Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation

Detalhes bibliográficos
Autor(a) principal: Pukhova, Alona
Data de Publicação: 2021
Outros Autores: Moreno, Ana Tsui, Llorca, Carlos, Huang, Wei-Chieh, Moeckel, Rolf
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.17645/up.v6i2.4021
Resumo: Every sector needs to minimize GHG emissions to limit climate change. Emissions from transport, however, have remained mostly unchanged over the past thirty years. In particular, air travel for short-haul flights is a significant contributor to transport emissions. This article identifies factors that influence the demand for domestic air travel. An agent-based model was implemented for domestic travel in Germany to test policies that could be implemented to reduce air travel and CO2 emissions. The agent-based long-distance travel demand model is composed of trip generation, destination choice, mode choice and CO2 emission modules. The travel demand model was estimated and calibrated with the German Household Travel Survey, including socio-demographic characteristics and area type. Long-distance trips were differentiated by trip type (daytrip, overnight trip), trip purpose (business, leisure, private) and mode (auto, air, long-distance rail and long-distance bus). Emission factors by mode were used to calculate CO2 emissions. Potential strategies and policies to reduce air travel demand and its CO2 emissions are tested using this model. An increase in airfares reduced the number of air trips and reduced transport emissions. Even stronger effects were found with a policy that restricts air travel to trips that are longer than a certain threshold distance. While such policies might be difficult to implement politically, restricting air travel has the potential to reduce total CO2 emissions from transport by 7.5%.
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spelling Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviationaviation emissions; long distance travel; mode choice modelling; transport emissions; transport modellingEvery sector needs to minimize GHG emissions to limit climate change. Emissions from transport, however, have remained mostly unchanged over the past thirty years. In particular, air travel for short-haul flights is a significant contributor to transport emissions. This article identifies factors that influence the demand for domestic air travel. An agent-based model was implemented for domestic travel in Germany to test policies that could be implemented to reduce air travel and CO2 emissions. The agent-based long-distance travel demand model is composed of trip generation, destination choice, mode choice and CO2 emission modules. The travel demand model was estimated and calibrated with the German Household Travel Survey, including socio-demographic characteristics and area type. Long-distance trips were differentiated by trip type (daytrip, overnight trip), trip purpose (business, leisure, private) and mode (auto, air, long-distance rail and long-distance bus). Emission factors by mode were used to calculate CO2 emissions. Potential strategies and policies to reduce air travel demand and its CO2 emissions are tested using this model. An increase in airfares reduced the number of air trips and reduced transport emissions. Even stronger effects were found with a policy that restricts air travel to trips that are longer than a certain threshold distance. While such policies might be difficult to implement politically, restricting air travel has the potential to reduce total CO2 emissions from transport by 7.5%.Cogitatio2021-06-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.17645/up.v6i2.4021oai:ojs.cogitatiopress.com:article/4021Urban Planning; Vol 6, No 2 (2021): Cities, Long-Distance Travel, and Climate Impacts; 271-2842183-7635reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttps://www.cogitatiopress.com/urbanplanning/article/view/4021https://doi.org/10.17645/up.v6i2.4021https://www.cogitatiopress.com/urbanplanning/article/view/4021/4021https://www.cogitatiopress.com/urbanplanning/article/downloadSuppFile/4021/1723Copyright (c) 2021 Alona Pukhova, Ana Tsui Moreno, Carlos Llorca, Wei-Chieh Huang, Rolf Moeckelhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessPukhova, AlonaMoreno, Ana TsuiLlorca, CarlosHuang, Wei-ChiehMoeckel, Rolf2022-12-20T10:59:40Zoai:ojs.cogitatiopress.com:article/4021Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:21:51.623005Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation
title Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation
spellingShingle Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation
Pukhova, Alona
aviation emissions; long distance travel; mode choice modelling; transport emissions; transport modelling
title_short Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation
title_full Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation
title_fullStr Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation
title_full_unstemmed Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation
title_sort Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation
author Pukhova, Alona
author_facet Pukhova, Alona
Moreno, Ana Tsui
Llorca, Carlos
Huang, Wei-Chieh
Moeckel, Rolf
author_role author
author2 Moreno, Ana Tsui
Llorca, Carlos
Huang, Wei-Chieh
Moeckel, Rolf
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Pukhova, Alona
Moreno, Ana Tsui
Llorca, Carlos
Huang, Wei-Chieh
Moeckel, Rolf
dc.subject.por.fl_str_mv aviation emissions; long distance travel; mode choice modelling; transport emissions; transport modelling
topic aviation emissions; long distance travel; mode choice modelling; transport emissions; transport modelling
description Every sector needs to minimize GHG emissions to limit climate change. Emissions from transport, however, have remained mostly unchanged over the past thirty years. In particular, air travel for short-haul flights is a significant contributor to transport emissions. This article identifies factors that influence the demand for domestic air travel. An agent-based model was implemented for domestic travel in Germany to test policies that could be implemented to reduce air travel and CO2 emissions. The agent-based long-distance travel demand model is composed of trip generation, destination choice, mode choice and CO2 emission modules. The travel demand model was estimated and calibrated with the German Household Travel Survey, including socio-demographic characteristics and area type. Long-distance trips were differentiated by trip type (daytrip, overnight trip), trip purpose (business, leisure, private) and mode (auto, air, long-distance rail and long-distance bus). Emission factors by mode were used to calculate CO2 emissions. Potential strategies and policies to reduce air travel demand and its CO2 emissions are tested using this model. An increase in airfares reduced the number of air trips and reduced transport emissions. Even stronger effects were found with a policy that restricts air travel to trips that are longer than a certain threshold distance. While such policies might be difficult to implement politically, restricting air travel has the potential to reduce total CO2 emissions from transport by 7.5%.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-09
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv https://doi.org/10.17645/up.v6i2.4021
oai:ojs.cogitatiopress.com:article/4021
url https://doi.org/10.17645/up.v6i2.4021
identifier_str_mv oai:ojs.cogitatiopress.com:article/4021
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.cogitatiopress.com/urbanplanning/article/view/4021
https://doi.org/10.17645/up.v6i2.4021
https://www.cogitatiopress.com/urbanplanning/article/view/4021/4021
https://www.cogitatiopress.com/urbanplanning/article/downloadSuppFile/4021/1723
dc.rights.driver.fl_str_mv Copyright (c) 2021 Alona Pukhova, Ana Tsui Moreno, Carlos Llorca, Wei-Chieh Huang, Rolf Moeckel
http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Alona Pukhova, Ana Tsui Moreno, Carlos Llorca, Wei-Chieh Huang, Rolf Moeckel
http://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Cogitatio
publisher.none.fl_str_mv Cogitatio
dc.source.none.fl_str_mv Urban Planning; Vol 6, No 2 (2021): Cities, Long-Distance Travel, and Climate Impacts; 271-284
2183-7635
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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