An analysis of monthly household energy consumption among single-family residences in Texas, 2010

Valenzuela, Carlos; Valencia, Alelhie; White, Steve; Jordan, Jeffrey A.; Cano, Stephanie; Keating, Jerome P.; Nagorski, John; Potter, Lloyd B.

doi:10.1016/j.enpol.2013.12.009

Cited by 53 publications

(24 citation statements)

References 34 publications

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“…We use a dummy variable for ownership status that takes a value of 1 if the house is owner occupied and 0 otherwise. Owneroccupied households tend to invest in energy-efficient appliances and thereby consume less energy than renter occupied households [13,29]. However, Kaza [7] demonstrated that owners may have an incentive to use more energy-efficient cooling systems, but they are more likely to own more energy-consuming appliances.…”

Section: Dwelling Attributesmentioning

confidence: 97%

“…The household disposable income variables are deflated using the consumer price index, whose base year is 2011. Most studies have indicated that income has a positive effect on household energy consumption [3,6,8,13]. In addition, household income influences the implicit discount rate when households purchase equipment.…”

Section: Householdmentioning

confidence: 98%

“…The characteristics of household head include age, gender and education. The age of household head, measured by a continuous variable, is considered a factor because it reveals the life-cycle stage of a household and determines household electricity use [13,30]. Older families may have more economic ability and consume more electricity than younger families.…”

Section: The Independent Variablesmentioning

confidence: 99%

“…Previous studies have found that household energy consumption is related to various factors, including economic ability [2e6], socio-demographic characteristics [7e10], physical characteristics of the dwelling [11e13], residential location [4,11,14], environmental and climate factors [12,13], and the costs of energy use [15,16]. Most of these studies investigate the factors that contribute to household energy consumption.…”

Section: Introductionmentioning

confidence: 99%

“…To avoid sampling bias, Kaza [7] and Valenzuela et al [13] employed a QR (quantile regression) approach that used the entire sample to estimate the effects of explanatory variables on the energy use distribution. Their studies suggested that explanatory variables generally have different effects on household energy use depending on whether household energy use is in a high or low quantile.…”

Section: Introductionmentioning

confidence: 99%

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The determinants of household electricity consumption in Taiwan: Evidence from quantile regression

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Section: Dwelling Attributesmentioning

confidence: 97%

Section: Householdmentioning

confidence: 98%

Section: The Independent Variablesmentioning

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Recurrent carbon footprint assessment and forecasting for conventional housing in tropical regions: A Malaysian case study

Shafiq

Gardezi

Nuruddin

et al. 2017

Env Prog and Sustain Energy

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Almost all life cycle assessment methods of carbon footprint for conventional housing in tropical region have either ignored the impacts of recurrent carbon or considered based on random assumptions. To ensure the green and sustainable urban built environment, particularly in tropical regions, an accurate monitoring and control of carbon footprint requires a comprehensive assessment of all phases of project life cycle, including maintenance. This article presents an innovative and dynamic approach of forecasting of carbon footprint including the recurrent carbon by combining a statistical (simple and multiple regression) and simulation (3D parametric prototyping) technique in a partial LCA (cradle to site) study for a tropical built environment. The developed multivariable regression model satisfied the statistical criteria of an efficient and consistent tool as the average percentage error of the predicted and observed values was found within the acceptable range. The average contribution of CO2 ranged from 2.0 tons to 40 tons for 25 to 100 year of service life span. Ceramic tiles, false ceiling, plaster and roof tiles remained the top materials. The model validation observed an acceptable average error range from (−) 3.24% to (+) 8%. The study analyzed the factual recurrent carbon footprint impacts and justified the importance of its inclusion in preemptive mitigation toward sustainability concerns at very early stage of the housing project. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 839–849, 2018

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A Case Study: Modeling Energy Markets by the Means of Quantile Regression

Uribe

Guillén

2020

Quantile Regression for Cross-Sectional and Time Series Data

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An analysis of monthly household energy consumption among single-family residences in Texas, 2010

Cited by 53 publications

References 34 publications

The determinants of household electricity consumption in Taiwan: Evidence from quantile regression

The determinants of household electricity consumption in Taiwan: Evidence from quantile regression

Recurrent carbon footprint assessment and forecasting for conventional housing in tropical regions: A Malaysian case study

A Case Study: Modeling Energy Markets by the Means of Quantile Regression

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