Differences among regional industries in scales are the results and manifests of competition and cooperation among the industries, and the competition-and-cooperation relationships are based on the commercial activities governed by the basic supply-and-demand theory. Multi-directional supply-and-demand relationships among the various industries constitute complex networks of commercial activities. There are two common themes in the previous researches on industrial networks. First, they focus on the characteristics of entire network involving all industries and the general statistical characteristics of multi-dimensional network topology. Second, although there are several studies on statuses and roles of different industries (nodes) in the entire network, there is no study on the intrinsic relationships between the status and properties, or the role and properties, of each industry (node) in the network. In other words, it is important to study the intrinsic relationship between the structure of an individual industry's network and its effects on the scale of the industry (node). By using the data of supply-and-demand chart in 2002, we constructed a 42×42 two value (0, 1) symmetric industrial linkage/ transaction network, and analyzed the network's structure characteristics and its external effects. The results are shown as follows. (1) There are eight major suppliers or demanders (industries) of each industry in the network, and they are significantly different from one another. For example, electric heat product and supply industry, chemical industry, construction, transportation, leasing and business services, get the maximum numbers of trading linkages in the market. These industries are the foundation of the regional economy. On the contrary, waste product, garment, leather, down and their manufacturing industry, metal and non-metal mining and selecting, wood processing and furniture manufacturing, gas product and supply industry, petroleum and natural gas industry, each have only one industry as a trading partner. These industries are mostly local industries. In addition, each industry's linkage/trading network is in linear distribution, and the scale of the network is in exponential distribution. (2) Eight industries, for example, waste product and garment, leather, down and their manufactured goods, mining and selecting of metal ore product and nonmetal ore product, are under the highest degree of constraint by the network. Chemical industry, construction, electric heat production and supply industry, transportation, leasing and business services, financial insurance industry, are under the lowest degree of constraint by the network. The constraint on each industry reduces as the scale of the network expands. (3) The competition structures of the industrial networks can be divided into three groups: with strong constraint, imbalance and small scale network; with medium constraint, medium balance, medium scale network; and with minimum constraint, balance, and large scale network, each of which has 13, 18, and 8 industries, respectively. The first one's trading partner industries and constraining markets are minimal, and distribution of industrial trading is concentrated. The second one's scale of trading network is relatively large, trading and constraint distribution are relatively balanced, and constraint market is relatively higher. The last one's suppliers or requirements is the highest, constraint value is minimum, and the coefficient of constraint hierarchy is small. (4) Degree of network constraint, constraint hierarchy coefficient and manufacturing virtual dummy variables directly have negative impact on the industry's scale. The trading constraints and manufacturing virtual dummy variables have the most significant effects on the industry, and the former affects more than the later. Network trading constraints have more absolute effect on manufacturing than non-manufacturing industries.

Since the reform and opening-up policies first started, China has experienced rapid economic growth. From 1979 to 2011, average annual growth for China's GDP is nearly 10 percent. Economic growth is always the focus of attention of the general public. Economic theories emphasize that the quality and quantities of the input factors affect economic growth. With the development of economic geography, the new economic geography, represented by Krugman, provides a new direction for the study of economic growth. Under the framework of imperfect competition and increasing returns to scale, new economic geography takes the geographic factors into the mainstream economics to explain the phenomenon of spatial agglomeration and diffusion. At present, China is in a special period of economic transition. On the one hand, due to the tax reform, administrative decentralization and the economic performance evaluation system for local officials by the central government, market segmentation in China is serious, manifested by regional malignant competition, redundant construction and local protection. On the other hand, in recent years, China promulgated a number of regional development plans to coordinate the regional development, and urban agglomeration has become the leading area of economic development. Why market segmentation exists in some regions, but regional integration exists in some other regions? Considering the differences in market segmentation and the differences in economic growth, we can't help but wonder how market segmentation affects economic growth and whether the relationship between the two changes from region to region, and from industry to industry. Solving these problems is of great significance to the current situation of China's economic growth. In this paper, based on the actual situation of China's regional economic development, we study the impact of market segmentation, characteristic of China's economic transition period, on economic growth.We focus on the differences among the regions and industries and take the manufacturing industry as the breakthrough point. In the frame of new growth theory and new economic geography, this paper constructs a linear model for the impact of market segmentation on economic growth. And from different geographic scales, the market segmentation is divided into international market segmentation, domestic market segmentation and geographic segmentation. Our study resulted in a panel data model based on manufacturing industries data from 2003 to 2009. By estimating random effects, the results were robust to prove that: (1) international market segmentation and geographic segmentation have significant impact on the growth of manufacturing industry in China. (2) In terms of regional differences, the growth of manufacturing industry is more sensitive to international market segmentation and geographic location segmentation in Eastern China, more sensitive to geographic location segmentation and topography segmentation in Central China, and more sensitive to geographic topography segmentation in Western China, respectively. (3) As far as industrial differences are concerned, international market segmentation, geographic topography segmentation and domestic market segmentation each has significant effect on labor intensive industry, capital intensive industry and technology intensive industry, respectively. Thus, for the specific type of region or industry, reducing the specific type of market segmentation is of great importance for the economic development in the future.

