The study of land use and land cover (LULC) change and the evolution of ecosystem service values (ESV) is important for optimizing the allocation of regional land resources and achieving sustainable development, but traditional land use analysis methods cannot dig deeper into the information on the internal transitions between different land types. Therefore, in this paper, we use the component analysis and Intensity Analysis methods to systematically analyze the land use landscape changes at multiple levels.

One of the world’s major issues is climate change, which has a significant impact on ecosystems, human beings, agricultural productivity, water resources, and environmental management. The General Circulation Models (GCMs), specially the recently released (coupled model intercomparison project six) CMIP6 are very indispensable to understand and support decision makers to identify adaptation strategies in response to future climate change in a basin. However, proper selection of skillful GCMs and future climate assessment is a prior task to climate impact studies.

Existing literature about the impact of farmland titling on farmland transfer takes no account of farmland plot characteristics, which makes them unable to effectively identify the causal relationship between farmland titling and farmland transfer. After the theoretical analysis, based on land plot level micro-survey data, we adopt the instrumental variable (IV) and conditional mixed process (CMP) methods to ease the endogeneity problem in the model and conduct a quantitative analysis.