Urban Flood Resilience in New York City, London, Randstad, Tokyo, Shanghai, and Taipei
Bibliographic record
Abstract
<p>Making cities resilient to natural disasters has become a priority for many policy makers. However, few studies of global cities analyze environmental vulnerability, disaster resilience, or urban flood resilience. This article analyzes global cities’ disaster resilience to flooding: New York City, London, Randstad, Tokyo, Shanghai, and Taipei. Case studies of those global cities will assist cities world-wide to prepare for the future. Results indicate that a hurricane could leave approximately 25% of New York City with severe economic losses by 2050. In London, 15% of the land is located in flood-prone areas. The Thames Barrier began to operate to protect London from flooding in 1982. However, this also encouraged housing development closer to the river, and resulted in higher exposure and vulnerability of flooding. Randstad has approximately 40% of its land areas in flood-prone areas, but Randstad is well prepared for flood risk reduction by land-use and environmental planning. In Tokyo, extensive urbanization suffers from severe damages once flooding occurs. In Shanghai, approximately 50% of its land is in flood-prone areas. Shanghai is the most vulnerable to floods of the coastal cities. Shanghai is still not well prepared in land-use and environment planning for urban flood resilience. In Taipei, flood-prone areas account for approximately 41% of its total land area in an extreme weather scenario. Among these six global cities, Asian cities should focus more on urban flood resilience since most of flooding hotspots by 2025 will be located in Asian nations with having weak policies and actions, inadequate risk information, insufficient budgets and poor implementation capacities (UNISDR 2013). This research suggests that global cities, particularly Asian cities, should promote policies of urban flood resilience, focusing on land-use and environmental planning for resilience as well as strengthening their organizations and funding to reduce disaster risk, maintain up-to-date risk and vulnerability assessment. Urban policies should include environmentally responsible development in the face of continued population and economic growth, and being resilient regarding natural disasters. Cities can also adopt a growth management policy to direct development away from flooding hotspots. Urban regeneration policy should require developers to improve storm sewers, water retention ponds, and permeable surfaces. Planning more space for rivers, more constructed wetlands, and more ecological ponds to accommodate water is important. Cities can promote an actuarially fair flood insurance program which can reflect actual flood risks. A bottom-up community resilience plan would assist achieving urban resilience.</p>
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".