Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns
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Host institution: Bern Business School, Switzerland (BFH)
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Starting month: M13
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Duration: 36 months
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Pillar 1: The need for explainable AI: methods and applications in finance (Bern University of Applied Sciences, 4 ECTs), Work Package 3
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Work Packages: WP3
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Lead Researcher: Rebecca Di Francesco
Objectives
The surge in interest in algorithmic fairness and sustainability is present in numerous fields of study, including finance and portfolio management in particular. This project's objective is to create new portfolio optimization models that address some of the difficulties associated with incorporating fairness and sustainability into investment management. The objective of the project is to increase understanding of the source and methods for eliminating algorithmic bias in finance in order to generate sustainable outcomes. The project will equip financial institutions with new sustainable and equitable algorithmic solutions to increase customer trust.
Planned Secondments
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ING, Max Baak and Raluca Voinea, M26, 6 months, sustainable finance, ESG modelling.
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ECB, Dr. Lukazs Kubicki, M33, 12 months, exposure to gloabally leading central bank research, training on EU principles
Feasibility of Sustainable Investing
We investigate how investors can reduce the carbon footprint of their portfolios using quantitative techniques, including:
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Reweighting assets based on carbon intensity
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Selecting lower-emission firms within sectors
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Constructing portfolios aligned with climate targets
The focus is on systematic, scalable approaches that can be implemented in real-world investment processes.
Portfolio Decarbonization Strategies
Sector-Level Differences in Decarbonization Potential
Not all sectors are created equal when it comes to decarbonization.
We analyze:
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How emissions profiles vary across industries
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Which sectors offer greater flexibility for carbon reduction
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Where structural constraints limit decarbonization without large portfolio deviations
This sectoral lens allows us to distinguish between:
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Reductions driven by sector shifts (e.g., moving away from energy)
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Reductions within sectors (e.g., selecting cleaner firms)
Trade-Offs: Carbon Reduction vs. Financial Risk
A central challenge is balancing sustainability goals with financial performance.
We examine:
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The relationship between carbon reduction and tracking error
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How much deviation from a benchmark is required to achieve decarbonization
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Whether low-carbon portfolios can remain financially efficient and stable
This helps quantify the true cost of sustainability in investment terms.
Key Insight
Sustainable investing must be financially realistic and interpretable - not just theoretically green.
We develop a sector-level framework for evaluating portfolio decarbonization in the S&P 500 under tracking error constraints.
Instead of looking only at the portfolio as a whole, we examine each sector separately, because sectors differ strongly in:
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their emissions structure,
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the concentration of carbon-intensive firms,
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and the cost of reducing carbon exposure while staying close to the benchmark.
To make sector-level decarbonization easier to interpret, we introduce a new composite measure:
Decarbonization Readiness Index (DRI)
The DRI captures how ready a sector is for benchmark-aware decarbonization.
Rather than focusing only on how much carbon can be reduced, the index evaluates whether decarbonization is:
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achievable early,
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flexible in implementation,
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stable under estimation uncertainty,
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and reliable out of sample.
In other words, the DRI helps identify sectors where climate-aware portfolio construction is not just possible in theory, but robust in practice.
Figure 1. Decarbonization Readiness Index (DRI) — Radar Profiles across Sectors.
To make the proposed framework more accessible and operational, we developed an interactive dashboard which allows users to explore sector-specific carbon–tracking-error trade-offs and diagnostic dimension profiles.