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Energy Journal Issue

The Energy Journal
Volume 36, Number 1

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A Microeconomic Framework for Evaluating Energy Efficiency Rebound and Some Implications

Severin Borenstein

DOI: http://dx.doi.org/10.5547/01956574.36.1.1
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Improving energy efficiency can lower the cost of using energy-intensive goods and may create wealth from the energy savings, both of which lead to increased energy use, a "rebound" effect. I present a theoretical framework that parses rebound into economic income and substitution effects. The framework leads to new insights about the magnitude of rebound when goods are not priced at marginal cost and when consumers are imperfect optimizers, as well as the role of technological progress in rebound. I then explore the implications of this framework with illustrative calculations for improved auto fuel economy and lighting efficiency. These suggest that rebound is unlikely to more than offset the savings from energy efficiency investments (known as "backfire"), but rebound likely reduces the net savings by roughly 10% to 40% from these energy efficiency improvements.

Recent Evidence for Large Rebound: Elucidating the Drivers and their Implications for Climate Change Models

Harry D. Saunders

DOI: http://dx.doi.org/10.5547/01956574.36.1.2
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New research using data spanning centuries reveals the presence of very large energy efficiency rebound magnitudes, calling into question the energy use forecasts relied on by international bodies investigating climate change mitigation policy. This article uses those recent results to highlight and explain the key drivers that future energy modelers need to incorporate.

Small Trends and Big Cycles in Crude Oil Prices

Xiaoyi Mu and Haichun Ye

DOI: http://dx.doi.org/10.5547/01956574.36.1.3
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We employ an unobserved components model to disentangle the long-term trend from cyclical movements in the price of internationally traded crude oil using data from 1861 to 2010. The in-sample estimation of the model identifies a deterministic quadratic trend and two types of cycles, with the short cycle having a period of 6 years and the long cycle of 29 years. Compared to the large amplitude of the cycles, the growth rate of the long-term trend is small. The out-of-sample forecasting performance of various competing models is compared to that of a "no change" random walk forecast. While the random walk forecast tends to be the most accurate at shorter horizons, it is outperformed by the trend-cycle models at horizons longer than one year. The results provide evidence of predictability in the price of crude oil at long horizons.

Ethanol Production and Gasoline Prices: A Spurious Correlation

Christopher R. Knittel and Aaron Smith

DOI: http://dx.doi.org/10.5547/01956574.36.1.4
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Ethanol made from corn comprises 10% of U.S. gasoline, up from 3% in 2003. This dramatic increase was spurred by recent policy initiatives such as the Renewable Fuel Standard and state-level blend mandates and supported by direct subsidies such as the Volumetric Ethanol Excise Tax Credit. Some proponents of ethanol have argued that ethanol production greatly lowers gasoline prices, with one industry group claiming it reduced gasoline prices by 89 cents in 2010 and $1.09 in 2011. The 2010 figure has been cited in numerous speeches by Secretary of Agriculture Thomas Vilsack. We show that these estimates were generated by implausible economic assumptions and spurious statistical correlations. To support this last point, we use the same statistical models and find that ethanol production "decreases" natural gas prices, but "increases" unemployment in both the U.S. and Europe. We even show that ethanol production "increases" the ages of our children. Overall, we see no compelling reason to believe that the effect of ethanol use on gasoline prices has been more than $0.10 per gallon.

How do Consumers Respond to Gasoline Price Cycles?

David P. Byrne, Gordon W. Leslie, and Roger Ware

DOI: http://dx.doi.org/10.5547/01956574.36.1.5
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This paper empirically studies how consumers respond to retail gasoline price cycles. Our analysis uses new station-level price data from local markets in Ontario, Canada, and a unique market-level measure of consumer responsiveness based on web traffic from gasoline price reporting websites. We first document how stations use coordinated pricing strategies that give rise to large daily changes in price levels and dispersion in cycling gasoline markets. We then show consumer responsiveness exhibits cycles that move with these price fluctuations. Through a series of tests we find that forward-looking stockpiling behavior by consumers plays a central role in generating these patterns.

The Optimal Share of Variable Renewables: How the Variability of Wind and Solar Power affects their Welfare-optimal Deployment

Lion Hirth

DOI: http://dx.doi.org/10.5547/01956574.36.1.6
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This paper estimates the welfare-optimal market share of wind and solar power, explicitly taking into account their output variability. We present a theoretical valuation framework that consistently accounts for the impact of fluctuations over time, forecast errors, and the location of generators in the power grid on the marginal value of electricity from renewables. Then the optimal share of wind and solar power in Northwestern Europe's generation mix is estimated from a calibrated numerical model. We find the optimal long-term wind share to be 20%, three times more than today; however, we also find significant parameter uncertainty. Variability significantly impacts results: if winds were constant, the optimal share would be 60%. In addition, the effect of technological change, price shocks, and policies on the optimal share is assessed. We present and explain several surprising findings, including a negative impact of CO2 prices on optimal wind deployment.

Estimating Short and Long-Run Demand Elasticities: A Primer with Energy-Sector Applications

John T. Cuddington and Leila Dagher

DOI: http://dx.doi.org/10.5547/01956574.36.1.7
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Many empirical exercises estimating demand functions, whether in energy economics or other fields, are concerned with estimating dynamic effects of price and income changes over time. This paper first reviews a number of commonly used dynamic demand specifications to highlight the implausible a priori restrictions that they place on short and long-run elasticities. Such problems are easily avoided by adopting a general-to-specific modeling methodology. Second, it discusses functional forms and estimation issues for getting point estimates and associated standard errors for both short and long-run elasticities - key information that is missing from many published studies. Third, our proposed approach is illustrated using a dataset on Minnesota residential electricity demand.