Carbon emissions increase the risk of climate change. As one of the primary sources of carbon emissions, road traffic faces a significant challenge in terms of reducing carbon emissions. Many studies have been conducted to examine the impacts of cities on carbon emissions from the perspectives of urbanization, population size, and economics. However, a detailed understanding of the relationship between road traffic and urban carbon emissions is lacking due to the lack of a reasonable set of road traffic metrics. Furthermore, there have been fewer studies that have conducted cluster analyses of the impact factors, which will be supplemented in this research. We established 10 impact metrics, including the highway network system, city road network system, public transit system, and land use system of streets and transportation, using 117 county-level cities in Hebei Province as the study area, which is one of the regions in China with the most acute conflicts between economic development and the environment. We built an ordinary least squares (OLS) model, a spatial lag model (SLM), a spatial error model (SEM), a spatial Durbin model (SDM), and a geographically weighted regression (GWR) model, and performed a cluster analysis on the key metrics. The results are as follows: (1) The difference in spatial distribution of urban land-average carbon emissions is obvious, highly concentrated in the areas surrounding Beijing and Tianjin. (2) The GWR model has a higher R² and a lower AICc than global models (OLS, SLM, SEM, and SDM) and performs better when analyzing the impact mechanism. (3) Highway network density, city road length, and density of the public transit network have significant effects on urban land-average carbon emissions, whereas the street and transportation land use systems have no significant effect, which indicates that the highway network and public transit systems should be prioritized. (4) The GWR model results show that the impact of the four metrics on the urban land-average carbon emissions exhibits clear spatial heterogeneity with a significant piecewise spatial distribution pattern. The highway network density has a relatively large impact on the northern region. The northwest is more affected by the density of the public transit network. The southwest is most impacted by the length of city roads. (5) The study area is divided into four distinct characteristic areas: the highway network dominant impact area, the public transit dominant impact area, the city road network dominant impact area, and the multi-factor joint impact area. Different traffic optimization strategies are proposed for different areas.

Global climate change has led to flood disasters increasing in terms of frequency and damage caused, which seriously threatens urban and rural security. The flood regulation (FR) service function of the ecosystem plays an important role in mitigating flood disaster risk. Previous studies on flood regulation ecosystem services (FRES) are still lacking in a cross-scale assessment of supply and demand, refined simulation of regional complex hydrology, and application of spatial zoning management. Taking the Fujian Delta as an example, this study established a cross-scale research framework based on the social-ecosystem principle. The SWAT model was used to simulate the regional hydrological runoff and calculate the macro-scale supply of FRES. Taking patches of land as units, a flood risk assessment model was constructed to calculate the micro-scale demand for FRES for urban and rural society. Through a comparison of supply and demand across spatial scales, a zoning management scheme to deal with flood disaster risk was proposed. The results showed that: (1) The supply of FRES differed greatly among the sub-basins, and the sub-basins with low supply were mostly distributed in the lower reaches of Jiulong River and the coastal areas. (2) The demand for FRES was concentrated in high-density urban built-up areas. (3) By comparing the supply and demand of FRES in sub-basin units, 2153 km² ecological space was identified as the primary ecological protection area, and 914 km² cultivated land and bare land were identified as the primary ecological restoration area. (4) By comparing the supply and demand of FRES of land patch units, 65.42 km² of construction land was identified as the primary intervention area. This study provides a decision-making basis for regional flood disaster management from the perspective of FRES.

Determining whether the supply–demand matching (SDM) of urban public health resources is reasonable involves important issues such as health security and the rational use of resources. Using the central urban area of Tianjin as the research area, this paper used the Gaussian-based 2-step floating catchment area method (Ga2SFCA), combined with multi-source data, and comprehensively considered public medical, natural, and physical resources to evaluate the SDM of single-category and integrated public health resources in the research area. The results showed the following: (1) there was a good fit between supply and demand for public medical and natural health resources in Tianjin’s central urban area. For public physical health resources, there was a poor fit between supply and demand; the population in the supply insufficient and scarce areas for 82.78% of the total and was mainly distributed in the marginal areas of the four districts around the city and the six districts of the inner city. (2) For integrated public health resources, the degree of SDM was generally good. It had a circular structure that gradually shrank from the core to the edge. In order to promote the supply–demand balance of urban public health resources, this paper proposed three strategies involving three aspects: the supply, accessibility, and demand of urban public health resources. These strategies involve the service supply level, urban traffic network and slow traffic, development intensity, and population scale.

High-density urban areas have spatial characteristics, such as complex functions, population gathering, and complex forms, that lead to more severe urban heat island effects. Systematically evaluating the thermal environmental benefits of urban spatial forms to optimize the urban physical environment is important. In this study, Tianjin’s central urban area, which is a typical representative of high-density urban areas, was selected to invert the multi-period land surface temperature by relying on the existing two- and three-dimensional morphological data set of communities. The multi-scale geographically weighted regression model was used to fit the regression relationship between the urban land surface temperature and spatial morphological parameters. From this, the influencing factors of different types of existing community spaces and their spatial stabilities were explored. The results show the following: (1) The summer surface temperature varies greatly in the central urban area, and the high-temperature areas are mainly distributed in the industrial, residential, and commercial districts. (2) The MGWR model has the better model-fitting ability. The positive influence coefficients of temperature include ISP and BD, while the negative influence coefficients are BSD, BH, NDVI, and SVF. (3) There is significant spatial heterogeneity in the impact coefficients among the blocks that can be targeted to mitigate the heat island effect. This study provides ideas for optimizing the spatial morphological parameters of surface temperature in urban centers. Future challenges include increasing the spatial morphological parameter selection range, dissecting the interactive relationships between spatial morphological parameters and their effectiveness on the surface temperature, and refining the study’s spatial and temporal granularity.

The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by “low in the southeast, high in the north, and medium in the center”. (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations.