GHG EMISSION COMPARISON BETWEEN E85 FLEX FUEL VEHICLE AND EV UPTAKE A Scandinavian perspective
Dissertation in partial fulllment of the requirements for the degree of
BACHELOR OF SCIENCE WITH A MAJOR IN SUSTAINABLE ENERGY TRANSITION
Uppsala University Department of Earth Sciences, Campus Gotland
Lucas Dewilde Cevelló
26 05 2021 GHG EMISSION COMPARISON BETWEEN E85 FLEX FUEL VEHICLE AND EV UPTAKE
A Scandinavian perspective
Dissertation in partial fulllment of the requirements for the degree of BACHELOR OF SCIENCE WITH A MAJOR IN SUSTAINABLE ENERGY TRANSITION
Uppsala University Department of Earth Sciences, Campus Gotland
Approved by:
Supervisor, Simon Davidsson Kurland
Examiner, Ola Eriksson
26 05 2021 i
Abstract
In this thesis the eects of two future greenhouse gas emission reducing strategies in the passenger transport sector are investigated. Three factors were modelled for 2021-2055; The life cycle emissions of four vehicle types using a well-to-wheel life cycle analysis tool called GREET, the growth curve of these vehicle types was analyzed and extrapolated to obtain total vehicle predictions and the mileage of these vehicles was extrapolated from existing governmental data. The resulting scenarios show that in the short term E85 ex fuel vehicles are capable of more avoided emissions, with EVs outperforming them in the long term. However limitations in the prediction of vehicle mileage leaves the overtake point to be determined.
Keywords Well-to-wheels, GREET, LCA, GHG emissions, Automotive fuels, Flex fuel vehicles, E85, Electric vehicles. ii
Acknowledgments
Firstly, I would like to thank my supervisor Simon Davidsson Kurland for his guidance and feedback throughout this process. In addition, I would like to thank all the teachers, department faculty and sta for making my time at Uppsala University Campus Gotland a great experience. I would also like to thank my friends who have been very supportive throughout my studies and my time on Campus Gotland. To my friends and classmates in Visby who made my experience at Uppsala University special and unforgettable; to group D who provided support and memorable times; to my roommates Ivy, Kadri, and Shaelyn who provided pressure and crucial feedback throughout the thesis process; and to Antii, Edward and Jamie with whom I made unforgettable memories. (Also thanks to Shaelyn for letting me copy her acknowledgments page) Finally, I would like to thank my family and girlfriend who supported me throughout this process and my studies for the past three years, especially my grandfather and my dad who endured through the unnished versions to provide valuable and much needed feedback. (Also thanks to Shaelyn for letting me copy her acknowledgments page) iii Nomenclature
BEV Battery electric vehicle E85 Fuel composed of 85% ethanol and 15% gasoline (gasoline % usually increases in winter to facilitate cold starts
EtOH Ethanol, C2H6O EV Electric vehicle, while an umbrella term for the purposes of this paper it is used to refer to BEVs GHG greenhouse gas HEV Hybrid electric vehicle ICEV Internal combustion engine vehicle LCA Life cycle analysis WTP Well to pump, referring to the path fuel takes from its well to the pump (charging station in the case of EVs) WTW Well to wheel, referring to the path fuel takes from its well to its use in a vehicle iv
Contents
1 Introduction 1 1.1 Aim ...... 2 1.2 Literature ...... 2
I Methodology 3
2 LCA 3
3 Data collection 4 3.1 GREET ...... 5 3.1.1 E85 ...... 5 Russian oil ...... 6 Norwegian oil ...... 7 Ethanol ...... 8 3.1.2 Electricity ...... 9 WTW data ...... 10 3.2 Number of Vehicles ...... 11
4 scenarios 12 scenarios 12
5 Future car uptake 14 5.1 Base scenario ...... 14 5.2 E85 ...... 15 First cycle ...... 15 Second cycle ...... 15 5.3 EVs ...... 16
II Results 16
6 Scenario emissions 17 6.1 EV scenario ...... 17 6.2 E85 scenario ...... 18
7 Comparing the scenarios 19
8 Ethical considerations 20 v
III Discussion and limitations 21
9 Scenario limitations 21
10 Discussion 22
IV Conclusion 24
V Appendix 30 List of Figures
1 GREET pathway: E85 Gasoline Blending and transportation to re- fueling station Sweden...... 6 2 GREET Pathway: Gasoline blendstock from crude oil for Use in CA Reneries...... 6 3 Oil pipeline route plotted on Calcmaps...... 7 4 Assumed tanker route Primorsk to Nynäshamn ...... 7 5 Assumed tanker route from Mongstad (NO) to the Preemra Lysekil Renery (SE)...... 8 6 Non Distributed European electricity mix (template)...... 10 7 Non Distributed Swedish electricity mix (mix created based on data from Svenska kraftnät 2020)...... 10 8 EV and Ethanol passenger vehicle numbers as a percentage of total passenger vehicles...... 12 9 EV and Ethanol passenger vehicle uptake (EV values scaled for Swe- den and uptakes synchronized)...... 12 10 First and second (projected) E85 cycle...... 16 11 EV projection and t to 2008-2020 data...... 17 12 Yearly projected EV emissions of the complete vehicle eet. . . . . 18 13 Yearly E85 emissions of the modelled second cycle...... 18 14 Cumulative emissions of the four scenarios...... 19 15 Relative cumulative emissions of the four scenarios...... 19 16 Cumulative emissions with same average mileage in all scenarios. . 20 17 Cumulative relative emissions with same average mileage in all sce- narios...... 20 18 Yearly E85 emissions, rst and second (modelled) cycle...... 23 19 Cumulative emissions of the scenarios (4 p. 12), with separate and general mileage (see section 9 p. 21)...... 23 20 Yearly emissions of the scenarios (4 p. 12), with separate and general mileage (see section 9 p. 21)...... 23 A.1 Transneft oil pipelines...... 30 A.2 GREET pathway: Ethanol Produced in the US for Gasoline Blend- ing Sweden...... 31 A.3 Modelled vehicle stock (see section 5), a combination of g. 10 and 11...... 32 vii
List of Tables
1 Ethanol shares used to modify the GREET Ethanol Produced in the US for Gasoline Blending pathway...... 9 2 Scenario overview...... 13 3 Growth model used (from toolbox by Höök et al. 2011)...... 14 4 Gompertz curve t values for the rst E85 cycle (2001-2020). . . . 15 5 Gompertz curve t values for the second E85 cycle (2021-2055). . . 16 6 Gompertz curve t values for the EV scenario...... 16 1
1 Introduction
