Author
Listed:
- Vera Komarova
(Daugavpils University, Latvia)
- Jānis Kudiņš
(Daugavpils University, Latvia)
- Aija Sannikova
(EKA University of Applied Sciences, Latvia)
- Edmunds Čižo
(Daugavpils University, Latvia)
- Oksana Ruža
(Daugavpils University, Latvia)
- Anita Kokarēviča
(Riga Stradins University, Latvia)
- Zane Zeibote
(University of Latvia, Latvia)
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.
Suggested Citation
Vera Komarova & Jānis Kudiņš & Aija Sannikova & Edmunds Čižo & Oksana Ruža & Anita Kokarēviča & Zane Zeibote, 2024.
"Using artificial intelligence (AI) for local territorial development: data-based machine diagnostics of Latvian municipalities,"
Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 12(2), pages 443-459, December.
Handle:
RePEc:ssi:jouesi:v:12:y:2024:i:2:p:443-459
DOI: 10.9770/y3784695648
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JEL classification:
- R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
- R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy
- O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
- Q01 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Sustainable Development
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