Analyzing the impact of urbanization quality on CO2 emissions: What can geographically weighted regression tell us?

2019 ◽  
Vol 104 ◽  
pp. 127-136 ◽  
Author(s):  
Yanan Wang ◽  
Xinbei Li ◽  
Yanqing Kang ◽  
Wei Chen ◽  
Minjuan Zhao ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Impact Of Urbanization ◽  
The Impact

Government failures and non-performing loans in European countries: a spatial approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ferdinando Ofria ◽  
Massimo Mucciardi
Keyword(s):  
Public Debt ◽  
Geographically Weighted Regression ◽  
European Countries ◽  
Weighted Regression ◽  
Second Step ◽  
Ols Regression ◽  
Content Type ◽  
The Impact ◽  
The Relationship ◽  
The Eu

PurposeThe purpose is to analyze the spatially varying impacts of corruption and public debt as % of GDP (proxies of government failures) on non-performing loans (NPLs) in European countries; comparing two periods: one prior to the crisis of 2007 and another one after that. The authors first modeled the NPLs with an ordinary lest square (OLS) regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the authors utilized the geographically weighted regression (GWR) to explore regional variations in the relationship between NPLs and the proxies of “Government failures”.Design/methodology/approachThe authors first modeled the NPL with an OLS regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the author utilized the Geographically Weighted Regression (GWR) (Fotheringham et al., 2002) to explore regional variations in the relationship between NPLs and proxies of “Government failures” (corruption and public debt as % of GDP).FindingsThe results confirm that corruption and public debt as % of GDP, after the crisis of 2007, have affected significantly on NPLs of the EU countries and the following countries neighboring the EU: Switzerland, Iceland, Norway, Montenegro, and Turkey.Originality/valueIn a spatial prospective, unprecedented in the literature, this research focused on the impact of corruption and public debt as % of GDP on NPLs in European countries. The positive correlation, as expected, between public debt and NPLs highlights that fiscal problems in Eurozone countries have led to an important rise of problem loans. The impact of institutional corruption on NPLs reports that the higher the corruption, the higher is the level of NPLs.

scholarly journals Valuing Environmental Amenities across Space: A Geographically Weighted Regression of Housing Preferences in Greenville County, SC

Land ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 147
Author(s):  
Hiebert ◽  
Allen
Keyword(s):  
Geographically Weighted Regression ◽  
City Planning ◽  
Spatial Dynamics ◽  
Ordinary Least Squares ◽  
Hedonic Model ◽  
Weighted Regression ◽  
Hedonic Price ◽  
Environmental Amenities ◽  
Forested Areas ◽  
The Impact

As global consumption and development rates continue to grow, there will be persistent stress placed on public goods, namely environmental amenities. Urban sprawl and development places pressure on forested areas, as they are often displaced or degraded in the name of economic development. This is problematic because environmental amenities are valued by the public, but traditional market analysis typically obscures the value of these goods and services that are not explicitly traded in a market setting. This research examines the non-market value of environmental amenities in Greenville County, SC, by utilizing a hedonic price model of home sale data in 2011. We overlaid home sale data with 2011 National Land Cover Data to estimate the value of a forest view, proximity to a forest, and proximity to agriculture on the value of homes. We then ran two regression models, an ordinary least squares (OLS) and a geographically weighted regression to compare the impact of space on the hedonic model variables. Results show that citizens in Greenville County are willing to pay for environmental amenities, particularly views of a forest and proximity to forested and agricultural areas. However, the impact and directionality of these variables differ greatly across space. These findings suggest the need for an integration of spatial dynamics into environmental valuation estimates to inform conservation policy and intentional city planning.

scholarly journals Examining the driving factors of industrial CO2 emissions in Chinese cities using geographically weighted regression model

2021 ◽  
Author(s):  
Huiping Wang ◽  
Xueying Zhang
Keyword(s):  
Spatial Heterogeneity ◽  
Geographically Weighted Regression ◽  
Service Industry ◽  
Industrial Sector ◽  
Weighted Regression ◽  
Impact Factors ◽  
Geographically Weighted Regression Model ◽  
Level Of Economic Development ◽  
Investment In Fixed Assets ◽  
The Impact

