Abstract
This paper introduces a new method to quantify physical climate risks for power generation projects at the portfolio level. Co-developed by WRI and the European Bank for Reconstruction and Development (EBRD), the approach is designed to be flexible enough to work with portfolios with different levels of data availability, leverage the latest science in climate and hydrology, and use machine-learning techniques such as recurrent neural networks.
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