The non-uniformity of the relationships between urban temperature and landscape has attracted board attention. The non-uniformity in urban areas is reflected in the spatial landscape’s heterogeneity and the difference of socio-economic functions. The former is shown as the spatial differentiation of land-cover, land-use, landscape composition, and configuration, while the latter leads to the difference of the intensity of human activities and population density, which are closely related with anthropogenic heat emission. Therefore, this study introduces urban functional zones (UFZs) to express urban spatial heterogeneity. This study also attempts to comprehend urban heat island (UHI) effects and discloses the variability of urban surface temperature (LST)–landscape relationships in different kinds of UFZs. There are two main technical difficulties—how to characterize the spatial heterogeneity of UFZs and how to quantify non-uniform LST effects. A three-level variable system is established from their attributes, inner structures, and interrelationships to characterize UFZs and their LST effects hierarchically. Considering the multi-collinearity among high-dimensional variables, the Elastic Net regression method is selected for quantitative analysis. The experimental results reveal the deficiency of uniform LST analysis for heterogeneous urban areas and verify the variable relationships of LST-landscaped with different kinds of UFZs.

The Qaidam Basin is a sensitive climate transition zone revealing a wide spectrum of local climates and their variability. In order to obtain an objective and quantitative expression of local climate regions as well as avoid the challenge to pre-define the number of heterogeneous local climates, the ISODATA cluster method is employed to achieve the fine-grained climate divisions of the Qaidam Basin, which can heuristically alter the number of clusters based on the input of monthly temperature and precipitation data. The fine-grained climate classification extends the traditional Köppen climate classification and represents the complex climate transformation processes in terms of fine-grained climate clusters. The following results are observed: (i) The Qaidam Basin is divided into an arid desert basin area and the surrounding alpine mountainous areas. The climate distribution is affected by both the altitude and the dryness ratio, which, employing the Budyko framework, largely characterizes the local energy–water fluxes at the surface and the related vegetation regimes (biomes). The fine-grained climate classification successfully captures their causal relationships and represents them well by the local climates: the climatic spatial differentiation in the mountainous areas is highly consistent with the topography and reveals an elevation-dependent circular distribution from the edges to the center of the basin; the climate heterogeneity within the basin presents a west-to-east meridional distribution due to the combined effect of the mid-latitude westerlies and the Indian monsoon. (ii) The climate gradients are spatially different over the Qaidam Basin. The surrounding mountainous areas have a large climate gradient compared to the inner basin; the southern mountain edge is governed by a more severe climate change than the north-eastern one; and the climate gradient is larger in the eastern than in the western basin. (iii) The lake regions within the basin show an obvious lake effect and reveal a local lake climate. Spatially, a common structure emerges with a dryer-climate zone or watershed embedding a wetter lake-affected area, which appears to migrate eastward becoming stepwise wetter from the very dry center to the wet eastern boundary of the Qaidam basin. This provides a topographically induced insight of the wet climate expansion of initially arid climates and is crucial to improve the Qaidam Basin’s ecological environment. Finally, although this work mainly focuses on the local-scale climates and their variability in the Qaidam Basin, the data-driven cluster methodology for climate refinement is transferable to regional- even global-scale climate studies, which offers broad application prospects.

Under the background of global warming, China with diverse climate types is experiencing dramatic climate change. In this study, we produced a series of climate type map of China at 0.1° resolution from 1979 to 2018 using Köppen-Geiger climate classification, which provided continuous fine-scale decadal climate classification data for climate researches in China. Based on these climate maps, we divided China into four main climate zones: arid climate zone located on the north of Tibetan Plateau and west of Inner Mongolian Plateau, temperate climate zones in southern China, cold climate zone occupied most of Northeast Plain and North China Plain, and polar climate zone on Tibetan Plateau. The distribution of main climate classes in China have not changed significantly over 40 years, while the climate change mainly occurs at the levels of climate type and subtype changes. The frequency of climate changes shows the climate sensitivity of the region, and we identified the transition areas of climate zones with a high sensitivity to climate change. The change of climate types shows an obvious trend of rising temperature in all climate zones of China and increasing precipitation in most climate zones of China (the cold climate zone shows no significant dry or wet change). Overall, the climate in China is generally getting warmer and wetter in the past 40 years. Furthermore, we analysed the landscape responses on climate change with land cover data, e.g. the vegetation type variations in southern China and the snow cover fluctuations on Tibetan Plateau.

Global population growth and land development are highly imbalanced, marked by 43% of population increase but 150% of built-up area expansion from 1990 to 2018. This results in the widely concerned ghost city phenomenon and runs against the sustainable development goals. Existing studies identify ghost cities by population densities, but ignore the spatial heterogeneity of land carrying capacities (LCC). Accordingly, this study proposes a general concept termed underload city to define cities carrying fewer people and lower economic strength than their LCC. The underload city essentially describes imbalanced human-land relationship and is understood in a broader context than the usually applied ghost city. In this study, very high-resolution satellite images are analyzed to obtain land functional structures, and further combined with population and GDP data to derive LCC. We empirically identify eight underload cities among 81 major Chinese cities, differing from previous findings of ghost cities. Accordingly, the proposed underload city considers heterogeneous human-land relationships when assessing city loads and contributes to sustainable city developments.

To control the growth of CO₂ emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator to explore the impact of COVID-19 on human activities, and thereby studied the urban resilience of different cities. Our research re-vealed that (i) the seasonal patterns of CO2 emissions in major cities of China could be divided into four types: Long High, Summer High, Winter High, and Fluctuations, which was highly related to the power and industrial sectors. (ii) The annual trends, which were strongly affected by the pan-demic, could be divided into four types: Little Impact, First Impact, Second Impact, and Both Impact. (iii) The recovery speed of CO2 emissions reflected urban resilience. Cities with higher levels of de-velopment had a stronger resistance to the pandemic, but a slower recovery speed. Studying the changes in CO2 emissions and their causes can help to make timely policy adjustments during the economic recovery period after the end of the pandemic, provide more references to urban resilience construction, and provide experience for future responses to large-scale emergencies.

China’s rapid industrialization and urbanization have led to significant and imbalanced CO₂ emissions, putting pressure on achieving sustainable development goals. This study analyzed the CO₂ emissions of 31 major cities in China from different sectors (total, power, industry, and transport) from 2019 to 2022. This study constructs a city-scale CO₂ emission correlation model to achieve nationwide and urban fine-scale research on CO₂ emission spatial networks from different sectors. This study revealed the following: (i) there is an increasing correlation among regions in China, and collaborative governance is crucial; (ii) there are differences in the structure, characteristics, and roles of CO₂ emission networks from different sectors; (iii) China’s CO₂ emission network is mainly concentrated in the northern and eastern regions, which play an important role in emission reduction; and (iv) the impact factors have different effects on CO₂ emissions from different sectors, and we should actively contribute to promoting emission reduction. Correctly understanding the spatial characteristics and influencing factors of CO₂ emissions can help us formulate targeted and efficient emission reduction policies.