1. The Impact of Coal-Electricity Linkage on The Cost Efficiency of China’s Thermal Power Plants Na Duan, Bai-chen Xie Tianjin University 1

2. October 27, 2015 2 Outline Introduction Literature Review Methodology Empirical Analysis Conclusions 2

3. October 27, 2015 3 Introduction Development & energy consumption of China’s thermal power industry Data source: China Electric Power Yearbook; China Energy Statistical Yearbook Installed CapacityGeneration Consumption

4. 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

5. October 27, 2015 5 The Coal-Electricity Linkage Policy TimePrice rangeFeed-in tariff adjustment 2007.09.015.140.96 2008.03.017.461.93 2008.09.0118.544.69 2009.09.01-5.12-1.44 2010.03.015.291.67 2011.02.015.131.16 Base on bargaining price on the vehicles of 5000-5500 kcal thermal coal-China Economic Database Reference: Lin boqiang. Design for coal-electricity linkage[M]. 2014 (in Chinese)

6. 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?

7. October 27, 2015 7 Literature Review Traditional 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

8. 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?

9. 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. 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.

10. October 27, 2015 10 Malmquist-Luenberger Index 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.

11. October 27, 2015 11 Data VariableUnit 200320072011 MeanStd.dev.MeanStd.dev.MeanStd.dev. Installed capacityMW 142.13456.02220.32519.39429.94912.16 Energy consumptionKtoe 537.881168.52630.671378.121117.162437.41 Auxiliary powerM kWh 116.13187.2298.38135.67181.40186.79 Power generatedM kWh 913.142252.751128.642611.682164.054390.36 Carbon emissionsKtons 1609.573496.681887.224123.853342.987293.68 1137 thermal power plants, 2003 – 2011

12. October 27, 2015 12 Results : Efficiency Scores The observations with high efficiency (over 0.9 ) become more and more

13. 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.

14. October 27, 2015 14 Double Bootstrap Analysis Regression results on the directional distance functions * Statistically significant at the 5% level. VariableCoefficient Constant0.2749 * Utilization-0.0014 * Age0.00451 * Size0.00485 * Size^2-0.000370 *

15. 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.

16. 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.

17. Bai-chen Xie College of Management and Economics, Tianjin University, Tianjin, China E-mail: xiebaichen@tju.edu.cn