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  • 2022
  • Working Paper
  • HBS Working Paper Series

Machine Learning Models for Prediction of Scope 3 Carbon Emissions

By: George Serafeim and Gladys Vélez Caicedo
  • Format:Print
  • | Language:English
  • | Pages:36
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Abstract

For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15 reported types of scope 3 emissions. The models utilize as inputs widely available financial statement variables, scope 1 and 2 emissions, and industrial classifications. We find that most reported scope 3 emission types can be predicted with higher accuracy using Adaptive Boosting machine learning algorithms relative to linear regression models and other supervised machine learning algorithms.

Keywords

Carbon Emissions; Climate Change; Environment; Carbon Accounting; Machine Learning; Artificial Intelligence; Digital; Data Science; Environmental Sustainability; Environmental Management; Environmental Accounting

Citation

Serafeim, George, and Gladys Vélez Caicedo. "Machine Learning Models for Prediction of Scope 3 Carbon Emissions." Harvard Business School Working Paper, No. 22-080, June 2022.

Supplemental Information

Machine Learning Models for Prediction of Scope 3 Carbon Emissions
  • SSRN
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About The Author

George Serafeim

Accounting and Management
→More Publications

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