Better sustainability assessment of green buildings with high-frequency data

Abstract

Reducing electricity consumption through green building certification is one key strategy for achieving environmental sustainability. Traditional assessments of the environmental benefits of green buildings rely on electricity consumption data at an aggregated level (such as monthly). Using such data can bias assessment results because marginal emissions factors vary throughout the day. We use panel data on hourly energy usage at the individual-building level from 2013–2016 in Arizona to provide a more accurate sustainability assessment for green buildings. For both Energy Star and Leadership in Energy and Environmental Design buildings, our estimated savings suggest that the majority of electricity savings in summer happen during electric load system peak hours. The estimated hourly savings and hourly marginal emissions damages reveal additional environmental gains in green-certified buildings. We show that traditional methods that ignore the intra-day timing of savings can underestimate the environmental benefit of green commercial buildings by 95%. We also demonstrate that our findings can be generalized to a broader geographical context.

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Fig. 1: Intra-day electricity savings by hour using a subsample with both pre- and post-treatment hourly consumption data.
Fig. 2: Marginal damages for the WECC (in US dollars from the year 2000) from carbon and air emissions by hour of day.
Fig. 3: Comparison of avoided environmental damages from CO2, SO2, NOX and particulate matter calculated using hourly electricity savings versus aggregate savings.
Fig. 4: Avoided environmental damages by industry type.

Data availability

The weather data are available from NOAA at https://www.ncdc.noaa.gov/cdo-web/. The Energy Star data are available from https://www.energystar.gov/index.cfm?fuseaction=labeled_buildings.locator. The LEED data are available from https://www.usgbc.org/projects. The high-frequency electricity data that support the findings of this study are available from the SRP, but restrictions apply to their availability. These data were used under a non-disclosure agreement in the current study, and so are not publicly available. However, they are available from the authors upon reasonable request and with permission from the SRP.

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Acknowledgements

Funding for this research was provided by the National Science Foundation under grant number 1757329. We thank A. Dock, L. Grant, M. Roberts and H. Bryan for helpful comments during preparation of this paper, and X. Bo for help with collecting the data.

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Y.Q. secured project funding, collected and cleaned the data, and conducted the statistical modelling. Y.Q. and M.E.K. designed the study, analysed the data and wrote the manuscript.

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Correspondence to Yueming Qiu or Matthew E. Kahn.

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The authors declare no competing interests.

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Supplementary Information

Supplementary Notes, Supplementary Figures 1–11, Supplementary Tables 1–10

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Qiu, Y., Kahn, M.E. Better sustainability assessment of green buildings with high-frequency data. Nat Sustain 1, 642–649 (2018). https://doi.org/10.1038/s41893-018-0169-y

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