Leveraging machine learning for developing a sustainable and stable pricing numeraire with ESG considerations

Introduction
Asset prices should be relevant to the rate of depletion of a basket of essential resources, rather than the money market numeraire, whose price is almost a deterministic function of time. Basket weights can then be calibrated to the stability of asset prices. Then in a complete market, we can argue that there is a risk-neutral measure under which the sensitively discounted asset prices are stable.
Methods
Our key considerations for developing the resource-linked numeraire will include:
• Choice of resource base: The numeraire could be tied to a basket of essential resources (e.g., energy, water, soil fertility, rare minerals). It could be weighted based on global availability, depletion rates, or ecological impact. Finding the optimal weight would require a number of methods to be applied, including, but not limited to, Machine Learning, Dimensionality Reduction, and Mathematical Optimization.
• Scarcity-driven valuation: As resources become scarcer, their contribution to the numeraire increases, making depletion more costly. This could be modeled similarly to commodity-backed currencies but dynamically adjusting for sustainability.
Results
The paper would result in a number of potential models such as, but not limited to,
• Energy-backed currency: Pegging the numeraire to available units of sustainable energy (e.g., solar, wind) instead of fossil fuel reserves.
• Eco-certificate scheme: A parallel financial system where economic activity is balanced against resource consumption quotas.
• Resource depletion index: An index tracking key essential resources, which could be used as a dynamic numeraire.
Conclusions
Developing a financial numeraire aligned with the depletion of essential resources is possible and could provide a more sustainable economic framework. The idea would be to create a unit of account that reflects the scarcity and consumption of critical natural resources, such as water, fossil fuels, arable land, or biodiversity.