手法を比較

	Post-Disaster Needs Assessment	Disaster Recovery Curve Analysis
分野	Disaster Studies	Disaster Studies
系統	Process / pipeline	Process / pipeline
提唱年	2008	2006
提唱者	European Union, World Bank (GFDRR) & United Nations Development Group	Scott B. Miles & Stephanie E. Chang
種類	Harmonized post-event assessment pipeline for damage, loss, and recovery needs	Time-trajectory model of functional recovery and resilience
原典	GFDRR, European Union, United Nations Development Group (2013). Post-Disaster Needs Assessments Guidelines, Volume A. Global Facility for Disaster Reduction and Recovery, World Bank.	Miles, S. B., & Chang, S. E. (2006). Modeling Community Recovery from Earthquakes. Earthquake Spectra, 22(2), 439-458.
別名	PDNA, Damage, Loss and Needs Assessment, Post-Disaster Damage and Loss Assessment	Recovery Trajectory Analysis, Resilience Curve Analysis, Community Recovery Modeling
関連	3	3
概要	The Post-Disaster Needs Assessment (PDNA) is a harmonized, government-led methodology for quantifying the effects of a disaster and costing a recovery program. Agreed in 2008 by the European Union, the World Bank (through GFDRR), and the United Nations Development Group, and codified in the PDNA Guidelines, it fuses two traditions: the ECLAC damage-and-loss accounting (DaLA), which values destroyed assets and the economic flows foregone during recovery, and a human-and-recovery-needs assessment, which captures impacts on people's lives, livelihoods, and access to services. Conducted sector by sector against a pre-disaster baseline, a PDNA produces a single consolidated picture of total disaster effects and feeds a costed Recovery Framework that increasingly embeds build-back-better resilience, giving governments and donors a common basis for mobilizing and prioritizing recovery resources.	Disaster recovery curve analysis represents the recovery of a community or system after a disaster as a trajectory of functionality over time and uses that trajectory to quantify resilience. Building on the resilience-triangle concept and formalized for community recovery by Scott Miles and Stephanie Chang in 2006, the approach tracks a performance indicator — population, employment, housing occupancy, service capacity, or composite functionality — from its pre-event baseline, through the abrupt drop caused by the disaster, along the path back toward (or beyond) the baseline. From the curve, analysts read the magnitude of the initial loss, the speed and shape of recovery, the time to return, and the cumulative resilience loss represented by the area between the baseline and the recovery path. Comparing curves across communities or scenarios reveals what drives faster, fuller recovery and complements loss-estimation models that stop at the moment of impact.