Development of systemic infrastructure governance mechanisms to reduce climate-health risks in cities
Bibliographic record
Abstract
Abstract Climate-related hazards can lead to multiple infrastructure risks that pose a threat to public health such as extreme heat conditions in housing and transport networks, the flooding of critical utilities and social infrastructure, and air pollution in buildings with vulnerable occupants. Business-as-usual approaches to manage physical infrastructure systems that result in the delivery of standalone interventions by individual city departments in isolated infrastructure systems are not sufficient to manage these systemic risks. The aim of this study is to explore how cities can develop systemic infrastructure governance mechanisms. Specifically, how stakeholders can draw together diverse information sources (including health data) and partnership funding mechanisms focused on the delivery of multiple benefits (including health benefits) to develop integrated infrastructure interventions to reduce climate-health risks. This study examines the infrastructure governance mechanisms developed in three cities: Ottawa (Canada), Belfast (Northern Ireland), and London (England) to draw insight into how cities can establish such systemic infrastructure governance mechanisms. The findings reveal how the development of multi-stakeholder partnerships, diverse information sources, and the use of holistic appraisal mechanisms to evaluate the wider benefits of infrastructure interventions have facilitated the implementation of integrated infrastructure measures to reduce climate-health risks in the case study cities. The research findings also show that these cities have implemented several types of integrated infrastructure measures to reduce climate-health risks such as deep housing retrofit projects and integrated grey-green-blue infrastructure measures to reduce flood risk and promote active travel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".