Sustainability analysis of overhead cable line powered freight trucks: a life cycle impact and techno-economic assessment toward transport electrification

We must move toward electrification of the transportation sector to help mitigate the adverse impacts of climate change. Carbon emission reduction in long-haul freight transport should be developed and administered given it accounts for 22% of transportation related emissions worldwide. Although electrified transport can make tail-pipe vehicle carbon emissions negligible, it does not mean that the entire system that supports electrified transport is carbon-neutral. We address this latter point in the present study by conducting a cradle-to-grave life cycle assessment of long-haul electric trucks that are powered by overhead cable lines (OCL). The environmental impacts were compared with those of battery electric vehicle trucks (BEV), and conventional diesel-powered trucks. The techno-economic analysis of long-haul freight OCL technology was conducted based on data from pilot-scale studies in Germany. The feasibility of implementing this technology in other countries was examined by comparing environmental impacts across respective electricity mixes. Results show that the environmental and economic impacts of OCL technology depends on the adoption percent. After analyzing different adoption rate scenarios, OCL adoption was found to be economically and environmentally beneficial at the 10% adoption rate or higher. We also found that use phase electricity accounts for over 83% of the net greenhouse gas emissions, thereby making the electricity mix powering this technology a determining factor for implementation around the world. Across their life cycles, the carbon footprint of both OCL and BEV was 2.5 times lesser than that of the conventional truck. Other findings reveal adaptable methods, a unique environmental-to-economic ratio measure, and equity considerations that can be leveraged for immediate decision-making activities and future studies alike.


Introduction
We must move away from the use of fossil fuels and more toward electrification of the transportation sector to help mitigate the adverse impacts of climate change (UN 2021, p 26). Transportation is responsible for approximately one quarter of all energy related GHG emissions which are predominantly fossil fuel based (UNEP 2022). Emission reduction strategies should be employed in several transportation technologies to meet the goal of limiting global temperature rise. This includes road freight transport given it accounted for 25% of transportation related emissions worldwide in 2019 (IEA 2021a). It is, therefore, imperative to develop environmentally friendly alternatives for existing road freight technologies.
While recognizing the impetus to replace existing fossil fuel powered vehicles with electric ones in personal use settings (Biresselioglu et al 2018, Schlüter and Weyer 2019, Jain 2022, commercial freight trucks play an important role in keeping the global supply chain operational (Pan et al 2013). Commercial freight trucks also tend to be fossil fuel reliant (González Palencia et al 2020). Alternatives to fossil-based freight transport includes BEV trucks, hydrogen powered trucks, and ERS technologies. ERS includes OCL, conductive rails, and wireless induction technologies (Schulte and Ny 2018).
Each of these alternatives to fossil-based freight transport (i.e. BEV, hydrogen powered trucks, and ERS) have their drawbacks. For instance, BEV trucks contain large batteries that can weigh up to five metric tons and requires significant amounts of metals like lithium and cobalt as part of the manufacturing process. By year 2050, these metals are projected to be scarce (Sverdrup et al 2017, Chitre et al 2020, Castelvecchi 2021. Hydrogen powered trucks can either utilize hydrogen fuel cells or directly combust hydrogen. There are challenges with using hydrogen as a fuel, in part due to its low energy density by volume such as onboard storage, safety concerns, and inaccessibility to refueling stations, along with economic feasibility and supply chain challenges (Hosseini and Butler 2020). As for ERS technologies, they necessitate relatively large investments in infrastructure to continuously power and charge the vehicles while in operation.

A focus on OCL-powered transport
ERS is an emergent alternative to conventional vehicles powered by internal combustion engines. As discussed above, ERS has three underlying concepts. Of the three, OCL-powered transport has been the most common, with its use in trams, bus rapid transit, and streetcars dating back to the late 19th century. The OCL trucks are powered by drawing electricity from overhead cables along with a smaller in-house battery, as opposed to only a high-capacity in-house battery which is the case for BEV. The scope of this technology has long been restricted to short-range transportation (e.g. inter-city public transport and mining operations). Our literature search found that only Sweden, Germany, and the United States of America (USA) thoroughly investigated OCL-powered freight trucks and these were at the pilot scale (Sundelin et al 2016). This was corroborated in our engagements with relevant industry partners.
Transitioning from a fossil-based transportation system to an ERS requires comprehensive planning and support. For example, public-private-partnerships have allowed the OCL technology to be piloted for public highways in Sweden and Germany. These studies have demonstrated the ability to seamlessly transport cargo over an electrified stretch of highway (Alakula andMarquez-Fernandez 2017, Hanesch et al 2022). However, the high upfront cost of infrastructure has prevented this technology from expanding. When exploring transport electrification alternatives, it is important to not overlook the lifecycle impacts of the associated infrastructure. That is, the environmental costs of constructing new road infrastructure are high and need to be accounted for (Fridell et al 2019). However, OCL technology does not necessarily need the construction of separate roads. Ancillary infrastructure (e.g. masts, pillars, and catenary cables) can be affixed to existing roadways. Still, the environmental impacts of affixing infrastructure in this manner can be significant. Scaling up OCL technology from the pilot phase for freight trucks is vital given that global freight volume is expected to double between the years 2015 and 2050 (ITF Transport Outlook 2021. As the drivetrain in OCL hybrid trucks are powered by electricity, the electricity mix of various regions can drastically change the associated environmental impacts. Electric mobility options can only be less impactful than their fossil-fuel alternatives if the environmental impacts due to the energy generation are reduced through shifts toward renewable energy generation (Ajanovic et al 2021).

Life cycle impact and techno-economic gaps in conventional-alternative studies
Meaningful LCAs of alternatives to conventional fossil-fuel powered freight transportation must be performed to assess viability and quantify environmental impacts. To our knowledge, there are limited studies that have investigated the life cycle impacts of alternatives to long-haul freight trucks. Furthermore, these previous studies lack a comprehensive investigation of geographic diversity and techno-economic viability.
For example, Zhao et al (2016) analyzed electric alternatives to conventional road transport with an emphasis on the importance of the regional electricity mix. It claimed that battery electric trucks have slightly higher energy consumption and GHG emissions than conventional trucks based on an environmentally-extended multi-regional input-output analysis. Furthermore, this study was conducted using a cradle-to-gate study boundary for class 3-5 trucks which included the manufacturing and operation phases, while assuming a truck lifespan of 240 000 km. It quantified the environmental impacts based on the US national average electricity mix and six regional entities within North America. Zhao et al (2016), however, did not include an economic analysis. The present study builds on this previous study by expanding from a cradle-to-gate to a cradle-to-grave study boundary and incorporating an economic analysis.
The importance of the regional electricity mix is further exemplified in Faria et al (2013) and Hawkins et al (2013). These studies conducted a cradle-to-grave comparative LCA of passenger EVs. Their results showed that BEVs are generally more environmentally sustainable than their conventional counterparts, with Hawkins et al 2013) finding a 10%-24% decrease in overall GHG emissions when using an average EU electricity mix. Faria et al (2013) also emphasized the need for improvement in battery technology and decarbonization of the electrical grid, but used Portuguese, Polish, and French electricity mixes. The present study is informed by these previous works in its analysis across regional electricity mixes within North America, Africa, Asia, and the EU so to augment geographic diversity.
Other relevant LCA studies emphasized techno-economic viability through the quantification of TCO. For instance, Ambrose et al (2019) compared the emissions and TCO of battery electric trucks with their conventional diesel counterparts in the state of California. Their results showed that 100% electrification of Class 8 trucks could reduce annual road freight transport emissions by nearly 30% in the state by year 2040. The previously mentioned Faria et al (2013) also incorporated a TCO analysis, further substantiating its evaluative importance. The present study follows these works by incorporating TCO as part of its economic analysis.
Previous works highlight further opportunities to strengthen the analytical breadth of comparative LCAs involving OCL technology. Hanesch et al (2022), for example, conducted a cradle-to-gate lifecycle assessment of OCL trucks in a pilot operation and compared its performance with a conventional diesel truck. Their results suggested that maximum environmental benefit-potentially 22% of GHG emissions savings-can be achieved when OCL infrastructure is fully utilized. Although their LCA was comprehensive, this study lacked a disposal scenario (i.e. end-of-life stage) for the trucks and infrastructure. Furthermore, the economic implications of the OCL technology were not investigated. The present study helps to fill both gaps by including LCA end-of-life activities and economic implications.
Other OCL analyses informed important parameters of the present study. For instance, Schulte and Ny (2018) included electricity mixes in their LCA of OCL truck technology to assess the feasibility in the EU region using average Nordic and EU electricity mixes. Wietschel et al (2019) conducted a comparative TEA of OCL trucks and conventional diesel trucks in Germany. Their results forecasted cost savings from large scale installation of OCL infrastructure, despite its high upfront capital costs. Lozanovski et al (2020) performed a lifecycle comparison of five low-emission freight transport technologies including OCL, BEV, fuel-cell EV, synthetic natural gas plug-in hybrid EV, and Fischer-Tropsch plug-in hybrid EV technology. They found that OCL had the lowest lifecycle GHG emissions.
Taken together, these previous works exemplify the complexities in evaluating OCL technology in comparison to more conventional technologies. Quality LCAs and TEAs are needed to ultimately improve decision-making outcomes in this regard (Dillman et al 2020). The next section further describes how the present study helps to do this through a study aims and need summarization.

Study aims and need summarization
It follows that there is a need for a combined LCA and TEA of OCL technology encompassing diverse geographic regions across the world. In the present study, we help fill this gap by performing a cradle-to-grave LCA of OCL-powered freight trucks, BEV trucks, and conventional diesel trucks by comparing their environmental impacts. We then perform a TEA of the OCL technology to test its economic viability, as it is a relatively novel freight transportation technology. Specifically, our study aims are to (1) analyze the environmental and techno-economic impacts of long-haul freight OCL technology, (2) compare its environmental impacts with BEV and diesel-powered trucks, and (3) assess the environmental performance of OCL technology in countries of varying electricity mixes.
Infrastructure and sustainability are key components of the most pressing challenges in society. A paradigm shift from fossil-fuel based transportation systems to a more sustainable alternative is an innovative solution that tackles climate change while also providing net-positive societal outcomes. This study attempts to quantitatively and qualitatively contribute to this paradigm shift. The present study analyzes the environmental impacts of these three technologies. It then shows the economic viability of the OCL as an emerging technology. Finally, it investigates the applicability of the OCL technology in different regions of the world. The present study not only provides meaningful data and frameworks for academicians, but it can also serve as a valuable reference for decision makers to aid in their transportation decarbonization efforts.

Materials and methods
To meet the present study aims, we developed and performed a meaningful LCA of the OCL-powered freight truck technology, BEV trucks, and diesel-powered trucks. A TEA of the OCL technology was also carried out. This model was primarily designed to inform decision-making activities using environmental impact and TCO estimations. The LCA was structured according to the ISO 14040:2006 standard which describes the principles and framework for LCA (ISO 2006). It was modeled in SimaPro 9.4.0.1, using the ecoinvent 3.8 database. For our study boundary, the 'allocation-cut-off by classification' system model was chosen. This type of system model does not credit the producers of waste for recycling or re-use of products (Steubing et al 2016). ReCiPe2016-hierarchist-was used to compute the environmental impact assessment. We explain and justify key modeling parameters in the remainder of this section.
We used the GWP of various compounds to estimate the net GHG emissions of system components. GWP is the ratio of radiative force of a substance to that of carbon dioxide. The formula below represents the principal approach in this regard (Forster et al 2007), where TH is the time horizon in years, RF i is the global mean radiative force of a compound 'i' , and RF CO2 is the global mean radiative forcing of carbon dioxide: Figure 1 shows the investigative boundary of the present study. The dotted line represents the system boundary for the LCA model. Truck Production and Use-phase Electricity/Fuel Consumption are major stages in the OCL, BEV and conventional trucks, while the OCL life cycle also includes Infrastructure Construction. The LCA model of the three technologies (i.e. OCL, BEV, and conventional) includes the impacts associated with material extraction, manufacturing, distribution, use-phase of the technologies, and the end-of-life waste management. Please note that the term conventional refers to the diesel-fueled truck technology. These terms and phrasings are used interchangeably throughout.

System boundary and transport technologies
The use-phase impacts are from the electricity consumed for the OCL, BEV, and the diesel combusted for the conventional trucks. The end-of-life impacts are from the disposal and waste management scenarios for the various components in each technology: metal and plastic recycling, disposal in landfills, and dismantling of the trucks. The use-phase and end-of-life assumptions for the three technologies are described below.
The OCL infrastructure was assumed to be built on an existing highway network and did not require the construction of new highway lanes. Also, the catenaries, masts, and substations were assumed to have a lifespan of 35 years (BMVI 2017, Hanesch et al 2022. The OCL trucks modeled to travel this highway were assumed to operate by being attached to the overhead catenary system using a retractable pantograph. This pantograph powers the vehicle while simultaneously charging the in-vehicle battery of roughly 125 kWh capacity (i.e. 100 km range) which weighs about 780 kg. This battery power is utilized by the truck once the truck is disconnected from the catenary cables, usually as a part of the last-mile travel off-highway where catenary infrastructure might not be available. The OCL trucks were assumed to utilize an average of 1.25 kWh of electricity per km travelled while engaged to the catenary system (Hanesch et al 2022). It was assumed that 700 km were powered by the infrastructure alongside of the highway, while the remaining 100 km was assumed to be powered by the in-vehicle battery (BMVI 2017). Furthermore, all the long-haul freight trucks were assumed to carry an average of 12.5 metric tons of cargo per trip (Hanesch et al 2022). This was done to mimic cargo being carried on the highway for most of its journey, as is the case in most long-haul freight transport scenarios.
The end of life of the OCL technology included the dismantling of the truck, the recycling of the metals used in the infrastructure, and disposal of the battery. There were important assumptions made to distinguish the model functionality between BEV and OCL trucks. The BEV trucks were assumed to house a battery of roughly 1000 kWh capacity at an 800 km range, which weighed 5000 kg. They were assumed to utilize an average of 1.44 kWh of electricity per km travelled on the highway. To establish OCL truck functionality in this regard, a kWh-to-km linear relationship was used between the weights of the BEV and OCL trucks (i.e. the OCL truck being 3600 kg lighter than the BEV truck) and OCL fuel efficiency (i.e. 1.25 kWh km −1 ). This resulted in an average of 1.44 kWh of electricity per km travelled on the highway for BEV trucks, accounting for the lower battery capacity and size of the OCL truck in comparison to the BEV truck. No charging infrastructure was assumed to be installed for this technology. The end of life of the BEV technology included the dismantling of the truck and disposal of the battery.
The average fuel consumption for the conventional truck was assumed to be 0.44 l km −1 (AFDC 2020). Construction of new fuel stations was not considered for this technology. Lastly, the end of life of the conventional truck included its dismantling.

Base case country selection
We had to choose a country to model the OCL, BEV, and conventional base case on. Germany is an interesting testbed for transportation emission reduction strategy in this regard. Its transportation sector has not kept pace in GHG emission reduction rates compared to other sectors such as energy, industry, and construction. For instance, total GHG emissions in the year 2019 fell by 36% relative to year 1990, yet, transportation emissions only fell by 10% in the same period (European Environment Agency et al 2022). Furthermore, transportation accounted for 13% of all GHG emissions in Germany in the year 1990, whereas it accounted for 20% in the year 2020 (European Environment Agency, European Commission, DG Climate Action 2022). The life cycle sustainability and electric mobility teams at Siemens provided a technical report which contained details and data on OCL technology implementation in Germany (BMVI 2017). For these reasons, we use Germany as the country to parameterize our OCL technology base case. We explain this further in the section 2.3.

