(276a) Biomass Feedstock Market Supporting the Emerging Biorefinery Industry: How Will It Develop?

An efficient and sustainable biomass feedstock production system is critical for the success of lignocellulosic biomass based bioenergy sector. However, there is currently a lack of understanding regarding the development of the feedstock value chain as well as the possible configuration of the mature value-chain. One of primary reasons for this is the involvement of multiple stakeholders, such as farmers, refinery, transportation companies, storage elevators, farm consultants and equipment renters that must participate and yet compete for a fraction of the profit. Moreover, the individual stakeholder behavior in reality is based on factual details as well as other aspects like experience and personal preferences. This adds to the complexity and makes the analysis difficult. The objective of this work is to take a complex adaptive systems approach to analyze the development and functioning of the feedstock market in the presence of such complexity. The power of this approach lies in its ability to simulate emergent system behavior through the modeling of key features and interrelations. It uses the agent-based modeling philosophy to model multiple stakeholders such as farmers and refinery in a typical feedstock production system. Each stakeholder is modeled as a class of independent agents using an object-oriented approach. The behavior and decision making of each class of agents is modeled with a set of rules. These rules are based not only on the economic rationale such as profit maximization, but also on agent-specific mental models and cognitive processes such as risk aversion. This enables the model to replicate decision making process that happen in reality to a certain extent. Key interactions between different classes of agents are modeled to mimic contractual negotiations and agreements in a real system. The role of the government, assumed to be very important to stimulate growth in this field, is also modeled in the form of policies that affect the interaction and decision making process for each agent. The model will be used to carry out simulation studies over multiple years to predict different possible development paths for the biomass feedstock market. The impact of agent diversity as well as policy interventions will be quantified. This should, subsequently, help in the identification of the most desirable incentives and policies to ensure a stable feedstock production system in the long run.