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

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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

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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)

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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?

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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.

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nb b

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z y p p y k

z p b p b

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max

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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.

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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

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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

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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

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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*

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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.

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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: xiebaichen@tju.edu.cn