Well-to-wheel greenhouse gas emissions of heavy-duty
transports: Influence of electricity carbon intensity
Marcus Gustafsson1*, Niclas Svensson1, Mats Eklund1, Joel Dahl Öberg1 and Aner Vehabovic1 1Environmental Technology and Management, Department of Management and
Engineering, Linköping University, SE-581 83 Linköping, Sweden *Corresponding author. E-mail: marcus.gustafsson@liu.se
Abstract
There are several alternatives for how to phase out diesel in heavy-duty transports, thereby reducing the sector’s climate change impact. This paper assesses the well-to-wheel (WTW) greenhouse gas (GHG) emissions of energy carriers for heavy-duty vehicles, analyzing the effect of the carbon intensity of the electricity used in production. The results show that energy carriers with high electricity dependence are not necessarily better than diesel from a WTW perspective. In particular, fuels produced through electrolysis are not well suited in carbon-intense electricity systems. Conversely, waste-based biofuels have low GHG
emissions regardless of the electricity system. Battery-electric buses show a large reduction of GHG emissions compared to diesel buses and many other alternatives, while battery-electric trucks have higher GHG emissions than diesel in carbon intense battery-electricity systems. Thus, electrifying transports or switching to renewable fuels will not suffice if the electricity system is not made renewable first.
Keywords: Well-to-wheel; Heavy transport; Greenhouse gas emissions; Carbon intensity;
Transport fuels
Nomenclature
Abbreviations
BEV battery-electric vehicle
Bio-CNG compressed biomethane
CNG compressed natural gas
FT Fischer-Tropsch
GHG greenhouse gas
HVO hydrotreated vegetable oil
ICE internal combustion engine
LCA life cycle assessment
SNG synthetic natural gas
TTW tank-to-wheel
WTT well-to-tank
WTW well-to-wheel
Symbols
CH₄ methane
CO₂ carbon dioxide
1. Introduction
The transport sector relies almost entirely on fossil fuels and is responsible for a large share of the greenhouse gas (GHG) emissions in Europe and globally. In the EU, commercial heavy-duty vehicles account for a quarter of the GHG emissions from road transports (EEA, 2018). This study takes on the energy use and GHG emissions of the transport sector by making a broad comparison of energy carriers for heavy-duty vehicles with varying driving cycles. The well-to-wheel (WTW) greenhouse gas emissions of electricity and renewable fuels are compared to conventional fossil diesel and natural gas for use in long haulage trucks, city buses and suburban buses. Seeing that many energy carriers rely on electricity in their production, the carbon intensity of the surrounding electricity system is taken into account. Thus, this study assesses the potentials and limitations of different propulsion systems when it comes to reducing the climate impact of the transport sector and which technologies can be employed without first having to replace fossil energy sources in the electricity system.
1.1.
Background
The European Commission has set goals for the transport sector to reduce GHG emissions by 20% from 2008 to 2030 and by 60% from 1990 to 2050 (European Commission, 2011). As the demand for transport work increases, the emissions from this sector can only be reduced by increasing the efficiency or changing the fuels. According to the EU Fuel Quality Directive, fuel and energy suppliers should reduce the life cycle greenhouse gas emissions by at least 6% per unit of energy, compared to the EU average level of fossil fuels, by the end of 2020 (European Commission, 2015). Promoting public transport as a more efficient alternative to individual transport by passenger car is also seen as an important strategy in the work towards climate change mitigation in the transport sector (European Commission, 2011). For a vehicle running on fossil fuel, most of the environmental impact over its lifecycle can usually be attributed to the use phase, while production and end-of-life treatment give a smaller contribution. A study by Nordelöf et al. (2019) on the climate change impact of buses showed that the use phase was responsible for 90% of the impact for a conventional diesel bus. For battery-electric vehicles (BEVs) and fuel cell electric vehicles, however, the
production and end-of-life phases typically have a larger impact (Ajanovic and Haas, 2019; Liu et al., 2020; Rosenfeld et al., 2019). Nordelöf et al. (2019) found that for electric buses operating in Sweden, the use phase would only contribute to 30% of the total climate change impact.
