Pre-publication: New and updated carbon intensities for transport processes

Date: February 15, 2024

1. Purpose

The purpose of this pre-publication is to present the proposed changes to the transport processes in the Fuel Life Cycle Assessment (LCA) Model (the Model) for the next formal publication in June 2024.

With this pre-publication, Environment and Climate Change Canada (ECCC) is providing a description of the proposed changes to existing processes and a description of the methodology and data sources for new processes. It is also providing the resulting carbon intensities (CIs) that can be used to assess the implications of this proposed update on the results generated by the Model and allow stakeholders to provide comments on the proposed update.

Pre-publications are not to be used for compliance with the Clean Fuel Regulations, or other programs or regulations, unless otherwise specified.

The CIs presented in the pre-publication may differ from those included in the next formal publication of the Model according to comments received and other changes to be implemented in the Model. 

This pre-publication includes this descriptive document and an openLCA module. The module includes, for each new and updated process, a system process located in the Data Library folder of the Model database and a unit process located in a new folder named Background Modelling. The system processes contain the rolled-up CI, while the unit processes showcase the inputs and outputs for each system process. The unit processes were developed to increase transparency with disaggregated data. The proposed new and updated CIs for transport processes are available in the module and are presented in Tables A1 and A2 in Annex A.

2. Context

The current Model database includes processes for the transport of various products, including natural gas, renewable natural gas, renewable propane, hydrogen, and feedstocks. Various transportation modes have been modelled for these products, including pipeline, truck, rail, and tanker ship. Note that each transportation mode has not been modeled for all products.

As the fuel consumption of each transport process is directly linked to the mass transported and the distance travelled, the functional unit of transport system processes in the Model is 1 tonne-kilometre (tkm - i.e. transport of one metric tonne of feedstock or fuel over a distance of one kilometer).

Transport processes currently in the Model consider the emissions associated to the amount of fuel consumed per tkm of transport while emissions associated to the manufacture of fuel transport infrastructure (i.e., pipelines, trucks, ships, and roads) is excluded from the scope of the Model. This methodology will not change with this pre-publication.

The transport processes in the current Model considers the average payload of a product but assumes that fuel efficiency (mpg or L/km) is constant for a transportation mode and does not change depending on the weight transported. This methodology will not change with this pre-publication.

Predefined transport scenarios are processes that assume a distance and transportation mode for some materials or equivalent impact to the CI of Low-CI Fuels. The methodology and values for these processes are not being updated in this pre-publication.

3. Description of the proposed changes to the Model

For the next formal update to the Model, it is proposed to update the following processes:

• Pipeline transport, of gas
• Pipeline transport, of renewable propane, dedicated pipeline, including flaring emissions
• Pipeline transport, of renewable propane, injected, including flaring emissions
• Pipeline transport, of RNG, dedicated pipeline
• Pipeline transport, of RNG, injected in natural gas pipeline
• Train transport, diesel
• Truck transport, of gaseous hydrogen
• Truck transport, of liquid hydrogen
• Truck transport, of liquid RNG

In addition, the process “Truck transport, diesel” will be replaced with three new processes:

• Truck transport, of agriculture feedstocks
• Truck transport, of other feedstocks and fuels^{Footnote 1} 
• Truck transport, of wood residue

Lastly, it is proposed to introduce the following new processes:

• Truck transport, of CNG
• Truck transport, of LNG
• Truck transport, of compressed RNG
• Truck transport, of liquid propane

Due to the addition of natural gas and propane transport processes, the folder names in openLCA will change to include these processes. The folders “Renewable natural gas transport” and “Renewable propane transport” will be renamed to “Natural gas transport” and “Propane transport”, respectively. Additionally, in the English version of the database only, the overall “Transportation” folder will be renamed to “Transport” to align the wording with the rest of the folders and processes. When importing this pre-publication into the Model database, all updated and new processes will be moved into a new folder named “Transport” while other processes remain in their original folders.

The new and updated CI values for the processes listed above are available for public review in Annex A of this document and in the module that can be uploaded in openLCA. See Section 4 for instructions on how to upload the module into openLCA.