Abstract The industrial sector is the sector with the largest CO2 emissions, and to reduce overall CO2 emissions, analysis of the impact factors holds significance. Based on the 2015 industrial CO2 emissions of 282 cities in China combined with economic and social data, and a geographically weighted regression (GWR) model, we analysed the characteristics of the spatial distribution of CO2 emissions and the influencing factors of spatial heterogeneity. The results show that China's urban industrial CO2 emissions present a significant spatial agglomeration state that includes Shandong, Beijing, Tianjin, Shanghai, Zhejiang, and Jiangsu, and the core of the coastal areas form a high-high (H-H) concentration; a low-low aggregation (L-L) is formed in less developed areas such as Guizhou, Yunnan, Sichuan and Guangxi. The influence of various factors on industrial CO2 emissions has significant spatial heterogeneity. The Industrial scale, industry share of GDP, and share of the service industry in GDP are factors that promote industrial CO2 emissions. The technological innovation, population density, and social investment in fixed assets are important factors that inhibit industrial CO2 emissions, but their impact on industrial CO2 emissions shows spatial differences. In contrast, the level of economic development, foreign direct investment, financial development and government intervention have a two-way impact on industrial CO2 emissions.

scholarly journals Spatial Analysis Of Foreign Migration In Poland In 2012 Using Geographically Weighted Regression

2014 ◽  
Vol 17 (4) ◽  
pp. 137-154 ◽  
Author(s):  
Karolina Lewandowska-Gwarda
Keyword(s):  
Geographically Weighted Regression ◽  
Graphical Representation ◽  
Emigration And Immigration ◽  
Weighted Regression ◽  
Model Parameters ◽  
Population Changes ◽  
Principal Influence ◽  
Gis Tools ◽  
Spatially Varying ◽  
The Impact

Migration has a principal influence on countries’ population changes. Thus, the issues connected with the causes, effects and directions of people’s movements are a common topic of political and academic discussions. The aim of this paper is to analyse the spatial distribution of officially registered foreign migration in Poland in 2012. GIS tools are implemented for data visualization and statistical analysis. Geographically weighted regression (GWR) is used to estimate the impact of unemployment, wages and other socioeconomic variables on the foreign emigration and immigration measure. GWR provides spatially varying estimates of model parameters that can be presented on a map, giving a useful graphical representation of spatially varying relationships.  

scholarly journals Application of Remote Sensing, GIS and Machine Learning with Geographically Weighted Regression in Assessing the Impact of Hard Coal Mining on the Natural Environment

2020 ◽  
Vol 12 (22) ◽  
pp. 9338
Author(s):  
Anna Kopeć ◽  
Paweł Trybała ◽  
Dariusz Głąbicki ◽  
Anna Buczyńska ◽  
Karolina Owczarz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Coal Mining ◽  
Geographically Weighted Regression ◽  
Machine Learning Algorithms ◽  
Weighted Regression ◽  
Hard Coal ◽  
Mining Operations ◽  
Hard Coal Mining ◽  
The Impact

Mining operations cause negative changes in the environment. Therefore, such areas require constant monitoring, which can benefit from remote sensing data. In this article, research was carried out on the environmental impact of underground hard coal mining in the Bogdanka mine, located in the southeastern Poland. For this purpose, spectral indexes, satellite radar interferometry, Geographic Information System (GIS) tools and machine learning algorithms were utilized. Based on optical, radar, geological, hydrological and meteorological data, a spatial model was developed to determine the statistical significance of the selected factors’ individual impact on the occurrence of wetlands. Obtained results show that Normalized Difference Vegetation Index (NDVI) change, terrain height, groundwater level and terrain displacement had a considerable influence on the occurrence of wetlands in the research area. Moreover, the machine learning model developed using the Random Forest algorithm allowed for an efficient determination of potential flooding zones based on a set of spatial variables, correctly detecting 76% area of wetlands. Finally, the GWR (Geographically Weighted Regression (GWR) modelling enabled identification of local anomalies of selected factors’ influence on the occurrence of wetlands, which in turn helped to understand the causes of wetland formation.

Sign in / Sign up

Export Citation Format

Share Document