Improving Transport Modeling in MESSAGE Energy Planning Model: Vehicle Age Distributions

In the European Green Deal, EU Commission has set a goal to reduce greenhouse gas emissions in the transport sector by 90% by 2050 compared to the 1990 level. Most likely, transport decarbonization will rely on a rapid expansion of electric and hydrogen vehicle fleet, which would seriously affect no...

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Autores principales:	Eimantas Neniškis, Arvydas Galinis, Egidijus Norvaiša
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	energy planning transport model vehicle age distribution Technology T
Acceso en línea:	https://doaj.org/article/ee804ff5b81f4a4c89d7e7b3aac59948
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spelling	oai:doaj.org-article:ee804ff5b81f4a4c89d7e7b3aac599482021-11-11T16:01:38ZImproving Transport Modeling in MESSAGE Energy Planning Model: Vehicle Age Distributions10.3390/en142172791996-1073https://doaj.org/article/ee804ff5b81f4a4c89d7e7b3aac599482021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7279https://doaj.org/toc/1996-1073In the European Green Deal, EU Commission has set a goal to reduce greenhouse gas emissions in the transport sector by 90% by 2050 compared to the 1990 level. Most likely, transport decarbonization will rely on a rapid expansion of electric and hydrogen vehicle fleet, which would seriously affect not just overall electricity demand, but also the shape of the electricity consumption curve. Consequently, our research focuses on integrated energy and transport modelling when analyzing its development pathways up to 2050 and beyond. This paper describes how already established transport modeling practices can be further improved by differentiating vehicles by age groups and setting vehicle age distributions to improve the representation of vehicle stock, fuel efficiencies and emissions, especially for countries that have non-declining vehicle age distributions. Modeling results using proposed and traditional approaches were compared for the Lithuanian case. It shows that the transport fuel shift using the proposed approach is more gradual than the traditional one. Diesel cars are phased out by 2050 versus 2040. Furthermore, the proposed approach provided more realistic CO<sub>2</sub> emissions, 7% lower emissions for 2018 than estimated based on statistical data, while traditional approach was 27% lower.Eimantas NeniškisArvydas GalinisEgidijus NorvaišaMDPI AGarticleenergy planningtransportmodelvehicleage distributionTechnologyTENEnergies, Vol 14, Iss 7279, p 7279 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	energy planning transport model vehicle age distribution Technology T
spellingShingle	energy planning transport model vehicle age distribution Technology T Eimantas Neniškis Arvydas Galinis Egidijus Norvaiša Improving Transport Modeling in MESSAGE Energy Planning Model: Vehicle Age Distributions
description	In the European Green Deal, EU Commission has set a goal to reduce greenhouse gas emissions in the transport sector by 90% by 2050 compared to the 1990 level. Most likely, transport decarbonization will rely on a rapid expansion of electric and hydrogen vehicle fleet, which would seriously affect not just overall electricity demand, but also the shape of the electricity consumption curve. Consequently, our research focuses on integrated energy and transport modelling when analyzing its development pathways up to 2050 and beyond. This paper describes how already established transport modeling practices can be further improved by differentiating vehicles by age groups and setting vehicle age distributions to improve the representation of vehicle stock, fuel efficiencies and emissions, especially for countries that have non-declining vehicle age distributions. Modeling results using proposed and traditional approaches were compared for the Lithuanian case. It shows that the transport fuel shift using the proposed approach is more gradual than the traditional one. Diesel cars are phased out by 2050 versus 2040. Furthermore, the proposed approach provided more realistic CO<sub>2</sub> emissions, 7% lower emissions for 2018 than estimated based on statistical data, while traditional approach was 27% lower.
format	article
author	Eimantas Neniškis Arvydas Galinis Egidijus Norvaiša
author_facet	Eimantas Neniškis Arvydas Galinis Egidijus Norvaiša
author_sort	Eimantas Neniškis
title	Improving Transport Modeling in MESSAGE Energy Planning Model: Vehicle Age Distributions
title_short	Improving Transport Modeling in MESSAGE Energy Planning Model: Vehicle Age Distributions
title_full	Improving Transport Modeling in MESSAGE Energy Planning Model: Vehicle Age Distributions
title_fullStr	Improving Transport Modeling in MESSAGE Energy Planning Model: Vehicle Age Distributions
title_full_unstemmed	Improving Transport Modeling in MESSAGE Energy Planning Model: Vehicle Age Distributions
title_sort	improving transport modeling in message energy planning model: vehicle age distributions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ee804ff5b81f4a4c89d7e7b3aac59948
work_keys_str_mv	AT eimantasneniskis improvingtransportmodelinginmessageenergyplanningmodelvehicleagedistributions AT arvydasgalinis improvingtransportmodelinginmessageenergyplanningmodelvehicleagedistributions AT egidijusnorvaisa improvingtransportmodelinginmessageenergyplanningmodelvehicleagedistributions
_version_	1718432449367638016

Improving Transport Modeling in MESSAGE Energy Planning Model: Vehicle Age Distributions

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