Received:
2024-09-11 | Accepted:
2024-12-09 | Published:
2024-12-30
Title
Using artificial intelligence (AI) for local territorial development: data-based machine diagnostics of Latvian municipalities
Abstract
The study investigates the application of artificial intelligence (AI), specifically the ChatGPT 4o tool, for data-based machine diagnostics of the local territorial development using Latvian municipalities as a case study. The topic is highly relevant due to the growing demand for precise, data-driven territorial diagnostics to address sustainable development and governance challenges. The study aims to evaluate AI tools' efficiency and contextual adaptability in performing municipalities' SWOT (Strengths, Weaknesses, Opportunities, Threats) analyses based on their annual public reports. Using discourse analysis as the methodological framework, the study focuses on five municipalities representing different typological clusters in Latvia: Riga City Municipality, Yelgava City Municipality, Liepaja City Municipality, Ropazhi County Municipality, and Augshdaugava County Municipality. Empirical results demonstrate the AI tool's ability to conduct detailed SWOT analyses, uncovering nuanced insights such as demographic challenges, economic dependencies, and opportunities for green transition initiatives. Notably, the tool highlighted innovative perspectives, such as the competitive impact of proximity to Riga on surrounding municipalities. The study identifies the AI tool’s capabilities, including flexibility in focus, contextual socioeconomic and environmental factors integration, and efficiency in processing complex datasets. However, challenges such as data limitations and the necessity of human oversight were also noted. The findings contribute novel insights into the feasibility and potential of AI for local territorial diagnostics, paving the way for broader applications in regional development planning and policymaking.
Keywords
machine diagnostics, AI tool (ChatGPT 4o), discourse analysis, SWOT analysis, cluster analysis, Territorial Analytic Data (TAD), annual public report, local territorial development, Latvian municipalities
JEL classifications
R11
, R58
, O33
, Q01
URI
http://jssidoi.org/jesi/article/1270
DOI
Pages
443-459
Funding
This research was funded by Daugavpils University, Latvia.
This is an open access issue and all published articles are licensed under a
Creative Commons Attribution 4.0 International License
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