Analysis of the energy justice in natural gas distribution with Multiscale Geographically Weighted Regression (MGWR)

• Published in: Energy reports Vol. 9; pp. 325 - 337
• Main Author: Senyel Kurkcuoglu, Muzeyyen Anil
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023; Elsevier
• Subjects: Energy justice; Geographically weighted regression; Multiscale geographically weighted regression; Natural gas investment; Spatial modeling; Urban energy distribution; Urban energy distribution; Natural gas investment; Energy justice; Multiscale geographically weighted regression; Geographically weighted regression; Spatial modeling
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2022.11.188

Energy justice is violated when particular customers and locations are excluded from a variety of urban energy service distribution. This study explores energy justice in terms of natural gas distribution by providing empirical evidence from 356 neighborhoods of Izmir Metropolitan Area (IMA). The aim is to reveal driving factors of natural gas investment and the spatial reflections of the relationships between investment, and socio-economic and physical characteristics. A global regression model, OLS, and two local spatial regression models, GWR and MGWR, are conducted. Population, income, employment and disadvantaged areas are the significant determinants of natural gas investments. Due to the presence of spatial autocorrelation in OLS residuals, GWR and MGWR are utilized to account for spatial variation in the response variable. Local models are superior to the global model according to AICc values, which are 607.3, 586.7, and 558.5, in OLS, GWR, and MGWR, respectively. MGWR further improves the overall fit with higher R-sq and lower AICc values. The local R-sq values indicate at least 70% variability is explained in 85% of the study area in MGWR, and in 73% of IMA in GWR. Parameters are slightly overestimated in GWR at the mean level. None of the local models are subject to multicollinearity according to local condition numbers, but local variance decomposition proportions indicate the effects of multicollinearity in some observations. Spatial modeling of investments helps to demonstrate local variations of energy injustice and to develop site-specific policies. Population and employment are related to potential customers, which lead to higher investments. Income depends on purchasing power, and there are economic barriers that need to be regulated through subsidies and incentives for the low-income households. Awareness raising policies can also be developed to better inform households about energy alternatives. Disadvantaged areas, either declared as urban transformation areas or currently under urban transformation, lack the natural gas service in general, while the investments can be considered in those areas along with the new development plans. •Energy justice in natural gas investment is analyzed with spatial regression.•Investments are related to socio-economic and physical characteristics.•MGWR provides improved results in spatial variations of investments.•MGWR explains at least 70% variability in 85% of the study area.•Spatial models help to show site-specific effects of the predictors.