scholarly journals Spatial Dynamics of Intercity Technology Transfer Networks in China’s Three Urban Agglomerations: A Patent Transaction Perspective

2019 ◽  
Vol 11 (6) ◽  
pp. 1647 ◽  
Author(s):  
Chengliang Liu ◽  
Caicheng Niu ◽  
Ji Han

Technology transfer has become a vital pipeline for acquiring external knowledge. The purpose of this paper is to portray the spatial dynamics of intercity technology transfer networks in China’s three urban agglomerations based on patent right transaction data from 2008 to 2015. The integration of social networks and spatial visualization is used to explore spatial networks and influencing variables of the networks. The results demonstrate that Beijing, Shanghai, and Shenzhen are emerging as hubs in the three urban agglomerations. The spatial distributions of degree and weighted degree are significantly heterogeneous and hierarchical. The larger cities play the role of a knowledge and technology incubator, highly related to their economic scale, research and development (R&D) input, and innovation output. The evolution of intercity technology linkages is driven by the networking mechanisms of preferential attachment, hierarchical and contagious diffusion, path dependence, and path breaking. Moreover, we found that the geographical proximity and technology gaps are determinants of the strength of intercity technology linkages. As a result, it has been discovered that the network in the Beijing–Tianjin–Hebei agglomeration is organized in a tree network, while the Yangtze River Delta features a polycentric network and the Pearl River Delta has multi-star characteristics.

2020 ◽  
Vol 12 (2) ◽  
pp. 458 ◽  
Author(s):  
Feng Wang ◽  
Wenna Fan ◽  
Xiangyan Lin ◽  
Juan Liu ◽  
Xin Ye

Population mobility accelerates urbanization convergence and mitigates the negative impact of the spatial agglomeration effect on urbanization convergence, which is the most important conclusion in this paper. Taking 38 cities in China’s three urban agglomerations (the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region) from 2005 to 2016 as research subjects, the study first shows that there is a large gap in the level of urbanization between the three major urban agglomerations, but the gap has been constantly narrowed and presents a trend of absolute convergence and conditional convergence. Furthermore, without adding a population mobility variable, the combination of the diffusion effect of high-urbanization cities and the high growth rate of low-urbanization cities causes the inter-regional urbanization level to be continuously convergent in the Yangtze River Delta region; however, the combination of the agglomeration effect of high-urbanization cities and the high growth rate of low-urbanization cities causes the inter-regional urbanization to be divergent in the Pearl River Delta and the Beijing–Tianjin–Hebei region. Under the influence of population mobility, the “catch-up” effect in low-urbanization regions is greater than the agglomeration effect in high-urbanization regions, which promotes the continuous convergence of inter-regional urbanization.


2021 ◽  
Vol 228 ◽  
pp. 01004
Author(s):  
Jianchao Hou ◽  
Jinhua Jian ◽  
Pingkuo Liu

With the aggravation of environmental pollution and the overuse of fossil energy, a sustainable transition to using the low-carbon and clean energy is perceived to be an inevitable trend. The Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta are the three most important economic circles in China. One purpose of energy transition in those Three Urban Agglomerations is to enable the energy system to have a higher share of clean energy. This paper introduces the current situation in terms of energy endowment, production and consumption in the three urban agglomerations, discusses the policy environment from the aspects of development planning, supporting mechanism and policy tools. We further analyse the barriers of the energy transition in the three urban agglomerations by using Institution-Economy-Technology-Behaviour (IETB) conceptual model. Through this research, we know that reducing the carbon emissions is a priority in energy transition and increasing the utilization of renewable energy has become the consensus in the three urban agglomerations. In addition, reasonable energy development policies can impel the energy investment and the technology innovation to accelerate energy transition. Moreover, in the designated “highly polluting” industry sectors, energy supply enterprises and energy-consuming enterprises establish green-development incentive mechanisms and adopt technological innovation in order to promote energy transition.