The eects of climate change are becoming a challenge for the world, among other things the eects on ecosystems, food and ber production, coastal regions and human health are expected to increase if nothing is done (Watson et al. 1998). Greenhouse gas emissions (GHGs) are one of the main drivers of climate change, mainly produced by burning fossil fuels. The consumption of fossil fuels has histor- ically been associated with economic development. Renewable energies (RE) can decouple economic development from growing GHG emissions (Edenhofer et al. 2012), however the pace at which REs have been growing has been dierent from sector to sector (IEA 2020). The transport sector produces the most CO2 emissions of any sector in Sweden. On the world scale transport is the second most emitting sector behind electricity and heat production (IEA 2021a; IEA 2021b). If climate change is to be halted, decarbonizing the transport sector will be a vital part of this process. E85 (a blend of 85% ethanol and 15% gasoline) is one of the emission-reducing alternatives to fossil fuel that is available in Sweden. E85 can provide a reduction in GHG emissions through the ethanol used in the fuel mix, which if produced sustainably can lead to signicant reductions of aforementioned GHGs (US EPA 2015) for the overall mix, the introduction of ethanol is also responsible for emis- sion reductions through its octane enhancing eects (Milovano et al. 2020). The sustainability therefore depends on the way this ethanol is produced, however even with 100% clean ethanol there would still be 15% fossil fuel left in the mix. The advantages of this approach are that E85 is generally used in ex-fuel vehicles that also run on regular gasoline, by also refueling the same way as regular gasoline ve- hicles the barrier for people that are used to gasoline or diesel vehicles is drastically lower than for the next alternative. Another strategy is based on the use of Electric vehicles, EVs possess the advan- tage of near 0 operational emissions when paired with 100% renewable electricity. The production emissions however are higher, especially because of battery produc- tion (Nordelöf et al. 2014). This leads to a situation where fresh o the assembly line a gasoline or ex fuel vehicle has a lower environmental impact than an electric vehicle. Over the lifetime of these vehicles the electric vehicles will emit much less or practically nothing, while the gasoline vehicle emissions will continue to rise over the life cycle of the car, with the same happening for the ex fuel vehicle but to a lesser degree. This could mean that a shift to electric vehicles would have short-term negative impacts before the low or zero operational costs show their benet. This is assuming current production methods, while battery production is bound to change within the time frame of this paper's projections, the paper has been delimited to currently viable solutions. Solid state batteries are an example of a so far unviable option that is making rapid progress, and while challenges still 2 exist (Boaretto et al. 2021; C. Wu et al. 2021; Yu et al. 2021) it is a promising step forward for the electric vehicle industry in the coming ve to ten years(Cooley 2012; Teague 2021; Cohen 2021). This paper compares these decarbonizing strategies for the transport sector: the use of an 85% ethanol fuel blend in vehicles and the introduction of electric vehicles (EVs). These alternatives are based on dierent technologies and bring with them their challenges, advantages, and disadvantages. The scope of this paper does not include exploring these challenges, rather it views them as alternative paths forward and explores what their dierences are in terms of emission reduction potential. The current highest share of fuel type for passenger vehicles is gasoline (Trakanalys 2021c), this will therefore be used as a baseline to compare the alternatives against.
1.1 Aim
The research question therefore becomes: Which is a better solution to avoid GHG emissions in the transport sector, ethanol ex fuel or electric vehicles? This report attempts to answer this question through the evaluation of 3 Al- ternative scenarios:
Ethanol is incentivized (based on Sweden in the 2000s).
EV's are incentivized (based on Norwegian EV uptake).
A base scenario where fossil fuels remains at their current share of the vehicle stock.
The impact of these scenarios between 2021 and 2055 will be compared to assess which has a lower environmental impact(in terms of GHG emissions). The target for this paper is Sweden, however due to the low number of EV vehicles in Sweden at the time of writing Norway was chosen as the example country due to its high EV uptake which has been brought about by strong policy and incentives favoring low-emission vehicles ((Norsk elbilforening 2020).
1.2 Literature
The literature on vehicle eet emission modelling was found to be lacking a direct comparison of E85 and electric vehicles' impact on GHG emissions in a Nordic context. Various studies come close to providing these insights, most relevant for this paper are the 2014 article by Fridell et al. 2014 and the 2017 study by Mansour and Haddad 2017 Fridell et al. 2014 models the dierence between E85 and gasoline vehicles for the Västra Götaland Region in regards to multiple emission gases concluding that E85 leads to an overall reduction in health risks. 3
Alhajeri, McDonald-Buller, and Allen 2011 model the air quality impacts of the Austin Metropolitan Statistical Area from eet electrication and use of biofuels. It nds that both have a positive impact, with electrication being capable of larger improvements at the cost of requiring expansion of the electrical grid capacity. The improvements from electrication would then depend on the emissions associated with this grid expansion. This partly provides an answer to the research question, however the modelled region diers in some key aspects; the electricity mix and the biofuel sources dier and a change in vehicle miles travelled was included. This paper treats vehicle mileage as an input and models vehicle emissions for a Swedish context. Similarly Orsi et al. 2016 found in their WTW analysis of dierent fuel systems, that E85 and EV both lead to signicant GHG emission reductions with the carbon intensity of the electricity mix determining which fuel system leads to the largest reduction. Another WTW paper that compares multiple fuel systems is by Huo, Y. Wu, and Wang 2009 , Huo et al. add the distinction of urban vs non-urban emissions. This is an aspect of LCA that this paper does not explore, however it is something to consider as it can provide context to LCA values. Mansour and Haddad 2017 conducts a WTW assesment of among other things E85 and EVs, expanding on the WTW assessment they also conduct a cost benet analysis. However being a case-study for Lebanon the results aren't necessarily applicable to Scandinavia, also the number of vehicles and their mileage were non- factors in this study.
