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

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  • Porse, Erik
  • Derenski, Joshua
  • Gustafson, Hannah
  • Elizabeth, Zoe
  • Pincetl, Stephanie

Abstract

Residential 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.

Suggested Citation

  • Porse, Erik & Derenski, Joshua & Gustafson, Hannah & Elizabeth, Zoe & Pincetl, Stephanie, 2016. "Structural, geographic, and social factors in urban building energy use: Analysis of aggregated account-level consumption data in a megacity," Energy Policy, Elsevier, vol. 96(C), pages 179-192.
    • Handle: RePEc:eee:enepol:v:96:y:2016:i:c:p:179-192
    DOI: 10.1016/j.enpol.2016.06.002
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    1. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    2. Zhang, Yuhang & Zhang, Yi & Yi Zhang, & Zhang, Chengxu, 2022. "Effect of physical, environmental, and social factors on prediction of building energy consumption for public buildings based on real-world big data," Energy, Elsevier, vol. 261(PB).
    3. Feng, Wei & Zhang, Qianning & Ji, Hui & Wang, Ran & Zhou, Nan & Ye, Qing & Hao, Bin & Li, Yutong & Luo, Duo & Lau, Stephen Siu Yu, 2019. "A review of net zero energy buildings in hot and humid climates: Experience learned from 34 case study buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    4. Xu, Guangyue & Wang, Weimin, 2020. "China’s energy consumption in construction and building sectors: An outlook to 2100," Energy, Elsevier, vol. 195(C).
    5. Lai, Xiaodong & Liu, Jixian & Shi, Qian & Georgiev, Georgi & Wu, Guangdong, 2017. "Driving forces for low carbon technology innovation in the building industry: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 299-315.
    6. Ruddell, Benjamin L. & Cheng, Dan & Fournier, Eric Daniel & Pincetl, Stephanie & Potter, Caryn & Rushforth, Richard, 2020. "Guidance on the usability-privacy tradeoff for utility customer data aggregation," Utilities Policy, Elsevier, vol. 67(C).
    7. Excell, Lauren E. & Jain, Rishee K., 2024. "Examining the impact of energy efficiency retrofits and vegetation on energy performance of institutional buildings: An equity-driven analysis," Applied Energy, Elsevier, vol. 357(C).
    8. Fournier, Eric D. & Federico, Felicia & Porse, Erik & Pincetl, Stephanie, 2019. "Effects of building size growth on residential energy efficiency and conservation in California," Applied Energy, Elsevier, vol. 240(C), pages 446-452.
    9. Anastasia Papangelou & Jean‐Baptiste Bahers & Lynda Aissani, 2023. "Drivers of urban metabolism: Toward a framework for urban transformations," Journal of Industrial Ecology, Yale University, vol. 27(5), pages 1389-1405, October.
    10. Yael Nidam & Ali Irani & Jamie Bemis & Christoph Reinhart, 2023. "Census-based urban building energy modeling to evaluate the effectiveness of retrofit programs," Environment and Planning B, , vol. 50(9), pages 2394-2406, November.
    11. Ki, Jaehong & Yoon, D.K., 2024. "The impact of urban form on residential electricity consumption: Panel data analyses of South Korean urban municipalities," Energy Policy, Elsevier, vol. 186(C).
    12. Porse, Erik & Fournier, Eric & Cheng, Dan & Hirashiki, Claire & Gustafson, Hannah & Federico, Felicia & Pincetl, Stephanie, 2020. "Net solar generation potential from urban rooftops in Los Angeles," Energy Policy, Elsevier, vol. 142(C).
    13. Stephanie Pincetl & Mikhail Chester & David Eisenman, 2016. "Urban Heat Stress Vulnerability in the U.S. Southwest: The Role of Sociotechnical Systems," Sustainability, MDPI, vol. 8(9), pages 1-13, August.

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