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2018 | OriginalPaper | Chapter

A Sub-national CGE Model for the European Mediterranean Countries

Authors : Francesco Bosello, Gabriele Standardi

Published in: The New Generation of Computable General Equilibrium Models

Publisher: Springer International Publishing

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Abstract

This chapter describes the methodology used to develop a Computable General Equilibrium model with sub-national detail for the Euro-Mediterranean area: Italy, France, Spain, Portugal and Greece. The main purpose of this exercise is to perform economic assessments of climate change impacts with a finer spatial resolution compared to that offered by standard CGE models and, in doing so, to increase the comparability of and the possibility to exchange information across economic and physical impact models. Indeed, aiming to represent the high spatial heterogeneity of climate drivers and environmental impacts, both climate models and physical process models (like e.g. land use, crop growth, flood risk models) are spatially detailed. This is not the case for macroeconomic models that typically feature large geo-political blocks or at best the country as the finest investigation units. Accordingly, when physical and economic models are interfaced to produce integrated assessments of climate change impacts, there is an unavoidable loss of richness both of input and output information. Developing a sub-national resolution for the economic analysis thus offers a first useful step to measure more accurately the economic consequences of climate change, to produce an information more relevant for local planners and businesses, and also to better capture the economic feedbacks between regions which can turn to be as important as the international ones. The study addresses conceptual and practical issues related to the regionalization process, and presents simple experiments aimed to test the robustness of the regionalized structure and understand the economic implications in terms of market integration.

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Footnotes
1
SLQs are so called “non-survey” techniques to derive input-output tables and SAMs. SLQs are not the most precise across non survey coefficients known in the literature (Bonfiglio and Chelli 2008). However they offer the great advantage to be of particularly easy application when large global databases are involved as in our case.
 
2
The RAS abbreviation stems from the names of the vectors (R and S) and matrix (A) used by Bacharach in the original formulation of the algorithm. According to McDougall (1999) RAS is a type of cross-entropy optimization method and it should be preferred in the absence of information about variation in column structure or row structure of the matrix.
 
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Metadata
Title
A Sub-national CGE Model for the European Mediterranean Countries
Authors
Francesco Bosello
Gabriele Standardi
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-319-58533-8_11