Construction of a Spatiotemporal Knowledge Graph for Carbon Source and Sink Dynamics Driven by Multi-Source Sensing Collaboration

Driven by global climate change and the “0Dual Carbon” goals, the precise analysis of spatiotemporal carbon sourcesink dynamics has become a central challenge in environmental governance. Traditional approaches relying on single data sources and static models often fail to adequately capture spatiotemporal heterogeneity. To address this, this study proposes a multi-source sensing collaborative-driven knowledge graph construction method for carbon source-sink dynamics. The framework integrates “space-air-ground-social” multi-source data to build a spatiotemporally continuous ontology model, featuring a four-dimensional representation system (semantic, spatial, temporal, and attribute features). It further incorporates domain knowledge-enhanced large language models to enable efficient extraction of unstructured text and structured data. Experimental results demonstrate that the method successfully achieves intelligent extraction of carbon-related entities and relationships from unstructured text, with the entity recognition accuracy of 92.3 %. A city-scale, dynamically updated carbon source-sink knowledge graph was constructed for Shenzhen, providing granular decision support for regional carbon sequestration potential assessment and emissions trading. This work advances carbon governance from an experience-driven to a data-driven paradigm.