This paper studies whether and how market segmentation affects exports from cities of China. Some existing research found that market segmentation promotes the export performance of firms in China, which is against what the New Economic Geography theories would predict. Although explanations were provided, these studies did not distinguish between short-term and long-run effects of market segmentation on exporting firms. Neither did they identify the mechanism of such impact. This paper tries to fill this gap. Based on the Chinese Industrial Enterprises Database, we use Heckman models to analyze the micro impacts of market segmentation on firms in 2007. The regression results of Heckman selection models show that market segmentation affects firms' behavior in two ways. Firstly, severe market segmentation reduces the scale of domestic market available to firms. As a result, firms enter the international market as a complementary market to pursue economy of scale. Secondly, market segmentation also restrains firms from specialisation because of the high cost of transporting intermediate goods between different areas. The impacts of these two pathways lead to the result that market segmentation increases the probability of firms' decision to export in the export decision-making stage to pursue economy of scale. After these firms are forced to enter the international market, however, their productivity and competitiveness decrease because they give up specialised production and lose the broad domestic market and are unable to benefit from the economy of scale. The characteristics of exports emerge from the microscopic analysis of firms' exporting behaviors. Market segmentation promotes exports of cities in the short term by increasing the number of exporting firms of a city. However, in the long run, market segmentation decreases the competitive of exporting firms. That is, the effect of market division policy is unsustainable. In addition, the impacts of market segmentation depend on enterprise ownership and geographic locations. The impacts are larger for state-owned enterprises and private enterprises, implying that foreign-owned enterprises hold super-national treatments. For firms in the central part of China, the impacts of market segmentation decrease when the distance from the city where the firms are located to the surrounding cities decreases. This phenomenon does not exist in the eastern and western regions. The role geographic location plays differs between regions, but there is little difference within the groups in eastern and western China. In eastern China, the transportation infrastructure is well developed, so transportation conditions do not seem to contribute significantly to creating market segmentation and therefore improving transport does not help decrease market segmentation. In western China, political barrier may have acted as the main reason causing market segmentation. Thus, improving transportation conditions, which are equally poor across the region, may not help as much as removing political barriers for reducing market segmentation. Although market segmentation promotes export of cities in the short term, it hurts export of firms in the long run. We recommend local governments make efforts to decrease domestic market segmentation to create a healthy environment for exporting firms. Local governments may open markets to neighboring provinces, encourage firms to balance between focusing on domestic and foreign markets, and improve transportation conditions. For cities in western China, it is necessary to take all these measures. In central China, it is more important to build high-quality roads and decrease transport costs.

Urbanization and employment are both key issues in China's current socioeconomic development; it is of practical significance to fully understand the interaction between these two key aspects of development for the purpose of facilitating a healthy and orderly transition of the Chinese economy and society. Using the Chenery model, existing studies have explored the interrelationship between urbanization level and non-agricultural employment in China but relatively little attention was paid to the issue of data quality. Therefore, based on a thorough understanding of statistic caliber changes in China's urban population and employment statistics, this paper used the data from the fifth census, the sixth census and relevant statistical yearbooks and employs the Moran's I index to investigate the spatial characteristics of urbanization and non-agricultural employment growth of prefecture- level administrative divisions of China; it also uses the geographically weighted regression (GWR) method to analyze the relationship between these two factors. The main conclusions are as follows. (1) Both China's urbanization and non- agricultural employment experienced considerable development during 2000- 2010, with urban population and non- agricultural employment increased by 13.46% and 14.18%, respectively; the growth was mainly distributed in the southeastern part of the country, with megacities leading the absolute growth and cities around megacities showing a faster relative growth. (2) The Moran's I index reveals that the spatial distribution of China's urbanization and non-agricultural employment growth show a polarized trend, and they have similar spatial distribution patterns; three regions had both high urbanization rate and high non-agricultural employment growth: the Yangtze River Delta, the Pearl River Delta and the Beijing-Tianjin-Hebei Area. Meanwhile, Tibet, Qinghai Province, Gansu Province, western Inner Mongolia and parts of Northeast China had both low urbanization rate and low non-agricultural employment development. (3) There was a significant positive spatial autocorrelation in China's urbanization and non-agricultural employment growth and spatial econometric models are needed to analyze their relationship. GWR was employed to reflect the regional differences of non- agricultural employment growth in promoting urbanization. By classifying the correlation coefficient the types of relationship between urbanization and non-agricultural employment growth of different regions can be determined. Urbanization and non-agricultural employment development are well coordinated in most parts of China. But Qinghai Province, the eastern part of Gansu Province and central Sichuan Basin show a trend of under- urbanization; Chongqing Municipality and its surrounding areas, Xinjiang and some provincial capital cities show a trend of over-urbanization. Thus, local governments should adopt different development policies that are in line with local conditions in promoting the development of urbanization and employment growth.

