scholarly journals Spatial econometric analysis of the regional commuting

2016 ◽  
pp. 42-53
Author(s):  
K. Sehida ◽  
S. Kostrikov
Keyword(s):  
Labor Migration ◽  
National Level ◽  
Econometric Analysis ◽  
Geographical Information ◽  
Research Approach ◽  
Administrative District ◽  
Spatial Econometric ◽  
Subject Field ◽  
Scale Levels ◽  
Spatial Econometric Analysis

Paper authors introduce the transitional research approach – the spatial econometric analysis (SEA) – upon commuting study within a regional workforce market. The authors emphasize that both the labor migration and the commuting are two dominant issues upon studying of any workforce market either at its national level, or at the regional one. In authors’ opinion, if we attempt to understand the lower, regional migration level, we have to choose just commuting as its dominant trend. SEA has been introduced as a subject field related to both Human Geography and Regional Studies, it also involves the applied GIS-science instruments as the mandatory research tools. The relationship between the usual labor migration and commuting has been shortly examined on the theoretical level only. This research has been provided with the use of geographical information system (GIS) software for spatial modeling, analysis and visualization. Several levels of the regional commuting geodatabase update with both empirical and derivative data have been described, and its relevant mathematical content introduced. Authors’ empirical / analytical GIS-model of regional commuting has become a key component of SEA completed consequently for two scale levels: for a single administrative district (“rajon”) => for whole Kharkiv administrative oblast – a region. Different visuals represent in details all commuting trends and peculiarities within Kharkiv region borders. Authors’ theoretical / empirical model has estimated an approximated number of regular daily-weekly commuters to Kharkiv-city from the region around one hundred and thirty thousand in the period of 2012-2014. The paper conclusion outlines that further commuting research needs to extend authors’ model presented with much broader number of cross-regional commuting factors. First of all, those commuting determinants caused by the war going on the East – the area neighboring to our region – must be taken into account in one way, or in another.

scholarly journals Evaluation and Influencing Factors of Industrial Pollution in Jilin Restricted Development Zone: A Spatial Econometric Analysis

2021 ◽  
Vol 13 (8) ◽  
pp. 4194
Author(s):  
Yanhua Guo ◽  
Lianjun Tong ◽  
Lin Mei
Keyword(s):  
Influencing Factors ◽  
Industrial Pollution ◽  
Production Capacity ◽  
Pollution Index ◽  
Green Technology ◽  
Econometric Analysis ◽  
Ecological Civilization ◽  
Entropy Weight ◽  
Spatial Econometric ◽  
Spatial Econometric Analysis

Winning the battle against pollution and strengthening ecological protection in all respects are vital for promoting green development and building a moderately prosperous ecological civilization in China. Using the entropy weight method, this paper establishes and evaluates a comprehensive industrial pollution index that contains and synthesizes six major industrial pollutants (wastewater, COD, waste gas, SO2, NOx, and solid waste) in the 2006–2015 period. Subsequently, this paper studies the spatiotemporal characteristics and influencing factors of industrial pollution via the Moran index and spatial econometric analysis. The empirical results indicate that (1) the temporal evolution of the industrial pollution index is characterized by an overall trend of first decreasing and then increasing. (2) The industrial pollution index of each county has certain geographical disparities and significant spatially polarized characteristics in 2006, 2009, 2012, and 2015. (3) The Moran test shows that there is a relatively significant spatial autocorrelation of the industrial pollution index among counties and that the geographical distribution of the industrial pollution index tends to show clustering. (4) Spatial regression models that incorporate spatial factors better explain the influencing factors of industrial pollution. The economic development level, technological progress, and industrialization are negatively correlated with industrial pollution, while population density and industrial production capacity are positively correlated. (5) Consequently, as relevant policy recommendations, this paper proposes that environmental cooperation linkage mechanisms, environmental protection credit systems, and green technology innovation systems should be established in different geographical locations to achieve the goals of green county construction and sustainable development.

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