On 16 March 2014, the State Council of China launched its first urbanization planning initiative dubbed “National New Urbanization Planning (2014–2020)” (NNUP). NNUP put forward 20 urban agglomerations and a sustainable development approach aiming to transform traditional Chinese urbanization to sustainable new urbanization. This study quantitatively evaluates the level of sustainability of the present new urbanization process in 20 Chinese urban agglomerations and provides some positive suggestions for the achievement of sustainable new urbanization. A three-level index system which is based on six fundamental elements in a city and a Full Permutation Polygon Synthetic Indicator evaluation method are adopted. The results show that China is undergoing a new urbanization process with a low level of sustainability and there are many problems remaining from traditional urbanization processes. There exists a polarized phenomenon in the urbanization of 20 urban agglomerations. Based on their own development patterns, the 20 urban agglomerations can be divided into seven categories. Every category has its own development characteristics. The analyses also show that waste of water resources, abuse of land resources, and air pollution are three big problems that are closely linked to traditional Chinese urbanization processes. To achieve sustainable new urbanization in China, four relevant suggestions and comments have been provided.

Since the twentieth century, China has been facing various kinds of environmental problems. It is necessary to evaluate and analyze the ecological status of the environment over China, which is of great importance for environmental protection measures. In this article, an Eco-environmental Quality Index (EQI) model is established using national remote sensing land-use data, NDVI data from MODIS and other statistical data. The model is used to evaluate the ecological status over China during 2005, 2008 and 2010, and spatial and temporal variations in EQI are analyzed during the period 2005–2010. We also discuss important factors affecting ecological quality, with special emphasis on meteorological conditions (including rainfall and sunshine duration) and anthropogenic factors (including normalized population and gross domestic product densities). The results show that, EQIs in northwestern China are generally lower than those in the southeast of the country, presenting a ladder-like distribution. There is significant correlation between EQI, rainfall and sunshine duration. Population density and GDP also have some relation to EQI. On the whole, the environmental quality results showed little variation during 2005–2010, with national average EQIs of 54.86, 55.07 and 54.43 in 2005, 2008 and 2010, respectively. During 2005–2010, differences in EQI were observed at the local level, but those at the provincial level were small.

Fine-scale population estimation is essential in emergency response and epidemiological applications as well as urban planning and management. However, representing populations in heterogeneous urban regions with a finer resolution is a challenge. This study aims to obtain fine-scale population distribution based on 3D reconstruction of urban residential buildings with morphological operations using optical high-resolution (HR) images from the Chinese No. 3 Resources Satellite (ZY-3). Specifically, the research area was first divided into three categories when dasymetric mapping was taken into consideration. The results demonstrate that the morphological building index (MBI) yielded better results than built-up presence index (PanTex) in building detection, and the morphological shadow index (MSI) outperformed color invariant indices (CIIT) in shadow extraction and height retrieval. Building extraction and height retrieval were then combined to reconstruct 3D models and to estimate population. Final results show that this approach is effective in fine-scale population estimation, with a mean relative error of 16.46% and an overall Relative Total Absolute Error (RATE) of 0.158. This study gives significant insights into fine-scale population estimation in complicated urban landscapes, when detailed 3D information of buildings is unavailable.

Identifying urban built-up area boundaries is critical to urban data statistics, size measurement, and spatial control. However, previous methods of extracting urban built-up area boundaries based on low-resolution remote-sensing data are frequently constrained by data accuracy. In this paper, a new method for extracting urban built-up area boundaries using high-resolution remote sensing images based on scale effects is proposed. Firstly, we generate a number of different levels of edge-multiplied hexagonal vector grids. Secondly, the impervious surface densities are calculated based on the hexagonal vector grids with the longest edge. Then, the hexagonal grids with higher impervious surface densities are extracted as the built-up area of the first level. Thirdly, we gradually reduce the spatial scale of the hexagonal vector grid and repeat the extraction process based on the extracted built-up area in the previous step. Eventually, we obtain the urban built-up area boundary at the smallest scale. Plausibility checks indicate that the suggested method not only guarantees the spatial continuity of the resultant urban built-up area boundary, but also highlights the prevailing orientation of urban expansion. The extracted Beijing built-up area boundary can serve as a reference in decision-making for space planning and land-use control.

Carbon dioxide (CO₂) is the most important anthropogenic greenhouse gas contributing to global climate change. SCIAMACHY on board ENVISAT (launched in 2002) is the first satellite instrument to monitor the changes in CO₂ concentration in the lowest atmospheric layers. The temporal and spatial distribution of CO₂ (2003–2009) concentration based on SCIAMACHY over China is presented and discussed. It shows an annual increase and a seasonal cycle. The CO₂ annual growth rate was about 1.8 ppm year⁻¹, with the highest value being in spring and the lowest in autumn. The CO₂ concentration variation is determined by many complex factors. In this article, we analyse the important factors affecting CO₂ variations, with special emphasis on terrestrial ecosystems and energy consumption. Terrestrial ecosystems are an important sink in the global carbon cycle. The relationship between CO₂ concentration and Moderate Resolution Imaging Spectroradiometer (MODIS) net primary production (NPP) in 2008 is analysed. CO₂ concentration is inversely proportional to NPP both in regions with high-density vegetation and in deserts. The Yunnan province has the highest NPP value and the lowest CO₂ concentration, whereas the Takla Makan Desert has the lowest NPP value and the highest CO₂ concentration. Energy consumption is the main emission source of atmospheric CO₂. CO₂ emissions from energy consumption show a steady increase in China since 1980. China's CO₂ concentration variation shows a high correlation with energy consumption (coefficient of determination (R ²) > 0.8). The regions with high energy consumption are major industrial regions such as Shandong, Guangdong, Jiangsu, Zhejiang, Hebei, and Henan.

Water resources are a key factor for regional sustainable development. However, the published water resource data in China is based on a large geographical scale, such as watershed units, and the data cannot reflect subtle differences in water resource distribution. The paper aimed to distribute water resources of the third-order basin of China into grid-cells of 1 km × 1 km. First, we used Moran’s I index to analyze the spatial pattern of water resources of the third-order basin. Second, we constructed a spatial autocorrelation model between water resources of third-order basins and the associated factors. Third, we applied the model to simulate the gridded water resource distribution and evaluated the simulation accuracy. The results indicated that significant spatial autocorrelation existed among the water resources of third-order basins. Northern China was the low-value clustering area of water resources and Southeast China was the high-value clustering area of water resources. Slope and precipitation were the main factors that influenced the amount of water resources. The simulating accuracy of water resource distribution was very high, apart from some extremely arid regions (Gurbantunggut Desert, Kumtag Desert, and Hexi Desert). On the whole, the gridded water resource distribution map was valid and was helpful for regional water resource management.