The land use changes directly reflected the economic and environmental development status of the region in a certain period, and the indicator can be applied in analyzing the role and economic benefits of various administrative departments. The Yangtze River Economic Belt, China is one of the “three major strategic development regions at the national level” implemented by the nation. The Markov prediction model was introduced to simulate the land use changes in the region and the average accuracy of the simulation was 99.54%. In the simulation the four regional development stages from 1992 to 2018 were identified in the model: primitive development, rapid urban expansion priority, ecological restoration priority and equilibrium on urban expansion and ecological restoration. Various scenarios with different transition probability matrix were characterized on diverse socio-economic conditions. The mean values were introduced in the prediction model. The land use changes in the Yangtze River Economic Belt in 2020-2030 were predicted and the characteristics of the changes in various scenarios were analyzed so as to provide scientific suggestions for decision makers on the sustainable utilization of the land in the densely populated and ecologically sensitive area.
The increase in human activities is one of the important factors affecting the value of ecosystem services. However, understanding of the driving mechanisms of human activities is limited. We established a deep learning model to approximate the ecosystem service value (ESV) of Nanjing City using 23 socioeconomic factors. A multi-view analysis was then conducted on feasible impact mechanisms using model disassembly. The results indicated that factors such as the proportion of ecological waters in the land-use structure and secondary industry output value had their own independent effects on ESV. Other intrinsically related factors, for instance, industrial water consumption and industrial electricity consumption, were likely to be composited together to affect ESV.