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This study introduces a tri-criterion model for sustainable finance, integrating carbon risk alongside return and risk. It helps investors manage climate risk by aligning financial flows with a low-carbon economy.

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genetic algorithmsinvestor’s preferenceslow-carbon economymulti-objective optimizationsustainable finance

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Area of Science:

  • Finance
  • Environmental Science
  • Economics

Background:

  • Sustainable finance integrates environmental, social, and governance (ESG) criteria into financial decisions.
  • The financial sector is crucial for global decarbonization efforts.
  • Investors need tools to manage climate risk beyond traditional return and risk metrics.

Purpose of the Study:

  • To extend Markowitz's mean-variance portfolio selection model.
  • To incorporate investor preferences for portfolio carbon risk exposure as a third criterion.
  • To develop a model for aligning financial flows with a low-carbon economy.

Main Methods:

  • A tri-criterion portfolio selection model was proposed.
  • An ε-dominance based multi-objective genetic algorithm (ev-MOGA) was used to approximate the 3D Pareto front.
  • An a-posteriori approach was employed to integrate investor preferences regarding carbon risk and loss aversion.

Main Results:

  • The study successfully extended portfolio selection to include carbon risk.
  • The ev-MOGA algorithm efficiently handled the multi-objective optimization problem.
  • The a-posteriori approach allowed for personalized portfolio construction based on investor preferences.

Conclusions:

  • The proposed model effectively embeds climate-related risk into investment portfolios.
  • This approach supports investors in managing climate risk and pursuing sustainable finance goals.
  • The methodology is applicable to socially responsible investment funds and can guide decarbonization strategies.