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Tianjin University
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Dynamic environmental efficiency analysis on China’s generation sector: Based on a Game Cross-Malmquist index approach
Bai-Chen Xie
College of Management and Economics, Tianjin University
June 19, 2019,
the 40th IAEE, Singapore
Tianjin University
Introduction
• Unbundling reform of 2002 aimed to introduce competition
into power market.
• Implement the supply side reform and began to Cut
excessive industrial capacity
• Not all the power generation groups perform as expectedunder the competitive market.
June 23, 2017 2
Xie B.-C., Fan Y., Qu Q.-Q., 2012, Does generation form influence environmental efficiency performance? An
analysis of China's power system. Applied Energy, 96: 261-271.
Tianjin University
Introduction
• China has overtaken the US to be the largest CO2 emissionscontributor in the world.
• There is growing concern over the mitigation of climatechange and power industry’s environmental performance.
• China committed to capping its GHG emissions around2030 or early and increasing the share of non-fossil fuel useto around 20% by 2030.
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“US-China joint announcement on climate change” published by White House in November 2014.
Tianjin University
Introduction
• Power industry has the biggest share of carbon dioxide (CO2)emissions.
• The electric industry is the key industries in implementingcarbon emission quota control mechanism.
• China’s power generation and consumption have leapt to thefirst in the world since 2010.
• Many researches are focusing on the performance of powerindustry .
June 23, 2017 4
Du, L.-M., He, Y.-N., Yan, J.-Y., 2013. The effects of electricity reforms on productivity and efficiency of China's
fossil-fired power plants: An empirical analysis. Energy Economics 40, 804-812.
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Introduction• Research on environmental efficiency of the OECD
countries.
• (Sueyoshi and Goto, 2013 ; Ewertowska et al.; 2016)
• Research on different regions of a country including China.
• (Bian et al., 2013 ; Zhou et al., 2013; Lin and Yang, 2014)
• performance studies target on the plant or power firms
• (Korhonen and Luptacik, 2004; Sueyoshi and Goto, 2011)
• Methods include CCR, BCC, SBM-DEA, DDF
June 23, 2017 5
Tianjin University
Introduction• Many researches are about the dynamic performance of power
industry.
• Dynamic changes in CO2 emission performance of fossil fuelpower plants in China and Korea (Zhang and Choi 2013)
• Emission performance of the world's 18 top CO2 emitters from1997 to 2004 (Zhou et al. 2010, Xie et al. 2012)
• Dynamic environmental efficiency of power industry (Xie et al.2014 ,Lin and Du, 2015)
• The influence of the competitiveness and dynamic environmentalefficiency changes of generation groups
• Regression on driving factors.
June 23, 2017 6
Tianjin University
Methodology• Malmquist index first proposed by Malmquist (1953), and
then developed by Caves et al. (1982) , Färe et al. (1994)
• The results are always deviant due to its subjectivity inselecting variable and inaccurateness.
• Examples, Zhou et al. (2010), Zhang and Choi (2013a), Arabi et al.(2014) and Munisamy and Arabi (2015)
• There are too many DMUs lying on the frontier.
June 23, 2017 7
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Methodology• Liang et al. (2008), game cross-efficiency model;
• Integrate cross-efficiency, Malmquist with game theory to
evaluate the environmental efficiency of generation groups;
• Incorporate undesirable outputs into model and apply the
conversion function, where represents the set of undesirable
outputs.
June 23, 2017 8
Tianjin University
Methodology
1
1 1
1
1 1
. . 0, 1,2, , ,
1,
0,
0, 1,2, , ,
0, 1,2, , .
sd
rj rj
r
m sd d
ij il rj rl
i r
md
ij ij
i
m sd d
d ij id rj rd
i r
d
ij
d
rj
Max y
s t x y l n
x
x y
i m
r s
1
1
, 1,2, , ,
s d
rj rjr
dj m d
ij iji
yd n
x
*
1 1
1 n sd
j rj d rj
d r
E yn
(1)(2)
(3)
June 23, 2017 9
The efficiency is of the productivity of DMU k in period t compared to the frontier
of t+1.
