Net Zero is not enough: ratcheting ambition for sustainable health systems through Reduce and Support
Bibliographic record
Abstract
Net Zero is the dominant framework for organising health system decarbonisation. Yet throughout Net Zero's rise to prominence, greenhouse gas emissions have remained on a dangerous trajectory. In this analysis, we synthesise strands of Net Zero critique from the climate policy literature, examine their implications for health systems and briefly present an alternative framework for decarbonisation. We begin by reviewing three families of Net Zero critique which have, to date, received little attention in the sustainable healthcare space: unambitious and inequitable pledges, accounting failures, and structural problems with the framework itself. Together, these critiques challenge the idea that the Net Zero agenda is best positioned to deliver upon the Paris Agreement commitment to limit temperature rise to below 1.5°C-2°C. We then consider how each challenge manifests in the health sector with examples from state and non-state actors. Finally, we briefly introduce an alternative 'reduce and support' approach which aims to address some of Net Zero's weaknesses. Reduce-and-support represents a conceptual pivot that would extend current best practices in science-based mitigation targets while exchanging the atomised trading of problematic carbon offsets for resource pooling towards collective efforts at deep decarbonisation. We discuss the moral, political and practical advantages of this framework and identify areas for future work. By considering the adoption of reduce-and-support, health systems can provide leadership for ratcheting climate ambition at this pivotal moment of accelerating climate breakdown.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".