  As an important step to fulfill the Paris Agreement and achieve net-zero
emissions by 2050, the European Commission adopted the most ambitious package
of climate impact measures in April 2021 to improve the flow of capital towards
sustainable activities. For these and other international measures to be
successful, reliable data is key. The ability to see the carbon footprint of
companies around the world will be critical for investors to comply with the
measures. However, with only a small portion of companies volunteering to
disclose their greenhouse gas (GHG) emissions, it is nearly impossible for
investors to align their investment strategies with the measures. By training a
machine learning model on disclosed GHG emissions, we are able to estimate the
emissions of other companies globally who do not disclose their emissions. In
this paper, we show that our model provides accurate estimates of corporate GHG
emissions to investors such that they are able to align their investments with
the regulatory measures and achieve net-zero goals.
