IDEAS home Printed from https://ideas.repec.org/a/spr/climat/v93y2009i1p39-54.html
   My bibliography  Save this article

The likelihood of holding outdoor skating marathons in the Netherlands as a policy-relevant indicator of climate change

Author

Listed:
  • H. Visser
  • A. Petersen

Abstract

“When I was born – in 1956 – the chance of realizing a Frisian Eleven City Ice Skating Marathon in Netherlands was 1 in 4. When my daughter was born – in 1999 – this chance had diminished to 1 in 10. An enormous change in one generation!” This quote was taken from a speech by J. P. Balkenende, prime minister of the Netherlands. It illustrates how a seemingly odd indicator of climate change, the chance of organizing large-scale outdoor ice-skating marathons, can play a role in the public and political debate on climate change. Outdoor skating has a very strong public appeal in the Netherlands, and the diminishing chances of holding such events provide an additional Dutch motive for introducing climate-policy measures. Here, “ice skating marathons” are approached from three angles: (1) the societal/political angle as described above, (2) the more technical angle, of how to derive annual chances for holding large-scale marathons such as the Eleven City Marathon (‘Elfstedentocht’), and (3) the role of (communicating) uncertainties. Since the statistical approach was developed in response to communicational needs, both statistical and communicative aspects are reported on in this article. Copyright Springer Science+Business Media B.V. 2009

Suggested Citation

  • H. Visser & A. Petersen, 2009. "The likelihood of holding outdoor skating marathons in the Netherlands as a policy-relevant indicator of climate change," Climatic Change, Springer, vol. 93(1), pages 39-54, March.
    • Handle: RePEc:spr:climat:v:93:y:2009:i:1:p:39-54
    DOI: 10.1007/s10584-008-9498-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10584-008-9498-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10584-008-9498-6?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.

    References listed on IDEAS

    as
    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    2. Genshiro Kitagawa, 1981. "A Nonstationary Time Series Model And Its Fitting By A Recursive Filter," Journal of Time Series Analysis, Wiley Blackwell, vol. 2(2), pages 103-116, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Martin Falk & Eva Hagsten, 2017. "Climate change threats to one of the world’s largest cross-country skiing races," Climatic Change, Springer, vol. 143(1), pages 59-71, July.
    2. Cheryl Mallen & Greg Dingle, 2021. "Organizing Sport for Climate Related Adaptations: Lessons from the Water and Forestry Industries," Sustainability, MDPI, vol. 13(18), pages 1-14, September.
    3. Geert Jan Oldenborgh & Karin Wiel & Sarah Kew & Sjoukje Philip & Friederike Otto & Robert Vautard & Andrew King & Fraser Lott & Julie Arrighi & Roop Singh & Maarten Aalst, 2021. "Pathways and pitfalls in extreme event attribution," Climatic Change, Springer, vol. 166(1), pages 1-27, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Victor Bystrov, 2018. "Measuring the Natural Rates of Interest in Germany and Italy," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(4), pages 333-353, December.
    2. Yukai Yang & Luc Bauwens, 2018. "State-Space Models on the Stiefel Manifold with a New Approach to Nonlinear Filtering," Econometrics, MDPI, vol. 6(4), pages 1-22, December.
    3. Fernández-Macho, Javier, 2008. "Spectral estimation of a structural thin-plate smoothing model," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 189-195, September.
    4. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model," Tinbergen Institute Discussion Papers 08-069/4, Tinbergen Institute.
    5. Avanzi, Benjamin & Taylor, Greg & Vu, Phuong Anh & Wong, Bernard, 2020. "A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 50-71.
    6. François R. Velde, 2009. "Chronicle of a Deflation Unforetold," Journal of Political Economy, University of Chicago Press, vol. 117(4), pages 591-634, August.
    7. Chen, Peimin & Wu, Chunchi, 2014. "Default prediction with dynamic sectoral and macroeconomic frailties," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 211-226.
    8. repec:zbw:bofitp:2019_008 is not listed on IDEAS
    9. Yue Zhao & Difang Wan, 2018. "Institutional high frequency trading and price discovery: Evidence from an emerging commodity futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(2), pages 243-270, February.
    10. Wen Xu, 2016. "Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters," Econometrics, MDPI, vol. 4(4), pages 1-13, October.
    11. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    12. Eric Heyer & Frédéric Reynès & Henri Sterdyniak, 2004. "Observable and unobservable variables in the theory of the equilibrium rate of unemployment, a comparison between France and the United States," Working Papers hal-01027420, HAL.
    13. Hári, Norbert & De Waegenaere, Anja & Melenberg, Bertrand & Nijman, Theo E., 2008. "Estimating the term structure of mortality," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 492-504, April.
    14. Brave, Scott A. & Gascon, Charles & Kluender, William & Walstrum, Thomas, 2021. "Predicting benchmarked US state employment data in real time," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1261-1275.
    15. Alejandro Rodriguez & Esther Ruiz, 2009. "Bootstrap prediction intervals in state–space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 167-178, March.
    16. Parrini, Alessandro, 2013. "Importance Sampling for Portfolio Credit Risk in Factor Copula Models," MPRA Paper 103745, University Library of Munich, Germany.
    17. Salman Huseynov, 2021. "Long and short memory in dynamic term structure models," CREATES Research Papers 2021-15, Department of Economics and Business Economics, Aarhus University.
    18. Bógalo, Juan & Poncela, Pilar & Senra, Eva, 2017. "Automatic Signal Extraction for Stationary and Non-Stationary Time Series by Circulant SSA," MPRA Paper 76023, University Library of Munich, Germany.
    19. Önundur Páll Ragnarsson & Jón Magnús Hannesson & Loftur Hreinsson, 2019. "Financial cycles as early warning indicators - Lessons from the Nordic region," Economics wp80, Department of Economics, Central bank of Iceland.
    20. Tsionas, Mike G., 2021. "Bayesian forecasting with the structural damped trend model," International Journal of Production Economics, Elsevier, vol. 234(C).
    21. Planas, Christophe & Roeger, Werner & Rossi, Alessandro, 2007. "How much has labour taxation contributed to European structural unemployment?," Journal of Economic Dynamics and Control, Elsevier, vol. 31(4), pages 1359-1375, April.

    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:spr:climat:v:93:y:2009:i:1:p:39-54. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.