Road transport is an important foundation for the development of a modern economy in accelerating the velocity factor of production, and plays an important role in promoting regional integration process. Thus transport efficiency has become an important index for road transport service evaluation. With the implementation of the sustainable strategy, China's economic growth will undergo a transition from extensive to intensive. It will require more efficient road transport and an optimized development mode. To make objective, fair and accurate comprehensive evaluation, different aspects of road transport efficiency during a certain period of operation are examined, including investment in highway facilities, transportation facilities, output of highway passenger, and efficiency of implementing comprehensive measurements of the functions of cargo transportation. High performance of highway transportation system means finding root causes of a potential problem in a timely fashion, facilitating quick solutions, and putting forward countermeasures. This article introduces undesirable outputs to build a road transport efficiency evaluation model, which uses the kilometers of highways, number of road transport employees, number of vehicles owned by road transport operators, and energy consumption by road transport as the indicators of investment made by provinces in road transport, uses the amounts of cargo and passenger turnovers in the scales of provinces and the municipalities directly under the central government as the indicators of desired output, and considers negative externalities of road transport output, such as road congestion, environmental pollution, traffic accidents, ecological damage as the results of undesirable outputs. In this article, using SBM-Undesirable model, we attempted to evaluate road transport efficiency from 1997 to 2010, describe the changes of road transport efficiency from a time perspective, and find ways to overcome the road transport inefficiencies. The results showed that: (1) The overall level of road transport efficiency in China is low, and it showed a fluctuating downward trend between 1997 and 2010, especially more prominent in 2008. (2) The negative external effects of road transport output reduce the overall level of efficiency. The change of pure technological efficiency is the main factor that affects the changes of overall road transport efficiency. (3) There are significant regional differences in road transport efficiency in China. Road transport efficiency in eastern region with higher level of economic development is also higher, while the transport efficiency in mid-west region with lower level of economic development is lower, and this difference shows a growing trend. (4) From the spatial perspective, the equilibrium of road transport efficiency in China tends to decrease, and it is gradually changing from relatively balanced state to unbalanced state, even to a polarized state in recent years. (5) The important approaches that could improve transport efficiency consist of improving resource utilization efficiency, optimizing resource allocation capability, reducing the negative external effects of output, and increasing the effective output of transport. In this article, due to limited availability of relevant data, the negative external effects of outputs only include road transport accidents and road transport carbon dioxide emissions for analysis and evaluation. In addition, the mechanism of changes of road transport efficiency is unclear.

