New evidence for the impact of financial agglomeration on urbanization from a spatial econometrics analysis

Ye, Changhua; Sun, Chuanwang; Chen, Litai

doi:10.1016/j.jclepro.2018.07.253

Cited by 79 publications

(31 citation statements)

References 24 publications

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“…The initial measure of spatial autocorrelation is based on the idea of binary adjacency between spatial units [51]. The form of w ij under this definition is [52] W ij = 1 When region i is adjacent to region j 0 When region i is not adjacent to region j; or i = j…”

Section: Global Moran Index and Local Moran Indexmentioning

confidence: 99%

“…If the values of Wald-spatial lag and Lr-spatial lag both pass the significance test, it indicates that SDM cannot be simplified to SAR or SEM. Otherwise, SDM can be simplified to SAR or SEM [52]. Elhorst referred the partial differential method to the spatial panel model and obtained the partial differential matrix by calculating the partial derivatives.…”

Section: Spatial Panel Data Modelmentioning

confidence: 99%

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Research on the Distribution of Pollution-Intensive Industries and Their Spatial Effects in China

et al. 2019

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Investigation of the spatial transfer laws and dynamic mechanisms of pollution-intensive industries (PIIs) is becoming a popular field in regional sustainable development. Based on the statistical data of 30 provinces (cities and districts) in China from 2000 to 2017, this paper applied the Gini coefficient and a redistribution index as well as spatial econometric approaches to explore the spatial distribution characteristics and spatial effects of China’s PIIs. PIIs in China have experienced two transition stages: ‘from north to south’ and ‘from east to central and west’, and the spatial distribution imbalance of PIIs has been gradually improved. In terms of industries, all PIIs in the northeast region were removed; PIIs in the eastern region not only transferred outward but also have experienced an agglomeration effect. The central and western regions were the main areas where transferring PIIs were settling. The distribution of PIIs in China showed a strong spatial correlation and a relatively stable path dependence. Through use of the spatial Dubin model, it is concluded that command-and-control environmental regulation and transportation costs had a negative impact on the distribution of PIIs in this region and a positive impact on the surrounding regions; thus, the pollution haven hypothesis was supported. Resource factors, technological innovation levels, and industrial structure—whether direct or indirect—had an inhibitory effect on the distribution of PII. Capital factors not only promoted the development of PIIs in this region, but also promoted it in other regions. Agglomeration economics had a positive impact on the distribution of PIIs in this region, and a negative impact on the surrounding regions.

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Section: Global Moran Index and Local Moran Indexmentioning

confidence: 99%

Section: Spatial Panel Data Modelmentioning

confidence: 99%

Research on the Distribution of Pollution-Intensive Industries and Their Spatial Effects in China

et al. 2019

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“…In terms of social impact, it is conducive to creating employment opportunities, improving urban staffing, and enhancing regional influence. In terms of environmental impact, it is conducive to improving urban structure, alleviating urban congestion, and reducing carbon emissions [55].…”

Section: Discussion and Suggestionmentioning

confidence: 99%

The Spatial and Temporal Evolution of Logistics Enterprises in the Yangtze River Delta

Zeng

et al. 2019

Sustainability

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With the deepening of economic globalization, the global freight volume has been constantly on the rise; and urban logistics space is gradually changing as well. Reorganization of urban logistics space is closely related to sustainable development. It has great influence on rational distribution of social resources, improvement of urban ecological environment, and balance of urban economic structure. This paper takes A-level logistics enterprises in the Yangtze River Delta as the object of study, aiming at revealing the spatial and temporal evolution of A-level logistics enterprises in Shanghai and Yangtze River Delta in 2005–2015 from the metropolitan and regional levels, respectively, and at providing reference for the rational planning of logistics space. The analysis result shows that the logistics sprawl occurs in various degrees in Shanghai and the Yangtze River Delta, and in the process of logistics enterprises moving from urban centers to the suburbs, the characteristics of logistics enterprises cluster keep emerging and gradually form a specific status. Then, we analyze the reasons underlying the formation of the spatial and temporal distribution pattern of logistics enterprises in light of relevant policies, geography and the sustainable development of economy, thus providing relevant suggestions for the government and logistics enterprises.

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“…ere is a lot of evidence that financial development has radiation and spillover effects. For instance, Ye et al [26] researched on the spatial spillover effect of financial agglomeration and indicate that financial agglomeration has a positive effect on economic urbanization level but insignificant effect on population urbanization level. Liao et al [27] studied on the spatial effect of Internet finance and suggested that developing Internet finance can promote the efficiency and technological progress of regional financial resource allocation and can enhance efficiency and technological progress in the allocation of financial resources in the neighboring areas.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Financial Development and Its Spatial Spillover Effect on Green Total Factor Productivity: Evidence from 30 Provinces in China

Zhong

2020

Mathematical Problems in Engineering

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The relationship between financial development and green economic growth has received much attention in recent years. Research on the relationship between financial development and green total factor productivity (GTFP) is of great importance to China and other countries. This study has attempted to reveal the spatial distribution of China’s provincial GTFP and impact of financial development on GTFP by using the method of GML index based on SBM-DDF and the spatial Durbin model (SDM) during the period 1996–2015. Innovation is added to the SDM to reflect the influencing mechanism of financial development on GTFP. The empirical results show the following: (1) The mean of China’s provincial GTFP showed a U-shaped curve in 1996–2015. (2) China’s provincial financial development promotes the growth of GTFP through innovation channel. The reason is that financial development boosts eco-friendly innovation and the introduction of energy saving technology, leading to a decrease in energy consumption and pollutant emissions. (3) Increasing the level of financial development in the surrounding areas will restrain local GTFP. Our results provide new evidence that China’s regional financial development has a spatial spillover effect. (4) China’s provincial GTFP has a significant spatial positive correlation. Finally, several policy implications can be summarized to China’s 30 provinces.

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New evidence for the impact of financial agglomeration on urbanization from a spatial econometrics analysis

Cited by 79 publications

References 24 publications

Research on the Distribution of Pollution-Intensive Industries and Their Spatial Effects in China

Research on the Distribution of Pollution-Intensive Industries and Their Spatial Effects in China

The Spatial and Temporal Evolution of Logistics Enterprises in the Yangtze River Delta

Impact of Financial Development and Its Spatial Spillover Effect on Green Total Factor Productivity: Evidence from 30 Provinces in China

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