Urban Flood Resilience in New York City, London, Randstad, Tokyo, Shanghai, and Taipei
Notice bibliographique
Résumé
<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>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».