Vehicle fuels and energy carriers are commonly analysed in a WTW perspective, from resource extraction to use in the vehicle. While electric vehicles have no tailpipe emissions and renewable fuels are considered to give no net CO₂ emissions through combustion, the production of these energy carriers can sometimes be associated with significant
environmental impact. This was evident in the WTW analysis by JRC, EUCAR and CONCAWE (Prussi et al., 2020a), which outlined the energy use and climate change impact of vehicle fuels, both well-to-tank (WTT) and tank-to-wheel (TTW). According to their results, cars running on electricity and renewable fuels often have a higher WTT energy use and CO₂ emissions than diesel and gasoline, although their total climate change impact is lower due to the low TTW GHG emissions. The Swedish knowledge centre for renewable transportation fuels (f3) investigated WTW data for different energy carriers for heavy-duty vehicles on the Swedish market (Hallberg et al., 2013). In the Swedish context, rapeseed methyl ester, ED95 ethanol and HVO were found to reduce the climate change impact by 60% compared to
fossil diesel, while biomethane reduced the climate change impact by at least 75%, even though the WTT impact was in some cases higher than for diesel.
The WTT impact of fuel production pathways is highly dependent on how and where the fuel is produced. An increasingly common approach when assessing electric vehicles is to take into account the carbon intensity—the CO₂ emissions per energy unit—of the electricity system. Several studies have applied this methodology in assessments of GHG emissions of electric cars, including Moro and Lonza (2018), Woo et al. (2017), Faria et al. (2013) and Doucette and McCulloch (2011). A similar approach was taken by Rupp et al. (2019) in their study on electric buses in Germany, by Correa et al. (2019) in an environmental analysis of diesel, hydrogen, hybrid and electric buses in South America, and by Edwards et al. (2014) in a comparison between different hydrogen production pathways. Likewise, Liu et al. (2021) discussed the influence of regional electricity grids on the WTW GHG emissions of battery-electric heavy-duty trucks in the US. Petrauskienė et al. (2020) found that in the current Lithuanian electricity system, BEVs would increase the climate change impact compared to fossil diesel or petrol vehicles. Ehrenberger et al. (2019) investigated the implications of increasing the number of electric vehicles, considering the electricity systems in the
countries with the world’s highest sales of passenger cars. This type of analysis highlights the importance of the surrounding energy system in the transition towards electrified
transports, as the benefit of electrification can be very small or even negative if the electricity system has a high carbon intensity. This could also be the case with other alternative vehicle fuels and energy carriers, which are to some extent dependent on electricity in their production. However, to the authors’ knowledge, there have been no studies prior to the present paper making a broad comparison of electricity, biofuels and fossil fuels for road transports and how their WTW GHG emissions depend on the carbon intensity of electricity.
2. Methodology
In this paper, alternative energy carriers for heavy-duty vehicles are compared through well-to-wheel (WTW) analysis. WTW is a form of life cycle assessment specifically designed for transport fuels and energy carriers, covering the pathway from resource extraction to use in the vehicle but excluding the life cycle of the vehicle itself. A WTW analysis can be divided into well-to-tank (WTT), encompassing the production and distribution of an energy carrier, and tank-to-wheel (TTW), referring to the use of the energy carrier for vehicle propulsion. Here, the assessment was limited to GHG emissions caused by production, distribution and use of the studied energy carriers. In the WTT part of the calculations, the carbon intensity (g CO₂-eq/kWh) of the electricity system was varied to illustrate its influence on the climate change performance of alternative energy carriers compared to diesel. Apart from fossil diesel, the analysis covered compressed natural gas (CNG), electricity, hydrogen (H₂), biomethane (bio-CNG), hydrotreated vegetable oil (HVO), Fischer-Tropsch (FT) diesel, synthetic natural gas (SNG), ethanol and ED95. The TTW part of the analysis included three different types of vehicles with distinct driving cycles: a 40-ton long haulage truck, a city bus and a suburban bus. The overall methodology of this study, shown in Figure 1, is based on the work by Dahl Öberg and Vehabovic (2018).
