This study aimed to analyze spatio-temporal changes in habitat quality in Chang-Zhu-Tan Urban Agglomeration during the 2000–2020 period and explore its topographic gradient effects. Using land use data from this period, the InVEST model was employed to assess the spatio-temporal variations in habitat quality. The bivariate spatial autocorrelation model was used to analyze the spatial correlation characteristics between habitat quality and various topographical factors. Additionally, terrain factor analysis was utilized to study the terrain gradient effects on habitat quality in the study area. The results reveal that: (1) The primary land use changes in the study area from 2000 to 2020 predominantly involve substantial arable land and forest conversions into urban development. (2) The average habitat quality indices for 2000, 2010, and 2020 in the Chang-Zhu-Tan Urban Agglomeration stand at 0.651, 0.622, and 0.606, respectively, indicating a consistent declining trend in habitat quality. The distribution of habitat quality grades demonstrates a spatial pattern of “lower in the central surroundings, higher in the surroundings.” (3) The Chang-Zhu-Tan Urban Agglomeration shows significant positive correlations between habitat quality and topographical gradients. Spatial aggregation tendencies between habitat quality and topographical gradients primarily exhibit “high-high” and “low-low” clustering. (4) The habitat quality of the Chang-Zhu-Tan urban agglomeration exhibits a significant topographic gradient effect, primarily characterized by an increase in habitat quality with the rise of the topographic gradient. The study outcomes contribute to unveiling the spatiotemporal variations in habitat quality within the Chang-Zhu-Tan Urban Agglomeration. Moreover, leveraging different habitat types’ distinctive terrain distribution characteristics, it proposes targeted habitat conservation measures, thereby offering theoretical support for biodiversity conservation and territorial spatial planning in the study area.

With the acceleration of urbanization, the disturbance to urban landscape patterns causes changes to urban surface runoff and increases the risk of urban waterlogging. We studied the response relationship between landscape pattern change and surface runoff in the Chang–Zhu–Tan metropolitan area for the period from 2000 to 2020, analyzing the driving factors that affected surface runoff. The influence of landscape pattern evolution on surface runoff was studied using the SCS-CN flow generation model, the moving window method, and Pearson’s analysis. The analysis showed that between 2000 and 2020, the forest area decreased, while the impermeable land area increased substantially. At the same time, the landscape spread degree (CONTAG) decreased, whereas the landscape fragmentation degree (DIVISION) increased, and the Shannon diversity index (SHDI) and landscape shape index (LSI) increased. The surface runoff in the main urban areas of Chang–Zhu–Tan increased substantially. The results showed that surface runoff is negatively correlated with SHDI, LSI, and DIVISION but displays a positive relationship to CONTAG. Soil texture and precipitation had the greatest impact on surface runoff. This study emphasizes the importance of landscape pattern evolution to surface runoff in rapidly developing metropolitan areas in terms of reducing surface runoff, alleviating urban waterlogging and preventing regional floods. Our research additionally seeks to optimize the landscape pattern of the Chang–Zhu–Tan metropolitan area.

Urban spatial expansion affects almost every dimension of sustainable urban development. A good grasp of the relationship between urban spatial evolution and carbon emissions can be the key to urban spatial governance. As a central city in the central region and a national low-carbon pilot city, Changsha has experienced a rapid expansion of construction land and growing carbon emissions. In this paper, four variable factors and five variable factors of carbon emission were used for the case city Changsha in 1979, 1996, 2003, 2008, 2013 and 2016. Based on the “double carbon” constraint target, the total carbon emissions, carbon-emission intensity and per capita carbon emission constraint indices were forecasted until 2030. They are 87.29 million t-CO₂, 0.45 t-CO₂/CNY10⁴ and 8.73 t-CO₂/person, respectively. The scale of urban land is controlled at 889.61; the constraint indices of residential, commercial service land, industrial land and road square land scales are 231.3 km², 143.88 km², 150.17 km² and 135.83 km², respectively. The land expansion intensity, urban compactness and shortest travel distance constraint indices are 6.19, 0.236 and 96,086.76 km, respectively. The results of this analysis can provide scientific guidance for the next step in territorial spatial master planning and low-carbon governance.

Coordination between regional economic development and carbon pressure is essential for sustainable regional development. However, existing research on carbon pressure and studies on the relationship between economic growth and carbon pressure needs to be more comprehensive. This study analyzes the decoupling impact of economic growth and carbon pressure in different regions of urban agglomeration in the middle reaches of the Yangtze River by revealing the evolution of the geographical and temporal characteristics of carbon pressure from 2000 to 2020. We investigate the drivers of carbon pressure in the middle reaches of the Yangtze River urban agglomeration using the LMDI index decomposition model. The results show that (1) the carbon pressure in the urban agglomeration of the middle reaches of the Yangtze River and its three sub-city agglomerations shows a declining tendency at the beginning and later exhibits an increasing tendency; (2) from 2000 to 2020, the carbon pressure of the majority of cities increased, with Wuhan and Ezhou in the high carbon pressure area and Ji’an, Fuzhou, and Shangrao in the carbon sink surplus area; (3) the rate of decoupling climbs from 45% to 96% over time, then declines to 67%, and reaches 90% by 2020; and (4) the most prominent influence on carbon pressure in the 31 cities is energy consumption, followed by economic expansion. The research in this paper is beneficial for cities to explore solutions to coordinate economic development and carbon pressure despite the constraints of imposed by the two.