Greenhouse Gases Emissions: Estimating Corporate Non-Reported Emissions Using Interpretable Machine Learning
- DOI
- 10.3390/su15043391
- Published
- 2023-02-13
- Container
- Sustainability
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/su15043391,
title = {Greenhouse Gases Emissions: Estimating Corporate Non-Reported Emissions Using Interpretable Machine Learning},
author = {Jérémi Assael and Thibaut Heurtebize and Laurent Carlier and François Soupé},
year = {2023},
journal = {Sustainability},
doi = {10.3390/su15043391},
url = {https://doi.org/10.3390/su15043391}
}RIS
TY - JOUR TI - Greenhouse Gases Emissions: Estimating Corporate Non-Reported Emissions Using Interpretable Machine Learning AU - Jérémi Assael AU - Thibaut Heurtebize AU - Laurent Carlier AU - François Soupé PY - 2023 JO - Sustainability DO - 10.3390/su15043391 UR - https://doi.org/10.3390/su15043391 ER -
APA
Assael, J., Heurtebize, T., Carlier, L., & Soupé, F. (2023). Greenhouse Gases Emissions: Estimating Corporate Non-Reported Emissions Using Interpretable Machine Learning. Sustainability. https://doi.org/10.3390/su15043391
Source records
- crossref · retrieved 2026-09-26T17:30:31.305Z