Limitations and future work

The model used in this study could be used in future research to examine the effects of local policy interventions such as congestion charges on vehicle mileage. It might also be used to investigate the effects of changes in public transport provision over time. Doing so would require data at a higher level of geographical detail than the postcode area data provided in the publicly available MOT dataset used in this study (see appendix B.10).

One limitation in the data used is the size of postcodes, which are somewhat socially and geographically heterogeneous. The ideal data would have the geographical resolution to better distinguish between urban and rural areas as public transport and environmental policies are predominantly present in urban contexts. The effects of local policies could be investigated using dummy variables on the year of introduction in each city and public transport trips could be introduced as an additional time series continuous variable. More granular geographical resolution may also identify even higher ‘hot spots’ of price elasticity and may be able to better control for average income and other socio-economic factors.

If public service provision is indeed a factor determining the fuel price elasticity of mileage it would draw into question the convention of assuming rebound effects are of equal magnitude to fuel price elasticities and thus merits further research. Similarly, possible vehicle switching (discussed in section 4.5.3) would also mean ηP_E(S) may not equal -η_ε(S), though would likely still remain a similar order of magnitude at the aggregate level across all vehicles.

Additional considerations cover possible non-linearities in the response to fuel prices.

The average fuel price across driving periods changed by a maximum of ±20% over the time period investigated (see table B.2). Changes outside this range may lead to larger elasticities.

This could have implications for rebound effects as shifting to an electric vehicle can lead to marginal travel cost savings in the order of 50-70%.

4.6 Policy Considerations

This chapter investigates the effect that vehicle efficiency improvements might have on stimulating higher mileage, known as the rebound effect. The magnitude of this effect in Great Britain between 2006 and 2017 is estimated by quantifying the response of vehicle mileage to fuel price changes. The findings show that vehicle efficiency improvements are unlikely to trigger short-term increases in vehicle mileage. This means the direct rebound effect is likely to be small ≈0.046 and any additional mileage stimulated by efficiency improvements is unlikely to significantly reduce energy savings in the short-term.

British drivers’ mileage is shown to be inelastic to fuel price changes. A 10% increase in fuel price would lead to a 0.46% decrease in mileage; for the average vehicle travelling

≈7400 miles per year, this is equivalent to a decrease of just 34 miles. Fuel taxes are therefore unlikely to have an important short-term effect on vehicle mileage.

Whilst these effects are small, the findings show that drivers of larger and less fuel efficient vehicles are more responsive to fuel price changes than average. Drivers in rural areas with relatively high annual mileage are also found to be less responsive to fuel price changes than drivers in more populous areas, which are possibly less dependent upon the private vehicle. Since a number of these rural areas have lower than average income, this raises social equity concerns. If car dependent drivers are unable to adjust their mileage in response to changes in fuel price (whether from changes in fuel tax or from market fluctuations) they may have to absorb the additional costs of travel.

Chapter 5

The future potential energy savings of efficiency improvements

5.1 Estimating future transport emissions

This PhD thesis aims to understand how much technical changes in vehicles, be they incre-mental efficiency improvements or sales shifts to new technologies, might impact future energy use and emissions. The findings of chapter 3 showed that sales shifts between petrol, diesel and hybrid vehicles played a relatively minor role in determining the energy intensity of vehicles between 2001 and 2018, though this is likely to change in future.

Chapter 3’s findings show that 65% of the past technical improvements were offset by the increasing size and power of vehicles. This means for every unit of technical improvement, the energy intensity of vehicles improved by just 0.35 units. In chapter 4, the direct rebound effect is shown to be ≈0.046. This means for every unit of energy intensity improvements, the energy savings will be 0.964. Bringing the findings from these two chapters together shows that in the past two decades the majority of lost potential energy savings are likely to have been from increasing vehicle size and power rather than any increased mileage from the rebound effect (summarised in figure 5.1). Even if the rebound effect is larger in the long-term, it would in all likelihood still remain far less significant than the effect of vehicle attributes. This result shows that 66% of the energy saving potential of technical improvements has been offset by driving slightly more, in larger and more powerful vehicles.

Technical Improvement

Energy Intensity

Improvement Energy Savings

1 unit 0.35 unit 0.34 unit

X 35% = X 96.4% =

Fig. 5.1 Summary of findings from chapters 3 & 4. For every unit of technical improvement, energy intensity improved by 0.4 units due to increasing size and power. Energy savings then improve by 96.4% of this due to the rebound effect.

The aim of chapters 3 & 4 was to estimate the magnitude of these effects historically.

This chapter builds on these historical findings and investigates the impact of incremental technical efficiency improvements, changes in vehicle attributes, technology switching and rebound effects on future energy use and CO₂emissions.

Estimating future emissions is imperative to make informed decisions about limiting the damaging effects of climate change. To determine the impacts of emissions reduction measures, and prioritise between them, it is important to quantify their likely impact. As discussed in chapter 2.3, past work has predominately relied on scenario analysis to prioritise policies. Analyses of this type can be useful, but they are hampered by the uncertainty of future trends and are particularly dependent upon subjective assumptions of parameter values.

This constraint is overcome using formal sensitivity analysis techniques that have not yet been applied to study future emissions from the transport sector.

The aim of this chapter is to explore the likely range of possible futures for the passenger vehicle fleet in the UK, quantifying the CO₂emissions in each using a vehicle stock model.

For the first time, Sobol sensitivity analysis is used to rank the importance of input variables on future transport CO₂emissions. The use of this novel method can evaluate the relative importance of input variables and thereby help to prioritize interventions to reduce emissions and highlight critical variables to consider in future work.