## 5 Conclusions and Policy Recommendations  
Our study focused on the analysis of AI applications in local and regional studies. The results provide a comprehensive understanding of the dynamics and impact of existing research, highlighting international trends and scientific collaborations. The analysis revealed a significant increase in interest in AI applications in local and regional studies since 2018, as demonstrated by the growing number of papers published annually. Similar observation have been made in other studies [113; 114; 115], where AI has been shown to contribute to solving urbanization challenges and to the development of sustainable local policies. This growth aligns with rapid technological advancements and the widespread implementation of AI-based solutions across various industries. Annual citation rates have also shown notable fluctuations, with significant spikes over the years where major technological launches occurred. The most cited studies cover a wide range of AI applications, from water resource planning to flood-prone area prediction, demonstrating the versatility and potential of these technologies in addressing complex local and regional problems. The use of AI applications has a significant impact on optimizing processes and improving decisions at local and regional levels. For example, AI can positively influence public transportation and Industry 4.0, improving resource management efficiency and risk anticipation. ML algorithms and artificial neural networks are essential for modeling, classification, and prediction. Studies on multi-agent systems have demonstrated that AI can optimize industrial processes and support land-use planning and urban sprawl. The main objective of the study was to provide the reader with a comprehensive bibliometric analysis of the applicability of AI in local or regional studies, based on a total of 237 papers extracted from the WoS database, over a period from 2002 to 2023. From the very beginning of the analysis, some key scientific questions were set, the answers to which were followed throughout the research in order to observe the evolution of the field. For the analyzed period, 2002-2023, until 2017 the number of published articles ranged from 0 to 2 (in 2011). The number of published articles was not very high until 2017; the period of ascension started in 2018. Between the years 2018 and 2023, there were between 9 and 73 published papers, which indicates a growing interest in the application of AI in local or regional studies. In terms of the number of publications in the field by country, China recorded the highest number of publications, with 215 articles, followed by the United States of America with 64 articles, Vietnam with 44, the United Kingdom with 35, Italy with 30, and India with 23. The remaining countries recorded numbers of publications during the period under analysis of less than 20 articles. Throughout the analysis, China constantly stood out at the top of the results obtained, which indicates a particular interest in local or regional studies. Another important factor in our analysis was the authors. There was a total of 992 authors, who, according to their preferences, chose to work individually or in collaboration with other researchers. Among those whose contribution was the most significant, we have [PERSON] and [PERSON], with six published papers each, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], with three papers each, and the rest of the authors participated with the publication of two papers each at most. Among the authors' favorite journals to publish their work, the most used journal was _Remote Sensing_, where a total of 16 articles were published. In addition, we can also mention _Sustainability_ (15 articles), _PLOS ONE_ (8 articles), _Mathematical Problems in Engineering_ (7 articles), _Computational Intelligence and Neuroscience_ (4 articles), _IEEE Access_ (4 articles), _Sensors_ (4 articles), and _Soft Computing_ (4 articles).