This study integrates the analysis of surface temperature data with natural and anthropogenic factors closely related to the urban thermal environment in Nanjing from 2000 to 2020, exploring the spatiotemporal variation characteristics of the urban heat island effect and the interactive relationships among its influencing factors. The research findings are as follows: (1) Between 2000 and 2020, the urban heat island effect in Nanjing exhibited an expansion trend radiating from the city center to the periphery, with the heat island phenomenon primarily concentrated in the old urban areas characterized by developed commerce, industry, and dense populations. Surface temperatures gradually decreased from the city center to the suburbs, forming a distinct spatial distribution gradient. Both the standard deviation ellipse and the centroid of high-temperature areas showed a southward shift. (2) Significant differences in surface temperatures were observed across different land use types, with built-up areas and arable land maintaining relatively stable and higher surface temperatures, while water bodies and forests exhibited lower and stable surface temperatures. (3) Vegetation coverage, normalized water body index, elevation, dispersion, and the Shannon diversity index were negatively correlated with surface temperature, while the normalized difference bare land index, building index, dispersion index, and patch cohesion index were positively correlated with surface temperature. In Nanjing, the interactive effects of dual factors on the urban heat island effect were found to be greater than those of individual factors, with vegetation coverage identified as the most critical factor affecting surface temperature. Considering multidimensional factors together enhances the understanding of the spatial patterns and causes of the urban heat island effect, clarifies the interrelationships and degrees of influence among natural, socio-economic, and landscape pattern factors, and provides a scientific basis for improving the quality of the living environment in Nanjing.

As urbanization accelerates, the increasing density of urban buildings and the prevalence of multi-unit high-rise residential areas have emerged as significant factors affecting residents’ comfort. Effective green space planning within residential areas can mitigate residents’ thermal discomfort. This study utilizes methods including the construction of two-dimensional and three-dimensional landscape indices and meteorological data simulation to examine the relationship between residents’ comfort levels at various heights in residential buildings and the 3D landscape patterns of residential areas, based on semantic three-dimensional grid data from a residential complex in Wuhan. The results indicate that (1) The characteristics of 3D landscape patterns vary across different regions within multi-unit high-rise residential areas. The landscape patches in the central and southern regions are more balanced compared to other areas, while there is minimal height variation in residential buildings in the northeastern region. (2) There are notable differences in comfort levels at varying heights across different areas of the residential district. In summer, residents expressing satisfaction with environmental comfort are primarily located in high-rise buildings in the central-southern region, whereas in winter, satisfaction is concentrated among residents in lower and mid-rise buildings in both the northern center and southern areas. (3) The degree of landscape fragmentation, the dominance of certain patches, and the distribution of buildings and vegetation at different heights significantly influence residents’ comfort. Achieving a balanced distribution of green spaces, reducing building density, and ensuring a uniform arrangement of trees of varied heights can effectively enhance the living environment for residents on lower floors, providing practical strategies for the planning of green spaces and built environments that improve overall resident quality of life. This research provides a theoretical foundation and reference for evaluating thermal comfort in high-rise residential areas and optimizing green space configurations.

Against the backdrop of global warming and the “dual carbon” goals, scientifically assessing the spatiotemporal patterns of regional carbon emissions is of great significance for formulating differentiated emission reduction policies. Taking counties of Jiangsu Province as the basic analytical unit, this study integrates NPP-VIIRS nighttime light data and energy statistical yearbook data from 2000 to 2020. An IPCC carbon emission coefficient method was adopted to construct a county-level carbon emission estimation model. Spatial autocorrelation analysis, hot spot detection, Theil–Sen trend estimation, and the Mann–Kendall significance test were comprehensively applied to systematically reveal the spatiotemporal evolution characteristics of energy consumption carbon emissions in Jiangsu Province. The results indicate that county-level carbon emissions in Jiangsu Province exhibit a stable spatial pattern of “higher in the south, lower in the north, and agglomeration along the Yangtze River,” and the total carbon emissions in the southern core area show a statistically significant increasing trend. The spatial pattern of carbon emissions has transformed from “unipolar high-intensity agglomeration” to “zonal diffusion coexisting with multi-point agglomeration.” High per capita carbon emission areas persistently cluster along the Yangtze River, whereas high-carbon-emission-intensity areas have shifted to certain counties in northern Jiangsu.