I work as the product manager on the team focusing on the carbon accounting solution from Ignite. As an experiment to make the carbon accounting domain more approachable, and because I had some time to spare and our software solution ready at hand, I have created what might be one of the most detailed personal greenhouse gas (GHG) inventories out there (I would very much like to be challenged on this as I haven't found much else on the topic online, and I would like inspiration to improve it further).
One of the reasons for doing this is to exemplify what many companies are working on these days as a result of stricter requirements for sustainability reporting, while using data that is relatable for most people. It also allows me to go into detail on the process and numbers in a way that I couldn't do with any of our customers' data or Ignite's own calculations.
Figure 1: Here’s an overview of my 2022 market-based household emissions. 2022 was a relatively average year with regards to my emissions when looking at the last 3.5 years.
Most of what I have done so far is described below. I'm very interested in getting questions, comments, feedback, or suggestions for improvements before I plan to share an updated version of this more broadly later in the year. Be warned that this post became longer than I planned for and is quite detailed, yet I hope it might be of interest to some of you!
Highlight summary:
- My personal yearly emissions over the last 3.5 years have been approximately 9.5 tCO₂e on average (market-based scope 2, the location-based value was around 7.5 tCO₂e). For comparison, I filled in the questionnaires for 7 different online calculators for personal carbon footprints, with extremely varying results from 1.6 to 24.3 tCO₂e (average = 9.3 and median = 8).
- To get these estimates, I started with a full transcript of my bank statements for the last few years, cleaned the data, normalised suppliers, classified to spend categories, and mapped to Exiobase emission factors for initial spend-based estimates.
- I also added emission activities that were either not covered by my spending or to overwrite spend-based estimates for certain categories such as electricity, where consumption in kWh should be used instead. This gave me a good overview of my total emissions and where to focus further efforts to improve data quality.
- It was clear that the three main sources of emissions were travelling by air (no big surprise there), electricity consumption (only the market-based numbers for us in Norway) and groceries.
- As most of our household grocery spending is with one chain, and we have accounts there, it was possible to extract product-level data on all our purchases in their stores for the last three years. I then did some work with this data to get both much more granular spend-based estimates and weight-based calculations on almost all the products.
- Analysing those estimates made it clear which food categories contribute the most to our emissions and highlighted the top items to prioritise for looking up specific estimates to further improve the accuracy.
- So far, this has made it possible to quantify the impact of our personal decisions like travel, dietary preferences, and how purchasing renewable energy certificates for our electricity consumption can make a difference. However, it also highlights how different the results sometimes can be from different calculation methods for the same activity (not a big surprise for most people working with carbon accounting I guess).
- I have spent approximately 40 hours on this whole project, including all the work with data for cleaning, normalising and classifying it, estimating spend-based emissions, collecting and estimating emissions from activity and product data, and writing this post. It would certainly not have been possible in that timeframe without a system like Ignite and my experiences over the last three years working in Ignite. This shows that for major corporations with much more complex data than me, getting to this level of detail is of course challenging and time-consuming. However, if I'm able to do it, then they certainly should be able to. And they should have much more reason for doing so as their emissions are typically many, many orders of magnitude larger than mine.
Some caveats before diving into it:
- I have used the GHG Protocol framework (I will abbreviate it to GHGP in parts of this text) with scopes 1, 2 and 3 to categorise the emissions and for calculation guidance. I know it was not created for personal GHG inventories, but it has worked surprisingly well also for this purpose.