Using physical, behavioral, and demographic variables to explain suite-level energy use in multiresidential buildings

Brown, Craig; Gorgolewski, Mark; Goodwill, Alasdair M.

doi:10.1016/j.buildenv.2015.02.039

Cited by 13 publications

(7 citation statements)

References 26 publications

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“…Overall, the socioeconomic and demographic findings presented here build upon and support findings in numerous empirical studies, such as [1,[18][19][20][21][22][23][24][25][26]51]. Such studies highlight the many ways that individual heterogeneity in socio-demographics and behavioral choice accounts for significant variation between predicted and actual energy use in buildings, and participation in energy efficiency programs.…”

Section: Policy Implications and Recommendationssupporting

confidence: 76%

“…This can be due to any number of intervening factors (for example, some researchers highlight the role of habits as a potential explanatory variable for the continued increase in energy consumption despite rising environmental awareness [40][41][42][43][44]). Thus, the demographic and socioeconomic focus is taking a center stage in energy research as policy makers realize that even among nearly identical physical structures or households, there may be vast differences in the energy consumption or efficiency behaviors undertaken [26]. Innovative cities and municipalities-driven by innovative and forward-thinking policymakers-are crafting new policy mechanisms that take such individualized factors into account; thus, for those policymakers, demographic and socioeconomic data and findings can offer important direction.…”

Section: Energy Efficiencymentioning

confidence: 99%

“…Some researchers point to the intention-behavior gap as a driving force behind lack of energy efficiency program participation, whereby stark differences exist between what individuals intend to do and actually do [10][11][12][13][14]; others argue that the technical and cost-saving potential for efficient buildings exists but that intervening economic factors such as information asymmetries, heterogeneous risk tolerance thresholds among individuals, and inaccurate personal discount rates lead to lack of participation in such cost-savings potential [3,4,6,[15][16][17]. Thus, many researchers are turning to socioeconomic and demographic factors as drivers of energy efficiency decisions to complement the field's knowledge of physical building characteristics that influence energy consumption [1,[18][19][20][21][22][23][24][25][26]; the present study builds upon this large and growing body of research.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Drivers of National-Level Energy Audit Behavior: Demographics and Socioeconomic Characteristics

Hewitt

Wang

2020

Sustainability

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The energy audit-an assessment of a home's energy systems performed by a trained auditor in order to provide the resident with strategies for saving energy and money-is provided by many utility companies throughout the United States for free or at a reduced cost. The uptake of such programs is generally low, and little is known about audit participants. Importantly, as more evidence points to the need to look beyond physical building characteristics to increase energy efficiency, this work explores if specific characteristics of the individual are correlated with increased participation in audit programs. This research analyzes the most recent (2015) national level Residential Energy Consumption Survey (RECS) data through a binary logit regression to determine what socioeconomic and demographic factors, if any, are statistically significant in linking to the decision to undertake an audit, while controlling for physical building characteristics. The findings indicate that age has a significant and positive relationship with the decision to undertake an audit, as does being non-white, while renting has a significant and negative relationship. Knowledge about national-level participation in audit programs can help policy makers craft more strategic incentives to increase participation and, ultimately, help connect the audit decision to the more important next step of retrofits and upgrades to save energy.

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Section: Policy Implications and Recommendationssupporting

confidence: 76%

Section: Energy Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding the Drivers of National-Level Energy Audit Behavior: Demographics and Socioeconomic Characteristics

Hewitt

Wang

2020

Sustainability

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“…As well, in the case of buildings with individually submetered suites and separate thermostats, further complexity is introduced by the fact that occupants may choose different setpoints based on their preferences. A recent study of highrise MURBs within Toronto showed that even in high-performing buildings, the EUI of suites can vary by as much as a factor of 7, due to occupant behaviour (Brown, Gorgolewski, & Goodwill, 2015).…”