Figure 1 – Overview of the methodology of the study. WTW results were built upon data for production and distribution (WTT) and use (TTW) of the studied energy carriers.
An overview of the studied scenarios is given in Figure 2, schematically showing the
pathways from raw material input through processing into energy carriers and use with the respective engine technologies and the different driving cycles. Data for most of the WTT pathways was gathered from the report and datasets presented by Prussi et al. (2020b), which was considered to be the most complete, recent compilation of WTT data for vehicle fuels. TTW data was taken from other sources, as described in Section 2.2.
2.1.
Well-to-tank scenarios
The WTT pathway for diesel was modelled in accordance with Prussi et al. (2020b). The diesel, starting from crude oil production, was refined and distributed to a fuel depot and then to dispensing sites by truck. Distribution from the production site to the fuel depot was done 60% by pipeline, 20% by truck and 20% by a 9,000-ton barge.
The second fossil fuel scenario considered was natural gas (CNG), for which the WTT pathway was also modelled according to Prussi et al. (2020b). It was assumed to be
distributed by pipeline and then through regional and local grids to retail sites, where it was compressed to 200 bar.
For electricity, the WTT GHG emissions are equal to the carbon intensity of the electricity system, plus transmission losses. In a sensitivity analysis, the impact of including production of the batteries for the vehicles was considered, assuming them to be NCM batteries with a climate change impact of 110 kg CO₂-eq/kWh storage capacity (when produced with EU average electricity) (Peters et al., 2017). The lifetime of the batteries was varied between 1,000 cycles, at a depth of discharge of 80 %, and 5,000 cycles, at a depth of discharge of 50 % (Peters et al., 2017).
Hydrogen was considered to be produced through either steam reforming of natural gas or electrolysis of water, with WTT data from Prussi et al. (2020b). In the electrolysis scenario, hydrogen was produced using high voltage electricity in a process with 65% efficiency, distributed through pipelines at 30 bar and compressed to 880 bar at the retail site for storage and vehicle fueling. The natural gas required for the steam reforming scenario was assumed to be produced and distributed as previously described.
Alternative production pathways for biomethane included anaerobic digestion (AD) of municipal organic waste (Prussi et al., 2020b), with internal heat production through the combustion of biogas, and thermal gasification (TG) of wood chips (Börjesson et al., 2016). Both of these pathways include valuable by-products, which were also included in the WTT calculations. For the AD pathway, the digestate was considered to be used as a biofertilizer, replacing mineral fertilizer, while the TG pathway produces excess heat and electricity, which were considered to replace other heat and electricity production. The electricity replaced by TG excess energy was assumed to vary according to the variation of the carbon intensity parameter, while the replaced heat was assumed to be produced from wood chips. Transports for the collection of waste for AD were not included, as they were considered to occur regardless of whether the waste was used for biogas production or not.
The alternative pathways for HVO include production from energy crops (rapeseed oil) or from waste (Prussi et al., 2020b). A sensitivity analysis was also made on HVO from energy crops as a low-blend (20 %) component in fossil diesel. Hydrogen used in the
hydrotreatment process was assumed to be produced through steam reforming of natural gas. For the energy crop pathway, credit was given for by-products used as animal feed, replacing other fodder. The waste oil pathway did not include transports of the waste for the same reasons as with biogas production from municipal waste. The HVO was distributed from the production site to a fuel depot and handled the same way as diesel.
