From Glaciers to Courtrooms: Translating Natural Science Concepts into Legal Frameworks for Climate Litigation
Bibliographic record
Abstract
The integration of natural science concepts into climate change litigation, particularly in cases related to glacier lake outburst floods (GLOFs) in mountainous regions like the Andes, faces significant challenges due to the differing nature of scientific and legal frameworks.Scientific understanding of climate change impacts on phenomena such as GLOFs relies heavily on scenarios, modeling, and projections that evolve over time with advancements in technology and knowledge. These models need to be comprehensive, and consider an array of factors including glacier retreat, temperature changes and various risk factors. However, legal standards often require definitive proof of causation. There may arise a discrepancy creating a gap in case of prevailing uncertainties inherent to high-mountain processes which may not always meet the exacting evidentiary requirements of litigation.An illustrative example of this challenge is the case of a citizen in Huaraz, in the Andes of Peru, using a major German energy producer over the risks of a catastrophic flood from a GLOF at Lake Palcacocha. The German court’s decision to admit this case is groundbreaking in climate litigation. It implies a recognition of legal responsibilities of large emitters for potential losses and damages caused by anthropogenic climate change globally, provided a causal relation between emissions and risk can be established. This case exemplifies the challenge in linking complex scientific causation with legal accountability.In the Palcacocha case, the German court defined to distinguish between i) the hazard and risk posed to the plaintiff in Huaraz, and ii) the attribution to anthropogenic climate change and the emissions produced by the defendant. Here we report on the geoscientific studies undertaken to analyze the hazard situation posed by potential rock and ice avalanches, impacting the glacial lake and producing potentially devastating floods in the city of Huaraz. Critical among other are concepts and methods to quantify probability of occurrence of an event, and the effect of cascading slope and mass flow processes.In conclusion, the challenges in adapting natural science concepts for climate change litigation, particularly regarding GLOFs, stem from different concepts, standards of proof, and conceptual understandings in science and law. Bridging this gap is essential for effective climate litigation and requires innovative interdisciplinary approaches that facilitate the translation of scientific findings into legally cogent arguments. The framework, methods and standards we applied in the case of Palcacocha could serve for other litigation cases in similar environments, highly impacted and vulnerable to anthropogenic climate change.
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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 | 0.058 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.021 | 0.074 |
| Scholarly communication | 0.041 | 0.040 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.026 | 0.024 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".