吴先雯, 郭风成. 基于Invest模型和Flus模型的江苏省碳储量变化模拟与预测[J]. 中国生态农业学报 (中英文), 2024, 32(2): 230−239. DOI: 10.12357/cjea.20230457
引用本文: 吴先雯, 郭风成. 基于Invest模型和Flus模型的江苏省碳储量变化模拟与预测[J]. 中国生态农业学报 (中英文), 2024, 32(2): 230−239. DOI: 10.12357/cjea.20230457
WU X W, GUO F C. Analysis and prediction of carbon storage changes in Jiangsu Province based on the Invest model and Flus model[J]. Chinese Journal of Eco-Agriculture, 2024, 32(2): 230−239. DOI: 10.12357/cjea.20230457
Citation: WU X W, GUO F C. Analysis and prediction of carbon storage changes in Jiangsu Province based on the Invest model and Flus model[J]. Chinese Journal of Eco-Agriculture, 2024, 32(2): 230−239. DOI: 10.12357/cjea.20230457

基于Invest模型和Flus模型的江苏省碳储量变化模拟与预测

Analysis and prediction of carbon storage changes in Jiangsu Province based on the Invest model and Flus model

  • 摘要: 为明确土地利用变化对碳储量的具体影响, 实现耕地、林地等生态环境的更好保护, 以江苏省为例, 基于2000年、2010年和2020年土地利用数据, 采用Invest模型的碳储存和固定模块以及Flus模型ANN模块、CA模块、Markov Chain模块, 分析了江苏省不同土地利用类型碳储量变化特征, 并预测了自然发展情景和生态保护情景下2030年和2050年江苏省碳储存与固定情况。结果表明, 2000年、2010年与2020年, 江苏省碳储量整体呈下降趋势, 从1803.948×106 t下降至1641.008×106 t, 其中耕地碳储量下降最多, 为239.494×106 t。这种现象的发生与江苏省工业化和城市化导致的耕地和林地等面积的减少并转为建筑用地有关。此外, 各市碳储量差异较显著, 其中盐城市碳储量最大, 占全省总碳储量的16.12%, 无锡市在全省碳储量中占比最小(4.12%)。基于ArcGIS 10.2软件和GeoDa 1.20软件分析发现, 2000—2010年, 南京、无锡、常州的碳储量分布呈低-低聚集, 连云港市呈低-高聚集; 2020年无锡、常州、镇江呈现低-低聚集, 连云港市呈现高-高聚集。本研究预测结果表明2030年和2050年的生态保护情景下碳存储能力较自然发展情景有一定提升, 分别提升6.069×106 t和5.861×106 t 。本研究结果可为江苏省更好地建设生态环境提供数据支撑。

     

    Abstract: Determination of the impact of land-use changes on carbon storage is crucial for protecting cultivated land, forest land, and other ecological environments. Various modules, including the carbon storage and fixation module of the Invest model, the Artificial Neural Network (ANN) module, the Cellular Automata (CA) module, and the Markov Chain module of the Flus model, were used to analyze the changes in carbon storage for different land use types in 2000, 2010, and 2020, and to predict the carbon storage and fixation in 2030 and 2050 under natural development and ecological protection scenarios in Jiangsu Province. The carbon stock in Jiangsu Province continuously decreased from 2000 to 2020, with a decrease of 39.111×106 t from 2000 to 2010 and 123.829×106 t from 2010 to 2020. The largest reduction, a decrease of 239.494×106 t, was observed in the carbon storage of cultivated land. This decline was attributed to the conversion of cultivated and forested lands into building land due to rapid industrialization and urbanization in Jiangsu Province. Furthermore, the study highlighted significant variations in carbon storage among different cities in Jiangsu Province. Yancheng City exhibited the highest carbon storage, accounting for 16.12% of the total carbon stock of the province, while Wuxi City had the lowest share (4.12%). The analysis using ArcGIS 10.2 and GeoDa 1.20 software indicated that from 2000 to 2010, Nanjing, Wuxi, and Changzhou showed a low-low aggregation pattern in carbon storage distribution, while Lianyungang demonstrated a low-high aggregation pattern. In 2020, Wuxi, Changzhou, and Zhenjiang exhibited a low-low aggregation pattern, whereas Lianyungang showed a high-high aggregation pattern. Based on these predictions, the study revealed that under the ecological protection scenario, the carbon storage capacity in 2030 and 2050 would improve compared with that under natural development scenario, with increases of 6.069×106 t and 5.861×106 t, respectively. These findings provide valuable data for promoting a better ecological environment in Jiangsu Province.

     

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