SNG and FT diesel were produced through the synthesis of hydrogen and carbon dioxide (Prussi et al., 2020b). The hydrogen used was assumed to be produced through electrolysis, the same way as in the hydrogen scenario but without pressurization to 880 bar for
distribution. The CO₂ emitted from the combustion of the fuels (TTW) was not taken into account, as it was balanced by the CO₂ input in production. After distribution to the retail site, the SNG and FT diesel were handled the same way as CNG and diesel, respectively. Ethanol was assumed to be produced from corn, the most common feedstock for ethanol in Europe (ePURE, 2019) and the US (Renewable Fuels Association, 2019). Similar to HVO, by-products from ethanol production were considered to replace other animal feed. For the 40-ton truck scenario, ethanol was included in the form of ED95, which is a mix of 95%vol
ethanol and 5%vol blending components that increase the heating value and improve
performance in heavy-duty engines.
For all diesel fuels (fossil diesel, FT diesel and HVO), AdBlue was included in the WTW pathways. AdBlue is a mix of urea and water commonly added in diesel to reduce NOx
emissions. The urea was considered to be produced through Haber-Bosch and Bosch-Meiser processes from hydrogen and natural gas, with hydrogen produced through steam
reforming. Energy use and GHG emissions from the Haber-Bosch process were allocated between the products urea and ammonia according to their typical weight ratio (Canadian Industry for Energy Conservation, 2008).
2.2.
Tank-to-wheel scenarios
Three TTW scenarios were considered: a 40-ton long haulage truck, a city bus and a suburban bus. The average speed for the respective driving cycle was 85 km/h (Fröberg, 2019), 15 km/h and 43 km/h (Anttila, 2016). Data regarding combustion and driving cycles were based on simulations conducted at Scania CV AB, a global truck and bus manufacturer. Data on fuel economy for diesel, methane and electricity used in city buses and suburban buses were obtained from a master’s thesis at Scania CV AB (Anttila, 2016), while the
corresponding data for the 40-ton truck, as well as TTW data for hydrogen, was produced by the authors in collaboration with Scania CV AB. The fuel economy of ethanol in buses and ED95 in trucks was assumed to be equal to that of methane and diesel, respectively (Fröberg, 2019). The efficiency for fuel cells was set to 50% (US Department of Energy, 2015). For battery-electric and fuel cell electric vehicles, it was assumed that some energy could be recovered through regenerative braking when the vehicle stopped. TTW energy use for the studied energy carriers and driving cycles is presented in Table 1.
Table 1 – Tank-to-wheel energy use (kWh fuel per km) for the studied energy carriers and driving cycles. TTW energy use, kWh/km
Vehicle type Diesel Hydrogen Methane Electricity Ethanol ED95
40-ton truck 2.74 2.93 3.24 1.53 n.a. 2.74
City bus 6.10 2.91 7.20 1.55 7.20 n.a.
Suburban
bus 3.18 2.11 3.76 1.12
3.76 n.a.
2.3.
Carbon intensity
The influence of the electricity system carbon intensity was investigated by varying the carbon intensity of low-voltage electricity in the calculations from 0 to 1,000g CO₂-eq/kWh. In the figures illustrating this dependence, the carbon intensity of 5 of the 20 largest
economies and top 12 electricity-producing countries in the world (Ang and Su, 2016) is indicated, as well as an average value for the EU. The EU average is also used to exemplify
the division between WTT and TTW GHG emissions of the studied energy carriers. Data for carbon intensity (Table 2) of electricity included GHG emissions from all the steps of
electricity production and distribution (Climate Transparency, 2019). As seen in Table 2, the differences between the countries with the lowest and the highest carbon intensity are quite large due to variations in how the electricity is produced. While the electricity production in China and India is largely based on hard coal, France has a large share of nuclear power and Canada has about 2/3 renewable energy in its electricity production mix (Climate
Transparency, 2019).