Part I Methodology
The scenarios were constructed from 3 yearly time series data sets: The LCA of the dierent vehicles (calculated with the GREET model), the number of vehicles on the road (projected with growth curve analysis) and the average yearly distance traveled by the dierent vehicle types (projected through TREND and GROWTH functions).
2 LCA
As mentioned in the 1.2 section p.2 life cycle analysis (LCA) is a common tool when analyzing transportation, LCA analysis aims to account for the environmental performance of a product taking into account its entire life cycle, from raw material 4 mining to end of life disposal. LCA analysis for transport can be approached from two perspectives, the fuel life cycle and the vehicle life cycle. In the tool utilized by this paper these are the well to pump (WTP) pathways for fuel and the well to wheel (WTW) for the vehicle assembly, fuel use and vehicle disassembly. The papers presented in section 1.2 vary in their focus, papers like the one by (Alhajeri, McDonald-Buller, and Allen 2011) have a stronger focus on the fuel aspect while papers like (Milovano et al. 2020) shift focus to the WTW aspect. Regardless of focus, to model the emissions from a vehicle the entire life cycle needs to be considered. Only considering the fuel emissions paints an incomplete picture, as for example the construction emissions for electric vehicles account for a larger share of total emissions than for ICEVs (Nordelöf et al. 2014).
3 Data collection
For each alternative the LCA of the vehicle type is calculated with a tool called GREET, `The Greenhouse gases, Regulated Emissions, and Energy use in Tech- nologies Model' (Argonne National Laboratory 2021). This tool is used because carrying out an LCA from scratch is unfeasable due to time constraints. This tool combines well-to-wheel (WTW) and well to pump (WTP) pathways that can be modied to reect local conditions. The changes made to the model are discussed in section 3.1. GREET has been used in this eld before, whether partly for its well-to-tank analysis (Gupta et al. 2020; Khan et al. 2019) or for its complete WTW assessment (Lin et al. 2018; Basili and Rossi 2018; Strecker, Hausmann, and Depcik 2014) After modelling the LCA, data from the Swedish Trakanalys 2021c for ethanol vehicles and from the Norwegian Statistics Norway 2021a for Electric vehicles will be used to determine the potential year over year uptake of these vehicle types in the vehicle stock (section 3.2 p.11). This data comes from governmental organizations that are tasked with providing this type of data. The use of data from separate countries was deemed appropriate by the author because the countries in question are in close geographical proximity and have a similar human development index and gross domestic product per capita (United Nations Development Programme 2020; World Bank Group 2019). In section 5 the uptake numbers of the future growth will be projected using growth curve analysis of the existing data and an adoption ceiling based on Brazil. Brazil has experienced a rapid transition to ex fuel that in 10 years saw the number of new ex fuel vehicles rise to 88.5% of all new vehicles (Bruno Moraes 2018). As the values for GHG emissions from the GREET model are expressed in
CO2 eq/km the distance traveled by each vehicle must be taken into account. To do this the average mileage for each vehicle type was extrapolated using data 5 from the same sources as the vehicle numbers (Statbank Norway 2021; Trakanalys 2021b).
3.1 GREET
As mentioned in section 3 GREET is used to calculate LCA. This tool, developed by Argonne National Laboratory will be used to assess the life cycle emissions of the dierent fuels analyzed in this report. GREET combines the emissions as- sociated with fuel production (well to pump) with the associated emissions from vehicle production (raw material mining to vehicle disposal) (Argonne National Laboratory 2021). This model comes in two forms, a .net version with a GUI and an excel model. For this report the .net version (Wang et al. 2020) was used as it was deemed more accessible by the author who has no previous experience with GREET. GREET uses predened resources (that have metadata describing them in terms of energy use) and creates pathways consisting of processes that repre- sent resource transformation, use and transport of products along the pathway. It hereby creates a well to product/ well to pump (WTP) path and calculates the emissions from this path. This is then combined with Well to wheel (WTW) path- ways that describe the construction and engine characteristics of dierent vehicles. This results in emission data for vehicles that can be modied by means of altering the existing pathways or creating new paths. For the scope of this report the use of GREET will be limited to using existing resources, technologies and processes. Own mixes and pathways based on these were added to better reect the Swedish energy mix.
3.1.1 E85
The E85 pathway for Sweden (g.1) was based on the E85 Gasoline Blending and transportation to refueling station pathway. The modications made to these pathways to reect the Swedish system involve adjusting upstream processes in the pathway. Starting with the gasoline blendstock used in the E85 blending process; this was based on the Gasoline blendstock for use in CA reneries(referring to Californian reneries). Adjustments in this pathway were made to the transporta- tion processes, this is important as Sweden imports most of its Crude oil From Russia and Norway (Drivkraft Sverige AB 2021). The transportation process for CA Crude oil is therefore not representative of the Swedish situation. Two path- ways were created based on the Gasoline blendstock from crude oil for Use in CA Reneries (g. 2), these were modied to the extent that the transport processes in these pathways were changed to reect the paths oil would take from Russia and Norway. The pathways were then combined in a mix (Mix blendstock NO and RU blendstock) that will be used in the forthcoming sections to provide a representation 6 for Swedish gasoline.
