Marie Sevenet and Ines Imbert's presentation on methods to measure transport and domestic energy poverty with the "low-income-high-costs" method for the international workshop "Energy-related economic stress at the interface between transport poverty, fuel poverty and residential location", held at the University of Leeds, 20th – 21st May 2015.

1. The two faces of energy poverty
Marie Sevenet, Ines Imbert
European Institute for Energy Research
Households’ energy burden in the residential and mobility sectors
at the city level
(t)ERES Seminar
ITS Leeds, 20th – 21st May 2015

2. Focus of our study
 Proposition of a methodology for the analysis of households’
double energy burden in the housing and transport sector
 Based on the Low-Income-High-Costs (LIHC) indicator (Hills
2011, 2012)
 Coupling of different databases
˃ Data on social and environmental aspects
˃ Data on transport and housing
 Local level  case study on the city of Strasbourg
 French context
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3. Context
 Currently, in France, efforts to develop tools for the analysis of
fuel poverty at the local/territorial level
˃ Studies help to better understand the spatial dimension of fuel
poverty and facilitate the exchange/collaboration between
concerned actors.
˃ But: Due to data restrictions, studies do not allow to precisely
detect/locate the concerned households. Analyses are more
accurate at a higher scale.
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4. Linking housing and transport
 Energy expenses in housing and transport  different focuses
4
Housing Transport
Terms used Fuel poverty Energy vulnerability
Elements of interest - Low incomes
- High energy prices
- Low energy efficiency
- Socio-economic
characteristics
- Location
- Settlement density
- Car dependence
- Accessibility
Indicators - 10% (energy burden)
- LIHC
Mostly based on an energy
burden (different variants)

5. Linking housing and transport
 Multitude of indicators, no consensus on their adequacy to
measure the phenomenon
 Our approach:
˃ Based on LIHC indicator.
˃ Allows to combine energy costs for both sectors and consider
them in the same measurement.
˃ Energy costs threshold is context-specific and does not have to
be fixed in advance.
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6. The LIHC indicator
 Two thresholds:
˃ Energy costs threshold (in our case: transport and housing)
˃ Income threshold (deduction of fixed housing and transport costs)
 Allows to assess extent of fuel poverty (number of households concerned)
as well as its depth (how severely households are affected)
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Source: Hills 2011, p. 139

7. Data
 In France, no database available that provides all the necessary
data:
˃ Income data
˃ Data on energy costs in the housing sector
˃ Data on energy costs in the transport sector
 Necessary to merge data from different databases:
˃ National dwelling survey (ENL)
˃ Census (RGP)
˃ Mobility surveys (ENTD, EMD – HH travel survey)
 Data is merged according to household and spatial characteristics
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8. Variables
 Income
˃ LIHC indicator: After housing costs income measure
˃ Our approach: After housing and transport costs income measure
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Energy consumption
Housing Transport
Energy need For the time being, no
established norms for energy
need
Tool has been developed at
EIFER, based on method
DPE/3CL, which allows to model
energy need with limited data
Use of real consumption data
Variable costs as a function of the
type of vehicle and fuel (unit fuel
consumption) + travelled distance
Use of heating scenarios, as
proposed by DECC
Use the real travelled distance per
household profile

9. Data disaggregation
9
Schema simplifié  Marie
 Spatial pattern
 Household patterns
 Dwelling patterns

10. Case study: Strasbourg
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The question of thresholds:
 Analyse local data using:
˃ National thresholds: allows to compare cities with one
another
˃ Local thresholds: allows to focus on local inequalities
 City context:
˃ Dwelling structure
˃ Mobility structure
˃ Household structure

11. Case study: Strasbourg
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Transport
LIHC
Housing
LIHC
Housing +
transport
LIHC
9%15% 15%
682€/household658€/household 543€/household
Local thresholds:
Average fuel poverty gap

12. Case study: Strasbourg
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Housing
LIHC
12%
Total gap
10 million €
Average gap
682€/household
National thresholds:
Local data Housing
LIHC
10%
Total gap
1.4 billion €
Average gap
555 €/household
National data