3.1 Pipeline

There is no methodological change for pipeline transport processes. The proposed changes are associated to the data sources used. In the current Model, the amount of electricity and natural gas consumed per tkm of pipeline transport was based on 2018 GREET model. The proposed update uses data from GREET 2022 model (GREET 2022) for all pipeline transport processes. In the current model, data on fugitive, venting and flaring emissions from natural gas pipelines is from Canadian Energy Partnership for Environmental Innovation (CEPEI, 2021). The proposed update uses the 2021 reference year data collected for 2023 Canada’s National Inventory Report (NIR).

For more information on the methodology, see Annex B.

3.2 Train

There is no methodological change for train transport processes. The proposed changes are associated to the data sources used. In the current Model, the amount of diesel consumed per tkm of train transport was based on 2016 data from Statistics Canada on the freight mass, the distance travelled and the annual quantity of diesel consumed. The proposed update uses 2021 data from the Rail Association of Canada (RAC, 2023).

For more information on the methodology, see Annex B.

3.3 Truck

There are methodological changes for truck transport processes, as well as updated data sources. In the current Model, the amount of diesel consumed per tkm of truck transport was calculated based on 2016 fuel efficiency data from the North American Council for Freight Efficiency (NACFE). The proposed update uses fuel consumption factors for two types of trucks; B Train trucks and other heavy-duty trucks. The fuel consumption factors were developed considering the average fuel consumptions taken from multiple sources (NRCan, 2000; Kabir and Kumar, 2012), empty fuel consumption ratio (Kabir and Kumar, 2012), fugitive emission factors (GREET 2022), where applicable, and the distance traveled empty.

For B Train trucks, it is assumed that 26% of the distance is being travelled empty between loads based on the American Transportation Research Institute (ATRI, 2021). For other heavy-duty trucks, it is assumed that transporting compressed and cooled fuels requires specialized equipment, limiting the type of material the truck can transport. Therefore, a worst-case scenario is used, assuming that emptied trucks will return directly to their origin for another load, or that 50% of the distance travelled is while the truck is empty.

The generic process “Truck transport, diesel” has been separated into three processes to better model the emissions associated with different products being transported, which is impacted by the product’s average payload.

The new truck transport processes allow for alternate transportation modes to be modelled for some fossil- and bio-fuels.

The average payload of the product transported is used in the modelling. It represents the average amount of product that a truck can transport. These payloads have a significant impact on the CI of the processes, as a smaller payload would require more trips to transport the same amount of product. Table 1 provides the payloads that are used in the proposed update.

For more information on the methodology, see Annex B.

4. Instructions on how to upload the Module in OpenLCA

The module is available in the “2024.02 Updated carbon intensities for transport processes” folder of the ECCC Data Catalogue and can be imported either into an empty database or into the Model Database.

Importing the module in an empty database allows users to see only the new and revised processes. No CI calculations can be performed when importing the module in an empty database.

When importing the module in the Model Database users can usually recalculate their CIs without any additional steps. However, because this pre-publication replaces one process with three processes, users may have additional steps if the process “Truck transport, diesel” is used.

It is important to note that the import of the module in the Model Database will update the values of the existing processes and the changes are irreversible. Consequently, users should always import the module into a copy of their original database.

Please refer to the Instructions on how to import a module into openLCA.

5. How to submit comments on this pre-publication

Stakeholders are invited to review this pre-publication and provide comments to ECCC within 30 days following the pre-publication at modeleacvcarburant-fuellcamodel@ec.gc.ca.

Please indicate the following in the subject line: Comments on the pre-publication: Updates carbon intensities for transport processes.

For any questions related to this pre-publication, please contact modeleacvcarburant-fuellcamodel@ec.gc.ca with the following subject line: Questions on the pre-publication: Updates carbon intensities for transport processes.

Annex A: CIs Comparison

The CI values presented in this annex use the global warming potential (GWP) for the 100-year time horizon of the International Panel on Climate Change (IPCC) 5th Assessment Report (AR5).

Annex B: Proposed revised methodology for transport processes

The methodology for transport processes in the Model can be found in Section 3.8 of the Fuel Life Cycle Assessment Model Methodology.