Figure 1: GREET pathway: E85 Gasoline Blending and transportation to refueling station Sweden.
Figure 2: GREET Pathway: Gasoline blendstock from crude oil for Use in CA Reneries.
Russian oil 35% Of Swedish oil imports come from Russia, the biggest share- holder with the next largest (Norway) trailing with a 28% share and trumping the third largest by a factor of three (Drivkraft Sverige AB 2021). To reect this in the transport process for the gasoline blendstock from crude oil pathway, estimations were made for the path oil takes from Russian oil elds to Sweden. First, from a 2019 analysis by Kamyshnikov and Kolpakov 2019 it was determined that the biggest oil and gas province by far is West Siberia. From a report by Clarke et al. 1977 on the West Siberian basin it was found that `Two large regional uplifts here, the Surgut and Nizhne-Vartov domes, yield most of the oil produced in the West Siberian Basin.' One eld that is located on the Nizhne-Vartov dome is mentioned in particular in this report and referred to as a `supergiant' oil eld, the Samotlor oil eld. This oil eld was chosen as the starting point for Russian oil travel to Sweden. From a map by Transneft 2021, the Russian state owned company that has a monopoly on oil transport in Russia(g.A.1), the route distance of oil from the eld up to the Baltic sea was approximated. This was done with the use of CalcMaps (CalcMaps 2021) , a simple online tool that uses google maps to pro- vide the distance of a user provided path. The path input for the oil pipeline is shown in gure 3. As shown this path only leads up to the Baltic sea, for the next stretch of the journey it was assumed that the oil travels by tanker to Nynäshamn, the nearest Swedish oil renery (A Barrel Full 2014). The same technique with CalcMaps was then applied to the route from Primorsk to Nynäshamn (g.4) and added to the transport process in GREET. 7
Figure 3: Oil pipeline route plotted on Calcmaps.
Figure 4: Assumed tanker route Primorsk to Nynäshamn
Norwegian oil For the Norwegian share of oil a very similar process to the Russian path was used, this time information from norskpetroleum.no was uti- lized, this site run in cooperation by the Ministry of Petroleum and Energy and the Norwegian Petroleum Directorate. provides all sort of information on oil and gas production in the North Sea. Both the production per oil eld (Norwegian Petroleum 2021b) and a map of the oil pipelines on the continental shelf (Norwe- gian Petroleum 2021c) were found here. However the site for Norwegian Petroleum provided the lengths of the pipelines in the NCS, therefore this value was used in GREET for the oil's route to shore (to Mongstad, NO). The transportation method to Sweden was not specied, however there is some more general information stat- ing that oil from the NCS is exported either by ship or by pipeline (Norwegian Petroleum 2021a). As no direct pipeline from Norway to Sweden could be identi- ed it is assumed that the oil travels by ship to the nearest renery with a shipping dock for raw oil, which is the Preemra Lysekil Renery (A Barrel Full 2014). To note is that this renery is not located far from Gothenburg (relatively speaking), the next largest renery and the only other renery in Sweden apart from the previously mentioned Nynäshamn renery. The approximated distance came out to 381 nautical miles(Vesselnder Limited 2021)(g. 5), which translates to 706 km. The transport process therefore becomes: Sverdrup (the most productive oil eld at time of writing) to Mongstad by pipeline (283 km) then from Mongstad to Sweden by oil tanker (706 km). 8
Figure 5: Assumed tanker route from Mongstad (NO) to the Preemra Lysekil Renery (SE).
Ethanol GREET provides a few dierent alternatives for ethanol production but does not include all the dierent ethanol types used in Sweden, with the scope of this project in mind no new resources were added to GREET. It was therefore needed to evaluate which of the ethanol resources should be used, i.e., which ethanol mix comes closest to the one used in Sweden. However, on closer inspection it was discovered that the CO2 values per MJ of the dierent ethanol types do not coincide with average LCA values from the review by Jeswani, Chilvers, and Azapagic 2020 nor the reference values set out by The European Parliament and The Council Of The European Union 2018. The review combines results from more than 80 LCA studies on bioethanol to provide a clear understanding on the current standing of dierent biofuels while Directive 2018/2001 provides European reference values for most ethanol biofuel sources including the ones used in Sweden. On top of this the types of ethanol that are included in the GREET model do not include wheat, the second biggest source of Swedish ethanol (Svenska Petroleum & Biodrivmedel Institutet 2020). If only the available GREET parameters were used and the unavailable sources were covered by the next most common fuel in Sweden the
CO2eq. value for the ethanol mix would be more than double what is set out by The European Parliament and The Council Of The European Union 2018.
Modication approach It was therefore deemed best to use a mix of the ethanol types whose average CO2 emissions match the average emissions of the Swedish mix (using the mix by Svenska Petroleum & Biodrivmedel Institutet 2020 and the emission averages by The European Parliament and The Council Of The European Union 2018). This was deemed indicative as the aggregation of LCA studies used had a high percentage of European studies with 36% being European, the highest contributing percentage. The other contributing studies were `26% in North America, 20% in Asia, 12% in South America, 4% in Africa and 1% in Australia'. While it could be argued that it is a global representation, it is 9 expected to be more representative of Sweden than the default GREET parameters. The GREET defaults have a mostly North American reference base, the values from the EU directive are of course based on European production. With the EU reference values and the previously mentioned shares from the Svenska petroleum biodrivmedelinstitutet the CO2 eq. average was calculated to be 23.706 grams of
CO2 eq/MJ for Swedish ethanol. To note is that this value is less than half of the GREET CO2 eq. value of 53.19 g CO2 eq/MJ for corn-based ethanol (corn ethanol comprising more than 94% of total ethanol used in the US according to the default GREET pathway mix).