2017 ◽  
Vol 05 (01) ◽  
pp. 1750006
Author(s):  
Xuan SUN

The level of coordinated industrial development in a region is considered as an important factor of measuring the construction of urban agglomerations. As the economic development patterns and stages vary in regions, a single-standard evaluation system is generally insufficient in evaluating and analyzing the coordinated industrial development of urban agglomerations. This paper, with multivariate values and diversified development demands considered, quantitatively describes the industrial development of urban agglomerations from four dimensions: economics, specialization, balance, and friendliness. On this basis, it synthesizes the indicator parameters effectively and proposes a multi-indicator evaluation model. Through the model, the paper comparatively analyzes the present status and development course of coordinated industrial development of typical urban agglomerations (Beijing–Tianjin–Hebei urban agglomeration, the Yangtze River Delta urban agglomeration, and the Pearl River Delta urban agglomeration) in China. The results show that Beijing–Tianjin–Hebei urban agglomeration has the clearest division of industries, but its industrial spillover effect is limited, the industrial structure of small and medium cities is too simple, and the economic gap among cities narrows at a very slow rate. The core cities in the Yangtze River Delta urban agglomeration exert certain driving effect upon the economy of their surrounding areas. However, they hardly give full play to their comparative advantages due to a low level of regional integration and high industrial similarity among cities. Compared with the above two urban agglomerations, the Pearl River Delta urban agglomeration enjoys more reasonable division of industries among cities, significant driving effect of core cities, and higher level of coordinated industrial development as driven by the market economy.


2020 ◽  
Vol 206 ◽  
pp. 02014
Author(s):  
Wang Hongmei ◽  
Lu Zhihui

The importance of close cooperation among cities can be seen from the development experience of mature urban agglomerations in the world. Compared with the Yangtze river delta and the Pearl River Delta urban agglomeration, cities in Beijing Tianjin Hebei urban agglomerations are in poor connection. This paper studies the internal linkages of Beijing Tianjin Hebei Urban Agglomerations, and finds that: in recent years, the overall spatial linkages of the urban agglomerations have been several times higher than that in 2007, but they show the characteristics of geographically “dense in the South and sparse in the north”; the main connection tracks within the urban agglomerations are roughly “inverted L”, that is, the connection of “Beijing-Tianjin”, “Beijing-Baoding-Shijiazhuang” and “Xingtai-Handan”.


2019 ◽  
Vol 11 (17) ◽  
pp. 4590 ◽  
Author(s):  
Jiqun Wen ◽  
Xiaowei Chuai ◽  
Shanchi Li ◽  
Song Song ◽  
Yuanwei Li ◽  
...  

Land-use change, particularly urban expansion, can greatly affect the carbon balance, both from the aspects of terrestrial ecosystems and anthropogenic carbon emissions. Coastal China is a typical region of rapid urban expansion, and obvious spatial heterogeneity exists from the north to south. However, the different urban change characteristics and the effect on carbon balance remain undetermined. By unifying the spatial-temporal resolution of carbon source and sink data, we effectively compared the carbon budgets of three coastal urban agglomerations in China. The results show that all of the three urban agglomerations have undergone an obvious urban expansion process, with the built-up area increasing from 1.03 × 104 km2 in 2000 to 3.06 × 104 km2 in 2013. For Beijing–Tianjin–Hebei (BTH), the built-up area gradually expanded. The built-up area in the Yangtze River Delta (YRD) gradually changed before 2007 but rapidly grew thereafter. The built-up expansion of the Pearl River Delta (PRD) passed through three growing stages and showed the largest mean patch size. Carbon emission spatial patterns in the three urban agglomerations are consistent with their economic development, from which the net ecosystem production (NEP) spatial patterns are very different. Compared to carbon emissions, NEP has a carbon sink effect and can absorb some carbon emissions, but the amounts were all much lower than the carbon emissions in the three urban agglomerations. The carbon sink effect in the Yangtze River Delta is the most obvious, with the Pearl River Delta following, and the lowest effect is in Beijing–Tianjin–Hebei. Finally, a scientific basis for policy-making is provided for viable CO2 emission mitigation policies.


