Institution Change, Government Behavior and Private Economic Development of Pearl River Delta

Author(s):  
Xiaoshan Cai ◽  
He Chen
2019 ◽  
Vol 11 (2) ◽  
pp. 362 ◽  
Author(s):  
Qingsheng Yang ◽  
Hongxian Zhang ◽  
Kevin M Mwenda

Measuring destination attractivity and finding the determinants of attractivity at the county scale can finely reveal migration flows and explain what kinds of counties have higher attractivity. Such understanding can help local governors make better policies to enhance county attractivity and attract more migrants for regional development. In this study, the county-scale relative intrinsic attractivity (RIA) of Guangdong Province is computed using the number of migrants and the corresponding distances between origins and destinations. The results show that the RIA has a higher positive correlation with the flows of migrants to destination and demonstrates an obvious phenomenon of distance decay. The RIA decreases faster when the distance between origins and destinations increases. Spatially, the RIA reveals a core-periphery belt pattern in Guangdong Province. The center of the Pearl River Delta is the highest core of RIA and the outside areas of the delta represent the low-RIA belt. The highest RIA is 6811 in Dongguan City and the lowest RIA is 1 in Yangshan County. The core area includes Dongguan, Shenzhen City and the southern regions of Guangzhou, Foshan and Zhongshan City where the RIA value is higher than 1000. The second belt is mainly composed of the periphery districts of the Pearl River Delta, which include Shunde, Nanhai, Luohu, Tianhe Huicheng, Panyu, Haizhu, Huiyang, Huadu, Yuexiu, Xiangzhou and the Yuexiu, Huangpu and Boluo, where the RIA values are higher than 100 and lower than 1000. The third belt includes the western wing, eastern wing and northern area. Most of these RIA values range from 1 to 2. In this belt, there are three areas with relatively higher RIA attractivity scattered in the ring: the downtowns of Zhanjiang City, Chaozhou and Shantou Cities and Shaoguan City. The areas farther away from the core have a lower RIA score. Determinants analysis indicates that the RIA is positively determined by destination economic development level, social service and living standard level and destination population quality. A region will be more attractive if it has higher per capital GDP, tertiary industry level, investment and number of industrial enterprises involved in economic development. A region with a high annual average wage of employees and high social service and living standards will be more attractive, while a region with low destination population quality, including aspects such as the adult illiteracy rate, will be less attractive.


2021 ◽  
Author(s):  
Jiansheng Wu ◽  
Xuechen Li ◽  
Yuhang Luo ◽  
Danni Zhang

Abstract Since the implementation of the Chinese economic reforms, economic development in the coastal cities has resulted in serious degradation of habitat quality; however, the concept of "ecological civilization" has improved this situation. For quantitative analysis of the correlation between the Pearl River Delta urban expansion and changes in habitat quality under the influence of the policy, we first analyzed the habitat quality change based on the InVEST model and then measured the impact of construction land expansion on the habitat quality through habitat quality change index (HQCI) and contribution index (CI) indicators. Finally, the correlation between urbanization level and habitat quality was evaluated using geographically weighted regression (GWR) and the Self-organizing feature mapping neural network (SOFM). The results indicated that: (1) during the study period, the habitat quality index decreased from 0.7181 to 0.6672 owing to urban expansion, and the decrease was most significant from 2000 to 2010. (2) The urbanization index had a negative effect on the habitat quality, but this improved after 2000, reflecting the positive effect of policies such as "ecological civilization construction" (3) The importance of ecological civilization varies greatly among cities in the study area: Shenzhen, Dongguan, Foshan, and Zhongshan have the best level of green development. These results reflect the positive role of policies in the prevention of damage to habitat quality caused by economic development and provide a reference for the formulation of sustainable urban development policies with spatial differences.


2020 ◽  
Vol 12 (19) ◽  
pp. 8020
Author(s):  
Binbin Du ◽  
Qiaoya Zheng ◽  
Xue Bai ◽  
Longyu Shi ◽  
Xian Shen

The coordinated development of environment and economy is an important way to achieve sustainable development. As the Guangdong-Hong Kong-Macao Greater Bay Area has been included in the national agenda, Guangdong province faces a turning point in its economic, social, and environmental development. Taking Guangdong province as an example, this paper analyzes the spatial evolution and correlation of economic development and environmental pollution by means of center of gravity (COG) and geo-information system (GIS). The results show the shift of economic development COGs are smaller than that of environmental pollution. Environmental pollution COGs are negatively correlated with economic scale and quality COGs, whereas it is positively correlated with economic growth COG, which depends on the industrial structure and local policies. The continuous transformation of the industrial structure of the Pearl River Delta Region (PRD) is conducive to improving its environment and promoting economic development of Non-Pearl River Delta Region in Guangdong province (Non-PRD) through bilateral causality. As the receiving place of industrial transfer, eastern Guangdong has obvious effects of environmental pollution transfer from the secondary industries. In this study, the logical spatial evolution path of the economic development and environmental pollution COGs is established. It provides theoretical and practical references for the study of interrelationship between economy and environment.


TERRITORIO ◽  
2015 ◽  
pp. 121-129 ◽  
Author(s):  
Peter Bosselmann ◽  
Francesca Frassoldati ◽  
Ping Su ◽  
Haohao Xu

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