How to measure and quantitatively assess hydrological drought (HD) in the inland river basins of Northwest China is a difficult problem because of the complicated geographical environment and climatic processes. To address this problem, we conducted a comprehensive approach and selected the Aksu River Basin (ARB) as a typical inland river basin to quantitatively assess the hydrological drought based on the observed data and reanalysis data during the period of 1980–2010. We used two mutual complementing indicators, i.e., the standardized runoff index (SRI) and standardized terrestrial water storage index (SWSI), to quantitatively measure the spatio-temporal pattern of HD, where the SRI calculated from the observed runoff data indicate the time trend of HD of the whole basin, while SWSI extracted from the reanalysis data indicate the spatial pattern of HD. We also used the auto-regressive distribution lag model (ARDL) to show the autocorrelation of HD and its dependence on precipitation, potential evapotranspiration (PET), and soil moisture. The main conclusions are as follows: (a) the western and eastern regions of the ARB were prone to drought, whereas the frequency of drought in the middle of the ARB is relatively lower; (b) HD presents significant autocorrelation with two months’ lag, and soil moisture is correlated with SWSI with two months’ lag, whereas PET and precipitation are correlated with SWSI with 1 month’ lag; (c) the thresholds of HD for annual PET, annual precipitation, and annual average soil moisture are greater than 844.05 mm, less than 134.52 mm, and less than 411.07 kg/m², respectively. A drought early warning system that is based on the thresholds was designed.

This research investigates the optimum spatial scale of regional tourism cooperation based on the tourism flows spillover in Mainland China. Using panel data from 341 prefecture-level cities in Mainland China, spatial econometric modeling is employed to estimate the spillover effects to determine the optimum spatial scale of regional tourism cooperation. The findings reveal that there are spatial correlations of tourism flow. In addition, tourism economic fundamental factor, surrounding market factor, tourism transportation facility factor, and tourist attraction factors have a positive and significant impact not only on local tourism flows but also on the surrounding areas. Finally, it is found that the optimum spatial scale of the regional tourism cooperation in Mainland China is [5, 15] cities, whereas for the eastern, western, southern, and northern regions, the optimum scale of the regional tourism cooperation is [5, 21], [5, 26], [5, 26], and [5, 11] cities, respectively.

The location choice and livelihoods of rural-urban migrants are critical to the sustainable development of cities. By using data from the China Migrants Dynamic Survey (CMDS) in 2017, this paper extant the Rosen–Roback’s model by adding factors of urban social network and air pollution to the function of the individual utility of migrants. Both the Probit Model and IV estimates imply evidence of an inverse U-shaped pattern of city size and migrants’ permanent settlement in urban China. This view proves that Chinese migrants like to settle permanently in large cities, but not mega-cities, such as Beijing and Shanghai. The internal mechanism is explained by the agglomeration economies and the crowing effect brought by city size. In mega-cities, the attractiveness of the city caused by wage premium cannot offset the combined repulsive force caused by the high housing price, bad urban social network, air pollution, and health deterioration. It is worth noting that air pollution has a significant negative impact on the settlement intention of migrants, such as health conditions and precipitation. Besides, there is heterogeneity among high-skilled migrants and low-skilled migrants in different city sizes. Our findings enhance the understanding of “Escape from megacities” in China and have implications for the reform of the housing security system and the exploration of the urbanization path.

Under the controversial background of “Northwestern China is gradually developing towards warm and humid”, how hydrological drought responds to meteorological drought at the endorheic basin is of great significance. To address this problem, we first analyzed the spatiotemporal variation of meteorological and hydrological droughts at Tarim Basin River from 1960 to 2014 by using the daily standardized precipitation index (SPI) and daily standardized terrestrial water storage index (SWSI) based on the reanalysis data. Thereafter, we explored the spatiotemporal response of hydrological drought to meteorological drought on the multi-time scale by using the cross-wavelet transform method, Ensemble Empirical Mode Decomposition (EEMD), and correlation analysis. We find that: (1) both meteorological and hydrological droughts signified a gradually weakened trend in time; (2) meteorological and hydrological drought have significant resonance periods on the 10-month time scale and the 8-year time scale; (3) hydrological drought generally lags behind the meteorological drought by 7 days in plains areas, while it can last as long as several months or even a year in mountainous areas.

Global warming caused by extensive carbon emissions is a critical global issue. However, the lack of county-level carbon emissions data in China hampers comprehensive research. To bridge this gap, we employ a deep learning method on nighttime light data sets to estimate county-level carbon emissions in mainland China from 1997 to 2019. Our key contributions include the successful derivation of more reliable data, revealing the evolution of spatial dynamics and emissions epicenters. Moreover, we identify a novel inverted N-shaped relationship between gross domestic product per capita and carbon emissions in the eastern and western regions, as well as an N-shaped relationship in the central region, challenging mainstream wisdom. Additionally, we highlight the significant impacts of population density, industrial structure, and carbon intensity on carbon emissions. Our study also unveils the nuanced effects of government spending, which exhibits both inhibitory and region-specific influences. These findings serve to enhance our understanding of the factors influencing carbon emissions and contribute to informed decision-making in addressing climate change-related challenges.

Complex temperature processes are the coupling results of natural and human processes, but few studies focused on the interactive effects between natural and human systems. Based on the dataset for temperature during the period of 1980–2012, we analyzed the complexity of temperature by using the Correlation Dimension (CD) method. Then, we used the Geogdetector method to examine the effects of factors and their interactions on the temperature process in the Yangtze River Delta (YRD). The main conclusions are as follows: (1) the temperature rose 1.53 °C; and, among the dense areas of population and urban, the temperature rose the fastest. (2) The temperature process was more complicated in the sparse areas of population and urban than in the dense areas of population and urban. (3) The complexity of temperature dynamics increased along with the increase of temporal scale. To describe the temperature dynamic, at least two independent variables were needed at a daily scale, but at least three independent variables were needed at seasonal and annual scales. (4) Each driving factor did not work alone, but interacted with each other and had an enhanced effect on temperature. In addition, the interaction between economic activity and urban density had the largest influence on temperature.