Abstract

How do our food choices affect climate change?

Different approaches were proposed to predict the carbon footprint of products from the different datasets provided by CodeCheck.

Multivariate linear regression and random forest regression models perform well in predicting carbon footprint, especially when - in addition to the nutrition information - the product categories, learned through Latent Dirichlet Allocation (LDA), were used as extra features in the models.

The prediction accuracy of the models that were considered varied across datasets. A potential way to display the footprint estimates in the app was proposed.

Citation information

Data Study Group team. (2018, September 13). Data Study Group Final Report: Codecheck. Zenodo. http://doi.org/10.5281/zenodo.1415344

Additional information

Angus Williams, Quantum Black
Ayman Boustati, University of Warwick
Diego Arenas, University of St Andrews
Jan-Hendrik de Wiljes, University of Hildesheim
Marina Chang
Marton Varga, INSEAD
Matthew Groves, University of Warwick
Reza Drikvandi Reza, Imperial College London
Taha Ceritli, University of Edinburgh

Turing affiliated authors

Research areas