<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-22T06:05:06Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/62106" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/62106</identifier><datestamp>2026-06-06T01:06:13Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131023</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">R. John Hansman.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Kar, Rahul, 1979-</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Massachusetts Institute of Technology. Technology and Policy Program.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Engineering Systems Division</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Technology and Policy Program</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2011-04-04T17:43:26Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2011-04-04T17:43:26Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2010</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2010</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/1721.1/62106</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">707937305</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2010.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">Increasing demand for air transportation and growing environmental concerns motivate the need to implement measures to reduce CO₂ emissions from aviation. Case studies of historical changes in the aviation industry have shown that the implementation of changes generally followed S-curves with relatively long time-constants. This research analyzed the diffusion characteristics of a portfolio of CO₂ emission mitigating measures and their relative contribution to cumulative system wide improvements. A literature review identified 41 unique measures, including (1) technological improvements, (2) operational improvements, and (3) the use of alternative fuels. It was found that several operational changes can be implemented in the short term but are unlikely to significantly reduce CO₂ emissions. Technology retrofits and some operational changes can be implemented in the medium term. 2 nd and 3rd generation biofuels can significantly reduce carbon emissions but are likely to have long diffusion times and may not be available in sufficient quantities to the aviation industry. Technology measures in the form of next generation aircraft have the highest CO₂ reduction potential, but only in the long term due to slow fleet turnover. An Aircraft Diffusion Dynamic Model (ADDM) was developed using System Dynamics modeling techniques to understand how the fleet efficiency will be influenced by the entry of various generations of aircraft with different levels of emissions performance. The model was used to evaluate effects of several future potential scenarios on the US narrow body jet fleet as well as their sensitivity to S-curve parameters. Results from the model showed that strategies that emphasize the early entry into service of available technology, as opposed to waiting and delaying entry for more fuel efficient technology, have greater potential to improve fleet fuel-burn performance. Also, strategies that incentivize early retirement of older aircraft have marginal potential for reducing fuel burn. Future demand scenarios showed that the infusion of fuel-efficient aircraft alone is unlikely to reduce emissions below 2006 levels. Instead, a portfolio of measures that also include demand reduction mechanisms, operational improvements, and adoption of alternative fuels will be required in order to limit the growth of CO₂ emissions from aviation.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Rahul Kar.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">S.M. in Technology and Policy</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">115 p.</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">M.I.T. theses are protected by &#xd;
copyright. They may be viewed from this source for any purpose, but &#xd;
reproduction or distribution in any format is prohibited without written &#xd;
permission. See provided URL for inquiries about permission.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Engineering Systems Division.</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Technology and Policy Program.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Dynamics of implementation of mitigating measures to reduce CO₂ emissions from commercial aviation</dim:field>
   <dim:field mdschema="dc" element="type" lang="en_US">Thesis</dim:field>
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	&lt;Language>eng&lt;/Language>
   	&lt;Title>Dynamics of implementation of mitigating measures to reduce CO₂ emissions from commercial aviation&lt;/Title>
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    	&lt;Publication>
      	&lt;/Publication>
   	&lt;/PublishedIn>
   	&lt;PublicationDate>2010&lt;/PublicationDate>
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        	&lt;DisplayName>Kar, Rahul, 1979-&lt;/DisplayName>
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            &lt;DisplayName>Massachusetts Institute of Technology&lt;/DisplayName>
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    &lt;License>http://dspace.mit.edu/handle/1721.1/7582&lt;/License>
    &lt;Keyword>Engineering Systems Division.&lt;/Keyword>
    &lt;Keyword>Technology and Policy Program.&lt;/Keyword>
   	&lt;Abstract>Increasing demand for air transportation and growing environmental concerns motivate the need to implement measures to reduce CO₂ emissions from aviation. Case studies of historical changes in the aviation industry have shown that the implementation of changes generally followed S-curves with relatively long time-constants. This research analyzed the diffusion characteristics of a portfolio of CO₂ emission mitigating measures and their relative contribution to cumulative system wide improvements. A literature review identified 41 unique measures, including (1) technological improvements, (2) operational improvements, and (3) the use of alternative fuels. It was found that several operational changes can be implemented in the short term but are unlikely to significantly reduce CO₂ emissions. Technology retrofits and some operational changes can be implemented in the medium term. 2 nd and 3rd generation biofuels can significantly reduce carbon emissions but are likely to have long diffusion times and may not be available in sufficient quantities to the aviation industry. Technology measures in the form of next generation aircraft have the highest CO₂ reduction potential, but only in the long term due to slow fleet turnover. An Aircraft Diffusion Dynamic Model (ADDM) was developed using System Dynamics modeling techniques to understand how the fleet efficiency will be influenced by the entry of various generations of aircraft with different levels of emissions performance. The model was used to evaluate effects of several future potential scenarios on the US narrow body jet fleet as well as their sensitivity to S-curve parameters. Results from the model showed that strategies that emphasize the early entry into service of available technology, as opposed to waiting and delaying entry for more fuel efficient technology, have greater potential to improve fleet fuel-burn performance. Also, strategies that incentivize early retirement of older aircraft have marginal potential for reducing fuel burn. Future demand scenarios showed that the infusion of fuel-efficient aircraft alone is unlikely to reduce emissions below 2006 levels. Instead, a portfolio of measures that also include demand reduction mechanisms, operational improvements, and adoption of alternative fuels will be required in order to limit the growth of CO₂ emissions from aviation.&lt;/Abstract>
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