TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Hellwig, Michael
A1  - Finck, Steffen
T1  - Joining emission data from diverse economic activity taxonomies with evolution strategies
JF  - Machine Learning, Optimization, and Data Science. 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part I
N2  - 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.
KW  - Evolution Strategies
KW  - Application
KW  - Sustainability
KW  - Constrained Optimization
KW  - Self-Adaptation
Y1  - 2024
SN  - 1611-3349
SS  - 1611-3349
SN  - 978-3-031-53969-5
SB  - 978-3-031-53969-5
U6  - https://doi.org/10.1007/978-3-031-53969-5_31
DO  - https://doi.org/10.1007/978-3-031-53969-5_31
VL  - 14505
SP  - 415
EP  - 429
ER  -