Energy Expenditure in Egypt: Empirical Evidence Based on a Quantile Regression Approach

Abstract

This paper investigates the key factors affecting household energy expenditure in Egypt. Based upon the latest 2015 Egyptian HIECS Survey, we develop a quantile regression model with an innovative variable selection approach via Adaptive Lasso Regularization technique to untangle the spectrum of household energy expenditure. Unsurprisingly, income, age, household size, housing size, and employment status are salient predictors for energy expenditure. Housing characteristics have a moderate impact, while socioeconomic attributes have a much larger one. The largest variations in household energy expenditures in Egypt are mainly due to variations in income, household size, and housing type. Our findings document substantial differences in household energy expenditure, originating from the asymmetric tails of the energy expenditure distribution. This outcome highlights the added value of implementing quantile regression methods. Our empirical results have various interesting policy implications regarding residential energy efficiency and carbon emissions reduction in Egypt.

Impact of Awareness Program for Carbon Footprint Mitigation among Nursing Students. A Step toward Sustainability

“…The mean students' CF score increases with higher household income, confirming that higher consumption is correlated with a higher economic level. This finding was reinforced by Belaid and Rault, (2021) (49), who found that EGYPT energy expenditure positively correlates with household-income. Additionally, Lévay et al, (2021) (50), found income and household CF are strongly correlated.…”

The Impact of Urban Form and Density on Residential Energy Use: A Systematic Review

“…on REC The role of urban form publications It affects the amount of REC [8,9,11,[14][15][16][17]19,[81][82][83][84][85][86] It does not affect the amount of REC [10,23,[71][72][73][74] The role of density on REC The role of density publications Positive correlation [34,35,40,54,67,75] Negative correlation [8,9,28,[30][31][32][33][36][37][38][39][41][42][43]46,47,[49][50][51][52]56,57,60,64,…”

“…Positive correlation [34,35,40,54,67,75] Negative correlation [8,9,28,[30][31][32][33][36][37][38][39][41][42][43]46,47,[49][50][51][52]56,57,60,64,66,68,[76][77][78] Not disclosed (Not. Sig) [29,45,48,53,55,59,61,62,65,[69][70][71][72][73][74]82] Note.…”

“…Hence, scholars should find some ways to cope with the shortage of needed information. For instance, some publications estimate the REC by considering household energy expenditures [34,41,54,70,73] or taking total energy use in summer months as the proxy of the cooling energy consumption [59,60], while others use existing datasets.…”

Do Children Matter to the Household Debt Burden?

“…The quantile regression on the microeconomic level has already been used in income analysis (Demir et al, 2020) as well as household expenses (Belaid & Rault, 2021) and savings (Hua & Erreygers, 2019). Examples of applied quantile regression can also be found in studies on indebtedness focused on analysis of the amount of loan (Pastrapa & Apostolopoulos, 2015), debt repayments (Han & Shi, 2009), and debt-to-income ratio (Tseng & Hsiao, 2022).…”