The functional unit and other important assumptions
The more commonly used functional units for LCA studies on transportation are lifetime of the vehicle and per tkm (Dente and Tavasszy 2018). In the case of freight transport, the functional unit of tkm makes it easier to compare different freight transportation options of varying payloads. We, therefore, adopted tkm as the primary functional unit of the present study.
Other assumptions were made to strengthen the present study model. Previous works estimated the lifespan of these freight trucks to be between 8 and 10 years or up to one million kilometers travelled (Wadud 2017, Simpson et al 2019, Tanco et al 2019, Hanesch et al 2022. Given these findings, we used a more conservative lifespan of 800 000 km in the present study. Each truck was assumed to cover 100 000 km of these in a year, yielding an 8 years lifespan.
There were several other key assumptions made regarding the highway infrastructure itself. We assumed that there was no significant change in the overall road traffic compared to current levels. This meant that there was no additional road wear accounted for. A detailed list of the assumptions and associated calculations is provided in the supplementary material.

Special OCL considerations for freight transport
Unlike the BEV and conventional technologies, the OCL technology was modeled to incorporate new infrastructure development. The lifespan of vital OCL components need to be assessed to ensure lifecycle impacts of the associated infrastructure are not overlooked. It is also important to mention that moderate to severe weather events can reduce the lifespan of transmission infrastructure drastically (Beard et al 2010). Nevertheless, for the OCL infrastructure modeled in the present study, we set the lifespan to 35 years to align with previous study parametrization (BMVI 2017, Hanesch et al 2022. The disposal or end-of-life phase of OCL transportation systems involve many dynamic factors. Metals like copper and steel form the bulk of construction material for the catenary infrastructure, whereas battery equipment for the BEV trucks needs metals like lithium, cobalt, and rare earth metals (Martins et al 2021). It further adds to the complexity that metal recycling practices change with geographical location, time, and design among other material characteristics (Graedel et al 2011. Moreover, when it comes to transportation systems, the end-of-life is not a one-off instance. Rather, it is an iterative process where components are replaced and refurbished independent of the infrastructure as a whole (Saxe and Kasraian 2020). Keeping this in mind, the end-of-life disposal scenario for the material inventory was included in the lifecycle model. This scenario accounts for the dismantling and disposal of all the components of the OCL inventory (see supplementary material: section 1).

Model scenarios, conditions, and parameters
The LCA and TEA model impacts were calculated based on differing levels of technology adoption. Technology adoption in the present study refers to the number of conventional fuel-powered freight trucks that were assumed to be replaced either by OCL or BEV trucks. All three long-haul freight trucks (i.e. OCL, BEV, and conventional) were assumed to travel a stretch of 800 km in total.
The model traffic data was based on Section 3809 of the Autobahn A9 and sourced from the Federal Highway Research Institute, German Federal Ministry of Transport (BASt 2021). The frequency of heavy-duty truck use (i.e. trucks weighing 20 metric tons and above) on Autobahn A9 was estimated to be 6500 per day (BASt 2021). This value was used to model the adoption rates, where 1% adoption meant the replacement of 65 existing trucks with either OCL or BEV trucks. Similarly, 10% adoption meant the replacement of 650 existing trucks, and so on. This approach aligns with another pilot-scale adoption rate analysis of OCL technology (Hanesch et al 2022). Material requirements of setting up the OCL infrastructure were assumed to be the same for all adoption rates. However, the number of trucks using the OCL infrastructure increase as the adoption rate increases.
For the base case, we assumed that German electricity supplied power to the OCL infrastructure and BEV trucks to help meet the study aims. Furthermore, to assess the sensitivity that the electricity mix had on the performance of OCL and BEV trucks, we conducted the LCA considering the electricity mixes of countries across the world: China, USA, India, Russia, Nigeria, South Africa, Norway, Saudi Arabia, Australia, and Brazil. These countries include the top domestic electricity consumers and CO 2 emitters (i.e. China, USA, India, and Russia). Norway and Brazil were chosen in this analysis as they have a high share of renewable energy in their electricity mix (Enerdata 2022). Nigeria and South Africa are the largest economies in Africa but have starkly differing electricity profiles (IEA 2021b, 2022a). Saudi Arabia and Australia were chosen as they similar electrical grid carbon intensities (Our World in Data 2021) but have very different electricity mixes (IEA 2022b(IEA , 2022c. The EU is a global leader in decarbonization efforts across all sectors. Major inroads have been made regarding electrification of the transportation sector in the EU (Zhang and Fujimori 2020). Hence, this study also considered the ramifications of the OCL technology in a EU electricity mix context.
The life-cycle inventory for all three technologies is provided in detail in the supplementary material (see supplementary material: section 1). It provides details for the major components of the OCL technology, including the infrastructure (i.e. masts, catenary cables, and substations), OCL truck inventory data, and use-phase electricity consumption. Inventory and use-phase data is also provided for the BEV (e.g. truck had a 1000 kWh battery) and conventional diesel truck (e.g. diesel-powered internal combustion engine truck) here.