13. Policy recommendations
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 Interdependencies between the two aspects  Urban planning
should take the two aspects into account conjointly
 Efficient settlement planning: dense infrastructures, mix of uses,
urban settlement planning along public transport lines, intermodal
connection points etc.
 Example: Location Efficient Mortgage
˃ Program implemented in some housing markets in the US*
˃ Idea: Increase the size of the mortgage for homebuyers that locate
in near transit locations (based on future savings on
transportation)
* see for example: http://www.nrdc.org/cities/smartgrowth/qlem.asp#what

14. Limits / further research
 No recent data available, data from 2009 had to be used
 Surveys for national and local level are not carried out the same
year  use of national thresholds problematic
 A more fine-grained analysis of transport costs might be
possible (see Verry and Vanco, 2009; CERTU, 2011; Nicolas et al.
2012) and should be tested in further studies.
 For the time being, different approaches to determine energy
costs in the housing sector (required energy) and transport
sector (real energy consumption).
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15. Limits / further research
 Research needed to define mobility norms taking into account
accessibility and households’ alternatives for choosing between
different transport modes (see Jouffe/Massot 2013 for a discussion)
 Carry out same case study for other cities  comparison
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16. Thank you!
Contact
Marie Sevenet
sevenet@eifer.org
+49 (0)721 - 6105 1490
Ines Imbert
mayer@eifer.org
+49 (0)721 - 6105 1449
EIFER
Emmy-Noether-Straße 11
76131 Karlsruhe
Germany
www.eifer.org
Copyright © EIFER 2015
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Co-Authors
Patrice Nogues
Elise Nimal

17. Case study: Strasbourg
17
TEE
15%
(Transport &
housing)
5%
TEE
18%
(Transport)
2,5%
TEE
18%
(Transport &
Housing)
16%
Housing
LIHC
15%
TEE
10%
9%

18. Case study: Strasbourg
 Households without children and singles are more affected by energy poverty than other
household types.
 Unfortunately, no reference available
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19. Characteristics of the dwelling
rural/urbanPrivate or social housing
privation or normal
Location
Transport mode
Behaviour
Characteristics of mobility
Fuel poverty

20. Type of space
Scale?
Transports budget
(fixed and variable costs)
associated with
Housing budget (fixed
and variable costs)
Households
Localised in +
Compared to living standards per unity of consumption
Global method
associated with

21. Transport costs per year per transport mode
Method - Transport
For each household type and each type of urban area
Personal Car
Public
transport
WalkingBicycle
Two-wheeled
motor vehicles
Gas
Electric
Diesel
Petrol
See methodology for motorized
modes
Annual €
Data:
ENTD
EMD
BDF
0€
Skateboard
Scooter

22. Transport cost per year
Distance
(Real or average)
Method – Transport:
Personal car and two-wheeled motor vehicles
Cost / km
Type of space (urban area,
city or agglomeration, ville ou
agglomération, fine layers)
Consumption
(per type of car - technology-
horsepower ou cubic capacity,
fuel type)
Household type
(21 categories, INSEE)
Averaged
Incluced
Determines
Variable costs
Houeshold type
For each household type and each type of urban area

23. Method – Transport: car & moto
Cost per km
Linked
Multiply
Fuel tariff
€
Average consumption
(ENTD)
Typology of car
(EMD, EDGT, EDVM)
For each household type and type of urban area
Type of fuel
Engine tax rating or size
Composed of

24. Method – Housing
Housing costs
Each dwelling (household)
Fixed costs Variables costs
Rent / Mortgage reimbursement
Type of energy

25. Method – Housing
For each dwelling
(household) Energy
Detailed databases for
household characteristics
(Insee, 20XX)
Average consumption per
uses and type of dwelling
kWh/m²/year
Consumption of each
dwelling
kWh/year
domestic fuel tariff
euros/kWh
(Pegase, 20XX)
Energy expenses per
household
euros/year