Fuel efficiency and motor vehicle travel: the declining rebound effect
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Citations
Effects of U.S. Tax Policy on Greenhouse Gas Emissions
The unusual suspects: The impact of non-transport technologies on social practices and travel demand
Are there Carbon Savings from US Biofuel Policies? Accounting for Leakage in Land and Fuel Markets
Impacts of fuel economy improvements on the distribution of income in the U.S
A Semiparametric Analysis of Gasoline Demand in the US: Reexamining The Impact of Price
References
Econometric Analysis of Cross Section and Panel Data
How Much Should We Trust Differences-In-Differences Estimates?
Estimation and inference in econometrics
Estimation and Inference in Econometrics
Energy efficiency and consumption — the rebound effect — a survey
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Frequently Asked Questions (15)
Q2. What future works have the authors mentioned in the paper "Fuel efficiency and motor vehicle travel: the declining rebound effect" ?
The question of CAFE ’ s effects remains an interesting area for future research, and the authors believe their approach offers a better chance of resolving it than previous attempts. To make further progress probably requires estimating models that disaggregate the passenger-vehicle fleet into the two categories, cars and light trucks, that are regulated differently under CAFE.
Q3. What is the importance of obtaining reliable measures of it?
Obtaining reliable measures of it is important because it helps determine the effectiveness of measures intended to reduce fuel consumption and because increased driving exacerbates congestion and air pollution.
Q4. How do the authors eliminate spurious effects of such correlations?
In their work, the authors eliminate the spurious effects of such crosssectional correlations by using fixed-effects specification, i.e. by including a dummy variable for each state.
Q5. What is the reason why Goldberg estimates the rebound effect?
Goldberg (1998) estimates the rebound effect using the Consumer Expenditure Survey for the years 1984-1990, as part of a larger equation system that also predicts automobile sales and prices.
Q6. What makes the simplified specification a suitable estimator?
the stability of the simplified specification lends support to the view that the model is well specified, making 3SLS a suitable estimator.
Q7. What is the reason why the recent studies use micro data?
Two recent studies use micro data covering several different years, thereby takingadvantage of additional variation in fuel price and other variables.
Q8. Why does their model have a dynamic component?
Because their model has a dynamic component, it could predict the year-by-year response to such a policy while taking into account projected changes in income and fuel prices — although the reliability of doing so diminishes if projected values lie outside the ranges observed in their data.
Q9. What is the estimate of the rebound effect for the US?
Their best estimates of the rebound effect for the US as a whole, over the period 1966-2001, are 4.5% for the short run and 22.2% for the long run.
Q10. What is the significance of the aggregate studies?
These aggregate studies highlight the possible importance of lagged dependent variables(inertia) for sorting out short-run and long-run effects.
Q11. What is the elasticity of the VMT with respect to lane miles?
22 Their implied long-run elasticity of VMT with respect to road-miles is 0.020//(1-0.7907)≈0.1, considerably smaller than the long-run elasticities with respect to lane-miles of 0.8 found by Goodwin (1996, p. 51) and Cervero and Hansen (2002, p. 484).
Q12. What is the conservative approach to explain the rebound effect?
In terms of policy, the full specification with 3SLS also happens to be the most conservative approach in explaining their main result, which is that the rebound effect declines with income.
Q13. How much is the rebound effect in the long run?
Thus the average rebound effect in this sample is estimated to be approximately 4.5% in the short run, and 22.2% in the long run.respectively.
Q14. What is the reason why the richer specification is unreliable?
the authors believe that this richer specification is unreliable because it over-fits the data: coefficients on a variable and its lag are in several instances large and opposite in sign, and the predicted desired fuel intensity show implausible oscillations over time.
Q15. What is the suitable base specification for the study?
the authors believe their base specification is the most suitable one given the short time period over which the authors can observe pre-CAFE behavior.