Natural disasters devastate property and infrastructure systems, impeding sustainable development. Low-income communities, due to economic, physical, and social disparities, face heightened exposure and vulnerability. These communities endure severe and long-lasting infrastructure damage, experiencing a fourfold increase in deaths per disaster and delayed recovery efforts. Consequently, they resort to constructing informal housing and infrastructure, worsening post-disaster challenges and vulnerabilities. This study aims to address post-disaster challenges in low-income communities by proposing two novel approaches that remain understudied despite their significant potential: (1) a short-term solution of origami temporary emergency housing for swift shelter post-disaster, enabling a return to routine activities while homes and infrastructure systems are being repaired or rebuilt; and (2) a long-term solution, including effective pedagogy, such as teaching methods and instructional tools, to educate and train low-income individuals to aid in sustainable post-disaster reconstruction while providing the added benefit of social mobility. To validate the feasibility of origami TEH and the need and effectiveness of the pedagogy, a survey among architecture, engineering, and construction experts in Puerto Rico, a region prone to natural disasters, was conducted. The results, analyzed using statistical measures including descriptive statistics and ordered probit regression analysis, emphasize the urgent need for sustainable TEH that can be quickly assembled and education for low-income individuals in construction trades. Implementing these solutions will significantly impact communities by addressing post-disaster challenges and promoting social mobility and job equity.

Natural disasters cause extensive infrastructure and significant economic losses, hindering sustainable development and impeding social and economic progress. More importantly, they jeopardize community well-being by causing injuries, damaging human health, and resulting in loss of life. Furthermore, communities often experience delayed disaster response. Aggravating the situation, the frequency and impact of disasters have been continuously increasing. Therefore, fast and effective disaster response management is paramount. To achieve this, disaster managers must proactively safeguard communities by developing quick and effective disaster management strategies. Disruptive technologies such as artificial intelligence (AI), machine learning (ML), and robotics and their applications in geospatial analysis, social media, and smartphone applications can significantly contribute to expediting disaster response, improving efficiency, and enhancing safety. However, despite their significant potential, limited research has examined how these technologies can be utilized for disaster response in low-income communities. The goal of this research is to explore which technologies can be effectively leveraged to improve disaster response, with a focus on low-income communities. To this end, this research conducted a comprehensive review of existing literature on disruptive technologies, using Covidence to simplify the systematic review process and NVivo 14 to synthesize findings.