Adaptation to climate change in urban areas: Climate-greening London, Rotterdam, and Toronto
Bibliographic record
Abstract
This article aims to gain insight into the governance capacity of cities to adapt to climate change through urban green planning, which we will refer to as climate-greening. The use of green space is considered a no-regrets adaptation strategy, since it not only absorbs rainfall and moderates temperature, but simultaneously can contribute to the sustainable development of urban areas. However, green space competes with other socio-economic interests that also require space. Urban planning can mediate among competing demands for land use, and, as such, is potentially useful for the governance of adaptation. Through an in-depth case study of three frontrunners in adaptation planning (London, Rotterdam, and Toronto), the governance capacity for climate-greening urban areas is analysed and compared. The framework we have developed utilizes five sub-capacities: legal, managerial, political, resource, and learning. The overall conclusion from the case studies is that the legal and political subcapacities are the strongest. The resource and learning sub-capacities are relatively weak, but offer considerable growth potential. The managerial sub-capacity is constrained by compartmentalization and institutional fragmentation, two key barriers to governance capacity. These are effectively blocking the mainstreaming of adaptation in urban planning. The biggest opportunities to enhance governance capacity lie in the integration of adaptation considerations into urban-planning processes, the establishment of links between adaptation and mitigation policies, investment in training programmes for staff and stakeholders in adaptation planning, and providing infrastructure for learning processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".