Is sustainable tourism actually serving the SDGs? Insights from scientometrics and BERTopic analysis of research evolution and future trajectories
Maniraj Bonkuri, N. Akshaya, S. YaminiPurpose
Sustainable tourism is increasingly recognised as a strategic instrument for advancing the United Nations Sustainable Development Goals (SDGs). However, the field lacks a data-driven synthesis that traces the evolution of themes over time and links emerging research trajectories to SDG-aligned governance frameworks. This study maps the intellectual landscape of sustainable tourism research and forecasts its future direction using an integrated scientometric and machine-learning methodology.
Design/methodology/approach
Scientometric analysis was combined with BERTopic modelling applied to 3,223 peer-reviewed articles (2005–2025) from Scopus and Web of Science. The pipeline integrated MiniLM-L6-v2 sentence embeddings, UMAP dimensionality reduction, HDBSCAN clustering, c-TF-IDF topic representation, GPT-4-assisted labelling and manual SDG mapping. Prophet-based time-series forecasting was then used to project research trajectories through 2030.
Findings
Eighteen thematic clusters organised into four super-topics (consumer behaviour core, cultural and place-based convergence, policy and governance and environmental operations) demonstrate how sustainable tourism research engages with 6 key SDGs (8, 11, 12, 13, 15 and 17). Forecasting identifies four trajectory clusters: sharp growth in tourist behaviour and green hotel practices; gradual acceleration in carbon management and smart tourism technologies; decline in traditional sustainability indicators and stable activity in cultural heritage and rural tourism.
Research limitations/implications
The findings offer KPI frameworks for hotel operators, evidence-based governance tools for policymakers and DMOs and a forward-looking research agenda for scholars.
Originality/value
This study is among the first to combine BERTopic with prophet forecasting within an SDG-linked tourism corpus, transforming bibliometric review into a predictive, decision-support instrument.