Finding t Finding the he fue fuel poor: an l poor: an - - PowerPoint PPT Presentation

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Finding t Finding the he fue fuel poor: an l poor: an exploration of challenges and ex practicable pract icable s solut olutions ions. Postgraduate symposium on household energy consumption, technology and efficiency, University of

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Lauren Probert School of Civil and Building Engineering Supervisors: Dennis L. Loveday and Victoria Haines

Postgraduate symposium on household energy consumption, technology and efficiency, University of Birmingham, 6th June 2012

Finding t Finding the he fue fuel poor: an l poor: an ex exploration of challenges and pract practicable icable s solut

  • lutions

ions.

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“the inability to afford adequate warmth in the home”

(Lewis, 1982, in Boardman, 1991: 1)

Photo Credit: Paolo Margari [www.flickr.com/photos/paolomargari]

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“…that as far as reasonably practicable persons do not live in fuel poverty”

WARM HOMES AND ENERGY CONSERVATION ACT 2000

Photo Credit: Anonymous [http://www.flickr.com/people/cheddarcheez/]

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Expenditure on WFP in 2009/10: £2.8bn (DECC, 2010). Proposed Annual ECO Affordable Warmth expenditure: £350m (DECC, 2011).

Winter F Fuel Pa Payment

(Source: English Housing Survey, 2009)

In Receipt of WFP Not In Receipt of WFP

Fuel Poor Households

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Expenditure on WFP in 2009/10: £2.8bn (DECC, 2010). Proposed Annual ECO Affordable Warmth expenditure: £350m (DECC, 2011).

Winter F Fuel Pa Payment

(Source: English Housing Survey, 2009)

In Receipt of WFP Not In Receipt of WFP

Fuel Poor Households

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Expenditure on WFP in 2009/10: £2.8bn (DECC, 2010). Proposed Annual ECO Affordable Warmth expenditure: £350m (DECC, 2011).

Winter F Fuel Pa Payment

(Source: English Housing Survey; DCLG, 2009)

Fuel Poor Not Fuel Poor

Winter Fuel Payment Recipients

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Expenditure on WFP in 2009/10: £2.8bn (DECC, 2010). Proposed Annual ECO Affordable Warmth expenditure: £350m (DECC, 2011).

Winter F Fuel Pa Payment

(Source: English Housing Survey; DCLG, 2009)

Fuel Poor Not Fuel Poor

Winter Fuel Payment Recipients

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CERT Super Priority Group

15% of savings be achieved by households that meet tightly defined criteria: (Ofgem, 2011: 55)

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(Dubois, 2012: 2)

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Target Group Eligibility Criteria/ Proxies Data Sources Training Data Set Prediction/ Identification Model Identified Households

Framework for fuel poverty identification (high level overview)

Political Input Validation

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Target Group

  • Official definition (e.g., houses who would need to use 10% of

income to keep their home affordably warm).

  • Modified definition (i.e., Hills Review definition, fuel poverty gap,

blunter definitions).

  • Definition tied to actual energy use – those who are using a lot

more or less than might be expected.

  • Self-reported/subjective fuel poverty.
  • Additional vulnerable groups, e.g., households with children,
  • lder people.
  • Fuel poverty gap (prioritising those with large values).
  • Those home where an investment will have the greatest impact

(Sefton, 2002). OR COMBINATIONS OF THE ABOVE

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Target Group Eligibility Criteria/ Proxies Data Sources Training Data Set Prediction/ Identification Model Identified Households

Framework for fuel poverty identification (high level overview)

Political Input Validation

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Data Sources

(a) Direct identification through database crossing. (b) Geographical identification as a proxy. (c) Decentralised identification. (Dubois, 2012) Further distinguishing element: identification vs. prediction?

Level Identification Prediction Household Local Authority Housing Database/Benefits Data Credit Reference Data Regional

  • Fuel Poverty Indicator
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Identification

  • Requires population data for households under consideration.
  • Very detailed dwelling survey (BREDEM modelling) and income data

required to establish classification (though SAP/rdSAP could provide a close approximation).

  • Assuming perfect data and modelling, perfect outcome is expected.

Prediction

  • Data mining methods have been used previously to predict fuel

poverty classification (see Waddams Price et al., 2012; Fahmy et al., 2011; Hills, 2012, also appears to have used this method).

  • Requires data for a sample representative of the population under

consideration.

  • Does not require same level of detailed data, but variables that are

expected to be predictors (i.e., dwelling age, benefits).

  • Requires Training Data, e.g., English Housing Survey Housing Stock

Dataset.

  • Predicts risk on a household (occupant/dwelling) or geographic

level, doesn’t classify precisely.

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Predictors and Model

  • Should achieve horizontal and vertical efficiency (see Sefton, 2002).
  • Need a robust model, i.e., a regression model, validated, without overfitting.
  • For implementation, must match real world data, not be overly complex.
  • Predictors should be intelligently developed to meet practical requirements.
  • Strong predictors could be used to develop proxies/eligibility criteria to improve

policy efficiency.

Data

  • Need data sufficient to gain significant results for population under

consideration, complete information from predictors.

