Search Results (2)

This research study assesses the effectiveness of the Socio-Technical Assistance (STA) program when combined with owner-driven housing reconstruction on rural private housing recovery after Nepal’s 2015 earthquake, particularly regarding vulnerable households. Through a quantitative, 304-question survey, the study reveals that 96% of households credited STA activities for accelerating reconstruction, with 95% acknowledging its significance and 78% emphasizing its necessity. Notably, 89% expressed dependency on STA for reconstruction, and 85% believed it heightened disaster risk reduction awareness. In conclusion, the study establishes that STA activities significantly contributed to the successful reconstruction of houses for vulnerable households, addressing such critical aspects as financial support, technical assistance, housing accessibility, earthquake-resilient construction, improved livelihoods, and safety enhancements. The field study presents crucial recommendations for enhancing the effectiveness of Socio-Technical Assistance (STA) activities in post-earthquake, rural private housing reconstruction. Emphasizing the need for tailored, demand-driven interventions, the study cautions against relying solely on an owner-driven reconstruction model, challenging the one-size-fits-all strategy. The study proposes integrating tailored interventions into overarching recovery strategies, advocating for coordinated efforts to enhance disaster risk reduction (DRR) awareness and to cultivate resilient communities in particularly vulnerable households as aligned with United Nations Sustainable Development Goal 11, which focuses on sustainable cities and communities. This research aims to enhance the literature on post-disaster humanitarian shelter and settlement by emphasizing the significance of inclusive and comprehensive approaches to recovery and reconstruction. Full article

In this paper, we introduce, model, and solve a clustered resource allocation and routing problem for humanitarian aid distribution in the event of an earthquake and subsequent tsunami. First, for the preparedness stage, we build a set of clusters to identify, classify, sort, focus, and prioritize the aid distribution. The clusters are built with k-means method and a modified version of the capacitated p-median model. Each cluster has a set of beneficiaries and candidate delivery aid points. Second, vehicle routes are strategically determined to visit the clusters for the response stage. A mixed integer linear programming model is presented to determine efficient vehicle routes, minimizing the aid distribution times. A vulnerability index is added to our model to prioritize aid distribution. A case study is solved for the city of Iquique, Chile. Full article