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Carbon emission and sustainable development have attracted global attention. Promoting urban green innovation (UGI) in the Yellow River Basin (YRB) will help in lowering the intensity of carbon emissions and improve the safety and sustainability. A SBM-DEA model was constructed to measure carbon emission efficiency (CEE) and the degree of coupling and coordination with UGI was calculated in 73 prefecture-level cities in the YRB. The spatial association network of CEE coupled with UGI is constructed by using a modified gravity model, social network analysis and the quadratic assignment procedure (QAP), to analyze spatial potential energy, network characteristics and clustering characteristics. The study found that: (1) The coupling coordination degree of CEE and UGI in the YRB shows fluctuating growth, mutual promotion and continuous coordinated development. (2) The spatial linkage between CEE and UGI is gradually close, and the potential energy of the spatial linkage increases year by year, with obvious spatial spillover effect, indicating that the radiation and influence between cities are gradually increasing. In contrast to the middle stream, the upstream and downstream regions show a higher percentage of spatial potential energy in the entire network, and their network structure is more intricate and robust. (3) The clustering patterns of the three major urban clusters are examined using the block model, exploring the positioning and functions of various cities in these urban conglomerations, which includes the net spillover, net benefit, two-way spillover and broker plate, so as to strengthen the connection and coordinated development between cities. (4) Factors such as spatial adjacency, industrial structure, population density, digital economy and urbanization level, and energy intensity significantly impact the spatial association network, along with temporal and regional heterogeneity. Therefore, tailored policies are needed in the YRB to strengthen collaboration between CEE and UGI, fostering the development of a circular economy and promoting sustainable development.

Exploration of urban spatial connections and network structures of urban agglomeration in the Yangtze River Delta, as well as its influencing factors, is of great significance regarding optimization of the development pattern of the Yangtze River Delta urban agglomeration and promotion of regional high-quality development. Therefore, based on Baidu index data in 2015 and 2019, this paper first analyzes the spatiotemporal variation characteristics of information-flow connections in the Yangtze River Delta urban agglomeration. Then it uses social network analysis to explore the information-flow network structure in the Yangtze River Delta urban agglomeration, and finally explores the influencing factors of information-flow intensity in the Yangtze River Delta urban agglomeration. The main conclusions are as follows: (1) The total amount of information flow in the Yangtze River Delta urban agglomeration has had no obvious change, and the coverage of information flow in the central urban circle has expanded. (2) The network hierarchy presents a relatively stable “pyramid” distribution pattern, which tends to develop into a “spindle” pattern. (3) The overall network density of the Yangtze River Delta urban agglomeration is high and is increasing. The backbone network is a “triangle” structure. The central cities in the region are stable, and the subgroups are adjacent to each other geographically. (4) Gross Domestic Product, resident population of the region and the number of Internet broadband subscribers all have important effects on the total information flow, among which the number of Internet broadband subscribers has the greatest effect on the total information flow. In addition, urban functions and their positioning, urban events, history and culture, and other factors that are difficult to quantify also have a certain impact on the information-flow network among cities.

The water-economy-ecology (WEE) system exhibits a complex coupling relationship, presenting significant challenges to sustainable urban agglomeration development. This study proposes a novel evaluation framework to explore the coupling coordination degree (CCD) of the regional WEE system for sustainable urban agglomeration development. By integrating the EFAST-Cloud model (ECM) to quantify the comprehensive development level of each subsystem, and applying an improved coupling coordination degree model (ICCDM), the spatio-temporal evolution of CCD in China’s Central plains urban agglomeration (CPUA) was analyzed. Results indicate: (1) The comprehensive evaluation index (CDI) of the WEE system shows an upward trend, with Xinyang, Zhengzhou, Huaibei, and Jiyuan in the leading position in their respective subsystems and the WEE system. (2) From 2011 to 2020, CCD exhibited a fluctuating but increasing trend, with the dominant coupling relationship shifting from water-economy to water-ecology. The core development area (CDA) consistently outperformed the non-core area (NCDA), with Jiyuan, Huaibei, and Zhengzhou achieving the highest CCD values. (3) Spatial analysis indicated a gradual strengthening of global spatial autocorrelation, while local autocorrelation was dominated by L-L and H-L clusters with limited spatial extent. The gravitational effect of CCD became increasingly pronounced by 2020, with Zhengzhou consistently emerging as the dominant center of gravity (COG) for CCD distribution. This study could not only provide a robust methodological framework for WEE system research but also offer new theoretical and practical insights into sustainable development in urban agglomerations.

