Carbon emissions caused by economic growth are the main cause of global warming, but controlling economic growth to reduce carbon emissions does not meet China's conditions. Therefore, how to synergize economic growth and carbon emission reduction is not only a sustainable development issue for China, but also significant for mitigating global warming. The territorial spatial functional pattern (TSFP) is the spatial carrier for coordinating economic development and carbon emissions, but how to establish the TSFP of synergizing economic growth and carbon emission reduction remains unresolved. We propose a decision framework for optimizing TSFP coupled with the multi-objective fuzzy linear programming and the patch-generating land use simulation model, to provide a new path to synergize economic growth and carbon emission reduction in China. To confirm the reliability, we took Qionglai City as the demonstration. The results found a significant spatiotemporal coupling between TSFP and the synergistic states between economic growth and carbon emission reduction (q ≥ 0.8220), which resolves the theoretical uncertainty about synergizing economic growth and carbon emission reduction through the path of TSFP optimization. The urban space of Qionglai City in 2025 and 2030 obtained by the decision framework was 6497.57 hm² and 6628.72 hm² respectively, distributed in the central and eastern regions; the rural space was 60,132.92 hm² and 56,084.97 hm², concentrated in the east, with a few located in the west; and the ecological space was 71,072.52 hm² and 74,998.31 hm², mainly located in the western and southeastern areas. Compared with the TSFP in 2020, the carbon emission intensity of the TSFP obtained by the decision framework was reduced by 0.7 and 4.7 tons/million yuan, respectively, and realized the synergy between economic growth and carbon emission reduction (decoupling index was 0.25 and 0.21). Further confirming that TSFP optimization is an effective way to synergize economic growth and carbon emission reduction, which can provide policy implications for coordinating economic growth and carbon emissions for China and even similar developing countries.

Long-term stability is crucial in cropland for maintaining stable agricultural production and ensuring national food security. However, relatively few studies have been conducted on the long-term stability of cropland at the national level. This study assessed the long-term stability of cropland in China from 1990 to 2018 using a fine-resolution land use dataset. The experimental results indicated that the average area of unstable cropland in China from 1990 to 2018 amounted to 2.08 $\times {10}^6$ km², 47.31% of the total. The Qinghai–Tibet Plateau exhibited the highest average proportion of unstable cropland at 65.9%, followed by the northern arid and semiarid region, Southern China, and the Yunnan–Guizhou Plateau. The quantity of unstable cropland in China initially declined before increasing, reaching a final growth rate of 5.09%. Furthermore, this study explored the relevant driving factors of cropland’s long-term stability from both natural factors and human activities based on artificial neural networks. The relative importance of distance to vegetation reached a value of 0.30, indicating that it had the most significant influence on the long-term stability of cropland, followed by relief amplitude and soil type. This phenomenon may be attributed to the inadequate execution of the Grain for Green Policy and the requisition–compensation balance of cropland policy, along with the depletion of young and middle-aged laborers due to urban migration from rural areas. Local governments should focus on addressing the unsustainable exploitation of sloped land in rural mountainous or hilly regions while preventing urban developers from appropriating fertile cropland to compensate for less fertile areas.