Advancing the modeling of technological change : applications in energy and climate policy analysis
Abstract/Contents
- Abstract
- There is a strong consensus that the climate is changing, that human activities are the dominant cause of this change, and that continued climate change will have negative impacts on human societies. To analyze energy and climate policy remedies, researchers have developed a diverse collection of integrated assessment models (IAMs) that represent the linked energy, economic, and earth systems in an interdisciplinary framework. Some IAMs are cost-benefit models designed to compute optimal policy interventions, while others are cost-effectiveness models used to determine the technology pathways that enable an emissions or climate goal to be achieved at least cost. Although IAM representations of technological change are critical determinants of model outcomes, underlying processes are poorly understood and models typically feature fairly crude formulations. The goal of the three projects that constitute this dissertation is to develop more advanced representations of technological change that capture a wider range of endogenous drivers. Scenario analyses based on these representations reveal their implications for energy and climate policy, as well as technology transitions this century. Chapter 2 describes the development of a system of technology diffusion constraints that endogenously respects empirically observed spatial diffusion patterns. Technologies diffuse from an advanced core to less technologically adept regions, with adoption experiences in the former determining adoption possibilities in the latter. Endogenous diffusion constraints are incorporated into the MESSAGE framework and results suggest that IAMs based on standard exogenous diffusion formulations are overly optimistic about technology leapfrogging potential in developing countries. Findings also demonstrate that policies which stimulate initial deployment of low-carbon technologies in advanced economies can be justified from a global common goods perspective even if they fail the cost-benefit test domestically. In Chapter 3, learning-by-doing is formulated as a firm-level rather than an industry-level phenomenon. Wind and solar PV manufacturers strategically choose output levels in an oligopoly game with learning and inter-firm spillovers. This game-theoretic representation of renewable technology markets is coupled to MESSAGE so that the energy system planner can only invest in wind and solar PV capacity at the equilibrium prices the market would charge for the desired quantities. Findings illustrate that the most ambitious emissions reduction pathways include widespread solar PV diffusion, which only occurs if competitive markets and spillovers combine to reduce prices sufficiently. The relationship between price and cumulative capacity is similar to that between unit cost and cumulative capacity under competitive markets, but a combination of market power, strong climate policy, and weak spillovers can cause prices to rise with cumulative capacity even though unit costs decline. The bilevel modeling framework of Chapter 4 is built to determine the optimal combination of technology-push and demand-pull subsidies for a given technology policy application. Firms (inner agents) solve a two-stage stochastic profit maximization problem in which they choose process and product R& D investments in the first stage, then choose output levels in the second stage. The policymaker (outer agent) seeks to identify the combination of policies that induces the firms to reach an equilibrium with the highest possible expected welfare. Numerical simulation results show that technology policy can enhance welfare under a wide range of parameter settings. Spillovers reduce product R& D expenditures but generally improve welfare by making R& D more effective. Welfare decreases with competition in the no-policy case, but increases with competition if optimal technology policies can be imposed. Each of the three projects focuses on a distinct aspect of technological change, but the formulations developed for these studies reflect several important themes: endogenous mechanisms, multiple decision-making agents, game-theoretic interactions, market power, spillovers, regional heterogeneity, and uncertainty. While the research presented in this dissertation advances the modeling of technological change, a number of formidable challenges remain. The final chapter discusses some of these challenges and ideas for future research to address them.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Leibowicz, Benjamin Daniel |
|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering. |
| Primary advisor | Weyant, John P. (John Peter) |
| Thesis advisor | Weyant, John P. (John Peter) |
| Thesis advisor | Grübler, Arnulf, 1955- |
| Thesis advisor | Sweeney, James L |
| Advisor | Grübler, Arnulf, 1955- |
| Advisor | Sweeney, James L |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Benjamin Daniel Leibowicz. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Benjamin Daniel Leibowicz
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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