Estimation of tourism-induced electricity consumption: The case study of Balearics Islands, Spain
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 the 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. However, in order to evaluate the impact of this sector on energy use the approaches used in the literature consider tourism in its disaggregated way. This paper assesses the electricity demand pattern and investigates the aggregated contribution of tourism to electricity consumption 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 the different population growth rate scenarios on electricity loads is also investigated. The results show that, in terms of electricity consumption, tourism cannot be considered a very energy-intensive sector.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Soares, Lacir Jorge & Souza, Leonardo Rocha, 2003. "Forecasting electricity demand using generalized long memory," FGV/EPGE Economics Working Papers (Ensaios Economicos da EPGE) 486, FGV/EPGE - Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
- Jose Ramon Cancelo & Antoni Espasa, 1996. "Modelling and forecastng daily series of electricity demand," Investigaciones Economicas, Fundación SEPI, vol. 20(3), pages 359-376, September.
- Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
- Yee Yan, Yuk, 1998. "Climate and residential electricity consumption in Hong Kong," Energy, Elsevier, vol. 23(1), pages 17-20.
- 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.
- Macintosh, Andrew & Wallace, Lailey, 2009. "International aviation emissions to 2025: Can emissions be stabilised without restricting demand?," Energy Policy, Elsevier, vol. 37(1), pages 264-273, January.
- 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.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- 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.
- 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.
- Ranjan, Manish & Jain, V.K., 1999. "Modelling of electrical energy consumption in Delhi," Energy, Elsevier, vol. 24(4), pages 351-361.
- 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.
- 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).
- 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.
- 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.
- 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.
- Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
- Zachariadis, Theodoros & Pashourtidou, Nicoletta, 2007. "An empirical analysis of electricity consumption in Cyprus," Energy Economics, Elsevier, vol. 29(2), pages 183-198, March.
- 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.
- 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.
- 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.
- 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.
- 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. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:33:y:2011:i:3:p:437-444. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
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 references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.