Speakers:

Ben McNeil

Using machine learning models to predict corporate carbon emissions

date:

Thursday, Jun 2, 2022

Time:

2:30 pm

Summary:

The quality and disclosure of corporate carbon emissions is critical in benchmarking progress towards net-zero emission targets for regulators and the wider market. in addressing climate change. We investigate the use of machine-learning algorithms to predict and forecast corporate carbon footprints, in particular we compare ML models for Scope 1, 2 and 3 emissions, comparing their performance against traditional OLS linear models. We forecast carbon footprints based on the availability of predictors in a three-step framework (Historical Model, Energy Model and Financial Model).  We find that machine learning algorithms improve prediction accuracy for firms without historical emission disclosure data. The largest gain comes from Linear Random Forest model. In contrast, past emission data is the best predictor for disclosing firms, and machine learning algorithms under-perform the traditional OLS estimator for these firms.

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