Pathway modications The GREET pathway E85 Gasoline Blending and Transportation to Refueling Station was modied to use the previously men- tioned Mix blendstock NO and RU blendstock(p. 3.1.1) to both denature and blend the ethanol with gasoline. On the ethanol side of the pathway, the upstream path- way Ethanol Produced in the US for Gasoline Blending conveniently contains the dierent ethanol production sources, by modifying the Average Ethanol produced in the US process of this pathway a mix was obtained that reects the GHG emis- sions of 23.706 g CO2 eq/MJ (see image A.2). To calculate the share for each ethanol type the GHG-100 emissions per MJ of each type were introduced in excel and the Solver tool was used on a simple sum of the shares multiplied by the GHG emissions/MJ. The nal share of each ethanol type used is shown in table 1.
Table 1: Ethanol shares used to modify the GREET Ethanol Produced in the US for Gasoline Blending pathway.
Ethanol source Share in GREET Corn 0.23705 Forest residue 0.06052 Poplar 0.06251 Sorghum 0.13042 Switchgrass 0.06623 Willow 0.41579
In summary, the resulting pathway: E85 Gasoline Blending and Transporta- tion to Refueling Station Sweden was modied from the base pathway in terms of the ethanol sources used and the crude oil transportation.
3.1.2 Electricity
For the energy source of electric vehicles the Swedish distributed electricity mix was created, this was done by taking the `Distributed European electricity mix' 10 and modifying it to reect the Swedish electricity mix. One mix and one pro- cess were modied in for this; the `Non Distributed - European Mix'(g. 6) was changed to reect the Swedish electricity mix (g.7) and the `Electric Transmis- sion and Distribution in Europe' was modied to reect the Swedish transmission losses. Both of these changes were based on electricity statistics from (Svenska kraftnät 2020). This results in a pathway called Distributed Sweden electricity mix that reects both the electricity source type and the transmission eciency of the Swedish electricity grid.
Figure 6: Non Distributed Figure 7: Non Distributed Swedish European electricity mix electricity mix (mix created based on data (template). from Svenska kraftnät 2020).
WTW data After modifying the fuel sources, the parameters for their use need to be set. This is where the Well to wheel aspect of GREET comes in, here the vehicles and their use of energy is dened. For the Well-to-wheel aspect not much adjustment is needed. In the case of elec- tric vehicles the vehicle model Car: EV100 - Electricity (Type 1 Li-Ion/NMC111 Conventional Material) was used as a base. The only changes made were to the Energy source (upstream pathway or mix) which was adjusted from Pathway: Dis- tributed U.S. mix to Pathway: Distributed Sweden electricity mix(see Electricity p.9). On the ethanol side the vehicle model Car: SI ICEV - Dedi. EtOH was initially used as a base, however it was discovered that this vehicle model only contained WTW data (no data on Assembly, Disposal and Recycling (ADR) or on other uids other than fuel during use). It was therefore chosen to use the `vehicle powerplant' of this vehicle model and use it in an ICEV model that contains information re- garding ADR. A basic ICEV model with this information was found in the Car: SI ICEV - E10 (Type 1 Conventional Material), an E10 fueled vehicle (90% gasoline blended with 10% ethanol). The reason this vehicle was chosen is for the simi- larities between gasoline-only and E85 fueled vehicles (most components are the 11 same, except for some ethanol-compatible parts like the fuel pump and injection system (AFDC 2021). The particular variant of gasoline, E10 is utilized due to its availability in multiple EU member states and its compatibility with most ve- hicles constructed after 2000 (most modern vehicles go further and are optimized to run on E10 as it has been the European test fuel since 2016) (ePURE 2021). The only changes made to the vehicle powerplant used from the dedicated ethanol vehicle were modifying the upstream pathway mix to set E85 Gasoline Blending and Transportation to Refueling Station Sweden as the energy source. To provide more context the same energy source modication was repeated except with the vehicle Car: SI HEV EtOH (a Hybrid electric vehicle model) to show a dierent vehicle type in the E85 fuel category. Lastly for the baseline scenario an E10 vehicle was used, the same that was mod- ied to run on E85 in the previous section. The only modication made was again to the energy source to use the pathway Reformulated Gasoline (E10) blending and transportation to refueling station Sweden. The vehicles were set to be recorded by the scenario manager and the total
WTW GHG-100 value for each vehicle (more widely recognized as the CO2 eq.) was monitored. A new scenario was created, and simulations were run for each year from 2001 to 2055. This provided the GHG-100 values per m travelled which were multiplied by 1000 to convert the values to CO2 eq/km.
3.2 Number of Vehicles
Historical vehicle stock data for EV's and E85 vehicles were obtained from the ocial statistics bureaus from Norway and Sweden, Statistics Norway 2021b and Trakanalys 2021a respectively. Both oer a page for the selection of desired statistics that includes sorting by fuel (`drivmedel' in the Swedish case). This way of obtaining the needed values however has a shortcoming as it does not allow for the consideration of all vehicle types as the sorting method groups ethanol and E85 in the `other fuel' category when it comes to lorries, motorcycles, buses, and other vehicle types. In summary this means the yearly number of Swedish registered passenger ve- hicles for 2001 to 2020 (described as `personbil'), and the number of Norwegian private cars for 2008 to 2020 were obtained. Both sorted by fuel type and with the total amounts by year for Sweden. For the Norwegian data this was calculated by adding up the amount of cars of each fuel category. The data sets were exported as excel les and combined into one document, then the percentages of E85 and electricity fueled vehicles were calculated using the total yearly number of vehicles from the same data set (g. 8). After the uptake was expressed as a percentage, the EV share was adapted to the Swedish eet by multiplying the yearly percentage shares of EVs with the yearly total passenger 12 cars in Sweden. When the amount of ethanol vehicles and the adjusted amount of EV vehicles is presented in the same graph and EV numbers shifted 7 years back the `initial' uptake can be observed to be surprisingly similar (g.9) . `Initial' here should be taken with a grain of salt as the data does not go back further than 2001 for ethanol and 2008 for EVs. However, the yearly uptake for the rst few accounted for years is less than 0.1%, enough to be considered insignicant in the scope of this report.
