For megacities experiencing rapid urbanization in China, urban growth boundaries (UGBs) have been considered as a useful means to control urban sprawl and to promote sustainable urban development. However, scientific methods and tools to delineate sound UGBs by planners are few and far between. Using metropolitan Chengdu as the study area, this paper applies the system dynamics (SD) and conversion of land use and its effects at small region extent (CLUE-s) models to delineate UGBs. In this study, land use demand was simulated in the SD model temporally at a macro-level and allocated in the CLUE-s model spatially at a micro-level. Key social-economic elements and spatial pattern factors were used in the simulation process for the period of 2013–2030. The simulation results under various scenarios showed that areas along the major corridors and belt roads of the main Chengdu metropolitan area and its satellite towns have higher chances to be developed. The areas most likely to be developed were used to establish the UGBs for 2020, 2025, and 2030. This research demonstrates that the integrated framework of SD and CLUE-s models provides a feasible means of UGB delineation under different development scenarios.

Given the complexity of the poverty problem, efforts and policies aiming at reducing poverty should be tailored to local conditions, including cultural, economic, social, and geographic aspects. Taking the Sichuan Province of China as the study area, this paper explores the impact of physical geographic factors on poverty using the Ordinary Least Squares (OLS) Regression and the Geographically Weighted Regression (GWR) models at the county level. In total, 28 factors classified in seven groups were considered as variables, including terrain (relief degree of the land surface, altitude, and slope); vegetation (forest coverage rate); water (drainage network density); climate (temperature, annual average rainfall); and natural disaster (landslide, debris flow, and torrential flood). The 28 variables were then tested using correlations and regressions. A total of six physical variables remained significant for the OLS and GWR models. The results showed that the local GWR model was superior to the OLS regression model and, hence, more suitable for explaining the associations between the poverty rate and physical geographic features in Sichuan.

The quick and reliable quantification of the relationship between ecosystem and economic system is important in policymaking for sustainable urban agglomerations facing enormous pressure from high population density and development intensity. This is especially true in China, where urban agglomeration has been part of the country’s strategy for reform, modernization, and urbanization. This study applied the coupling coordination degree (CCD) model to assess the coupling coordination relationships between the ecosystem and economic system at the county level for the Chengdu–Chongqing agglomeration for the period of 2005–2019, and then, the local indicator of spatial association analysis (LISA) was used to illustrate the spatial distribution of CCDs further, hoping to capture the spatiotemporal dynamics of CCDs. The results found that (1) fringe counties and districts in the urban agglomeration were on the brink of ecological–economic disorder with low CCDs, (2) urbanized areas near Chongqing coordinated well with high CCDs, and (3) sound spatial governance and territorial planning may be better achieved by using the county-level unit than the city-level unit.

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