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Joining emission data from diverse economic activity taxonomies with evolution strategies

  • In this paper, we consider the question of data aggregation using the practical example of emissions data for economic activities for the sustainability assessment of regional bank clients. Given the current scarcity of company-specific emission data, an approximation relies on using available public data. These data are reported in different standards in different sources. To determine a mapping between the different standards, an adaptation to the Covariance Matrix Self-Adaptation Evolution Strategy is proposed. The obtained results show that high-quality mappings are found. Nevertheless, our approach is transferable to other data compatibility problems. These can be found in the merging of emissions data for other countries, or in bridging the gap between completely different data sets.

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Metadaten
Author:Michael Hellwig, Steffen Finck
DOI:https://doi.org/10.1007/978-3-031-53969-5_31
ISBN:978-3-031-53969-5
ISSN:1611-3349
Parent Title (English):Machine Learning, Optimization, and Data Science. 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part I
Document Type:Article
Language:English
Year of publication:2024
Release Date:2024/02/29
Tag:Application; Constrained Optimization; Evolution Strategies; Self-Adaptation; Sustainability
Volume:14505
First Page:415
Last Page:429
Organisationseinheit:Forschung / Forschungszentrum Business Informatics
Forschung / Josef Ressel Zentrum für Robuste Entscheidungen
DDC classes:500 Naturwissenschaften und Mathematik / 510 Mathematik
Open Access?:nein
Peer review:wiss. Beitrag, peer-reviewed
Publicationlist:Finck, Steffen
Hellwig, Michael