Figure 8: EV and Ethanol passenger vehicle numbers as a percentage of total passenger vehicles.
Figure 9: EV and Ethanol passenger vehicle uptake (EV values scaled for Sweden and uptakes synchronized).
After obtaining the emission and vehicle stock data the third and nal set of data, the average yearly distance driven by cars of each fuel type was obtained. This was from the same sources and in the same way as the vehicle stock data, it was again available from the ocial statistics bureaus for Sweden and Norway Statbank Norway 2021; Trakanalys 2021b. In this step the average travel distance of all vehicle types was obtained as well.
4 scenarios
From the collected data three scenarios were created: 13
Table 2: Scenario overview.
Fuel E85 Electricity E10 Propulsion method ICEV HEV EV ICEV E85 scenario EV scenario Base scenario
To construct each scenario ve factors are utilized;
Et: Life cycle emissions/km travelled over the vehicle lifespan (from GREET, section WTW data p.10) at year t
EF t: Life cycle emissions/km travelled over the base [E10 fueled] vehicle lifespan (from GREET, section WTW data p.10) at year t
Rt: Average distance travelled in year t
St: Studied vehicle stock (number of vehicles of the scenario propulsion method) at year t
SF t: Fossil vehicle stock (number of fossil fuel vehicles) at year t
From St and SF t follows that:
Tt: Total Vehicle stock at year t (Tt = St + SF t)
After the studied vehicle stock is modelled using Growth curve analysis the total emissions at year t: GHGt are given by:
GHGt = St ∗ Rt ∗ Et + SF t ∗ RF t ∗ EF t (1) An the total emissions by 2055 will be:
2055 X St ∗ Rt ∗ Et + SF t ∗ RF t ∗ EF t (2) t=2021 For the E85 scenarios two alternative engine types are used, internal combustion (ICEV) and hybrid electric (HEV). ICEV is currently by far the most popular type, the HEV alternative is modelled to provide values for the performance of a more ecient vehicle running on the same E85 fuel. In the scenarios a large push for EV and E85 fueled vehicles is modelled, this is characterized by an 88.5% adoption of each fuel type by 2055. 88.5% after the Brazil market, as it was discovered to be the practical ceiling due to the importance of diesel in the light commercial vehicles market (Bruno Moraes 2018) (Coincidentally or not, this is very close to the percentage of gasoline and diesel vehicles in sweden 2020, 89.0%) . 2055 was chosen as a conservative number, as it allows time for adoption considering an 14 average car lifespan of 9.9 years in 2018 (helgianalyticsAgeCarAverage2021). To calculate this upper limit the total future vehicle numbers (Tt) were extrapolated from the existing values (see Number of Vehicles p.11) using the TREND() function.
These same values were utilized to calculate SF t using St.
5 Future car uptake
To model the car uptake for each fuel type, a growth curve was used (see 5.2 p.15 on parameter denition). The use of growth curves for analysis is one of the most widely developed and understood statistical techniques for modelling growth (Höök et al. 2011). From image 9, it can be observed that the growth of ethanol vehicles can be approximated by a bounded growth curve. Bounded growth is common in resource constrained scenarios Höök et al. 2011. Several possible growth models were obtained. From these the Gompertz curve (Table 3) was found to be the best tting for the existing EV and E85 vehicle values through the coecient of determination (R2). This is a calculated value
(unrelated to the previously dened Rt) that assesses how well the function ts the data(the closer this value is to 1 the better the t) (Brown 2001). This value is based on the dierence between the y(t) values and the provided values (which the growth model was tted to).
Table 3: Growth model used (from toolbox by Höök et al. 2011).
Model Equation for y(t) Inection point Asymptote Gompertz A ∗ exp(−b ∗ exp(−kt)) e−1 ≈ 0.37 A with:
y(t) A b k Number of scaling factor factor factor vehicles at year inuencing inuencing t curve shape growth rate
5.1 Base scenario
Before going ahead with the E85 and EV scenarios, a base was established. As alluded to earlier, the E10 vehicle (see WTW data section p. 10) is used for this. To obtain the yearly emissions the GHG-100/km value from GREET was multi- plied by the average km driven and the amount of fossil fuel vehicles. While the GREET WTW is calculated for an E10 gasoline vehicle, both gasoline and diesel cars are used for the extrapolated number of vehicles in this scenario. This was 15 done because the baseline scenario is meant to represent the fossil fueled part of the vehicle eet. Using the GREET data for an E10 vehicle could seem inappro- priate for this, however the WTW emissions were found to be in line with the EU fossil fuel reference (0.09 kg/MJ compared with the EU 0.094 kg/MJ reference). This reference value is used to represent for both diesel and gasoline vehicles (The European Parliament and The Council Of The European Union 2018).
5.2 E85
First cycle
In the case of E85 vehicles, the vehicle uptake has already attened o. To simulate future uptake a new inux of E85 vehicles is modelled, this new inux is referred to as the 'second cycle'. The rst one being the growth and later stagnation due to falling gasoline prices, introduction of 'green' diesel vehicles and increased negative press (growth from 2001 to 2008 and further stagnation until 2020)(F. Sprei 2018). These factors are not covered in this paper, but are considered to not be a further problem in the E85 diusion. Before emulating a second cycle the rst was veried by tting a Gompertz curve A ∗ (−b ∗ exp(−kt)) to the existing values using Solver. Solver is an excel add-in program used for what-if analysis, in this case it was used to nd the optimal values in the cells for the A, b and k variables of the logistic curve formula to minimize the sum of the squared residuals (SSR). The sum of the squared dierences between the y values (input) and the tted y values(output), called the SSR is used by solver to check if the changing of a variable results in a better or worse t of the curve to the input data. Then to verify the results the R2 value was used (coecient of determination). The values Solver arrived at for the rst cycle were:
Table 4: Gompertz curve t values for the rst E85 cycle (2001-2020).
