Editing IPCC:AR6/WGIII/Chapter-10 (section)

=== 10.4.3 Land-based Freight Transport ===

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As is the case with passenger transport, thereis growing interest in alternative fuels that could reduce GHG emissions from freight transport. Natural gas-based fuels (e.g., CNG, LNG) are an example, however these may not lead to drastic reductions in GHG emissions compared to diesel. Natural gas-powered vehicles have been discussed as a means to mitigate air quality impacts ( [[#Khan--2015|Khan et al. 2015]] ; [[#Cai--2017|Cai et al. 2017]] ; [[#Pan--2020|Pan et al. 2020]] ), but those impacts are not the focus of this review. Decarbonisation of medium- and heavy-duty trucks would likely require the use of low-carbon electricity in battery electric trucks, low-carbon hydrogen or ammonia in fuel-cell trucks, or bio-based fuels (from sources with low upstream emissions and low risk of induced land-use change) used in ICE trucks.

Freight rail is also a major mode for the inland movement of goods. Trains are more energy efficient (per tkm) than trucks, so expanded use of rail systems (particularly in developing countries where demand for goods could grow exponentially) could provide carbon abatement opportunities. While diesel-based locomotives are still a major mode of propulsion used in freight rail, interest in low-carbon propulsion technologies is growing. Electricity already powers freight rail in many European countries using overhead catenaries. Other low-carbon technologies for rail may include advanced storage technologies, biofuels, synthetic fuels, ammonia, or hydrogen.

Figure 10.8 presents a review of lifecycle GHG emissions from land-based freight technologies (heavy- and medium-duty trucks, and rail). Each panel within the figure represents data in GHG emissions per tonne-kilometre of freight transported by different technology and/or fuel types, as indicated by the labels to the left. The data in each panel came from a number of relevant scientific studies ( [[#Tong--2015a|Tong et al. 2015a]] ; [[#Frattini--2016|Frattini et al. 2016]] ; [[#Nahlik--2016|Nahlik et al. 2016]] ; [[#Zhao--2016|Zhao et al. 2016]] ; CE Delft 2017; [[#Isaac--2017|Isaac and Fulton 2017]] ; [[#Song--2017|Song et al. 2017]] ; [[#Valente--2017|Valente et al. 2017]] ; [[#Cooper--2019|Cooper and Balcombe 2019]] ; [[#Lajevardi--2019|Lajevardi et al. 2019]] ; [[#Hill--2020|Hill et al. 2020]] ; [[#Liu--2020a|Liu et al. 2020a]] ; [[#Merchan--2020|Merchan et al. 2020]] ; [[#Prussi--2020|Prussi et al. 2020]] ; [[#Gray--2021|Gray et al. 2021]] ; [[#Valente--2021|Valente et al. 2021]] ). Similar to the results for buses, technologies that offer substantial emissions reductions for freight include: ICEV trucks powered with the low-carbon variants for biofuels, ammonia or synthetic diesel; BEVs charged with low-carbon electricity; and FCVs powered with renewable-based electrolytic hydrogen, or ammonia. Since ammonia and Fischer-Tropsch diesel are produced from hydrogen, their emissions are higher than the source hydrogen, but their logistical advantages over hydrogen are also a consideration ( [[#10.3|Section 10.3]] ).

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'''Figure 10.8''' | '''Lifecycle greenhouse gas intensity of land-based freight technologies and fuel types.''' Each bar represents the range of the lifecycle estimates, bounded by minimum and maximum energy use per tkm, as reported for each fuel/powertrain combination. The ranges are driven by differences in vehicle characteristics and operating efficiency. For energy sources with highly variable upstream emissions, low, medium and/or high representative values are shown as separate rows. For trucks, the primary x-axis shows lifecycle GHG emissions, in gCO 2 -eq tkm –1 , assuming 100% payload; the secondary x-axis assumes 50% payload. The values in the figure rely on the 100-year GWP value embedded in the source data, which may differ slightly from the updated 100-year GWP values from WGI. For rail, values represent average payloads. For trucks, main bars show full lifecycle, with vertical bars disaggregating the vehicle cycle. ‘Diesel, high’ references emissions factors for diesel from oil sands. ‘Advanced biofuels’ refers to the use of second-generation biofuels and their respective conversion and cultivation emission factors. ‘IAM EMF33’ refers to emissions factors for advanced biofuels derived from simulation results from the EMF33 scenarios. ‘PM’ refers to partial models, where ‘CLC’ is with constant land cover and ‘NRG’ is with natural regrowth. DAC FT-Diesel, wind electricity refers to Fischer- Tropsch diesel produced via a CO 2 direct air capture process that uses wind electricity. ‘Ammonia and Hydrogen, low-carbon renewable’ refers to fuels produced via electrolysis using low-carbon electricity. ‘Ammonia and Hydrogen, natural gas SMR’ refers to fuels produced via steam methane reforming of natural gas.

Trucks exhibit economies of scale in fuel consumption, with heavy-duty trucks generally showing lower emissions per tkm than medium-duty trucks. Comparing the lifecycle GHG emissions from trucks and rail, it is clear that rail using internal combustion engines is more carbon efficient than using internal combustion trucks. Note that the rail emissions are reported for an average representative payload, while the trucks are presented at 50% and 100% payload, based on available data. The comparison between trucks and rail powered with electricity or hydrogen is less clear – especially considering that these values omit embodied GHG from infrastructure construction. One study reported embodied rail infrastructure emissions of 15 gCO 2 per tonne-kilometre for rail ( [[#International%20Union%20of%20Railways--2016|International Union of Railways 2016]] ), although such embodied emissions from rail are known to vary widely across case studies ( [[#Olugbenga--2019|Olugbenga et al. 2019]] ). Regardless, trucks and rail with low-carbon electricity or low-carbon hydrogen have substantially lower emissions than incumbent technologies.

