Machine learning research to predict corporate carbon emissions published
After more than one year collaborating with the University of Otago on machine learning models to better predict corporate carbon emissions, we’ve published a working paper in the United States Association for Energy Economics. You can read it here.
Big differences in various data provider (Bloomberg, Refinitiv/Eikon, and ISS) estimates of Scope 3 depending on the methodology.
The application of machine learning algorithms can improve the prediction accuracy of the aggregated Scope 3 emissions (up to 6%) and its components, especially when each category is estimated individually and aggregated into the total Scope 3 emissions values (up to 25%).
Overall accuracy of predictions is low (~70-80%) however it is easier to predict upstream emissions than downstream emissions. Prediction performance is primarily limited by low observations in particular categories, and predictor importance varies by category.