model

This paper examines the impact of declining energy prices on biofuels production and use and its implications to agricultural commodity markets. It uses PEATSim, a dynamic partial equilibrium, multi-commodity, multi-country global trade model of the agriculture sector to analyze the interaction between biofuel, crop and livestock sectors. The ability of countries to achieve their energy goals will be affected by future direction of petroleum prices. A 50 percent decline in petroleum prices (absent of mandates) would result in rapid decline in biofuel use worldwide accompanied by a decline in feedstock and biofuel prices. About a 21 percent decline in U.S. cost of ethanol production is needed to make ethanol competitive with gasoline and to offset the effect of lower energy prices.

Using System Dynamics to Model the Transition to Biofuels in the United States Preprint, B. Bush, M. Duffy, and D. Sandor, National Renewable Energy Laboratory S. Peterson, Peterson Group To be presented at the Third International Conference on Systems of Systems Engineering Monterey, California June 2-4, 2008 Conference Paper NREL/CP-150-43153 June 2008

Today, the U.S. consumes almost 21 million barrels of crude oil per day; approximately 60% of the U.S. demand is supplied by imports. The transportation sector alone accounts for two-thirds of U.S. petroleum use. Biofuels, liquid fuels produced from domestically-grown biomass, have the potential to displace about 30% of current U.S. gasoline consumption. Transitioning to a biofuels industry on this scale will require the creation of a robust biomass-to-biofuels system-of-systems that operates in concert with the existing agriculture, forestry, energy, and transportation markets. The U.S. Department of Energy is employing a system dynamics approach to investigate potential market penetration scenarios for cellulosic ethanol, and to aid decision makers in focusing government actions on the areas with greatest potential to accelerate the deployment of biofuels and ultimately
reduce the nation’s dependence on imported oil.

Understanding the Growth of the Cellulosic Ethanol Industry, D. Sandor and R. Wallace, National Renewable Energy Laboratory, S. Peterson The Peterson Group, Technical Report, NREL/TP-150-42120 April 2008

This paper presents an overview of multi-agent system models of land-use/cover change (MAS/LUCC models). This special class of LUCC models combines a cellular landscape model with agent-based representations of decisionmaking, integrating the two components through specification of interdependencies and feedbacks between agents and their environment. The authors review alternative LUCC modeling techniques and discuss the ways in which MAS/LUCC models may overcome some important limitations of existing techniques. We briefly review ongoing MAS/LUCC modeling efforts in four research areas. We discuss the potential strengths of MAS/LUCC models and suggest that these strengths guide researchers in assessing the appropriate choice of model for their particular research question. We find that MAS/LUCC models are particularly well suited for representing complex spatial interactions under heterogeneous conditions and for modeling decentralized, autonomous decision making. We discuss a range of possible roles for MAS/LUCC models, from abstract models designed to derive stylized hypotheses to empirically detailed simulation models appropriate for scenario and policy analysis. We also discuss the challenge of validation and verification for MAS/LUCC models. Finally, we outline important challenges and open research questions in this new field. We conclude that, while significant challenges exist, these models offer a promising new tool for researchers whose goal is to create fine-scale models of LUCC phenomena that focus on human-environment interactions.

This paper describes a methodology to explore the (future) spatial distribution of biofuel crops in Europe. Two main types of biofuel crops are distinguished: biofuel crops used for the production of biodiesel or bioethanol, and second-generation biofuel crops. A multiscale, multi-model approach is used in which biofuel crops are allocated over the period 2000?2030. The area of biofuel crops at the national level is determined by a macroeconomic model. A spatially explicit land use model is used to allocate the biofuel crops within the countries. Four scenarios have been prepared based on storylines influencing the extent and spatial distribution of biofuel crop cultivation. The allocation algorithm consists of two steps. In the first step, processing plants are allocated based on location factors that are dependent on the type of biofuel crop processed and scenario conditions. In the second step, biofuel crops are allocated accounting for the transportation costs to the processing plants. Both types of biofuel crops are allocated separately based on different location factors. Despite differences between the scenarios, mostly the same areas are showing growth in biofuel crop cultivation in all scenarios. These areas stand out because they have a combination of well-developed infrastructural and industrial facilities and large areas of suitable arable land. The spatially explicit results allow an assessment of the potential consequences of large-scale biofuel crop cultivation for ecology and environment.