The game-d cross efficiency can be defined as:
Tianjin University
Methodology
1
1 1
1 1
1
1
1 1
. . 0, 1,2, , ,
1,
0,
0, 1,2, , ,
0, 1,2, , .
il rl
sd
rj rj
r
m sd t d t
ij rj
i r
md
ij ij
i
m st d d
d ij id rj rd
i r
d
ij
d
rj
Max y
s t x y l n
x
x y
i m
r s
(4)
June 23, 2017 10
Tianjin University
Methodology• Steps
1. We get and from equation (1).
2. We adopt the above equation (4) to get and in similar way.
3. Finally, we use , , and
to get the Game-cross Malmquist index(GMI), efficiency change (EC) and frontier shift( FS).
𝐸𝑘𝑡 𝑥𝑘
𝑡 , 𝑦𝑘𝑡 𝐸𝑘
𝑡+1 𝑥𝑘𝑡+1, 𝑦𝑘
𝑡+1
𝐸𝑘𝑡+1 𝑥𝑘
𝑡 , 𝑦𝑘𝑡
𝐸𝑘𝑡 𝑥𝑘
𝑡+1, 𝑦𝑘𝑡+1
𝐸𝑘𝑡 𝑥𝑘
𝑡 , 𝑦𝑘𝑡 𝐸𝑘
𝑡+1 𝑥𝑘𝑡+1, 𝑦𝑘
𝑡+1 𝐸𝑘𝑡+1 𝑥𝑘
𝑡 , 𝑦𝑘𝑡
𝐸𝑘𝑡 𝑥𝑘
𝑡+1, 𝑦𝑘𝑡+1
June 23, 2017 11
Tianjin University
Methodology
1 1 1,
,
t t t
k k k
k t t t
k k k
E x yEC
E x y
1
1 1 2
1 1 1 1
, ,
, ,
t t t t t t
k k k k k k
k t t t t t t
k k k k k k
E x y E x yFS
E x y E x y
1
1 1 1 1 1 2
1
, ,
, ,
t t t t t t
k k k k k k
k t t t t t t
k k k k k k
E x y E x yGMI
E x y E x y
k k kGMI EC FS
June 23, 2017 12
The efficiency bigger than unity means improvement; less than unity stands for decrease，and the unity means constant.
Tianjin University
Empirical study
Ave Min 1/4Quantile Median 3/4Quantile Max
Energy (Mtce) 63.68 0.80 4.83 16.43 133.72 265.39
Labor 11072.10 38.00 1599.50 4066.50 17825.00 49894.00
Installed Capacity(MW)
3660.35 118.00 368.50 812.50 7094.25 16063.00
Electricity(TWh) 155.24 5.800 14.33 38.25 303.38 639.70
CO2(Mt) 126.08 1.58 9.58 32.54 26.48 52.54
Table 1 Statistics of input and output index
June 23, 2017 13
• 16 listed generation corporations in Shanghai and Shenzhen stock market
Tianjin University
Empirical study
Fig. 1 The average GMI, EC and FS
0.920.930.940.950.960.970.980.99
11.011.021.03
GMI EC FS
June 23, 2017 14
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Empirical study
June 23, 2017 15
Fig. 2 Wind power curtailment after 2011
Tianjin University
Empirical study
N2007/
2008
2008/
2009
2009/
2010
2010/
2011
2011/
2012
2012/
2013
2013/
2014
2014/
2015Averge
HNG 1.0012 0.9970 1.0712 1.0022 1.0285 1.0518 0.9520 0.8945 0.9998
DTG 0.9837 0.9773 1.0366 1.0213 1.0577 1.0081 0.9895 0.9144 0.9986
HDC 1.0200 1.0229 0.9404 1.0502 1.0826 1.0008 0.9942 0.9046 1.0020
GDC 1.0271 1.0558 0.9662 1.0068 1.0254 1.0878 0.9510 0.8938 1.0017
SPIC 1.0589 0.9976 0.9390 1.0716 1.0620 1.0053 0.9909 0.8970 1.0028
GDYDG 1.0473 0.9865 0.9946 1.0456 0.9856 1.0139 0.9644 0.8926 0.9913
SDIC 1.0151 0.9656 1.0285 1.0229 0.9315 1.0735 1.0000 1.0000 1.0046
HBCIG 0.9931 0.9951 0.9879 1.0222 1.0002 0.9620 0.9834 0.9554 0.9874
SZE 1.0020 1.0005 1.0000 0.9886 1.0098 0.9541 0.9691 0.8423 0.9708
