Spatial interaction modeling of interregional commodity flows

Çelik, Hüseyin Murat; Guldmann, Jean‐Michel

doi:10.1016/j.seps.2005.10.003

Cited by 26 publications

(10 citation statements)

References 20 publications

(24 reference statements)

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“…The Gross Value Added (GVA) of the specific industry of the origin country is also always positive and significant. The employment in trade of the origin country as a proxy, for the inner-national transport promoting distribution network is significant and positive as expected in four out of six industries (see Murat Celik and Guldmann, 2007). The coefficients on manufacturing employment of the origin country is only negative and significant as expected in the industry 'Chemicals'.…”

Section: Regional Samplementioning

confidence: 55%

Does Globalization Affect Regional Growth? Evidence for Nuts-2 Regions in EU-27

Polasek

Sellner

2013

Danube

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We analyze the influence of newly constructed globalization measures on regional growth for the EU-27 countries between 2001 and 2006. The spatial Chow-Lin procedure, a method constructed by the authors, was used to construct, on a NUTS-2 level, complete regional data for exports, imports and FDI inward stocks, which serve as indicators for the influence of globalization, integration and technology transfers on European regions. The results suggest that most regions have significantly benefited from globalization measured by increasing trade openness and FDI. In a non-linear growth convergence model, the growth elasticities for globalization and technology transfers decrease with increasing GDP per capita. Furthermore, the estimated elasticity for FDI decreases when the model includes a higher human capital premium for CEE countries and a small significant growth enhancing effect accrues from the structural funds expenditures in the EU.

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Section: Regional Samplementioning

confidence: 55%

Does Globalization Affect Regional Growth? Evidence for Nuts-2 Regions in EU-27

Polasek

Sellner

2013

Danube

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“…As a measure of the relationship, the gravity model originated from the formula of universal gravitation in physics, which considering the quality and geographical factors, can fully show the spatial effect of city elements attracting. The gravity model is used to measure the spatial relationship and has been widely used in urban transportation [28], commodity and energy [29,30], sustainable development [31,32], and other fields in the urban network.…”

Section: The Gravity Modelmentioning

confidence: 99%

Analysis on the Housing Price Relationship Network of Large and Medium-Sized Cities in China Based on Gravity Model

Chong

et al. 2021

Sustainability

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The relationship among cities is getting closer, so are housing prices. Based on the sale price of stocking houses in thirty-five large and medium-sized cities in China from 2010 to 2021, this study established the modified gravity model and used the method of social network analysis to explore the spatial linkage of urban housing prices. The results show that: (1) from the overall network structure, the integration degree of housing price network in China is still at a low stage, and the influence of housing price is polarized; (2) from the individual network structure, Beijing, Shanghai, Shenzhen, Nanjing, Hangzhou, and Hefei have a higher degree of centrality. Chengdu, Xining, Kunming, Urumqi, and Lanzhou stay in an isolation position every year; (3) from the results of cohesive subgroup analysis, different cities play different roles in the block each year and have different influences on other cities. (4) Emergencies, such as outbreaks of COVID-19, also have an impact on the housing price network. Structural divergence among urban housing prices has become more pronounced, and the diversity of house price network has been somewhat reduced. Based on the above findings, this paper puts forward some recommendations for the healthy development of housing market from the perspective of housing price network.

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“…The gravity model is a useful method to measure the forces in space between two regions, which can be used to quantitatively describe the attraction between cities. Since its proposal, the gravity model has been widely used in urban network research, including urban transportation networks [46], migration networks [47,48], and commodity flow networks [49]. In recent years, some scholars have applied it to the field of energy and CO 2 emissions.…”

Section: Constructing a Spatial Association Network Of Co 2 Emissionsmentioning

confidence: 99%

Spatial Association and Effect Evaluation of CO2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis

et al. 2018

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Urban agglomeration, an established urban spatial pattern, contributes to the spatial association and dependence of city-level CO 2 emission distribution while boosting regional economic growth. Exploring this spatial association and dependence is conducive to the implementation of effective and coordinated policies for regional level CO 2 reduction. This study calculated CO 2 emissions from 2005-2016 in the Chengdu-Chongqing urban agglomeration with the IPAT model, and empirically explored the spatial structure pattern and association effect of CO 2 across the area leveraged by the social network analysis. The findings revealed the following: (1) The spatial structure of CO 2 emission in the area is a complex network pattern, and in the sample period, the CO 2 emission association relations increased steadily and the network stabilization remains strengthened; (2) the centrality of the cities in this area can be categorized into three classes: Chengdu and Chongqing are defined as the first class, the second class covers Deyang, Mianyang, Yibin, and Nanchong, and the third class includes Zigong, Suining, Meishan, and Guangan-the number of cities in this class is on the rise; (3) the network is divided into four subgroups: the area around Chengdu, south Sichuan, northeast Sichuan, and west Chongqing where the spillover effect of CO 2 is greatest; and (4) the higher density of the global network of CO 2 emission considerably reduces regional emission intensity and narrows the differences among regions. Individual networks with higher centrality are also found to have lower emission intensity.

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Spatial interaction modeling of interregional commodity flows

Cited by 26 publications

References 20 publications

Does Globalization Affect Regional Growth? Evidence for Nuts-2 Regions in EU-27

Does Globalization Affect Regional Growth? Evidence for Nuts-2 Regions in EU-27

Analysis on the Housing Price Relationship Network of Large and Medium-Sized Cities in China Based on Gravity Model

Spatial Association and Effect Evaluation of CO2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis

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