Essays in energy resource economics
Abstract/Contents
- Abstract
- This dissertation consists of three chapters whose common theme is innovation and tax policies as they concern emerging energy technologies and resources in the United States and Canada. The first chapter investigates whether there is econometric evidence of learning-related externalities in the U.S. wind development business that might substantiate learning-by-doing as a basis for costly policies to support wind energy. I develop a modeling framework that: (i) disentangles firms' experience installing wind capacity from other determinants of wind power projects' costs -- in particular, input prices, scale economies, and exogenous technical change; and (ii) allows for alternative measures of experience and multiple channels through which experience can accumulate. For a variety of specifications, I find evidence of firm-specific learning-by-doing but no evidence of knowledge spillovers across firms, which calls into question the need for policies to support U.S. wind on the grounds that there are learning-related externalities. The second chapter develops a model of production and investment in the upstream natural gas business in an effort to characterize and evaluate an efficient and government revenue-maximizing royalty regime for shale gas in Canada. The model suggests that efficient production, investment, and royalty revenue outcomes can be achieved via a royalty on shale gas producers' operating profits, with royalty rates sensitive to gas wells' locations and market prices for gas. Simulation results based on this model suggest that the royalty regime currently in place in British Columbia -- the province with the best shale gas resources in Canada -- over-taxes shale gas producers at low prices, and under-taxes shale gas producers at high prices, compared to the proposed alternative royalty regime. The third chapter revisits the issue of identifying and estimating learning-by-doing in U.S. wind (and renewable energy technologies more generally). I explain why statistical inference based on learning curves -- a class of regression models employed in much existing empirical research on learning-by-doing in renewables -- is suspect and should not inform policy. Specifically, I show that popular learning curve models make strong assumptions that: (i) preclude the ability to distinguish between inter-firm knowledge spillovers and firm-specific learning-by-doing (i.e. learning that does and does not entail externalities); and (ii) disregard factors besides learning in explaining the evolution of firms' costs over time.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Anderson, John Wesley |
|---|---|
| Associated with | Stanford University, Department of Economics. |
| Primary advisor | Wolak, Frank A |
| Thesis advisor | Wolak, Frank A |
| Thesis advisor | Einav, Liran |
| Thesis advisor | Reiss, Peter C. (Peter Clemens) |
| Advisor | Einav, Liran |
| Advisor | Reiss, Peter C. (Peter Clemens) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | John Wesley Anderson. |
|---|---|
| Note | Submitted to the Department of Economics. |
| Thesis | Ph.D. Stanford University 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by John Wesley Anderson
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...