A recent study by researchers from Banaras Hindu University investigates significant bibliometric trends and concept-evolution trajectories in AI applications for Sustainable Development Goals (SDGs). The research highlights that AI has been increasingly applied to various SDGs globally. 

The study finds that AI is most prominently used in SDGs 3 (Good Health & Well-being) and 7 (Affordable and Clean Energy). For SDG 3, AI techniques like deep learning are applied to precision and personalized medicine. In SDG 7, AI is employed to enhance the integration of solar power systems and improve building energy efficiency. Additionally, AI approaches are also applied to SDGs 4 (Quality Education), 13 (Climate Action), 11 (Sustainable Cities and Communities), and 16 (Peace, Justice, and Strong Institutions). 

Despite the growing recognition of AI's potential in achieving SDG targets, there is limited in-depth knowledge about the extent of research on AI for SDGs, key focus areas, and prominent techniques used. This study aims to address the research gap through bibliometric and content analysis methods. 

Key Findings

The study examines four major aspects: temporal, regional, collaborative, and quantitative.

• Temporal Analysis: Tracks publication trends and citations over 20 years for all SDGs. 
• Regional Analysis: Identifies global research hotspots based on publication geotags.
• Collaborative Analysis: Reveals collaboration patterns among authors from different regions.
• Quantitative Analysis: Quantifies research output by individual SDGs and identifies the most active regions. 

The total number of publications on AI for SDGs (AI4SDG) has grown exponentially since the UN adopted the SDGs in 2015. The most active regions include the United States, Western Europe, China, Japan, Australia, and India. The US, UK, China, Canada, Australia, and Germany are notable for their international collaborations. A total of 20,511 articles were published across 360 journals, with the top 20 journals being particularly prominent. 

The study also explores the major problem areas addressed by AI techniques within various SDGs. Only goals with high publication counts (over 1000) were selected for detailed analysis: SDG 3, SDG 7, SDG 4, SDG 11, SDG 13, and SDG 16. This approach assumes that higher research output indicates more exploration and AI integration in these areas. 

Impact of the Study

This study offers a thorough analysis of the literature on AI for SDGs. It highlights key activity areas, identifies prominent literature, and tracks research trends over time. The results show an increasing trend in AI applications across different regions, with SDGs 3 and 7 being the most researched. The study also maps knowledge flows in AI research related to SDGs and identifies major application areas and methods. 

The findings can guide researchers and universities to focus on emerging or under-explored SDG areas. Governments may use these insights to adjust policies and programs based on regional and national priorities. While some observations, particularly those from the content analysis, are subjective and may evolve with future research, they offer valuable insights into the development of this field.

Full Study