Link to the website with documentation and download instructions for the PNNL Global Change Assessment Model (GCAM), a community model or long-term, global energy, agriculture, land use, and emissions. BioEnergy production, transformation, and use is an integral part of GCAM modeling and scenarios.
This report is a collective effort of the Scientific Committee on Problems of the Environment (SCOPE), including contributions from 137 researchers of 82 institutions in 24 countries. It concludes that land availability is not a limiting factor to bioenergy production and that bioenergy can contribute to sustainable energy supplies even with increasing food demands, preservation of forests, protected lands, and rising urbanization.
Landscape implications of bioenergy feedstock choices are significant and depend on land-use practices and their environmental impacts. Although land-use changes and carbon emissions associated with bioenergy feedstock production are dynamic and complicated, lignocellulosic feedstocks may offer opportunities that enhance sustainability when compared to other transportation fuel alternatives.
Land-use change (LUC) is a contentious policy issue because of its uncertain, yet potentially substantial, impact on bioenergy climate change benefits. Currently, the share of global GHG emissions from biofuels-induced LUC is small compared to that from LUC associated with food and feed production and other human-induced causes. However, increasing demand for biofuels derived from feedstocks grown on agricultural land could increase this contribution. No consensus has emerged on how to appropriately isolate and quantify LUC impacts of bioenergy from those of other LUC drivers.
Increasing demand for crop-based biofuels, in addition to other human drivers of land use, induces direct and indirect land use changes (LUC). Our system dynamics tool is intended to complement existing LUC modeling approaches and to improve the understanding of global LUC drivers and dynamics by allowing examination of global LUC under diverse scenarios and varying model assumptions. We report on a small subset of such analyses.