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October 27, Introduction Development & energy consumption of China’s thermal power industry Data source: China Electric Power Yearbook; China Energy Statistical Yearbook Installed CapacityGeneration Consumption
Citation preview
The Impact of Coal-Electricity Linkage on The Cost Efficiency of China’s Thermal Power Plants
Na Duan, Bai-chen XieTianjin University
1
October 27, 2015 2
Outline • Introduction • Literature Review
• Methodology
• Empirical Analysis
• Conclusions 2
October 27, 2015 3
Introduction• Development & energy consumption of China’s thermal power industry
2003200420052006200720082009201020110
200
400
600
800
1000
1200
50%
60%
70%
80%
installed capacity of thermal power industry
total installed capacity(GW)
capacity proportion
Inst
alle
d ca
paci
ty/G
W
2003 2004 2005 2006 2007 2008 2009 2010 20110
500 1000 1500 2000 2500 3000 3500 4000 4500 5000
70%
75%
80%
85%
thermal power generatedtotal power generated(TWh)generation proportion
pow
er g
ener
ated
(TW
h)
2003 2004 2005 2006 2007 2008 2009 2010 20110
500
1000
1500
2000
2500
3000
3500
0%
5%
10%
15%
20%
25%
energy consumption of thermal power industry(million tce)national energy consumptionenergy consumption proportion
ener
gy co
nsum
ption
(mili
on tc
e)
Data source: China Electric Power Yearbook; China Energy Statistical Yearbook
Installed Capacity Generation
Consumption
October 27, 2015 4
Introduction• Dual-track mechanism• Straighten the relationship between coal and electricity
– Marketization – Administrating pricing– Coal-electricity linkage
4
• Based on bargaining price on the vehicles
Measurement of the fuel costs
• 6 months→Annual (2012)
Adjustment cycle
• 30%→10%-(2012)
Self absorption rate
•=coal price adjustment * conversion factor•Conversion factor =(1-digestibility rate)*standard coal consumption for power supply*7000/natural coal's calorific value*(1+17%)/(1+13%)
Feed-in tariff adjustment
October 27, 2015 5
The Coal-Electricity Linkage Policy
Time Price range Feed-in tariff adjustment
2007.09.01 5.14 0.96
2008.03.01 7.46 1.93
2008.09.01 18.54 4.69
2009.09.01 -5.12 -1.44
2010.03.01 5.29 1.67
2011.02.01 5.13 1.16
Base on bargaining price on the vehicles of 5000-5500 kcal thermal coal-China Economic DatabaseReference: Lin boqiang. Design for coal-electricity linkage[M]. 2014 (in Chinese)
October 27, 2015 6
Research Questions
– Does “enhanced “ linkage between coal cost and electricity price lead to an improvement in environmental efficiency of China’s thermal power plants?
– To what extent the change in cost efficiency can be explained by covariates such as plant size, vintage, utilization?
October 27, 2015 7
Literature ReviewTraditional methodology:• Parametric approach: Stochastic frontier approach• non-parametric estimates of productive efficiency • environmental variables
Zhou (2010), data envelopment analysis (DEA) and Malmquist index, static and dynamic perspectives.
Extension: Chung (2007, 2013): directional distance function (DDF), Malmquist-Luenberger productivity index (ML).
Fare et al(2013). DDF with endogenously determined direction vectors.
Difficulties:• Lack of a coherent data-generating process (DGP)• Existence of serial correlation
October 27, 2015 8
Literature Review
Statistical inference:•Stochastic environmental DEA (Jin, 2007), tolerances approach (Sala-Garrido), •Simar et al. (2013) bootstrap procedures for original DEA and DDF estimates
•Simar & Wilson(2014): Double bootstrap regression
Questions:1) Is it possible and necessary to combine Bootstrap with DDF to build the corresponding productivity index?2) Will endogenous directional distance vector be applicable for the cases with multiple inputs and outputs?
October 27, 2015 9
Endogenous Directional Vector• Färe R, Grosskopf S, Whittaker G. 2013 • Generalize: multiple inputs and outputs, alternative input/output orientations.
The distance of a given point in the production set to the cost frontier can be used to calculate the relative cost efficiency of that point.
0 01
0 01
0 01
1 1
,
max
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g , 1, 2, ,K
g ,m 1, 2, , Ms. t .
1
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nx x
i ij ij j ji
ny y
i ik ik k k ki
nb b
i im im k k mi
K M
k mk m
k m
z p x p x j J
z y p p y k
z p b p b
g g
g
0 01
0 01
0 01
,
max
, 1,2, ,
, 1,2, , Ks.t .