The city of Guangzhou has proposed an urban spatial development strategy characterized by "advancing to the east, connecting with the west, expanding to the south, and optimizing in the north" in 2000. The initial target of this strategy was to form an urban spatial pattern of "two centers and four towns" in the city of Guangzhou by 2010. Later "adjusting in the central area" was added to the strategy to further optimize the layout of urban development, and to form a multi-center, clustering, and network type of urban spatial architecture by 2020.The implementation of this urban spatial development objective and the strategic adjustment require the essential support provided by the urban transport system, which demands higher quality development of the system. Urban transport infrastructure construction and the layout not only reflect the level of development of the city, but also are the important indicator and object of study and investigation of the differences in urban accessibility. With the development of urban transport in Guangzhou, especially the urban rail rapid transit (metro) network development in recent years, urban transport accessibility of the city has undergone significant changes, and also has an essential impact on and plays a key role in the structure and layout of urban spatial extension. In this context, this paper applies a grid-based partition method in a GIS (Geography Information System) environment to calculate the transportation infrastructure densities (including road density and bus line density) and analyze their spatial distribution. Moreover, this study measures the urban rail rapid transit network time-based accessibility based on matrix analysis and comprehensively investigates urban accessibility and its spatial characteristics in Guangzhou. The results show that the spatial pattern of urban accessibility in the city of Guangzhou is characterized by a concentric ring structure radiating outwards from the core area of the city. There are large regional variations between different areas in urban accessibility and the urban spatial pattern and structure of the city should be further improved and optimized. On the basis of multiple indicators, including time-based accessibility indices at each metro station and regional urban transport infrastructure densities, the study area is divided into different zones and the relationship between location and transportation accessibility is explored. At last, this paper integrates the spatial distributions and differences of urban public, commercial and residential facilities to further examine the characteristics and differentiation of urban spatial pattern in the city of Guangzhou, in order to provide scientific supports for the urban construction and spatial development.

Ratio of green space area and per capita green space are often used to measure the greening degree of cities in urban planning. Such indices can reflect the general characteristics of urban green space but fail to reveal the actual situation of resources allocation. Previous studies suggest that evaluating green space accessibility is a feasible way to solve the problem. However, the current methods of green space accessibility analysis have paid little attention to supply and demand relationship between green space and residents as well as the carrying capacity of green spaces, which made these methods less useful in practice. In this study, the Gaussian based 2-step floating catchment area (2SFCA) method, which can overcome these drawbacks, was employed to analyze the green space accessibility of Shenyang. The paper discusses a new method for green space accessibility assessment. Meanwhile, it offers an in-depth knowledge of green space of Shenyang. The spatial pattern of green space accessibility of Shenyang shows a clear declining trend from north to south and from east to west. Four high-value centers and three low-value areas can be recognized. The high-value areas are mainly located in the periphery of the core urban area, and the low-value areas are mainly distributed in the far southern and western suburb of Shenyang. It is also found that the spatial pattern of green space accessibility shows strong polarization characteristics, and more than 70% of the streets have lower green space accessibility than the average level around the city, while only a few streets exhibit higher accessibility. This phenomenon is attributed to the spatial mismatch between green space and population. Four recommendations are made for promoting the equal provision of green space services: (1) Enhance the quantity and quality of the green spaces in the core urban area. (2) Make scientific planning for the new development areas, to achieve the goal of equal accessibility of green space service and realize a coordinated development of population, transport, and green space. (3) Emphasize the people-oriented principle, enhance the aesthetics value of green space, public participation in their development and accessibility by considering the comments and suggestions from the public. (4) Stress the construction of green belt along the river, large public green space and roadside green space, create a balanced layout of small green spaces and improve the connection between these green spaces, to build a green space network comprised of points, belts and areas. The paper also discusses the drawbacks and improvements of the Gaussian based 2SFCA method. First, it is unable to create an accessibility surface because the result is expressed as values at the center points of streets and these values are discrete. Kriging spatial interpolation can be used to address this issue. Second, in this study size is used as the single indicator for the service ability of each green space, but factors determining the attractiveness of individual green space are much more complex and therefore more indicators need to be included in future studies. Third, the user group of green space is generalized in this model without considering the differences in needs, preference, and behavior between population groups. Green space accessibility for different age, ethnic, income and occupation groups should be assessed separately in future studies. Overall, the Gaussian based 2SFCA method is an effective method for evaluating the accessibility of the urban green space in Shenyang.