BJEIG 0.7783 1.2557 0.8944 1.0992 1.0748 1.0391 0.9687 1.0004 1.0138
AHEG 0.9893 0.9060 0.9753 0.9683 1.1746 1.0344 0.9736 0.9440 0.9957
GZDG 1.0635 0.9996 0.9414 0.9948 1.0160 0.9683 0.9849 0.9140 0.9853
JSIG 1.1498 1.0160 1.0407 0.9558 1.0287 1.0717 1.0329 0.9959 1.0364
HBEG 1.1844 0.8560 0.9804 1.0983 1.0979 0.8353 1.0772 0.9716 1.0126
SXIEG 1.0284 1.0028 1.0071 0.8515 1.0266 0.9492 0.7939 0.9469 0.9508
GSEIG 0.8170 1.0109 1.0918 0.9931 1.1149 0.8837 0.9234 0.8620 0.9621
1.0099 1.0028 0.9935 1.0120 1.0448 0.9962 0.9718 0.9268 0.9947
Table 2 Changes in GMI of different corporation from 2007/2008 to 2014/2015
June 23, 2017 16
Tianjin University
Empirical study
N2007/
2008
2008/
2009
2009/
2010
2010/
2011
2011/
2012
2012/
2013
2013/
2014
2014/
2015Averge
HNG 1.0000 1.0000 1.0003 0.9999 1.0001 0.9999 1.0000 1.0000 1.0000
DTG 1.0000 1.0000 1.0000 0.9999 1.0001 0.9999 1.0000 1.0000 1.0000
HDC 1.0000 1.0000 1.0000 0.9999 1.0001 0.9999 1.0000 1.0000 1.0000
GDC 1.0000 1.0000 1.0000 1.0000 1.0001 0.9999 1.0000 1.0000 1.0000
SPIC 1.0000 1.0000 1.0000 0.9999 1.0001 0.9999 1.0000 1.0000 1.0000
GDYDG 1.0000 1.0000 0.9999 1.0000 1.0001 0.9999 1.0000 1.0000 1.0000
SDIC 1.0000 0.9999 1.0002 0.9999 1.0001 0.9999 1.0000 1.0000 1.0000
HBCIG 1.0000 1.0000 1.0000 0.9999 1.0001 0.9999 1.0000 0.9999 1.0000
SZE 1.0000 1.0000 1.0000 0.9999 1.0001 0.9999 0.9999 1.0000 1.0000
BJEIG 1.0000 1.0000 1.0000 1.0000 1.0002 0.9999 1.0000 1.0000 1.0000
AHEG 1.0000 1.0000 1.0000 0.9995 1.0005 0.9997 1.0000 0.9999 1.0000
GZDG 1.0000 1.0000 1.0000 0.9997 1.0003 0.9997 0.9998 0.9999 0.9999
JSIG 1.0000 1.0000 1.0003 0.9997 1.0001 0.9999 1.0000 1.0000 1.0000
HBEG 1.0000 0.9994 1.0008 0.9995 1.0004 0.9997 0.9999 1.0000 1.0000
SXIEG 1.0000 1.0000 0.9999 0.9992 1.0008 0.9994 0.9999 0.9999 0.9999
GSEIG 1.0000 0.9998 1.0003 0.9996 1.0002 0.9999 0.9999 1.0000 1.0000
1.0000 0.9999 1.0001 0.9998 1.0002 0.9998 1.0000 1.0000
Table 3 Technological progress of different corporations from 2007/2008 to 2014/2015
June 23, 2017 17
Tianjin University
Empirical study
N2007/
2008
2008/
2009
2009/
2010
2010/
2011
2011/
2012
2012/
2013
2013/
2014
2014/
2015Averge
HNG 1.0012 0.9970 1.0709 1.0022 1.0283 1.0519 0.9521 0.8945 0.9998
DTG 0.9837 0.9773 1.0366 1.0214 1.0576 1.0082 0.9896 0.9144 0.9986
HDC 1.0200 1.0229 0.9404 1.0504 1.0825 1.0009 0.9942 0.9046 1.0020
GDC 1.0271 1.0558 0.9662 1.0069 1.0253 1.0879 0.9510 0.8938 1.0018
SPIC 1.0589 0.9976 0.9390 1.0717 1.0619 1.0053 0.9910 0.8970 1.0028
GDYDG 1.0473 0.9865 0.9946 1.0456 0.9855 1.0140 0.9644 0.8926 0.9913
SDIC 1.0151 0.9658 1.0283 1.0230 0.9314 1.0736 1.0000 1.0000 1.0047
