Companies worldwide contribute to climate change, emitting significant amounts of greenhouse gases (GHGs). Yet, most do not report their direct or Scope 1 emissions, resulting in a large data gap in corporate emissions. This study aims to fill this gap by training several decision-tree machine learning models to predict company-level Scope 1 emissions. Our results demonstrate that the Extreme Gradient Boosting and LightGBM models perform best, where the former shows a 19% improvement in prediction error over a benchmark model. Our model is also of reduced complexity and greater computational efficiency; it does not require meta-learners and is trained on a smaller number of features, for which data is more common and accessible compared to prior works. Our features are uniquely chosen based on concepts of environmental pollution in economic theory. Predicting corporate emissions with machine learning can be used as a gap-filling approach, which would allow for better GHG accounting and tracking, thus facilitating corporate decarbonization efforts in the long term. It can also impact representations of a company’s carbon performance and carbon risks, thereby helping to funnel investments towards companies with lower emissions and those making true efforts to decarbonize.