IDEAS home Printed from https://ideas.repec.org/a/bla/inecol/v27y2023i4p1076-1088.html
   My bibliography  Save this article

Scaling in urban building energy use and its influencing factors

Author

Listed:
  • Chunyan Wang
  • Hanying Jiang
  • Hao Wu
  • Yi Liu
  • Siyue Guo
  • Ming Xu

Abstract

Several studies have reported scaling relationships for energy consumption with respect to city size and other indicators. However, such scaling relationships have rarely been reported at the suburban level. This study explored the scaling relationships between energy use (EU) and building size (gross floor area [GFA]) at the building level in 16 urban regions in the United States from 2011 to 2019. We found that the scaling exponents of most of the examined regions changed from either super‐linear or sub‐linear to linear (β = 1) over the years. The scaling exponents of some cities (e.g., New York City) fluctuated around 1. These scaling exponents are negatively correlated with regional climate. This study reports that the scaling relationships between energy consumption and GFA at the building level in heterogeneous cities are evolving toward linear scaling. This study also found that different building types and building energy structures significantly impact building energy consumption. Hotels in New York City had the highest scaling exponent (β = 1.02), and strong correlations were observed between the scaling exponents and the share of electricity in building EU. These insights reveal the common nature of the relationships between building EU and GFA and the intersections between scaling exponents and building attributes. Our study highlights the importance of energy efficiency management in hotels and electricity‐dominated buildings.

Suggested Citation

  • Chunyan Wang & Hanying Jiang & Hao Wu & Yi Liu & Siyue Guo & Ming Xu, 2023. "Scaling in urban building energy use and its influencing factors," Journal of Industrial Ecology, Yale University, vol. 27(4), pages 1076-1088, August.
    • Handle: RePEc:bla:inecol:v:27:y:2023:i:4:p:1076-1088
    DOI: 10.1111/jiec.13395
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jiec.13395
    Download Restriction: no
    File URL: https://libkey.io/10.1111/jiec.13395?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ryu, Jun-Yeol & Kim, Dae-Wook & Kim, Man-Keun, 2021. "Household differentiation and residential electricity demand in Korea," Energy Economics, Elsevier, vol. 95(C).
    2. Huang, Jianhua & Gurney, Kevin Robert, 2016. "The variation of climate change impact on building energy consumption to building type and spatiotemporal scale," Energy, Elsevier, vol. 111(C), pages 137-153.
    3. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
    4. M. Batty & R. Carvalho & A. Hudson-Smith & R. Milton & D. Smith & P. Steadman, 2008. "Scaling and allometry in the building geometries of Greater London," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(3), pages 303-314, June.
    5. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    6. Pan, Yue & Zhang, Limao, 2020. "Data-driven estimation of building energy consumption with multi-source heterogeneous data," Applied Energy, Elsevier, vol. 268(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
    3. Salah Vaisi & Saleh Mohammadi & Benedetto Nastasi & Kavan Javanroodi, 2020. "A New Generation of Thermal Energy Benchmarks for University Buildings," Energies, MDPI, vol. 13(24), pages 1-18, December.
    4. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation," Energy, Elsevier, vol. 244(PB).
    5. Andrews, Abigail & Jain, Rishee K., 2022. "Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking," Applied Energy, Elsevier, vol. 327(C).
    6. Mohammed Hammam Mohammed Al-Madani & Yudi Fernando & Ming-Lang Tseng, 2022. "Assuring Energy Reporting Integrity: Government Policy’s Past, Present, and Future Roles," Sustainability, MDPI, vol. 14(22), pages 1-24, November.
    7. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
    8. Chen, Yanguang, 2014. "An allometric scaling relation based on logistic growth of cities," Chaos, Solitons & Fractals, Elsevier, vol. 65(C), pages 65-77.
    9. Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
    10. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    11. Thill, Jean-Claude & Dao, Thi Hong Diep & Zhou, Yuhong, 2011. "Traveling in the three-dimensional city: applications in route planning, accessibility assessment, location analysis and beyond," Journal of Transport Geography, Elsevier, vol. 19(3), pages 405-421.
    12. Zhang, Shaohui & Guo, Qinxin & Smyth, Russell & Yao, Yao, 2022. "Extreme temperatures and residential electricity consumption: Evidence from Chinese households," Energy Economics, Elsevier, vol. 107(C).
    13. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    14. Jungwon Yoon & Sanghyun Bae, 2020. "Performance Evaluation and Design of Thermo-Responsive SMP Shading Prototypes," Sustainability, MDPI, vol. 12(11), pages 1-35, May.
    15. Lai, Yuan & Papadopoulos, Sokratis & Fuerst, Franz & Pivo, Gary & Sagi, Jacob & Kontokosta, Constantine E., 2022. "Building retrofit hurdle rates and risk aversion in energy efficiency investments," Applied Energy, Elsevier, vol. 306(PB).
    16. Filippín, Celina & Ricard, Florencia & Flores Larsen, Silvana & Santamouris, Mattheos, 2017. "Retrospective analysis of the energy consumption of single-family dwellings in central Argentina. Retrofitting and adaptation to the climate change," Renewable Energy, Elsevier, vol. 101(C), pages 1226-1241.
    17. Shi, Zhengyu & Wu, Libo & Zhou, Yang, 2023. "Predicting household energy consumption in an aging society," Applied Energy, Elsevier, vol. 352(C).
    18. Chen, Guangwu & Zhu, Yuhan & Wiedmann, Thomas & Yao, Lina & Xu, Lixiao & Wang, Yafei, 2019. "Urban-rural disparities of household energy requirements and influence factors in China: Classification tree models," Applied Energy, Elsevier, vol. 250(C), pages 1321-1335.
    19. Ye, Zhongnan & Cheng, Kuangly & Hsu, Shu-Chien & Wei, Hsi-Hsien & Cheung, Clara Man, 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach," Applied Energy, Elsevier, vol. 301(C).
    20. Shen, Meng & Li, Xiang & Lu, Yujie & Cui, Qingbin & Wei, Yi-Ming, 2021. "Personality-based normative feedback intervention for energy conservation," Energy Economics, Elsevier, vol. 104(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:inecol:v:27:y:2023:i:4:p:1076-1088. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=1088-1980 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.