Spatiotemporal Dynamics and Projections of Carbon Storage Using Integrated PLUS-InVEST Modeling: A Case Study of the Guanzhong Plain Urban Agglomeration, China

Rapid urbanization has driven land-use transitions, leading to the continuous replacement of land-use types with high carbon storage capacity by those with lower capacity. A deeper analysis of the drivers behind these changes and predictions of their future development is essential for optimizing land-use patterns and enhancing regional carbon sink functions. This study takes the Guanzhong Plain Urban Agglomeration (GPUA) as a case study. It employs the PLUS and InVEST models to simulate land use and land cover (LULC) dynamics from 2000 to 2020 and to project the LULC and associated spatial clustering characteristics of carbon storage in 2030. The results show that: (1) From 2000 to 2020, LULC changes in the region were dominated by the conversion of cropland to built-up land, primarily concentrated in urban areas and along the Wei River corridor. By 2030, built-up land is expected to continue expanding along transportation corridors and urban peripheries, further reducing the area of cropland. (2) Changes in carbon storage were mainly driven by LULC transitions, with an overall declining trend observed from 2000 to 2020 (decreasing from 2754.69 Mt to 2741.79 Mt) despite the buffering effect of ecological restoration, and a projected continued decrease to 2734.28 Mt by 2030. (3) The spatial distribution of carbon storage was characterized by a strengthening polarization. The proportion of hotspot areas increased from 30.38% to 32.33% over the 2000–2020 period, with a concentration in ecological function zones such as the Qinling Mountains, Ziwuling, and Huanglongshan. Concurrently, coldspot areas also expanded. Future efforts should prioritize the protection of high-carbon-sink mountainous zones, strictly control the outward expansion of built-up land, and enhance carbon storage capacity in agricultural areas to support low-carbon development and spatial optimization in the GPUA.