Improving the vitality of cities has long been considered an important goal of planning. However, people’s understanding of how complex and diverse built environment factors affect urban vitality is still limited. In recent years, the emergence of new data provides a new perspective for the study of urban vitality. In this article, the spatio-temporal variation of urban vitality was quantitatively measured by using Baidu heat map, and the influence of built environment factors on urban vitality is further analyzed by geographically weighted regression model. The analysis was conducted at the block level, taking into account differences between weekdays and weekends. The results show that Shenzhen presents a vitality pattern of three centers and two sub-centers, and the average vitality level of weekdays is higher than that of weekends. Distance to subway station, road density, residential density, land mixed use, and compactness have significant influence on block vitality, but the influence varies from block to block, showing strong spatial heterogeneity. Commercial facility density and floor space ratio show significance only on weekends and weekdays, respectively. The findings reveal that we need to take regional differences into consideration and develop more targeted urban planning policies to facilitate block vitality.

Carbon emissions are closely related to global warming. More than 70% of global carbon emissions have been generated in cities. Many studies have analyzed the effects of cities on carbon emissions, from the perspective of urbanization, economics, and land use, yet a detailed understanding of the relationship between urban form and carbon emissions is lacking due to the absence of a reasonable set of urban form metrics. The aim of this research is to explore the effects of urban form on carbon emissions through empirical research. By eliminating collinearity, we established a set of urban form landscape metrics comprising Class Area (CA), Mean Perimeter–Area Ratio (PARA-MN), Mean Proximity Index (PROX-MN), and Mean Euclidian Nearest Neighbor Distance (ENN-MN) representing urban area, complexity, compactness, and centrality, respectively. Through spatial autocorrelation analysis, the results show that there is a positive spatial autocorrelation of carbon emissions. The high–high agglomeration regions are located in the Beijing–Tianjin–Hebei and Yangtze River Delta, while the low–low agglomeration regions are concentrated in the Southwest and Heilongjiang Province. Based on a spatial error model, for the whole study area, CA, PARA-MN, and ENN-MN show a positive correlation with carbon emissions, but PROX-MN is the opposite. Based on ordinary least squares, PARA-MN in the Northeast and East, PROX-MN in the North and Mid-South, and ENN-MN in the North are significantly correlated with carbon emissions. These findings are helpful for low-carbon urban planning.

China is one of the largest carbon emitting countries in the world. Numerous strategies have been considered by the Chinese government to mitigate carbon emissions in recent years. Accurate and timely estimation of spatiotemporal variations of city-level carbon emissions is of vital importance for planning of low-carbon strategies. For an assessment of the spatiotemporal variations of city-level carbon emissions in China during the periods 2000–2017, we used nighttime light data as a proxy from two sources: Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The results show that cities with low carbon emissions are located in the western and central parts of China. In contrast, cities with high carbon emissions are mainly located in the Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD). Half of the cities of China have been making efforts to reduce carbon emissions since 2012, and regional disparities among cities are steadily decreasing. Two clusters of high-emission cities located in the BTH and YRD followed two different paths of carbon emissions owing to the diverse political status and pillar industries. We conclude that carbon emissions in China have undergone a transformation to decline, but a very slow balancing between the spatial pattern of high-emission versus low-emission regions in China can be presumed.

Air pollution contributes to a large fraction of the total mortality estimated under the global burden of disease project (GBD) of the World Health Organization (WHO). This paper discusses an integrated study to obtain the spatiotemporal characteristics of particulate matter (PM₁₀ and PM_2.5) and trace gases (O₃, SO₂, NO₂, and CO) pollutants in Hangzhou City (China) for the years 2014–2016. Our detailed analysis shows a relationship between air pollutants and land-use/land-cover change. Air quality parameters (PM_2.5 and PM₁₀) and trace gases (SO₂, NO₂, and CO) show strong monthly variations in the months of January (higher values) and July (lower values). During monsoon and summer seasons, air quality and trace gases show low values, whereas ozone (O₃) is higher in the summer and lower in the winter. The spatial distribution of air pollutants is retrieved using the kriging method at the monitoring sites in Hangzhou City. We have considered normalized difference vegetation index (NDVI) and land surface temperature (LST) from the Landsat 8 data. The correlation between air pollutants and land use at the street-town unit suggests that areas with low NDVI, high road density, large built-up density, and LST are consistent with high concentrations of particulate matter and SO₂, NO₂, and CO. Among the trace gases, NO₂ is found to be the most sensitive element affected by land use patterns, and O₃ shows weak correlation with land use. SO₂ shows a strong positive correlation with road density and LST, whereas CO shows positive correlation with the built-up density, LST, and population density.

PM_2.5 is a major component of air pollution in China and has a serious threat to public health. It is very important to quantify spatial characteristics of the health effects caused by outdoor PM_2.5 exposure. This study analyzed the spatial distribution of PM_2.5 concentration (45.9 μg/m³ national average in 2016) and premature mortality attributed to PM_2.5 in cities at the prefectural level and above in China in 2016. Using the Global Exposure Mortality Model (GEMM), the total premature mortality in China was estimated to be 1.55 million persons, and the per capita mortality was 11.2 per 10,000 persons in the year 2016, resulting in higher estimates compared to the integrated exposure‐response model. We assessed the premature mortality attributed to PM_2.5 through common diseases, including ischemic heart disease (IHD), cerebrovascular disease (CEV), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and lower respiratory infections (LRI). The premature mortality due to IHD and CEV accounted for 68.5% of the total mortality, and the per capita mortality (per 10,000 persons) for all ages due to IHD was 3.86, the highest among diseases. For the spatial distribution of disease‐specific premature mortality, the top two highest absolute numbers of premature mortality associated with IHD, CEV, LC, and LRI, respectively, were found in Chongqing and Beijing. In 338 cities of China, we have found a significant positive spatial autocorrelation of per capita premature mortality, indicating the necessity of coordinated regional governance for an efficient control of PM_2.5.