Energy Efficiency Drivers and Trends David I. Stern Arndt-Corden Department of Economics, Crawford School of Economics and Government, Australian National University E-mail: sterndavidi@yahoo.com Website: http://www.sterndavidi.com Economics and Environment Network Symposium 2010
Energy Intensity & GDP per Capita: 99 Countries 1971-2007 1 Energy Intensity (E/GDP) kgoe/ppp$ 0.1 0.01 100 1000 10000 100000 1000000 GDP per Capita (2005 PPP$)
Energy Intensity & GDP per Capita 0.7 0.6 Energy Intensity kgoe/ppp$ 0.5 0.4 0.3 0.2 China Canada USA 0.1 India Japan Australia Germany UK 0 100 1000 10000 100000 GDP per Capita (2005 PPP$)
Key Variables (2007): Australia & Other Countries Winter Temp Mining & Utilities Coal TFP PPP Fossil Reserves / GDP Australia 15.2 10% 44% 0.88 1.15 55.87 Canada -20.4 10% 11% 0.85 1.11 26.06 China -5.8 14% 66% 0.39 0.32 7.40 Germany 0.2 2% 26% 0.84 1.21 1.06 India 17.1 5% 41% 0.36 0.25 10.27 Japan 0.8 3% 22% 0.77 1.07 0.07 UK 3.4 4% 18% 0.98 1.33 0.68 USA -2.7 4% 24% 1 1 10.02
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
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
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, 1971-2007
Advantages of Between Estimator
Advantages of Between Estimator Potentially consistent estimator of long-run relationship:
Advantages of Between Estimator Potentially consistent estimator of long-run relationship: o z i variables address potential Cov( X i,u i ) 0
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
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
Econometric Results: z i Variables Constant 9.278 Ger/Scand L.O. -0.251 1.59-1.22 ln TFP -1.296 French L.O. -0.107-1.63-1.23 ln PPP 0.884 Former Comm. 0.538 5.79 2.17 ln Open 0.113 " v 0.220 1.38 3.04 Transparency -0.050 " u 0.011-1.47 0.01 Fossil Res. 0.008 1.42
Underlying Energy Efficiency: Chindia & Developed Economies 3 ln Relative Underlying Energy Efficiency 2.5 2 1.5 1 0.5 0 China India Australia Japan USA -0.5 Germany 1970 1975 1980 1985 1990 1995 2000 2005 2010
Underlying Energy Efficiency: Chindia & Developing Economies 3.5 ln Relative Underlying Energy Efficiency 3 2.5 2 1.5 1 0.5 0 China India Indonesia South Africa Brazil -0.5 Mexico 1970 1975 1980 1985 1990 1995 2000 2005 2010
Decomposition of Increase in Global Energy Use, 1971-2007 300% 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
Decomposition of Increase in Global CO2 Emissions, 1971-2006 300% 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
Summary
Summary New approach to estimating technology trends in panel data
Summary New approach to estimating technology trends in panel data Higher TFP! higher energy efficiency
Summary New approach to estimating technology trends in panel data Higher TFP! higher energy efficiency Higher exchange rate! lower energy efficiency
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
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
Energy Efficiency Drivers and Trends David I. Stern Arndt-Corden Department of Economics, Crawford School of Economics and Government, Australian National University E-mail: sterndavidi@yahoo.com Website: http://www.sterndavidi.com Economics and Environment Network Symposium 2010
Extra Slides
Econometric Results: X i Variables Constant -4.006 Primary Elec. -0.897-1.71-1.90 Capital -0.153 Biomass -0.543-1.50-1.53 Human Capital -0.422 Agriculture 0.270-2.80 0.27 Winter 0.015 Mining 1.796 1.50 1.33 Coal -0.998 Services 1.038-3.00 1.11 Natural Gas -0.653-1.21
Decomposition of Increase in Global Energy Use and Carbon Emissions Energy 1971-2007 Carbon 1971-2006 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 -55.45% -56.88% Global Scale 269.24% 252.42% Global Shift 6.93% 8.54% Total 123.20% 105.86% Residual -2.38% -3.17% Change in Energy and Emissions 120.81% 102.70%
Mitigation Targets Cuts Relative to BAU 2020: Brazil: 38.9% South Africa: conditional 34% Indonesia: 26% Cuts in Emissions Intensity: China: 40-45% 2005-2020 India: 20-25% 2005-2020 Cuts in Emissions: Australia: 5-25% 2000-2020 US: 17% 2005-2020 EU: 20-30% 1990-2020
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China: Energy & Emissions Trends 1971-2008 kg OE or kg CO2 per PPP $ 1.6 Carbon Intensity 1.4 1.2 1 0.8 Emissions Intensity 0.6 0.4 0.2 Energy Intensity 0 1970 1975 1980 1985 1990 1995 2000 2005 3.5 3 2.5 2 1.5 1 0.5 0 kg CO2 per kg OE
India: Energy & Emissions Trends 1971-2008 0.45 Carbon Intensity 2.5 0.4 kg OE or kg CO2 per PPP $ 0.35 0.3 0.25 0.2 0.15 0.1 Emissions Intensity Energy Intensity 2 1.5 1 0.5 kg CO2 per kg OE 0.05 0 1970 1975 1980 1985 1990 1995 2000 2005 0
Chindia & USA: Underlying Energy Efficiency 3 2.5 2 China US India 1.5 lnuit 1 0.5 0-0.5-1 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
China Historical and Projected Energy and Emissions Intensity 0.9 0.8 kg OE or kg C02 per PPP $ 0.7 0.6 0.5 0.4 0.3 0.2 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 0.1 0 1995 2000 2005 2010 2015 2020
India Historical and Projected Energy and Emissions Intensity 0.4 0.35 kg OE or kg CO2 per PPP $ 0.3 0.25 0.2 0.15 0.1 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 0.05 0 1995 2000 2005 2010 2015 2020
Reductions in Emissions Intensity: 2005-2020 China India Scenario 1 (Convergence to US) -24% -29% Scenario 1 + 15% Non-Fossil Target -26% Scenario 2 (2000-2007 rate of tech -33% -28% change) Scenario 3 (1971-2007 rate of tech -38% -2% change) US Average Energy Efficiency Applied to New Investment -46%