This study analyzed data from 1995, 2005, and 2015 using mathematical calculations, spatial analysis, and a geographically weighted regression model. The results showed that from 1995 to 2015, the comprehensive regional development degree (RDD) of urban agglomeration in the middle of Jilin Province increased overall, with the average RDD increasing from 0.250 in 1995 to 0.323 in 2015. Especially in Changchun, a sub-provincial city, the RDD increased by nearly one-third, and the gap between this and other cities has been increasing. However, the ecosystem service value (ESV) decreased overall, with the average ESV decreasing from 108.3 in 1995 to 105.4 in 2015, and showed a strong spatial correlation. The maximum quantile in southeast–northwest direction was 1.712, with good homogeneity. The spatial influence coefficient of the RDD on the ESV showed a trend from positive to negative in the northwest–southeast direction. This value decreased continuously while the negative agglomeration area was gradually expanding, corresponding to the stressful effects of the RDD on ESV. The results of this study can provide a reference for urban planning and development as well as encourage reasonable regional spatial planning to ensure the sustainable development of urban agglomerations.

Global climate change is causing various negative impacts on urban ecosystems and energy systems. To effectively mitigate and adapt to these changes, it is important to understand the contributions of background climate and local effects to urban thermal environment variation. This study utilized the empirical orthogonal function (EOF) approach to deconstruct long-term MODIS land surface temperature (LST) datasets to obtain the main features of change in daytime and nighttime thermal environments. Local bivariate spatial autocorrelation analysis was used to explore the underlying causes of these changes. The main EOF modes explained 73.14% and 81.33% of daytime and nighttime thermal environment variation, respectively. The correlation coefficient between the time coefficient of the main modes and the average LST was > 0.99, reflecting the role of global effect caused by background climate change. The secondary EOF modes explained 12.51% and 4.12% of daytime and nighttime thermal environment variation, respectively, and were spatially correlated with changes in landscape thermal intensity, reflecting local effect caused by landscape change and anthropogenic heat emissions. In expansion and renewal areas, industrial zones and compact high-rise buildings had the most obvious warming effect on the daytime thermal environment, while mid-to-high-rise buildings had the most obvious warming effect on the nighttime thermal environment. The results of this study provide valuable insights into the mechanisms of background climate and local effects on the urban thermal environment, and provide a reference for formulating effective strategies for mitigating and adapting to change in urban areas, and for promoting sustainable development.

How to explore the allocation and green utilization level of urban construction land resources has an important role in the sustainable development of the city. Taking 47 counties and cities in Jilin Province as an example, this paper evaluates the green utilization efficiency of urban construction land (GUEUCL) in 2011 and 2015 by using the unexpected output super-SBM model and explores the spatial-temporal differentiation characteristics and influencing factors of GUEUCL by using GIS and machine learning methods. The results show that (1) the GUEUCL in Jilin Province is low, mainly distributed in small- and medium-sized areas, with significant positive spatial correlation. The L-L concentration area is mainly distributed in the eastern region, but the degree of spatial concentration is small, the spatial structure characteristics of the two periods are different, and the spatial heterogeneity is large; (2) the internal factor decomposition shows the impact of pure technical efficiency on the comprehensive efficiency and the restriction ability is stronger than the scale efficiency, that is to say, the factors such as management and technology have a greater impact on the comprehensive efficiency; (3) the relative importance of external factors has always been ranked as socioeconomic factors, urban development factors, and natural science and technology factors. This paper focuses on the temporal and spatial characteristics of each county and city and the influencing factors, which provides a certain value reference for the pilot of ecological construction and the development of ecoenvironmental benefit economic system in Jilin Province.

Studying land comprehensive carrying capacity (LCCC) is the foundational and key requirement for determining land development planning and urban spatial development patterns of a region. However, the traditional evaluation method discounts the fuzziness and randomness of the evaluation index and its results. The cloud model combines randomness and fuzziness to reveal the correlation between randomness and fuzziness using numerical feature entropy and is used to represent the granularity of a qualitative concept. This study used the Liaoning Province as the study area, and developed a multi-dimensional evaluation index system for LCCC using a normal cloud model. Based on this, the relationship between the different elements of geological condition, resources and environment, economic scale and urban construction were studied using the coupling coordination degree model that reflected not only the system interactions but also the strengths of its degree of coordination. Our results were as follows: (1) numerical feature entropy were evaluated to determine the carrying capacity level of the land, and comprehensive land carrying capacity evaluations were conducted in terms of both quantitative results and the reliability of the results, promoting the scientific application of uncertainty theory in the field of comprehensive land evaluation as well as carrying capacity. (2) Liaoning Province’s prefecture-level cities had distinctly different LCCC, demonstrating “low in the west and high in the east” spatial distribution characteristics. Cities with established economies and relatively strong infrastructures had larger comprehensive land carrying capacities. Overall, there was considerable consistency across the region, though the “low in the west and high in the east” spatial distribution characteristics affected the degree of coordination.

Extreme heat events occur frequently in urban areas, seriously affecting human well-being and productivity. Therefore, this study aimed to quantify the impact of and regional differences in the UHI effect within the broader context of achieving sustainable development goals. To this end, we combined footprint analysis with principal component analysis and multivariate linear regression analysis to quantify the spatiotemporal distribution of heat island intensity and footprint within the Beijing–Tianjin–Hebei (BTH) urban agglomeration as well as the impact of regional differences. We found that the surface urban heat island intensity (SUHII) value was higher during the daytime than at night. In 2005, 2010, 2015, and 2018, the average daytime values of SUHII were 0.21°C, 0.03°C, 0.35°C, and 0.53°C higher than those at night, respectively. High daytime values of SUHII mainly occurred in larger cities (e.g., Beijing), and high nighttime values of SUHII mainly occurred at higher latitudes. Additionally, we determined that the maximum values of the SUHIF were concentrated in densely populated areas such as Beijing, Tianjin, and Shijiazhuang. Furthermore, principal component analysis revealed that PM2.5 was negatively correlated with SUHII, whereas population density (PD) and enhanced vegetation index (EVI) were positively correlated with SUHII. In contrast, PM2.5 and EVI were negatively correlated with SUHIF, whereas PD and SUHIF showed a negative correlation. This study elucidates the changes in and influencing mechanisms of the urban heat island intensity and footprint and provides an important reference for mitigating the UHI effect and rationally planning urban land use.