,m 1, 2, ,M
0
k mk m
nx x
i ij ij j ji
ny y
i ik ik k k ki
nb b
i im im k k mi
k m
z p x p x j J
z p y p y k
z p b p b
kk
k mk m
mm
k mk m
g
g
Reference: Färe R, Grosskopf S, Whittaker G. Directional output distance functions: endogenous directions based on exogenous normalization constraints[J]. Journal of Productivity Analysis, 2013, 40(3): 267-269.Bilotkach V, Gitto S, Jovanović R, Mueller J, Pels E. Cost-efficiency benchmarking of European air navigation service providers. Transportation Research Part A: Policy and Practice. 2015;77:50-60.
October 27, 2015 10
Malmquist-Luenberger Index
1/2(1 ( , , ; , ))(1 ( , , ; , ))
,(1 ( , , ; , ))(1 ( , , ; , ))
t t t t t tk k k k k
t t t t t tk k k k k
t t t t t tk k k k k
t t t t t tk k k k k
D x y u y uD x y u y u
ML t tD x y u y uD x y u y u
1 ( , , ; , ),
1 ( , , ; , )
t t t t t tk k k k k
t t t t t tk k k k k
D x y u y uTECH t t
D x y u y u
1/21 ( , , ; , )1 ( , , ; , )
,1 ( , , ; , )1 ( , , ; , )
t t t t t tk k k k k
t t t t t tk k k k k
k t t t t t tk k k k k
t t t t t tk k k k k
D x y u y uD x y u y u
TCH t tD x y u y uD x y u y u
1 2, , , 1, , , 1, , ,t t t t t T t T t t
1 12 2
DM DFTECHGN GH
DM DE GN GH DF GKTCHDM DF GN GK DE GH
Graphical illustration of ML index
Reference: Zhou P, Ang B, Han J. Total factor carbon emission performance: a Malmquist index analysis. Energy Economics. 2010;32:194-201.
October 27, 2015 11
Data
Variable Unit2003 2007 2011
Mean Std.dev. Mean Std.dev. Mean Std.dev.
Installed capacity MW 142.13 456.02 220.32 519.39 429.94 912.16
Energy consumption Ktoe 537.88 1168.52 630.67 1378.12 1117.16 2437.41
Auxiliary power M kWh 116.13 187.22 98.38 135.67 181.40 186.79
Power generated M kWh 913.14 2252.75 1128.64 2611.68 2164.05 4390.36
Carbon emissions Ktons 1609.57 3496.68 1887.22 4123.85 3342.98 7293.68
others36%
capacity of the sample plants
64%
Coverage of the sample plants capacity in total thermal power industry in 2011
Installed capacity
Energy consumption
Auxiliary power
Power generated
Carbon emissions
Profile change of the sample power plants
2011 2007 2003
1137 thermal power plants, 2003 – 2011
October 27, 2015 12
Results: Efficiency Scores
0.5
0.6
0.7
0.8
0.9
1
effi
cien
cy sc
ore
2003 2007 2011
The observations with high efficiency (over 0.9 ) become more and more
October 27, 2015 13
Cumulative Probability Estimation
With coal-electricity linkage, more observations achieve efficiency score over 0.71, no matter the original estimates or the bootstrap ones, demonstrated by the estimation of cumulative distribution function.
-0.2 0 0.2 0.4 0.6 0.8 1 1.20
0.2
0.4
0.6
0.8
1
effiency score
cum
ulat
ive
prob
ablit
y
without linkagewith linkage
October 27, 2015 14
Double Bootstrap Analysis
• Regression results on the directional distance functions
* Statistically significant at the 5% level.
Variable Coefficient
Constant 0.2749*
Utilization -0.0014*
Age 0.00451*
Size 0.00485*
Size^2 -0.000370*
October 27, 2015 15
Discussions• The enhanced linkage between coal cost and electricity
pricing lead to statistically significant efficiency improvement.
• The estimation results through traditional directional distance functions varies a lot.
• The bootstrapped total factor cost efficiencies are different from the estimated ones in the traditional perspective.
October 27, 2015 16
Conclusions
Combined with other policies, the ‘coal-electricity linkage’ policy may further enhance the environmental efficiencies?
Environmental factors affect the cost efficiency.
The bootstrap procedure is indispensable for the cost efficiency estimations.
The generalized endogenous optimal vector method makes the estimated efficiency scores perform better.
Bai-chen XieCollege of Management and Economics,
Tianjin University, Tianjin, China E-mail: [email protected]