Abstract
As an important ecological security barrier, land use change on the Qinghai-Xizang Plateau has a profound impact on regional carbon storage functions. Exploring the impact of the spatiotemporal evolution of land use on carbon storage in Lhasa has significant reference value for improving regional carbon cycle theory and formulating differentiated low-carbon development strategies. This study takes Lhasa City as the research area, uses land use data from 2000 to 2020 as the benchmark, and couples the PLUS and InVEST models to simulate land use patterns under three different scenarios—natural development (NDS), urban development (UDS), and ecological protection (EPS)—in 2030, thereby revealing the spatiotemporal dynamic evolution differences of carbon storage under each scenario. The results show that: (1) From 2000 to 2020, the urbanization process in Lhasa was remarkable. Construction land, forest land, cultivated land, water area, and unused land all showed an increasing trend, with grassland being the main source of transformation. From 2000 to 2020, carbon storage in Lhasa showed a fluctuating trend of first rising and then falling, with the historical peak occurring in 2010 (207.70 Mt), declining to 206.53 Mt in 2020. The spatial pattern was high in the west and low in the east, with the west being the main high-carbon storage area. (2) The carbon storage of the three scenarios in 2030 differs: NDS is 205.80 Mt, UDS is 206.11 Mt, and EPS is 208.16 Mt. Among them, NDS and UDS are lower than the 2020 level (206.53 Mt), while EPS is not only higher than the 2020 level but also exceeds the historical peak of 2010 (207.70 Mt). The total carbon differences among the three scenarios are relatively small on a macro level, but the micro-level differentiation is significant. EPS performs best in grassland and forest land carbon storage, while NDS is relatively higher in cultivated land and unused land carbon storage. (3) Based on the 2020 baseline carbon storage, the Geodetector analysis shows that soil type (q = 0.264) and GDP (q = 0.253) have the strongest statistical associations with the spatial differentiation of carbon storage, followed by elevation (q = 0.160) and annual average temperature (q = 0.115). It should be noted that this analysis only reveals statistical associations and cannot be interpreted as an independent causal driving mechanism for carbon storage. Moreover, the results are subject to uncertainties in fixed carbon densities, data resampling, and scenario settings. This research can provide a scientific basis for ecological protection strategies under territorial spatial planning and carbon neutrality goals in Lhasa City.