Pith. sign in

REVIEW 2 cited by

Estimation of Corporate Greenhouse Gas Emissions via Machine Learning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2109.04318 v1 pith:P3X6U5WR submitted 2021-09-09 cs.LG stat.ML

Estimation of Corporate Greenhouse Gas Emissions via Machine Learning

You Han , Achintya Gopal , Liwen Ouyang , Aaron Key This is my paper
classification cs.LG stat.ML
keywords emissionsmeasurescompaniesinvestorsableachievealigncorporate
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. GHGbench: A Unified Multi-Entity, Multi-Task Benchmark for Carbon Emission Prediction

    cs.LG 2026-05 unverdicted novelty 7.0

    GHGbench is a new multi-entity benchmark for company- and building-level carbon emission prediction that shows building tasks are harder, out-of-distribution gaps dominate, and multimodal data aids generalization.

  2. GHGbench: A Unified Multi-Entity, Multi-Task Benchmark for Carbon Emission Prediction

    cs.LG 2026-05 unverdicted novelty 7.0

    GHGbench supplies a harmonized dataset and multi-task benchmark for company and building carbon emission prediction, with baselines showing large OOD gaps and benefits from multimodal embeddings.