Legal implications of the climate-health crisis: A case study analysis of the role of public health in climate litigation
Bibliographic record
Abstract
BACKGROUND: Strong scientific evidence affirms that climate change is now a public health emergency. Increasingly, climate litigation brought against governments and corporations utilizes international human rights, environmental and climate laws and policies to seek accountability for climate-destructive and health-harming actions. The health impacts of climate change make litigation an important means of pursuing justice and strategically challenging legal systems. Yet there is scant documentation in the literature of the role that public health has played in climate litigation and the legal weight public health narratives are given in such contexts. Therefore, we assessed to what extent courts of law have used public health harm in legal adjudication and sought to provide practical recommendations to address barriers to positioning legal arguments in public health-centric frames. METHODS: We reviewed legal databases to identify all publicly reported, documented, cases of climate litigation filed in any country or jurisdiction between 1990 and September 2020. For the 1641 cases identified, we quantified the frequency of cases where health concerns were explicitly or implicitly raised. FINDINGS: Case numbers are trending upwards, notably in high income countries. Resolution remains pending in over half of cases as the majority were initiated in the past three years. Cases were primarily based in climate and human rights law and brought by a wide range of groups and individuals predominantly against governments. About half of the decided cases found in favour for the plaintiffs. Based on this, we selected the 65 cases that were directly linked to public health. We found economic forces and pricing of health risks play a key role, as courts are challenged by litigants to adjudicate on the responsibility for health impacts. CONCLUSIONS: While courts of law are receptive to public health science, significant legal reform is needed to enhance leveraging of public health evidence in legal judgements of climate litigation cases. The integration of a public health mandate into a new eco-centric legal paradigm will optimize its potential to promote human well-being-the core objective underpinning both international law, human rights, and public health. Existing legal doctrines and practices can be enhanced to increase the weight of public health arguments in climate legal action and consequently ensure legal rulings in climate litigation prioritize, protect and promote public health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".