Climate and health: a path to strategic co-financing?
Bibliographic record
Abstract
Leveraging the co-benefits of investments in health and climate can be best achieved by moving away from isolated financing approaches and adopting co-financing strategies, which aim to improve the outcomes of both sectors. We propose a framework for studying co-financing for health and climate that considers the degree of integration between sector funding, and whether arrangements are 'passive', when cross-sectoral goals are indirectly affected, or 'strategic', when they are pre-emptively supported to build resilience and sustainability. We conducted a rigorous, evidence-focused review to describe co-financing mechanisms according to a framework, including the context in which they have been employed, and to identify enablers and barriers to implementation. We searched the international literature using Pubmed and Web of Science from 2013 to 2023, the websites of key health and climate agencies for grey literature and consulted with stakeholders. Our review underscores the significant impact of climate change and related hazards on government, health insurance and household health-related costs. Current evidence primarily addresses passive co-financing, reflecting the financial consequences of inaction. Strategic co-financing is under explored, as are integrative co-financing models demanding cross-sectoral coordination. Current instances of strategic co-financing lack sufficient funding to demonstrate their effectiveness. Climate finance, an under used resource for health, holds potential to generate additional revenue for health. Realizing these advantages necessitates co-benefit monitoring to align health, climate mitigation and adaptation goals, alongside stronger advocacy for the economic and environmental benefits of health investments. Strategic co-financing arrangements are vital at all system levels, demanding increased cross-sectoral collaboration, additional funding and skills for climate integration within health sector plans and budgets, and mainstreaming health into climate adaptation and mitigation plans. Supporting persistent health needs post-disasters, promoting adaptive social protection for health and climate risks, and disseminating best practices within and among countries are crucial, supported by robust evaluations to enhance progress.
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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 | 0.047 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.013 | 0.035 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".