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

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.