Making physical climate risk assessments relevant to the financial sector – Lessons learned from real estate cases in the Netherlands
Bibliographic record
Abstract
Climate change can be an important additional risk for the financial sector. For (large) investments in real estate, it is becoming increasingly important to take climate related risks into account. Yet, generating tailored physical climate risk information to make meaningful decisions about investment portfolios remains difficult. Using literature review, semi-structured interviews and reflection on four case studies implemented in the Netherlands, this paper presents lessons learned and recommendations for improving Physical Climate Risk Assessments (PCRA) for the financial sector. Results from the literature review show that simply selecting a PCRA methodology does not guarantee uptake of information by end-users, because there is no single approach that is suitable for all contexts. From the case interviews, we conclude that effective PCRA information is helpful for the financial sector in several ways; first, it supports investors to pinpoint which assets need attention and how much money is required to mitigate the impacts. Second, they serve as a template upon which clients make purchasing decisions. Third, they serve as a tool for determining the choice of building materials and the structure of properties. Fourth, they assist firms in the development of plausible adaptation strategies. Furthermore, we identified five cardinal points (that incorporate the perspectives of both providers and end-users) to improve the PCRA process: 1) Engagement and co-production, 2) Needs identification, 3) Data availability and quality, 4) Internal integration, and 5) Communication. These recommendation points will serve as a valuable reference to guide the selection and implementation of the most appropriate PCRA method for a given situation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".