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CO2 Footprinting - Progress and Insights: Transportation and Food Overshadow Everything Else


My CO2 footprinting app in its current state allows me to model the things I do everyday and annotate my “events” with emitted CO2.

So one could say I advanced from the development stage to a data curation stage. Because what I have to do now is mostly to look up the emission data of the everyday things I do, up to a point to which they can be resolved and cast them into a couple of numerical values that can be passed to the emission functions of my actions to calculate emission values. Examples are, the ingredients of meals I ate, those ingredients' amounts and their respective production emission, distances between places I drove, the emission of our car per km, power consumption of the devices I use, or emission per kWh of our electricity plan.

Some surprises


I made some surprising observations on the way that put some of the things I do into perspective. Before I continue describing them though, I have to put out a disclaimer, which I'm going to motivate in the paragraphs below: The data I have in my app is of course highly incomplete! Thus, conclusions drawn from it may be highly biased. I do however think, that some general trends can be seen.

First of all, I always thought my computers' power consumption was one of the major drivers of my emissions. However, putting in the numbers I can clearly see that those are significantly lower than the ones of the true main factors. And those are: food and transportation. Since food is something one really consumes regularly, many times a day, this accumulates to much. And some of the things I eat are causing a surprisingly high amount of emissions. I underestimated the amount of variation between the different foods, and was most of all shocked by how much emissions are caused by dairy products. Also was I shocked by the amount of emissions caused by car transportation, even if driving short distance and using a small car.

Computers and such


We have an Intel NUC, Synology NAS (including 4 disks), 1x raspberry pi2 and 1x pi3 running 24h a day. The emissions of these were the first things I calculated using my app, as I assumed they were the biggest factors. They amounted to about 220-370g co2e daily (and this is a range because this is as accurate an emission value I could find for power providers in Denmark). There are also other factors I neglected because of lack of time, such as electrical consumer devices running in standby. I did investigate one other device though, and that was my stereo which uses 30W when I just let it be turned on when I'm not actually using it. Doing this 24h a day, would cause 115.20-209.52g co2e. I started switching it into standby immediately, where it then uses less than 1W. Another thing I calculated were the LED lamps in our living room. We have them turned on in the evenings. Assuming that they are turned on for about 5h, this causes 20.00-36.38g co2e per day.

Eating


I use to eat a bowl of yogurt most evenings. That bowl causes an estimated 520g co2e daily. In comparison, that is more than my NUC, NAS (Including 4 disks), 1x raspberry pi3 and 1x pi3 produce together daily when running 24h (~220-370g)! This really shocked me. A similar story is the daily oat meal I eat in the morning. It's nothing fancy: oat flakes, raisins and milk. ~311g co2e. And I didn't stop here. Another finding that didn't actually surprise me too much but rather confirmed my bad expectations is the emissions caused by my coffee consumption. Since I couldn't find emission values for the exact coffee bean I use to buy, I had to calculate a rather rough estimate across all kinds of possible coffee beans and producers. Here, the caused emissions vary extremely. When putting in all these numbers, a liter of coffee, including bean production and power consumption of grinding and brewing causes emissions somewhere between 130-900g! So even if I assume that the fair trade ecological coffee bean I buy is somewhere in the average, it's still causing around ~500g/L. And these days I – unfortunately – drink about two liters a day. So that is also quite the sizable sum of 1kg co2e daily. Damn.

Transportation


The most surprising – and at the same time shocking – insight though was, that transportation easily overshadowed all of the other things of my everyday life. Taking a comparably small Aygo 1.0i for a 5km ride to the grocery store and back causes between 515.27-567.32g co2. Averaging it out and assuming we do this tour three times a week, this would cause around 270g co2 daily. This feels like an insane amount for just 5km! Then I wanted to know how much a drive of about 140km to the family from Jydland to Odense and back causes: 28-31kg co2. Assuming we did this drive maybe once a month, it would still mean it caused about 1kg co2 per day when averaging it out. Lastly, I calculated how much it causes when we visit my family in Germany (with the Aygo 1.0i). One trip is around 900km, and driving there and back causes between 180-200kg co2. Before Covid19 we did this trip once every half year, leading to a daily average of around 1-1.5kg co2. Summing all these transportations up I was left baffled when seeing how few such drives already cause emissions to climb up rapidly.