The next sections provide the proposed updates to the Methodology for the next formal version of the Model. Only the sections affected by the proposed update are included in this annex. Note that the section numbers and the text could change in the final version of the Fuel Life Cycle Assessment Model Methodology.

3.8.1 Generic transport

Train transport

The amount of diesel consumed per tkm of train transport is based on 2021 data from the Rail Association of Canada on the freight mass, the distance travelled, and the annual quantity of diesel consumed. The processes can be used regardless of geographical location.

Truck transport

The truck’s diesel consumption is directly related to the mass transported and the distance traveled. Hence, the Model uses units of tkm (metric tonne*kilometers) so the process can be used in any fuel pathway.

The Model assumes B Train trucks are used for generic truck transport. The fuel consumption factor modeled for B Train trucks is 60.98 L/100km. This factor considers the fuel efficiency for full trucks of 50 L/100km, averaged from multiple sources (NRCan, 2000; Kabir and Kumar, 2012), an empty fuel consumption ratio of 0.6097 (Kabir and Kumar, 2012), the distance travelled empty between loads of 26% from the American Transportation Research Institute (ATRI, 2021), and the payload.

Truck transport payloads are modelled by considering the bulk density of products (Kabir and Kumar, 2012) and the capacity of B Train trucks (Kabir and Kumar, 2012; CoMT, 2019). The average payloads are 22.2 tonnes for agriculture feedstocks, 30.5 tonnes for wood residue, and 40.0 tonnes for other feedstocks and fuels. The modeling takes into consideration average loading, therefore, there is no distinction between partial or fully loaded trucks.

This process is considered representative of transport by truck in Canada. However, some data are sourced from U.S. references. The processes can be used regardless of geographical location.

Gas Pipeline transport

The amount of energy consumed per tkm of gas pipeline transport is based on GREET 2022. Weighted average energy to transport 1 MJ over a distance of 1 km by pipeline is used to model the energy use. Natural gas is responsible for 98% of the energy needed for pump operations. The remainder is assumed to be coming from electricity. The average Canadian grid electricity is used to reflect the emissions due to the average electricity usage across Canada.

Flaring, fugitive, venting and emergency response emissions were included to calculate the CI of natural gas transport. The 2021 reference year data collected for 2023 Canada’s National Inventory Report (NIR, 2023) is used to quantify fugitive, venting and flaring emissions from natural gas pipelines.

This process is considered representative of transport by pipeline in Canada. However, some data was sourced from U.S. references. The processes can be used regardless of geographical location.

3.8.2 Hydrogen transport

Truck transport

The truck’s diesel consumption is directly related to the mass transported and the distance travelled. Hence, the Model uses units of tkm (metric tonne*kilometers) so the process can be used in any fuel pathway.

The Model assumes heavy-duty trucks other than B Train trucks are used for hydrogen truck transport. The fuel consumption factor modeled for heavy-duty trucks is 69.09 L/100km. This factor considers the fuel efficiency for full trucks of 40 L/100km, averaged from multiple sources (NRCan, 2000; Kabir and Kumar, 2012), an empty fuel consumption ratio of 0.7273 (Kabir and Kumar, 2012), and the payload.

It also assumes that transporting compressed and cooled fuels requires specialized equipment, limiting the type of material the truck can transport and requires the trucks to travel further while empty. A worst-case scenario is assumed, with 50% of the distance is being travelled empty between loads, as emptied trucks will return directly to their origin for another load.

Truck transport average payloads are based on GREET 2022 and are 3.6 tonnes for liquid hydrogen and 1.0 tonne for gaseous hydrogen. The modelling takes into consideration average loading, therefore, there is no distinction between partial or fully loaded trucks.

This process is intended to be representative of truck transport of hydrogen in Canada. However, some data was sourced from US references. The processes can be used regardless of geographical location.

3.8.4 Natural gas transport

Truck transport, of liquid RNG

The truck’s diesel consumption is directly related to the mass transported and the distance traveled. Hence, the Model uses units of tkm (metric tonne*kilometers) so the process can be used in any fuel pathway.