TEA conditions for the OCL technology
A TEA was performed to estimate the TCO of commercial OCL technology deployment since the OCL technology is a novel freight transportation alternative with only pilot scale operations. It included construction of the infrastructure, manufacturing of the trucks, electricity usage, and maintenance. The cost data for the truck procurement and infrastructure construction was sourced from the German Federal Ministry of Transport report (BMVI 2017) (see supplementary material: section 3). This dataset considered the best (i.e. $1.46 million km −1 ) and worst case (i.e. $1.93 million km −1 ) for the cost of materials and equipment on a per-km basis.
The base-or lower-cost of electricity was assumed to be $0.09 kWh −1 which is in line with the industrial cost of electricity (EMBER 2023). However, electricity prices in Europe in year 2022 were highly volatile. This was due to higher heating demand, a relatively harsh winter season, and a rise in natural gas and coal prices connected to the ongoing Russia-Ukraine war (Zakeri et al 2022). For instance, electricity prices peaked at $0.51 kWh −1 in Germany in August 2022 (EMBER 2023)-more than five times higher than the present study's electricity cost. We, therefore, also use the $0.51 kWh −1 electricity cost in the present study TEA as our higher cost.
It is important to highlight that there was no TEA performed for the BEV and conventional diesel truck technologies as a few previous studies have explored the same (Mojtaba Lajevardi et al 2019, Zhang et al 2022). These studies also include the TEA of OCL technology. However, the present study uses cost data from the pilot scale operation and the technical report (BMVI 2017), thereby making the TEA more robust with lesser cost uncertainties.
The cost of electricity influences the effect that subsidies might have on this technology. The OCL infrastructure was assumed to require a major investment. This would mean that the rate of OCL truck adoption heavily influences the TCO conditions required to economically compete with more traditional fuel-powered trucks. Given these factors, discount rates of 5%, 7%, and 10% were applied to analyze capital cost recovery differences (Vassallo 2010, Ferreira andSantos 2013). Specifically, a rate of 7% for road transportation infrastructure was selected, given it is impossible to forecast the exact discount rate (Jones et al 2014). The lower and higher discount rates-5% and 10%, respectively-were applied to account for uncertainty in this regard.

Sensitivity analysis
Unlike the BEV and conventional diesel technologies which are already commercially viable (NACFE 2022), the OCL technology is still at the pilot scale (Hanesch et al 2022). Nonetheless, the OCL technology has the potential to move from pilot-scale to commercial deployment. This justifies a sensitivity analysis on the environmental and economic effects of the OCL technology. This helped to identify OCL system vulnerabilities. To help calculate this, we increased the GHG emissions and associated costs of the three core OCL components (i.e. truck manufacturing, infrastructure construction, and use phase electricity) by 10% (Murphy et al 2004). The country-to-country analysis provides the sensitivity of the environmental impacts of the OCL technology to differing electric grid carbon intensities. The consideration of two different costs of electricity (i.e. the lower $0.09 kWh −1 and higher $0.51 kWh −1 ) uncovered key sensitivity insights, which we explain in section 3.1.3. This allowed for a systematic assessment of the influence these core components had on LCA and TEA outcomes.

Results and discussion
All the results shown in this section represent cradle-to-grave impacts of the three technologies (i.e. OCL, BEV, and conventional truck) confined to the system boundary (refer to section 2.1). Furthermore, the base case results use Germany for its contextual testbed.

Base case environmental impacts
The net GHG emissions during the life cycle of the conventional truck is more than twice of its electric alternatives (i.e. OCL and BEV) across all adoption rates (starting from 1%). Setting up the infrastructure of OCL has an environmental impact of 7.6 kiloton CO 2 eq. This impact is amortized over a larger number of trucks as the adoption rate increases. Infrastructure is the most environmentally impactful component until an adoption rate of 0.23% (i.e. 15 trucks) is reached, after which, electricity in the use-phase starts becoming more impactful. The impacts due to manufacturing of the trucks is higher than that of the infrastructure, starting from an adoption rate of 1.22% (i.e. 80 trucks). Net impacts of the OCL technology are higher than those of the conventional truck technology until 0.1% adoption (i.e. 7 trucks), after which the impacts due to conventional truck technology are higher. Similarly, net impacts of the OCL technology are higher than those of the BEV technology until 1.67% (i.e. 110 trucks), after which the impacts due to BEV technology are higher. This suggests that the environmental impacts of the OCL infrastructure are inconsequential when compared to those of the use-phase electricity at higher adoption rates.
Among the electric alternatives, the OCL technology has a lower carbon footprint compared to the BEV technology. This can be attributed to the lower weight of the OCL truck batteries (i.e. about 780 kg) compared to the BEV batteries (i.e. 5000 kg). As another attribution in this regard, the GHG emissions stemming from the OCL infrastructure amortizes more over its life cycle (i.e. 35 years) as the adoption rate increases (e.g. more OCL trucks are introduced into the fleet), whereas the BEV technology has no new infrastructure components incorporated as a condition of the present study. At 15% adoption, the OCL technology has a 10% less environmental impact than the BEV technology, after which, the difference in impact increases slowly and peaks at 11% less environmental impact with a 50% adoption. Figure 2 shows the GHG emissions normalized per functional unit (g.CO 2 eq./tkm) for the three technologies in the 10% adoption case and presents a component impact breakdown. We chose to display the 10% adoption case here, rather than the 50% adoption case for instance, to highlight the component effects at a lower overall truck count, and to represent a realistic implementation of a nascent technology. The carbon footprint associated with the conventional truck technology amounts to 178 g.CO 2 eq./tkm which is the highest among the three technologies. The BEV technology follows with a carbon footprint of 76 g.CO 2 eq./tkm, whereas the OCL technology has the lowest at 62 g.CO 2 eq./tkm. Emissions associated with the use-phase electricity consumption are higher in the case of BEV trucks (i.e. 59.3 g.CO 2 eq./tkm) owing to their lower fuel efficiency when compared to OCL trucks (i.e. 51.6 g.CO 2 eq./tkm). The GHG emissions of the conventional diesel truck technology is over 2.5 times that of its electric alternatives. This is primarily due to the high carbon intensity of the combustion of diesel during the use phase. Another interesting point is that the life cycle GHG emissions stemming from the heavier BEV battery (i.e. 16.6 g.CO 2 eq./tkm per truck) is higher than the OCL infrastructure and battery emissions combined due to, in part, a lighter OCL battery (i.e. 10.9 g.CO 2 eq./tkm per truck).
The use-phase electricity or fuel accounts for the highest proportion of GHG emissions in each of the three technologies (i.e. 83% for OCL, 78% for BEV, and 96% for conventional diesel trucks). This indicates that the source electricity mix-such as the share of clean energy as opposed to fossil-fuel-based energy provided by the system grid-plays a key role in overall GHG emissions. Please note that the BEV technology also shares these implications. Since the OCL technology has the highest proportion of use-phase GHG emissions of the electric alternatives at 83%, we follow this section with results of a country-to-country analysis of source electricity mixes for the OCL technology.