A b k R2 220648 104.394 0.683477 0.9961
With this high R2 value the Gompertz curve was considered an applicable ap- proximation method for the uptake.
Second cycle
To emulate the second cycle the same technique was employed, however the A constant was altered and the values to approximate were reduced. The A value was set to 88.5% of the 2055 total Swedish vehicles minus the number of vehicles on the road in 2020 (see Number of Vehicles section p. 3.2). This locks the asymptote 16
Figure 10: First and second (projected) E85 cycle. leaving the b and k values to be modied by Solver, the values of 2001 to 2008 were used to t. This limit was chosen because it has been identied as the point in time where the swedish diusion of E85 was stied (Frances Sprei 2013; Pacini and Silveira 2011; F. Sprei 2018). While
Table 5: Gompertz curve t values for the second E85 cycle (2021-2055).
A b k R2 (relative to 2001-2008 uptake) 5751360 32.7161 0.2786721 0.815434
This t results in a second cycle after the numbers are oset by the 2020 number of vehicles (Fig. 10).
5.3 EVs
In the case of the EV curve no second cycle was needed, a Gompertz curve A ∗ exp(−b ∗ exp(−kt)) was tted to the adjusted EV car amounts (see Number of Vehicles section p. 3.2). The A value was again the asymptote and set to 88.5% of the 2055 Swedish vehicle stock, this time oset by the number of EVs on the road in Sweden 2020. The solver function was then again used on the SSR, except with the A constant set, the program was left with the b and k variables to modify. The result was a graph (Fig. 11) that t the (adjusted) 2008-2020 data well with an R2 value of 0.999449 using the constant values of:
Table 6: Gompertz curve t values for the EV scenario.
A b k R2 5889530 9.435276 0.1089395 0.99944 17
Figure 11: EV projection and t to 2008-2020 data.
Part II Results
6 Scenario emissions
6.1 EV scenario
To construct the EV scenario the modelled uptake numbers of the previous section were combined with the CO2 eq/km and average km travelled series (3.1.2 section p.10)in equation (1). This returns the yearly emissions of a hypothetical hard push for EV adoption ('EV-total'). To compare the emission reductions across scenarios the same was repeated but using the dierence between these and the baseline scenario values ('EV-relative'), showing the potential GHG savings of this scenario. This was repeated using the same travel distance (Rt) for both vehicles (the total averages for Sweden, extrapolated)('EV-relative, general mileage'). This was done because extrapolating the average mileage for EVs is expected to lead to inaccuracies as not much data was available to extrapolate from, and the trend that was found counters the trends for the other 2 fuel types by trending up in mileage instead of down (more on this in the 9 section p.21). The results for these 3 variations are shown in g. 12. To note in this gure is the trend towards 0 of the EV-relative emissions, this is due to the year over year increases in mileage for the EV scenario which as mentioned are expected to misrepresent future mileage. 18
Figure 12: Yearly projected EV emissions of the complete vehicle eet.
6.2 E85 scenario
The same method as in the EV scenario was used to calculate the yearly emissions. To maintain comparability the same three representations of the scenario were made, except that it was done twice; once with the internal comubtion engine (ICEV) and another time with the hybrid electric vehicle (HEV) LCA data. Here we see the emissions of a hypothetical second push for E85, where the adoption does not get halted like in the rst cycle. The results for this scenario are shown in g. 13.
Figure 13: Yearly E85 emissions of the modelled second cycle. 19
7 Comparing the scenarios
Now that both scenarios have been constructed a comparison of their projected impact can be made, this was done by plotting their Cumulative total emissions for 2021-2055 (gure 14) and the cumulative relative emissions (relative to the E10 scenario) (g 15).
Figure 14: Cumulative emissions of the four scenarios.
Figure 15: Relative cumulative emissions of the four scenarios.
From these gures it can be concluded that even in the long term horizon of this study, which 34 years, E85 outperforms EVs in terms of emission reductions. Their high vehicle stock uptake and low mileage are by far the biggest contributing factors to this. While the EV GHG-100 values out of GREET were 37% and 25% that of the HEV and ICEV E85 vehicles, the higher number of displaced E10 vehicles and fewer kilometers driven in the E85 scenarios make up for this dierence. This can be explained with the slow adoption, E85 vehicle numbers reach their peak much sooner than EVs do (see g. 10 and 11 or g. A.3 for a combined graph). It can 20 also be seen that not only the fuel, but the type and eciency of the vehicle can have a large impact on the total emissions as the more ecient HEVs are able to pull ahead with 21% less emitted GHGs compared with ICEVs running on the same fuel mix. Taking the dierence in mileage out of the equation by using the same average kilometers for every scenario (the extrapolated values from the Swedish average mileage of all vehicles) results in g. 17 and 16. This shows the inuence mileage had on the previous gures, without the mileage advantage The slow EV uptake still holds it back however by 2055 it is able to get within reach of the ICEV scenario only emitting 3% more emissions.
Figure 16: Cumulative emissions with same average mileage in all scenarios.
Figure 17: Cumulative relative emissions with same average mileage in all scenarios.
8 Ethical considerations
The type of research is unobtrusive as publicly available documents are used, by being present as a researcher the contents of said documents are not aected. The data collected was unaected by the researcher. When extrapolating, data was inuenced, the choice between growth and trend functions to extrapolate certain data sets (e.g. the future total vehicle numbers) was made according to the researcher's best judgment. These choices were made before the inuence on the nal results were clear, however they are still choices 21 that inuenced the nal results. The researcher does not and has not had any nancial interest in either of the alternatives at the time of writing, although the study direction can attest to the interest of the author in renewable energies and alternatives to fossil fuels.