Location of urban housing directly affects housing price. Choice of housing involves considerations of various public service facilities such as schools, job accessibilities, among many others, which have been widely discussed in existing literature. In this paper, we explore the spatial correlation of landscape accessibility with housing price in Beijing. Based on ArcGIS spatial analysis method and geographically weighted regression model, this paper examines the spatial heterogeneity and the main determinants of the second-hand housing prices in the urban area of Beijing. Through major real estate dealer websites, we collected the second-hand housing data on prices in January 2012 for downtown Beijing, with a total number of 3174 samples. After establishing the housing spatial database, spatial interpolation and kernel density estimation are applied to explore the spatial distribution and heterogeneity of housing price. The kernel density map shows that the residential space in downtown Beijing has evident agglomeration characteristics in general, that is, density decreases gradually from Tian'anmen Square to the periphery. High density also occurs at sub-centers formed near the subway transfer stations, and the sub-centers in Shijingshan and Tongzhou have begun to take shape. With the help of spatial interpolation analysis in ArcGIS, we mapped the spatial pattern of housing price in Beijing. It can be clearly seen from the result that housing price also decreases from city center to the periphery, which is similar to the spatial pattern of housing density. Housing price reaches the peak within the Second Ring Road, with some high price sub-centers emerge between the 3rd and the 4th Ring Road or at the outer suburban districts along the subway lines. Finally, by using geographically weighted regression model, we analyzed the influencing factors of housing price, including traffic factors, locational features, maintenance cost and landscape accessibility (green space coverage, distance to the nearest lake or river, distance to the nearest mountain) and so on. The results show that the distance to sub-centers has the most significant impact on housing price, and there is a certain degree of correlation between landscape accessibility and housing price. Specifically, houses with high greening rate and those located near a mountain is much more expensive; due to the poor water quality, waterscape has a negative impact on housing price; sewage treatment plants, burial grounds and other sources of pollution also exert negative impact on housing price. People prefer houses far from sources of pollution and near pleasant landscape features; low plot ratio and high green space coverage are also favored. The spatial correlation analysis of landscape accessibility and residential housing prices provides a foundation for the planning of urban residential space and references for the planning and management departments of the city government.