Table 2 – Carbon intensity of electricity production mixes in China, India, USA, Canada and France and average for the European Union (Climate Transparency, 2019)
Electricity production mix, % Carbon intensity,
g CO₂-eq/kWh
Country Renewables Nuclear Gas Oil Coal
China 26 4 3 0 67 555 India 19 2 5 2 73 708 USA 18 19 34 1 28 401 Canada 66 15 9 1 8 140 France 20 72 5 0 2 48 EU av. 33 25 19 2 20 269
3. Results and Analysis
In an electricity system with a carbon intensity of 269 g CO₂-eq/kWh (EU average), WTT electricity use is a major contributor to WTW GHG emissions for FT diesel, electricity, hydrogen from electrolysis, SNG, bio-CNG from AD, ethanol and ED95 (Figure 3). For FT diesel, electricity, hydrogen and SNG, the impact of WTT electricity use is so large that it more or less eradicates the benefit of displacing fossil diesel. Thus, the carbon intensity of the electricity system will have a major impact on the total results for these energy carriers. For biomethane and ethanol, there is a partly negative contribution to GHG emissions in the WTT stage. For biomethane from AD, this results from the displacement of mineral fertilizer by the use of digestate from the process, while for biomethane from TG and ethanol, the negative contribution is from surplus electricity and heat. It should be noted that it is not possible to make a direct WTW comparison between energy carriers from this figure, as this shows the GHG emissions per kWh and the energy use per km varies between different engine technologies. TTW GHG emissions for biofuels are very low according to the calculations (< 1 g CO₂-eq/kWh) and are therefore not visible in the figure.
Figure 3 – Total greenhouse gas emissions per kWh for the studied energy carriers, divided into WTT (electricity/other) and TTW (combustion). The numbers shown are representative of an average European electricity mix.
Figure 4 – Figure 6 show the WTW GHG emissions per km for energy carriers used in
different vehicle applications, relative to the WTW GHG emissions of diesel. The driving cycle of the long haulage truck (Figure 4) is characterized by constant speeds at around 85 km/h and no mid-stops. This enables the internal combustion engine (ICE) to work at its optimal load and maximum efficiency to a larger extent than in the city- and suburban bus cycles. For electric vehicles, the constant speed and lack of mid-stops limit the potential for energy recovery during braking to just a few percent of the total electricity use.
FT diesel and hydrogen generate higher GHG emissions than diesel, even with the relatively low carbon intensity of the electricity used. With a high carbon intensity, electricity also gives higher GHG emissions than diesel. ED95 has an opposite trend due to a surplus of electricity in the ethanol production process, making it more beneficial when the alternative electricity production has a high carbon intensity. ED95, electricity and HVO almost intersect at a carbon intensity of 360 g CO₂/kWh, where they all give around 40% lower GHG
emissions than diesel. Biomethane has the lowest GHG emissions in most electricity systems. With a carbon intensity of electricity below 30 g CO₂/kWh, the GHG emissions of
biomethane are actually negative due to the displacement of mineral fertilizer by the use of digestate from the AD process.
-100 0 100 200 300 400 500 600 700 G H G e m is sion s, g CO₂ -e q /kW h Energy carrier
Figure 4 – Well-to-wheel greenhouse gas emissions (relative to diesel) of energy carriers for a 40-ton long haulage truck, as a function of the carbon intensity of the electricity system
The city bus driving cycle (Figure 5) involves low speed (on average 15 km/h) and frequent starts and stops. This favors battery-electric vehicles (BEVs) over ICEs, as more energy can be recovered when the vehicle brakes, while the efficiency of an ICE suffers from the varying speed and the many stops. Hydrogen also has lower WTW GHG emissions with this type of driving cycle, but the lowest emissions are seen for biomethane. All alternative fuels except FT diesel and ethanol reduce the GHG emissions compared to diesel for any electricity system with a carbon intensity < 1,000 g CO₂/kWh. FT diesel requires a carbon intensity < 140 g CO₂/kWh to have lower WTW GHG emissions than diesel, while ethanol, on the other hand, only reduces the GHG emissions in more carbon-intense electricity systems, at > 70 g CO₂/kWh.
