Carbon footprint for your shopping list

In this blog post, I’m going to be showing how I calculated a hypothetical family shopping list emission. We don’t know the carbon footprint of the items we get in the supermarket. So, I thought it will be a nice project to work the carbon footprint of an average shop.

To develop a shopping list. I had to match it closer to what most families will buy. I first got a list of general categories that people spent in the supermarket from money advice service.  

Here they have a graph showing what the average family buys in the supermarket.

As the list shows the average spent in the UK. I used this as a template to make a shopping list. I started finding items in each category. For example, the first item in the list says, “soft drinks”. So, I googled Coca-Cola and clicked to a supermarket page. Of a 6-pack box of the drink. I copied the price and the name into the notion database. Then I moved one to the next category in the list.

After adding all the items. I started to calculate the carbon footprint. First, I had to find the carbon footprint online.  For the highland spring water. It was easy as I found the carbon footprint of one 1.5 litre bottle then I multiplied it by six. As it was a 6-pack item. But this was not done for most of the items in the list. For example, for the apple juice example above. I had to find the carbon footprint 1000 litres of apple juice in this research paper [FIND LINK]. Then I had to find a way to scale it down towards 4 litres.

First, I used this:

90kg co2e per 1000 litres of apple juice

90 / 1000 = 0.09 CO2e per litre

0.09 * 4 = 0.36 grams

I knew the calculation was likely to be wrong, but I was not sure how to fix it.

I used that process for many items for the first time around. For a few products on the list, I calculated the carbon footprint from the amount of protein in the food. I used the data from Our World in Data. To do this I found the item on the Our World in Data copied the carbon footprint it produces into the notion database for reference. Then worked out the protein in the item itself. And used the calculated protein to work out how carbon is generated from the item.

First-time calculations for 6 pack of eggs:

4.21 kg co2e per 100g protein

7.5 protein per egg

7.5 * 6 = 45g protein

4.21kg/100g

4.21 kg co2e / 45g protein = 10.6 co2e

After doing this process for all the items. I found while working carbon footprint out. That I’m dealing with ratios. As process simply working out how grams of protein or net content produces how many grams of carbon.  So, as I was trying to out in ratio fashion. I started struggling. That’s when I got to YouTube. To land on a few khan academy videos explaining how to calculate contents from ratios. I used these two videos to help me. Intro to ratios and Rate Problems.

After a few hits and misses, I found a process that worked for me.

Back to the apple juice example:

90-111kg CO2e/1000 litres

https://link.springer.com/chapter/10.1007%2F978-94-007-1899-9_37

We have the carbon footprint from the research paper.

1000 litres = 1000000 ml

90kg  = 90,000 g

I convert both numbers into the same units. This major mistake did not see the first time around.

90,000 g : 1000000 ml

9 g : 100 ml

I convert the numbers into a ratio format. The ratio is simplified using this website. For further calculations.

100 ml : 9 g

100/100: 9/100 = 0.09

I swapped the ratios around so I can divide apple juice contents, not the CO2e emissions.

After I convert the ratios into base units by dividing dominator by itself. I used the same number for the carbon number in the ratio.

1 ml: 0.09 g co2e

This is the unit rate. For 1 millilitre of apple juice. We get 0.09 grams of greenhouse emissions.

1 * 4000 : 0.09 * 4000 = 4000 : 360

To match it to the item we have. We multiply the apple juice contents by 4000 ml. (4 pack of 1-litre apple juice) with the ratio. And get a ratio that matches are the item. 4000ml of apple juice produces with 360g of emissions.

This process used for all the items. With adjustments. For different measurements and/or multipacks.

They are some exceptions. When I wanted to work out A “Bonne Maman Strawberry Conserve 370G” strawberry jam. I had to work out two carbon footprints. Because a strawberry jam carbon footprint did not exist.

So, my calculations were here:

370 g

Prepared with 50g of Strawberries per 100g, Total Sugar content: 60g per 100g

This information was found the nutritional information.

50 : 100g berries

100g  : 50g berries

100 / 50 : 50/50 = 2 : 1

2/2 : 1/2 = 1 : 0.5

1 * 370 : 0.5 * 370  = 370 : 185

185g strawberries

This section worked out the number of strawberries in the item using the information at the top.

60 g sugar : 100g content

3g sugar : 5g content

5g content : 3g sugar

5/5 : 3/5 = 1 : 0.6

1*370 : 0.6 * 370 = 222

222g sugar

Worked out the amount of sugar.

kg CO2-eq./kg strawberries 0.91 - https://saiplatform.org/uploads/Library/WG%20Fruit%20-%20ART%20Final%20Report.pdf

1kg : 0.91kg

1000g : 910g

100g : 91g

91g berries : 100g co2e

91/91 : 100/91 = 1 : 1.09

1 * 185: 1.09 * 185= 185: 201.65

Now we work out the carbon footprint of strawberries in the product.

201.65g co2e from strawberries

0.6g of CO2 equivalent is produced for every gram of sugar made.  

https://www.fwi.co.uk/arable/carbon-footprint-of-sugar-revealed

1g sugar : 0.6 co2e

1 * 222g : 0.6 * 222 = 222 : 133.2

133.2g from sugar

Working out the carbon footprint of sugar in the product

201.65g + 133.2g = 334.85g CO2e

Adding the carbon footprint of sugar and strawberries. To have the total carbon footprint of the item.

