Energy Efficiency in Fossil-Fuel Electricity Generation: A Panel Data Empirical Analysis

Transcription

1 Energy Efficiency in Fossil-Fuel Electricity Generation: A Panel Data Empirical Analysis Elena Verdolini Catholic University Milan and FEEM IEFE Milan, 22 January 2010

2 0. Outline A. Motivation 1. Forecasts of CO2 emissions: the Power sector 2. Forecasts of Electricity production 3. Curbing CO2 emissions in the electricity sector B. Energy Efficiency in Fossil Fuel Electricity Generation 1. Definition 2. Data for OECD C. Review of the literature D. Simple framework to look at energy efficiency E. Knowledge stocks as proxies of technological development and availability F. Results G. Conclusions and Next Steps 1

3 A.1 CO2 Emission Forecasts Widespread agreement that unless we take significant actions in a Business-As-Usual (BAU) scenario global anthropogenic CO2 emissions grow rapidly, oil and gas prices are high and energy security concerns increase. ETP (2008) revises previous estimated of CO2 emissions upward by 7%, pointing once more to the role of developing economies. CO2 emissions in 2050 are 130% above the level of

4 A.1 CO2 Emissions Forecasts: the Power Sector Higher global emissions reflect rapid economic growth and increasing carbon intensity of energy use, which overwhelm decoupling of economic activity and energy use 3

5 A.2 Electricity Production Forecasts BAU: electricity demand increases by 2.2%/yr (DCs: 3.8%) Factors: rapid population and income growth in DCs, increase in the number of electricity consuming devices (homes and commercial buildings), growth in electrically driven industrial processes. Fossil Fuels will remain the main input 4

6 A.3 Curbing CO2 Emissions in the Electricity Sector The power sector is the sector in which higher emission reductions can be achieved 5

7 A.3 Curbing CO2 Emissions in the Electricity Sector 6 (1) Improving efficiency in energy-intensive sector/end-use Will global energy demand really decrease? (2) Reduce the share of FFs power generation through A. Decarbonization of the electricity sector -- substitution with non-fossil energy sources (nuclear and renewable) or some break-through Technical limitations (grids) and social acceptance? B. Carbon Capture and Storage (CCS) General concerns for storage? Location? Existing power plants? Moreover, capturing CO2 from low efficiency is not economically viable (3) Increase energy efficiency of FF electricity production Attractive option as it would combine reduce impact on environment and energy security

8 A.3 Curbing CO2 Emissions in the Electricity Sector 7

9 A.3 Curbing CO2 Emissions in the Electricity Sector: EE All the projections of future power mix, as well as of future efficiency levels, are based on the optimal behaviour of the economic agents. In many cases, such as widespread deployment of renewables or nuclear, as well as other frontier technologies, such assumptions are necessary because we can t observe past performance. On the other hand, the dynamics of fossil-fuel electricity generation and its efficiency over time can be studied, since the technology has been used for a long time, data is available for a number of countries. 8 A study of how the efficiency of power plants has developed over time can allow for a cross-country comparison as well as shed light on the determinants of technical efficiency

10 B.1 Energy-efficiency in fossil-fuel power generation Energy efficiency for the power sector is often referred to as technical efficiency, as it relates to the ability to extract the energy content of FFs and transform it into electricity. In its definition, it is necessary to take into account that electricity can be produced in a traditional plants or in CHP plants, where heat is a by product of the electricity generation process plant (Phylipsen et al., 1998 and Graus et al., 2007). 9 EL = Electricity produced with FF (GWh*3.6) H = Heat produced with FF (TJ) s = Correction factor between electricity and heat (s=1.75) I = FF inputs (coal, gas, oil) (TJ) EE i = EL i +( H I i i * s )

11 B.1 Energy-efficiency in FF power generation: OECD 10

12 C. Review of the Literature Aspects of power sector have been researched: 1980s: rate of return regulation, environmental controls 1990s: natural monopoly (distribution), ownership restructuring of the industry, break in constituent parts (transmission, distribution, retail) and competition introduced in wholesale and retail market Soderholm (1999): Interfuel substitution in FF power sector Recently, interest in energy efficiency of FF power plants: Some case studies at the country level Grauss et al (2007) and (IEA 2008): descriptive analysis Grauss et al (2008): links energy efficiency to age of power plant and to carbon intensity 11

13 C. Review of the Literature This paper provides a quantitative analysis of the factors influencing energy efficiency in fossil-fuel power generation at the national level, with the novelty of taking into account the impact of technological availability in the market The aim is to substantiate known facts with an empirical analysis that allows to go past simple case study approaches to the explanation of improvements in energy efficiency In particular, previous descriptive studies point to the fact that energy efficiency in the power sector is negatively correlated with the age of the capital stock and positively correlated with the load factor, the type of technology used in the plant and the prices of input. 12

14 D. Framework 1/3 A representative firm produces electricity using FF EL = h( QFF ( C, G, O), L, Z, K, A) Assuming that the firm is a price taker in the market for inputs and that fossil- fuel inputs and the non-energy inputs are weakly separable, in the short-run the production choices of the firm can be characterized by a cost minimization problem min SRC = g(w s.t. EL = H(Q FF FF (C, (w C, w O, w G G, O), K, A) ), K, EL, A) 13 where SRC is the short-run cost function of the firm and w FF is a function that aggregates the fossil fuel input prices, namely coal (w C ), gas (w G ), and oil (w O ).