How much is OK?


After calculating all those numbers I was quite puzzled, as to whether they would leave me in the low or high end of the range – and whether the number I got out would possibly even be unsustainable. I googled, and found a page that claimed, that 3t per person per year are sustainable. OK. Other than that page, I only found pages that said, that the exact limit was not known. Which I also believe to be the true answer here. But for the sake of my argument, I would assume that 3t are the correct value. If I sum up all the values I outlined above, I would end up at about 5kg per day (correct me if I'm wrong). That equates to ca. 1,8t per year. Wow. That is already quite a lot, considering that I wasn't even close to having put in all my data into the app. So I fear that it it actually ends up being a lot higher.

These things made several things quite clear to me that we (I and everyone) have to achieve if we want to solve this climate crisis:
  • all of us need to reconsider and rethink how and what we are eating. Obviously, dairy and meat are the biggest emitters foodwise so it can't possibly be sustainable if many of us eat these things on a daily basis in large quantities.
  • transportation needs to be minimized, at least as long as the co2e/km is as high as with today's cars (and I believe that to include electrical cars, as their production footprint is so much higher). This is something, that has to be tackled on the political level by governments: jobs and families have to be located close together. I don't see this working out, if people need to commute and drive long ways with their cars.

Ok. What are the challenges?


While using the app and filling it with life by putting in the emission values and functions, I stumbled over several challenges.

The biggest one is clearly, that emission data is only available up to a certain degree and resolution. The emission of our car for instance is only given as a single average emission value, but not as a function of the speed. Hence, assuming this value for a fast ride will underestimate the emitted co2.

Also, some data is simply not available at all. While I could find production emissions for the basic foods, for more complex composite foods such as Prinzenrolle there is simply no value available. My solution here is that I calculate the emission as the sum of the production emissions of the food's individual ingredients.

Another example is, that I couldn't find an emission value per kWh for our exact electricity provider and power plan, but only a general average one for whole Denmark. This completely neglects that some people intentionally pay more for a regenerative power plan.

The same lack of data resolution was present also for other things:
  • imported vs. local foods: the transportation chain seems to be mostly neglected by the sources/databases for emission values. Here it will make a huge difference whether a food is consumed locally or after export
  • same type of food but of different producers: most databases provide a general value per food production, completely independent of where it is being produced or by which company
So generally, there is only so much one can do in terms of tracking the own emissions, simply because for many things there is no emission data available. I would love to build this project up and provide producers of goods with an API that they can use to feed in their emission data directly. Right now they don't even have this kind of data themselves, and hence I do think that this is another aspect governments should put focus on. Being able to trace the caused emissions as far down these complex paths, would allow consumers to make more sustainable life decisions about what to eat, where to live, how often to drive, or where to look for a work place.

Apart from the data availability and quality problem, another problem is the usability of the app. It's simply not motivating, having to go through an unresponsive website to fill in complex data about what one eats, where one drivers or which device one uses. This process should be much simpler, for people to actually keep using it. I observed this with myself. Once I reached an OK level, I just stopped filling in data because it was too tedious.

Despite these challenges though, I would still claim that I gained valuable insights into my emission footprint caused by things I do everyday. I don't think this idea is a dead end, but of course its accuracy is bound by the resolution and accuracy of the data it curates. But if people only got as many insights into where their emissions are coming from as I did, that would already be a huge success for me.

What next?


There are many things one could do next. They basically follow from the challenges outlined above.
  • Manual data curation: Filling in more data, and thereby making my own footprint more complete and accurate
  • Mobile app: Using the website to fill in the things while I'm doing them is unacceptable and inaccessible. I would love to develop a mobile app to be able to connect to the website. Doing data curation on the fly, when only one hand is free would automatically boost step 1. (at least for me)
  • Connecting this project with other emission data providers, providing query interfaces, maybe importers.
  • Getting good producers on board, that could directly contribute emission data for their products.
As of now, I'm undecided what I will do next. I put the project on hold for about a month now, due to personal and professional stress levels. I'll have to see, when I'll have the energy to do some more work on it.