Tri-criterion model for constructing low-carbon mutual fund portfolios: a preference-based multi-objective genetic algorithm approach
A. Hilario-Caballero, A. Garcia-Bernabeu, J. V. Salcedo, M. Vercher

TL;DR
This paper introduces a tri-criterion portfolio model that incorporates carbon risk preferences into sustainable investing, extending traditional methods with a multi-objective genetic algorithm to better align portfolios with climate goals.
Contribution
It develops a novel tri-criterion model and an efficient genetic algorithm to include carbon risk preferences in portfolio selection, enhancing sustainable finance strategies.
Findings
Effective approximation of the 3D Pareto front for portfolio trade-offs.
Demonstrates how investor preferences influence portfolio carbon risk exposure.
Shows potential for embedding climate risk considerations in European SRI funds.
Abstract
Sustainable finance, which integrates environmental, social and governance (ESG) criteria on financial decisions rests on the fact that money should be used for good purposes. Thus, the financial sector is also expected to play a more important role to decarbonise the global economy. To align financial flows with a pathway towards a low-carbon economy, investors should be able to integrate in their financial decisions additional criteria beyond return and risk to manage climate risk. We propose a tri-criterion portfolio selection model to extend the classical Markowitz mean-variance approach in order to include investors preferences on the portfolio carbon risk exposure as an additional criterion. To approximate the 3D Pareto front we apply an efficient multi-objective genetic algorithm called ev-MOGA which is based on the concept of e-dominance. Furthermore, we introduce an a…
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