Residential energy demand in the United States: Analysis using static and dynamic approaches

Salari, Mahmoud; Javid, Roxana J.

doi:10.1016/j.enpol.2016.09.041

Cited by 54 publications

(21 citation statements)

References 57 publications

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“…For designing intervention aimed at changing the future land use plans for the cities across the world, the micro scale analysis is crucial. The global energy demand has raised many concerns across the world particularly for the U.S. that has the highest demand among countries [1]. A complete assessment of households' energy consumption and its relationships to spatial urban structure need to be provided to help engineers and planners in their approach toward sustainability.…”

Section: Discussionmentioning

confidence: 99%

“…The impact of climate change and temperature on residential energy consumption has been well documented [1,27,65,66]. There is a non-linear relationship between temperature and energy use [66,67] as electricity and gas demand increase for both low and high temperatures and decrease for Intermediate temperatures [67].…”

Section: Temperaturementioning

confidence: 99%

“…Worldwide, energy consumption and its associated greenhouse gas (GHG) emissions has increased over the past few decades [1] and is expected to grow by 28% between 2015 and 2040 [2]. The U.S. households alone are responsible for about 20% of annual global GHG emissions, yet they represent only 4.3% of the global population [3].…”

Section: Introductionmentioning

confidence: 99%

“…More differentiated local knowledge (e.g., land-use regulations, policies, and practices) that plays particular role in energy consumption is essential for community-scale urban design [22]. Research addressing spatial aspects thus far has been conducted mostly at the macro scales such as country, region, state or city [1,23,24]. Microscale (e.g., census tract) energy consumption analysis remains under explored [17].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Temperaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2018

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“…Tabasi, et al, [7] studied the behavior of indicating parameters and performed forecasting up-to 2022, by evaluating historical data from , for Germany in the year of (2016) using linear regression analysis. Salari and Javid, (2016) [8] investigated the indicator of energy consumption and presents the alternative scenarios for reducing the residential energy demand of United States by using both static and dynamic estimation models. Mohamed and Bodger, [9] performed an critical analysis over correlation between the electricity usage and its demographical variables, and estimated the future energy consumption by using multiple linear regression analysis for New Zealand.…”

Section: Introductionmentioning

confidence: 99%

Investigating Future Consumption of Electricity Estimates in Pakistan using Linear Regression Analysis

Waseera¹,

Soomro²

2020

IJEW

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In this study, an inclusive effect of economy, gross domestic product (GDP) and other socio-economic terms are deeply taken into account for long lasting and future projection of electricity usage in Pakistan. The explaining variables considered in this study are gross domestic product GDP, per capita GDP, electricity consumption price/kwh and population, to develop the different regression models. The historical data considered for time period of past 47 years from 1970-2016. This paper is divided into two parts, in first part estimation of income, price and GDP elasticities are evaluated for residential, non-residential and total electricity consumption models. These elasticities showed that long-run and short-run price elasticities for domestic model are -0.30 and -0.32, for non-domestic model 0.42 and 0.64 where-as for total electricity consumption model price elasticities are 0.50 and o.93. In addition GDP/per capita and GDP resulted higher values. In second part, different statistical models are presented by using linear regression, which are based on the stationary or co-integrated time series data. Moreover for checking the validity of proposed models different statistical tests are conducted. A comparison with available national forecasts, which are proposed through different econometric models, like support-vector model or Pakistan's long range-energy alternative plaining (LEAP) model was exmined, resulting that proposed regression model has compatibility with national projections, with deviation of ±2% to ±12% for the best and worst case, these deviations are acceptable in the time span taken into account.

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Saving energy in residential buildings: the role of energy pricing

et al. 2021

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A zero-carbon society requires dramatic change everywhere including in buildings, a large and politically sensitive sector. Technical possibilities exist but implementation is slow. Policies include many hard-to-evaluate regulations and may suffer from rebound mechanisms. We use dynamic econometric analysis of European macro data for the period 1990–2018 to systematically examine the importance of changes in energy prices and income on residential energy demand. We find a long-run price elasticity of −0.5. The total long-run income elasticity is around 0.9, but if we control for the increase in income that goes towards larger homes and other factors, the income elasticity is 0.2. These findings have practical implications for climate policy and the EU buildings and energy policy framework.

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Residential energy demand in the United States: Analysis using static and dynamic approaches

Cited by 54 publications

References 57 publications

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

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

Investigating Future Consumption of Electricity Estimates in Pakistan using Linear Regression Analysis

Saving energy in residential buildings: the role of energy pricing

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