2018 ◽  
Vol 10 (11) ◽  
pp. 4179 ◽  
Author(s):  
Chengliang Liu ◽  
Tao Wang ◽  
Qingbin Guo

Continuous aggregation of socioeconomic factors is the key issue of sustainable development in urban agglomerations. To date, more attention has been paid to single urban agglomeration than to multiple agglomerations. In this paper, China’s 19 urban agglomerations were selected as the case study and their spatial differences in factors aggregating ability were portrayed comparatively. Firstly, the spatial pattern of urban factors aggregating ability is relatively well distributed in all China’s cases, most noticeably in the Yangtze River Delta urban agglomeration, closely followed by the Beijing-Tianjin-Hebei and the Pearl River Delta urban agglomerations. However, more significant differences on factors aggregating ability are noticeably seen between cities than among urban agglomerations. Meanwhile, the rank-size structure distribution of factors aggregating ability in China’s 19 cases is in line with the Zipf’s law of their urban systems, and divided into three types: Optimized, balanced, and discrete. Furthermore, the urban factors aggregation ability in one urban agglomeration is roughly negatively correlated with its primacy ratio of factors aggregating ability distribution. Lastly, urban agglomerations with higher average values of factors aggregating ability are concentrated on the three major urban agglomerations: The Yangtze River Delta, the Beijing-Tianjin-Hebei and the Pearl River Delta. Otherwise, high-high clusters in the three urban agglomerations are distinctly observed as well.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Likun Zhao ◽  
Junsen Tian ◽  
Yanqi Liu ◽  
Rui Liu

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.


2020 ◽  
Vol 26 (1) ◽  
pp. 135-164 ◽  
Author(s):  
Ke Li ◽  
Jianying Qu ◽  
Pan Wei ◽  
Hongshan Ai ◽  
Pinrong Jia

The technological progress in favor of energy conservation and emission reduction will help increase green total factor productivity and thus mitigate China’s environmental problems. This study adopts the data envelopment analysis (DEA) to measure the total factor productivity (TFP) index of the Chinese three urban agglomerations from 2005 to 2014, and the reasons for its changes are also analyzed. Furthermore, the biases of technological progress from two perspectives of inputs and outputs (including the undersirable output, measured by CO2 emissions) are estimated. Main results are: (i) During the sample period, the TFP of the three urban agglomerations continues to increase, and the main driving force is technological change. (ii) From the perspective of inputs, the Beijing-Tianjin-Hebei prefers to use electricity, whereas the Pearl River Delta and the Yangtze River Delta urban agglomerations tend to use capital and save labor. (iii) From the perspective of outputs, the technological progress of the three major urban agglomerations is significantly biased toward GDP with a slight difference among the three urban agglomerations, which means its technological progress is conducive to reduce CO2 intensity, symbolizing low carbon development. From this point of view, their economic growth shows a low-carbon trend.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rui Feng ◽  
Rong Zhou ◽  
Weiwei Shi ◽  
Nanjing Shi ◽  
Xuekun Fang

AbstractWe focus on the causes of fluctuations in wintertime PM10 in nine regional core cities of China using two machine learning models, Random Forest (RF) and Recurrent Neural Network (RNN). RF and RNN both show high performance in predicting hourly PM10 using only gaseous air pollutants (SO2, NO2 and CO) as inputs, showing the predominance of the secondary inorganic aerosol and implying the existence of thermodynamic equilibrium between gaseous air pollutants and PM10. Also, we find the following results. The correlation of gaseous air pollutants and PM10 were more relevant than that of meteorological conditions and PM10. CO was the predominant factor for PM10 in the Beijing-Tianjin-Hebei Plain and the Yangtze River Delta while SO2 and NO2 were also important features for PM10 in the Pearl River Delta and Sichuan Basin. The spatial heterogeneity and temporal homogeneity of PM10 in China are revealed. The long-range transported PM10 was substantiated to be insignificant, except in the sandstorms. The severity of PM10 was attributable to the lopsided shift of thermodynamic equilibrium and the phenology of indigenous flora.


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