For the land use demands of Jing-Jin-Ji urban agglomeration cooperative development strategies, four kinds of land use policies, i.e. status quo continuation, food security, nature protection and urban expansion were made. A modified Cobb-Douglas utility function was developed to quantify the influence of different policies on various land use demands, and a CLUE-S model was built to simulate the spatial and temporal evolution of Jing-Jin-Ji land use under different policies. Results show that, compared with year 2010, great changes occur on land use areas and patterns in the year 2020 under different policies. “Status quo continuation” is characterized by the continuous expansion of urban groups, increasing the construction land by 2280 km². “Food Security” significantly increases the arable land by 3611.4 km², while reducing the ecological land including forest and grassland. “Nature protection” greatly reduces the area of arable land by 3082.13 km², while increasing forest, grassland and water area by 3726.4 km². “Urban expansion” substantially increases the construction land by 3375 km², while decreasing other types of land use. Spatially, every land use policy has its significant regional characteristics in land use conversion. The increase of construction land always comes together with the decrease of arable land, which tends to occur in the existing urban surroundings. The increase of ecological land is more often seen in Bashang plateau, Yanshan Mountains as well as Taihang Mountains. This study has great reference value in designing optimal land use policies, especially in the gradual implementation stage of the Jing-Jin-Ji collaborative development strategies.

Based on the GWR (geographically weighted regression) model supported by ArcGIS, the research explores the multi-scale relationship between vegetation change, climatic factors, and the sensitivity of vegetation to climate factors using AVHRR vegetation cover data combined with temperature and precipitation data in China from 1982–2010. Compared with the general linear regression (Ordinary Least Square, OLS) model, GWR gives a much better fitting result, with the goodness of fit increased from 0.3 to 0.6. The relationship between NDVI, annual rainfall, and average annual temperature has a significant spatial heterogeneity. Regression coefficients of climatic factors decrease from north to south and are higher in the northwest dry region of China. Temperature is more influential than rainfall on NDVI in most areas of China. Each ecological zone has different spatial scales when NDVI and the climatic factors maintain a stable relationship.

Based on Meta-analysis, the ecosystem service value regression models for cultivated land, forest land, pasture, garden land, waters and unused land were established respectively. In addition to time and value method, the geographical division and socio-economic factors (including population density and GDP per capita) were also incorporated into the models. The performance of model indicates that these factors play a significant role in explaining ecosystem service value change. Using value transfer approach, the authors valued ecosystem service of land use types in Beijing, Tianjin, and 11 cities in Hebei Province. The results show that land use types ranked by ecosystem service value are waters, forest land, pasture, garden land, cultivated land and unused land. The ecosystem service values per unit area of cultivated land, forest land, pasture, garden land and water area in Beijing and Tianjin are more than other areas. The research results on the one hand can enrich the methodology and technology of ecosystem service valuation, on the other hand provide scientific support for land use sustainable management in study area.