Using Financial and Sustainability Ratios to Map Sectors: An Approach With Compositional Data

Altres autors/es

Agencia Estatal de Investigación

Data de publicació

2026-01-20



Resum

The article aims to visualize in a single graph Spanish fish and meat processing companies with respect to solvency, energy, waste and water intensity and gender employment gap. These financial, environmental, and social indicators are ratios, which require specific statistical analysis methods. We use the compositional data methodology and the principal-component analysis biplot. Fish-processing companies have more homogeneous financial, environmental, and social performance. Firms with higher solvency tend to be less efficient in energy use. Firms can be visually ordered along all indicators simultaneously and thus can visually see their areas of improvement in financial, environmental, and social performance compared to their competitors. This is the first time in which visualization tools have combined financial, environmental, and social indicators. The main limitation is the small sample size. As of now, few Spanish firms publish reports according to the EU Corporate Sustainability Reporting Directive


Research funding: Generalitat de Catalunya. Grant Numbers: 2021SGR00403, 2021SGR01197, 2023-CLIMA-00037; Ministerio de Sanidad. Grant Number: CIBERCB06/02/1002; Ministerio de Ciencia, Innovación y Universidades; ERDF. Grant Number: PID2021-123833OB-I00 i Department of Research and Universities, AGAUR. Open Access funding provided thanks to the CRUE-CSIC agreement with Wiley


11

Tipus de document

Article


Versió publicada


peer-reviewed

Llengua

Anglès

Publicat per

Wiley

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info:eu-repo/semantics/altIdentifier/doi/10.1002/csr.70412

info:eu-repo/semantics/altIdentifier/issn/1535-3958

info:eu-repo/semantics/altIdentifier/eissn/1535-3966

PID2021-123833OB-I00

info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-123833OB-I00/ES/GENERATION AND TRANSFER OF COMPOSITIONAL DATA ANALYSIS KNOWLEDGE/

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