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Estimating energy savings from benchmarking policies in New York City

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  • Meng, Ting
  • Hsu, David
  • Han, Albert

Abstract

A growing number of governments have begun to implement benchmarking or energy disclosure policies. By requiring owners to measure and disclose their energy use, these policies are intended to transform the market for energy-efficient investments in existing buildings. To improve future policy efforts, two critical questions are: first, how much energy do these policies save? and second, what particular aspects of these policies are most effective? To answer these questions, this study explores how different aspects of these policies were phased-in to different groups of buildings over the first four years of the City of New York's benchmarking ordinance. By applying a novel difference-in-differences strategy, we can causally attribute observed declines in energy consumption to specific owner behaviors and policy mechanisms. Our analysis indicates that in comparison with the control group and before the policies were implemented in 2011, total disclosure of both energy use and Energy Star together can be credited with a 6% reduction in building energy use intensity (EUI) three years later and a 14% reduction in EUI four years later. Disclosure of Energy Star scores decreased building EUI by 9% three years later and 13% four years later. These two separate findings are a consequence of the policy design and different control groups.

Suggested Citation

  • Meng, Ting & Hsu, David & Han, Albert, 2017. "Estimating energy savings from benchmarking policies in New York City," Energy, Elsevier, vol. 133(C), pages 415-423.
  • Handle: RePEc:eee:energy:v:133:y:2017:i:c:p:415-423
    DOI: 10.1016/j.energy.2017.05.148
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    Cited by:

    1. Andrews, Abigail & Jain, Rishee K., 2022. "Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking," Applied Energy, Elsevier, vol. 327(C).
    2. Mohammed Hammam Mohammed Al-Madani & Yudi Fernando & Ming-Lang Tseng, 2022. "Assuring Energy Reporting Integrity: Government Policy’s Past, Present, and Future Roles," Sustainability, MDPI, vol. 14(22), pages 1-24, November.
    3. Bertoldi, Paolo & Mosconi, Rocco, 2020. "Do energy efficiency policies save energy? A new approach based on energy policy indicators (in the EU Member States)," Energy Policy, Elsevier, vol. 139(C).
    4. Excell, Lauren E. & Jain, Rishee K., 2024. "Examining the impact of energy efficiency retrofits and vegetation on energy performance of institutional buildings: An equity-driven analysis," Applied Energy, Elsevier, vol. 357(C).
    5. ChungYeon Won & SangTae No & Qamar Alhadidi, 2019. "Factors Affecting Energy Performance of Large-Scale Office Buildings: Analysis of Benchmarking Data from New York City and Chicago," Energies, MDPI, vol. 12(24), pages 1-17, December.
    6. Livingston, Olga V. & Pulsipher, Trenton C. & Anderson, David M. & Vlachokostas, Alex & Wang, Na, 2018. "An analysis of utility meter data aggregation and tenant privacy to support energy use disclosure in commercial buildings," Energy, Elsevier, vol. 159(C), pages 302-309.
    7. Ali Movahedi & Sybil Derrible, 2021. "Interrelationships between electricity, gas, and water consumption in large‐scale buildings," Journal of Industrial Ecology, Yale University, vol. 25(4), pages 932-947, August.
    8. Luming Shang & Sofia Dermisi & Youngjun Choe & Hyun Woo Lee & Yohan Min, 2023. "Assessing Office Building Marketability before and after the Implementation of Energy Benchmarking and Disclosure Policies—Lessons Learned from Major U.S. Cities," Sustainability, MDPI, vol. 15(11), pages 1-23, May.
    9. Job Taminiau & John Byrne & Jongkyu Kim & Min‐whi Kim & Jeongseok Seo, 2021. "Infrastructure‐scale sustainable energy planning in the cityscape: Transforming urban energy metabolism in East Asia," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    10. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
    11. Zhang, Yan & Teoh, Bak Koon & Zhang, Limao, 2023. "Exploring driving force factors of building energy use and GHG emission using a spatio-temporal regression method," Energy, Elsevier, vol. 269(C).
    12. Job Taminiau & John Byrne, 2020. "City‐scale urban sustainability: Spatiotemporal mapping of distributed solar power for New York City," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(5), September.
    13. Yu, Xinran & Zou, Zhengbo & Ergan, Semiha, 2023. "Extracting principal building variables from automatically collected urban scale façade images for energy conservation through deep transfer learning," Applied Energy, Elsevier, vol. 344(C).
    14. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    15. Oak, Hena & Bansal, Sangeeta, 2022. "Enhancing energy efficiency of Indian industries: Effectiveness of PAT scheme," Energy Economics, Elsevier, vol. 113(C).
    16. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    17. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    18. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
    19. Roth, Jonathan & Rajagopal, Ram, 2018. "Benchmarking building energy efficiency using quantile regression," Energy, Elsevier, vol. 152(C), pages 866-876.
    20. Lai, Yuan & Papadopoulos, Sokratis & Fuerst, Franz & Pivo, Gary & Sagi, Jacob & Kontokosta, Constantine E., 2022. "Building retrofit hurdle rates and risk aversion in energy efficiency investments," Applied Energy, Elsevier, vol. 306(PB).

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