Technology Diffusion in Climate Mitigation Modeling and Implications for Mitigation Targets

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dc.contributor.advisorRuth, Matthiasen_US
dc.contributor.advisorPatwardhan, Ananden_US
dc.contributor.authorde la Chesnaye, Francisco Carlosen_US
dc.contributor.departmentPublic Policyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2014-06-26T05:38:37Z
dc.date.available2014-06-26T05:38:37Z
dc.date.issued2014en_US
dc.description.abstractGlobal climate mitigation analyses have been used to evaluate the challenges of reducing greenhouse gases and to inform climate change policymaking for over 30 years. Studies traditionally focus on projections of greenhouse gases over the 21st century based on key drivers such as population growth, economic growth, and the rate of technological change especially in climate mitigation or energy technologies. Any one of these factors can have an appreciable impact on emissions levels and the cost of mitigation particularly in the face of stringent mitigation targets. One area that has not been sufficiently studied is the impact of different rates of technology diffusion of advanced energy technologies between high-income and low- and middle-income countries. This is the topic of this dissertation. The standard approach in climate economic modeling is to assume that all technologies are available at the same time and rate across countries with different incomes and technological capabilities. This study applies the literature related to economic and technological convergence to first develop new estimates of technology diffusion for energy-related sectors across 112 countries of varying income levels. Then new greenhouse gas scenarios are developed with the Global Change Assessment Model (GCAM) to test the importance of different assumptions on technology diffusion versus other key modeling assumptions. The modeling results from this research show that the cost of meeting the same climate target could be as high as 60% to 80% in marginal cost terms and about 30% greater in total policy costs when different assumptions on diffusion rates of climate mitigation technologies between countries are used. These results clearly point to the need for greater evaluation on the importance of technology diffusion in climate mitigation modeling and also in the consideration of these results for climate change policy decision making.en_US
dc.identifier.urihttp://hdl.handle.net/1903/15465
dc.language.isoenen_US
dc.subject.pqcontrolledClimate changeen_US
dc.subject.pqcontrolledEnvironmental economicsen_US
dc.subject.pqcontrolledPublic policyen_US
dc.subject.pquncontrolledClimate Mitigationen_US
dc.subject.pquncontrolledClimate Targetsen_US
dc.subject.pquncontrolledEnergy Economicsen_US
dc.subject.pquncontrolledMitigation Modelingen_US
dc.titleTechnology Diffusion in Climate Mitigation Modeling and Implications for Mitigation Targetsen_US
dc.typeDissertationen_US

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