Induced Demand and Rebound Effects in Road Transport

Transcription

1 Induced Demand and Rebound Effects in Road Transport Kent M. Hymel, Kenneth A. Small, and Kurt Van Dender Department of Economics, UC Irvine February

2 Measure three effects of policies or technological changes that reduce the cost of driving. Recent examples of such policies: Light truck fuel economy standards Capacity expansion along congested freeways Since driving is a normal good, reducing its cost should increase it. 3

3 1a. Induced demand: accessibility - Increasing highway accessibility reduces costs of reaching destinations, maybe increases set of possible destinations. - Measure as accessibility-related road capital, K1 (road miles / land area). - Makes motor vehicle travel more attractive, so more of it takes place. 4

4 1b. Induced demand: capacity - Increasing highway capacity reduces congestion. - Reduced congestion lowers travel times and the generalized cost of driving. - As the cost falls, driving and congestion increase. 5

5 2. Rebound Effect: - Making vehicles more fuel efficient reduces the cost of driving. - Reduced cost of driving increases quantity demanded. - Therefore an X% improvement in vehicle fuel efficiency lead to <X% reduction in fuel consumption. - The difference is called a rebound in energy use, from its (naïve) expected value. 6

6 2. Rebound Effect: (continued) - Measuring rebound effect gives the elasticity of vehicle-miles traveled (VMT) with respect to fuel cost of driving. - Thus it also gives us elasticity of VMT wrt fuel price. - This is one component of total price-elasticity of fuel consumption 7

7 3. Congestion Effect: - Increasing fuel efficiency leads to an increase in driving via the rebound effect. - This extra driving leads to increased congestion a potential policy problem. - Complication: congestion dampens the rebound effect. Measure simultaneously. 8

8 Prior Estimates of Induced Demand Usually measured as the elasticity of vehicle-miles traveled with respect to lane-miles. Recent estimates with best controls: ~0.1 short run, ~0.4 long run. Typically restricted to freeways and primary roads. No previous study has distinguished between accessibility and capacity. All measure one or the other or the two combined. 9

9 Prior Estimates of the Rebound Effect Usually measured as negative of elasticity of vehiclemiles traveled (VMT) with respect to the per-mile cost of driving, expressed as % rather than fraction. Most estimates are between 10-15% in the short run and 20-30% in the long run. Some studies don t distinguish short & long run. Small & Van Dender (2007) find it declines with income. They estimate long-run value of 22% over , but only 11% over and still declining. 10

10 Prior Estimates of the Congestion None. Effect Portney et al. (2003) cite the congestion effect from higher CAFE standards as a serious disadvantage of such standards. CAFE = Corporate average fuel efficiency 11

11 Empirical Model Simultaneous equations model of: Demand for driving Vehicle stock Fuel efficiency Congestion The data are a cross section of US states between 1966 and

12 Empirical Model - continued Congestion data Texas Transportation Institute 2005 Urban Mobility Report Provide annual measure of travel delay per adult for 85 largest metropolitan areas. We aggregate to state level. 13

13 Travel demand equation (all in logs) Vehicle miles traveled/adult Per-mile fuel cost ( vma) t m mv m m m = α ( vma) i, t 1 + α ( veh) t + β1 ( pm) t + β 2 ( cong) t + β K1 ( cap1) t + β m 3 X m t + u m t Vehicle stock/adult Travel delay Road-miles/area 14

14 Vehicle stock equation (per adult, in logs) Vehicle stock Price of new vehicle (veh) t = α v (veh) t 1 +α vm (vma) t + β 1 v ( pv) t + β 2 v ( pm) t + β 3 v X t v + u t v Vehicle miles Per-mile fuel cost 15

15 Fuel intensity equation (in logs) Fuel intensity Fuel price ( fint) t = α f ( fint) t 1 +α fm (vma) t + β 1 f ( pf ) t + β 2 f (cafe) t + β 3 f X t f + u t f Vehicle miles Strictness of CAFE 16

16 Congestion equation (in logs) Travel delay Highway capacity ( cong) t cm c c c = α ( vma) t + β K 2 ( cap2) t + β3 X t + ε c t Vehicle miles traveled 17

17 Variables are measured per adult Log-log specification State fixed effects Autocorrelated errors Three-stage least squares 18

18 Vehicle Miles Equation (extract) Variable Coefficient Std. Error lagged veh-miles (***) income (inc) (***) congestion (cong) (***) cong*inc (***) cong*pm (***) fuel cost/mile (pm) (***) pm*pm (***) pm*inc (***) road-miles/land-area (**) autocorrelation (***) Observations 1938 Adjusted R

19 Vehicle Miles Equation (extract) Variable Coefficient Std. Error lagged veh-miles (***) income (inc) (***) congestion (cong) (***) cong*inc (***) cong*pm (***) fuel cost/mile (pm) (***) pm*pm (***) pm*inc (***) road-miles/land-area (**) autocorrelation (***) Observations 1938 Adjusted R

20 Vehicle Miles Equation (extract) Variable Coefficient Std. Error lagged veh-miles (***) income (inc) (***) congestion (cong) (***) cong*inc (***) cong*pm (***) fuel cost/mile (pm) (***) pm*pm (***) pm*inc (***) road-miles/land-area (**) autocorrelation (***) Observations 1938 Adjusted R

21 Vehicle Miles Equation (extract) Variable Coefficient Std. Error lagged veh-miles (***) income (inc) (***) congestion (cong) (***) cong*inc (***) cong*pm (***) fuel cost/mile (pm) (***) pm*pm (***) pm*inc (***) road-miles/land-area (**) autocorrelation (***) Observations 1938 Adjusted R

22 Vehicle Miles Equation (extract) Variable Coefficient Std. Error lagged veh-miles (***) income (inc) (***) congestion (cong) (***) cong*inc (***) cong*pm (***) fuel cost/mile (pm) (***) pm*pm (***) pm*inc (***) road-miles/land-area (**) autocorrelation (***) Observations 1938 Adjusted R

23 Congestion Equation (extract) Table 5. Congestion Equation (extract) Variable Coefficient Std. Error urban-lane-miles/adult (***) vma+urban (**) pop. density (***) percent trucks (***) Observations 1938 Adjusted R

24 Congestion Equation (extract) Table 5. Congestion Equation (extract) Variable Coefficient Std. Error urban-lane-miles/adult (***) vma+urban (**) pop. density (***) percent trucks (***) Observations 1938 Adjusted R

25 Congestion Equation (extract) Table 5. Congestion Equation (extract) Variable Coefficient Std. Error urban-lane-miles/adult (***) vma+urban (**) pop. density (***) percent trucks (***) Observations 1938 Adjusted R

26 Table 6. Summary of Elasticities (extract) (Evaluated on entire sample) Short Run Long Run Total induced demand effect: from expansion in total road mileage from expansion in urban lane widths Rebound effect: VMT with respect to per-mile fuel cost: Congestion effect: Travel delay with respect to fuel efficiency

27 Our estimates of induced demand lower than prior estimates: long-run elasticity 0.20, compared to ~0.40 in literature Effect of increased accessibility is more than twice that of congestion relief. The measures of capacity and usage we use are broader than what has been used in prior studies. 28

28 Rebound effect is weakly affected when we account for endogenous congestion. Rebound effect has appprox. same magnitude & variation as in Small & Van Dender We estimate very small elasticity of VMT with respect to congestion. Congestion effect is quite small. 29