## 4 Discussion  
Multidimensional Validation of the Robustness of the Territorial Carbon Budgets Accounting Framework in Lake-Intensive Regions  
Lake-intensive regions, characterized by distinct water-land interlaced landscapes and diverse ecosystems, possess inherent carbon sequestration potential. Lakes and their surrounding wetlands, forests, and grasslands play a crucial role in carbon sequestration. These areas absorb and store substantial carbon emissions, regulate regional climate, and maintain ecological balance. Therefore, using lake-intensive regions to achieve carbon balance is scientifically justified and provides a solid theoretical basis for developing precise, region-specific low-carbon strategies. To ensure the reliability of the study's results, this research systematically evaluates the model's robustness through parameter sensitivity tests, spatial consistency checks, and robustness assessments of driving factor decomposition. First, parameter sensitivity analysis shows that although the selection of carbon source and sink coefficients has minimal impact on total estimates, it is crucial for identifying spatial variation patterns [13]. To ensure reliable carbon balance results in lake-intensive regions, this study adopts the IPCC-recommended direct estimation method for carbon emissions and an indirect method based on energy coefficients, integrating remote sensing imagery and socioeconomic data for systematic carbon source and sink calculations. Additionally, comparing carbon budgets under varying carbon emission and sequestration coefficients in the Dongting Lake region confirms the consistency of carbon balance trends [38]. This reinforces the robustness of the findings. However, high-value territorial carbon budget areas are more sensitive. When the forest carbon sequestration coefficient is reduced by 20%, the L-L clustered area in edge forests shrinks by 12%. This suggests that underestimating the carbon sequestration capacity of ecological spaces may weaken their spatial adverse feedback effects. Second, this study employs a multi-scale and multi-resolution cross-validation approach to eliminate potential biases in selecting spatiotemporal exploratory analysis methods. The spatial autocorrelation results of carbon budgets on a 5 km \(\times\) 5 km grid show strong consistency with Moran's I index at the urban scale (\(p\) < 0.01). Moreover, SDE analysis of carbon budgets reveals that high-value areas significantly overlap with significant water systems, consistent across scales. After adjusting the grid resolution to 3 km \(\times\) 3 km, the spatial coverage variation of each cluster remains below 5%, indicating that the core conclusions exhibit low sensitivity to spatial unit scale. These findings further reinforce the robustness of spatiotemporal variation patterns in carbon budgets. Furthermore, spatial clustering and SDE analysis results align with trends reported in the relevant literature [39], reinforcing the reliability of this study's conclusions. Finally, the LMDI model remained stable in ranking driving factor contributions across scenarios. Even when excluding contentious parameters (e.g., potential ecological carbon sink coefficients), the economic development effect (\(\Delta\)Cg) and land use efficiency effect (\(\Delta\)Cy) retained their dominance as primary positive and negative contributors. This finding parallels [PERSON]'s Yellow River Basin decomposition results, underscoring the model's robustness in isolating critical driving mechanisms [20]. In summary, this study's \"carbon source-carbon sink\" spatiotemporal accounting framework demonstrates strong robustness in facing the threefold challenges of parameter uncertainty, spatial heterogeneity, and driver complexity.