Projection for Ecological Carrying Capacity Based on the Interpretable CAXO Model: The Case of China.
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| Title: | Projection for Ecological Carrying Capacity Based on the Interpretable CAXO Model: The Case of China. |
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| Authors: | Tang, Xiaoyan1,2 (AUTHOR) xytang@xju.edu.cn, Liu, Funan1,2 (AUTHOR), Feng, Jingyu1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1690. 27p. |
| Subjects: | Ecological carrying capacity, Shapley Additive Explanations, Ecological forecasting, Sustainable development |
| Geographic Terms: | China, Northwest China |
| Abstract: | Highlights: What are the main findings? We proposed a novel interpretable CAXO hybrid model for ECC projection, achieving an OA of 90.01% and a Kappa of 87.11%, outperforming ARIMA-LSTM, ANN, and PLUS models. China's ECC shows a persistent "high southeast, low northwest" spatial pattern and has improved continuously from 2000 to 2020. SHAP and LIME analyses reveal spatially heterogeneous driving mechanisms and critical thresholds across ECC levels. Multi-scenario projections indicate significant divergence in ECC patterns under three SSP-RCP pathways by 2030 and 2050. What are the implications of the main findings? SHAP and LIME interpretability analyses identify NDVI, soil moisture and precipitation as core ECC drivers, with heterogeneous factor contributions across ECC levels, filling the gap of systematic interpretability in ECC projection models. The ECC evaluation system and CAXO model provide a scientific tool for ecological security early warning, and differentiated regional governance strategies can be formulated based on multi-scenario ECC projection results to promote sustainable development. Ecological carrying capacity (ECC) is a vital indicator for regional sustainable development, reflecting an ecosystem's support for human activities while maintaining core functions. Research on ECC has largely focused on static assessment, while exploration of dynamic prediction has been relatively limited. This study constructed a comprehensive evaluation system using the AHP-EW model with multidimensional indicators and developed a CAXO hybrid model for multi-scenario ECC projection of China. ECC patterns were classified into five levels, with SHAP and LIME adopted to interpret ECC changes. The results show that China's ECC exhibits a "high in the southeast and low in the northwest" spatial pattern and has improved continuously from 2000 to 2020, with the proportion of Level V areas increasing from 10.86% to 14.61%. Significant regional disparities exist, with more favorable ECC conditions east of the Hu Huanyong Line and poorer conditions in the west. The CAXO model achieves reliable performance (OA = 90.01%, Kappa = 87.11%) and outperforms traditional models. SHAP analysis identifies NDVI (2.17) as the most critical driving factor, followed by soil moisture (0.53) and precipitation (0.52), while LIME reveals heterogeneous factor contributions across ECC levels. Northwestern China faces severe ecological constraints (Level I: 53.96%), whereas eastern China exhibits the optimal ECC status (Level V: 70.07%). Multi-scenario projections to 2050 show that Level V areas will reach 28.22% under SSP1-2.6, Level III will account for 27.70% under SSP2-4.5, and Level I will rise to 22.44% under SSP5-8.5. The proposed ECC framework and CAXO model provide scientific support for ecological security early warning and sustainable development policy-making. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We proposed a novel interpretable CAXO hybrid model for ECC projection, achieving an OA of 90.01% and a Kappa of 87.11%, outperforming ARIMA-LSTM, ANN, and PLUS models. China's ECC shows a persistent "high southeast, low northwest" spatial pattern and has improved continuously from 2000 to 2020. SHAP and LIME analyses reveal spatially heterogeneous driving mechanisms and critical thresholds across ECC levels. Multi-scenario projections indicate significant divergence in ECC patterns under three SSP-RCP pathways by 2030 and 2050. What are the implications of the main findings? SHAP and LIME interpretability analyses identify NDVI, soil moisture and precipitation as core ECC drivers, with heterogeneous factor contributions across ECC levels, filling the gap of systematic interpretability in ECC projection models. The ECC evaluation system and CAXO model provide a scientific tool for ecological security early warning, and differentiated regional governance strategies can be formulated based on multi-scenario ECC projection results to promote sustainable development. Ecological carrying capacity (ECC) is a vital indicator for regional sustainable development, reflecting an ecosystem's support for human activities while maintaining core functions. Research on ECC has largely focused on static assessment, while exploration of dynamic prediction has been relatively limited. This study constructed a comprehensive evaluation system using the AHP-EW model with multidimensional indicators and developed a CAXO hybrid model for multi-scenario ECC projection of China. ECC patterns were classified into five levels, with SHAP and LIME adopted to interpret ECC changes. The results show that China's ECC exhibits a "high in the southeast and low in the northwest" spatial pattern and has improved continuously from 2000 to 2020, with the proportion of Level V areas increasing from 10.86% to 14.61%. Significant regional disparities exist, with more favorable ECC conditions east of the Hu Huanyong Line and poorer conditions in the west. The CAXO model achieves reliable performance (OA = 90.01%, Kappa = 87.11%) and outperforms traditional models. SHAP analysis identifies NDVI (2.17) as the most critical driving factor, followed by soil moisture (0.53) and precipitation (0.52), while LIME reveals heterogeneous factor contributions across ECC levels. Northwestern China faces severe ecological constraints (Level I: 53.96%), whereas eastern China exhibits the optimal ECC status (Level V: 70.07%). Multi-scenario projections to 2050 show that Level V areas will reach 28.22% under SSP1-2.6, Level III will account for 27.70% under SSP2-4.5, and Level I will rise to 22.44% under SSP5-8.5. The proposed ECC framework and CAXO model provide scientific support for ecological security early warning and sustainable development policy-making. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111690 |
