As the outlook for oil prices remains uncertain, this paper develops a method to assess which areas of England would be most vulnerable to future motor fuel price increases. Building on previous research, we define and operationalise three dimensions of vulnerability: exposure (the cost burden of motor fuel), sensitivity (income) and adaptive capacity (accessibility with modes alternative to the car). We exploit unique data sets available in England, including the ‘MOT’ vehicle inspection data and DfT Accessibility Statistics. This allows us to map vulnerability to fuel price increases at a spatially disaggregated level (Lower-layer Super Output Areas), taking into account motor-fuel expenditure for all travel purposes, and the ability of households to shift to other modes of travel. This is an advancement on the ‘oil vulnerability’ indices developed in previous international research.

1. Institute for Transport Studies
FACULTY OF ENVIRONMENT
Developing an index of vulnerability to
motor fuel price increases in England,
based on vehicle inspection data and
accessibility statistics
Giulio Mattioli, Ian Philips, Jillian Anable & Tim Chatterton
Institute for Transport Studies, University of Leeds
I.Philips@leeds.ac.uk
UTSG Annual Conference, Dubllin
5th January 2017

2. The (t)ERES project
(2014-2016)
‘Car-owning households who need to spend a
disproportionately high share of their income to
get where they need to go, with negative
consequences in terms of restricted activity
spaces and/or spending cuts in other essential
areas’
≈ ‘forced car ownership’, ‘transport poverty’…

3. Indicators
1. A material deprivation-based
indicator of CRES
2. A ‘low-income high-costs’
indicator of CRES
3. A spatial index of vulnerability
to fuel price increases
Data
1. EU-SILC 2005-2014 (UK)
2. Living Costs and Food Survey
(LCFS) 2006-2014 (UK)
3. Anonymised MOT tests with
keeper and derived results data
, income data and accessibility
statistics (England LSOA)
3
(t)ERES studies

4. The context: Motor fuel and oil
prices, UK 1990-2016
Source:
DBEIS, 2016
Policy-
driven
Market-
driven

5. ‘Oil vulnerability’ research
Dodson et al. (e.g. Dodson & Sipe, 2007)
Australian city = “regressive city” –
2 urban structural effects:
1. “low socioeconomic status and high car dependence are strongly co-
located” (Dodson & Sipe, 2007, p.57)
2. “socioeconomically less advantaged households are spatially co-distributed
with less efficient motor vehicle technologies” (Dodson et al., 2013, p.10)
BUT “the socio-spatial structure of Australian cities differs from many overseas
jurisdictions, particularly (…) Europe (…) given different socio-spatial and
transport geographies” (Dodson & Sipe, 2007, p.58)

6. 3 spatial components of vulnerability
to fuel price increases - England
6
1. Exposure:
Cost burden ratio = per
household expenditure
on fuel / median income
2. Sensitivity
Median household
income
3. Adaptive capacity
Travel time to 8 key
services by public
transport / walking
(Anonymised MOT tests and results) (Experian Median Income data) (UK Government Accessibility Statistics)

7. A spatial index of vulnerability to fuel
price increases - England, 2011
Standardise each component variable (z-scores)
vulnerability to fuel price increases (VFP)
VFP = f(Exposure , Sensitivity , Adaptive Capacity)
VFP = cost burden – income + travel time

8. A spatial index of vulnerability to
fuel price increases - England, 2011
8

9. English city regions, 2011
London West Midlands Greater Manchester Sheffield CR

10. Income & car dependence:
a regressive spatial distribution?
r = +0.10 (England)
r = +0.22 (excluding London)

11. Income & car dependence:
a regressive spatial distribution?
West Midlands Greater Manchester West Yorkshire Sheffield CR
r = +0.23 r = +0.22 r = +0.23 r = +0.22

12. Fuel economy

13. Income & fuel economy:
a regressive spatial distribution?
r = +0.60

14. Index in context of domestic
“fuel poverty”
VFP FP

15. Where do VFP and FP
interact?
There is no correlation between the two
forms of vulnerability, but ‘doubly
vulnerable’ areas exist and have a distinct
profile. That could be quite interesting to
develop.

16. • Spatial patterns:
• no sign of “regressive city” – but maybe “regressive country”?
• Australian pattern not seen in England. Vulnerability to fuel price increases is
a problem here too, but it plays out differently
• Scope for contextualising the indicator in a wider framework – IMD, Housing,
Physical capability to travel by active modes other possible adaptations.
• Regional metrics
Conclusions

17. Institute for Transport Studies
FACULTY OF ENVIRONMENT
Thank you for your attention!
I.Philips@leeds.ac.uk @ianphilipsITS
G.Mattioli@leeds.ac.uk @giulio_mattioli
https://teresproject.wordpress.com/
@TranspPoverty
www.demand.ac.uk
@DEMAND_CENTRE
www.MOTproject.net