The introduction of the carbon peak and carbon-neutral targets by many countries' central governments has put low-carbon-oriented spatial planning at the forefront of discussions. However, few studies have focused on the balance of carbon emission reduction and economic goals in spatial planning, and the governance influence on land use change simulation. This study addresses this gap by conducting an empirical analysis in the rapidly urbanizing area of Hangzhou, China, taking into consideration low-carbon constraints and economic development demands. Using the stochastic impacts by regression on population, affluence, and technology (STRIPAT) model and linear programming–Markov, we simulate the governance decision-making process to calculate the optimal land-use structures under both low-carbon and baseline scenario, then simulated land use patterns by using artificial-neural-network-based cellular automata (ANN-CA). The results showed 12.35% and 2.5% growth in urban and forest land, and 9.69% and 6.4% decline in farm and rural land under the low-carbon scenario. 92.31% of urban land change occur in the downtown districts and suburbs; while 59.77% of farm land change and 95.53% of forest land change occur in the exurban districts. The low-carbon performance of land use was reflected in carbon storage release, carbon emission capability change, and low-carbon capability. The most common conversion of land use categories under the low-carbon scenario was between farm and forest land, and between rural and urban land, which resulted in less carbon storage release and carbon emissions compared with the baseline scenario. Furthermore, under the low-carbon scenario, the compactness of construction land increased by 2 × 10⁻⁵, while its fragmentation decreased by 0.0027. This study sheds light on the impact of low-carbon-oriented land use planning on urban land expansion, providing empirical evidence for city governments in rapid urbanization areas to improve land use efficiency.