Climate change Impacts on Tourism in Europe and research ideas for the Russian Far East Climate Change Constrains and Opportunities in the Asian Pacific Region: Human-Biosphere- Atmosphere Interactions and Green Growth Andrea Bigano CMCC, FEFU, FEEM FEFU Vladivostok, Russia, 24-26 October 2017
Tourism in the EU: quick facts Tourism in the EU27 provided about 10% of total GDP in the last decade (WTTC 2017) For example in 2013, it generated 1.6 trillion (10.2% EU27 GDP,) 11.4 million jobs (5% of total EU27 employment) Coastal and maritime tourism employs over 3.2 million people, generates 183 billion in gross value added and represents over one third of the maritime economy (Ecorys, 2013). 51% of bed capacity in hotels across Europe is concentrated in regions with a sea border. (Source : EU Commission-Maritime Affairs) Cultural and Muntain tourism also attract many tourists: Ile de France (Paris) is the second most popular destination in the EU, and about 100 million tourists visit the Alps each year (WTO,2013)
Coastal vs. non-coastal tourism in Europe
Tourism across the EU Number of nights spent in tourist accommodation establishments in the top 20 EU-28 tourist regions, by NUTS 2 regions, 2015
Tourism and climate change: main methodological approaches The typical climate at origin and destination is a crucial factor for determining tourism-related decisions. Main approaches: Tourism Comfort Index (TCI) and derived indicators (e.g. HCI) (Mieczkowski, 1985), Scott et al. (2016)). Push-pull models of tourist flows (Hamilton et al. 2005, Bigano et al. 2007). Local empirical analyses of tourists and operators attitudes towards climate (EEA, 2016) 5
TCI - seasonal and regional patterns Projected changes in the tourism climatic index for the four seasons Climate is generally at its best for tourism in the reference period in southern Europe (left column). Over this century, climate change is projected to shift the latitudinal band of favourable climate northward, thereby improving climate resources in northern and central Europe in most seasons (central column). Southern Europe s tourism suitability drops strikingly in the summer holiday months; this drop is partially compensated for by improvements in other seasons (right column). Source: Perch-Nielsen et al., 2010 Tourism Climatic Index (TCI) for four seasons in 1961 1990 (left), under future climate change (2071 2100, middle), and change between periods (right). Based on the SRES A2 scenario simulations of RCMs in the PRUDENCE project.
Push-pull models of tourist flows: HTM 0.4 0.3 0.2 0.1 0 Canada -0.1-0.2-0.3-0.4 Finland Switzerland Belarus China Andorra United Kingdom Slovenia Bosnia and Herzegovina New Zealand Albania Uzbekistan Argentina Morocco Rwanda Israel Reunion Iraq Botswana Egypt Western Sahara Lao People's Dem Rep Arrivals Departures Cape Verde Sao Tome & Principe El Salvador Colombia Cameroon Haiti Aruba Papua New Guinea Liberia Eritrea Oman Trinidad and Tobago Barbados Singapore Cambodia Sri Lanka Qatar Benin Palau Kiribati Changes in arrivals and departures according to the HTM model due to climate change w.r.t their values in absence of climate change. Countries are ranked according to their average temperature in the period 1961-1990. Source: Hamilton et al., 2005 7
Modeling tourism and SLR and tourism impact jointly Bigano, Bosello and Roson (2008) Sea Level Rise: Land loss is modeled as a negative supply-side shock on the endowment Land (which is an exogenous variable in the model). Tourism: Changes in tourism demand are modeled as changes in the demand for recreational services (within the market service sector), income flows determined by additional foreign expenditure are modelled. 8
Tourism and sea level jointly 0.3 0.2 30 0.1 0-0.1 10-10 -0.2-0.3-30 -0.4-50 -0.5-0.6-70 -0.7-90 USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA ROW SLR&TOU SLR TOU %D SLR&TOU-SUM 9
Combining impacts and adataptation: ToPDAd project Change in summer overnight stays in [%] (2035-2065 vs. baseline) in beach dominated regions for RCP4.5/SSP4, when change destination(left) or both month and destination (right). (Perrels et al., 2015)
Downscaling impacts: Italy Change in international internazionali tourists (%) Change in total tourist flows (%) Different impacts stem from different international popularity: Sicily loses 4 times the market share of Calabria; follow Lazio, Tuscany and Umbria. In the worst cases, more than a fifth of total tourists and almost one third of international tourists will be lost in comparison to an unchanged climate. Domestic tourists compensate partially for the losses, quite substantially in some provinces.
Ideas for the Russian Far East Russia is already included in global studies, but downscaling is needed to understand regional implications; TCI and HCI need to be fine tuned. The views of local and likely international tourists about treshold values need to be elicited; Mapping and assssessing of touristic potential of specific natural area in the Far East, now and under a changing climate; Identification of adaptation options and policy measures, minding that short medium term opportunities may be followed by long term criticalities; Adapting means also knowing the climate of tourist destinations - climate services for tourism ( eg. seasonal forecasts of HCI) can prove an important tool and opportunity.
Thank You! andrea.bigano@cmcc.it