HBCIG 0.9931 0.9951 0.9878 1.0223 1.0002 0.9621 0.9834 0.9555 0.9874
SZE 1.0020 1.0005 1.0000 0.9887 1.0097 0.9542 0.9691 0.8424 0.9708
BJEIG 0.7783 1.2557 0.8944 1.0992 1.0746 1.0393 0.9687 1.0005 1.0138
AHEG 0.9893 0.9060 0.9753 0.9688 1.1740 1.0348 0.9737 0.9441 0.9958
GZDG 1.0635 0.9996 0.9414 0.9951 1.0156 0.9686 0.9851 0.9141 0.9854
JSIG 1.1498 1.0160 1.0406 0.9561 1.0286 1.0718 1.0329 0.9959 1.0365
HBEG 1.1843 0.8565 0.9796 1.0988 1.0974 0.8356 1.0773 0.9716 1.0126
SXIEG 1.0283 1.0028 1.0072 0.8522 1.0258 0.9497 0.7940 0.9470 0.9509
GSEIG 0.8169 1.0111 1.0915 0.9935 1.1147 0.8837 0.9235 0.8621 0.9621
1.0099 1.0029 0.9934 1.0122 1.0446 0.9964 0.9719 0.9269
Table 4 Efficiency Changes of corporations from 2007/2008 to 2014/2015
June 23, 2017 18
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Empirical study
0.999
0.9992
0.9994
0.9996
0.9998
1
1.0002
average min first quartile median third quartile max
large enterprises small enterprises
Fig 3 Statistics of GMI for large and small enterprises
June 23, 2017 19
Tianjin University
Empirical study
June 23, 2017 20
0.85
0.90
0.95
1.00
1.05
1.10
Generation groups Groups with CHP
Fig 4 GMI of corporations with and without Combined heat and power system
Tianjin University
Empirical study
Cross enterprises regression analysis
• Economic growth
• Ownership
• The growth rate of installed capacity
• Whether it is pilot carbon trade permit area
• GMI(-1)
June 23, 2017 21
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Empirical study
Variable Coefficient Std. Error Prob.
GMI(-1) -0.1729 0.0907 0.0593
Economic growth rate 0.0105 0.0009 0.0000
Carbon trade pilot -0.0159 0.0146 0.2774
Ownership 0.0338 0.0136 0.0146
Capacity growth rate 0.0110 0.0337 0.7440
Table 5 Regression results
June 23, 2017 22
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Conclusions• The environmental Malmquist index fluctuate around 1 over
the period and showed a slightly decline in the later period.
• The changes of GMI is mainly driven by the changes of EC.
• A large room for power enterprises to enhance their GMI,especially.
• Economic development and ownership affect thecompetitiveness of enterprises.
June 23, 2017 23
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Suggestions
• Encourage the technical innovation and application.
• Adjust the energy structure and install new generatorcapacity according to the supply and demand of electricpower
• We should conduct market oriented carbon emissiontrading mechanism to encourage the power enterprises toreduce carbon dioxide emissions.
June 23, 2017 24
