Decarbonizing intraregional freight systems with a focus on modal shift
Open accessAbstract
Road freight transportation accounts for around 7% of total world energy-related carbon dioxide emissions. With the appropriate incentives, energy savings and emissions reductions can be achieved by shifting freight to rail or water modes, both of which are far more efficient than road. We briefly introduce five general strategies for decarbonizing freight transportation, and then focus on the literature and data relevant to estimating the global decarbonization potential through modal shift. We compare freight activity (in tonne-km) by mode for every country where data are available. We also describe major intraregional freight corridors, their modal structure, and their infrastructure needs. We find that the current world road and rail modal split is around 60:40. Most countries are experiencing strong growth in road freight and a shift from rail to road. Rail intermodal transportation holds great potential for replacing carbon-intense and fast-growing road freight, but it is essential to have a targeted design of freight systems, particularly in developing countries. Modal shift can be promoted by policies targeting infrastructure investments and internalizing external costs of road freight, but we find that not many countries have such policies in place. We identify research needs for decarbonizing the freight transportation sector both through improvements in the efficiency of individual modes and through new physical and institutional infrastructure that can support modal shift.
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Consolidation enables shipments to use rail and water modes for part of the journey.
“As rail and water modes require much larger loads than trucks, consolidation also enables shipments to use these modes for part of the journey [407].”
From Tackling climate change with machine learning (2023) · cites this paper as [407]
AM can produce lighter parts in vehicles that reduce energy consumption.
“AM (3-D printing) can produce lighter parts in vehicles, such as road vehicles and aircraft, that reduce energy consumption [341,407].”
From Tackling climate change with machine learning (2023) · cites this paper as [407]
Additive manufacturing has limited potential to reduce freight transport by producing lighter goods and enabling production closer to the consumer.
“For example, additive manufacturing (AM) or 3-D printing has (limited) potential to reduce freight transport by producing lighter goods and enabling production closer to the consumer [407].”
From Tackling climate change with machine learning (2023) · cites this paper as [407]
Some high-GHG modes such as trucks can be cost-competitive in regions with lax enforcement because they can overload and not obey labor or safety rules.
“Some modes with high GHG emissions, such as trucks, can be particularly cost-competitive in regions with lax enforcement of regulation, as they can benefit from overloading and not obeying labor or safety rules [407].”
From Tackling climate change with machine learning (2023) · cites this paper as [407]
Intermodal transportation relies on low-carbon modes such as rail and water for part of the journey.
“Such intermodal transportation relies on low-carbon modes such as rail and water for part of the journey [407].”
From Tackling climate change with machine learning (2023) · cites this paper as [407]
Electrofuels and biofuels have potential as low-carbon drop-in fuels retaining fossil fuel properties and compatibility with existing vehicles and infrastructure.
“Electrofuels and biofuels have the potential to serve as low-carbon drop-in fuels that retain the properties of fossil fuels, such as high energy density, while retaining compatibility with the existing fleet of vehicles and the current fuel infrastructure [407].”
From Tackling climate change with machine learning (2023) · cites this paper as [407]
Intelligent transport systems of different modes could be combined and enable more efficient multimodal freight transportation.
“Intelligent transport systems of different modes could be combined and enable more efficient multimodal freight transportation [407].”
From Tackling climate change with machine learning (2023) · cites this paper as [407]
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- Authors
- Kaack, L. H., Vaishnav, P., Morgan, M. G., Azevedo, I. L., & Rai, S.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Kaack, Lynn H
- Venue
- Environmental Research Letters
- Publisher
- IOP Publishing
- Published
- 2018-08-15
- Volume
- 13
- Issue
- 8
- Pages
- 083001
- ISSN
- 1748-9326 (ISSN-L)
- OpenAlex
- W2884628636
- Cited by
- 166
- References
- 168
- Citations by year
- 2026: 15 · 2025: 30 · 2024: 22 · 2023: 32 · 2022: 23 · 2021: 22 · 2020: 13 · 2019: 7 · 2018: 2
- FWCI
- 13.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Environmental Research Letters (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- OPUS 4 (Zuse Institute Berlin) (submittedVersion)
- Funders
- National Science Foundation (SES- 0949710)
- Topics
- Social Sciences › Transportation › transportation and logistics systems
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Environment, and Transportation Policies
- Physical Sciences › Engineering › Building and Construction › Urban and Freight Transport Logistics
- Keywords
- modal shift, road freight transport, rail freight, modal split, CO2 emissions, internalization of external costs, developing countries, transport policy
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