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
Climate risk, sustainable finance and international business: a research agenda
“… since 2015 investors have increased their visibility through their climate pledges and ESG performance. However, they have mostly failed to divest from fossil fuels. We draw on this evidence to propose a novel international climate finance research agenda.” Lire
Scope 3 Emissions: Data Quality and Machine Learning Prediction Accuracy
“We conclude that users of the Scope 3 emission datasets should consider data source, quality and prediction errors when using data from third party providers in their risk analyses.” Lire