Nexus of Sustainability Understanding of FEWSE Systems ІI

Free Download Nexus of Sustainability: Understanding of FEWSE Systems ІI
English | 2026 | ISBN: 3032036151 | 657 Pages | PDF (True) | 28 MB
This book emphasizes the need for harmonized policies and adaptive strategies that can address the complexities of local, national, and global systems simultaneously. The interconnected challenges of our time-climate change, resource depletion, systemic risks, and socio-economic inequalities -demand a multidisciplinary and integrated approach to sustainable development. This second volume, Nexus of Sustainability: Understanding of FEWSE Systems II, builds upon the foundations laid in the first volume, deepening our exploration of the complex interplay among Food, Energy, Water, Social, and Environmental (FEWSE) systems. It highlights both the vulnerabilities within these systems and the transformative opportunities that arise when they are considered as interconnected components of a greater whole. This book comes at a pivotal moment in global sustainability discourse. Building on the outcomes of the Joint Research Project between the National Academy of Sciences of Ukraine (NASU) and the International Institute for Applied Systems Analysis (IIASA), this book synthesizes advanced systems analysis, innovative modeling methodologies, and robust decision-making frameworks. These approaches aim to address emerging systemic risks-including those exacerbated by the COVID-19 pandemic, geopolitical instabilities, and accelerating environmental change-and to develop practical, evidence-based strategies for ensuring the resilience and security of FEWSE systems. The collaborative efforts between NASU and IIASA have been instrumental in shaping the insights presented in this volume. With contributions from leading disciplinary institutes and research groups, this work integrates knowledge from academia, industry, policy, and community stakeholders. The research spans diverse methodologies, including stochastic optimization, machine learning, downscaling techniques, distributed model linkage, and robust estimation methods.