Country-to-country analysis (OCL technology)
We have established that the electricity consumed during OCL truck operation accounts for a relatively large share of the net GHG emissions across adoption rates. It follows that overall GHG emissions is highly dependent on the electricity generation profile of the country that may employ an electric alternative. Figure 3 depicts the difference in the associated GHG emissions per tkm for countries that have varying electricity mixes (i.e. Germany, USA, China, India, Russia, Norway, Brazil, Saudi Arabia, Nigeria, South Africa, and Australia) for the 10%-adoption case.
It can be observed that Norway yields the lowest GHG emission impact (i.e. 13.4 g.CO 2 eq./tkm), given 99% of its electricity production is from renewable energy sources (Enerdata 2022). The use-phase electricity accounts for 55% of its total GHG emissions, whereas the production of trucks, and setting up of infrastructure accounted for 40% and 5% of the emissions, respectively. For countries with a profile such as this, reducing the impacts from truck production can further reduce the net GHG emissions.
In contrast, India which uses coal for 65% of its electricity generation, yields the highest emissions (i.e. 164.2 g.CO 2 eq./tkm) (IEA 2021c). The net-positive impacts of replacing conventional trucks with OCL technology are marginal (only 8% reduction). This suggests that a country or region with this energy profile needs to consider investing in increasing its share of renewable and clean energy capacity before employing an electric alternative technology. Figure 4 depicts the environmental impacts of implementing the OCL technology across the EU. There is a wide variation in the applicability of this technology in the EU due to varying electricity mixes among the member states. Sweden (15.77 g. CO 2 /tkm) and Poland (114.5 g. CO 2 /tkm) are on the opposite ends of the impact spectrum. While the electricity mix of Sweden is dominated by clean and renewable energy sources, Poland's mix comprises mainly of coal, oil, and natural gas (IEA 2021d, 2021e). France, where nuclear energy makes up 69% of its electricity mix would also benefit from the implementation of electric alternatives to Taken together, this highlights how important it is for decision makers to pay attention to all system components before adopting transportation technology. For instance, it is vital that countries and regions shift to a cleaner electricity grid to reduce overall GHG emissions rather than just deploying electric vehicle alternatives.

