A data- and expert-driven framework for establishing land cover-related essential variables for SDG monitoring and assessment

Abstract

Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals (SDGs). Although land cover information has long been recognized as an essential component for monitoring SDGs, a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist. Therefore, we propose a novel expert- and data-driven framework for identifying, refining, and selecting a priority list of Essential Land cover-related Variables for SDGs (ELcV4SDGs). This framework integrates methods including expert knowledge-based analysis, clustering of variables with similar attributes, and quantified index calculation to establish the priority list. Applying the framework to 15 specific SDG indicators, we found that the ELcV4SDGs priority list comprises three main categories, type and structure, pattern and intensity, and process and evolution of land cover, which are further divided into 19 subcategories and ultimately encompass 50 general variables. The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment, thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local, national, and global levels.

Keywords

Essential variable / Land cover / SDG / Spatial monitoring and assessment / Interactive analysis / Refinement and selection

Data availability statement

All data used in the analysis are publicly accessible. The data presented in this manuscript [Data-01 the number of the reviewed literature referred to each SDG indicator, candidate variable and the frequency these occurred together; Data-02 the data specification estimates of candidate land cover related variables assembling from published literature and data products] are stored and available from the following data repository: 10.17632/ddz4zr9bfp.1.

CRediT authorship contribution statement

Hao Wu: Writing - review & editing, Writing - original draft, Methodology, Conceptualization. Ping Zhang: Writing - review & editing, Writing - original draft, Visualization, Validation, Methodology, Funding acquisition, Formal analysis, Data curation. Jun Chen: Writing - review & editing, Supervision, Funding acquisition, Conceptualization. Songnian Li: Writing - review & editing, Supervision, Conceptualization. Jing Li: Writing - review & editing, Validation. Shu Peng: Validation, Data curation. Dongyang Hou: Validation, Investigation, Formal analysis. Jun Zhang: Validation, Resources. Hao Chen: Validation, Resources.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was supported by the Key Program of National Natural Science Foundation of China (Grant No. 41930650) and Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 42301310).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2025.100397.

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