Off-campus UMass Amherst users: To download campus access theses, please use the following link to log into our proxy server with your UMass Amherst user name and password.
Non-UMass Amherst users: Please talk to your librarian about requesting this thesis through interlibrary loan.
Theses that have an embargo placed on them will not be available to anyone until the embargo expires.
Access Type
Campus Access
Degree Program
Industrial Engineering & Operations Research
Degree Type
Master of Science in Industrial Engineering and Operations Research (M.S.I.E.O.R.)
Year Degree Awarded
2010
Month Degree Awarded
September
Keywords
R&D Portfolio, Climate Policy
Abstract
We build a two-stage stochastic R&D portfolio model for climate policy analysis. This model can help policy makers allocate a limited R&D budget to minimize the total social cost. We develop several methods, including genetic programming and a greedy algorithm, to deal with the computational challenges of the model that arise due to the inclusion of uncertainties. From the R&D model, we have several key results. First, the optimal portfolios are robust against the climate risks. Second, policy makers should put most of their investment into Carbon Capture and Storage (CCS) projects when the R&D budget is relatively low. We further show Fast Reactor (FR) and 3rd generation PV are the two most unattractive technologies in the portfolio. Finally, more sophisticated expert elicitations on climate change energy technologies should be done in the future, because the potential benefit can be up to 11 billion dollars.
First Advisor
Erin D. Baker