Mining minerals plays a fundamental role in promoting social-economic progress. Intensive mining operations. However, it also brings huge ecological risks, and may lead to significant changes in land use/land cover (LULC), further affecting the urban landscape and ecological environment. Based on changes in LULC from 2000 to 2020 in the Jiawang district, a resource-based city, this study uses an ecological risk assessment as the theoretical basis for evaluating regional landscape ecological risk. The results show that: (1) From 2000 to 2020 significant changes in LULC were the increase the areas of built-up land and those covered with water, and decreases occurred in the cultivated, industrial, and mining land areas. The conversion of industrial and mining lands to areas of water is a noteworthy ecological compensation area. (2) Over the past two decades, the average ecological risk in the study area was 0.19, 0.21, and 0.14, in 2000, 2010 and 2020, respectively, first increasing and then decreasing. The area ratio of the lowest-risk ecological risk areas dropped from 31.60% to 4.00%. (3) The global Moran's I values in 2000, 2010, and 2020 were 0.6047, 0.6280, and 0.6392, respectively. The positive values indicate that the autocorrelation and spatial aggregation of ecological risks in Jiawang district gradually increased. This study can provide a basis for other resource-based cities to rationally allocate land resources and manage urban ecological risks.

Urban blue-green infrastructure (BGI) forms the basis of a regional ecosystem. Quantitative calculations can identify the weak points of a typical ecological environment, which is helpful for providing a basis for the spatial planning and ecological environment protection of developing cities. Currently, assessment of BGI ecosystem services focuses on local temperature, climate, and entertainment aesthetics, and the integrity of ecological indicators needs improvement. The assessment is usually conducted within large blue-green areas such as parks and rivers, and street greening is typically ignored. Roof gardens and unmanaged blue-green spaces also have ecosystem service functions. Therefore, our study aimed to extract the basic design distribution of urban blue-green spaces more accurately and monetize the value of its ecosystem services. Changsha, one of the top ten ecologically competitive cities in China, was the research focus. First, four types of BGI, forest, grassland, wetland, and waterbody, were extracted using remote sensing images and ArcGIS10.8. Second, the adjusted value coefficient was used to quantify the service value and geographic spatial distribution of the four BGI ecosystems in monetary terms. The results showed that in 2020, the total economic value of ecosystem services (ESV) generated by BGI in the study area was CNY 36.25 billion. Among ecosystem services, forest land climate regulation and waterbody hydrological regulation accounted for the largest proportion, at CNY 6.543 and 15.132 billion, respectively. The urban center had the weakest climate regulation capacity, and the Xiangjiang River Basin had the strongest hydrological regulation capacity. The urban center had the lowest ESV, thus requiring the attention of urban planners in the future. This study evaluated and optimized the distribution of BGI in Changsha according to the ESV of the existing BGI to help improve the ESV of the city center and create a green, ecological, and healthy city.

The built environment of cities has increasingly affected the travel mode of residents and led to changes in energy consumption, which is crucial to maintaining urban sustainability. Ningbo is a typical representative of urbanization on the east coast of China, and its energy consumption is in a period of rapid growth. Therefore, using the survey data of 22,112 traffic trip samples from nine streets in Ningbo, this paper establishes a regression analysis model, systematically analyzes the relationship between the built environment and domestic energy consumption from multiple dimensions, and reveals the impact mechanism of the built environment on domestic energy consumption. We find that (1) social and economic conditions are the main factors affecting traffic energy consumption. (2) The population density has a significant negative correlation effect on the energy consumption of transportation trips. When the population density increases by 1%, the energy consumption of total transportation trips, commuting trips, high-energy-consumption trips, and low-energy-consumption trips decreases by 0.094%, 0.115%, 0.273%, and 0.124%, respectively. (3) When the degree of mixed use of land increases by one percentage point, the energy consumption of total transportation trips, commuting trips, high-energy-consumption trips, and low-energy-consumption trips decreases by 0.415%, 0.421%, 2.574%, and 1.197%, respectively. (4) The density of road intersections has a significant negative correlation effect on the energy consumption of traffic trips. (5) The impact of the built environment on the energy consumption of transportation trips is greater than that of residential buildings.