Plotting Data

After I was happy with carbon footprint calculations. I wanted to plot them. I did this using pandas and matplotlib.

This was the first plot. Using the footprint and price. I don’t think it’s that helpful. Also, I noticed the y-axis is in the thousands because of grams. But most items are 2,000 grams at most. So, I decided to covert y-axis into kg.

The y-axis looked somewhat cleaner. So, I decided to look for more ways to improve the graphs. This led me to add more columns to the database which may have a closer correlation with the carbon footprint. I decided the add the weight of the item. And protein per 100g. As some items that had high protein may have high emissions like meat-based products.

Got the weight and protein numbers from the supermarket product page.

These are the graphs:

To my surprise, there is less of a difference. But I think some the outliers may be messing with the graph. The biggest outlier in the database is Talia rice 10kg and has a carbon footprint of over 35kg. Most items in the list weigh less than 4kg. So, I replaced with 1kg version. To prevent it from skewing the rest of the dataset.

We can see y-axis is now only 3500 grams. To get an idea which items are we looking at. I created another graph which labels all the items.

Labelling all the items makes the graph look very cluttered. But we can see some outliers.

The one that stands out is ice cream. The Ice cream produces a lot of emissions. The rate of a vanilla ice cream I found from this research paper. Is 3.94 kg co2e = 1kg of ice cream consumed. So for every 1 kilogram of ice cream 3.84 kilograms of carbon equivalent is produced. This is the highest rate of any items that we have on the list. And something that came as a big shock. The little research I have done is normally meat. Mainly beef.  With 100g of protein-producing 49.89kg co2e. That is one of the most polluting foods. This is because of the land use. As cow graze the land, they produce a lot of methane. And the land is changed to accommodate this. Which meaning chopping down trees to make for farmland.

Ben and Jerry did a life cycle analysis of their ice cream. And worked one pint (568.261 ml) of ice cream produced 2 lbs (0.9 kg) of emissions. And most of the emissions (52%) comes from the ingredients. Mainly diary. Because of the same issues with beef. Cows produce a lot of methane a very potent greenhouse gas. Methane is 21x more powerful than CO2. Also, Land use change adds a lot to the carbon footprint. Due deforestation to make up for cattle land.

Observations:

As the graphs don’t make it that clear on how much items have the biggest carbon footprint. I sorted the dataframe using pandas sort values. To find the items with the biggest footprint.

df_sorted = df.sort_values(by=['Carbon Footprint (kg)'], ascending=False)

This is the top five items with the highest footprint. As ice cream as the biggest outlier. Then potatoes as far second. But notice these are slightly heavy items the potatoes and sunflower oil. As they weight multiple litres and kilograms. Unlike most items in the list which are simply in grams.

The least polluting items in the list are.

The smallest footprint is mango. As the ratio between a mango and the emission. 1 kg of mango to 0.45kg co2e. Which is one of the smallest rates. Also, the item only weighs 250g so the net content is low. The highland spring water produces a low amount. As for each 1.5-litre bottle, only 45g is produced.

The total carbon footprint of the shopping is 105.97 kg co2e. 

This show how much carbon we produce. We don’t do this by purpose. As we have little feedback loops. Highlighting this. We get this problem. A carbon tax may be the answer to have a stronger feedback loop.

Comparisons:

Let’s make comparisons on some of the items in the list.

The average new car emits 120.1g/km of CO2  - lightfoot

The ice cream has a carbon footprint of 36.2. Driving a car for 300 kilometres (186 miles) produces 36 kg of co2. So, your ice cream has the has amount of emissions to the short road across your country.

The next comparison is potatoes. The potatoes have a carbon footprint of 12kg. A car trip to produce 12kg is 100 kilometres (62 miles). This can be a simple road trip to escape the city.

Now the total carbon footprint of the shopping list 105.97 kg co2e. A car trip equivalent of that will 880 kilometres (546.8 miles). A trip like that produces 105.6 kg co2e. That’s doing a pretty major road trip you’re going to do.

Conclusion:

Doing regular shopping can create a lot of emissions. Granted most of the emissions are not in our control when we get to the supermarket.  As most emissions generated when making the product itself. Long before it gets transported to the supermarket by freight. Transport emissions for food only account for 6% of global food emissions.

But this is not to say all hope is lost. You make personal changes reduce your carbon footprint. You can stop eating foods that have a high carbon footprint. Which means giving up most meat-based products. (I love burgers too much, so I won’t take this advice myself.) But if you are open to the idea of becoming a vegetarian. I will add the numbers to support the point.

·       Beef (beef herds) produces 49.89kg co2 per 100 grams of protein.

·       Lamb and mutton produce 19.85kg per 100g of protein.

·       Farmed prawns 18.19kg co2e per 100g of protein.

·       Diary produces 16.87 kg per 100g protein.

https://ourworldindata.org/grapher/ghg-per-protein-poore

While Tofu produces 1.98kg co2e. Oatmeal produces 1.91kg co2e. And nuts only contribute 0.26 per 100g protein.