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Developing an index of vulnerability to motor fuel price increases in England, based on vehicle inspection data and accessibility statistics

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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.

Published in: Environment
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Developing an index of vulnerability to motor fuel price increases in England, based on vehicle inspection data and accessibility statistics

  1. 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. 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. 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. 4. The context: Motor fuel and oil prices, UK 1990-2016 Source: DBEIS, 2016 Policy- driven Market- driven
  5. 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. 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. 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. 8. A spatial index of vulnerability to fuel price increases - England, 2011 8
  9. 9. English city regions, 2011 London West Midlands Greater Manchester Sheffield CR
  10. 10. Income & car dependence: a regressive spatial distribution? r = +0.10 (England) r = +0.22 (excluding London)
  11. 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. 12. Fuel economy
  13. 13. Income & fuel economy: a regressive spatial distribution? r = +0.60
  14. 14. Index in context of domestic “fuel poverty” VFP FP
  15. 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. 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. 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

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