Taxis have always been an important part of the urban transportation system in metropolises since they can meet many demands and greatly improve travel efficiency. This study explores the distributions of hot spot destinations of taxi, land surface temperature data, and VIIRS nighttime light data in Beijing, Shanghai and Wuhan. This study further selects point of interests (POIs) that are very close to taxi ridership patterns. This paper finally uses a geographically weighted regression (GWR) model to examine the relationship between population flow by taxi and urban vitality, which is represented by land surface temperature, nighttime light data and influential POIs. The results suggest that urban structure has an impact on the distribution of drop-off points and nighttime light. The implementation of the relevant policies is revealed to have a certain influence on the alleviation of the urban thermal environment. Traffic stations and financial facilities contribute to an increase in travel, and their influences vary spatially. Similarly, the relationships between the drop-off points and LST and NTL vary spatially. This research aids in understanding population flow and urban vitality development. In addition, this study provides valuable results for policy-makers to promote urban vitality development.

Soil wind erosion is a global problem that leads to increasingly serious regional land degradation, where the need for windbreak and sand fixation services (WSFS) is substantial. Inner Mongolia plays an important role in global semiarid and arid areas and the severe land degradation resulting from soil wind erosion warrants an urgent solution. However, the mechanism of influence of various driving factors on windbreak and sand fixation services is still not fully studied. In this paper, the revised wind erosion equation (RWEQ) model was used to synthesize the monthly spatiotemporal dynamics of soil wind erosion modulus (SWEM) and WSFS in Inner Mongolia from January 2000 to February 2020 from a semi-monthly scale. The influencing factors of WSFS were examined from both natural and anthropogenic aspects. Results show that over the past 20 years, the average SWEM in Inner Mongolia was 118.06 t ha−1 yr−1, the areas with severe wind erosion were mainly concentrated in the desert areas in the southwest of Inner Mongolia, and the forests in the northeast suffered less soil wind erosion. Meanwhile, the average WSFS was 181.11 × 108 t yr−1, with the high-value areas mainly located in major deserts, sandy land, and the area bordering Mongolia in the north and the low-value areas mainly located in the densely forested northeast and the Gobi Desert in the northwest. Both SWEM and WSFS showed a clear downward trend and a certain periodicity over the past 20 years. WSFS showed two peaks a year (April and October). Among the natural factors, precipitation and NDVI showed a significant correlation with WSFS and were identified as the main driving factors of WSFS, whereas temperature had no significant correlation. Among the anthropogenic factors, farming and animal husbandry intensity and GDP showed a positive correlation with WSFS, whereas population showed a negative correlation. These four types of factors were identified as socio-economic factors that drive WSFS. Meanwhile, WSFS did not show any significant correlation with the administrative area. Land use change contributed to a large proportion of WSFS change, thereby suggesting that the intensity of human activities is another central driver of WSFS.

A high rate of urbanization comes with high environmental costs, leading to reductions in biodiversity and ecosystem services (BES). How to maximize the efficiency and representation of BES in cities is of utmost urgency. However, in the process of spatial prioritization identification, it remains unclear whether a preference for ecosystem services (ES) promotes or detracts from biodiversity. In this study, a Marxan‐based spatial conservation prioritization framework is provided to achieve a win–win situation for ES provision and biodiversity protection. Using Hohhot city, China as a study case, it sets up different weighting scenarios with species and five ES to determine the optimal protected area network for each possible combination. At the same time, it tests the conservation costs, protected features, and spatial overlap of different scenarios with existing protected areas to quantify their conservation efficiency. We found that (1) closed deciduous broadleaved forests, closed evergreen needle‐leaved forests, and deciduous shrublands could support both high biodiversity and abundant ES at altitudes of 1600–2000 m. (2) Although a positive association is found between ES and biodiversity, there is some spatial variation. The geographical overlap rate with biodiversity prioritization was only 29.72% when only ES were considered. (3) Conservation of ecological hotspots by increasing the weight of ES can reduce conservation expenses by 0.69%–20.32% compared to meeting solely biodiversity targets. Our analyses highlight the need for an appropriate weighting of ES in decisions seeking to identify protected areas. This study provides methodological support for the integration of ES and biodiversity, facilitating more comprehensive conservation planning decisions.

The Minimal Living Standard Allowance System (MLSAS), established by the Chinese central government in the late 1990s, was intended to provide basic needs for urban and rural low‐income populations. Although the subsidy standards of MLSAS have increased rapidly over the years, its distributions in time and space were found imbalanced. Using the per capita subsidy income (PCSI) data of 338 Chinese cities from 2008 to 2016, this study quantified the spatiotemporal patterns of the urban‐rural gap and regional differences of MLSAS throughout China and identified the major influential socioeconomic factors of the observed patterns. The results showed that the PCSI of China's low‐income populations increased rapidly but with large variations between urban and rural residents and between geographic regions. The PCSI in rural areas was much lower than that in urban areas, whereas the Gini coefficient of PCSI in urban areas was lower than that in rural areas, indicating the allowance from MLSAS was more unequal among rural residents. Additionally, the higher PCSI was concentrated mainly in three urban agglomerations in eastern China. Most cities in central and western China lagged in terms of PCSI. Correlation analysis between PCSI and socioeconomic factors indicated that the income and GDP per capita were the most important influencing factors. With a better understanding of the overall situation of the urban‐rural gap and regional differences in implementing MLSAS, the current study should help improve the subsistence subsidy policies in China.