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The report provides financial institutions with a state-of-the-art blueprint for evaluating physical risks and opportunities. Complete with case studies from participating banks, the report investigates leading practices for five critical topics related to physical risks and opportunities:
1. Extreme events data and data portals – reviewed example...
Citations
... The scientific research community has made a considerable contribution to the implementation of the TCFD recommendations, particularly researchers specializing in the field of enterprise and business management (Achenbach, 2021;Bose & Hossain, 2021;Cummings, 2022;Goto, 2020;Lin et al., 2020;Scholten et al., 2020;Siew, 2021;Suortti, 2021;Vola & Gelmini, 2021). A common thread in the resulting literature is the development of physical and transition risk assessment methods to support TCFD, including projects such as Aqueduct Tools (from the World Resources Institute, WRI), the Climanomics platform (from the Climate Service), and the Climate Value-at-Risk model (from Carbon Delta) (Connell et al., 2020;Liu & Tung, 2020;Smith, 2021). ...
... Moreover, scientific education on climate change should be strengthened to ensure the consistency of use by people from all walks of life, and to avoid misinterpretation and misuse of data." As mentioned by Connell et al. (2020), tools and data supporting physical risk and opportunity assessments in banking must be based on strong scientific evidence, must be used in the context of the other data, tools and banking systems, and should ensure comparability between banks. Furthermore, due to the fact that there is a gap in the use of climate data in the industry, in terms of corporate TCFD reports and sustainability reports, Taiwan's financial industry and large companies generally perform better than non-financial industries and small and medium enterprises. ...
Since the inception of the Task Force on Climate Related Financial Disclosures (TCFD) and its publication of a series of guidelines to instruct companies how to respond to climate change, physical and transition risks have become must‐know terms for businesses around the world. The development of various physical and transition risk assessment tools has become an urgent task for not only climate service companies but also for the scientific research community. Nevertheless, there is still an obvious gap between the contribution of the scientific research community and enterprise needs for clarification of the TCFD requirements. This article draws on interviews from representatives of more than a dozen related corporations and institutions in Taiwan, including government agencies, private firms, research units, and industry associations, to understand and summarize their experience and expectations for the research community's contributions to TCFD alignment. The interview findings point out that physical risk assessment, transition risk assessment, data/information disclosure and integration, and policies and systems are the most concerned aspects of the interviewees. Based on these findings, the paper then provides suggestions for improvement by the research community, policy makers, and decision makers, including appropriate policies and systems, localized high‐quality climate services, and international competition, helping to effectively bridge the gap between daily research work and external expectations for the research results.
... Unfortunately, quantifying the physical climate risks in the loan and equity portfolios of power generation assets has been challenging. It requires data and methods that translate climate science into impacts on power plants and onwards to financial metrics used by banks (Connell et al. 2020). ...
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.
