The ecological environment of resource-based cities is threatened by multiple ecological risks. The main objective of this study is to investigate the dynamics of land use and landscape ecological risk in resource-based cities. Huaibei, a typical resource-based city in China, was taken as a case study. By using the Markov model and landscape metrics, this study first analysed the land use changes in 1989, 2002 and 2016. Then an assessment model was developed to examine the ecological risk caused by the land use changes. Additionally, a spatial autocorrelation analysis was adopted to reflect the spatial heterogeneity of the landscape ecological risk. The results showed that urbanization has a greater impact on land use than the mining activity in Huaibei. The level of landscape ecological risk showed a tendency to initially increase and then decrease. In 1989 and 2002, the extremely high and high-risk regions were concentrated in the northern mining area, but these regions were reduced and scattered throughout the northern and middle areas of Huaibei in 2016. The area of medium-risk regions continuously increased throughout the study period. The spatial autocorrelation of the landscape ecological risk was positive and was weakened in 2016. The results suggest that the pattern of land use and the landscape ecological risk in resource-based cities showed an obvious spatiotemporal variation. Understanding the phase characteristics is necessary for ecological restoration.

Since 1978, China's rapid urbanization and industrialization have significantly increased carbon emissions. This study employs spatial autocorrelation, kernel density estimation, and spatiotemporal geographically weighted regression (GTWR) methods to analyze the spatiotemporal evolution characteristics of carbon emissions across 336 Chinese cities from 1978 to 2020. It also explores the dominant influencing factors for different cities at various stages of development. The findings reveal that carbon emissions in Chinese cities exhibit a stepwise growth pattern: "slow growth (1978-1995) - low-level stability (1996-2000) - rapid growth (2001-2012) - high-level stability (2013-2020)." The gap between cities has widened rapidly, and spatially, the distribution follows a "core-periphery" pattern. The increase in carbon emissions in core cities has transformed the urban hierarchy from a "generally low-carbon" structure to a "pyramid" structure. Compared to 1995, the influence of population size on carbon emissions decreased in 2020 (0.54-0.38), while the impact of infrastructure development and technological advances increased (0.02-0.25, 0.09 to 0.19). Due to the varying stages of urban development across regions, the influencing factors of carbon emissions exhibit spatial heterogeneity. Specifically, population size has a stronger positive impact on carbon emissions in the Southeast, technological advances in East and North China, and industrial structure in the Yangtze River Basin region. Infrastructure construction and investment levels show a dampening effect on carbon emissions in the Yangtze River Basin. Finally, the study proposes policy recommendations focusing on implementing regional "gradient" carbon reduction and promoting regional collaborative carbon reduction driven by core cities.