The Model assumes heavy-duty trucks other than B Train trucks are used for liquid RNG truck transport. The fuel consumption factor modeled for heavy-duty trucks is 69.09 L/100km. This factor considers the fuel efficiency for full trucks of 40 L/100km, averaged from multiple sources (NRCan, 2000; Kabir and Kumar, 2012), an empty fuel consumption ratio of 0.7273 (Kabir and Kumar, 2012), and the payload.

It also assumes that transporting compressed and cooled fuels requires specialized equipment, limiting the type of material the truck can transport and requires the trucks to travel further while empty. A worst-case scenario is assumed, with 50% of the distance is being travelled empty between loads, as emptied trucks will return directly to their origin for another load.

The average Liquefied Natural Gas (LNG) payload from GREET 2022 of 13.6 tonnes was used as a proxy for liquefied RNG. The modelling takes into consideration average loading, therefore, there is no distinction between partial or fully loaded trucks.

Boil-off emissions during truck transport of LNG from GREET 2022 are used as a proxy for the transport of liquefied RNG.

This process is intended to be representative of truck transport of renewable natural gas in Canada. However, some data was sourced from U.S. references. The processes can be used regardless of geographical location.

LNG truck transport (New)

The truck’s diesel consumption is directly related to the mass transported and the distance traveled. Hence, the Model uses units of tkm (metric tonne*kilometers) so the process can be used in any fuel pathway.

The Model assumes heavy-duty trucks other than B Train trucks are used for LNG truck transport. The fuel consumption factor modeled for heavy-duty trucks is 69.09 L/100km. This factor considers the fuel efficiency for full trucks of 40 L/100km, averaged from multiple sources (NRCan, 2000; Kabir and Kumar, 2012), an empty fuel consumption ratio of 0.7273 (Kabir and Kumar, 2012), and the payload.

It also assumes that transporting compressed and cooled fuels requires specialized equipment, limiting the type of material the truck can transport and requires the trucks to travel further while empty. A worst-case scenario is assumed, with 50% of the distance is being travelled empty between loads, as emptied trucks will return directly to their origin for another load.

The average LNG payload from GREET 2022 was used at 13.6 tonnes. The modeling takes into consideration average loading, therefore, there is no distinction between partial or fully loaded trucks.

Boil-off emissions during truck transport are based on data from GREET 2022.

This process is considered to be representative of truck transport of LNG in Canada. However, some data was sourced from U.S. references. The processes can be used regardless of geographical location.

Compressed natural gas (CNG) and RNG truck transport (New)

The truck’s diesel consumption is directly related to the mass transported and the distance traveled. Hence, the Model uses units of tkm (metric tonne*kilometers) so the process can be used in any fuel pathway.

The Model assumes heavy-duty trucks other than B Train trucks are used for CNG and compressed RNG truck transport. The fuel consumption factor modeled for heavy-duty trucks is 69.09 L/100km. This factor considers the fuel efficiency for full trucks of 40 L/100km, averaged from multiple sources (NRCan, 2000; Kabir and Kumar, 2012), an empty fuel consumption ratio of 0.7273 (Kabir and Kumar, 2012), and the payload.

It also assumes that transporting compressed and cooled fuels requires specialized equipment, limiting the type of material the truck can transport and requires the trucks to travel further while empty. A worst-case scenario is assumed, with 50% of the distance is being travelled empty between loads, as emptied trucks will return directly to their origin for another load.

The average payload for CNG is 6 tonnes and is used as a proxy for compressed RNG.^{Footnote 3} The modeling takes into consideration average loading, therefore, there is no distinction between partial or fully loaded trucks.

Boil-off emissions during truck transport of LNG from GREET 2022 are used as a proxy for the transport of CNG and compressed RNG.

These processes are considered to be representative of truck transport of CNG and RNG in Canada. However, some data was sourced from U.S. references. The processes can be used regardless of geographical location.

Pipeline transport of RNG

The amount of energy consumed per tkm of renewable natural gas pipeline transport is based on GREET 2022, using natural gas pipelines as a proxy.

Weighted average energy to transport 1 MJ over a distance of 1 km by pipeline was used to model the energy use. Renewable natural gas is responsible for 98% of the energy needed for pump operations. The remainder is assumed to be coming from electricity. Canadian average grid is applied to reflect the emissions due to the average electricity usage across Canada.

