This study investigates the evolution and stability of China’s carbon emission trading system, addressing disparities between China’s carbon market development and international practices. By constructing a three-party evolutionary game model involving the government, enterprises, and consumers, this paper identifies key factors influencing system stability and offers policy recommendations. The main contributions include: (1) Developing a dynamic model that incorporates consumer behavior, including the herd effect, to reveal the interplay between demand-side dynamics and market evolution. (2) Demonstrating that government regulatory costs, consumer preferences, and enterprise investment costs significantly impact market stability and green transitions. (3) Highlighting that carbon prices alone have limited influence on enterprise participation, underscoring the importance of comprehensive incentives. This work provides actionable insights to optimize policy design, enhance market efficiency, and promote low-carbon transformation in China.

Soil organic carbon (SOC) is a key indicator of cropland quality, soil fertility, and the carbon sequestration potential of agroecosystems. Accurate characterization of its spatial distribution is essential for black soil conservation and regional soil carbon management. However, regional-scale SOC prediction is often constrained by limited field observations, which can reduce model generalizability and predictive reliability. In this study, we developed a limited-sample SOC prediction framework for the Liaohe Plain using 310 surface (0–20 cm) soil samples collected in 2025 and multi-source environmental covariates, including climate, vegetation, soil spectral, and terrain variables. The framework used the Tabular Prior-Data Fitted Network (TabPFN), whose performance was compared with that of Random Forest, Support Vector Machine, CatBoost, K-Nearest Neighbors, and XGBoost. Model performance was evaluated using 100 repetitions of random 80:20 holdout validation and repeated five-fold spatial cross-validation based on spatially constrained clustering, while sampling-induced relative uncertainty was quantified using 100 repeated random sampling and model-fitting runs. Under random holdout validation, TabPFN showed competitive predictive performance, with mean R² and RMSE values of 0.608 ± 0.038 and 4.026 ± 0.184 g kg⁻¹, respectively. Repeated spatial cross-validation yielded more conservative performance estimates, with mean R² and RMSE values of 0.535 ± 0.072 and 4.33 ± 0.31 g kg⁻¹, respectively, indicating that random splitting may overestimate model performance when sampling sites are spatially clustered. Spatial prediction showed that cropland SOC ranged from 4.63 to 27.04 g kg⁻¹, with generally lower values in the west and higher values in the northeast. Areas with high sampling-induced relative uncertainty were mainly concentrated in the northern, northeastern, and marginal regions. These findings provide a methodological basis for SOC mapping, supplementary sampling optimization, and regional soil carbon management under limited-sample conditions, although the temporal robustness of the results requires confirmation using independent data from additional years.