Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
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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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 |
format |
article |
status_str |
publishedVersion |
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 instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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1799130665048866816 |