Artificial Intelligence plays a strategic role in enhancing environmental resilience in the digital era by optimizing resource management, improving energy efficiency, and supporting sustainable environmental policies.
Abstract
The rapid development of Artificial Intelligence (AI) has created significant opportunities to address environmental challenges in the digital era. Increasing issues such as climate change, pollution, waste management problems, and natural resource depletion highlight the need for innovative and sustainable solutions. The main problem addressed in this study is how Artificial Intelligence can enhance environmental resilience in the digital era. Therefore, the objective of this study is to analyze the role of AI in supporting environmental resilience and sustainable environmental management. This study employs a bibliometric analysis approach using scientific publications indexed in the Scopus database. The data were analyzed to identify publication trends, keyword relationships, research networks, thematic clusters, and emerging research directions related to Artificial Intelligence and Environmental Resilience. The results show that Artificial Intelligence contributes significantly to environmental monitoring, climate prediction, renewable energy optimization, waste management, and natural resource conservation. The findings reveal that the integration of AI with digital technologies improves decision-making accuracy, increases operational efficiency, reduces environmental risks, and supports data-driven sustainability initiatives. In conclusion, Artificial Intelligence plays a strategic role in enhancing environmental resilience in the digital era by optimizing resource management, improving energy efficiency, and supporting sustainable environmental policies. The adoption of AI should therefore be encouraged while maintaining principles of sustainability, ethics, and environmental responsibility.
The findings reveal that AI significantly contributes to environmental resilience by enabling advanced data analysis, early detection of environmental risks, predictive assessment, resource optimization, and evidence-based decision-making.
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