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 \nchange through urban green planning, which we will refer to as climate-greening. The use of green \nspace is considered a no-regrets adaptation strategy, since it not only absorbs rainfall and moderates \ntemperature, but simultaneously can contribute to the sustainable development of urban areas. However, \ngreen space competes with other socio-economic interests that also require space. Urban planning can \nmediate among competing demands for land use, and, as such, is potentially useful for the governance \nof adaptation. Through an in-depth case study of three frontrunners in adaptation planning (London, \nRotterdam, and Toronto), the governance capacity for climate-greening urban areas is analysed and \ncompared. The framework we have developed utilizes five sub-capacities: legal, managerial, political, \nresource, and learning. The overall conclusion from the case studies is that the legal and political subcapacities \nare the strongest. The resource and learning sub-capacities are relatively weak, but offer \nconsiderable growth potential. The managerial sub-capacity is constrained by compartmentalization and \ninstitutional fragmentation, two key barriers to governance capacity. These are effectively blocking the \nmainstreaming of adaptation in urban planning. The biggest opportunities to enhance governance capacity \nlie in the integration of adaptation considerations into urban-planning processes, the establishment of links \nbetween adaptation and mitigation policies, investment in training programmes for staff and stakeholders \nin adaptation planning, and providing infrastructure for learning processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".