This project examined how energy efficient Ireland’s homes are using data published by Ireland’s Central Statistics Office (CSO) from 2009 to 2019. The data as presented in the article allowed readers to understand how efficient houses are both on a national and local level by utilising the data collected on homes which have had energy assessments carried out. The data also documented the dominant type of heating used in each county.
With climate change to the forefront of many people’s minds, this story provided an insight both into the energy efficiency of Ireland’s homes and also showed how reliant the country is on using fossil fuels to heat them. Analysing the data showed that just one in 20 homes has the top energy rating of A, the majority of these in Dublin, the capital. The county with the lowest number of A rated homes was in Leitrim, the least populated part of the country, which along with Roscommon, also had the highest proportion of G rated homes (the least energy efficient rating) in the country. While analysing the data showed discrepancies in the energy efficiencies between different counties, it also showed the differences in energy ratings within each of the Dublin postcodes showing that older, more “established” areas (ie Dublin 4), had less A rated houses than “newer areas”, further away from the city, such as Dublin 13 which had the highest number of A rated houses in Dublin.
The primary tool I used for this project was Google Spreadsheets. I downloaded the information from the CSO website and carried out all cleaning and analysis using this programme. The cleaning primarily involved grouping different ratings together to provide an easy to understand picture of each rating (ie merging together A1 and A2 ratings to give an overall A rating for each county). This was done in line with the SEAI’s own categorisation of ratings (ie A, B, C). I then ran simple analysis by filtering the various columns to ascertain the counties with the highest – lowest energy ratings etc. I also ran calculations to ascertain the number of houses and apartments BERs had been carried out in so I could inform the reader ie, “One-fifth of all assessments have been carried out in apartments, with the rest being houses”. I also used Google Spreadsheets when analysing the main space heating fuel used in each home and again used the filter option to ascertain if there were any fuels that were more dominant in some counties than others (ie the reliance on solid fuel in Offaly). Datawrapper was then used to create the graphs.
What was the hardest part of this project?
There has been much talk in Ireland, as in most countries, about climate change and the steps needed to reduce our carbon footprint. I believe this article should be considered as retrofitting of homes and making them more energy efficient is one of the issues that comes up often in the context of fighting climate change and as such I felt it necessary to examine how efficient, or indeed inefficient, our homes really are and provide some context through data around the issue. I believe the article does just that; gives an insight into the true energy efficiency of our homes on a national scale and also provides insight into our reliance on fossil fuels (particularly gas and oil) to heat our homes. I believe this project adds to the important conversation around climate change and analysing and presenting the data as per the article gives people a greater understanding of not just the scale of retrofitting that may be required to reduce our home’s energy consumption and therefore carbon footprint, but also how their home, particularly for those with a low rated home, is having some impact on emissions. The most difficult part of the project was the condsieration given to how to present it in an easy way for the reader. While maps were considered and tested, presenting the graphs in the format found in the article was found to be the easiest way for the viewer to get to grips with the data.
What can others learn from this project?
From this project other journalists could learn the value of looking past the press releases issued by bodies such as the Central Statistics Office and examining the vast amount of data that informs them to see what other stories can be gleaned from the data.