Frequent occurrences of extreme weather events substantially impact the lives of the less privileged in our societies, particularly in agriculture-inclined economies. The unpredictability of extreme fires, floods, drought, cyclones, and others endangers sustainable production and life on land (SDG goal 15), which translates into food insecurity and poorer populations. Fortunately, modern technologies such as Artificial Intelligent (AI), the Internet of Things (IoT), blockchain, 3D printing, and virtual and augmented reality (VR and AR) are promising to reduce the risk and impact of extreme weather in our societies. However, research directions on how these technologies could help reduce the impact of extreme weather are unclear. This makes it challenging to emploring digital technologies within the spheres of extreme weather. In this paper, we employed the Delphi Best Worst method and Machine learning approaches to identify and assess the push factors of technology. The BWM evaluation revealed that predictive nature was AI's most important criterion and role, while the mass-market potential was the less important criterion. Based on this outcome, we tested the predictive ability of machine elarning on a publilcly available dataset to affrm the predictive rols of AI. We presented the managerial and methodological implications of the study, which are crucial for research and practice. The methodology utilized in this study could aid decision-makers in devising strategies and interventions to safeguard sustainable production. This will also facilitate allocating scarce resources and investment in improving AI techniques to reduce the adverse impacts of extreme events. Correspondingly, we put forward the limitations of this, which necessitate future research.
Enterprise Architecture as a Dynamic Capability for Scalable and Sustainable Generative AI adoption: Bridging Innovation and Governance in Large Organisations
Causal machine learning for sustainable agroecosystems
Universitat de València（瓦伦西亚大学）；Wageningen University and Research（瓦赫宁根大学及研究中心）；Arizona State University（亚利桑那州立大学）；World Food Program（世界粮食计划署）；Helmholtz Centre for Environmental Research – UFZ（亥姆霍兹环境研究中心）；National Observatory of Athens（雅典国家天文台）
Challenging Techno-Solutionism: The Role of ICT Innovation and the Value of Technological Growth
University of Edinburgh（爱丁堡大学）；University of Oslo（奥斯陆大学）；Université Catholique de Louvain（天主教鲁汶大学）；Witten/Herdecke University（维滕/海尔韦克大学）；Universidade de Vigo（维戈大学）
Culturally-Aware AI for Cross-Boundary Community Learning: Undergraduate Innovation at the Intersection of Computation and Design
Duke Kunshan University（杜克昆山大学）；Zhouzhuang Mystery of Life Museum（周庄生命之谜博物馆）；Digital Innovation Research Center and Social Science Division（数字创新研究中心和社会科学系）
Artificial Intelligence in Rural Healthcare Delivery: Bridging Gaps and Enhancing Equity through Innovation
University of Illinois College of Medicine Rockford（伊利诺伊大学罗克福德医学院）；National Center for Rural Health Professions（农村健康专业中心）；Sri Sivasubramaniya Nadar College of Engineering（塞维萨布拉曼尼亚那德尔工程学院）；M. Kumarasamy College of Engineering（M. Kumarasamy 工程学院）；The Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医疗中心）；iWorks Corporation（iWorks 公司）；Central South University（中南大学）；Bacha Khan University（巴哈汗大学）
