Porównaj metody

	Build Back Better Recovery Evaluation	Disaster Recovery Curve Analysis
Dziedzina	Disaster Studies	Disaster Studies
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2014	2006
Twórca	Sandeeka Mannakkara, Suzanne Wilkinson & Regan Potangaroa	Scott B. Miles & Stephanie E. Chang
Typ	Principle-based evaluation framework for resilient post-disaster recovery	Time-trajectory model of functional recovery and resilience
Źródło pierwotne	Mannakkara, S., Wilkinson, S., & Potangaroa, R. (2014). Build back better: implementation in Victorian bushfire reconstruction. Disasters, 38(2), 267-290.	Miles, S. B., & Chang, S. E. (2006). Modeling Community Recovery from Earthquakes. Earthquake Spectra, 22(2), 439-458.
Inne nazwy	BBB Evaluation, Build Back Better Assessment, Resilient Recovery Evaluation	Recovery Trajectory Analysis, Resilience Curve Analysis, Community Recovery Modeling
Pokrewne	3	3
Podsumowanie	Build Back Better (BBB) recovery evaluation is a principle-based framework for assessing whether post-disaster reconstruction reduces future risk rather than merely restoring pre-disaster conditions. Formalized by Sandeeka Mannakkara, Suzanne Wilkinson, and Regan Potangaroa and demonstrated in the 2009 Victorian bushfire reconstruction, the framework organizes recovery around three categories: disaster risk reduction (safer structures and siting), community recovery (social and economic restoration), and effective implementation (stakeholder engagement, regulation, and monitoring). Under each category sit concrete principles against which a recovery effort is evaluated, comparing what was achieved with what resilient recovery requires and with the pre-disaster baseline. Endorsed within the Sendai Framework and embedded in post-disaster needs assessment, BBB evaluation turns the slogan 'build back better' into an auditable standard for resilient reconstruction.	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.