DISSEMINATION AND FUEL POVERTY STATISTICS Presentation to the Oslo Group Andrew Ray Department for Business, Energy and Industrial Strategy (UK central government ministry) 10 May 2017

Dissemination in the UK: Recent Progress Shorter publications, better infographics Starting to move away from spreadsheet tables: developing an energy statistics API, data extraction and tabulator tools A user consultation event on electricity statistics to find out more about what people would like Focus of talk today is on fuel poverty, to show how what we publish is shaped by policy and delivery. Mention most recent publication, that it’s for England only (unlike EE & NEED) and that it relates to 2014 data Fuel Poverty definition May need to touch on after housing cost income

In England we consider a household to be fuel poor if: What is Fuel Poverty? In England we consider a household to be fuel poor if: they have required fuel costs that are above the national average were they to spend that amount, they would be left with a residual income below the official poverty line Households in fuel poverty face both lower incomes and higher energy needs compared to typical households. Mention most recent publication, that it’s for England only (unlike EE & NEED) and that it relates to 2014 data Fuel Poverty definition May need to touch on after housing cost income Annual Fuel Poverty Statistics for England 2014 published 30th June 2016 Based upon English Housing Survey data

The International Context Examples of other ‘objective measures’ from the EU: Households spending more than 10% of their income on energy – Ireland, the rest of Britain. An unofficial definition in France. Where average monthly expenditure on energy represents a ‘substantial’ share of average monthly income of the household - Slovakia. Subjective measures have also been used No internal consensus on definitions of energy poverty. Mention most recent publication, that it’s for England only (unlike EE & NEED) and that it relates to 2014 data Fuel Poverty definition May need to touch on after housing cost income Sources: EU Energy Poverty Handbook launched in 2016 and ‘Manual for Statistics on Energy Consumption in Households’ (Eurostat, 2013 Chapter 7)

Depth of FP would reduce if fewer with very high fuel costs. A relative measure… 14.7% 35.3% Always half below the median but income changes can alter the % in the FP quadrant. 10.6% 39.4% Depth of FP would reduce if fewer with very high fuel costs. Low Income, High Costs Arrows represent individual household (FP gap) Not necessarily about getting people out of FP but reducing the inequality in society by reducing the FP gap Low Income High Costs is a dual indicator, which allows us to measure not only the extent of the problem (how many fuel poor households there are) – which is represented by the shaded quadrant in the bottom left hand corner, but also the depth of the problem (how badly affected each fuel poor household is). The depth of fuel poverty is calculated by taking account of the fuel poverty gap. This is a measure of the additional fuel costs faced by fuel poor households compared to the non-fuel poor household thresholds – as shown by the vertical blue arrows in the fuel poor quadrant. Households can move in and out of fuel poverty in a number of ways – high level explanation of crossing vertical and horizontal thresholds and moving diagonally (combination). However, it is worth noting that as the LIHC indicator is a relative measure, it is difficult to isolate absolute reason for change.

Fuel poverty in England 2003-2014 Emphasis on the fairly stable nature of the indicator…... Explain blue bar fuel poverty level - remained fairly stable Explain orange dots and trend line represents fuel poverty gap which increased between 2003-2009; started to come back down again from 2011 Text from previous slide which includes the changes: In 2014, the number of households in fuel poverty in England was estimated at around 2.38 million, representing approximately 10.6 per cent of all English households. This is The number of households in fuel poverty in England was estimated at 2.38 million (10.6%). This is an increase (1.4%) from 2.35 million households in 2013. The average fuel poverty gap (the amount needed to meet the fuel poverty threshold), fell from £379 to £371 (2%). The aggregate fuel poverty gap across all fuel poor households also reduced over this period, from £890 million to £882 million (1%).

