Diamant, Adam (Electric Power Research Institute (EPRI), 1805, Arlington Blvd., El Cerrito, CA, 94530; Phone: 510-260-9105; Email: firstname.lastname@example.org)
Estimating and Comparing the Levelized Cost ($/ton CO2e) of Greenhouse Gas Emissions Reductions Associated with Four Different Approaches to Developing and Managing Forest Carbon Sequestration Projects
A. Diamant *
Electric utilities can implement a variety of strategies to offset their greenhouse gas emissions. One strategy that has received considerable attention is forest carbon sequestration. In recent years a number of electric utilities have invested in pilot forest carbon sequestration projects both here in the U.S. and internationally. While forest carbon sequestration appears to be a potentially low-cost way to offset GHG emissions from electric utilities, electric companies continue to wrestle with how to analyze these kinds of projects from a financial analysis perspective, and how to compare different approaches that can be used to implement these projects. The Electric Power Research Institute (EPRI) has attempted to address these issues by developing a new, “real-options”-enabled computer simulation model that is designed to estimate the levelized cost ($/ton CO2e) of GHG emissions reductions associated with four different approaches electric utilities and others can use to contract for and manage forest carbon sequestration projects. EPRI\'s new Greenhouse Gas Emissions Reduction Cost Analysis Model (GHG-CAM v1.1) uses an advanced discounted cash flow (DCF) analysis methodology to evaluate the revenues, costs and expected after-tax gross margins expected to flow from investments that could be made by an electric utility company in different kinds of GHG emissions reduction projects, including forest carbon sequestration. The GHG-CAM model incorporates sophisticated statistcal and economic analysis tools, including Monte Carlo simulation, real-options analysis, and decision analysis methods that make it possible to explicitly incorporate risk, statistical uncertainty and contingent decision making into the analysis of specific GHG emissions abatement strategies such as forest carbon sequestration.