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Nature stress test: Assessing exposure of five African banking systems
This report by FSD Africa and McKinsey Sustainability presents a nature stress test for the banking systems of Ghana, Mauritius, Morocco, Rwanda, and Zambia. It evaluates how nature-related physical and transition risks impact real economy profits and subsequent credit losses for financial institutions across three scenarios: current policies, a disorderly transition, and an orderly transition aligned with the Global Biodiversity Framework (GBF). The findings highlight that while portfolio-level risks may appear moderate, they are heavily concentrated in priority sectors like agriculture and mining, where unweighted losses can be severe. The report argues that a coordinated, orderly transition can significantly mitigate these financial risks and provide macroeconomic benefits.
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Document type: Report
Nature and financial institutions in Africa: A first assessment of opportunities and risks
This report by FSD Africa provides a first quantitative risk assessment and stress-testing framework for financial institutions in Africa to evaluate nature-related opportunities and risks. Using the Taskforce on Nature-related Financial Disclosures (TNFD) LEAP approach, the study analyzes the impact of nature loss and nature-positive transitions on equity and lending portfolios across several African countries and private banks. It highlights that while aggregate portfolio impacts may appear small due to limited direct exposure, the risks are material for nature-intensive sectors like agriculture and extractives, where nature-related risks could double expected credit losses by 2030.
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Document type: Report
Assessing Physical Climate Risks for the European Bank for Reconstruction and Development's Power Generation Project Investment Portfolio
This research paper presents a new method, co-developed by the World Resources Institute (WRI) and the European Bank for Reconstruction and Development (EBRD), to quantify physical climate risks for power generation portfolios. The method utilizes machine-learning techniques and climate science to estimate electricity generation losses across different climate scenarios (RCP 4.5 and RCP 8.5), specifically pilot-testing the approach on EBRD's thermal and hydropower assets.
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This summary is written by a language model reading the source document. It is not the publisher's words and is not a substitute for the original.
Document type: Research paper