Figure 5 – Well-to-wheel greenhouse gas emissions (relative to diesel) of energy carriers for a city bus, as a function of the carbon intensity of the electricity system
The characteristic driving cycle of a suburban bus (Figure 6) lies somewhere between the long haulage truck and the city bus, with an average speed of 43 km/h and less frequent stops than a city bus. Biomethane, followed by electricity, has the lowest WTW GHG emissions in most electricity systems, and all alternative fuels except FT diesel and ethanol reduce the GHG emissions compared to diesel for any electricity system with a carbon intensity < 1,000 g CO₂/kWh. Ethanol, electricity and HVO nearly intersect at a carbon intensity of 560 g CO₂/kWh, all giving around 40% lower GHG emissions than diesel.
Figure 6 – Well-to-wheel greenhouse gas emissions (relative to diesel) of energy carriers for a suburban bus, as a function of the carbon intensity of the electricity system
Figure 7 shows alternative production scenarios for some of the studied fuels and a sensitivity analysis on the inclusion of battery production for the BEV. The results shown represent the suburban bus driving cycle. For the BEV (top left), the battery production adds 30 – 100 g CO₂-eq/km to the total WTW GHG emissions if the battery lasts 5,000 cycles and 90 – 300 g CO₂-eq/km if it lasts just 1,000 cycles. With a carbon intensity similar to the US (around 400 g CO₂-eq/kWh), the battery production accounts for 12 – 38% of the WTW GHG emissions, depending on how long the battery lasts. The intersection with fossil diesel is moved back from 1,000 g CO₂-eq/kWh carbon intensity to 920 or 760 g CO₂-eq/kWh. In the latter case, this means that the BEV would have nearly as high climate change impact as a diesel vehicle in an electricity system similar to India. For the long haulage truck driving cycle, this occurs at a carbon intensity of 450 – 590 g CO₂-eq/kWh, while for the city bus cycle, the BEV is better than diesel up to a carbon intensity of 1,110 – 1,320 g CO₂-eq/kWh. HVO produced from waste oil reduces GHG emissions by more than 60% compared to HVO from energy crops (top right). While the end product in both cases is the same renewable fuel, the waste oil pathway does not include energy use and related GHG emissions for the production of the feedstock. The GHG emissions reduction of blending HVO (from rapeseed oil) into diesel is proportional to the blend ratio; in this example, a 20% HVO content gives a 6 – 7% reduction of WTW GHG emissions. All HVO scenarios have a relatively low
dependence on the carbon intensity of the electricity, as the amount of electricity used in production and distribution is quite low. The results are very similar for other driving cycles.
Hydrogen production through steam reforming of natural gas requires less electricity than electrolysis (bottom left), making it better from a GHG point of view in electricity systems with high carbon intensity. The two pathways intersect at a carbon intensity of about 250 g CO₂-eq/kWh, which is fairly low from an international perspective. With a carbon intensity above 320 g CO₂-eq/kWh, the scenario with H₂ from electrolysis has higher WTW GHG emissions than diesel. In systems with very low carbon intensity, on the other hand, the GHG emissions of the electrolysis scenario are also very low. For the long haulage truck driving cycle, H₂ from steam reforming has higher WTW GHG emissions than diesel regardless of the electricity system. For the city bus driving cycle, the steam reforming scenario results in 30 – 50% lower GHG emissions than diesel.
The last figure (bottom right) shows a comparison of different ways to produce methane: compressed biomethane (bio-CNG) through anaerobic digestion (AD) or thermal gasification (TG), synthetic natural gas (SNG) and fossil compressed natural gas (CNG). As already shown in Figure 4 – Figure 6, bio-CNG from AD gives a significant reduction of GHG emissions compared to diesel in any electricity system. Bio-CNG from TG includes a net production of electricity, which makes it more beneficial in electricity systems with a high carbon intensity, in contrast to other fuel scenarios. The WTW GHG emissions of the AD and TG pathways are equal at about 500 g CO₂-eq/kWh carbon intensity. SNG has a similar production pathway and similar WTW GHG emissions as FT diesel, with a very high dependence on the electricity system carbon intensity. It has higher GHG emissions than diesel in systems with a carbon intensity above 140 g CO₂-eq/kWh. CNG gives a reduction of just 15% compared to diesel but is not very dependent on the electricity system. The results of the methane fuels are very similar for other driving cycles.
