 −0.029 0.713 8 −0.039
Zhejiang 0.554 10 −
0.036 0.844 17 −0.008
0.674 10 −0.016 0.695 10 −0.025
Anhui 0.402 19 −0.007 0.751 23 −0.001 0.577 12 −0.018 0.613 15 −0.005
Fujian 0.757 6 0.038 0.983 5 0.011 0.86 6 0.009 0.797 6 0.019
Jiangxi 0.425 16 0.017 0.785 22 0.020 0.711 8 0.022 0.609 17 0.002
Shandong 0.587 8 −0.062 0.941 8 −0.010 0.539 14 −0.031 0.697 9 −0.040
Henan 0.389 23 −0.016 0.834 18 0.008 0.452 21 −0.011 0.592 22 −0.016
Hubei 0.356 28 −0.021 0.678 29 −0.011 0.453 20 −0.019 0.604 19 −0.013
Hunan 0.419 17 −0.004 0.894 15 0.012 0.531 16 0.014 0.609 16 −0.012
Guangdong 0.997 2 −0.003 1.017 2 −0.008 0.995 2 0.000 0.992 2 0.000
Guangxi 0.358 26 0.006 0.834 19 0.035 0.612 11 0.018 0.567 25 −0.008
Hainan 0.912 4 0.020 0.953 7 0.010 0.973 3 0.014 0.921 4 0.017
Chongqing 0.446 14 −0.006 0.789 21 0.018 0.519 17 −0.009 0.594 21 −0.012
Sichuan 0.405 18 −0.022 0.808 20 0.012 0.443 23 −0.013 0.619 14 −0.019
Guizhou 0.318 29 0.002 0.743 24 0.043 0.298 29 −0.009 0.538 29 −0.003
Yunnan 0.356 27 0.005 0.691 27 0.021 0.452 22 0.006 0.586 23 −0.002
Shaanxi 0.394 21 −0.004 0.73 25 −0.001 0.496 18 −0.005 0.562 26 −0.002
Gansu 0.372 25 0.011 0.68 28 0.031 0.426 25 0.018 0.597 20 0.006
Qinghai 0.957 3 0.004 1.004 3 0.000 0.938 4 0.007 0.935 3 0.000
Ningxia 0.488 12 0.082 0.913 12 0.000 0.43 24 0.104 0.624 12 0.046
Xinjiang 0.288 30 0.020 0.558 30 −0.006 0.346 26 0.045 0.553 28 0.003
Average 0.531 −0.005 0.853 0.003 0.587 −0.002 0.674 −0.006
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We also measure the confidence intervals (iterations = 1000, α = 0.05) of eco-effi-
ciency and its three sub efficiencies by bootstrapping methods illustrated in Toma et al.
(2017), more details can be found in Appendix B. We are appreciated for the constructive
comment of the anonymous reviewer.
J. Huang et al.
Ecological Indicators 85 (2018) 674–697
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