Author
Listed:
- Zhen Liu
(State Key Laboratory for Tunnel Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Department of Architecture, School of Mechanics and Civil Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)
- Xiaodan Li
(State Key Laboratory for Tunnel Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Department of Architecture, School of Mechanics and Civil Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)
- Qi Yang
(State Key Laboratory for Tunnel Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Department of Architecture, School of Mechanics and Civil Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)
- Shuai Mao
(State Key Laboratory for Tunnel Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Department of Architecture, School of Mechanics and Civil Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)
- Xiaosai Li
(State Key Laboratory for Tunnel Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Department of Architecture, School of Mechanics and Civil Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)
- Zhiping Liu
(State Key Laboratory for Tunnel Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Department of Architecture, School of Mechanics and Civil Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)
Abstract
Robust and transferable approaches for evaluating research capacity—whose measurable expression is reflected in research output—are essential for evidence-based science policy and strategic research management. This study develops an integrated framework to assess global scholarly capacity and regional disparities by combining semantic-similarity-based literature filtering, bibliometric mapping, dynamic performance assessment, and spatial analytical techniques into a coherent and replicable model. A Sentence-BERT model ensures thematic precision and dataset consistency, while CiteSpace 6.1.R3 is used tomap publication trajectories, thematic evolution, and influential contributors. A dynamically weighted TOPSIS model incorporates temporal variation to quantify national research capacity, and spatial analyses—including gravity center analysis, Theil index decomposition, spatial autocorrelation, gray relational analysis, and the Geographical Detector Model—identify disparity patterns and their explanatory associations. Applied to urban ecosystem security research (2001–2023), an emerging interdisciplinary field within sustainability science, the framework shows that China and the United States dominate research output, whereas European journals exert strong academic influence. The field has advanced through three stages, with increasing emphasis on ecosystem services and sustainable development. GDP, environmental pressure, and urbanization rate show the strongest explanatory associations with research capacity, and interactive effects—especially those involving GDP—exceed single-factor explanatory strength. Ecological baseline conditions such as NDVI and climate exhibit only limited associations, functioning mainly as contextual factors. Policy implications highlight four priorities: strengthening interdisciplinary and cross-regional collaboration in developing regions; promoting equity-oriented research agendas in developed regions; establishing unified definitions and validated evaluation frameworks; and advancing dynamic, systems-based approaches to ecosystem security analysis. By shifting attention from ecological status assessment to the dynamics of scientific knowledge production and research capacity, this study advances methodological foundations for research evaluation and enriches analytical approaches in urban ecosystem security, offering a generalizable framework for identifying capacity differences and supporting evidence-informed policy design.
Suggested Citation
Zhen Liu & Xiaodan Li & Qi Yang & Shuai Mao & Xiaosai Li & Zhiping Liu, 2026.
"A Multi-Method Framework for Assessing Global Research Capacity and Spatial Disparities: Insights from Urban Ecosystem Security,"
Land, MDPI, vol. 15(3), pages 1-43, March.
Handle:
RePEc:gam:jlands:v:15:y:2026:i:3:p:512-:d:1900900
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