Advancing Nature-Based Solutions with Artificial Intelligence: A Bibliometric and Semantic Analysis Using ChatGPT

In response to escalating climate change and ecological degradation, nature-based solutions (NBSs) have emerged as a critical paradigm for sustainable environmental governance. Simultaneously, artificial intelligence (AI) offers powerful capabilities for addressing the complexity and uncertainty inherent in natural systems. This study investigates the integration of AI within NBS through a hybrid bibliometric and semantic-enhancement framework. Drawing on 535 peer-reviewed articles from the Web of Science Core Collection (2011–2024), we employ keyword co-occurrence analysis via CiteSpace and semantic refinement using ChatGPT-4.0 to identify 15 key thematic clusters. Results reveal that AI is widely applied in ecological monitoring, carbon emission reduction, urban climate adaptation, and green infrastructure optimization—substantially improving the responsiveness, precision, and scalability of NBS interventions. The proposed methodology enhances both structural insight and semantic coherence in bibliometric review, offering a robust foundation for future interdisciplinary research. This study contributes to the theoretical development and practical implementation of AI-enhanced NBS, supporting data-driven, adaptive strategies for climate resilience and sustainable development.