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Co-benefits of energy efficiency in residential buildings

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  • Baniassadi, Amir
  • Heusinger, Jannik
  • Gonzalez, Pablo Izaga
  • Weber, Stephan
  • Samuelson, Holly W.

Abstract

Local and state governments find it challenging to adopt aggressive residential building codes that require energy-efficiency upgrades beyond those with a reasonable payback. Thus, economic considerations inhibit the progress towards a more energy-efficient housing stock and often account for direct utility savings. A widely discussed solution is to look beyond energy costs and consider other impacts of energy-saving strategies that affect their financial attractiveness. In this paper, we examine the case of a public housing project in Phoenix, AZ, using several tools to calculate different economic, environmental, and health metrics associated with the three levels of energy efficiency. Our results show that while the payback calculated from direct energy costs may not be attractive, we should consider other savings. We demonstrate that avoided health and climate costs could total around 40% of the direct utility savings. In addition, we quantify how energy-saving strategies can cool the neighborhood, make buildings more resilient to heat, improve indoor air quality, and reduce the transmission of airborne disease. These benefits could be translated to avoid costs in the future.

Suggested Citation

  • Baniassadi, Amir & Heusinger, Jannik & Gonzalez, Pablo Izaga & Weber, Stephan & Samuelson, Holly W., 2022. "Co-benefits of energy efficiency in residential buildings," Energy, Elsevier, vol. 238(PB).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221020168
    DOI: 10.1016/j.energy.2021.121768
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    References listed on IDEAS

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    Cited by:

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    3. Liu, Xiangfei & Ren, Mifeng & Yang, Zhile & Yan, Gaowei & Guo, Yuanjun & Cheng, Lan & Wu, Chengke, 2022. "A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings," Energy, Elsevier, vol. 259(C).

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