Climate change and agriculture in computable general equilibrium models: alternative modeling strategies and data needs

Abstract

Agricultural sectors play a key role in the economics of climate change. Land as an input to agricultural production is one of the most important links between economy and the biosphere, representing a direct projection of human action on the natural environment. Agricultural management practices and cropping patterns exert an enormous effect on biogeochemical cycles, freshwater availability and soil quality. Agriculture also plays an important role in emitting and storing greenhouse gases. To consistently investigate climate policy and future pathways for the economic and natural environment, a realistic representation of agricultural land use is essential. Top—down Computable General Equilibrium (CGE) models have increasingly been used for this purpose. CGE models simulate the simultaneous equilibrium in a set of interdependent markets, and are especially suited to analyze agricultural markets from a global perspective. However, modeling agricultural sectors in CGE models is not a trivial task, mainly because of differences in temporal and geographic aggregation scales. This study surveys some proposed modeling strategies and highlights different tradeoffs involved in the various approaches. Coupling of top-down and bottom-up models is found to be the most applicable for comprehensive analysis of agriculture in prism of climate change. However, linking interdisciplinary data, methods and outputs is still the major obstacle to be solved for wide-scale implementation.