Abstract
This study analyses the potential impact of climate change on tourism demand in the European Union (EU) and provides long-term (2100) projections accounting for climate adaptation in terms of holiday duration and frequency. Our analysis is based on hedonic valuation of climatic conditions combining accommodation and travel cost estimations. Our results suggest that climatic change is likely to affect the relative attractiveness of EU regions for tourism activities. In certain regions, most notably the Southern EU Mediterranean regions, climate condition in 2100 could under current economic conditions, lower tourism revenues for up to −0.45 % of GDP per year. On the contrary, other areas of the EU, most notably Northern European regions would gain from altered climatic conditions, although these gains would be relatively more modest, reaching up to 0.32 % of GDP on an annual basis. Our results also suggest that the change in holiday duration would be more beneficial than the change in holiday frequency in view of mitigating the cost of climate change. These two time dimensions of adaptation are likely to be conditioned by broader societal and institutional factors, however.
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Notes
In this paper we focus on sun tourism and do not consider alternative tourism activities such winter tourism and skiing although this type of activity is also very likely to be altered by climate change.
Aspects of tourism adaptation could include for instance institutional arrangements on the timing of school holidays or the rise in retired population which is less constrained in the timing of holiday choices.
The TCI is a weighted average of the value taken by climatic variables relevant for tourism comfort and in particular sun-tourism. Amelung and Moreno (2012) include in the TCI the maximum and mean daily temperature, the minimum daily relative humidity, mean daily relative humidity, precipitation, sunshine and wind speed. The grouping of EU regions into NUTS groups follows the Eurostat nomenclature.
TRANS-TOOLS (TOOLS for TRansport Forecasting ANd Scenario testing) has been developed in collaborative projects funded by the European Commission’s DG MOVE and the European Commission Joint Research Centre. The present study was conducted with the 2.5.0 version of TT.
Note that for simplicity we omitted the differentiation by month given that each elasticity is in fact estimated on the interaction between the monthly dummies and the specific climatic variable considered.
See http://ensembles-eu.metoffice.com/index.html for a description of the ENSEMBLES project.
The data on annual number of bednights by residents and non-residents by NUTS2 region were obtained from the table “Nights spent at tourist accommodation establishments by NUTS 2 regions (Table code: tour_occ_nin2)” accessible at the EUROSTAT database website. This data can be found at the EUROSTAT data navigation tree under the heading “General and regional statistics / Regional statistics by NUTS classification / Regional tourism statistics (reg_tour)”. The data on annual bed capacity per NUTS2 region were taken from the table “Number of establishment, bedrooms and bed-places by NUTS 2 regions (Table code: tour_cap_nuts2)” also accessible at the EUROSTAT database website under the heading “Industry, Trade and Services / Tourism (tour)”. The EUROSTAT database website is accessible at http://ec.europa.eu/eurostat/data/database.
Of course in order to do so we must assume that the distribution of tourists by country of origin and region of destination applies also on a monthly basis.
The country-level data on monthly bednights with information on the country of origin of tourists were obtained from the table “Nights spent by non-residents at tourist accommodation establishments – 1990–2011 –world geographical breakdown – monthly data (Table code: tour_occ_ninrmw)” accessible at the EUROSTAT database under the heading “Industry, Trade and Services / Tourism (tour) / monthly data on tourism industries (tour_indm). The EUROSTAT database website is accessible at http://ec.europa.eu/eurostat/data/database.
No comparable data was available for 2010 or 2011 which are the years the present study focuses on. The figures mentioned here are accessible at http://ec.europa.eu/eurostat/statistics-explained/index.php/Seasonality_in_tourism_demand.
The share of non-EU residents is especially high in northern EU countries such as Finland (46.6 %), Sweden (46.7 %), Germany (37.4 %), Denmark (36.6 %) while in Mediterranean countries where sun-tourism predominates such as Croatia (17 %), Portugal (20.3 %), Spain (22.3 %) or Greece (29.9 %), this share is markedly lower. Source: Eurostat and authors’ calculations.
Specifically this implied running alternative specifications including and excluding successively explanatory variables so as to maximise the R-square and avoiding including explanatory variables (including the climatic variables) with a high degree of correlation. Note also that we also experimented with a season specification of our hedonic price model, i.e., considering all variable averaged around their seasonal mean. The results obtained on the predicted hotel price index were highly corrected (in cases more than 0.9) with the monthly specification used here.
See Barrios and Ibañez (2014) for a detailed exposition of these econometric results.
The other climatic variables, i.e., precipitations, wind speed and humidity, had a less straightforward interpretation, and it is unclear what their optimum level should be in combination with the temperature and rain variables. Leaving these variables in our baseline estimations remains important however given that they capture other important aspects of tourism demand. The rest of control variables (ii) to (vii) usually displayed statistically significant coefficient (in most cases at 1 %) level, independently of the holiday duration considered.
This means that we took the relative weights of each holiday duration given by the Eurostat data in 2010 for the period 2011–2040. We then modified the weights for the 2041–2070 and 2071–2099 periods according to the number of bednights estimated for the previous period.
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Acknowledgements
We would like to thank Mate Rozsai for useful assistance with the data and Alessandro Dosio for providing the climate data. We would also like to thank Juan Carlos Ciscar, Michael Hanemann and Mac Callaway for their very useful comments and suggestions.
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The view expressed in this paper are those of the authors and shall not be attributed to the European Commission. Errors and omissions are those of the authors only.
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Barrios, S., Ibañez, J.N. Time is of the essence: adaptation of tourism demand to climate change in Europe. Climatic Change 132, 645–660 (2015). https://doi.org/10.1007/s10584-015-1431-1
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DOI: https://doi.org/10.1007/s10584-015-1431-1