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Forecasting uptake of retrofit packages in office building stock under government incentives

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  • Higgins, Andrew
  • Syme, Mike
  • McGregor, James
  • Marquez, Leorey
  • Seo, Seongwon

Abstract

As government and industry plan to reduce energy consumption in building stock, there is a need to forecast the uptake of retrofit packages across building stock over time. To address this challenge a diffusion model was set up and applied to office building stock across New South Wales (NSW) in Australia, accommodating a high spatial resolution and temporal capability for projecting uptake of technology packages characterised by multiple variables. Six retrofit packages were set up for the diffusion model, which ranged from inexpensive services and manuals through to mid-priced packages involving energy efficient T5 lighting and solar hot water through to expensive packages such as chilled beams and Solar PV. We evaluated the model using a base case and two policy programs, representing the Green Building Fund and Environmental Upgrade Agreements. These were recent incentive programs funded by the Australian government to accelerate the uptake of retrofit packages, by providing financial support to upfront expenditures and removing barriers to retrofit. By forecasting uptake of each retrofit package to 2032 under each program, we demonstrate how the model can be a valuable resource in tailoring expensive government programs and increasing their effectiveness.

Suggested Citation

  • Higgins, Andrew & Syme, Mike & McGregor, James & Marquez, Leorey & Seo, Seongwon, 2014. "Forecasting uptake of retrofit packages in office building stock under government incentives," Energy Policy, Elsevier, vol. 65(C), pages 501-511.
    • Handle: RePEc:eee:enepol:v:65:y:2014:i:c:p:501-511
    DOI: 10.1016/j.enpol.2013.10.041
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    References listed on IDEAS

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    1. Curtis, Jim & Walton, Andrea & Dodd, Michael, 2017. "Understanding the potential of facilities managers to be advocates for energy efficiency retrofits in mid-tier commercial office buildings," Energy Policy, Elsevier, vol. 103(C), pages 98-104.
    2. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
    3. Gui, Xuechen & Gou, Zhonghua, 2021. "Understanding green building energy performance in the context of commercial estates: A multi-year and cross-region analysis using the Australian commercial building disclosure database," Energy, Elsevier, vol. 222(C).
    4. Gliedt, Travis & Hoicka, Christina E., 2015. "Energy upgrades as financial or strategic investment? Energy Star property owners and managers improving building energy performance," Applied Energy, Elsevier, vol. 147(C), pages 430-443.
    5. Yamaguchi, Yohei & Kim, Bumjoon & Kitamura, Takuya & Akizawa, Kotone & Chen, Hemiao & Shimoda, Yoshiyuki, 2022. "Building stock energy modeling considering building system composition and long-term change for climate change mitigation of commercial building stocks," Applied Energy, Elsevier, vol. 306(PA).
    6. Barnes, Belinda & Southwell, Darren & Bruce, Sarah & Woodhams, Felicity, 2014. "Additionality, common practice and incentive schemes for the uptake of innovations," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 43-61.
    7. Vidushini Siva & Thomas Hoppe & Mansi Jain, 2017. "Green Buildings in Singapore; Analyzing a Frontrunner’s Sectoral Innovation System," Sustainability, MDPI, vol. 9(6), pages 1-23, May.

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