  • Should be high quality, reliable, and “clean”.
  • Ideally need to include data on both dwellings and occupants to capture nature
  • f fuel poverty (especially under Hills definition).
  • For implementation, cost and accessibility need to be factored in to data usage.
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Target Group Eligibility Criteria/ Proxies Data Sources Training Data Set Prediction/ Identification Model Identified Households

Framework for fuel poverty identification (high level overview)

Political Input Validation

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Real-World D Data S Sources

  • Energy Supplier Data
  • LA Housing Databases
  • Housing Stock Databases (e.g., The Homes Energy

Efficiency Database (HEED))

  • Credit Reference Data
  • Occupants
  • Government
  • Advanced Technology (e.g., thermal imaging)
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Existing Schemes Eligibility Criteria

Warm Homes Discount Scheme Core group: Identified via benefits data as far as possible.

  • aged under 80 and receiving only the Guarantee Credit element of

Pension Credit and no Savings Credit.

  • aged 80 or over and are receiving the Guarantee Credit element of

Pension Credit. Broader Group: Discretionary (approved by Ofgem) and customers need to apply.

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Existing Schemes Eligibility Criteria

Energy Company Obligation (ECO) Affordable Warmth Component “people who are in receipt of state Pension Credit, Child Tax Credit under the ‘free school meals’ income threshold, or people in receipt of either Income Support, Income Related Employment and Support Allowance (where this includes a work related activity or support component), Income Based Jobseeker’s Allowance and at least one of the following:

  • parental responsibility for a child under the age of 5 who ordinarily

resides with the person

  • child tax credit which includes a disability or severe disability element
  • a disabled child premium
  • A disability premium, enhanced disability premium or severe disability

premium

  • A pension premium, higher pension premium or enhanced pensioner

premium.“ (DECC, 2012)

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Target Group Eligibility Criteria/ Proxies Data Sources Training Data Set Prediction/ Identification Model Identified Households

Framework for fuel poverty identification (high level overview)

Political Input Validation

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Fuel Poverty Indicator

www.fuelpovertyindicator.org.uk (CSE, 2012)

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Fuel Poverty Indicator

www.fuelpovertyindicator.org.uk (CSE, 2012)

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hi4em

www.hi4em.org.uk (George, 2011)

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Self-Reported Data

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Conclusions

  • Link between policy and practice isn’t strong, and should be

complementary.

  • We have better data than is being used! There is potential for

targeting to be improved, for example, by using Local Authority and credit reference agency data.

  • A system of rewarding better targeting – government could provide
  • incentives. Could encourage data collection and mapping, improving

efficiency.

  • Local Authorities also have a role to play “sense checkers”,

especially given that the majority of models are predictive – should not be excluded from ECO delivery. Currently relies on Local Authority initiative.

  • Need to consider the development of outcomes and how they link to

policy, i.e., subjective measures of fuel poverty, actual usage, blunter targets.

  • Criteria should be fit for purpose, not so complex as to create

barriers to delivery. Balance is key.

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l.probert@lboro.ac.uk

Thank you.

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Re References

Boardman, B., 1991, Fuel Poverty: From cold homes to affordable warmth. (Belhaven Press, London). CSE, 2012, ‘Fuel Poverty Indicator: targeting fuel poverty at a local level in England’. http://www.fuelpovertyindicator.org.uk. Department for Communities and Local Government (DCLG), 2009, English Housing Survey, 2009, Housing Stock Data [computer file]. Colchester, Essex: UK Data Archive [distributor], July 2011. SN: 6804, http://dx.doi.org/10.5255/UKDA-SN-6804-1. Department for Energy and Climate Change (DECC), 2010, ‘Fuel Poverty Monitoring Indicators 2010’. (DECC, London). http:// www.decc.gov.uk/assets/decc/statistics/fuelpoverty/612-fuel-poverty-monitoring-indicators-2010.pdf DECC, 2011, ‘The Green Deal and Energy Company Obligation: Consultation Document’. (DECC, London). http://www.decc.gov.uk/en/ content/cms/consultations/green_deal/green_deal.aspx Dubois, U., 2012, ‘From targeting to implementation: The role of identification of fuel poor households’. Energy Policy (2012), doi:10.1016/ j.enpol.2011.11.087. Fahmy, E., Gordon, D., Patsios, D., 2011, ‘Predicting fuel poverty at small-area level in England’. Energy Policy 39: 4370–4377. George, D., 2011, ‘Housing Intelligence for the East Midlands (hi4em)’. Presentation to the NEA East Midlands Fuel Poverty Forum, 16th September 2011. Hills, J., 2012, Getting the measure of fuel poverty: Final report of the Fuel Poverty Review. (DECC and LSE, London). Available at: http:// sticerd.lse.ac.uk/dps/case/cr/CASEreport72.pdf Ofgem, 2011, ‘Carbon Emissions Reduction Target (CERT) 2008-2012 Supplier Guidance - Version 3’. (Ofgem, London). http:// www.ofgem.gov.uk/Sustainability/Environment/EnergyEff/InfProjMngrs/Documents1/CERT%20supplier%20guidance%20V3.pdf Sefton, 2002, ‘Targeting Fuel Poverty in England: Is The Government Getting Warm?’. Fiscal Studies 23(3): 369-399. Waddams Price, C., Brazier K., Wang, W., ‘Objective and subjective measures of fuel poverty’. Energy Policy (2012), doi:10.1016/j.enpol. 2011.11.095 Warm Homes and Energy Conservation Act 2000, London: HMSO.