A biblio-ecological study on sustainable land planning research: Evidence from machine learning methods

Sustainable land planning (SLP) plays a vital role in addressing the ecological, social, and economic pressures of urbanization, land degradation, and climate change. As global interest in land-use governance grows, there is a need to clarify the conceptual structure of SLP and its evolving policy relevance. This study analyzes 4883 publications indexed in Scopus from 2021 to 2024 using a mixed-method bibliometric approach. Ensemble community detection on keyword co-occurrence networks is combined with Multiple Correspondence Analysis (MCA) and hierarchical clustering to identify robust thematic clusters and semantic cores. The results reveal six major conceptual areas: smart technologies for land monitoring and degradation control; stakeholder engagement and nature-based solutions aligned with the Sustainable Development Goals (SDGs); tourism's ecological impacts and mitigation strategies; valuation of ecosystem services amid urban expansion; integration of green infrastructure and urban agriculture for livable cities; and spatial planning responses to rising temperatures and green space loss. These clusters reflect the interdisciplinary nature of SLP and its responsiveness to emerging environmental and governance challenges. The study introduces a set of biblio-ecological indicators that support evidence-based policymaking and strategic planning. By enhancing the robustness of thematic detection, the proposed framework offers a replicable tool for tracking conceptual shifts and guiding future research. It is recommended for use in ecological economics, urban governance, and sustainability science to foster integrated land-use strategies and resilient planning practices.