With the accelerated urbanization and frequent occurrence of climate extremes, the regional ecosystem service level has ushered in a great challenge, and the resilience of the ecological network has gradually weakened, leading to lower ecological benefits and production levels. As a core ecologically sensitive area in the middle reaches of the Yangtze River, Wuhan metropolitan area has been expanding outward with rapid urbanization, crowding out surrounding arable and ecological land, and facing serious challenges to the sustainable development of the national space, while current cross-regional ecological protection measures need to be strengthened urgently, and exploring the structural resilience of its ecological network is of great significance to promote regional stability. In this study, Wuhan metropolitan area is taken as an example, and we explore the evolution and laws of ecological network structure from the perspective of network analysis by constructing ecological networks in Wuhan metropolitan area in 2000, 2010, and 2020. Firstly, we select regions from the ecological control line developed in China as ecological source sites, and also select multivariate data to supplement them. Then, the ecological network was established using the MCR model. Finally, network analysis was applied to discuss the evolution of network structure under multiple times and propose corresponding conservation strategies. The results show that (1) the major ecological resistance of Wuhan urban area has increased by 5.24% in 20 years. (2) The centrality and connectivity of the network nodes have increased over the 20-year period, and the overall structure of the network has stabilized and the resilience of the network has increased. (3) There is a strong link between changes in the network as a whole and local resilience. The results of the study will help analyze the relationship between the network as a whole and the region, and provide reference for optimizing the ecological network and constructing the systematic management of ecological security pattern.

This paper analyses the carbon dioxide emissions caused by industrial energy consumption of Tianjin from 2005 to 2012. The carbon emissions decomposition illustrated that the scale of production factor played a major role in the growth of Tianjin industrial carbon emissions and the average contribution of carbon emissions is up to 220.8975% in the statistical period; the intensity of energy factor played an important role in slowing down the growth of industrial carbon dioxide emissions. The average contribution of carbon emissions was −136.1994% in the statistical period. The prediction model based on carbon emissions data from industrial energy consumption from 2003 to 2012 reached a high accuracy, with an average error of 1.78% for stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, 2.41% for the Logistic regression model and an average error of 1.54% for the grey model. This research can contribute to predict the carbon emission and through it some suggestions can be made.

With deepening global higher education reforms, outcome-based education has emerged as the core paradigm for teaching model innovation. This study investigates the structural dependencies and teaching effectiveness of the Environmental Design curriculum at Hubei Engineering University in medium-sized cities, China, addressing challenges of enrollment decline and market contraction critical for urban sustainability. Using network analysis, we construct curriculum support and contribution networks and course temporal networks to assess structural dependencies and teaching effectiveness, revealing structural patterns and optimizing the OBE-based Environmental Design curriculum to enhance educational quality and student competencies. Analysis reveals computer basic courses as knowledge transmission hubs, creating a course network with a distinct core–periphery structure. Technical course reforms significantly outperform theoretical course reforms in improving student performance metrics, such as higher average scores, better grade distributions, and reduced performance gaps, while innovative practice courses show peripheral isolation patterns, indicating limited connectivity with core curriculum modules, which reduces their educational impact. These findings provide empirical insights for curriculum optimization, supporting urban sustainable development through enhanced professional talent cultivation equipped to address environmental challenges like sustainable design practices and resource-efficient urban planning. Network analysis applications introduce innovative frameworks for curriculum reform strategies. Future research expansion through larger sample validation will support urban sustainable development goals and enhance professional talent cultivation outcomes.

The rapid growth of urban populations has resulted in a scarcity of land, thus making sustainable urban development an urgent matter. Although Shenzhen has implemented land policies and optimized its functional layouts, these measures have inadvertently contributed to a shortage of available land for development. The city’s exponential population growth and expansive urban expansion have outpaced the supply of land. This study endeavors to identify urban commercial patterns by employing multiple data sources and applying machine learning and network analysis to predict future commercial areas. The results demonstrated that the identification of commercial points of interest and analysis of land surface temperature distributions made Futian district the primary area for ongoing commercial development, while also revealing a positive correlation between these two datasets. By leveraging network analysis to thoroughly examine this data, Bao’an district was highlighted as the future focal point for Shenzhen’s commercial sector, with 22 core nodes identified in total. Finally, by assessing the network centrality within the spatial networks, and utilizing clustering algorithms to categorize nodes into groups, the economic clustering pattern was determined as the predominant model for Shenzhen’s commercial growth. This research represents a significant contribution to the realm of sustainable urban development and presents a valuable framework for other cities to adopt.

Rapid global urbanization has intensified the need for cities to transition from growth-oriented models to sustainable development frameworks that prioritize livability, environmental quality, and social equity, positioning urban physical examination as an essential methodology for guiding this transformation. This study analyzes the spatial–temporal evolution of Shenzhen’s sustainable urban transformation from 2020 to 2024, employing urban physical examination methodologies combined with hypernetwork analysis to evaluate livability enhancement strategies. The research develops an economic vitality index incorporating urban points of interest density, Habitat Environment Index, and land surface temperature. Through spatial optimization analysis and hypernetwork modeling, the study examines the evolution of Shenzhen’s economic vitality and sustainable development patterns, with a particular focus on the impacts of economic centralization on regional sustainability and habitability. Results show Shenzhen’s economic vitality index increased by 10.47% from 2020 to 2024. However, regional disparities persist, with western and central regions displaying higher vitality than eastern coastal areas. The hypernetwork analysis reveals clustering patterns in livable spaces, with connectivity indicators ranging from 3.75 to 3.86. The uneven distribution of public facilities in Longgang and Yantian Districts highlights the need for improved resource allocation. These findings provide evidence-based support for sustainable urban space strategies in rapidly developing cities, emphasizing the importance of equitable resource allocation and community-centered planning approaches.

This paper is to discussing traffic accessibility and point of interest spatial network on the relationship between urban point of interest and the urban road network, First, we introduced the distribution of POI (points of interest, such as business, banking, catering, and attractions), and determining the road traffic and accessibility of cities through urban map data. Secondly, the spatial network formed by the POI is used to find the main factors, analyze the correlation between road traffic density and the road traffic density. Finally, the GIS platform is used for data organization for system analysis, mining urban data to obtain feature factors, and urban space optimization strategy. The studies results show urban POI affect road traffic through functional attributes, leading to changes in urban space.