As the world’s largest developing country, China has actively implemented the UN Sustainable Development Goals (SDGs). Sustainable development of urban human settlements is the result of localization and the deepening of sustainable development theory in China. This study combines SDGs to construct an evaluation index system for the sustainable development of urban human settlements in China, using optimization methods, such as natural breaks (Jenks), exploratory spatial data analysis, and GeoDetector, to conduct systematic research on the spatiotemporal evolution of the current sustainable development level and analyze the core driving forces of urban human settlements in 285 prefecture-level cities in China from 2000 to 2019. Our study revealed that: (1) The overall sustainable development level of urban human settlements and their subsystems in China has improved steadily, but the levels of subsystems are quite different; (2) the sustainable development level of the urban human settlements in China can be expressed as a spatial pattern of “high in the east and low in the west, high in the south and low in the north” and has relatively significant spatial correlation characteristics; notably, the development level of each subsystem has different spatial characteristics; (3) the sustainable development level of urban human settlements is mainly based on medium sustainability, and the main development model is to progress from a medium-low development level to a medium-high development level; (4) the sustainable development level of urban human settlements is mainly driven by the per capita gross domestic product (GDP), housing price-to-income ratio, investment in education and scientific research, Internet penetration, and PM_2.5.

The rapid development of the urban network has led to the fact that cities are no longer single individuals, and the network has changed the urban development environment. The interaction between cities has gradually become an important factor for the high-quality development (HQD) of cities. From the perspective of externalities, it is of great significance to explore the impact of agglomeration externalities and network externalities on the HQD of cities to promote the high-quality and sustainable development of the region. Taking the urban agglomeration in the middle reaches of the Yangtze River as an example, this study constructs a theoretical framework to empirically study the influence of agglomeration externalities and network externalities on the HQD of the city. The results show that the integrated network of the urban agglomeration from 2011 to 2020 had a high clustering coefficient and a small average path length with the characteristics of a “small world”. The centrality of urban nodes was hierarchical and had a “pyramid” structure. From 2011 to 2020, the high-quality development level (HQDL) of the urban agglomeration steadily improved and the regional “development gap” gradually narrowed. Wuhan, Changsha, and Nanchang were in a relatively advantageous position in the urban agglomeration. Furthermore, there was a spatial agglomeration effect and a spatial spillover effect in the HQD of urban agglomeration. Network externalities presented difference in different cities, and the influence of agglomeration externalities on HQD presented a u-shaped nonlinear relationship. Network externalities could significantly promote HQD, and the indirect effect of HQD was greater than its direct effect. In addition, factors such as government capacity and level of opening to the outside world also had a significant impact on the HQD of the region.

Land resources in cities are limited, and the cost of green space construction is high. Compared with increasing the amount of green space, maximizing the cooling effect of limited green space has important theoretical and practical significance. Green fractal is a new innovative branch of urban fractal that uses a fractal index to quantify the green space structural index in studying the thermal environmental effect. Multi-source data, such as high-resolution remote sensing images, were used, and spatial regression models and inconsistency indices were applied to explore the spatial pattern of the urban thermal environment at the block scale, and the mechanism of green space fractal characteristics in terms of correlation and spatial heterogeneity, to assess the quality of green space development. This study shows the following: (1) In 2019, the land surface temperature in Dalian formed a spatial distribution structure of “high in the central region and low in the surrounding region” at the block scale, and the fractal indices of different green spaces show the spatial distribution structure of “dual-core” and “multi-core” spaces. (2) The driving direction and force of the fractal index of green space on the spatial pattern of land surface temperature differs. The influence of the green space structure index (grid and boundary dimensions) is greater than that of the quantity index (area and circumference), and the influence of the grid dimension is the most significant. (3) The spatial heterogeneity between the fractal index of block-scale green space and land surface temperature in Dalian is significant, showing a centralized and contiguous spatial pattern, with a trapezoidal structure decreasing from north to south. (4) The spatial adaptation between the fractal and thermal environments of green spaces can be evaluated using the inconsistency index. The development quality of green space can be divided into three types: advanced, relative coordination, and lagged green spaces. Finally, this study proposes specific suggestions for the development of block-scale green spaces and thermal environment management in Dalian City.

The sustainable development of the human settlements (HS) has become a global universal program. The comparison of cities in different countries is of great significance to provide international experience for future urban construction. Combined with the UN 2030 Sustainable Development Goals (SDGs), this paper establishes an evaluation index system for the sustainable development ability of urban HS and constructs a three‐dimensional research framework of “development‐coordination‐sustainability,” which compares the sustainable development ability of the HS of Dalian, China, and Kobe, Japan, from 2005 to 2018 and explores the spatial evolution characteristics and obstacle factors of the HS of the two cities. The results show that (1) the development degree of the HS of the two cities is on the rise. The development level of Kobe is always higher than that of Dalian, and the gap is gradually narrowing; Kobe has advantages in natural and residential environment, while Dalian has advantages in cultural and economic environment. (2) The coordination degree of the development of the HS of the two cities has improved steadily, and the coordination degree of Kobe is better than that of Dalian. (3) The sustainability of the development of the HS of the two cities is fluctuating, and the average sustainable growth rate of Dalian is higher than that of Kobe. (4) The sustainable development space of the HS in Dalian presents a pattern of “high in the south and low in the north,” and the spatial characteristics of the subsystems are different; the main obstacles have changed from economic‐natural to economic‐natural‐cultural‐public services, and the obstacles to development in districts are different. (5) The sustainable development space of the HS in Kobe has a high level of development in the southeast, radiating to the surrounding area, and the spatial characteristics of the subsystems are different; the main obstacles have changed from economic‐cultural‐natural to economic‐natural‐population, and the obstacles to development in districts are different. Finally, it puts forward targeted suggestions for the sustainable construction of Dalian. This paper can provide methodological reference for quantitative assessment of the sustainable development of HS and provide policy reference for scientific planning of the construction of HS.

This study investigates the coupling and coordination mechanisms between virtual and physical spatial heat in coastal internet-famous streets under the influence of social media. Taking Dalian’s coastal internet-famous street as a case study, user interaction data (likes, favorites, shares, and comments) from the Xiaohongshu platform were integrated with multi-source spatio-temporal big data, including Baidu Heat Maps, to construct an “online–offline” heat coupling and coordination evaluation framework. The entropy-weight method was employed to quantify online heat, while nonlinear regression analysis and a coupling coordination degree model were applied to examine interaction mechanisms and spatio-temporal differentiation patterns. The results show that online heat demonstrates significant polarization with strong agglomeration in the Donggang area, while offline heat fluctuates periodically, rising during the day, stabilizing at night, and peaking on holidays at up to 3.5 times weekday levels with marginal diminishing effects. Forwarding behavior is confirmed as the core driver of online popularity, highlighting the central role of cross-circle communication. The coupling coordination model identifies states ranging from high-quality coordination during holidays to discoordination in daily under-conversion or overload scenarios. These findings verify the leading role of algorithmic recommendation in redistributing spatial power and demonstrate that the sustainability of coastal check-in destinations depends on balancing short-term traffic surges with long-term spatial quality, providing practical insights for governance and sustainable urban planning.