Integrated Assessment of Climate Change, Land-Use Changes, and Regional Carbon Dynamics in United States

Abstract:

The fact that climate change is likely to accelerate throughout this century means that climate-sensitive sectors such as agriculture will need to adapt increasingly to climate change. This fact also means that understanding the potential for agricultural adaptation, and how it could come about, is important for ongoing technology investments in the public and private sectors, for infrastructure investments, and for the various policies that address agriculture directly or indirectly. This paper is an interdisciplinary study by collaborating with climate scientist, agronomists, economists, and ecologists. We first use statistical models to estimate impacts of climate change on major crop yields (wheat, corn, soybeans, sorghum, and cotton) and predict changes in crop yields under future climate condition using downscaled climate projections from CMIP5. Then, we feed the predicted yield changes to a partial equilibrium economic model (FASOM-GHG) to evaluate economic and environmental outcomes including changes in land uses (i.e., cropland, pastureland, forest land, urban land and land for conservation) in United States. Finally, we use outputs from FASOM-GHG as inputs for the ST-SIM ecological model to simulate future carbon dynamics through changes in land use under future climate conditions and discuss the rate of adaptation through land-use changes. Findings in this paper have several merits compared to previous findings in the literature. First, we add economic components to the carbon calculation. It is important to include socio-economic conditions when calculating carbon emission and/or carbon sequestration because human activities are the major contribution to atmosphere GHG emissions. Second, we use the most recent downscaled climate projections from CMIP5 to capture uncertainties from climate model projections. Instead of using all GCMs, we select five GCMs to represent the ensemble. Third, we use a bottom-up approach because we start from micro-level data (e.g., counties in the United States) and aggregate to states or eco-logical regions. Alternative approach used in previous literature is the top-down approach, which depends on global economic model projections and downscaling methods. Compared to the top-down approach, our approach is more realistic by using observed data.