# _6.3 Inclusion of Tourism Impacts in a CGE Model_

Our estimates of net currency inflows are meant to be used as inputs in a CGE model, assessing economic impacts of climate change. The exogenous shock can be inserted as a variation in international income transfers and, possibly, as a shift in the pattern of final consumption.

Most CGE models are based on a “territorial” definition of income. In other words, GDP rather than GNP is taken as the reference value for income and other macroeconomic variables. This implies that there is no distinction between nationals and foreigners when income is spent inside a country boundaries. However, the purchasing power of foreigners comes from income generated abroad. In order to consider this important aspect, Berrittella et al. (2006) and Bigano et al. (2008) simulate the occurrence of some international income transfers, whose magnitude corresponds to the estimated change in net currency inflows.

Since foreign tourists are unlikely to have a structure of consumption similar to that of the representative household in a country, a further step is simulating an exogenous increase (or decrease) in the consumption of tourism (hotels, restaurants, recreation facilities) and domestic transport services, which can be implemented by inserting some shifting parameters in the final demand for these items.

# **7. Climate change impact #6: Household Energy Demand**

Household energy demand is directly affected by variations in temperature. This relationship is rather complex, as the impact on energy consumption depends on the season, the source of energy and the climatic condition of the country.

For instance, an increase in winter temperatures would cause a decrease in energy used for heating purposes, whereas an increase in summer temperatures is likely to cause an increase of energy consumed for cooling purposes, depending on the latitude of the country (i.e., tropical, temperate, cold).

In what follows, the impact of increasing average temperature on energy demand is computed, taking into account all these factors.

# _7.1 Methodology_

Our estimates are based on De Cian, Lanzi and Roson (2013), who computed parameters of a model for household energy demand, by energy source and season, using econometric techniques and a global panel database. Energy demand is expressed as dependent, among other factors, on the (natural logarithm of) seasonal average temperature, expressed in °F.

Seasonal long run temperature elasticities by energy source and by climate region (Table 4) are those estimated by De Cian, Lanzi and Roson (2013.). Since we are interested in the variation of total energy demand, elasticities in Table 4 have been scaled down by considering the share of energy used for heating and cooling purposes (Table 5). The adjusted elasticities are shown in Table 6.

Data on average seasonal temperature by country are obtained from the Weather Database (www.weatherbase.com), whereas each country has been classified as Cold, Mild or Hot, according to its latitude.<sup>3</sup> Applying the model estimated by De Cian, Lanzi and Roson (2013), to the percentage variation in temperature corresponding to 1°C (and 2, 3, 4, 5°C) increase in seasonal average temperature has been multiplied by the elasticities reported in Table 6.

> 3 Hot countries: latitude<27°; mild countries: 27°<latitude<63°; cold countries: latitude>63°. For aggregated regions the latitude has been computed as a weighted sum of the latitude of each single country.

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