Ivan and Tishya's awesome hack!

Motivation

We wanted to build a calculator to help better assess the impact of our food choices on the environment.

Methodology

Through our research, we found out in the supply chain for a given food product, the processing of food and transportation of food where most greenhouse gasses (GHG) are emitted.

We used a dataset provided by Our World in Data to determine the amount of GHG emitted by each category of food item (meat, vegetables etc) during production. Based on our research on the country of origin for these categories of food in the UK, we then calculated the amount of GHG emitted by each category of food item during transportation. From our research, most of the food consumed in the UK is produced in the UK. At this stage, all food items sourced from outside the UK were assumed to be from the EU (which is largely true), and shipped by boat (as most food items are).

Further improvements

As of now, the country of origin for each category of food is "hard coded" into our dataset (e.g. vegetables are sourced from the UK). In reality, the country of origin for the actual food item used in the recipe might be different (e.g. broccoli from Australia). Thus, the GHG emissions due to transportation may be higher than what is reported in the calculator.

An area for further improvement would thus be to pull data on country of origin for an individual food item, perhaps from a supermarket stocking database.

Technical Details We used Flask as the main web technology, using basic HTML for the front end and using REST APIs from Spoonacular to get the recipes and ingredients. We also sourced the carbon footprint data (CSV) from our world in data, as we were unable to find free APIs for this. With more data, we could scale this project to encompass more food types.

Data flow: User enters food -> This food is searched for in the recipes API of Spoonacular -> Using this recipe, we get the ingredients of this recipe from Spoonacular -> Match these ingredients against our dataset of carbon footprints and output the final result as a table on the front end

Challenges It was not easy working with new technology in such a short period of time, and ensuring a working prototype. Moreover, we found it rather hard to find the appropriate data sources for this project. A special thanks to the organisers, Spoonacular and Our World in Data for their help and resources :)

Discord usernames and skill ieeps#4477 Beginner Ivan Tan #5831 Beginner

Built With

• api
• flask
• html
• python
• rest