Edited by Ariel Dinar and Robert Mendelsohn
Chapter 6: Farm-level Impacts of Climate Change: Alternative Approaches for Modeling Uncertainty
Dannele E. Peck and Richard M. Adams INTRODUCTION For more than two decades agricultural policy makers and stakeholders have expressed concern about the potential implications of global climate change for food security, resource management and the well-being of rural economies. In response, economists have applied several alternative methods at a variety of spatial and temporal scales to provide estimates of and insights into these potential impacts. Research has focused largely on estimating national and global impacts using highly aggregated data (e.g. Adams et al., 1990; Adams et al., 1999; Cline, 2007; Mendelsohn et al., 1994; Parry et al., 2004). Farm-level analyses, especially those employing mathematical programming techniques, have played a smaller role in climate change research. This is attributable, in part, to a lack of farm-level data for model parameterization, and the difficulty of downscaling climate change predictions to the local level. Heterogeneity of farms and operators also makes it difficult to scale farm-level results up to estimate aggregate impacts of climate change and associated policies. Despite these challenges, farm-level mathematical programming models are useful because they can explicitly capture the decision-making environment in which climate change impacts and adaptations occur. This enables researchers to systematically alter components of the decision environment (e.g. management objectives and activities, resource constraints and information) to explore the effects of climate change on producer decisions; identify economically optimal adaptations; estimate climate change impacts, and test the value of information. Most large-scale (e.g. sector-level) models, in contrast, only implicitly capture the farm-level decision environment by...
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