US long-term energy intensity: Backcast and projection

Energy intensity of the economy is often modeled as being determined by the combined effect of a fixed price elasticity of demand, and an exogenously specified, fixed technical change parameter denoted as the autonomous energy efficiency improvement (AEEI). Typically, the AEEI rate is set to 0.5–1.5% improvement per annum. Here, we study historic aggregate energy intensity trends for the US from 1954 to 1994. We show that the historic trends are inconsistent with an autonomous model of improved energy efficiency—especially when the model is used to inform policies that impact energy prices. As an alternative we propose a model of price-induced efficiency, π, in which aggregate energy intensity trends respond to changes in energy prices beyond price elasticity of demand e. Our exercise reveals that the aggregate price elasticity of energy demand of the US economy has declined by roughly 15% over the past four decades. But beyond this decline, bringing our simulations and historical data into close correspondence requires π to change sign before and after 1974. Before 1974, after accounting for price elasticity of demand, the economy was growing less energy efficient. After 1974, after accounting for the price elasticity of demand, the economy was growing more energy efficient. Furthermore, since 1984, the rate of energy efficiency gain has been declining. When projections of long-term energy use are compared, those with a price-induced energy efficiency formulation generate significantly more price sensitive energy use and emissions trajectories. When in the business as usual scenario energy prices are expected to be rising, climate policies involve lower shadow carbon prices with π than with AEEI formulations. In scenarios where energy prices are relatively flat, energy intensity rises leading to CO2 emissions far higher than standard business as usual projections utilizing AEEI assumptions.

[1]  M. Granger Morgan,et al.  Mixed Levels of Uncertainty in Complex Policy Models , 1999 .

[2]  William W. Hogan,et al.  Productivity Trends and the Cost of Reducing CO2 Emissions , 1991 .

[3]  Fridtjof Unander,et al.  Oil Crises and Climate Challenges: 30 Years of Energy Use in IEA Countries , 2004 .

[4]  Richard G. Richels,et al.  The Attached Material Is Posted on Regulation2point0.org with Permission. the Impact of Learning-by-doing on the Timing and Costs of Co 2 Abatement the Impact of Learning-by-doing on the Timing and Costs of Co 2 Abatement , 2022 .

[5]  Andreas Löschel,et al.  Technological Change in Economic Models of Environmental Policy: A Survey , 2002 .

[6]  Alan S. Manne,et al.  ETA-MACRO: A model of energy-economy interactions , 1977 .

[7]  Anna Monis Shipley,et al.  The Technical, Economic and Achievable Potential for Energy-Efficiency in the U.S. - A Meta-Analysis of Recent Studies , 2004 .

[8]  Mark Jaccard,et al.  Modeling the cost of climate policy: Distinguishing between alternative cost definitions and long-run cost dynamics , 2003 .

[9]  A. S. Manne ETA-MACRO: A user's guide , 1981 .

[10]  Mark Jaccard Greenhouse Gas Abatement: Controversies in Cost Assessment , 2004 .

[11]  James C. Williams,et al.  Energy in World History , 1994 .

[12]  Hadi Dowlatabadi,et al.  A Bayesian technique for refining the uncertainty in global energy model forecasts , 1995 .

[13]  Nebojsa Nakicenovic,et al.  Dynamics of energy technologies and global change , 1999 .

[14]  Max Henrion,et al.  Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis , 1990 .

[15]  David W. Dilks,et al.  Development of Bayesian Monte Carlo techniques for water quality model uncertainty , 1992 .

[16]  Stephen C. Peck,et al.  CO2 Emissions Control Agreements: Incentives for Regional Participation , 1999 .

[17]  A. Manne,et al.  Buying greenhouse insurance: The economics costs of carbon dioxide emission limits , 1992 .

[18]  Andreas Schäfer,et al.  Structural change in energy use , 2005 .

[19]  Paul Ekins,et al.  Global warming and energy demand , 1995 .

[20]  Reuven Y. Rubinstein,et al.  Simulation and the Monte Carlo Method , 1981 .

[21]  Hadi Dowlatabadi,et al.  A REVIEW OF TECHNICAL CHANGE IN ASSESSMENT OF CLIMATE POLICY , 1999 .

[22]  J. Edmonds,et al.  Global Energy: Assessing the Future , 1985 .

[23]  Alan S. Manne,et al.  The Kyoto Protocol: A Cost-Effective Strategy for Meeting Environmental Objectives? , 1999 .

[24]  David Popp,et al.  Induced Innovation and Energy Prices , 2001 .

[25]  D. R. Bohi,et al.  An Update on Econometric Studies of Energy Demand , 1984 .

[26]  Andy S. Kydes,et al.  Beyond the horizon: Recent directions in long-term energy modeling , 1995 .

[27]  R. Sepanski,et al.  TRENDS '90: A compendium of data on global change , 1991 .

[28]  Richard G. Newell,et al.  Technological Change and the Environment , 2001 .

[29]  Domestic Commerce,et al.  Survey of current business , 1921 .

[30]  Stephen C. Peck,et al.  International CO2 emissions control : An analysis using CETA , 1995 .

[31]  J. M. Griffin,et al.  Price Asymmetry In Energy Demand Models: A Proxy for Energy-Saving Technical Change? , 2005 .

[32]  Alan S. Manne,et al.  MERGE. A model for evaluating regional and global effects of GHG reduction policies , 1995 .

[33]  Cutler J. Cleveland,et al.  Encyclopedia of Energy , 2004 .

[34]  C. Hitch Modeling energy-economy interactions : five approaches , 2015 .

[35]  A. Jaffe,et al.  The Induced Innovation Hypothesis and Energy-Saving Technological Change , 1998 .