The determinants of domestic energy consumption in France: Energy modes, habitat, households and life cycles

Lévy, Jean-Pierre; Bélaïd, Fateh

doi:10.1016/j.rser.2017.06.022

Cited by 58 publications

(26 citation statements)

References 19 publications

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“…(Greening and Jeng 1994;Jensen 2008;Lenzen et al 2006;Pachauri 2004;Prskawetz et al 2004;Pucher et al 1998;Van den Bergh 2008). These sociodemographic features affect energy profiles and some studies identify subpopulations according to their mode of consumption and behavior (Belaïd 2016;Lévy and Belaïd 2017 Inequality levels remained high, with small fluctuations (see Figure 1). Between 1998Between -2004, the Mexican Gini coefficient declined, but this indicator is still one of the highest in the world (Esquivel 2011).…”

Section: Electrical Appliances Ownershipmentioning

confidence: 99%

Diffusion of Electricity Consumption Practices in Mexico

Castillo

Peña

2017

Social Sciences

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Abstract:In recent decades, residential energy consumption has grown in Mexico despite high poverty levels. While inequalities in energy have been documented, less attention has been paid to practices of consumption. Particularly, we sustain that it is necessary to account for changes in associated behaviors, which shape energy use, such as the acquisition of electrical appliances. This paper analyzes if there is evidence of diffusion of energy practices from higher to lower-income households. We hypothesize that more intensive energy practices expand across groups beyond their demographic and socioeconomic characteristics. Employing a harmonized dataset of thirteen Income and Expenditure Household Surveys, we assess changes in electrical appliances and electricity consumption. Using latent class analysis, we construct energy profiles that identify underlying consumption behaviors from sociodemographic and residential characteristics. We find support for the argument that intensive energy practices expanded from high to lower socioeconomic groups. While this trend reflects improvements in living conditions in Mexico, it also highlights the environmental challenges that increasing consumption poses for sustainable development goals.

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Section: Electrical Appliances Ownershipmentioning

confidence: 99%

Diffusion of Electricity Consumption Practices in Mexico

Castillo

Peña

2017

Social Sciences

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“…The authors concluded that residential gas consumption is mainly determined by the physical characteristics of the building, while electricity consumption is more related to household composition, in particular income and family composition. Lévy and Belaid [32] analysed the impact on energy consumption of household profiles and building profiles by investigating global consumption, consumption per m 2 , and per capita. The study highlighted the impact of the demographic characteristics of households.…”

Section: Impact Of Drivers On Energy Consumptionmentioning

confidence: 99%

Analysis of the EU Residential Energy Consumption: Trends and Determinants

et al. 2019

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This article analyses the status and trends of the European Union (EU) residential energy consumption in light of the energy consumption targets set by the EU 2020 and 2030 energy and climate strategies. It assesses the energy efficiency progress from 2000 to 2016, using the official Eurostat data. In 2016, the residential energy consumption amounted to 25.71% of the EU’s final energy consumption, representing the second largest consuming sector after transport. Consumption-related data are discussed together with data on some main energy efficiency policies and energy consumption determinants, such as economic and population growth, weather conditions, and household and building characteristics. Indicators are identified to show the impact of specific determinants on energy consumption and a new indicator is proposed, drawing a closer link between energy trends and policy and technological changes in the sector. The analysis of these determinants highlights the complex dynamics behind the demand of energy in the residential sector. Decomposition analysis is carried out using the Logarithmic Mean Divisia Index technique to provide a more complete picture of the impact of various determinants (population, wealth, intensity, and weather) on the latest EU-28 residential energy consumption trends. The article provides a better understanding of the EU residential energy consumption, its drivers, the impact of current policies, and recommendations on future policies.

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“…Occupant characteristics have been identified as one of the main factors affecting domestic energy use [11]. Socioeconomic and demographic characteristics of HH such as income, education, size, and age determine households' social class and their lifestyle [34].…”

Section: Socioeconomic Characteristics and Demographicsmentioning

confidence: 99%

“…HH size and age are other significant factors influencing HH energy consumption [11]. A larger number of HH members is associated with higher energy consumption for appliances, larger vehicles, and longer trips [27,41,44,45].…”

Section: Socioeconomic Characteristics and Demographicsmentioning

confidence: 99%

“…The influence of various factors such as urban form, socioeconomic, demographic, climate, and housing on energy consumption have been widely studied [11][12][13][14][15][16], yet the empirical evidence is inconclusive [9,17]. The disagreements may exist due to the limitation of datasets, different methodologies, variations in countries, the scale being used, and lack of locational contexts analysis being conducted [18,19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach

2018

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The U.S. household (HH) energy consumption is responsible for approximately 20% of annual global GHG emissions. Identifying the key factors influencing HH energy consumption is a major goal of policy makers to achieve energy sustainability. Although various explanatory factors have been examined, empirical evidence is inconclusive. Most studies are either aspatial in nature or neglect the spatial non-stationarity in data. Our study examines spatial variation of the key factors associated with HH energy expenditures at census tract level by utilizing geographically weighted regression (GWR) for the 14 metropolitan statistical areas (MSAs) in North Carolina (NC). A range of explanatory variables including socioeconomic and demographic characteristics of households, local urban form, housing characteristics, and temperature are analyzed. While GWR model for HH transportation expenditures has a better performance compared to the utility model, the results indicate that the GWR model for both utility and transportation has a slightly better prediction power compared to the traditional ordinary least square (OLS) model. HH median income, median age of householders, urban compactness, and distance from the primary city center explain spatial variability of HH transportation expenditures in the study area. HH median income, median age of householders, and percent of one-unit detached housing are identified as the main influencing factors on HH utility expenditures in the GWR model. This analysis also provides the spatial variability of the relationship between HH energy expenditures and the associated factors suggesting the need for location-specific evaluation and suitable guidelines to reduce the energy consumption.

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The determinants of domestic energy consumption in France: Energy modes, habitat, households and life cycles

Cited by 58 publications

References 19 publications

Diffusion of Electricity Consumption Practices in Mexico

Diffusion of Electricity Consumption Practices in Mexico

Analysis of the EU Residential Energy Consumption: Trends and Determinants

Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach

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