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Integrated Socio-Economic Assessment (The Economic Point of View)

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Regional Assessment of Climate Change in the Mediterranean

Part of the book series: Advances in Global Change Research ((AGLO,volume 51))

Abstract

This section introduces the main methodologies used by the climate change impact science to assess economically the consequences of climate change. Furthermore it presents the main findings of this literature focusing specifically on possible future economic consequences of climate change in the Mediterranean area emphasizing the new knowledge in this field brought by the CIRCE project. The robust finding of the literature points out a low economic vulnerability of Euro-Mediterranean countries (with losses ranging from −0.25 to −1.4% of GDP for extreme temperature scenarios or even slight gains), and a higher vulnerability of North African and Eastern-Mediterranean countries (of roughly 2% of GDP by the mid of the century). Against this background the CIRCE project proposes one of the first attempts to perform a detailed integrated impact assessment exercise focusing on the Mediterranean area. With the IPCC A1B SRES scenario as ­reference, impacts related to energy demand, sea-level rise and tourism, have been economically assessed by a general equilibrium model. The Mediterranean as a whole loses 1.2% of GDP with the Northern-Mediterranean countries clearly less ­vulnerable than the Southern-Mediterranean ones. Among the former the average loss in 2050 is 0.5% of GDP, while among the latter this more than doubles. Particularly adversely affected are Cyprus, Albania and the Eastern Mediterranean region (−1.6, −2.4, −1.5% of GDP respectively in 2050). In terms of impact types, tourism and sea-level rise are clearly the most threatening, while GDP impacts induced by demand re-composition of energy use is less of an issue and often positive.

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Notes

  1. 1.

    This definition is still broadly valid, however it is also increasingly blurring, especially because of the growing flexibility of software packages and of the computational power of computers.

  2. 2.

    The distinction between hard and soft linked models somehow translates into the distinction between policy optimization vs. policy simulation. Policy optimization IAMs perform “normative” exercises: basically they answer the question on what would be the optimal (utility-maximizing) level of environmental externality (say GHG emissions and thus climate change damage), given the cost and benefits of climate policies or of available strategies to reach a given environmental target. Policy simulation IAMs perform “positive” if-then exercises by assessing the (direct and higher order) costs of environmental policies.

  3. 3.

    Benefits of emission reductions are non excludable. They are felt not only by who is abating and bearing the costs, but also by others. This creates an incentive to understate the willingness to abate hoping to charge the burden of abatement on other parties. This is the “free riding” mechanism, one of the major obstacles in the practical implementation of a stringent and largely participated international mitigation agreement. Strictly intertwined to this is the “leakage” effect. This relates to the fact that when a country or group decide an abatement policy, emissions in non abating countries increase. Two are the reasons: part of the polluting productions processes are re-located abroad to escape the stringent environmental regulation; products of the abating countries tend to become more costly in international market as charged by the cost of the cleaning activities. Thus demand shifts to cheaper products of non abating countries which accordingly produce and pollute more.

  4. 4.

    Although adaptation to warmer temperature does not necessarily have to be through extra energy consumption, i.e. it can be partially implemented through passive building cooling, design, ­behavioural change, etc.

  5. 5.

    We assume that “Africa and High Income OPEC” are partly representative of North African countries; and that Western Europe indicates the minimum impact that Southern Europe can experience due to its higher exposure compared to the Northern part.

  6. 6.

    On sea-level rise, agriculture, health, energy demand, tourism flows, Eboli et al. (2010); on sea-level rise, agriculture, health, energy demand, tourism flows, forestry, Bosello et al. (2009a ); on sea-level rise, agriculture, health, energy demand, tourism flows, forestry, fisheries, extreme events, electricity supply Aaheim and Wey (2010); on sea-level rise, agriculture, tourism, river floods Ciscar et al. (2011).

  7. 7.

    Probably the most important difference across the quoted studies, all making use of CGE models, is that pertaining to static and dynamic exercises. In the first case no transitions, dynamics and inertias of the economic system and linking the economic system to climate impacts are captured. In the second they are taken into account. Accordingly, studies results would not be immediately comparable. It can be noted that, typically, dynamic model results are larger than those of static models as inertias tend to amplify both positive and negative effects.

  8. 8.

    Note that these estimates obtained through a direct cost methodology are closer to Nordhaus and Boyer (2000) estimates.

  9. 9.

    Although climate change impacts on agriculture are presently not part of the CGE assessment, CIRCE deliverable D10.9.5: “Economic evaluation of climate change impact on agriculture in the Mediterranean region – a CGE versus partial equilibrium analyses” will provide these inputs. In addition, work performed by CIRCE WP10.9 allowed to build inside the CGE model and estimate a new and more realistic description of agricultural production. By the same token climate change impacts on migration flows will be included as long as results from WP10.3 will be completed.

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Authors and Affiliations

  1. Department of Economics, Management and Quantitative Methods, University of Milan, Milan, Italy

    Francesco Bosello

  2. Climate Impact and Policy Division, Centro Euro Mediterraneo sui i Cambiamenti Climatici CMCC, Venezia, Italy

    Francesco Bosello

  3. Fondazione Eni Enrico Mattei – FEEM, S. Giorgio Maggiore, Venice, Italy

    Francesco Bosello

  4. Natural Resource and Environmental Research Center, Haifa University, Haifa, Israel

    Mordechai Shechter

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Correspondence to Francesco Bosello .

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  1. Climate Change (CMCC), Euro-Mediterranean Center on Climate Change (CMCC), Lecce, Italy

    Antonio Navarra

  2. IDDRI SciencesPo, Paris, France

    Laurence Tubiana

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Bosello, F., Shechter, M. (2013). Integrated Socio-Economic Assessment (The Economic Point of View). In: Navarra, A., Tubiana, L. (eds) Regional Assessment of Climate Change in the Mediterranean. Advances in Global Change Research, vol 51. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5772-1_10

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