{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116258"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116258","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Designing electric vehicle incentives to meet emission reduction targets","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_has_math":false,"creators":["Wu, Yen-Chu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Kontou, ‪Eleftheria"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:56Z","subjects":["Electric vehicles","Rebate","Charging stations","Incentives","Optimization model","Simulated annealing algorithm","Emissions reduction"],"languages":["en","eng"],"rights":["Copyright 2022 Yen-Chu Wu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116258","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kontou, ‪Eleftheria"]},{"key":"dc:creator","label":"Author","values":["Wu, Yen-Chu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-19"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electric vehicles","Rebate","Charging stations","Incentives","Optimization model","Simulated annealing algorithm","Emissions reduction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Yen-Chu Wu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116258"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Yen-Chu Wu, accepted the attached license on 2022-07-15 at 22:23.","The student, Yen-Chu Wu, submitted this Thesis for approval on 2022-07-15 at 22:34.","This Thesis was approved for publication on 2022-07-19 at 10:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18352 on 2022-11-15 at 18:21:21","Electric vehicles are expected to reduce transportation emissions. We design and allocate rebates and charging infrastructure investments to induce electric vehicle adoption and achieve emission reduction targets. A nonlinear mixed-integer mathematical model is proposed to optimize the investment allocation over a planning horizon. Logistic functions describe the vehicle demand driven by capital and ownership costs and network externalities. A simulated annealing algorithm is used to solve the nonlinear programming problem that is applied using data representative of the United States and the State of Illinois markets. Our analysis indicates that rebates should be provided earlier than chargers due to neighborhood effects of electric vehicle adoption and the minimization of expenditure; availability of home charging influences consumers' choice and the drivers electrified travel distance; rebates are more effective for modest drivers while charging stations should be prioritized for frequent drivers; network externalities should be further investigated because of their impact on electric vehicle demand."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Designing electric vehicle incentives to meet emission reduction targets"]}]}],"canonical_facts":{"dc:contributor":["Kontou, ‪Eleftheria"],"dc:creator":["Wu, Yen-Chu"],"dc:date":["2022-08","2022-07-19"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Yen-Chu Wu, accepted the attached license on 2022-07-15 at 22:23.","The student, Yen-Chu Wu, submitted this Thesis for approval on 2022-07-15 at 22:34.","This Thesis was approved for publication on 2022-07-19 at 10:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18352 on 2022-11-15 at 18:21:21","Electric vehicles are expected to reduce transportation emissions. We design and allocate rebates and charging infrastructure investments to induce electric vehicle adoption and achieve emission reduction targets. A nonlinear mixed-integer mathematical model is proposed to optimize the investment allocation over a planning horizon. Logistic functions describe the vehicle demand driven by capital and ownership costs and network externalities. A simulated annealing algorithm is used to solve the nonlinear programming problem that is applied using data representative of the United States and the State of Illinois markets. Our analysis indicates that rebates should be provided earlier than chargers due to neighborhood effects of electric vehicle adoption and the minimization of expenditure; availability of home charging influences consumers' choice and the drivers electrified travel distance; rebates are more effective for modest drivers while charging stations should be prioritized for frequent drivers; network externalities should be further investigated because of their impact on electric vehicle demand."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116258"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Yen-Chu Wu"],"dc:subject":["Electric vehicles","Rebate","Charging stations","Incentives","Optimization model","Simulated annealing algorithm","Emissions reduction"],"dc:title":["Designing electric vehicle incentives to meet emission reduction targets"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}