The Agglomeration Mechanism of Network Emerging E-commerce Industry Based on Social Science

Network emerging e-commerce refers to the development of wireless broadband technology, smart terminal technology, near-field network, etc. as the driving force. It is the emerging e-commerce represented by the continuous development of modern e-commerce and the integration of commerce. This paper proposes to use Michael Porter’s cluster theory method, income increasing algorithm, and spatial Gini coefficient method to sort out and analyze the research results of industrial agglomeration problems, further study the relationship of e-commerce industry agglomeration mechanism, and build agglomeration simulation model , the construction of the centripetal force model of the industrial agglomeration area, through the analysis of the production factors of the e-commerce industry, and then study the influence of each factor on the development of the e-commerce industry. Finally, this paper selects and uses 16 standard mechanical data sets to investigate and analyze the agglomeration mechanism of the e-commerce industry, which verifies the accuracy and overall applicability of the method.

Study on the Non-linear Effect of Tourism Industry Agglomeration on Economic Growth in China——Based on Inter-provincial Panel Data

Objectives: There are few studies on the non-linear effect of tourism industry agglomeration on economic growth. Based on this, this paper uses the panel data of provinces in 2007-2017 to analyze the spatial characteristics of China's tourism industry agglomeration, and uses the threshold regression model to analyze the role of China's tourism industry agglomeration in promoting economic growth. The results show that: China's tourism industry shows obvious characteristics of spatial agglomeration. The provinces with high degree of industrial agglomeration are mainly Beijing, Shanghai, Yunnan, Guangdong, Guizhou, Sichuan and Shanxi; The non-linear effect of China's tourism industry agglomeration on economic growth is significant. When the level of economic development is less than the threshold value of 10.552, tourism industry agglomeration promotes economic growth. When the level of economic development is greater than the threshold value of 10.552, the impact of tourism industry agglomeration on economic growth is negative. Williamson hypothesis of China's tourism industry agglomeration is established.

Tobacco Industry Agglomeration and Industrial Pollution Emission: Nonlinear Test Based on Panel Data

In the past 40 years, the agglomeration of tobacco industry plays a vital role in economic growth in China. Meanwhile, the rapid economic development has paid serious environmental and energy costs.Based on the panel data of 31 provinces in China from 2005 to 2015, this paper uses the differential GMM method to empirically test the relationship between the agglomeration of tobacco industry and industrial pollution emissions. The study found that the spatial agglomeration of the tobacco industry has a significant negative impact on industrial wastewater, sulfur dioxide and industrial dust emissions. Capital labor, enterprise scale, and pollution control investmentall have a significant impact on pollution emissions.Therefore, the increase in the agglomeration of China’s tobacco industry is conducive to reducing industrial pollution emissions.

How tourism industry agglomeration improves tourism economic efficiency?

The agglomeration of the tourism industry has important effects on its efficiency. This article used panel data on the Chinese provincial tourism industry for the 2011–2016 period, applied the location quotient index and three-stage data envelopment analysis method to, respectively, measure the degree of agglomeration and efficiency, and explained the impact of agglomeration on tourism efficiency. The empirical results of this study indicate the following. (1) China’s tourism industry shows a trend towards agglomeration, revealing gradient differences where the highest degree of agglomeration is in the eastern region, followed by the western and central regions. (2) After eliminating random and environmental factors, the adjusted efficiencies are lower than the unadjusted efficiencies. The average overall tourism efficiency is higher in the eastern region than in the central and western regions. (3) From the national perspective, industrial agglomeration can significantly improve the overall efficiency (TE), pure technical efficiency (PTE), and scale efficiency of the tourism industry. (4) Based on regional analysis, the agglomeration of the eastern tourism industry can significantly enhance its TE and PTE. Agglomeration for the western area has a significant positive impact on PTE. There is no significant relationship between agglomeration and efficiency in the central region.

An Empirical Study on the Agglomeration Characteristics of China’s Construction Industry Based on Spatial Autocorrelation and Spatiotemporal Transition

The spatiotemporal agglomeration of industries is the most prominent geographical feature of economic activities. Based on the analysis of the spatiotemporal distribution of China’s construction industry agglomeration, this paper analyzes the characteristics and evolution trend of the spatiotemporal agglomeration of construction industry in 31 provinces and cities of China from 2010 to 2019 by using Moran’s index and the spatiotemporal transition measurement model. The findings are as follows: (1) China’s construction industry has experienced two stages in terms of time: steady rise and turbulent rise. Spatially, China’s construction industry, as a whole, the space takes the shape of one horizontal and two vertical, similar to the letter “H” being crossed. And the difference of “East-West” two ends of the industrial agglomeration level is obvious. (2) The Yangtze River Delta Urban Agglomerations (Shanghai, Jiangsu, and Zhejiang), the Pearl River Delta Urban Agglomerations (Guangdong), Beijing-Tianjin-Hebei Urban Agglomerations, and the western region (Xinjiang and Tibet) have significant local features. The four major types of China’s construction industry cluster, which are H-H, H-L, L-H, and L-L, are formed. (3) The time-space transition of China’s construction industry is dominated by the “stable transition” mode. The transition inertia is significant. The regional development has strong path dependence and spatial locking characteristics.