Accurately identifying the boundary of urban clusters is a crucial aspect of studying the development of urban agglomerations. This process is essential for comprehending and optimizing smart and compact urban development. Existing studies often rely on a single category of data, which can result in coarse identification boundaries, insufficient detail accuracy, and slight discrepancies between the coverage and the actual conditions. To accurately identify the extent of urban clusters, this study proposes and compares the results of three methods for identifying dense urban areas of three major agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Guangdong–Hong Kong–Macao Greater Bay Area. The study then integrates the results of these methods to obtain a more effective identification approach. The social economic method involved extracting a density threshold based on the fused nuclear density of socio-economic vitality data, including population, GDP, and POI, while the remote sensing method evaluated feature indices based on remote sensing images, including the density index, continuity index, gradient index, and development index. The traffic network method utilizes land transportation networks and travelling speeds to identify the minimum cost path and delineate the boundary by 20–30 min isochronous circles. The results obtained from the three methods were combined, and hotspots were identified using GIS overlay analysis and spatial autocorrelation analysis. This method integrates the multi-layered information from the previous three methods, which more comprehensively reflects the characteristics and morphology of urban clusters. Finally, the accuracy of each identification result is verified and compared. The results reveal that the average overall accuracy (OA) of the three areas delineated by the first three methods are 57.49%, 30.88%, and 33.74%, respectively. Furthermore, the average Kappa coefficients of these areas are 0.4795, 0.2609, and 0.2770, respectively. After performing data fusion, the resulting average overall accuracy (OA) was 85.34%, and the average Kappa coefficient was 0.7394. These findings suggest that the data fusion method can effectively delineate dense urban areas with greater accuracy than the previous three methods. Additionally, this method can accurately reflect the scope of urban clusters by depicting their overall boundary contour and the distribution of internal details in a more scientific manner. The study proposes a feasible method and path for the identification of urban clusters. It can serve as a starting point for formulating spatial planning policies for urban agglomerations, aiding in precise and scientific control of boundary growth. This can promote the rational allocation of resources and optimization of spatial structure by providing a reliable reference for the optimization of urban agglomeration space and the development of regional spatial policies.

Nature-based solutions are some of the most effective strategies to promote sustainable city development; however, existing research on NbS is mostly comprised of single variable studies rather than multiple variables. The purpose of this study was to explore the possibility of extending the NbS of a single variable to two variables for the better development of sustainable cities. Both forestation and wetland restoration are regarded as NbS for sustainable city development. The research approach of “forest–wetland” NbS was proposed and centers on the process and core issues of traditional NbS. Taking Tianjin as an example, the spatial patterns of forests and wetlands, correlation between the spatial distribution of forests and wetlands, and spatial correlation between the areas of forest growth and wetland growth within a certain distance in different years were studied using a spatial distribution pattern analysis, geographic concentration analysis, kernel density estimation and spatial autocorrelation analysis. Based on the core issues of NbS and the above spatial analysis, a “forest–wetland” spatial planning strategy was formulated. The main conclusions are as follows: forest and wetland were negatively correlated in the whole area of Tianjin, forest resources w mainly located in north, while wetland resources were mainly located in south. Compared with forests, the spatial distribution of wetlands in Tianjin was more balanced. There exist synergy and trade-offs between forest and wetland area under certain circumstances. Growth of forests was positively correlated with the growth of wetlands, within a distance of 0–400 m from 2000 to 2010, and within a distance of 0–600 m from 2010 to 2020. An increase in forest area will lead to an increase in evaporation, which in turn will hinder the growth of wetlands in Tianjin. Forest–wetland ecological network could promote synergistic between forest and wetland, and grey infrastructure to reduce potential trade-off between forest and wetland.

The research focuses on the air-conditioning system in a public area of a subway station. To address this, an optimization model based on the grid time segmentation method was constructed, specifically a GM (1,1) model. We explored the influence of the hourly passenger flow fluctuation on the load of the subway air-conditioning system, obtained the dynamic change law of the air conditioning system load in the subway station, and then dynamically adjusted the air conditioning system according to the dynamic change law to reduce the operation energy consumption of the system. Through the analysis of the simulation results, the model predicted that compared with the actual passenger flow data, the average maximum relative error was 14.97%. On this basis, the change law of the dynamic load of the subway air-conditioning system which caused by the change in passenger flow from time to time could be calculated and analyzed. Compared with the calculated load of the air conditioning system, the working day load was decreased by 1469.77 kW, or 22.00%. The findings indicate that in response to the dynamic load of fluctuations, timely adjustment of the air supply parameter of the air-conditioning system offers a significant reference point for optimizing energy efficiency in subway stations.

Carbon emissions affect sustainable urban development, and cities are the main carbon emission factors. To improve carbon performance from the perspective of urban density, the temporal and spatial evolution characteristics and associations, and indicative thresholds of urban density and carbon performance in the Chengdu-Chongqing urban agglomeration are analyzed by combining Slack Based Measure-Data Envelopment Analysis (SBM-DEA), Standard Deviation Ellipse Analysis, Regression Model, and Spatial Autocorrelation in this paper. The results show that: (1) there is no obvious trend in population density, road density increases faster than building density, and carbon performance shows a decreasing and then increasing trend. (2) The center of gravity of building density and population density migrates towards Chengdu-Chongqing. The road density develops in the southwest-northeast direction, and the spatial pattern of carbon performance shows the characteristic of “high in the southwest and low in the northeast”. (3) The building density, population density, road density, and carbon performance show an upward curve, an “N” curve, and an inverted “N” curve, respectively, and the order of influence is building density > road density > population density. (4) There is a positive spatial correlation between urban density and carbon performance in the Chengdu-Chongqing urban agglomeration, but it is not uniformly distributed, and there is a phenomenon of clustering in specific local areas, especially in the core city areas such as Chengdu.