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With the rapid development of urban economy and the continuous expansion of urban scale, the limitations of urban carrying capacity begin to appear. For the sustainable development of the city, more and more scholars are paying attention to the research onurban carrying capacity. Basedon the continuous research of the authors’ research group over the past ten years, this paper uses a multiscale geographically weighted regression model and method to explore the impact of geographical location, floor area ratio, public transportation, residents’ consumption level, the density of high-tech enterprises, and the ecological environment on the carrying capacity of the Shanghai metropolis. The results show that (1) the impact of geographical location on the bearing capacity decreases from downtown to the outer areas and from the northeastern area to the southwestern area of Shanghai. (2) On the whole, the elasticity of the average floor area ratio to the urban carrying capacity is 0.52%. In different regions, most of the central urban areas have exceeded the optimal average plot ratio. With an increase in the average plot ratio, the urban carrying capacity presents a downward trend. Other sample areas generally did not reach the average optimal plot ratio, especially the southwestern area of Shanghai. With an increase in the average plot ratio, the urban carrying capacity of this area improved significantly. (3) The elasticity of public transportation convenience to the urban carrying capacity is 0.23%; that is, the average increase in the urban carrying capacity is 0.23% for every 1% increase in public transportation convenience. The elasticity of residents’ consumption level is −0.18%; in other words, every 1% increase in residents’ consumption level will reduce the urban carrying capacity by 0.18% on average. The elasticity of the density of high-tech enterprises is 0.08%; hence, when the density of high-tech enterprises increases by 1%, the urban carrying capacity increases by 0.08% on average. Lastly, the elasticity of the eco-environmental status index is 0.17%; that is, every 1% increase in the eco-environmental status index increases the urban carrying capacity by 0.17% on average.

The "comparative attitude" of urban agglomerations involves multidimensional perspectives such as infrastructure, ecological protection, and air pollution. Based on monitoring station data, comparative studies of multispatial, multitimescale and multiemission pollution sources of air quality on 19 urban agglomerations during the 13th Five-Year Plan period in China were explored by mathematical statistics. The comparison results are all visualized and show that clean air days gradually increased and occurred mainly in summer, especially in South and Southwest China. PM2.5, PM10 and O3 were still the main primary pollutants. PM2.5 is mainly concentrated in December, January and February, and PM10 is mainly concentrated in October–November and March–April. The O3 pollution in the Pearl River Delta and Beibu Gulf urban agglomerations located in the south is mainly concentrated from August to November, which is different from others from May to September. Second, from 2015 to 2019, the increasing rate of O3 concentration in any hour is higher than that of particulate matter (PM). Diurnal trends in O3 concentration in all directions also showed a single peak, with the largest increments that appeared between 13:00 and 16:00, while the spatial distribution of this peak was significantly regional, earlier in the east but later in the west. Third, this analysis indicated that the annual average air quality index (AQI) showed a gradually decreasing trend outward, taking the Central Plain urban agglomeration as the center. The ambient air pollutants are gradually moving southward and mainly concentrated in the Central Plains urban agglomeration from 2015 to 2019. Furthermore, in each urban agglomeration, the cumulative emission of PM2.5 is consisted of the four average emissions, which is approximately 2.5 times of that of PM10, and industries are the main sources of PM2.5, PM10 and VOCs (volatile organic compounds). VOCs and NOX increased in half of the urban agglomerations, which are the reasons for the increase in ozone pollution. The outcomes of this study will provide targeted insights on pollution prevention in urban agglomerations in the future.

Rural residential concentration was one of the important tasks of the “Three Concentrations” strategy implemented in the suburbs of Shanghai in the mid-1990s. The aims of this paper are to comprehensively evaluate the process, pattern and effects of residential concentration in the suburbs of Shanghai over the past 20 years, clarify the direction and focus of development, and propose suggestions for existing deficiencies. Based on remote sensing images and statistical data, the implementation and effects of the rural residential concentration strategy from 1990 to 2015 were analysed using landscape indexes and geospatial analysis. The results are as follows: (1) according to the changes in the landscape pattern and spatial structure, the trends in population concentration in the suburbs of Shanghai are obvious. (2) Before 1995, the trend of population diffusion was conspicuous. After 1995, the period of population diffusion gradually shifted to a period of population agglomeration. The rate of population concentration increased rapidly from 2000 to 2010 and then became moderate after 2010. (3) In 1990, most of the rural residential areas were distributed within 14–52km of the city centre, the distribution of residential area in each ring was relatively uniform, and the overall distribution was scattered and uniform. By 2015, the rural population gradually converged in the inner suburbs, and the centralized distribution gradually changed to within 16–32km of the city centre. (4) In 1990, most of the rural residential areas were located north-northwest, southeast, and southwest of the People’s Square. By 2015, the areas southwest and southeast of the People’s Square became the focus of rural residential distribution. These findings provide a useful reference for future rural planning and construction.

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