Towards a socio-ecological system understanding of urban flood risk and barriers to climate change adaptation using causal loop diagrams
Bibliographic record
Abstract
While cities are facing increasing challenges of flood risk due to combined effects of climate change and socioeconomic development, understanding of the complexity of urban flood risk is still limited, hampering decision-making and urban adaptation planning. This study presents a qualitative system dynamics modelling framework to investigate urban flood risk and adaptation under climate change in a coupled socio-ecological system, the city of Hamburg. The developed integrated conceptual model provides a holistic understanding of key physical and socio-economic processes and the role of feedback loops underlying the urban system, and contributes to the understanding of vicious cycles of barriers that perpetuate and hinder adaptation processes within cities. The qualitative approach can help to break down silo-thinking in urban flood risk assessments. Decision-makers could use the framework to understand the complexity of interactions among multiple drivers of flood risk to overcome barriers and lock-in effects to adaptation in cities.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".