15 D. Framework 2/3 Applying Sheppard s lemma, the conditional demand of FF input can be derived SRC = Q FF = f ( w FF, K, EL, A) wff Log-linearizing we can write the following equation: 14 ln QFF = β 1 + β 2 ln w FF + β 3 ln K + β 4 ln EL + β 5 ln and, subtracting ln(el) from both sides, we obtain: QFF ln = β 1 + β 2 ln w FF + β 3 ln K + ( β 4 1) ln EL + β 5 ln EL Note: QFF 1 EL = EE A A

16 D. Framework 3/3: Definition of Variables ln EE = α 1 + α 2 ln w FF + α 3 ln K + α 4 ln EL + α 5 ln A EE w FF K EL A Input weighted EE of FFs EE Input weighted index of fossil fuel prices (oil, coal, gas), Defined as (stock of capital * utilization ratio) Stock of capital Kt = It + ( 1 δ ) Kt 1 Load factor L = EL / PEL Electricity production (proxied by GDP) Index of technological availability built using patent data for fossil fuel electricity technologies = EE * I i I i i α 3 > α 4 > α 5 > α 2 > 0 15

17 E. Index of Technological development/availability 1/2 Patents are costly. They are: indicator of the outcome of the innovation process, Indicator technological availability in the market Limitations of patent data are well-known (Griliches 1990) Identified classes that include energy-efficient technologies for fossil-fuel power generation Coal Gasification, Fluidised-bed combustion, Integrated gasification combined cycle (IGCC), Process heaters and superheaters, Compressed ignition engines, Efficiency improving gas turbines, Co-generation, Combined cycle combustion Built a dataset that includes all singular patents, claimed priorities and duplicate patents 16

18 E. Innovation: Environmental/Energy-Efficient Technologies 17

19 E. Index of Technological development/availability 2/2 Following Popp 2002, Bottazzi and Peri 2005, Verdolini and Galeotti 2009, build a measure of knowledge stock using patent data Measure built using claimed priorities only, cps and duplicates and cps, dup and singulars to account for different value of patents Effect lagged (10 years) to account for time differences between innovation and deployment 18

20 E. Innovation: Environmental/Energy-Efficient Technologies 19

21 F. The sample Sample of 20 OECD over the period USA, Japan, Germany, France, UK, France, Canada, Italy, Netherlands, Austria, Korea, Finland, Belgium, Hungary, Spain, Czech Republic, Mexico, Portugal, Turkey, Slovak Republic Input, output and capacity installed data from IEA Electricity Information Database 2008 Patents from EPO/OECD PATSTAT Database (2008) Pooled OLS estimation with robust standard errors Country fixed effects are included 20

22 F. Results 1/2 21 (1) (2) (3) (4) Input Weighted *** *** *** *** Price Index ( ) ( ) ( ) ( ) Capital Stock *** ** ( ) ( ) Technological *** *** Availability (CP) ( ) ( ) Technological *** *** *** *** Availability (CP+SIN) ( ) ( ) ( ) ( ) GDP Per Capita 0.108*** *** (0.0182) (0.0194) GDP 0.119*** 0.105*** (0.0192) (0.0210) Country Fixed Effects yes yes yes yes Nr of Cases R Square

23 F. Results 2/2 22 (5) (6) (7) Input Weighted *** *** *** Price Index ( ) ( ) ( ) Capital Stock *** *** *** ( ) ( ) ( ) Technological *** *** *** Availability (CP) ( ) ( ) ( ) GDP 0.134*** 0.147*** 0.119*** (0.0212) (0.0201) (0.0220) Average age of Plant ** ** ** (0.0178) (0.0168) (0.0181) Share of Electricity Imports 0.105*** (0.0346) Share of Electricity Exports 0.185*** (0.0617) Nr of Cases R Square

24 G. Conclusions Good fit of the model Consistent results with expectations, robust to different specifications of the model Need to improve Sample size: unreliable data for a few countries Measures of environmental regulation Relax assumption of global knowledge stock and construct national knowledge stocks to study spillovers Further examine how technical change and innovation contribute to CO2 emissions reductions 23

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