But we target those with highest energy costs Aim to eradicate these. FPEER F/G = most non-energy efficient properties after factoring in additional government support The government has a target to get as many fuel poor homes up to a C FPEER rating by 2030. The latest Fuel Poverty Strategy outlined interim targets of Band E by 2020, and Band D by 2025. Chart shows that progress has been made since 2010 with the majority of fuel poor homes now classified as a D rather than an E The proportion of households in Band E and above has increased over the year from 86.8 per cent in 2013 to 88.4 per cent in 2014, and has seen a steady increase from 78.7 per cent in 2010. This shows progress towards the interim target of getting as many households as reasonably practicable to Band E or above by 2020. Majority of FP HH now classified as band D rather than Band E

with interim targets of: So this year’s publication needs to focus more on the target than on the overall number in fuel poverty… FUEL POVERTY TARGET Raise as many fuel poor homes as reasonably practicable to a minimum energy efficiency rating of Band C by 2030; with interim targets of: Band E by 2020 Band D by 2025  As Peter touched on earlier “ ”….. … and this is important in the fuel poverty context……. By improving a households energy efficiency up to a Band C, in line with the fuel poverty target, you don’t necessarily make them ‘not fuel poor’ – but what we do achieve is an improvement of that households’ ability to be able to heat their home and potential reduction in the fuel poverty gap which may or may not move a household out of fuel poverty.

SE & E have lowest levels To achieve targets we need to know where the fuel poor live LAs with highest levels of FP concentrated in South West, West Midlands and North. In general LAs in SE & E have lowest levels We have been publishing these figures each year down to quite small areas BUT we are reviewing this methodology.

However, we only have estimates based on a survey The English Housing Survey is a continuous national survey of people's housing circumstances and the condition and energy efficiency of housing in England. It is the only survey in England to collect information on both the occupants and physical characteristics of dwellings. But it only covers about 12,000 out of 22.5 million households EHS: This is run by DCLG - they collect data (via interview) every year from around 12000 households Around half of these go on to have a physical survey of their property which includes taking measurements of the rooms, any insulation, they type of walls etc. We use the data from the physical survey to base our fuel poverty estimates on. English Housing Survey (EHS)

The new published figures won’t be detailed enough If we change to publishing more robust figures for Local Authorities with confidence intervals we will please statisticians but not policy makes. Is there a way to target fuel poverty interventions in the absence of detailed figures? Two approaches: Data science methods to try to classify the fuel poor using existing available data Legislation to enable additional datasets to be used. EHS: This is run by DCLG - they collect data (via interview) every year from around 12000 households Around half of these go on to have a physical survey of their property which includes taking measurements of the rooms, any insulation, they type of walls etc. We use the data from the physical survey to base our fuel poverty estimates on. English Housing Survey (EHS)

Data science pilot Take the ‘known’ fuel poverty sample in the survey data Link in a wide range of additional data available nationally on household characteristics Run machine learning algorithms to try to classify fuel poverty – used TPOT In theory can then use same approach model to classify all households http://rhiever.github.io/tpot/ Performs better than simple regression models for fuel poverty But still not as helpful as more relevant real data... 12

Data Can we obtain more data? Within government the data needed to do accurate LIHC targeting exists at address-level, but it is sensitive. Tax Credits ESA JSA Universal Credit Pension credits Housing benefit Etc… Household income Household size Housing costs Low Income (equivalised, after bills) Valuation Office NEED Ordnance Survey EPC Register Property age Property size Property type Central heating Fuel type Boiler info Etc… High Costs 13

Data sharing legislation New primary legislation required before tax credit and VOA data can be used. Proposals were consulted on last year and legislation has just gone through Aiming for new targeting model in 2018 Will be able to provide more accurate maps and data to energy companies 14

Summary Fuel Poverty annual publication has been improved and made more relevant to the targtes we actually use But this has raised questions about the local level statistics – are they robust? In the absence of published statistics that can be used for targeting we will be able to use other means (data linking) to pursue delivery of fuel poverty policies. However legislation doesn’t necessarily allow us to publish these more detailed numbers