Meeting the Global Sustainable Development Goals on a Changing Planet with Limited Land and Water Resources
Incorporating water into a computable general equilibrium (CGE) model operating at global scale can be extremely demanding due to the absence of standardized data, the sheer dimensions caused by intersecting river basins with countries, and difficulties to model demand for and supply of water. This has led many authors to introduce water in their CGE modeling framework in different ways and at different spatial and sectoral aggregation levels. Of course, simplifying market for water and sacrificing the geographical realism risk introducing errors caused by inappropriate aggregation. In this paper we use a global CGE model which distinguished between rainfed and irrigated crops and traces supply of and demand for water at river basin by agro ecological zone (AEZ) and country to investigate the three most commonly practiced simplifications: 1) tackling global questions in a national level model; 2) collapsing irrigated and rainfed crop production into a single sector; and 3) removing river basin boundaries within a country. In each case, we compare their performance in predicting the impacts of future irrigation scarcity on international trade, crop output, prices and land use change, relative to the full scale model. As might be expected, the single region model does a pretty good job of matching outcomes for that region, although changes in bilateral trade can entail significant errors. When it comes to the elimination of sub-national river basins and irrigation location, we find that, if the research question has to do with changes in national-scale trade, production, consumption, and prices, it may be sufficient to ignore the sub-national water and land heterogeneity in global economic analysis of water scarcity. However, when decision makers have an interest in the distribution of inputs and outputs within a region, preserving the river basin and sectoral detail in the model brings considerable added value to the analysis.
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