Assessing urban atmospheric environmental efficiency and factors influencing it in China

Quality of life assessment and its spatial correlation in impoverished districts and counties: A case study of Guizhou Province

China’s rapid urbanization has greatly boosted the quality of life of its traditionally impoverished regions. Research into the spatial distribution characteristics, evolution and spatial correlation of the quality of life in impoverished regions can help illuminate the experience of successful development and construct a knowledge base for authorities to devise development strategies. This study focuses its attention on the historically impoverished districts and counties (which are designated as parallel administrative units in China) of Guizhou Province in southwestern China. Extensively citing official statistics on districts and counties released by China’s National Bureau of Statistics and local governments, it assesses the quality of life of those places in the 3 years of 2000, 2010, and 2020 from the four dimensions of economy, society, culture, and environment. The aim is to illustrate the distribution characteristics and the evolution of quality of life in Guizhou’s historically impoverished districts and counties. In order to understand the characteristics of spatial clustering as well as the patterns of evolution of the quality of life of Guizhou’s impoverished districts and counties, the study incorporates spatial autocorrelation analysis into a spatio-temporal analysis of local quality of life. It could presumably help enrich the knowledge base that local authorities draw on to formulate development strategies that are scientific and adapted to local conditions. The study found that while the overall quality of life in all the impoverished districts and counties of Guizhou Province has improved, large gaps in quality of life between eastern and western regions of the province persisted. In addition, the driving force behind the evolution in the overall quality of life of those places changed with time, as did the characteristics of the spatial aggregation in quality of life.

The spatiotemporal evolution and influencing factors of urban green innovation in China

Green innovation (GI) is an important way to build an ecological civilization and an innovative country. The study on urban green innovation (UGI) is of great significance for enriching the research on GI and rationally formulating high-quality urban development policies. The green patent data obtained using a web crawler was used to represent the level of UGI. The spatiotemporal evolution and influencing factors of UGI in China were analyzed by standard deviation ellipses, spatial autocorrelation, and Geodetector. The research shows that: From 2005 to 2020, the level of UGI in China tended to rise rapidly. The center of gravity of UGI in China was located in the southeast of China's geometric center and tended to move to the south and west. The standard deviation ellipse was distributed in a "northeast-southwest" pattern, the area was gradually shrinking, and the length of the two semi-axes was shortening. UGI in China showed obvious global and local spatial autocorrelations. The degree of global spatial autocorrelation was gradually increasing. Among the types of local spatial autocorrelation, the largest number of low-low agglomeration cities was mainly located in the northwest and southwest part of China, while high-high agglomeration cities were distributed in Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta. The government intervention expressed by the proportion of scientific and technological expenditure in fiscal expenditure and environmental regulation is the dominant factor affecting UGI.

Regional Differences, Distribution Dynamics, and Convergence of Air Quality in Urban Agglomerations in China

The urban agglomeration (UA), with a high concentration of population and economy, represents an area with grievous air pollution. It is vital to examine the regional differences, distribution dynamics, and air quality convergence in UAs for sustainable development. In this study, we measured the air quality of ten UAs in China through the Air Quality Index (AQI). We analyzed regional differences, distribution dynamics, and convergence using Dagum’s decomposition of the Gini coefficient, kernel density estimation, and the convergence model. We found that: the AQI of China’s UAs shows a downward trend, and the index is higher in northern UAs than in southern UAs; the differences in air quality within UAs are not significant, but there is a gap between them; the overall difference in air quality tends to decrease, and regional differences in air quality are the primary contributor to the overall difference; the overall distribution and the distribution of each UA move rightward; the distribution pattern, ductility, and polarization characteristics are different, indicating that the air quality has improved and is differentiated between UAs; except for the Guanzhong Plain, the overall UA and each UA have obvious σ convergence characteristics, and each UA presents prominent absolute β convergence, conditional β convergence, and club convergence.

Spatial Differences and Influential Factors of Urban Carbon Emissions in China under the Target of Carbon Neutrality

Cities are areas featuring a concentrated population and economy and are major sources of carbon emissions (CEs). The spatial differences and influential factors of urban carbon emissions (UCEs) need to be examined to reduce CEs and achieve the target of carbon neutrality. This paper selected 264 cities at the prefecture level in China from 2008 to 2018 as research objects. Their UCEs were calculated by the CE coefficient, and the spatial differences in them were analyzed using exploratory spatial data analysis (ESDA). The influential factors of UCEs were studied with Geodetector. The results are as follows: (1) The UCEs were increasing gradually. Cities with the highest CEs over the study period were located in the urban agglomerations of Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, middle reaches of the Yangtze River, and Chengdu–Chongqing. (2) The UCEs exhibited certain global and local spatial autocorrelations. (3) The industrial structure was the dominant factor influencing UCEs.

Agricultural Water Utilization Efficiency in China: Evaluation, Spatial Differences, and Related Factors

Agricultural water utilization efficiency (AWUE) reflects the rational utilization of water resources in agricultural production. Improving AWUE is important for both improving the levels of agricultural production and reducing consumption of water resources, and it is significant to explore the spatial differences between different cities and regions and the various factors related to AWUE, both theoretically and practically. The AWUE of totally 281 cities at the prefecture level or above in China between 2003 and 2018 was evaluated using the super-efficiency slacks-based measure (SBM). The spatial differences in AWUE were simulated by exploratory spatial data analysis (ESDA), and the various factors affecting AWUE were simulated using the graphical statistical tool, Geodetector. The results of this study are as follows: (1) The mean value of AWUE across the country was merely 0.23 when it registered a record high in 2018, indicating that the AWUE in China was low; (2) AWUE showed significant spatial differences judging from the results of ESDA, and the low-low type was the principal spatial type, which was distributed mainly in the North China Plain and the Loess Plateau; and (3) agricultural technology was the main factor affecting AWUE.