Energy Funding: Benefiting from benefit data?
Disclaimer: I'd like to start by apologising for the following festive analogy - I'm well aware that 'tis isn't the season to be jolly' (yet), however I feel it makes a necessary point. So, you're just going to have to deal with it.
We've all received Christmas presents that - although given with the best of intentions - have left you scratching your head pondering, 'when on earth am I going to use this?' The contents of attics, basements, and the backs of wardrobes are testament to this; filled with an array of toasty makers, neon disco balls and yoga mats. Similarly, certain datasets can often be a lot like unwanted Christmas presents. We take one look at the data, accept it for what it is, then leave it in a file somewhere on a hard drive - never to be thought of again. However, just as we decide (usually out of the blue) that we'd quite like a toasted Nutella sandwich, to hold a rave in our front room or have the ability to touch our toes again, there are times when previously undesirable data is exactly what is required.
As part of the ODI Leeds Energy Data Infrastructure project - collaborating with Leeds City Council and SmartKlub - we previously outlined our intention to investigate existing energy funding mechanisms. This is set to be published in December, however we've decided to produce a short prequel to the report, detailing the framework and some of the key points. We've left it as an open doc, so if you have any questions or would like to make any specific comments then please take the chance to do so.
One of the reports key outcomes is to detail the recent mechanism changes, such as to ECO and the RHI, highlighting how these will impact the data requirements for attracting funding into an area. One of the most important changes to ECO (now referred to as ECO2t) relates to the allocation of funding between obligations. As a result, the home heating cost reduction obligation (HHCRO) has been earmarked for an increase in funding; rising from an estimated £288m/year to £448m/year. More importantly though, is the simplification of eligibility criteria for HHCRO - specifically those in receipt of certain benefits. Claimants receiving financial support, such as job seekers allowance (JSA), income support (IS) or employment and support allowance (ESA), now fall within the 'help to heat group' without having to meet any further sub-criteria. This is important, simplifying the prospect of identifying the geographical locations of those potentially eligible under ECO2t (in case you were wondering, this is where unwanted Christmas presents and dataset equivalents come into things).
The Department for Work and Pensions (DWP) release quarterly statistical updates on benefit recipients, made available principally at a parliamentary constituency (PC) level. By combining this data with ONS population estimates, it is possible to determine which areas contain higher percentages of people on specific benefits. We've visualised this data for the entirety of Great Britain in our constituency Hex Maps, however for our funding report the geographical focus has been narrowed to the areas of Leeds, Bradford and Calderdale.
Please forgive the imminent barrage of acronyms but, by using the IS, JSA and ESA benefit data - as a percentage of constituents, while also considering the constituency areas - it is possible to visualise higher concentrations of potential ECO2t-eligible residents. This is fine on a national level, but for use at city level we're going to need data that's a little more detailed. And that's what we've been doing. Using MSOA and LSOA data located on Nomis (ONS labour statistics platform) and Stat-Xplore - a frustrating and difficult to use, DWP online data tool - we've been trying to better highlight where these areas exist.
Of course, this forms just a section of the data that's necessary to unlock ECO2t funding, however it perfectly displays that you can find value from places you'd not previously considered. Who would have guessed that access to energy funding could be improved by utilising benefits data; perhaps there's hope for those old Christmas presents too?