论文标题
温室气体排放：使用可解释的机器学习估算公司非报告的排放
Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning
论文作者
论文摘要
截至2022年，对于所有公司和测量和估计方法，温室气体（GHG）的排放报告和审计尚未强制性。我们提出了一个基于机器学习的模型，以估算尚未报告的公司的范围1和范围2温室气体排放。我们的模型专门设计为透明且完全适应此用例，能够估算大型公司宇宙的排放。它显示出良好的样本外全球表演以及在由国家，国家或收入存储库评估时出现的良好样本外观表演。我们还将我们的结果与其他提供商的结果进行比较，并发现我们的估计值更准确。得益于使用Shapley值提出的解释性工具，我们的模型是完全可解释的，用户能够了解哪些因素分开解释了每个特定公司的温室气体排放。
As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, specifically designed to be transparent and completely adapted to this use case, is able to estimate emissions for a large universe of companies. It shows good out-of-sample global performances as well as good out-of-sample granular performances when evaluating it by sectors, by countries or by revenues buckets. We also compare our results to those of other providers and find our estimates to be more accurate. Thanks to the proposed explainability tools using Shapley values, our model is fully interpretable, the user being able to understand which factors split explain the GHG emissions for each particular company.
