Human Development Report - Croatia 2008 Tourism^59
Box 4-1: Information about models dealing with climate variables and tourismHamburg Tourism Model (HTM)The Hamburg Tourism Model (HTM) can be used
to estimate changes in global tourism flows due to
climate change. This approach involves, first, using
aggregate data to develop an empirical model of
the relationship between a measurement of tourist
participation (such as overnights or arrivals or depar-
tures) and a number of explanatory variables, among
which are climate variables. The empirical model is
then used to simulate the effects of changes in the
climate variables on tourist participation. The HTM
and models like it tend to be relatively crude instru-
ments for simulating the effects of climate change
on recreation demand. This is because they use ag-
gregate data on tourism and climate variables; they
often do not discriminate between different types
of recreation or tourism, and fail to take into account
important trade-offs caused by changes in the desir-
ability of the climate at different sites relative to the
costs of travelling to those sites. These effects can be
captured much better through the use of travel cost
models. Travel cost models are similar to participa-
tion models with one critical difference: they include
not only climate among the explanatory variables,
but also the travel costs between origins and desti-
nations. Models of this type have been developed to
look at the determinants of the travel behaviour of
UK^21 and Dutch^22 residents. However, these particular
models were not developed to simulate the effects of
climate change on destination choice in the EU.Tourism Climate Index (TCI)A second way of incorporating climate as a variable
for tourists is through the use of a Tourism Climate
Index (TCI). The TCI indicates the desirability of a tour-
ist site based on local climate features, consisting of
maximum and minimum daily temperatures, mean
daily temperature, mean and minimum daily relative
humidity, total precipitation, total hours of sunshine,
and average wind speed.^23 These variables are used
to construct three “comfort indexes” whose weighted
sum constitutes the TCI. The TCI, as such, provides a
method to systematically rate the tourist locationsaround the world, using a scale from -20 to 100. The
scale is divided into 11 categories, where 50-59 is “ac-
ceptable” as a tourism climate, 80-89 is “excellent,” and
90-100 is “ideal.”
The TCI can be used to identify six different “represen-
tative” TCI distribution shapes (Figure 4-4). A location
with year-round tourism and a consistently favourable
climate would be called “optimal” and would have
a TCI rating of 80 or above all year, while a country
with “poor” tourism would have a TCI rating below 40
throughout the year. A location that is more attractive
for summer tourism (such as Croatia) will have higher
TCI values during the summer months (summer peak)
while a location with more winter tourism will have
higher TCI values during the winter (winter peak).
Locations where climate is attractive for tourism in
both the spring and autumn months will have higher
TCI values during those months (bimodal shoulder
peaks), while areas with distinct wet and dry seasons
will generally have the highest TCI values during the
dry season (dry season peak).^24 This implies that in the
future, tourists will be most likely to visit countries
during the times with the highest TCI values.‘Optimal’ ‘Poor’‘Summer Peak’ ‘Winter Peak’‘Bimodal - Shoulder Peaks’ ‘Dry Season Peak’Figure 4-4: Conceptual tourism climate distributions
according to TCI. Source: Scott and McBoyle 2001.