Making Global Climate Action work for nature and people: Priorities for Race to Zero and Race to Resilience
Bibliographic record
Abstract
There is increasing recognition in science and policy that the current nature and climate change crises are highly intertwined, and that these need to be jointly addressed. Within the United Nations Framework Convention on Climate Change (UNFCCC), the Race to Zero (R2Z) and the Race to Resilience (R2R) campaigns foster climate action by cities, regions, businesses, investors, and civil society organizations for mitigation and adaptation. The campaigns are part of UNFCCC-backed institutional arrangements linking intergovernmental climate governance with actions beyond national commitments to support the implementation of the Paris Agreement, also referred to as the Global Climate Action Agenda (GCAA). Both mobilization campaigns highlight and promote the contribution of nature to climate mitigation, adaptation, and resilience. Yet, the integration of nature in climate ambition is more complex than indicated in the calls to action. We here identify key areas of concern in the alignment of climate and biodiversity goals, discussing the biophysical and socio-ecological considerations relative to (i) practices for enhancing land-based and marine sinks to limit warming; (ii) the unpredictability of biodiversity dynamics under climate change; (iii) the spatial scale at which actions can be implemented; and (iv) the types of metrics that can be used for tracking progress. We provide recommendations for the two mobilization campaigns to integrate in their criteria and metrics frameworks to support effective and equitable actions that deliver for climate, but also for nature and people. We then make a call to action for transdisciplinary knowledge production and dissemination that strengthens science-policy interactions. • Nature's contribution to climate mitigation and adaptation is increasingly recognized in science and policy. • The integration of nature in climate actions presents risks for climate, biodiversity and people. • Risks encompass bio-physical challenges, social trade offs, and equity concerns. • UN-backed mobilization campaigns can promote alignment goals for climate, nature, and people. • This article makes recommendations to strengthen standards, transparency, and accountability.
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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 | 0.051 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.030 | 0.040 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".