Structural, geographic, and social factors in urban building energy use: Analysis of aggregated account-level consumption data in a megacity

Abstract

Building energy use varies widely across metropolitan Los Angeles. Building age, household income, home ownership rates, and land use are all correlated with energy consumption. High-income areas use more energy per building, while lower-income areas use more energy per square-foot. Account-level energy use data can help local governments devise conservation strategies. Energy efficiency programs need evaluated using energy consumption data. a r t i c l e i n f o b s t r a c tResidential and commercial buildings comprise approximately forty percent of total energy consumption and carbon dioxide emissions in the U.S. Yet, while California spends $1.5 billion annually on energy efficiency programs, limited research has explored how building energy consumption varies within cities, including the social and structural factors that influence electricity and natural gas use. We present results from an analysis of aggregated account-level utility billing data for energy consumption across the over two million properties in Los Angeles County. Results show that consumption in L.A. County varies widely with geography, income, building characteristics, and climate. Several higher-income areas have greater total energy use per building even in cooler climates, while many lower-income regions rank higher for energy use per square-foot. Energy consumption also correlates with building age, which varies widely throughout the region. Our results demonstrate the many complex and interrelated factors that influence urban energy use. While billing data is critical for devising energy efficiency programs that actually realize estimated savings and promote more sustainable cities, opening access to such data presents significant challenges for protecting personal privacy. The presented approach is adaptable and scalable to cities seeking to develop data-driven policies to reduce building energy use.

The Hestia fossil fuel CO<sub>2</sub> emissions data product for the Los Angeles megacity (Hestia-LA)

“…"Bottom-up" approaches, by contrast, include a mixture of direct flux measurement, indirect measurement and modeling. Common among the bottom-up approaches are those that include flux estimation based on a combination of activity data (population, number of vehicles, building floor area) and emission factors (amount of CO 2 emitted per activity), socioeconomic regression modeling, or scaling from aggregate fuel consumption (VandeWeghe and Kennedy, 2007;Shu and Lam, 2011;Zhou and Gurney, 2011;Gurney et al, 2012;Jones and Kammen, 2014;Ramaswami and Chavez, 2013;Patarasuk et al, 2016;Porse et al, 2016). Direct endof-pipe flux monitoring is often used for large point sources such as power plants (Gurney et al, 2016).…”

Local and national determinants of household energy consumption in the Netherlands

“…they presume that there are some generic rules applicable to all locations. A variety of the studies following this presumption have cited global rules to explain levels of HEC, such as the following examples: the higher the income level, the higher the HEC (Druckman and Jackson 2008;Joyeux and Ripple 2007); per capita HEC drops in larger households (Kowsari and Zerriffi 2011;Isaac and Van Vuuren 2009); the older a building, the higher the HEC (Belaïd 2016;Steemers and Yun 2009); the higher the surface-tovolume ratio of the buildings, the higher the HEC (Steemers and Yun 2009;Druckman and Jackson 2008); HEC drops in areas with a higher population density (Porse et al 2016;Pachauri and Jiang 2008); the more cooling and heating degree days there are, the higher the level of consumption (Wiedenhofer et al 2013;Reinders et al 2003); the impact of windspeed on the heat loss of buildings is substantial enough to change the level of HEC (Sanaieian et al 2014;van Moeseke et al 2005); land surface temperature affects HEC in all urban areas (Azevedo et al 2016;Lee and Lee 2014).…”

Comparison of Global Downscaled Versus Bottom‐Up Fossil Fuel CO2 Emissions at the Urban Scale in Four U.S. Urban Areas

“…Spatiotemporally resolved FFCO 2 emission data products from the global to the urban are developed using two general approaches which we refer to here as "bottom-up" and "downscaling." Bottom-up approaches use direct flux monitoring and sectoral activity data gathered from various socioeconomic sources to develop spatiotemporally explicit, mechanistic FFCO 2 emissions (Brondfield et al, 2012;Gately et al, 2013;Gately & Hutyra, 2017;Gurney et al, 2009;Jones & Kammen, 2014;Parshall et al, 2010;Patarasuk et al, 2016;Pincetl et al, 2014;Porse et al, 2016;Shu & Lam, 2011;VandeWeghe & Kennedy, 2007;. At the urban scale, this approach has been pioneered by the Hestia Project which estimates FFCO 2 emissions for urban landscapes at the building/street spatial scale and hourly temporal scale with sectoral, fuel, and functional details (Gurney et al, 2012).…”