Machine Learning methods in climate finance: a systematic review

“Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic literature to assess how ML is enabling climate finance to scale up. The main contribution of this paper is to provide a structure of application domains in a highly fragmented research field, […]

Fat Tails, Tipping Points and Asymmetric Time Horizons: Dealing With Systemic Climate-Related Uncertainty in the Prudential Regime

“Even pioneering forward-looking stress tests cannot feasibly capture all possible tail risks. We propose supplementing the existing capital requirements regime by giving it a stronger precautionary and macroprudential focus, paying particular attention to the prevention of environmental tipping points to avoid systemic and catastrophic impacts on the financial system and macroeconomy.”    Lire