Part III Discussion and limitations
9 Scenario limitations
The inuence of driven distance on the performance of the vehicle types with re- spect to GHG emissions is very signicant, this is where the EV scenario falls short. Data used to extrapolate future travel distance was not as complete as for the other scenarios (only 2008-2020 data was available) and more concerning, the data was highly irregular. While the distances for E85 and gasoline vehicles created a highly regular downwards trend (never exceeding more than 1% variance year to year) the EV data saw the kilometers trend both up and downs with variations as large as 61% year to year (coming from equivalent governmental sources, this suggests the lower number of vehicles sampled together with rapid technological advancements are the cause for this variability). Multiple extrapolations were tried, using both the complete data set and only the last few years, all resulted in extremely high values by 2055 compared with E85 and gasoline vehicles (The approach taken here resulted in EVs having double the kilometers driven yearly by 2055). The reason the driven distance shouldn't be disregarded completely is therefore the same rea- son it can be so problematic, it can have a large impact on total emissions. Taking 2008 as an example, from the GREET emission data (section 3.1.2) and the av- erage yearly travel distance (section 3.2) the yearly emissions from an E85 ICEV vehicle are on average 3776 kg CO2 eq, while the yearly E10 ICEV emissions are
3727 kgCO2 eq. The average E85 vehicle, while emitting 41% less, still emits more when the driven distances are taken into account. Another limitation lays in the LCA section, with the current methodology the ADR emissions are considered together with tailpipe emissions and vehicle lifespan to provide emissions values per driven kilometer. Decoupling the operational and ADR emissions would lead to more accurate results, especially for electric vehicles in the sections without equal (general) mileage for the dierent scenarios. Another limitation that could disadvantage the EV scenario is that historic values to model the vehicle stock uptake does not allow for changes in technological maturity. As electric vehicles are experiencing more development their uptake 22 rate could be inuenced by factors that didn't exist during the period the uptake numbers were based on, adding another level of uncertainty to the EV scenario.
10 Discussion
The LCA section of the methodology could have been elaborated by adding ethanol pathways for the ethanol sources used in Sweden. This would have been done by making a mix that is as representative as possible of the Swedish mix, which is mostly based on corn (50%) and wheat (34%)(Svenska Petroleum & Biodrivmedel Institutet 2020). The addition of such detailed fuel pathways was not within the scope of this paper and the use of CO2 eq values without a further breakdown means the use of an external source to match the nal mix's emissions to the internal GREET pathways should not lead to inaccuracies. For further investigation into the share of urban and non-urban emissions this would have to be addressed. Regardless of the scenario deemed most benecial, a profound shift in the ve- hicles and fuels used (like the ones modelled) promises a signicant reduction in transport emissions. When assuming the same yearly travel, the E85 ICEV and EV scenarios achieve a 28% and 25% reduction in GHG emissions respectively if compared to the base E10 scenario (g.19). Interestingly the E85 HEV reduction comes out a lot stronger with a 43% emission reduction. This suggests that E85 as a fuel has the promise to improve eciency when ICEVs are considered. Another advantage of E85 which has not been explored in this report is the potential for conversion. As mentioned previously the dierences between an E85 ex fuel and a standard gasoline car are minimal, with the conversion of a few fuel related components most gasoline cars can be modied to run on both E85 and gasoline StepOne Tech Inc. and eFlexFuel Technology 2021; BSR Svenska AB 2021. This possibility further reinforces the promise of E85 as a transition fuel. This is a relatively short-term outlook in the grand scheme of things however, it could be argued that because electric vehicles have the lowest tailpipe and per km LCA emissions they should be prioritized in policy. While electric vehicles look like the most promising solution for a zero-emission transport sector, if meaningful change is to happen in the next 20 to 30 years E85 promises fast emission reductions by displacing fully fossil fueled vehicles while providing a lower barrier to entry and less impact on current road infrastructure and production methods compared to electric vehicles. In gure 18 the emissions from both E85 cycles are shown in succession. What would have happened if the rst cycle hadn't stagnated is naturally speculation, but from the second cycle it can be assumed that any increase in E85 ex fuel vehicles would have led Sweden to lower overall emissions and would have led Sweden to a 23 more sustainable position in terms of GHG emissions.
Figure 18: Yearly E85 emissions, rst and second (modelled) cycle.
Figure 19: Cumulative emissions of the scenarios (4 p. 12), with separate and general mileage (see section 9 p. 21).
Figure 20: Yearly emissions of the scenarios (4 p. 12), with separate and general mileage (see section 9 p. 21). 24
Part IV Conclusion
E85 hybrid electric vehicles result in the most avoided emissions, closely followed by E85 internal combustion engine vehicles and then electric vehicles. When assuming all vehicle types drive the same distance yearly the dierence between electric and E85 vehicles shrinks signicantly, most likely due to limitations in predicting electric vehicle mileage. The scenarios with a push for E85 are capable of much faster emission reductions compared to an EV scenario due to a more aggressive uptake curve (g. A.3). How far this advantage reaches is not certain, as at EV vehicle stock saturation the lower operational emissions comfortably outperform all alternatives. With a more accurate yearly travel distance estimation for the dierent vehicle types the point of advantage could be determined. This puts E85 in a good position to become a transition fuel as it can also leverage much more of the existing fueling infrastructure than electric vehicles. From the total emissions of the eet it has become apparent that on a national scale the amount and distance travelled by vehicles are signicant factors. While they may sound insignicant and are sometimes not considered when comparing the environmental impact of vehicle types (they can be overlooked in favor of directly comparing LCA values), they can have a large inuence on national transport emission. While the Scandinavian energy mix has allowed EV emissions to be as low as possible, E85 still is capable of more emission avoidance (g. 20) thanks to its higher fossil fuel displacement. 25
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Part V Appendix
Figure A.1: Transneft oil pipelines. 31
Figure A.2: GREET pathway: Ethanol Produced in the US for Gasoline Blending Sweden. 32
Figure A.3: Modelled vehicle stock (see section 5), a combination of g. 10 and 11.