Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Book Part Citation - Scopus: 2Using Night-Time Lights and Statistical Data to Measure Regional Inequality in Turkey(IGI Global, 2024-04-12) Ustaoglu, EdaPoverty and inequality are the outstanding challenges in both developing and developed countries in the globe. Using Suomi National Polar-orbiting Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) images and socio-economic data from administrative sources, this chapter focuses on the association between nighttime lights and economic activities with an aim of computing regional income inequality indices for the year 2015 in Turkey. Gini, the Atkinson and Theil statistics were used to establish regional inequality indices using both NTL and statistics data. The findings indicated that urban NTLs are strongly correlated with economic activity while the correlation is much weaker regarding rural nightlights and agricultural output. It can be noted that there was increasing regional inequality in north-west, south, and south-east regions whereas regional equality was more homogeneously distributed. The results indicated that NPP-VIIRS nightlight data can help to perform regional inequality assessments for the urban areas in Turkey. © 2024 Elsevier B.V., All rights reserved.Book Part Citation - Scopus: 1A Spatial Econometric Analysis of the Regional Variations of Residential Energy Consumption in Europe(IGI Global, 2024-03-22) Ustaoglu, EdaA residential energy consumption model was estimated by using socio-economic characteristics, economic activities, mobility, land cover, natural hazards, governance, energy, air quality, and green economy variables for the EU-27 and UK. Regional variations of the energy consumption were also investigated through focusing on European regional typologies including urban, intermediate, and rural regions. The residential energy consumption model was estimated by using spatial econometric approaches as well as specific regression models were estimated for the urban, intermediate, and rural regions. The key variables used in regression analysis were selected according to their importance using the Random Forest (RF) classification method. The results from the regressions confirm that socio-economic, environmental, governance, technology, and natural hazards related variables explain residential energy consumption in Europe. The variations of sign and coefficients of the variables according to different regional typologies were also uncovered. © 2024 Elsevier B.V., All rights reserved.