Techno-economic impacts (OCL technology)
There exist two cost scenarios which can be differentiated on the basis of their geographical location. One assumes the infrastructure is constructed in an urbanized area (e.g. worst case), and the second assumes the infrastructure is constructed in a rural area (e.g. best case). The differences in these costs mainly arise from setting up of feed lines, which applies to both rural and urbanized settings, and passive protection device installation which is unique to the urban scenario.
Expenses for construction management (e.g. project planning and tendering) are assumed to be 10% of the total capital costs. The TEA was performed using best-and worst-case cost estimates for the OCL infrastructure and industrial rate for electricity (i.e. $0.09 per kWh) (EIA 2022). Each truck was estimated to cost $150 000 and the annual maintenance was estimated to be 2% of the truck cost. Furthermore, the TCO was computed using three discount rates (i.e. 5%, 7%, and 10%) and by including the life span of the trucks and OCL infrastructure components per the present study boundary and parameters (refer to section 2.1). Figure 5 shows the trend of the TCO for a single OCL truck based on different adoption rates ranging from 1% to 50% for the worst-case cost estimate. Results show that the cost is highest when the adoption rate is low because the cost of the infrastructure is being amortized by a small number of OCL trucks. However, the TCO per truck drops steeply across each discount rate as the adoption rate increases, with an 85% reduction in cost realized at 10% adoption-compared to the TCO per truck at 1% adoption. After which, the TCO per truck declines gradually with a 93% reduction in cost at 50% adoption-compared to the TCO per truck at 1% adoption. These TEA results clearly indicate that this OCL technology should be implemented at a 10% adoption rate or higher to realize economic viability. A major hindrance to the adoption of OCL technology is the upfront capital costs required for setting up the infrastructure (Connolly 2017). Figure 6 shows the TCO with a component-wise breakdown of costs in the OCL technology (infrastructure, truck manufacture, use-phase electricity, and maintenance) in both absolute values ($/truck) and percent-wise breakdown. This shows that the cost of infrastructure influences the TCO per truck at lower adoption rates. It is noted that the influence of the infrastructure cost decreases steadily as the adoption rate increases, with the cost of the truck and use-phase electricity taking over.
At 1% adoption, the TCO per truck is approximately $5 million, where the infrastructure cost accounts for 95% of all costs, whereas the truck and use-phase electricity accounts for just over 4%. At 10% adoption, the TCO per truck reduces to $710 000, where the infrastructure cost accounts for 67%, the truck accounts for 21%, and use-phase electricity accounts for 9% of the TCO per truck. At 31% adoption, the TCO is equally influenced by the cost of the truck and the infrastructure (both 40% of total costs). With a 50% adoption of OCL, the TCO per truck further reduces to $330 000 with a cost breakdown of-29% infrastructure cost, 46% cost of truck, and 20% cost of use-phase electricity. Thus, understanding how the carbon savings interplay with TCO is crucial to qualifying for when circumstances require high upfront capital costs. Figure 7 shows the sensitivity of cumulative OCL system emissions using the base case (i.e. a German testbed and electricity profile) and a 10% increase in intensity of the three core OCL components (i.e. truck production, use-phase electricity) at an assumed cost of $0.09 kWh −1 , including infrastructure construction (refer to section 2.3.2.). It shows that environmental impacts are most sensitive to use-phase electricity consumption across all adoption rates (i.e. 1% -through 50%). However, the economic sensitivity of the use-phase electricity is not as high. Conversely, environmental sensitivity of infrastructure is the least among the three, while its economic sensitivity is the highest until 30% adoption. The environmental sensitivity of the truck is nearly constant across all adoption rates, but its economic sensitivity keeps increasing and is the most significant in adoption rates greater than 30%. These results show that different components of the OCL technology are hotspots for economic and environmental impact given particular adoption rates.   To model for an unusually high cost of electricity due various reasons-as discussed in section 2.3.1, the economic sensitivity of use-phase electricity was calculated using the cost of electricity at $0.51 kWh −1 . In the 1% adoption case of OCL, the economic sensitivity was 6%, rising to a sensitivity of 44% at 10% adoption, and peaking at 94% for 50% adoption. This highlights the economic vulnerability of any electric transportation alternative to fluctuations in the costs of electricity which can be unpredictable. Electrified transportation would continue to be affected by these fluctuations until energy independence is achieved in key regions worldwide. Table 1 shows the ratio of environmental-to-economic sensitivity for the three OCL components across different adoption rates. When this ratio has a relatively high value, the component has a significant environmental impact and relatively low economic impact. When this ratio has a low value, the environmental impact is lesser compared to its economic impact. Simply put, there is higher OCL environmental and techno-economic benefit when this ratio is low. This ratio is a novel metric for assessing the environmental impacts of transportation components that have varying economic burdens.

Sensitivity analysis results
Results reveal three major findings. The first is that the environmental-to-economic sensitivity ratio is the lowest for the infrastructure construction component across each adoption case. The second is that use-phase electricity has the highest environmental-to-economic sensitivity ratio across each adoption case. This ratio does decrease drastically at the 10% adoption rate-from 52.9 to 8.8-for the use phase component. This indicates that OCL technology implementation becomes much more techno-economically viable at a 10% adoption rate or higher, given that the source electricity mix contains a high enough share of renewable and clean sourcing. Lastly, the environmental-to-economic sensitivity ratios for each OCL system component decreases as the adoption rate increases. Taken together, this complements the previous findings that suggest a 10% adoption rate or higher makes this OCL technology viable in many cases.
These findings have advantageous decision-making implications given its synthetization of complex environmental and economic life cycle impacts into a simplified ratio. This ratio can be used to frame policies that aim to account for the cost of carbon emissions associated with relevant technologies and products. It can also be helpful in circumstances where technology system analyses fail to account for carbon emissions and capital viability in an integrated manner.

Equity considerations
Tailpipe emissions from conventional long-haul freight transport, which primarily include carbon dioxide, nitrous oxide, and particulate matter, have been a major source of pollution. This is especially the case for communities that are located close to highway corridors where these trucks operate (Patterson and Harley 2021). Exposure to oxides of nitrogen and particulate matter have been linked to adverse health impacts such as asthma, cancer, cardiovascular, and respiratory mortality among others (Barath et al 2010, Hamra et al 2015, Jacquemin et al 2015. Previous studies show that the communities living close to these highway corridors tend to be low-income and mostly comprise of people of color (Seo et al 2013, Clark et al 2014, 2017. This brings up a major environmental equity issue due to the localized air pollution caused by heavy truck traffic. Since the long-haul transportation of goods are unlikely to cease given its importance to the global supply chain (McKinnon 2016) developing and employing low-emitting system alternatives will play a significant role in improving air quality and human health outcomes for these communities. The present study finds, in part, that the OCL technology has lower GHG emissions than the conventional diesel technology at about the 10% adoption rate or higher. This is an important finding that positively contributes toward more equitable outcomes in transportation decision-making activities.