With the development of network science, many scholars abroad begin to focus on the research of centrality of traffic network based on MCA(Multiple Centrality Assessment) and its relationship with economic activities. Centrality of traffic network is calibrated in a MCA model composed of multiple measures such as closeness, betweenness, and straightness. MCA model is a very important indicator that measures the rate of land development and utilization, and is widely used both in the theoretical and empirical inquiries. In this paper, by using the tools developed by MIT to calculate centrality of traffic network and its relationship with economic activities precisely and efficiently, we investigated the geography of three centralities of traffic network and their correlations with economic density of tertiary industry in Shenyang City, and then applied the KDE method to both centralities of traffic network and economic density to examine the correlations between them. Since economic density is regional data based on subdistricts, we created fishnet in ArcGIS and then did spatial interpolation. The results indicated that centralities of traffic network are correlated with the spatial distribution of economic density of tertiary industry in Shenyang. Spatial distribution of economic activity density correlates highly with the betweenness of traffic network, which means that the multiple centers of the streets lead to multiple centralities of economic activities. But we found that only betweenness and straightness show clear multi-centricity. Closeness, however, just has single centrality. This also means closeness has less impact on economic activities than betweenness and straightness. The major contributions made by this research can be summarized as follows: (1) Improving overall understanding of the spatial distribution of street centralities in Shenyang, which can be one of the most powerful determinants for urban planners and designers to understand how a city works and to decide where renovation and redevelopment need to be placed, to guide economic layout. (2) The concept that central urban arterials should be conceived as the cores, not the borders, of neighborhoods has the importance of directing in the theory and practice of city planning. (3) By drawing lessons from foreign research experiences, this research can enrich the theory, methods, and practice of the street network centrality in our country. If we take into account the relative properties of different street grades and types, vehicle flow rate and capacity, one-way or two-way streets, and so on, and give them appropriate weights based on their properties, the results will be more actual and practical and can help us understand the centrality of traffic network and its relationship with economic activities more precisely. More works need be done in order to study centrality of traffic network and its relationship with economic activities more comprehensively: (1) By looking into centrality of traffic network and its relationship with every kind of economic activity, we can get clear dependent relationship between centrality of traffic network and economic activities profoundly, and better understand the different relationships between them. (2) If we can get different attributes of every traffic level and use them as weights when we do KDE analysis, the research results will be much more practical.

Based on the transformation of spatial research and the limitation of the economy of an administrative region, the linkage and cooperation between different administrative regions have become an important strategy for spatial governance of the interregional relationships. Cooperation has great influences in many regions of Europe, East Asia and North America. Different ways of cooperation are adopted in different regions as regional cooperation is sensitive to space scale. In Europe, as an example, the development of interregional cooperation is going through three stages in Euregios, Cross-border Regions and Cross-border Polycentric Metropolitan Region, respectively. Currently in the cross-border regions in Europe, money and resources have been already ensured for the improvement of the development. Interregional cooperation in North America shows a trend of industrial accumulation near the American borders both in Canada and Mexico. The cooperation in East Asia shows diversified trends to enlarge the economic influences. Spatial structure has specific influences and guidance on the urbanization and production effect flow. The research on spatial structure is divided into four parts: traditional pattern, activity pattern, structure pattern and information pattern. The method to assess the cooperation is based on economy and space. In economy part, border effect is the main research method while ESDA and DEA are the related research method. In space part, planning and governance modes are the main research method. The research method of city spatial structure studies city structure, space influence range, city potential mode and internal space structure, by using different space research methods. Based on related researches on regional cooperation and spatial structure worldwide, this paper attempts to review the thoughts and methodologies in the research on the development of the cooperation and its spatial effect. The focus of space research has changed from stable space to space flow. The research framework is based on the space economy, assuming that everyone is rational and conforms to economy maximum criterion. The research can be divided into four major parts: city's driving mechanism, cooperation, spatial effect and composite structure system. The first part discusses the components of city character, city activities and personal activities. The second part discusses the type, intensity and mode of cooperation. The third part discusses city form and city network. The fourth part discusses the structure cycle and special components. The results show that empirical research is dominant but the system is still lacking in China due to lack of data of relative flow between different administrative regions. The main characters for the research are traditional theories on location and transformation, and the systematic research is needed to improve the related researches on the regional characteristics. The research in China is divided into three topics: regional cooperation, governmental commission and single governmental dominance. The systematic research on regional cooperation's spatial effect can help recognize real developmental conditions and provide a theoretical basis for city cooperation and transformation.