Flaring, fugitive, venting and emergency response emissions were included to calculate the CI of RNG transport. The 2021 reference year data collected for 2023 Canada’s National Inventory Report (NIR, 2023) is used to quantify fugitive, venting and flaring emissions from RNG pipelines, using natural gas as a proxy.

This process is considered to be representative of pipeline transport of RNG in Canada. However, some data was sourced from U.S. references. The processes can be used regardless of geographical location.

3.8.5 Propane transport

Liquid propane truck transport (New)

The truck’s diesel consumption is directly related to the mass transported and the distance traveled. Hence, the Model uses units of tkm (metric tonne*kilometers) so the process can be used in any fuel pathway.

The Model assumes B train trucks are used for liquid propane transport. The fuel consumption factor modeled for B Train trucks is 60.98 L/100km. This factor considers the fuel efficiency for full trucks of 50 L/100km, averaged from multiple sources (NRCan, 2000; Kabir and Kumar, 2012), an empty fuel consumption ratio of 0.6097 (Kabir and Kumar, 2012), the distance travelled empty between loads of 26% from the American Transportation Research Institute (ATRI, 2021), and the payload.

The average payload for propane is 25 tonnes.^{Footnote 3}  These calculations take into consideration average loading, therefore, there is no distinction between partial or fully loaded trucks.

Fugitive propane emissions are not considered.

This process is considered to be representative of transport of liquid propane by truck in Canada. However, some data was sourced from U.S. references. The processes can be used regardless of geographical location.

Pipeline transport of liquid renewable propane

The amount of energy consumed per tkm of liquid renewable propane pipeline transport is based on GREET 2022, using natural gas pipelines as a proxy. Weighted average energy to transport 1 MJ over a distance of 1 km by pipeline was used to model the energy use. Renewable propane is responsible for 98% of the energy needed for pump operations. The remainder is assumed to be coming from electricity. The average Canadian grid electricity was used to reflect the emissions due to the average electricity usage across Canada.

Fugitive emissions, emergency response emissions and venting emissions were excluded for renewable propane transport and only flaring emissions were included. The 2021 reference year data collected for 2023 Canada’s National Inventory Report (NIR, 2023) is used to quantify flaring emissions from propane pipelines, using natural gas as a proxy.

This process is considered representative of pipeline transport of liquid renewable propane in Canada. However, some data was sourced from U.S. references. The processes can be used regardless of geographical location.

References

CoMT. Heavy Truck Weight and Dimension Limits for Interprovincial Operations in Canada. January 2019. Category 3: B Train Double, Part 2 - Weight Limits. Retrieved from: Council of Ministers

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (GREET 2022). Computer Software. USDOE Office of Energy Efficiency and Renewable Energy (EERE). 10 Oct. 2022. Retrieved from: Argonne National Laboratory

Kabir and Kumar. Comparison of the energy and environmental performances of nine biomass/coal co-firing pathways. 2012. Table 3: Inventory data for biomass transportation in different forms. Retrieved from: Comparison of the energy and environmental performances of nine biomass/coal co-firing pathways

Leslie, A. and Murray, D. An Analysis of the Operational Costs of Trucking: 2021 Update. November 2021. American Transportation Research Institute (ATRI). Retrieved from: An Analysis of the Operational Costs of Trucking: 2021 Update (PDF)

NRCan. Fuel Efficiency Benchmarking in Canada's Trucking Industry. March 2000. Fuel Efficiency: Page 3. Retrieved from: National Resources Canada, FleetSmart

Rail Association of Canada, 2023. Retrieved from: Locomotive emissions monitoring report (PDF)

Sultana and Kumar. Optimal configuration and combination of multiple lignocellulosic biomass feedstocks delivery to a biorefinery. 2011. Table 3: Weight carried by trucks for various forms of biomass (Payload of truck = 22.7 tonnes, Volume capacity of truck = 70 m3). Retrieved from: Optimal configuration and combination of multiple lignocellulosic biomass feedstocks delivery to a biorefinery