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Evaluating the energy performance of buildings within a value at risk framework with demonstration on UK offices

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  • Parkinson, Aidan
  • Guthrie, Peter

Abstract

Facility quality is dependent on the performance of utility infrastructure and local weather conditions in addition to social context. Theoretically, improvements in facility quality such as energy performance should reduce marginal costs of consumption for occupiers so as to increase asset values. This research explores the relationship between expectations of building energy performance and the financial value of real estate. The United Kingdom was selected as a leading case, being a large economy that has enacted legislation committing the government to delivering ambitious emission reductions to mitigate climate change. Appropriate instruments are identified and applied to a diverse set of case study offices. A scalable method is employed for calculating value at risk from energy performance for buildings. This involves a novel approach to testing supporting system capacity through an exploratory analysis of 2050 end-states and demonstration on real world contemporary cases as a feasibility study. In doing so, the significance of systematic risks to building energy performance can be quantified. By comparing systematic excess returns for energy performance with rental value for a large sample a Capital Market Line for building energy management emerges, providing a means to shadow price the social impacts of climate change.

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  • Parkinson, Aidan & Guthrie, Peter, 2014. "Evaluating the energy performance of buildings within a value at risk framework with demonstration on UK offices," Applied Energy, Elsevier, vol. 133(C), pages 40-55.
    • Handle: RePEc:eee:appene:v:133:y:2014:i:c:p:40-55
    DOI: 10.1016/j.apenergy.2014.07.074
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    Cited by:

    1. Nie, S. & Li, Y.P. & Liu, J. & Huang, Charley Z., 2017. "Risk management of energy system for identifying optimal power mix with financial-cost minimization and environmental-impact mitigation under uncertainty," Energy Economics, Elsevier, vol. 61(C), pages 313-329.
    2. Congedo, Paolo Maria & Baglivo, Cristina & D'Agostino, Delia & Zacà, Ilaria, 2015. "Cost-optimal design for nearly zero energy office buildings located in warm climates," Energy, Elsevier, vol. 91(C), pages 967-982.
    3. Handing Guo & Wanzhen Qiao & Jiren Liu, 2019. "Dynamic Feedback Analysis of Influencing Factors of Existing Building Energy-Saving Renovation Market Based on System Dynamics in China," Sustainability, MDPI, vol. 11(1), pages 1-16, January.
    4. Zhi-Fu Mi & Yi-Ming Wei & Bao-Jun Tang & Rong-Gang Cong & Hao Yu & Hong Cao & Dabo Guan, 2017. "Risk assessment of oil price from static and dynamic modelling approaches," Applied Economics, Taylor & Francis Journals, vol. 49(9), pages 929-939, February.
    5. Baglivo, Cristina & Congedo, Paolo Maria & D'Agostino, Delia & Zacà, Ilaria, 2015. "Cost-optimal analysis and technical comparison between standard and high efficient mono-residential buildings in a warm climate," Energy, Elsevier, vol. 83(C), pages 560-575.
    6. Maitra, Debasish & Guhathakurta, Kousik & Kang, Sang Hoon, 2021. "The good, the bad and the ugly relation between oil and commodities: An analysis of asymmetric volatility connectedness and portfolio implications," Energy Economics, Elsevier, vol. 94(C).
    7. Parkinson, Aidan & Hill, Stephen & Wheal, Richard, 2016. "An income-based analysis of the value premise for property energy performance," Energy, Elsevier, vol. 106(C), pages 162-169.
    8. Chai, Shanglei & Zhou, P., 2018. "The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems," Energy Economics, Elsevier, vol. 76(C), pages 64-75.

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