2025 Volume 13 Issue 1 Pages 230-256
Sustainable urban development is a growing area of research, with cities worldwide facing various economic, social, and environmental challenges due to rapid urbanization. This study provides a comprehensive review of sustainable urban development literature through content analysis and application of artificial intelligence techniques. The objectives of this research are: 1) To conduct a systematic literature review of research published between 2012-2022, analyzing dimensions studied and trends over time and across continents/countries. 2) To utilize BERT to automatically classify articles into sustainable urban development dimensions based on titles and abstracts. A systematic literature review was conducted by collecting data from articles indexed on Google Scholar with the keyword 'sustainable urban development.' The initial search yielded 790 articles, which were screened using predefined inclusion and exclusion criteria, resulting in a final selection of 705 articles. Subsequently, a panel of 30 experts was engaged to refine and prioritize dimensions for analysis. Using the Delphi method, a consensus was reached to identify five key dimensions: environmental-ecological, economic, socio-cultural, political (managerial-institutional), and demographic. Statistical analysis was performed on trends by continent, country, and year. The results show Asia had the largest share of articles, followed by Europe, Africa, North America, South America, and Oceania. Iran, Sweden, Egypt, the US, Brazil, and Australia ranked highest for their continents, respectively. The environmental-ecological dimension received the most attention. Statistical analysis of research trends over ten years across six continents and 92 countries provided novel insights. The BERT model was also able to automatically classify articles with 59% accuracy based on titles, abstracts, and topics, demonstrating its potential to organize vast literature efficiently. In conclusion, this research presents the most comprehensive review of sustainable urban development research to date and introduces an AI technique that can facilitate comparative studies and identification of best practices. The classification of literature into dimensions also provides a framework for structured analysis of trends.
