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The price of going green: Multi-objective optimization in the energy equity space

Author

Listed:
  • Platania, Federico
  • Toscano Hernandez, Celina
  • El Ouadghiri, Imane
  • Peillex, Jonathan

Abstract

This paper develops a three-objective portfolio optimization framework for the energy sector that simultaneously maximizes expected return, minimizes volatility, and improves environmental performance, measured by the Environmental Pillar Score. Using NSGA-III and rolling 60-month windows from 2010 to 2024, we analyze the U.S. and European equity markets and extract four representative strategies: Max-Return, Min-Volatility, Max-Environmental, and Balanced. Our findings show that trade-offs between financial performance and environmental impact are systematic and regionally specific. U.S. portfolios generally achieve higher returns with greater risk and lower environmental scores, while European portfolios offer stronger sustainability outcomes and lower volatility. The Balanced strategy proves to be a robust compromise among environmental scores, reduced risk, and moderate returns, offering a viable path for investors seeking alignment between financial and sustainability objectives. These results emphasize the value of multi-objective optimization in designing portfolios that align with both financial and sustainability goals.

Suggested Citation

  • Platania, Federico & Toscano Hernandez, Celina & El Ouadghiri, Imane & Peillex, Jonathan, 2026. "The price of going green: Multi-objective optimization in the energy equity space," Energy Economics, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:eneeco:v:158:y:2026:i:c:s0140988326001817
    DOI: 10.1016/j.eneco.2026.109302
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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

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