Figure 7 – Sensitivity analysis of assumptions for the studied scenarios, shown for the suburban bus driving cycle. Top left: Including or excluding the life cycle impact of the battery for BEV; top right: HVO produced from energy crops (rapeseed) or
from waste oil, and low-blend (20 %) HVO from energy crops in diesel; bottom left: Hydrogen produced through steam reforming or electrolysis; bottom right: methane produced through anaerobic digestion (Bio-CNG AD), thermal gasification
(Bio-CNG TG), synthesis of hydrogen and carbon dioxide (SNG), or from natural gas (CNG).
4. Discussion
As the results of this study show, the WTW GHG emissions of energy carriers for vehicles vary depending on the electricity system in which they are produced. Meanwhile, it should be noted that no alternative energy carrier will be enough to single-handedly replace fossil fuels; it will, rather, require a combination of several alternatives as well as an increased efficiency (European Expert Group on Future Transport Fuels, 2011; IPCC, 2014; NREL, 2013). With that in mind, the results of this study can be used as an indication of the order of priority for alternative energy carriers and in what type of vehicles they are best used. It is also clear that regions with a low share of fossil energy in their electricity mix will have a better chance of reducing the climate impact of their transport system. Building upon this work, future studies could include scenarios on how to combine energy carriers in different vehicle applications to reduce the GHG emissions of the transport sector.
A WTW analysis covers only a limited part of the environmental impact of transports, since it deals exclusively with the energy carriers and not with the life cycle of the vehicles. Prussi et al. (2020b) reviewed previous studies and found that production and end-of-life treatment
do have a significant impact on the environmental performance of vehicles, although this impact does not vary much between different engines technologies. Similar conclusions can be made based on the results from Nordelöf et al. (2019) and Marmiroli et al. (2020). Thus, the WTW perspective could be considered sufficient for a broad comparison of energy carriers for transports, such as in the present paper, although a full LCA would be necessary to get the whole picture. Indeed, the environmental performance of production and end-of-life treatment of vehicles can also be influenced by the properties of the local energy system (Kawamoto et al., 2019).
If the carbon intensity of the local electricity production system is very low, electrolysis could be a very advantageous way of producing vehicle fuels. This could, for example, be the case if hydrogen and other “electrofuels” (SNG, FT diesel) are produced for the intermittent storage of excess electricity from wind and solar power (Mansilla et al., 2018; Schiebahn et al., 2015).However, the competition over excess electricity can become tough as more and more processes in society are being electrified, which sets a limit on how much of these fuels will be available for transports.
It should be noted that the vertical lines in Figure 4 – Figure 7 indicate the average carbon intensity of the six selected electricity production mixes, as listed in Table 2. In practice, the carbon intensity of the available electricity for fuel production and charging of BEVs will vary over time, depending on the availability and costs of energy sources. Also, since the lines indicate electricity production, they do not represent the electricity market mixes, which would include import and export.
While most of the studied pathways have a net consumption of electricity, the ethanol and biomethane from thermal gasification (TG) give a net output of electricity. As a result, their WTW GHG emissions actually decrease the higher the carbon intensity of the electricity production they replace. For some of the other pathways, onsite electricity for use in internal processes could also be possible, thus reducing their dependence on the local electricity system. This could be the case with biomethane from anaerobic digestion (AD), HVO, hydrogen from steam reforming, natural gas and diesel. This would, of course, give a lower output of the respective energy carrier for use in vehicles.
