Spatial-temporal pattern evolution of manufacturing geographical agglomeration and influencing factors of old industrial base: A case of Jilin Province, China

2014 ◽  
Vol 25 (4) ◽  
pp. 486-497 ◽  
Author(s):  
Linshan Li ◽  
Yanji Ma
2013 ◽  
Vol 726-731 ◽  
pp. 5014-5019
Author(s):  
Jing Wang ◽  
Li Li Chang ◽  
Min Hang Yuan ◽  
Wen Yue Li

Following strategies of Coastal Open Go West and Reviving Northeastern Old Industrial Base the state put forward the strategy of Rising of Central China in order to promote its rapid development. Urban agglomeration in Central China is becoming academic focus with unprecedented development momentum. It applies multidisciplinary theory of human geography, regional economics, etc. and takes urban agglomeration of Hunan, Henan and Hubei provinces for example to empirical analysis. Firstly, analyzing the historical evolution, urbanization space development and patterns then comes to spatial association of urban and rural through comparison, Finally, showing the development characteristics of urban agglomeration in Central China and putting forward urbanization suggestion.


2019 ◽  
Vol 11 (23) ◽  
pp. 6743
Author(s):  
Jia Wan ◽  
Junping Yan ◽  
Xiaomeng Wang ◽  
Ziqiang Liu ◽  
Hui Wang ◽  
...  

Strengthening research on urban tourism competitiveness is vital in evaluating the current situation and potential of urban tourism, maintaining the sustainable development of the tourism economy and assisting in the regional macro decision making. In this study, an index system evaluation of urban tourism competitiveness in city agglomerations across the Guanzhong Plain is established by collecting cross-section data from the years 2017 and 2010. The entropy value method is adopted to determine the index weight. Cluster analysis is performed and the spatial-temporal pattern and evolution laws of urban tourism competitiveness among city agglomerations in the Guanzhong Plain are analyzed and the geographic detector utilized to discuss the influencing factors. Results show that the spatial gradient difference of urban tourism competitiveness of agglomerations in the Guanzhong Plain is significant. In 2010, it presented the characteristic of ‘the high and middle levels having a zonal distribution from east to west, and the low level was distributed along the north and south wings’. In 2017, the characteristic of ‘polarization’ became highly prominent, that is, the scope of high-level and low-level cities expanded and the scope of medium-level cities decreased. Urban tourism competitiveness in city agglomerations across the Guanzhong Plain exhibited a trend of ‘strengthening in the east, weakening in the west’. The competitiveness of resources and management shifted aggressively and supporting factors competitiveness underwent a slight change. The urban tourism competitiveness of city agglomerations in the Guanzhong Plain is generally low, while the urban tourism competitiveness of Xi’an had an absolute advantage in city agglomerations of the Guanzhong Plain. According to the cluster analysis results, resources and management competitiveness, supporting factors competitiveness, demand conditions competitiveness, situational conditions competitiveness and urban tourism competitiveness of Xi’an in 2010 and 2017 were all at an extremely high level, which was relatively higher than the index values of other cities in the city agglomerations of the Guanzhong Plain. Tourism resources, service support capacity, infrastructure support capacity, tourism income scale, tourism reception scale and economic development power are the core influencing factors of urban tourism competitiveness among city agglomerations in the Guanzhong Plain. The single factor explanatory power of destination management indicates a downward trend while the single factor explanatory power of the ecological environment condition shows an upward trend. Tourism resources are the leading interactive factor of urban tourism competitiveness, and destination management and ecological environment condition are the most significant indicators for the collaborative effect.


Author(s):  
Liu Liqin

Technology, economy, human capital and policy are essential facilities of undertaking international service outsourcing for an area based on analyzing the influencing factors. With principal component analysis, this paper evaluates the ability to undertake international service outsourcing in Jilin Province of China with the purpose of constructing an index system. It shows that the ability in Jilin Province is weak. It is essential for Jilin province of China to improve the technology, to train and introduce talents, and to perfect the soft environment in order to further develop the ability to undertake international service outsourcing.


2011 ◽  
Vol 110-116 ◽  
pp. 3895-3898
Author(s):  
Hong Qing Rong ◽  
Ying Yang

To speed up the construction of Liaoning to make it advanced equipment manufacturing base with international l competitiveness is the inevitable demand of reviving the old industrial base. It is also urgent needs and important practice to carry out the scientific outlook on development and adhere to the new industrial road with Chinese characteristics.


2013 ◽  
Vol 448-453 ◽  
pp. 4281-4284 ◽  
Author(s):  
Shao Bo Liu

Using IPCC methodology, the carbon emissions of Chinese Northeast Old Industrial Base is calculated, and the energy's synthesized impact on carbon emissions intensity is presented. The resulting shows that the carbon emissions in the three northeast provinces decreased 52.87% from 2000 to 2010, of which, Liaoning, Jilin and Heilongjiang are individually 60.09%, 45.47% and 54.14% lower. The implications are that the energy structure is one of the main factors in carbon emission in the Old Industrial Base of Northeast China, and its industrial structure is changing greatly due to energy consumption carbon emission. To adjust optimally the energy and industrial structure, and to develop the energy technology to promote energy utilization are recommended.


2014 ◽  
Vol 25 (4) ◽  
pp. 847-856 ◽  
Author(s):  
Hong Liang ◽  
Xiaoshuang Chen ◽  
Junguang Yin ◽  
Liangjun Da

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