Energy Efficiency Drivers and Trends

Transcription

1 Energy Efficiency Drivers and Trends David I. Stern Arndt-Corden Department of Economics, Crawford School of Economics and Government, Australian National University Website: Economics and Environment Network Symposium 2010

2 Energy Intensity & GDP per Capita: 99 Countries Energy Intensity (E/GDP) kgoe/ppp$ GDP per Capita (2005 PPP$)

3 Energy Intensity & GDP per Capita Energy Intensity kgoe/ppp$ China Canada USA 0.1 India Japan Australia Germany UK GDP per Capita (2005 PPP$)

4 Key Variables (2007): Australia & Other Countries Winter Temp Mining & Utilities Coal TFP PPP Fossil Reserves / GDP Australia % 44% Canada % 11% China % 66% Germany 0.2 2% 26% India % 41% Japan 0.8 3% 22% UK 3.4 4% 18% USA % 24%

5 Econometric Model of Energy Intensity Stochastic frontier model: ln E i Y i = "# 0 "# K ln K i Y i lnu i ~ N + ( 2 ''z i,( ) u lnv i ~ N( 2 0,( ) v 5 & j= 2 4 & k= 2 "# H ln H i "# W W i " $ j e ji + % k y ki Y i + lnu i + lnv i u i measures the distance from the best-practice frontier

6 z i Variables: Total factor productivity Capital/land ratio PPP Ratio Corruption (Transparency International) Regime (Polity IV) Openness to trade Fossil fuel reserves/gdp Gini coefficient Legal origin Former communist

7 Econometric Model of Energy Intensity Stochastic frontier model: ln E i Y i = "# 0 "# K ln K i Y i lnu i ~ N + ( 2 ''z i,( ) u lnv i ~ N( 2 0,( ) v 5 & j= 2 4 & k= 2 "# H ln H i "# W W i " $ j e ji + % k y ki Y i + lnu i + lnv i Estimated using between estimator: 85 Countries,

8 Advantages of Between Estimator

9 Advantages of Between Estimator Potentially consistent estimator of long-run relationship:

10 Advantages of Between Estimator Potentially consistent estimator of long-run relationship: o z i variables address potential Cov( X i,u i ) 0

11 Advantages of Between Estimator Potentially consistent estimator of long-run relationship: o z i variables address potential Cov( X i,u i ) 0 Do not need to model time dimension of technology

12 Computing Technology Trends ln ˆ u it = ln E it Y it α ˆ 0 α ˆ K ln K it Y it α ˆ H ln H it Y it ˆ α W W i 5 j= 2 ˆ β j e jit + 4 k= 2 ˆ γ k y kit ln ˆ v i Smoothed with Hodrick-Prescott filter

13 Econometric Results: z i Variables Constant Ger/Scand L.O ln TFP French L.O ln PPP Former Comm ln Open " v Transparency " u Fossil Res

14 Underlying Energy Efficiency: Chindia & Developed Economies 3 ln Relative Underlying Energy Efficiency China India Australia Japan USA -0.5 Germany

15 Underlying Energy Efficiency: Chindia & Developing Economies 3.5 ln Relative Underlying Energy Efficiency China India Indonesia South Africa Brazil -0.5 Mexico

16 Decomposition of Increase in Global Energy Use, % 250% Global Scale 200% 150% 100% 50% 0% H/GDP Fuel Mix Global Shift Sum Total -50% K/GDP Struct. Change Resid -100% Tech Change

17 Decomposition of Increase in Global CO2 Emissions, % 250% Global Scale 200% 150% 100% 50% 0% H/GDP Fuel Mix Global Shift Sum Total -50% K/GDP Struct. Change Resid -100% Tech Change

18 Summary

19 Summary New approach to estimating technology trends in panel data

20 Summary New approach to estimating technology trends in panel data Higher TFP! higher energy efficiency

21 Summary New approach to estimating technology trends in panel data Higher TFP! higher energy efficiency Higher exchange rate! lower energy efficiency

22 Summary New approach to estimating technology trends in panel data Higher TFP! higher energy efficiency Higher exchange rate! lower energy efficiency Convergence of energy efficiency over time

23 Summary New approach to estimating technology trends in panel data Higher TFP! higher energy efficiency Higher exchange rate! lower energy efficiency Convergence of energy efficiency over time Technological change most important in offsetting effect of growth in global scale on energy use and carbon emissions

24 Energy Efficiency Drivers and Trends David I. Stern Arndt-Corden Department of Economics, Crawford School of Economics and Government, Australian National University Website: Economics and Environment Network Symposium 2010

25 Extra Slides

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27 Econometric Results: X i Variables Constant Primary Elec Capital Biomass Human Capital Agriculture Winter Mining Coal Services Natural Gas

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29 Decomposition of Increase in Global Energy Use and Carbon Emissions Energy Carbon Capital/GDP Ratio -7.04% -6.85% Human Capital/GDP Ratio 44.79% 45.54% Local Fuel Mix 3.93% 1.82% Local Economic Structure -9.29% -9.58% Local Technology % % Global Scale % % Global Shift 6.93% 8.54% Total % % Residual -2.38% -3.17% Change in Energy and Emissions % %

30 Mitigation Targets Cuts Relative to BAU 2020: Brazil: 38.9% South Africa: conditional 34% Indonesia: 26% Cuts in Emissions Intensity: China: 40-45% India: 20-25% Cuts in Emissions: Australia: 5-25% US: 17% EU: 20-30%

31 !"#$%&'!()*++*&'+!#',!('-$./!0'1-'+*1/!!"#$%&' (")#"*+)&',-$./"' 4' 4' 12-3#' 5' (")#"*+)&',-$./"'!0+**+/"*'

32 China: Energy & Emissions Trends kg OE or kg CO2 per PPP $ 1.6 Carbon Intensity Emissions Intensity Energy Intensity kg CO2 per kg OE

33 India: Energy & Emissions Trends Carbon Intensity kg OE or kg CO2 per PPP $ Emissions Intensity Energy Intensity kg CO2 per kg OE

34 Chindia & USA: Underlying Energy Efficiency China US India 1.5 lnuit

35 China Historical and Projected Energy and Emissions Intensity kg OE or kg C02 per PPP $ Energy Intensity Emissions Intensity Scenario 1 E/Y Scenario 1 C/Y Scenario 2 E/Y Scenario 2 C/Y Scenario 3 E/Y Scenario 3 C/Y 40% Target 45% Target

36 India Historical and Projected Energy and Emissions Intensity kg OE or kg CO2 per PPP $ Energy Intensity Emissions Intensity Scenario 1 E/Y Scenario 1 C/Y Scenario 2 E/Y Scenario 2 C/Y Scenario 3 E/Y Scenario 3 C/Y 20% Target 25% Target

37 Reductions in Emissions Intensity: China India Scenario 1 (Convergence to US) -24% -29% Scenario % Non-Fossil Target -26% Scenario 2 ( rate of tech -33% -28% change) Scenario 3 ( rate of tech -38% -2% change) US Average Energy Efficiency Applied to New Investment -46%