Climate Greening London, Rotterdam and Toronto \nA comparative analysis of the governance capacity of adaptation to climate change in urban areas.
Bibliographic record
Abstract
This research addresses the question what the governance conditions are of the spatial planning policy field to “climate green cities”, that is to adapt cities to climate change by means of green space. The use of green space is a no-regret adaptation strategy, since it not only absorbs rainfall and moderates temperature, but it simultaneously contributes to the sustainable development of urban areas through its many co-benefits. However, green space competes with other short-term socioeconomic interests that require space. As a cross-divisional policy field spatial planning can mediate among these competing demands for land use and as such offers potential for the governance of adaptation. Through their effect on land use and spatial configurations in cities, spatial planning policies can affect resilience to the impacts of climate change. Nevertheless, climate change considerations have not yet had much impact on urban planning. Through an in-depth comparative case study of three frontrunner cities in adaptation planning, Rotterdam, London and Toronto, the governance capacity is analysed for each city. A framework of analysis was developed to analyse the governance capacity, broken down into five sub-capacities: legal, managerial, political, resource and learning capacity. The content analysis of key policy and strategy documents of each city has provided a top-down perspective, while in-depth semi-structured interviews with key actors and stakeholders in each city have provided the bottom-up perspective. This was complemented with a \nhorizontal perspective by comparing the cities in order to distinguish universal patterns. The overall conclusion is that the legal capacity of spatial planning appears to be most important for climate greening cities, while the managerial capacity is seriously hampered by the complexity of urban \ngovernance structures, leading to compartmentalisation and institutional fragmentation as the two key barriers to the governance capacity for climate greening cities. The political capacity is also well developed but not necessarily as a result of spatial planning, while the resource and learning capacity represent most potential for growth. The biggest opportunities for climate greening cities are the establishment of strong links between adaptation and other important societal governance themes, the most obvious one being climate change mitigation, as well as the integration of adaptation considerations into spatial planning processes and standards for sustainable building.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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".