Integration of urban climate research within the global climate change discourse
Bibliographic record
Abstract
Global climate science needs to address a fundamental challenge: the mismatch between the scales of anthropogenic processes driving change and the resulting climate impacts. While projected climate changes and impacts are global in extent, the drivers of this change, and the exposure to its impacts, are concentrated in densely populated urban areas. Despite occupying only 1–3% of the land, urban areas are home to most of the world’s population and responsible for ~70% of current greenhouse gas emissions [1]. By 2050, an additional 2.5 billion people are expected to live in urban areas, with up to 90% of this growth anticipated in the Global South with increased rates of vulnerability. The importance of cities in our climate change dialogue will therefore not diminish but rather become increasingly more significant. Despite the vital role of cities, urban-scale climates are poorly represented in global climate science (both in observations and models). Urban climate research has also been traditionally underrepresented in the assessments of the physical basis of climate, with the inclusion of cities not being formalized until the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), which emphasized adaptation and mitigation. This has several consequences. First, a rich body of existing knowledge on urban climates is ignored, particularly in IPCC science assessments. Second, there has been little incentive to measure and understand climate processes at urban scales, which further undermines the accuracy of current climate assessments. Third, urban policies to mitigate and adapt to climate change may not account for unique climate consequences in cities. This limits our ability to develop effective strategies for climate adaptation and mitigation, posing additional risks to the future resilience of societies.
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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 | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".