County land use scenario simulation and carbon emission effect analysis using CLUE-S model

Carbon emission from land-use change can be one of the most important parts of the national greenhouse gas emission inventory. In this study, Faku County, Shenyang City, Liaoning Province was taken as the study area. Based on the vector data of the land use change survey results in Faku County in 2013, the driving factors of land-use change were calculated using the Auto-Logistic model, and then the land-use change pattern in 2019 was simulated using the CLUE-S model, thereby verifying the simulation using the Kappa coefficient. The spatial pattern of land use distribution in 2030 was predicted using five scenarios, including baseline scenarios, agricultural development, construction development, ecological protection, and land use structure optimization. Finally, the carbon revenue and expenditure of land use were calculated in each scenario using the coefficient of carbon emission. The results showed that: 1) The CLUE-S model performed better to simulate the change of land use layout in the study area, where the Kappa coefficient and the overall accuracy of the simulation were 0.989 6, and 99.14%, respectively. It infers that the model and parameters were suitable for the prediction of future land use layout. 2) Among the five simulated scenarios, Faku County presented the best land use effect in 2030 under the optimized land use structure scenario, which was a more suitable land use mode in the middle and long-term development stage. Several hidden dangers were found in the land use under the other four simulation scenarios. Specifically, there was a risk to the regional ecology and food security under the baseline scenario. Some serious damages were found to the safety of ecological and water resources under the agricultural development scenario. The food and ecological security were also seriously damaged under the construction development scenario. There were some risks in the limited economic development of food security under the ecological protection scenario. 3) The carbon emissions increased by 5.53% from 2013 to 2019. Among the five simulation scenarios in 2030, the construction and development scenarios presented the largest carbon emission (121.79×103 t), whereas, the ecological protection scenario was the least (-218.71×103 t). Except for the ecological protection and agricultural development scenarios, the carbon emissions showed an upward trend under other scenarios, indicating the smallest growth rate (2.29%) of land use structure optimization scenarios. The auto-Logistic regression model was selected to calculate the driving coefficient of land-use change, leading to the high accuracy of the CLUE-S model to predict the regional land use pattern. The finding can provide an excellent evaluation system to calculate the carbon emission, and then predict the land use pattern for the decision-making on the carbon budget in other regions. Anyway, the land-use layout can be optimized for the land and space planning related to carbon emission reduction.