This paper illustrates the spatial variations in urban resource and environmental efficiency （REE） amongst 285 cities in China using a Data Envelopment Analysis （DEA） model, and examines the factors that have had the greatest effect on this spatial pattern by regression models. The results gave an average urban REE of 0.6381, and an average pure technical efifciency （PTE） and scale efifciency （SE） of 0.6964 and 0.9225, respectively. The results support the existence of a U-shaped relationship between REE and income level, which means that an increase in urban GDP does not result in an equivalent increase in environmental efficiency. Economic growth affects REE in three ways： scale effects （population scale and urbanization rate）; composition effects; and spatial effects. Improvements in urban resource use and environmental efifciency depend upon both technological innovation and effective governance. Policies designed to achieve these improvements should therefore be implemented at al levels of government and local enterprise.
Using datasets on high-tech industries in Beijing as empirical studies, this paper attempts to interpret spatial shift of high-tech manufacturing firms and to examine the main determinants that have had the greatest effect on this spatial evolution. We aimed at merging these two aspects by using firm level databases in 1996 and 2010. To explain spatial change of the high-tech firms in Beijing, the Kernel density estimation method was used for hotspot analysis and detection by comparing their locations in 1996 and 2010, through which spatial features and their temporal changes could be approximately plotted. Furthermore, to provide quantitative results, Ripley′s K-function was used as an instrument to reveal spatial shift and the dispersion distance of high-tech manufacturing firms in Beijing. By employing a negative binominal regression model, we evaluated the main determinants that have significantly affected the spatial evolution of high-tech manufacturing firms and compared differential influence of these locational factors on overall high-tech firms and each sub-sectors. The empirical analysis shows that high-tech industries in Beijing, in general, have evident agglomeration characteristics, and that the hotspot has shifted from the central city to suburban areas. In combination with the Ripley index, this study concludes that high-tech firms are now more scattered in metropolitan areas of Beijing as compared with 1996. The results of regression model indicate that the firms′ locational decisions are significantly influenced by the spatial planning and regulation policies of the municipal government. In addition, market processes involving transportation accessibility and agglomeration economy have been found to be important in explaining the dynamics of locational variation of high-tech manufacturing firms in Beijing. Research into how markets and the government interact to determine the location of high-tech manufacturing production will be helpful for policymakers to enact effective policies toward a m
