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Increase in household energy consumption due to ambient air pollution

Author

Listed:
  • Jiyong Eom

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Minwoo Hyun

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Jaewoong Lee

    (Korea Advanced Institute of Science and Technology (KAIST))

  • Hyoseop Lee

    (Encored Technologies)

Abstract

In response to acute environmental stresses such as air pollution, households may resort to quick and convenient adaptation measures that increase energy use, amplifying the environmental impact and requiring additional adaptation. This cycle of energy-intensive adaptation has so far received little consideration by the broader energy community. Here, we analyse the response of Korean households to PM2.5 (ultrafine dust), based on real-time hourly smart meter data. We show that a 75 μg m–3 increase in PM2.5 concentration led to an 11.2% increase in electricity consumption, equivalent to the impact of a 3.5 °C increase in the average summer temperature. The magnitude of the energy-intensive adaptation correlated with households’ lifestyles and was higher on weekends and during daytime hours on both weekdays and weekends. The responses also reflected seasonal differences and had a U-shape relationship with temperature. We illustrate the importance of integrating the broader impacts of air pollution into policymaking to strike a proper balance between its mitigation and adaptation.

Suggested Citation

  • Jiyong Eom & Minwoo Hyun & Jaewoong Lee & Hyoseop Lee, 2020. "Increase in household energy consumption due to ambient air pollution," Nature Energy, Nature, vol. 5(12), pages 976-984, December.
    • Handle: RePEc:nat:natene:v:5:y:2020:i:12:d:10.1038_s41560-020-00698-1
    DOI: 10.1038/s41560-020-00698-1
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    Cited by:

    1. Minseok Jang & Hyun Cheol Jeong & Taegon Kim & Dong Hee Suh & Sung-Kwan Joo, 2021. "Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape," Energies, MDPI, vol. 14(22), pages 1-15, November.
    2. Choi, Jongwoo & Lee, Il-Woo & Cha, Suk-Won, 2022. "Analysis of data errors in the solar photovoltaic monitoring system database: An overview of nationwide power plants in Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    3. Diego Moya & César Arroba & Christian Castro & Cristian Pérez & Sara Giarola & Prasad Kaparaju & Ángel Pérez-Navarro & Adam Hawkes, 2023. "A Methodology to Estimate High-Resolution Gridded Datasets on Energy Consumption Drivers in Ecuador’s Residential Sector during the 2010–2020 Period," Energies, MDPI, vol. 16(10), pages 1-16, May.
    4. Minkyu Kim & Chankook Park, 2021. "Academic Topics Related to Household Energy Consumption Using the Future Sign Detection Technique," Energies, MDPI, vol. 14(24), pages 1-24, December.
    5. Meixuan Teng & Hua Liao & Paul J. Burke & Tianqi Chen & Chen Zhang, 2022. "Adaptive responses: the effects of temperature levels on residential electricity use in China," Climatic Change, Springer, vol. 172(3), pages 1-20, June.
    6. Tingting Xiao & Zhong Liu, 2023. "Air Pollution and Enterprise Energy Efficiency: Evidence from Energy-Intensive Manufacturing Industries in China," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
    7. Liang, Jing & Qiu, Jiehong & Wang, Xiangrui & Wu, Jikai & Yang, Sansi & Zhou, You, 2024. "Examining energy inequality: The impact of household wealth on heterogeneous response to temperatures," Energy Policy, Elsevier, vol. 190(C).
    8. Icaza-Alvarez, Daniel & Jurado, Francisco & Tostado-Véliz, Marcos & Arevalo, Paúl, 2022. "Decarbonization of the Galapagos Islands. Proposal to transform the energy system into 100% renewable by 2050," Renewable Energy, Elsevier, vol. 189(C), pages 199-220.
    9. Jeseok Ryu & Jinho Kim, 2020. "Demand Response Program Expansion in Korea through Particulate Matter Forecasting Based on Deep Learning and Fuzzy Inference," Energies, MDPI, vol. 13(23), pages 1-14, December.
    10. Eslam Mohammed Abdelkader & Nehal Elshaboury & Abobakr Al-Sakkaf, 2022. "On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 13(1), pages 1-31, January.
    11. Huang, Xinyu & Du, Zhao & Li, Yuanji & Li, Ze & Yang, Xiaohu & Li, Ming-Jia, 2024. "Optimal design on fin-metal foam hybrid structure for melting and solidification phase change storage: An experimental and numerical study," Energy, Elsevier, vol. 302(C).

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