Title:
Learning in integrated optimization models of climate change and economy
Learning in integrated optimization models of climate change and economy
| dc.contributor.advisor | Thomas, Valerie M. | |
| dc.contributor.author | Shayegh, Soheil | |
| dc.contributor.committeeMember | Ayhan, Hayrie | |
| dc.contributor.committeeMember | Shapiro, Alexander | |
| dc.contributor.committeeMember | Vengazhiyil, Roshan Joseph | |
| dc.contributor.committeeMember | Nenes, Athanasios | |
| dc.contributor.department | Industrial and Systems Engineering | |
| dc.date.accessioned | 2015-09-21T15:52:32Z | |
| dc.date.available | 2015-09-22T05:30:06Z | |
| dc.date.created | 2014-08 | |
| dc.date.issued | 2014-06-27 | |
| dc.date.submitted | August 2014 | |
| dc.date.updated | 2015-09-21T15:52:32Z | |
| dc.description.abstract | Integrated assessment models are powerful tools for providing insight into the interaction between the economy and climate change over a long time horizon. However, knowledge of climate parameters and their behavior under extreme circumstances of global warming is still an active area of research. In this thesis we incorporated the uncertainty in one of the key parameters of climate change, climate sensitivity, into an integrated assessment model and showed how this affects the choice of optimal policies and actions. We constructed a new, multi-step-ahead approximate dynamic programing (ADP) algorithm to study the effects of the stochastic nature of climate parameters. We considered the effect of stochastic extreme events in climate change (tipping points) with large economic loss. The risk of an extreme event drives tougher GHG reduction actions in the near term. On the other hand, the optimal policies in post-tipping point stages are similar to or below the deterministic optimal policies. Once the tipping point occurs, the ensuing optimal actions tend toward more moderate policies. Previous studies have shown the impacts of economic and climate shocks on the optimal abatement policies but did not address the correlation among uncertain parameters. With uncertain climate sensitivity, the risk of extreme events is linked to the variations in climate sensitivity distribution. We developed a novel Bayesian framework to endogenously interrelate the two stochastic parameters. The results in this case are clustered around the pre-tipping point optimal policies of the deterministic climate sensitivity model. Tougher actions are more frequent as there is more uncertainty in likelihood of extreme events in the near future. This affects the optimal policies in post-tipping point states as well, as they tend to utilize more conservative actions. As we proceed in time toward the future, the (binary) status of the climate will be observed and the prior distribution of the climate sensitivity parameter will be updated. The cost and climate tradeoffs of new technologies are key to decisions in climate policy. Here we focus on electricity generation industry and contrast the extremes in electricity generation choices: making choices on new generation facilities based on cost only and in the absence of any climate policy, versus making choices based on climate impacts only regardless of the generation costs. Taking the expected drop in cost as experience grows into account when selecting the portfolio of generation, on a pure cost-minimization basis, renewable technologies displace coal and natural gas within two decades even when climate damage is not considered in the choice of technologies. This is the natural gas as a bridge fuel scenario, and technology advancement to bring down the cost of renewables requires some commitment to renewables generation in the near term. Adopting the objective of minimizing climate damage, essentially moving immediately to low greenhouse gas generation technologies, results in faster cost reduction of new technologies and may result in different technologies becoming dominant in global electricity generation. Thus today’s choices for new electricity generation by individual countries and utilities have implications not only for their direct costs and the global climate, but also for the future costs and availability of emerging electricity generation options. | |
| dc.description.degree | Ph.D. | |
| dc.embargo.terms | 2015-08-01 | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/54012 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Integrated assessment model | |
| dc.subject | Climate change | |
| dc.subject | Stochastic optimization | |
| dc.subject | Bayesian statistics | |
| dc.title | Learning in integrated optimization models of climate change and economy | |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Thomas, Valerie M. | |
| local.contributor.corporatename | H. Milton Stewart School of Industrial and Systems Engineering | |
| local.contributor.corporatename | College of Engineering | |
| relation.isAdvisorOfPublication | 1fc77587-4f8b-4a14-8e8c-fc53a957f2ec | |
| relation.isOrgUnitOfPublication | 29ad75f0-242d-49a7-9b3d-0ac88893323c | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| thesis.degree.level | Doctoral |