Limitations and future directions
There are several items that future studies could integrate to reveal complementary insights or tools. For example, the present study assumed average values of fuel and electricity consumption and for the load of cargo transported for the LCA model. Future studies could incorporate data on the times of day that long-haul trucks are driven, typical driving speeds, and efficiencies achieved during various portions of a trip. This would help forecast the peak loads required at different times of the day and the corresponding dynamic costs of electricity associated.
Energy consumption and its related emissions for electric transportation technologies can increase due to changes in weather and climatic conditions. This is usually exacerbated in regions which have cold winters, marking a significant drop in the range and efficiency of batteries (Yuksel and Michalek 2015). This study utilizes the current electricity mixes of all countries analyzed during the lifecycle of the OCL technology. Future studies can improve on the present study findings by forecasting the decarbonization of the electricity mix of a country during the operating lifecycle of OCL technology. Dead-head trips could be taken into consideration to account for a certain percentage of trips during which the trucks are empty and not carrying cargo.
The present study uses the ecoinvent database (global averages) to model all material, processes, and waste disposal/management scenarios in the LCA. Future LCA and TEA models of this kind could benefit from region-specific inventories, if and when available.
We do not consider the construction of new lanes in the present study. However, it was found that the addition of separate concrete lanes along the edges of e-highways increase the fuel efficiency of a truck compared to when it is driven over an asphalt road (Sumitsawan and Romanoschi 2009). New highway infrastructure projects, therefore, could benefit from the complementary installation of an electric alternative and outside lanes made of concrete. Nonetheless, a detailed LCA would need to be performed to weigh the tradeoffs here.
Furthermore, future studies should evaluate novel freight transport technologies not just with existing road infrastructure but also with the existing rail network available in the country or region. Countries with robust and extensive rail infrastructure might be less receptive to large investments in OCL technology. A major chunk of long-haul freight transportation in such countries is carried out through rail freight corridors (Kaack et al 2018). Countries like the USA, where rail infrastructure is unevenly established on the other hand, could greatly benefit from long-haul electrification.

Conclusion
The present study aimed to perform a cradle-to-grave LCA of three freight transport technologies (OCL, BEV, and conventional truck), and a TEA of the OCL technology. The specific aims were to analyze the techno-economic and environmental impacts of long-haul freight OCL technology, compare its environmental impacts with BEV and diesel-powered trucks, and to assess the environmental performance of OCL technology in countries of varying electricity mixes. These aims were met by revealing that the OCL technology is marginally less polluting than BEV, while both the electric alternatives are more than 2.5 times less polluting than the conventional diesel truck across all adoption rates. Findings suggest that to be environmentally and techno-economically viable, decision makers must adopt a 10% rate or higher for electric alternatives (i.e. OCL and BEV technologies) in countries or regions that have an electricity grid with a share of renewable and clean energy sourcing at a level close to or greater than Germany (e.g. USA, Brazil, France, Sweden, Belgium, Finland, and Norway). The share of renewable and clean energy sourcing should be a primary focus before electric alternative deployment for countries or regions that have fossil-heavy grids (e.g. India, South Africa, Poland, Estonia, and China). Nevertheless, balancing technology and electrification sourcing is a complex and critical decision-making factor.
Other important environmental and economic findings were revealed. One was that the construction of the OCL infrastructure becomes a relatively insignificant contributor to net GHG emissions as the adoption rate increases. This is because the impacts due to the infrastructure are amortized over a period of 35 years and is spread over a larger number of trucks as the adoption rate increases. Another was that the electricity mix of the grid is key when considering electric alternatives to freight transportation (i.e. OCL and BEV). Lastly, the environmental-to-economic ratio, as a measure, can inform decision making on long-haul freight truck components of varying economic burdens. For instance, these ratio findings showed that the OCL infrastructure construction system component has the lowest ratio (i.e. comparatively has an environmental and techno-economic benefit), whereas use phase electricity has the highest ratios across each adoption scenario (i.e. does not comparatively have an environmental and techno-economic benefit).
The present study also provides useful decision-making insights and measures. It makes clear that OCL technology adoption should be at a 10% adoption rate or higher when considering at-scale administration. Furthermore, the environmental-to-economic sensitivity ratio provides a novel tool to help assess both environmental and economic viability of transportation technology components. Taken together, these findings serve as a meaningful contribution toward electric alternative freight transport decision making. Countries, regions, and locales with a high share of fossil fuels in their electric mix should prioritize decarbonizing their grid before implementing large-scale electric mobility projects such as OCL and BEV technologies.

Data availability statement
All data that support the findings of this study are included within the article (and any supplementary information files). Davis Nuclear share of electricity generation in 2021 (available at: https://pris.iaea.org/pris/worldstatistics/nuclearshareofelectricity generation.aspx) (Accessed 21 January 2023) IEA 2021a Energy technology perspectives IEA 2021b Nigeria-electricity generation by source (IEA) (available at: www.iea.org/countries/nigeria) (Accessed 20 December 2022) IEA 2021c Electricity mix in India (International Energy Agency) (Accessed January-December 2020) IEA 2021d Poland-electricity generation by source (IEA) (available at: www.iea.org/countries/poland) (Accessed 21 January 2023)