Uncertainty Quantification in Portfolio Temperature Alignment

TL;DR

This paper introduces a Bayesian framework that quantifies uncertainty in portfolio temperature alignment models using advanced statistical and machine learning techniques, improving climate risk assessment and decision-making.

Contribution

It presents a novel Bayesian approach with an emulator for efficient uncertainty quantification in climate-aligned portfolio models, surpassing traditional linear methods.

Findings

Enhanced uncertainty quantification accuracy

Real-time simulation capability

Identification of key uncertainty sources

Abstract

We present a novel Bayesian framework for quantifying uncertainty in portfolio temperature alignment models, leveraging the X-Degree Compatibility (XDC) approach with the scientifically validated Finite Amplitude Impulse Response (FaIR) climate model. This framework significantly advances the widely adopted linear approaches that use the Transient Climate Response to Cumulative CO2 Emissions (TCRE). Developed in collaboration with right{\deg}, one of the pioneering companies in portfolio temperature alignment, our methodology addresses key sources of uncertainty, including parameter variability and input emission data across diverse decarbonization pathways. By employing adaptive Markov Chain Monte Carlo (MCMC) methods, we provide robust parametric uncertainty quantification for the FaIR model. To enhance computational efficiency, we integrate a deep learning-based emulator, enabling near real-time simulations. Through practical examples, we demonstrate how this framework improves climate risk management and decision-making in portfolio construction by treating uncertainty as a critical feature rather than a constraint. Moreover, our approach identifies the primary sources of uncertainty, offering valuable insights for future research.