Assessing Early Investments in Low Carbon Technologies under Uncertainty: The Case of Carbon Capture and Storage
Project: Assessing Early Investments in Low Carbon Technologies under Uncertainty: The Case of Carbon Capture and Storage
Sponsor: Carbon Sequestration Initiative
Climate change is a threat that could be mitigated by introducing new energy technologies into the electricity market that emit fewer greenhouse gas (GHG) emissions. We face many uncertainties that would affect the demand for each of these technologies in the future. The costs of these technologies decrease due to learning-by-doing as their capacity is built out. Given that we face uncertainties over future energy demands for particular technologies, and that costs reduce with experience, an important question that arises is whether policy makers should encourage early investments in technologies before they are economically competitive, so that they could be available in the future at lower cost should they be needed. If society benefits from early investments when future demands are uncertain, then there is an option value to investing today. This question of whether option values exist is investigated by focusing on Coal-fired Power Plants with Carbon Capture and Storage (CCS) as a case study of a new high-cost energy technology that has not yet been deployed at commercial scale.
A decision analytic framework is applied to the MIT Emissions Prediction Policy Analysis (EPPA) model, a computable general equilibrium model that captures the feedback effects across different sectors of the economy, and measures the costs of meeting emissions targets. Three uncertainties are considered in constructing a decision framework: the future stringency of the US GHG emissions policy, the size of the US gas resource, and the cost of electricity from Coal with CCS. The decision modeled is whether to begin an annual investment schedule in Coal with CCS technology for 35 years. Each scenario in the decision framework is modeled in EPPA, and the output measure of welfare is used to compare the welfare loss to society of meeting the emissions target for each case. The decision framework is used to find which choice today, whether to invest in CCS or not, gives the smallest welfare cost and is therefore optimal for society. Sensitivity analysis on the probabilities of the three uncertainties is carried out to determine the conditions under which CCS investment is beneficial, and when it is not.
The study finds that there are conditions, specified by ranges in probabilities for the uncertainties, where early investment in CCS does benefit society. The results of the decision analysis demonstrate that the benefits of CCS investment are realized in the latter part of the century, and so the resulting optimal decision depends on the choice of discount rate. The higher the rate, the smaller the benefit from investment until a threshold is reached where choosing to invest becomes the more costly decision. The decision of whether to invest is more sensitive to some uncertainties investigated than others. Specifically, the size of the US gas resource has the least impact, whereas the stringency of the future US GHG emissions policy has the greatest impact.
This thesis presents a new framework for considering investments in energy technologies before they are economically competitive. If we can make educated assumptions as to the real probabilities we face, then extending this framework to technologies beyond CCS and expanding the decision analysis, would allow policymakers to induce investment in energy technologies that would enable us to meet our emissions targets at the lowest cost possible to society.
Ereira, E., H. Herzog, and M. Webster, "Assessing Early Investments in Carbon Capture and Storage Technology under Uncertaintys," presented at the 10th International Conference on Greenhouse Gas Control Technologies, Amsterdam, The Netherlands, September (2010). <PDF>
Ereira, E.C., "Assessing Early Investments in Low Carbon Technologies under Uncertainty: The Case of Carbon Capture and Storage," M.I.T. Masters Thesis, June (2010). <PDF>