Against the backdrop of urban stock renewal, as the core area of a city rich in culture, aesthetics, and tourism resources, the assessment of landscape visual sensitivity of historic districts can provide an accurate, objective, and intuitive decision-making basis for the multi-purpose planning of districts. The main purpose of this study was to develop an assessment method based on the geographic information system (GIS) in order to make a visual sensitivity index map on a district scale. To this end, this study uses the multi-criteria evaluation (MCE) method, selects the visibility (VSv), the number of potential users (VSu), and remarkableness (VSe) as the main criteria, and constructs a comprehensive assessment model of the visual sensitivity of the historic landscape. The most well-protected Wudadao Historic District in Tianjin (Wudadao) was selected as the study area, and its visual sensitivity was assessed. The assessment results are divided into four levels: areas of high sensitivity, moderate sensitivity, low sensitivity, and very low sensitivity. Results indicate that after the optimization and improvement of the evaluation index for visual sensitivity of a large-scale forest landscape, it is feasible to evaluate the small-scale visual sensitivity of historic districts; the higher the sensitivity level, the more important it is to be protected, and the more cautious it should be in the renewal of districts; the higher the number of potential users, the higher the visual sensitivity level, and so on. Further attention needs to be paid to planning and design to improve visual quality.

Rapid urbanization causes serious air pollution and constrains the sustainable development of society. The influencing factors of urban air pollution are complex and diverse. Multiple factors act together to interact in influencing air pollution. However, most of the existing studies on the influencing factors of air pollution lack consideration of the interaction mechanisms between the factors. Using multisource data and geographical detectors, this study analyzed the spatial heterogeneity characteristics of air pollution in Shijiazhuang City, identified its main influencing factors, and analyzed the interaction effects among these factors. The results of spatial heterogeneity analysis indicate that the distribution of aerosol optical depth (AOD) has obvious agglomeration characteristics. High agglomeration areas are concentrated in the eastern plain areas, and low agglomeration areas are concentrated in the western mountainous areas. Forests (q = 0.620), slopes (q = 0.616), elevation (q = 0.579), grasslands (q = 0.534), and artificial surfaces (q = 0.506) are the main individual factors affecting AOD distribution. Among them, natural factors such as topography, ecological space, and wind speed are negatively correlated with AOD values, whereas the opposite is true for human factors such as roads, artificial surfaces, and population. Each factor can barely affect the air pollution status significantly alone, and the explanatory power of all influencing factors showed an improvement through the two-factor enhanced interaction. The associations of elevation ∩ artificial surface (q = 0.625), elevation ∩ NDVI (q = 0.622), and elevation ∩ grassland (q = 0.620) exhibited a high explanatory power on AOD value distribution, suggesting that the combination of multiple factors such as low altitude, high building density, and sparse vegetation can lead to higher AOD values. These results are conducive to the understanding of the air pollution status and its influencing factors, and in future, decision makers should adopt different strategies, as follows: (1) high-density built-up areas should be considered as the key areas of pollution control, and (2) a single-factor pollution control strategy should be avoided, and a multi-factor synergistic optimization strategy should be adopted to take full advantage of the interaction among the factors to address the air pollution problem more effectively.

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.

IntroductionThe shallow mountainous area in Hebei province is a crucial part of the ecological security barrier and regional ecological conservation construction in the Beijing-Tianjin-Hebei (BTH) region. In recent years, the contradictions in the development of the rural “production-living-ecological” function (PLEF) in shallow mountainous areas are prominent, so optimize its spatial pattern is beneficial to rural sustainable development. But there are significant problems in the existing research, such as the lack of fine-scale research and effective guidance for rural PLEF. Based on this, this study takes Quyang County as an example, starts from the perspective of PLEF coordinated development, finally puts forward the optimization strategy of rural production-living-ecological space (PLES) pattern by evaluating rural PLEF and its coupling co-scheduling.MethodsThis study first fused multi-source data such as POI and remote sensing images to build a comprehensive evaluation system of rural PLES, combined with entropy weight method and analytic hierarchy process to give weight to various indicators, and calculated the PLEF distribution of Quyang County on the 300 × 300m grid scale. Then the collaborative development of PLEF is measured by coupling coordination degree model. Finally, according to PLEF and its coupling and coordination, the functional space types are divided according to the principles of coordinated development and ecological optimization, and the optimization strategy of PLES pattern is proposed on the village scale.Results(1) The spatial distribution of PLEF in Quyang County is significantly different, and the order of functional intensity is: ecological space (ES) > production space (PS) > living space (LS). (2) The PLEF coupling coordination degree generally presents the spatial distribution characteristics of “low in the north and high in the south”, which is highly related to its topographic features. The high-value areas are mainly spread over southern plains with developed economy and rich ecological resources, while the low-value areas are located in the northern mountains and the central hills. (3) On the grid scale, the PLES pattern is identified as six types: production-living-ecological balance space (PLEBS), production-living space (PLS), production-ecological space (PES), living-ecological space (LES), ES and PS. Among them, the proportion of PLEBS and ES is larger. (4) On the village scale, it is suggested that PLEBS villages further emphasize high-quality coordinated development; ecological function leading optimization type (EFLOT) villages adhere to ecological priorities and ensure the development of ecological space functions; villages with composite functions should combine their own advantages and the spatial characteristics of the surrounding countryside, optimize and control infrastructure configuration, industrial structure, ecological protection and other aspects of classification, overcome shortcomings and improve the coordination of the PLEF.DiscussionBased on previous studies, this paper explored and improved the research scale, analysis methods, evaluation indexes and optimization ideas in the field of rural PLEF. Therefore, the results can guide for the high-quality coordinated development of territorial space and rural revitalization construction of counties in shallow mountainous areas.

Urban resilience, as an important ability to deal with disasters in the process of urbanization, has been paid more and more attention as the result of the increasing risks that are caused by rapid urbanization. China is taking the county level as the basic unit to promote new-type urbanization and constructing resilient cities has become one of the development strategies. However, to achieve this strategy researchers need to analyze the interaction between county urbanization and urban resilience and its driving mechanism, which have been paid little attention. Therefore, this paper selected 167 counties in Hebei Province as the investigation subject. Based on the statistical data from 2010 to 2020, a comprehensive index system was developed to quantify the degree of coupling coordination between urbanization and urban resilience, and the spatial Durbin model was used to analyze the driving mechanism of it. The study shows that: Firstly, the urbanization level of counties rose year after year, with there being a geographical distribution that was “lower from southeast to northwest”. The level of urban resilience increased year after year, showing a geographical distribution that was “higher from south to north” and a “core-edge” feature that was localized. Secondly, the coupling coordination degree increased steadily, and the overall level changed from a basic imbalance to a mild imbalance. In space, it is bounded by “Pingquan City—Pingshan County”, which showed the distribution of “high in the east and low in the west, high in the center and low on the outskirts”. Thirdly, the coupling coordination degree has spatial spillover effect. Government financial expenditure, innovation level, industrial upgrading level and urban shape index all influence the coupling coordination degree positively, with a successively decreasing impact, while the urban compactness has significant negative impacts. This study indicates that the regional differences exist in the coupling coordination degree, and the counties in different development stages need to adopt different strategies to promote the coordinated development of urbanized and resilient cities. Inter-regional support is also necessary in this process. Meanwhile, it is necessary for the government to govern various urban elements, especially in terms of their urban form.