The paper presents a general introduction to the structure and the characteristics of AGLINK, in comparison with the other most important global multi-commodity partial equilibrium models. Special reference is made to policy modelling, and particularly to the representation of the most important CAP tools, grouped into four types; direct price support, trade measures, supply management tools, and partially decoupled payments. The model is one of the most interesting efforts, especially in terms of its ability to effectively represent EU agricultural policy tools. The high level of institutional control exerted by the OECD member countries provides AGLINK with a particularly high-level of expertise, and a constant update of information; but it also prevents its results from being solely of a scientific nature.

Land-use change models are used by researchers and professionals to explore the dynamics and drivers of land-use/land-cover change and to inform policies affecting such change. A broad array of models and modeling methods are available to researchers, and each type has certain advantages and disadvantages depending on the objective of the research. This report presents a review of different types of models as a means of exploring the functionality and ability of different approaches. In this review, we try to explicitly incorporate human processes, because of their centrality in land-use/land-cover change. We present a framework to compare land-use change models in terms of scale (both spatial and temporal) and complexity, and how well they incorporate space, time, and human decisionmaking. Initially, we examined a summary set of 250 relevant citations and developed a bibliography of 136 papers. From these 136 papers a set of 19 land-use models were reviewed in detail as
representative of the broader set of models identified from the more comprehensive review of literature. Using a tabular approach, we summarize and discuss the 19 models in terms of dynamic (temporal) and spatial interactions, as well as human decisionmaking as defined by the earlier framework. To eliminate the general confusion surrounding the term scale, we evaluate each model with respect to pairs of analogous parameters of spatial, temporal, and decisionmaking scales: (1) spatial resolution and extent, (2) time step and duration, and (3) decisionmaking agent and domain. Although a wide range of spatial and temporal scales is
covered collectively by the models examined, we find most individual models occupy a much more limited spatio temporal niche. Many raster models we examined mirror the extent and resolution of common remote-sensing data. The broadest-scale models are, in general, not spatially explicit. We also find that models incorporating higher levels of human decision making are more centrally located with respect to spatial and temporal scales,
probably due to the lack of data availability at more extreme scales. Further, we examine the social drivers of land-use change and methodological trends exemplified in the models we reviewed. Finally, we conclude with some proposals for future directions in land-use modeling.

This paper offers a graphical exposition of the GTAP model of global trade. Particular emphasis is placed on the accounting, or equilibrium, relationships in the model. It begins with a treatment of the a one region version of GTAP, thereafter adding a rest of world region to highlight the treatment of trade flows in the model. The implementation of policy instruments in GTAP is also explored, using simple supply-demand graphics. The material provided in this paper was first developed as an introduction to GTAP for participants taking the annual short course. Based on its success in that venue, this paper has been placed on the ?highly recommended? reading list for individuals seeking an introduction and overview of the GTAP framework. It was modified in March of 2001 to reflect the changes in version 6.0 of the GTAP model.

Land-use change models are important tools for integrated environmental management. Through scenario analysis they can help to identify near-future critical locations in the face of environmental change. A dynamic, spatially explicit, land-use change model is presented for the regional scale: CLUE-S. The model is specifically developed for the analysis of land use in small regions (e.g., a watershed or province) at a fine spatial resolution. The model structure is based on systems theory to allow the integrated analysis of land-use change in relation to socio-economic and biophysical driving factors. The model explicitly addresses the hierarchical organization of land use systems, spatial connectivity between locations and stability. Stability is incorporated by a set of variables that define the relative elasticity of the actual land-use type to conversion. The user can specify these settings based on expert knowledge or survey data. Two applications of the model in the Philippines and Malaysia are used to illustrate the functioning of the model and its validation.

Growing concern about climate change and energy security has led to increasing interest in developing renewable, domestic energy sources for meeting electricity, heating and fuel needs in the United States. Illinois has significant potential to produce bioenergy crops, including corn, soybeans, miscanthus (Miscanthus giganteus), and switchgrass (Panicum virgatum). However, land requirements for bioenergy crops place them in competition with more traditional agricultural uses, in particular food production. Additionally, environmental and economic conditions, including soil quality, climate, and variable agricultural costs, vary significantly across Illinois. The intent of this study is to examine the spatial and economic conditions necessary for introducing bioenergy crops into the Illinois landscape. In this paper, we develop a spatial dynamic model to explore the process by which individual farmer agents optimize profits through crop selection and cost minimization. This dynamic agent-based modeling approach will allow us to determine the optimal spatial arrangement of crops throughout Illinois as it is influenced by several factors, including the use of subsidies, changes in travel costs and crop demand, and the introduction of new ethanol production plants. This article discusses model development and specification, and outlines future calibration procedures and scenario tests that will be formalized in future work.