For the scenario with biomethane from AD, using municipal solid organic waste as feedstock, the WTW GHG emissions are negative if the electricity system has a low carbon intensity (< 30 g CO₂/kWh). Depending on the feedstock used to produce bio-CNG, the limit for negative GHG emissions could move even further. Using manure as feedstock for AD increases the GHG benefit considerably, as the alternative ways of handling manure lead to higher emissions of methane (European Commission, 2018; Tufvesson et al., 2013).
Advanced biofuels, or second-generation biofuels, have a larger potential for reducing environmental impact because the feedstock used to produce them is waste from other processes. This becomes clear comparing HVO based on rapeseed oil or waste oil and is key to the low WTW GHG emissions of bio-CNG. Another aspect of using waste as feedstock is to avoid the much-debated competition between fuel- and food production. This issue could be brought up when discussing the scenarios HVO from rapeseed oil and ethanol/ED95 from corn if the arable land area is limited where the fuel is produced. Ethanol production is usually dependent on primary energy crops rather than waste. Worldwide, corn is, and is projected to continue to be, the most common feedstock for ethanol production
(OECD/FAO, 2018). In terms of energy use and GHG emissions, wheat or sugarcane could be better alternatives (Prussi et al., 2020a), although still competing for land with other crops. With hydrogen from steam reforming of natural gas, HVO production uses very little electricity, and thus the GHG emissions are almost not influenced at all by the carbon intensity of electricity. If, on the other hand, hydrogen from electrolysis is used, the electricity dependence would increase, and HVO might not reduce WTW GHG emissions compared to diesel in electricity systems with high carbon intensity. The choice of feedstock could also be explored further, for example, the use of PFAD from palm oil production, which constitutes a significant part of the global production of HVO. PFAD has been much debated from a sustainability perspective and leads to substantial greenhouse gas emissions (Nyström et al., 2019).
In future research, there are, of course, other factors to be considered than climate change impact. From a broader sustainability perspective, biofuel production can bring many positive side-effects locally, such as cleaner air, secure energy supply and job creation. Biogas or biomethane is perhaps the best example and is often reported to contribute to many of the UN sustainable development goals (Dada and Mbohwa, 2018; Hagman and Eklund, 2016; World Biogas Association, 2017). For the local environment, electric or fuel cell vehicles have certain advantages in terms of pollution and noise. On the other hand,
batteries for electric vehicles require scarce materials that may not be sustainable in other aspects. The WTW perspective, especially when focused on one type of environmental impact, only provides a limited sustainability assessment of the life cycle of vehicles. Moreover, the transport sector is only one piece in the societal puzzle of climate change mitigation. In order to find optimal solutions to reducing GHG emissions, other sectors should be considered as well.
5. Conclusions
This paper has presented an analysis of the well-to-wheel GHG emissions of different energy carriers for heavy-duty vehicles, taking into account varying driving cycles as well as the carbon intensity of the electricity system. The main conclusions of this work are:
The WTW GHG emissions of energy carriers are in many cases highly dependent on how the electricity used is produced. This is especially true for fuel pathways that include electrolysis, such as hydrogen, Fischer-Tropsch diesel and synthetic natural gas.
Energy carriers with a high electricity dependence are not necessarily better than fossil diesel from a WTW perspective.
In order to reduce the climate impact of the transport system, simply switching to electricity or renewable fuels will not suffice if the climate impact of the electricity system is not reduced first.
Bio-CNG is the energy carrier with the lowest GHG emissions for all investigated applications.
Electric engines are more advantageous in driving cycles with many starts and stops, compared to internal combustion engines, which work best at a constant speed. Following this study, future work could take on a broader perspective on the sustainability of vehicle fuels and energy carriers depending on the electricity system where they are
approach could also be used when constructing scenarios for fuel combinations to reduce the GHG emissions of road transports.
Acknowledgement
This research has received funding from the Swedish Biogas Research Center (BRC), which in turn is funded by the Swedish Energy Agency, grant number 35624-3. We would also like to extend our gratitude towards Magnus Fröberg at Scania CV AB for providing data for the studied driving cycles.
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