As an important part of city traffic, rail traffic has a profound impact on city space structure and resident travel behavior. Urban metro plays a critical role in the normal operation of the whole city system, attracting all sorts of city agglomeration in economic activities in surrounding areas, affecting the formation of agglomeration of different production and living space and to some extent, the development direction and landscape of a city. Metro construction also exerts a far-reaching influence on city spatial structure reorganization and distribution of industries. The relationship between metro and industrial agglomeration has become an urgent research question for scholars. This paper takes the business office space of Beijing urban area as an example for the study of such relationship. Using the GIS spatial analysis method this study examines the influence of urban metro transfer station on business office space agglomeration and distribution pattern of office space. It further analyzes the different stages of rail transportation development, and the dynamic change of Beijing urban business office concentration level and causes. The results show that: (1) There is a significant difference in business building density within different radius of metro transfer stations. Office building density is highest within 500 m from these stations, at 237.18/km2 and decreases to 118.40/km2 within 1500 m radius. (2) New office buildings tend to concentrate around the transfer sites: 54.3% of the new buildings are found within 1500 m from these stations. (3) With an increase of the range of influence of transfer stations, the number of high grade office agglomerations also increase. The most attractive zone for office activities is between 800~1500 m from transfer stations. (4) Dynamic analysis of Beijing metro transfer stations and office cluster scale before and after 2008 indicates that there is a clear difference between 800 and 1500 m radius for business office agglomeration degree; this is particularly true for the high-end office agglomerations. Some new transfer stations located in the concentrated area of existing business office buildings, such as the China World Trade Center station in the CBD core area and the Haidian Huangzhuang station in Zhongguancun, upgraded the original grade of existing office clusters and expanded their scope to a certain extent. (5) Functional properties of metro transfer stations influence the formation of office clusters. However, historical inertia, distance attenuation, external effects and dominant functions are the main factors that influence the numbers and grades of office clusters within different radius of metro transfer stations. Further studies should analyze functional properties of rail transit lines and transfer stations and their range of influence, define the functions and classes of metro lines and transfer stations, in order to guide the planning of residential and office space development along these lines and around these stations, and provide a reference for the construction of urban landscape corridors. In addition, in-depth investigation on the impact of urban metro on office space diffusion should be conducted as well.

Under the background of globalization and information economy, innovation has become the key factor for economic development of countries, regions and cities. Innovation is becoming a primary strategic choice for development of all countries. Because city is the main site where innovation takes place, urban innovation becomes a new research focus. Heilongjiang Province, as an old industrial base of the country, has abundant technology stocks. But due to the impact of the planned economy, its regional innovation environment is not very favorable and technological advantage cannot be used adequately. This paper analyzes the structure and development of urban innovation capability in Heilongjiang Province. It first built an urban innovation capability evaluation index system with four indicators (knowledge innovation capacity, technology innovation capacity, government support and service capacity, and innovation environment) and 24 variables and normalized the raw data and determined the weight of each variable and indicator. Using the weighted sum method, it calculated innovation capacity of each city, and analyzed the structure of innovation capacity. Then the paper analyzed the process of innovation capacity development in recent 10 years and the differences of innovation capability among 12 cities in Heilongjiang Province. The main results are as follows. (1) Innovation capacity of Harbin is much higher than other cities. The cities are divided into three categories according to the level of innovation capacity with the method of integrated cluster analysis. Harbin is in the first group; Daqing, Qiqihar and Mudanjiang are in the second group; and the others are in the third group. High values of innovation capacity occur in the Hadaqi (Harbin- Daqing-Qiqihar) industrial corridor. (2) Absolute differences among the cities of Heilongjiang Province are very large. By calculating the correlation coefficient of the 24 variables and urban innovation capability, an explanatory variable under each indicator, which has the highest correlation with urban innovation capability, is selected to represent the evaluation criterion for absolute differences among cities. The absolute differences among 12 cities as reflected by the four evaluation criteria are very large. Harbin's number of research and development staff is 155 times of Heihe's. (3) The pattern of urban innovation capability in Heilongjiang Province was stable over recent 10 years. The innovation capability of coal mining cities, Jixi, Qitaihe and Shuangyashan is declining. The main reason for this trend is its industrial structure that relies heavily on resource exploitation and lacks diversification of local economy and alternative industries. The regional differences and spatial polarization of urban innovation capability tend to increase. Especially since the state implemented the Strategy of Revitalizing Northeast China and Other Old Industrial Bases in 2003, such spatial differentiation accelerated. The government should not only pay attention to increasing the innovation capability but also coordinate the development within the region. Finally, this paper analyzed the problems in improving urban innovation capability of the major cities in Heilongjiang Province and put forward some policy recommendations. In the process of upgrading the cities' innovation capacity, the old industrial cities should build rational industrial innovation strategy according to the cities' own development characteristics; emphasize the importance of enterprises in the process of innovation and encourage them to improve technological innovation capability; strengthen the awareness for innovation; break barriers in the traditional planning system; and attract high quality human resources.