For trucks, Figure 10.8 includes two x-axes representing two different assumptions about their payload, which substantially influence emissions per tonne-kilometre. These results highlight the importance of truckload planning as an emissions reduction mechanism, for example, as also shown in [[#Kaack--2018|Kaack et al. (2018)]] . Several studies also point to improvements in vehicle efficiency as an important mechanism to reduce emissions from freight transport ( [[#Taptich--2016|Taptich et al. 2016]] ; [[#Kaack--2018|Kaack et al. 2018]] ). However, projections for diesel vehicles using such efficiencies beyond 2030 are promising, but still far higher emitting than vehicles powered with low-carbon sources.

Figure 10.9 shows the results of a parametric analysis of the LCC of trucks and freight rail technologies with the highest potential for deep GHG reductions. As with Figure 10.8, the vehicle efficiency ranges are the same as those from the LCA estimates (80% payload for trucks; effective payload as reported by original studies for rail). Vehicle, fuel and maintenance costs represent ranges in the literature ( [[#Moultak--2017|Moultak et al. 2017]] ; [[#Eudy--2018b|Eudy and Post 2018b]] ; [[#IEA--2019e|IEA 2019e]] ; [[#Argonne%20National%20Laboratory--2020|Argonne National Laboratory 2020]] ; [[#BNEF--2020|BNEF 2020]] ; [[#IRENA--2020|IRENA 2020]] ; [[#Burnham--2021|Burnham et al. 2021]] ; [[#IEA--2021c|IEA 2021c]] ), and the discount rate is 3% where applicable (Appendix 10.2). The panels for the ICEV can represent trucks and freight trains powered with any form of diesel, whether derived from petroleum, synthetic hydrocarbons, or biofuels. See discussion preceding Figure 10.7 for additional details about current global fuel costs. Under most parameter combinations, rail is the more cost-effective option, but the high efficiency case for trucks (representing fuel-efficient vehicles, favourable drive cycles and high payload) can be more cost-effective than the low efficiency case for rail (representing systems with higher fuel consumption and lower payload). For BEV trucks, cost ranges are driven by vehicle purchase price due to the large batteries required and the associated wide range between their current high costs and anticipated future cost reductions. For all other truck and rail technologies, fuel cost ranges play a larger role. Similar to transit technologies, the current state of freight ICEV technologies is best represented by cheap vehicles and low fuel costs for diesel (top left of each panel), and the current status of alternative fuels is better represented by high capital costs and mid-to-high fuel costs (right side of each panel; mid-to-bottom rows), with expected future increases in ICEV LCC and decreases in alternative fuel vehicle LCC. Electric and hydrogen freight rail are potentially already competitive with diesel rail (especially electric catenary ( [[#IEA--2019e|IEA 2019e]] )), but low data availability (especially for hydrogen efficiency ranges) and wide ranges for reported diesel rail efficiency (likely encompassing low capacity utilisation) makes this comparison challenging. Alternative fuel trucks are currently more expensive than diesel trucks, but future increases in diesel costs or a respective decrease in hydrogen costs or in BEV capital costs (especially the battery) would enable either alternative fuel technology to become financially attractive. These results are largely consistent with raw results reported in existing literature, which suggest ambiguity over whether BEV trucks are already competitive, but more consistency that hydrogen is not yet competitive, but could be in future ( [[#Zhao--2016|Zhao et al. 2016]] ; [[#Moultak--2017|Moultak et al. 2017]] ; [[#Sen--2017|Sen et al. 2017]] ; [[#White--2017|White and Sintov 2017]] ; [[#Zhou--2017|Zhou et al. 2017]] ; [[#Mareev--2018|Mareev et al. 2018]] ; [[#Yang--2018a|Yang et al. 2018a]] ; [[#El%20Hannach--2019|El Hannach et al. 2019]] ; [[#Lajevardi--2019|Lajevardi et al. 2019]] ; [[#Tanco--2019|Tanco et al. 2019]] ; [[#Burke--2020|Burke and Sinha 2020]] ; [[#Jones--2020|Jones et al. 2020]] ). There is limited data available on the LCC for freight rail, but at least one study IEA (2019g) suggests that electric catenary rail is likely to have similar costs to diesel rail, while battery electric trains remain more expensive and hydrogen rail could become cheaper under forward-looking cost reduction scenarios.

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'''Figure 10.9 | Life cycle costs for internal combustion engine vehicles, battery electric vehicles, and hydrogen fuel cell vehicles for heavy-duty trucks and freight rail.''' The range of efficiencies for each vehicle type are consistent with the range of efficiencies in Figure 10.8. The results for ICEV can be used to evaluate the lifecycle costs of ICE trucks and freight rail operated with any form of diesel, whether from petroleum, synthetic hydrocarbons, or biofuels, as the range of efficiencies of vehicles operating with all these fuels is similar. The secondary y-axis depicts the cost of the different energy carriers normalised in USD per GJ for easier cross-comparability.

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