Advancing climate resilient development pathways since the IPCC’s fifth assessment report
Bibliographic record
Abstract
Development processes and action on climate change are closely interlinked. This is recognised by the Intergovernmental Panel on Climate Change (IPCC) in its fifth assessment report, which reports on climate-resilient pathways, understood as development trajectories towards sustainable development which include adaptation and mitigation. The upcoming sixth assessment report dedicates a chapter to climate resilient development pathways. In this context, this paper asks what conceptual and empirical advances on climate resilient development pathways were made since the fifth assessment report. Through a literature review, this paper analyses goals and approaches for climate resilient development pathways, and discusses what conceptual advances have and could still be made. We find little evidence of dedicated concept development. Rather, we observe conceptual ambiguity. Literature showed four non-exclusive clusters of approaches: (a) climate action oriented, (b) social-learning and co-creation oriented, (c) mainstreaming oriented and (d) transformation oriented. We recommend operationalising climate resilient development pathways as the process of consolidating climate action and development decisions towards long-term sustainable development. This process requires explicit engagement with aspirations of actors, and connecting past developments with future aspirations and understandings of risk. Working with multiple pathways allows us to embed flexibility, anticipation and learning in planning. A greater focus is needed on issues linked to justice and equity as climate resilient development pathways will inevitably involve trade-offs. Substantiating the concept of climate resilient development pathways has the potential to bridge climate and development perspectives, which may otherwise remain separated in development and climate policy, practice and science.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".