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A new approach to estimating tourism-induced electricity consumption

Author

Listed:
  • Jaume Rosselló Nadal

    (Centre de Recerca Econòmica (UIB · Sa Nostra))

  • Mohcine Bakhat

    (Balearic Islands University)

Abstract

Tourism has started to be acknowledged as a significant contributor to the increase in environmental externalities, especially to climate change. Various studies have started to estimate and compute the role of different tourism sectors’ contributions to greenhouse gas (GHG) emissions. These estimations have been made from a sectoral perspective, assessing the contribution of air transport, the accommodation sector, or other tourism-related economic sectors. In this paper, the contribution of tourism to electricity consumption is investigated using the case study of the Balearic Islands (Spain). Using a conventional daily electricity demand model, including data for daily stocks of tourists, the impact of different tourist policy measures on electricity loads is also investigated. The results show that, in terms of electricity consumption, tourism cannot be considered a very energy-intensive sector.

Suggested Citation

  • Jaume Rosselló Nadal & Mohcine Bakhat, 2009. "A new approach to estimating tourism-induced electricity consumption," CRE Working Papers (Documents de treball del CRE) 2009/6, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
    • Handle: RePEc:pdm:wpaper:2009/6
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    References listed on IDEAS

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    1. Patterson, Trista M. & Niccolucci, Valentina & Bastianoni, Simone, 2007. "Beyond "more is better": Ecological footprint accounting for tourism and consumption in Val di Merse, Italy," Ecological Economics, Elsevier, vol. 62(3-4), pages 747-756, May.
    2. Zachariadis, Theodoros & Pashourtidou, Nicoletta, 2007. "An empirical analysis of electricity consumption in Cyprus," Energy Economics, Elsevier, vol. 29(2), pages 183-198, March.
    3. Gossling, Stefan & Hansson, Carina Borgstrom & Horstmeier, Oliver & Saggel, Stefan, 2002. "Ecological footprint analysis as a tool to assess tourism sustainability," Ecological Economics, Elsevier, vol. 43(2-3), pages 199-211, December.
    4. Becken, Susanne & Frampton, Chris & Simmons, David, 2001. "Energy consumption patterns in the accommodation sector--the New Zealand case," Ecological Economics, Elsevier, vol. 39(3), pages 371-386, December.
    5. Cottet R. & Smith M., 2003. "Bayesian Modeling and Forecasting of Intraday Electricity Load," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 839-849, January.
    6. Price, T. & Probert, S. D., 1995. "An energy and environmental strategy for the Rhymney Valley, South Wales," Applied Energy, Elsevier, vol. 51(2), pages 139-195.
    7. Smith, Michael, 2000. "Modeling and Short-term Forecasting of New South Wales Electricity System Load," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 465-478, October.
    8. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
    9. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
    10. Lacir J. Soares & Marcelo Cunha Medeiros, 2005. "Modelling and forecasting short-term electricity load: a two step methodology," Textos para discussão 495, Department of Economics PUC-Rio (Brazil).
    11. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    12. Hippert, H.S. & Bunn, D.W. & Souza, R.C., 2005. "Large neural networks for electricity load forecasting: Are they overfitted?," International Journal of Forecasting, Elsevier, vol. 21(3), pages 425-434.
    13. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
    14. Considine, Timothy J., 2000. "The impacts of weather variations on energy demand and carbon emissions," Resource and Energy Economics, Elsevier, vol. 22(4), pages 295-314, October.
    15. Pardo, Angel & Meneu, Vicente & Valor, Enric, 2002. "Temperature and seasonality influences on Spanish electricity load," Energy Economics, Elsevier, vol. 24(1), pages 55-70, January.
    16. Taylor, James W. & Buizza, Roberto, 2003. "Using weather ensemble predictions in electricity demand forecasting," International Journal of Forecasting, Elsevier, vol. 19(1), pages 57-70.
    17. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
    18. Moral-Carcedo, Julian & Vicens-Otero, Jose, 2005. "Modelling the non-linear response of Spanish electricity demand to temperature variations," Energy Economics, Elsevier, vol. 27(3), pages 477-494, May.
    19. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    20. Becken, S. & Simmons, D., 2008. "Using the concept of yield to assess the sustainability of different tourist types," Ecological Economics, Elsevier, vol. 67(3), pages 420-429, October.
    21. Mirasgedis, S. & Sarafidis, Y. & Georgopoulou, E. & Lalas, D.P. & Moschovits, M. & Karagiannis, F. & Papakonstantinou, D., 2006. "Models for mid-term electricity demand forecasting incorporating weather influences," Energy, Elsevier, vol. 31(2), pages 208-227.
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    Keywords

    Electricity demand; tourism contribution; sustainable tourism; daily data;
    All these keywords.

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