## 2 Materials and Methods  
This scoping review adopts a structured and exploratory approach to map existing research on the contribution of artificial intelligence and machine learning to the sustainable development of regional ecosystems. The goal is to uncover prevailing trends and critical challenges associated with AI integration at the local level, with a particular focus on its environmental, economic, and social implications. The review is guided by the PRISMA framework, which ensures methodological transparency and reproducibility. Relevant peer-reviewed sources were identified through a stepwise search strategy in the Scopus database, refined iteratively to ensure thematic relevance and comprehensive coverage. Initially, the following query string was constructed to identify relevant studies: TITLE-ABS-KEY ((\"Regional Ecosystem\") AND (\"Artificial Intelligence\" OR \"Machine learning\")) AND PUBYEAR > 2009 AND PUBYEAR < 2026. This query covered the period from 2010 to 2025 and targeted both foundational and recent research. However, as of 18 March 2025, it returned only 37 publications, which did not meet the requirements for a scoping review. In the second iteration, the query was expanded by including additional keywords to broaden the scope: TITLE-ABS-KEY ((\"Regional Ecosystem\" OR \"Local Ecosystem\") AND (\"Artificial Intelligence\" OR \"Machine learning\")) AND PUBYEAR > 2009 AND PUBYEAR < 2026. This modification increased the number of retrieved records to 70, which still fell short of the necessary threshold for a comprehensive review. During the third phase, the search terms \"Regional Ecosystem\" and \"Local Ecosystem\" were split to improve coverage, resulting in the following string: TITLE-ABS-KEY (\"Ecosystem\" AND (\"Regional\" OR \"Local\") AND (\"Artificial Intelligence\" OR \"Machine learning\")) AND PUBYEAR > 2009 AND PUBYEAR < 2026. While this search yielded 1728 documents, the results proved too broad, capturing a significant number of irrelevant publications and weakening the thematic focus. To enhance the thematic focus and methodological clarity, an additional filter was applied to restrict the results to studies explicitly addressing sustainability. The final search string was: TITLE-ABS-KEY (\"Ecosystem\" AND (\"Regional\" OR \"Local\") AND (\"Artificial Intelligence\" OR \"Machine learning\") AND (\"Sustainability\" OR \"Sustainable Development\")) AND PUBYEAR > 2009 AND PUBYEAR < 2026. As a result of the refined search strategy, a total of 198 publications were identified, providing a balanced dataset in terms of both relevance and comprehensiveness. The selected keywords were carefully tailored to align closely with the aims and research questions of this study. Known constraints of the search strategy (database, language, and disciplinary coverage) were identified a priori and are discussed in detail in Section Discussions. As of 18 March 2025, a temporal analysis of publication trends revealed that 73% of the articles were published between 2022 and 2025, indicating a sharp increase in academic interest in this topic over recent years (Figure 1). The number of publications has grown from 16 in 2021 to 57 in 2024, demonstrating exponential growth in the field. Figure 1: Literature review. Documents by years. Source: Scopus Analyzer. An analysis of the geographical distribution of the reviewed publications revealed that the highest number of studies originated from China (\(n=78\)), significantly outpacing all other countries. The United States ranked second with 27 publications, followed by India with 22. A considerably smaller number of studies was recorded in Germany, the United Kingdom, Italy, South Africa, and Spain, where publication counts ranged between 8 and 16. Other notable contributors included Australia, France, Portugal, and Singapore, each represented by six publications (Figure 2). The disciplinary classification of the reviewed literature (Figure 3) indicates that the largest proportion of studies falls under the field of Environmental Science (23%).