Based on geographic information system (GIS) spatial analysis technology, the spatial pattern of raster grid transport accessibility for the Guangdong-Hong Kong-Macao Greater Bay area was studied and the states of spatial connectedness were simulated using highway passenger transport, railway passenger transport, port passenger transport and aviation passenger transport data. The result shows that transport accessibility within the Guangdong-Hong Kong-Macao Greater Bay area costs ‘one hour’ and the spatial distribution of accessibility in the area presents clear ‘core-periphery’ spatial characteristics, with Guangzhou, Foshan, Shenzhen constituting the core. The transport accessibility of Guangdong-Hong Kong-Macao is high. Average accessibility of urban nodes as measured by travel time is 0.99 h, and the areas accessible within 1.42 h occupy 79.14% of the total area. Most of the areas with the lowest accessibility are found in the peripheral area, with the worst accessibility being 4.73 h. Compared with the west-side cities, the economically developed east-side cities of the Guangdong-Hong Kong-Macao Greater Bay area have higher connectivity with roads, railways, ports, and aviation transport. Guangzhou, Foshan, Zhuhai, Shenzhen, Hong Kong and Macao are closely linked. The higher the accessibility, the closer the intercity connectedness.

To support carbon stock assessment and ecological restoration under the “Carbon Neutrality” objective, this paper developed a high-precision vegetation biomass model for expressway corridors in Shanxi Province, China, by integrating Unmanned Aerial Vehicle (UAV) technology and the random forest algorithm. Based on climatic zoning and DEM data, 70 sample plots representing diverse vegetation and topography were selected. LiDAR point clouds and multispectral data were spatially connected using the BallTree algorithm, achieving an average matching rate of 73.98–82.01%. A joint biomass model incorporating tree height and crown width was constructed with spatial cross-validation. The results indicate that the model substantially outperformed single-factor models, with R2 values ranging from 0.839 to 0.934 (highest in the Hengshan–Wutaishan forest area). Accuracy was higher in forest-dominated zones but lower in areas with significant human disturbance. A representative sample library was established for model optimization. This paper provides a robust technical framework for biomass monitoring across comparable Northern Hemisphere latitudes, thereby supporting sustainable green transport development.

Climate change caused by CO2 emissions has become one of the most serious environmental problems facing the world today, and it has a strong relevance to sustainability. This paper measures the carbon emission efficiency of the Yangtze River Delta urban agglomeration from 2001 to 2019 using the U-S SBM model. The modified gravity model and social network analysis methods are used to explore its spatially correlated network structure, and QAP regression is used to explore the influencing factors. The results show the following: (1) The spatial correlation of the carbon emission efficiency in the Yangtze River Delta urban agglomeration increased during the study period, showing a complex network structure with multiple threads and directions, and a strong mobility of the network. (2) The spatial network of the carbon emission efficiency in the Yangtze River Delta urban agglomeration gradually formed a core−edge structure with southern Jiangsu as the core area, northern Zhejiang and central Jiangsu as the secondary core area, and central Anhui and southern Zhejiang as the edge area during the study period. (3) The spatial correlation network of carbon emission efficiency in the Yangtze River Delta urban agglomeration is divided into “net benefit”, “net spillover”, “two-way spillover”, and “broker”. (4) Differences in energy intensity, government environmental regulations, technology research and development, and economic export orientation are the main factors affecting the spatial correlation of carbon emission efficiency in the Yangtze River Delta urban agglomeration.

