Financial analysis and sustainability from a multi-criteria perspective in the automotive sector

Fernando García; María del Carmen García; Sorely García; Javier Oliver; Fernando García; María del Carmen García; Sorely García; Javier Oliver

doi:10.3934/GF.2026004

Green Finance

2026, Volume 8, Issue 1: 85-112. doi: 10.3934/GF.2026004

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Research article

Financial analysis and sustainability from a multi-criteria perspective in the automotive sector

1.
Department of Economics and Social Sciencies, Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain
2.
Department of Accounting, Universitat de València, Avinguda Tarongers S/N, 46022 Valencia, Spain
3.
Faculty of Administrative and Economic Sciences, affiliated with the Public Observatory Research Group, Technological University of Antioquia I.U, Medellín, Colombia

Received: 25 October 2025 Revised: 14 January 2026 Accepted: 09 February 2026 Published: 12 February 2026
JEL Codes: M14, N70, R40

To overcome the limitations of one-dimensional rankings and subjective expert-based evaluations, we introduced an objective multi-criteria framework for assessing corporate performance in the global automotive sector. The approach integrated financial indicators with Environmental, Social, and Governance (ESG) scores for a dataset of 430 manufacturers. Performance ranking was conducted using Extended Goal Programming (EGP), while Rough Set Theory (RST) was employed for group classification. The results indicated that the EGP model effectively identifies top-performing firms demonstrating balanced excellence across financial profitability and sustainability dimensions. Sensitivity analysis further revealed that ESG performance serves as the key differentiator under varying stakeholder priorities. Moreover, the RST classification framework substantially outperforms conventional Support Vector Machine (SVM) models, achieving accuracy levels around 90% compared to 80.95% obtained in the SVM model. This indicated that rough set model robustly addresses uncertainty and indiscernibility of data within industry-specific financial variables. The application of these methodologies provides investors and managers with a rigorous, data-driven tool for strategic benchmarking and sustainability-oriented decision-making.
- Sustainability,
- goal programming,
- rough set,
- rankings,
- automotive sector
Citation: Fernando García, María del Carmen García, Sorely García, Javier Oliver. Financial analysis and sustainability from a multi-criteria perspective in the automotive sector[J]. Green Finance, 2026, 8(1): 85-112. doi: 10.3934/GF.2026004

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Abstract

To overcome the limitations of one-dimensional rankings and subjective expert-based evaluations, we introduced an objective multi-criteria framework for assessing corporate performance in the global automotive sector. The approach integrated financial indicators with Environmental, Social, and Governance (ESG) scores for a dataset of 430 manufacturers. Performance ranking was conducted using Extended Goal Programming (EGP), while Rough Set Theory (RST) was employed for group classification. The results indicated that the EGP model effectively identifies top-performing firms demonstrating balanced excellence across financial profitability and sustainability dimensions. Sensitivity analysis further revealed that ESG performance serves as the key differentiator under varying stakeholder priorities. Moreover, the RST classification framework substantially outperforms conventional Support Vector Machine (SVM) models, achieving accuracy levels around 90% compared to 80.95% obtained in the SVM model. This indicated that rough set model robustly addresses uncertainty and indiscernibility of data within industry-specific financial variables. The application of these methodologies provides investors and managers with a rigorous, data-driven tool for strategic benchmarking and sustainability-oriented decision-making.

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