Section: Assessing Building Performance Gapsmentioning

confidence: 99%

Examining the performance gap in LEED certified MURBs

Carroll¹

2023

Preprint

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This study assesses the performance gap between actual energy performance and desired energy performance outcomes for a case study of 19 LEED-certified multi-unit residential buildings in the Greater Toronto Area. The study examines 1) how accurately design-stage energy modelling predicts actual energy use, 2) how much variation of energy performance can be seen between buildings of the same level of certification, and 3) the key contributing factors of this performance gap. Using EUI as the basis of comparison, trend analysis was carried out. It was determined that a performance gap between modelled and actual building energy use does exist. When compared to a larger sample of existing buildings, the case study buildings show no real improvement, on average. Regression models revealed no strong correlation between LEED Level or LEED EAc1 credits and reduced EUIs.

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“…Canadian electricity and natural gas use, emphasizing household characteristics. Though dwelling characteristics have been used in engineering models, less work exists on examining them from a statistical perspective; though it appears this area is becoming more common (e.g., Brounen, Kok, & Quigley, 2013;Brown, Gorgolewski, & Goodwill, 2015;Guerra Santin et al, 2009;Steemers & Yun, 2009). These statistical studies use methods such as quantile regressions, ordinary least squares regressions, neural networks, and decision trees to examine energy use (e.g., Kaza, 2010;Liao & Chang, 2002;Sanquist, Orr, Shui, & Bittner, 2012;Valenzuela et al, 2014), thus providing the direction for the present methodology (i.e., regression modeling).…”

Section: Introductionmentioning

confidence: 99%

Towards understanding the determinants of household energy use: exploring the role of individual, household, social, and physical characteristics on household electricity and gas use

Das¹

2021

Preprint

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This research is an investigation of people and their relationship with energy. This dissertation first presents an analysis of national surveys: Statistics Canada‘s 2013 Households and the Environment Survey and the 2013 Energy Use Supplement. These surveys were used to investigate the relationships between dwelling characteristics, household characteristics, electricity use, and natural gas use. Approximately 20% of the variance in overall energy use was explained by dwelling and household characteristics, leaving a large portion of the variance unaccounted for. It was also found that household characteristics have more impact on electricity use than natural gas use. In contrast, dwelling characteristics have more impact on natural gas use than on electricity use. In order to better gauge the unexplained variation in household energy use, an instrument was developed to measure public energy literacy, which was conceptualized using previous surveys as well as frameworks and models from academic and grey literature, as well as literacy and educational initiatives and programs. Such conceptualization created the parameters within which survey items could be developed, created, and therefore generated. Instrument development included pretesting, cognitive interviews, and quantitative and qualitative analysis of instrument items. The resulting survey is a 15-item, 5-item, and 9-item measure of energy-related knowledge, attitudes, and behaviours, respectively. Energy literacy, along with environmental concern, was assessed among a sample of Toronto homeowners. In general, participants demonstrated environmental concern, positive attitudes, performed energy-saving behaviours and demonstrated satisfactory understanding of energy-related knowledge (i.e., average score of 66 percent). Interesting relationships were found between the measures of energy literacy as well as some additional measures of knowledge and behaviour, suggesting that examination of energy use should be broken down into smaller examinable components, such as curtailment and efficiency behaviours. The attitudinal and behavioural subscales belonging to the measure of energy literacy added to the explanation of household energy use in the Toronto sample: for electricity 11% of the variation was explained and 4% for natural gas. Surveys and examination of household energy use need to therefore consider examining energy literacy variables in addition to socio-demographics and dwelling characteristics.

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Using physical, behavioral, and demographic variables to explain suite-level energy use in multiresidential buildings

Cited by 13 publications

References 26 publications

Understanding the Drivers of National-Level Energy Audit Behavior: Demographics and Socioeconomic Characteristics

Understanding the Drivers of National-Level Energy Audit Behavior: Demographics and Socioeconomic Characteristics

Examining the performance gap in LEED certified MURBs

Towards understanding the determinants of household energy use: exploring the role of individual, household, social, and physical characteristics on household electricity and gas use

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