RTI uses cookies to offer you the best experience online. By clicking “accept” on this website, you opt in and you agree to the use of cookies. If you would like to know more about how RTI uses cookies and how to manage them please view our Privacy Policy here. You can “opt out” or change your mind by visiting: http://optout.aboutads.info/. Click “accept” to agree.
Modeling bioenergy, land use, and GHG emissions with FASOMGHG: Model overview and analysis of storage cost implications
Beach, R., Zhang, Y., & McCarl, BA. (2012). Modeling bioenergy, land use, and GHG emissions with FASOMGHG: Model overview and analysis of storage cost implications. Climate Change Economics, 03(03), 1250012. https://doi.org/10.1142/S2010007812500121
Biofuels production has increased rapidly in recent years due to higher petroleum prices as well as heightened concerns regarding climate change and energy security. However, because commercially viable biofuels are currently produced primarily from agricultural feedstocks, higher production volumes increase pressure on land resources. Thus, large-scale biofuels production has important implications for the forest and agriculture sectors, land use, trade, and net greenhouse gas (GHG) emissions. Competition for land is expected to continue growing in the future as mandated biofuels volumes increase along with rising demand for food, feed, and fiber, both domestically and internationally. In response to heightened concern regarding impacts such as indirect land-use change and higher food prices, the U.S. policy is focusing on second-generation (cellulosic) feedstocks to contribute the majority of the mandated increase in biofuels volume through 2022. However, there has been little work exploring the logistics of supplying these feedstocks or examining feedstock mix and net GHG effects of combining renewable fuels mandates with climate policy. In this paper, we apply the recently updated Forest and Agricultural Sector Optimization Model with GHGs (FASOMGHG) to explore the implications of alternative assumptions regarding feedstock storage costs and carbon price for renewable energy production mix, land use, and net GHG emissions. The model is used to quantify the magnitude and regional distribution of changes in the optimal mix of bioenergy feedstocks when accounting for storage costs. In addition, we find that combining the biofuels volume mandate with a carbon price policy has additional implications for feedstock mix and provides a substantially larger net reduction in GHG than a renewable fuels mandate alone.