IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v23y1998i2p91-103.html
   My bibliography  Save this article

Natural gas consumption and climate: a comprehensive set of predictive state-level models for the United States

Author

Listed:
  • Sailor, David J.
  • Rosen, Jesse N.
  • Muñoz, J.Ricardo

Abstract

Separate models correlating natural gas (NG) consumption to climate have been developed for the residential and commercial sectors of the 50 U.S. states. The models relate a population-weighted average temperature to state per capita NG consumption on a monthly basis. The majority of the models have Pearson correlation coefficients greater than 0.90 supporting the use of temperature as the sole independent parameter. The sensitivities of the models to a 1°C increase in temperature, are compared for each state and the monthly sensitivity to climate integrated over the entire U.S. is investigated for a range of temperature perturbations. The predicted impact of a 1°C increase in mean monthly temperature on U.S. consumption is an 8.1% decrease in the residential sector and a 5.9% decrease in the commercial sector. In terms of the net consumption normalized over the study period (1984–1993) this corresponds to a 111.8TWh decrease in the residential sector and a 47.0TWh decrease in the commercial sector. The largest change for a single month occurs in January when consumption would decrease 19.7TWh in the residential sector and 7.4TWh in the commercial sector.

Suggested Citation

  • Sailor, David J. & Rosen, Jesse N. & Muñoz, J.Ricardo, 1998. "Natural gas consumption and climate: a comprehensive set of predictive state-level models for the United States," Energy, Elsevier, vol. 23(2), pages 91-103.
    • Handle: RePEc:eee:energy:v:23:y:1998:i:2:p:91-103
    DOI: 10.1016/S0360-5442(97)00073-X
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054429700073X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/S0360-5442(97)00073-X?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lam, Joseph C. & Tang, H.L. & Li, Danny H.W., 2008. "Seasonal variations in residential and commercial sector electricity consumption in Hong Kong," Energy, Elsevier, vol. 33(3), pages 513-523.
    2. Yau, Y.H. & Pean, H.L., 2011. "The climate change impact on air conditioner system and reliability in Malaysia—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4939-4949.
    3. Jianhua Huang & Kevin Robert Gurney, 2016. "Impact of climate change on U.S. building energy demand: sensitivity to spatiotemporal scales, balance point temperature, and population distribution," Climatic Change, Springer, vol. 137(1), pages 171-185, July.
    4. Fazle Wahid & Hamid Ullah & Sher Ali & Sajjad Ahmad Jan & Abid Ali & Azhar Khan & Imran Ali Khan & Maryam Bibi, 2021. "The Determinants and Forecasting of Electricity Consumption in Pakistan," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 241-248.
    5. Huang, Jianhua & Gurney, Kevin Robert, 2016. "The variation of climate change impact on building energy consumption to building type and spatiotemporal scale," Energy, Elsevier, vol. 111(C), pages 137-153.
    6. Ruth, Matthias & Lin, Ai-Chen, 2006. "Regional energy demand and adaptations to climate change: Methodology and application to the state of Maryland, USA," Energy Policy, Elsevier, vol. 34(17), pages 2820-2833, November.
    7. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2012. "Impact of climate change on energy use in the built environment in different climate zones – A review," Energy, Elsevier, vol. 42(1), pages 103-112.
    8. Spoladore, Alessandro & Borelli, Davide & Devia, Francesco & Mora, Flavio & Schenone, Corrado, 2016. "Model for forecasting residential heat demand based on natural gas consumption and energy performance indicators," Applied Energy, Elsevier, vol. 182(C), pages 488-499.
    9. Askari, S. & Montazerin, N. & Zarandi, M.H. Fazel, 2015. "Forecasting semi-dynamic response of natural gas networks to nodal gas consumptions using genetic fuzzy systems," Energy, Elsevier, vol. 83(C), pages 252-266.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:23:y:1998:i:2:p:91-103. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.