Advancing Environmental Resilience through Integrated Science, Technology, and Engineering: A Multidisciplinary Approach

Environmental degradation and climate change demand interdisciplinary solutions that integrate science, technology, and engineering to foster resilience. This study investigates how advanced tools such as AI, IoT, and remote sensing, combined with sustainable engineering practices, can address ecological and socio-economic stressors more effectively than traditional siloed approaches. We employ a multi-modal methodology, merging empirical data analysis, case study comparisons, and policy reviews, while leveraging global datasets from repositories like the UNEP Environmental Data Explorer. Data fusion techniques, including Convergent Cross Mapping and Structural Equation Modeling, are applied to synthesize socio-ecological and IoT sensor data, revealing non-linear causal relationships in stressed ecosystems. Furthermore, machine learning models, particularly attention-based architectures, are benchmarked for bias correction, significantly improving climate prediction accuracy. The results demonstrate that edge-computing IoT networks outperform centralized cloud systems, achieving 80–90% faster response times and 85% energy savings. However, governance gaps persist, with 40% of IoT devices lacking robust privacy protocols, necessitating federated learning frameworks to reconcile data utility with jurisdictional sovereignty. The findings highlight the transformative potential of integrated systems for environmental resilience while underscoring the need for equitable governance and bias-aware algorithms. This research contributes a scalable, policy-aligned framework that bridges theoretical innovation with practical implementation, advancing global sustainability goals.