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Journal Article

Coupling a Detailed Transport Model to the Integrated Assessment Model REMIND

Authors
/persons/resource/Rottoli

Rottoli,  Marianna
Potsdam Institute for Climate Impact Research;

/persons/resource/dirnaichner

Dirnaichner,  Alois
Potsdam Institute for Climate Impact Research;

Kyle,  Page
External Organizations;

/persons/resource/lavinia.baumstark

Baumstark,  Lavinia
Potsdam Institute for Climate Impact Research;

/persons/resource/Robert.Pietzcker

Pietzcker,  Robert C.
Potsdam Institute for Climate Impact Research;

/persons/resource/Gunnar.Luderer

Luderer,  Gunnar
Potsdam Institute for Climate Impact Research;

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Fulltext (public)

25508oa.pdf
(Publisher version), 4MB

Supplementary Material (public)
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Citation

Rottoli, M., Dirnaichner, A., Kyle, P., Baumstark, L., Pietzcker, R. C., Luderer, G. (2021): Coupling a Detailed Transport Model to the Integrated Assessment Model REMIND. - Environmental Modeling and Assessment, 26, 6, 891-909.
https://doi.org/10.1007/s10666-021-09760-y


Cite as: https://publications.pik-potsdam.de/pubman/item/item_25508
Abstract
The transport sector is a crucial bottleneck in the decarbonization challenge. To study the sector’s decarbonization potential in the wider systems perspective, we couple a large-scale integrated assessment model, Regionalized Model of INvestments and Development (REMIND), to a detailed transport model, Energy Demand Generator-Transport (EDGE-T). This approach allows the analysis of mobility futures in the context of long-term and global energy sector transformations, at a high level of modal and technological granularity and internal consistency. The runtime of the coupled system increases by ~ 15–20% compared with a REMIND standalone application, and first convergence tests are promising. To illustrate the capabilities of our modeling approach, we focus on a reference pathway for Europe. Preliminary results indicate that transport service demands grow in the next decades for both passenger and freight transport. Transport system emissions are expected to decrease in the same time range, due to a shift towards electric drivetrains, advanced vehicles, more efficient modes as well as a slight increase in the share of biofuels.