The role of urban land in climate change
Bibliographic record
Abstract
Introduction Recent IPCC reports have addressed the issue of urban land under the topic of industry, settlement, and society (IPCC, 2007). Since reviews of human settlements from the perspective of climate change have been primarily focused on climate change mitigation, topics of land cover and use, urbanization, land planning and management, land markets, property rights, and fiscal and legal issues, which will be key to responding to impacts of climate change, have not received extensive coverage. We argue in this chapter that it is important to focus on urban land as a sector or as the overarching framework in order to recognize the challenges of government coordination and integration necessary to address climate change. In incorporating urban land in climate change adaptation and mitigation efforts one would be able to include a fundamental set of strategies, such as policies concerning land conversion, land tenure, and urban land markets that have not been fully addressed. This chapter provides an introduction to the role of urban land in climate change, discusses the potential for urban planning and management to address climate change challenges, and reviews current planning efforts focused on climate change. It is organized into several sections. This introductory section develops several key concepts, such as recent trends in urbanization, and discusses their relation to urban land and climate change. The second section focuses on urban form, impacts on ecosystems, including the urban heat island effect, and discusses the vulnerability of informal and slum settlements to climate change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".