The study of the formation and development of urban agglomerations is of great significance, and the connection between cities is the critical foundation for shaping these agglomerations. However, the mechanism behind spatial connection between cities in the formation of urban agglomerations remains unclear. Using the Greater Bay Area (GBA) as a case study, we proposed a Bayesian network framework that integrated the spatial connection index and land use intensity. We constructed a dependency network of land use intensity from the perspective of spatial connection, and summarized the spatiotemporal evolution patterns of urban agglomeration combined with social network analysis methods. The results indicate that: (1) From 1980 to 2020, both land use intensity and spatial connection strength in the GBA have significantly increased, though the characteristics of different cities varied noticeably; (2) The spatial connection center of the urban agglomeration has shifted geographically from Hong Kong and Macao to the Pearl River Delta, and then to the east bank of the Pearl River. Hong Kong, Guangzhou, and Shenzhen are the three core cities in the spatial connection network, each with different development trajectories. (3) A dependency network of changes in land use intensity among cities at different stages from the perspective of spatial connection was constructed, identifying the evolving roles of each city in the development of the urban agglomeration. The study discussed a three-stage development model of urban agglomerations from the perspective of spatial connection, providing a new perspective for exploring the formation mechanism of urban agglomerations.

Campus community gardens (CCGs) can potentially improve student health and wellbeing, mitigate social and ecological problems, and nurture university-community relationships. However, CCGs are located in complex socio-political and ecological settings and many community gardens struggle or fail. However, few studies have assessed the socio-political/ecological context of a garden setting prior to its development to understand the potential barriers and enablers of success. Our study assessed the socio-spatial context of a proposed CCG at a student university accommodation site. We engaged diverse university and community stakeholders through interviews, focus groups and a survey to explore their perceptions of the space generally and the proposed garden specifically. Visual observations and public life surveying were used to determine patterns of behavior. Results confirmed known problems associated with an underutilized site that provides little opportunity for lingering or contact with nature; and unknown barriers, including socially disconnected stakeholders and community distrust of the university. The research also uncovered positive enablers, such as stakeholder appreciation of the social, wellbeing and ecological benefits that a CCG could deliver. Our findings suggest that an in-depth exploration of a proposed garden context can be an important enabler of its success.

Wetland is a suitable habitat for water birds, and it enhances cultural ecosystem services. But the rapid transformation of such habitat, especially in floodplain environments, is an emerging crisis. Wetland reclamation and fragmentation are two major issues leading to poor habitat and landscape. The present paper aimed to explore the spatio-temporal changes in the suitability of wetland bird habitat, wetland landscape pattern, and the connection between them. Two wetlands, including a wetland of national importance, were taken as cases for this study. Time series Landsat and Sentinel images were taken for developing modeling parameters and Land Use Land Cover (LULC) for the years 2016 and 2020. The first transformation of wetland was accounted from the LULC maps of both years. Machine learning algorithm-based spatial models were developed for mapping the poor landscape condition of the existing wetland parts. Finally, semi-subjective analytic hierarchy approach (AHP)-based models were developed for assessing waterbird habitat suitability. Results demarcated more than 48% area belonging primarily to edges and tiny patches of wetlands under a poor state in 2020. Although the total wetland area was reduced between 2016 and 2020, the wetland area found to be highly suitable habitat increased from 25.5 to 59.44% of the total area during that period. The suitability of edge-preferring bird habitat showed a 10% increase. The increasing poverty of the landscape was caused by declining edge-preferring bird habitat suitability. From 1990 to 2020, 27% of wetlands were converted to single-cropped lands, and 5% were converted to multi-cropped agricultural land. Since the study spatially identified the potential suitable area and trend of wetland habitat transformation, this could help policymakers define suitable planning for the restoration and conservation of such promising bird habitat.

This study evaluates the new urbanization (NU) quality and the ecological resilience (ER) of 33 districts and counties in the Urban Agglomeration of Central Guizhou from 2010 to 2020. For this purpose, we used a modified coupled coordination degree (CCD) model, spatial autocorrelation analysis, and trend surface analysis to analyze the spatiotemporal evolutionary characteristics of the CCD of NU and ER. Meanwhile, we used a modified gravity model and social network analysis to investigate the spatial connection network (SCN) characteristics of the CCD of NU and ER. The results show that (1) the general NU quality has increased significantly in the Urban Agglomeration of Central Guizhou. There is, however, a downward trend in ER. (2) For the CCD of NU and ER in the Urban Agglomeration of Central Guizhou, there is coupling dissonance, with a double U-shaped arc, characterized by west > north > south > east > central. (3) The network density increases and then decreases. Network connectivity is 1, and network efficiency decreases and then increases. (4) During the study period, the SCN is characterized by significant core–edge characteristics; there are no “island nodes” in the SCN.