Market diffusion of alternative fuels and powertrains in heavy-duty vehicles: A literature review

Rose, Philipp; Gnann, Till; Plötz, Patrick; Wietschel, Martin Article Market diffusion of alternative fuels and powertrains in heavy-duty vehicles: A literature review Energy Reports Provided in Cooperation with: Elsevier Suggested Citation: Rose, Philipp; Gnann, Till; Plötz, Patrick; Wietschel, Martin (2019) : Market diffusion of alternative fuels and powertrains in heavy-duty vehicles: A literature review, Energy Reports, ISSN 2352-4847, Elsevier, Amsterdam, Vol. 5, pp. 1010-1024, https://doi.org/10.1016/j.egyr.2019.07.017 This Version is available at: https://hdl.handle.net/10419/243647 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ Energy Reports 5 (2019) 1010–1024 Contents lists available at ScienceDirect Energy Reports journal homepage: www.elsevier.com/locate/egyr Review article Market diffusion of alternative fuels and powertrains in heavy-duty vehicles: A literature review Philipp Kluschkea,∗, Till Gnanna, Patrick Plötza, Martin Wietschela,b aFraunhofer Institute for Systems and Innovation Research ISI, Breslauer Strasse 48, 76139 Karlsruhe, Germany bInstitute for Industrial Production (IIP), Chair of Energy Economics, Karlsruhe Institute of Technology (KIT), Hertzstrasse 16, 76187 Karlsruhe, Germany highlights •Heavy duty vehicles (HDV) will become a relevant player in the electricity market. •Alternative fuels and powertrains (AFP) expected on low scale following current regulations. •High uncertainty regarding the emergence of a superior AFP technology for HDV. article info Article history: Received 4 March 2019 Received in revised form 25 July 2019 Accepted 27 July 2019 Available online xxxx Keywords: Heavy-duty vehicle Road freight transport Alternative fuel Alternative powertrain Decarbonization Low-carbon policy abstract With about 22%, the transport sector is one of the largest global emitters of the greenhouse gas CO2. Long-distance road freight transport accounts for a large and rising share within this sector. For this reason, in February 2019, the European Union agreed to introduce CO2emission standards following Canada, China, Japan and the United States. One way to reduce CO2emissions from long-distance road freight transport is to use alternative powertrains in trucks — especially heavy-duty vehicles (HDV) because of their high mileage, weight and fuel consumption. Multiple alternative fuels and powertrains (AFPs) have been proposed as potential options to lower CO2emissions. However, the current research does not paint a clear picture of the path towards decarbonizing transport that uses AFPs in HDVs. The aim of this literature review is to understand the current state of research on the market diffusion of HDVs with alternative powertrains. We present a summary of market diffusion studies of AFPs in HDVs, including their methods, main findings and policy recommendations. We compare and synthesize the results of these studies to identify strengths and weaknesses in the field, and to propose further options to improve AFP HDV market diffusion modelling. All the studies expect AFPs on a small scale in their reference scenarios under current regulations. In climate protection scenarios, however, AFPs dominate the market, indicating their positive effect on CO2reduction. There is a high degree of uncertainty regarding the emergence of a superior AFP technology for HDVs. The authors of this review recommend more research into policy measures, and that infrastructure development and energy supply should be included in order to obtain a holistic understanding of modelling AFP market diffusion for HDVs. ©2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Contents 1. Introduction..................................................................................................................................................................................................................... 1011 1.1. Carbon footprint of heavy-duty vehicles ........................................................................................................................................................ 1011 1.2. Alternative fuels and powertrains ................................................................................................................................................................... 1011 1.3. Research on alternative fuels and powertrains.............................................................................................................................................. 1013 1.4. Objective and research questions.................................................................................................................................................................... 1013 2. Material and method ..................................................................................................................................................................................................... 1013 2.1. Data collection ................................................................................................................................................................................................... 1013 2.2. Review method .................................................................................................................................................................................................. 1013 2.3. Overview of market diffusion studies on HDV .............................................................................................................................................. 1015 3. Results.............................................................................................................................................................................................................................. 1016 ∗Corresponding author. E-mail address: [email protected] (P. Kluschke). https://doi.org/10.1016/j.egyr.2019.07.017 2352-4847/©2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). P. Kluschke, T. Gnann, P. Plötz et al. / Energy Reports 5 (2019) 1010–1024 1011 3.1. Model objectives................................................................................................................................................................................................ 1016 3.2. Model designs .................................................................................................................................................................................................... 1017 3.2.1. Model parameters .............................................................................................................................................................................. 1017 3.2.2. Input parameters................................................................................................................................................................................ 1017 3.3. Model outputs.................................................................................................................................................................................................... 1017 3.4. Comparison of main findings........................................................................................................................................................................... 1021 3.5. Comparison of policy recommendations ........................................................................................................................................................ 1021 4. Summary and discussion............................................................................................................................................................................................... 1021 5. Recommendations for further research....................................................................................................................................................................... 1022 Declaration of competing interest................................................................................................................................................................................ 1023 Acknowledgements ........................................................................................................................................................................................................ 1023 Appendix ......................................................................................................................................................................................................................... 1023 References ....................................................................................................................................................................................................................... 1023 Abbreviations AFP Alternative Fuels and Powertrains BEV Battery Electric Vehicle BIO Biofuels CAT Catenary electric vehicle CNG Compressed Natural Gas eMET e-Methane eSYN e-Synfuel EU European Union GHG Greenhouse Gas Emissions HDV Heavy Duty Vehicle HYB Hybrid Electric Vehicle ICE Internal Combustion Engine FCEV Fuel Cell Electric Vehicle LDV Light Duty Vehicle LNG Liquefied Natural Gas LPG Liquefied Petroleum Gas TCO Total Cost of Ownership tkm Ton kilometres 1. Introduction The World Climate Report from 2014 describes one of the biggest challenges of the 21st century: climate change. A significant reduction in greenhouse gas (GHG) emissions is required (IPCC,2013) in order to keep its impacts on humans and the environment as low as possible. Many countries worldwide have defined both joint and individual targets to reduce the emissions of a major GHG: CO2(United Nations,1998;Wietschel et al.,2018;BMUB,2016;EC,2015). The transport sector is a key emitter of CO2, accounting for around 22% of the total global energy-related CO2emissions in 2015. Within this sector, road freight transport represents a very large share of approximately 40%, which will continue to increase (IEA,2017). 1.1. Carbon footprint of heavy-duty vehicles According to (IEA,2017), the global stock of trucks consists mainly of light-duty vehicles (<3.5t, approx. 70% of the fleet) and only a small proportion of heavy-duty vehicles (HDV >12t, less than 15% of the fleet). However, HDVs have a higher share in total truck mileage as they are mainly used for long-distance transport. Furthermore, their higher specific energy consumption per vehicle means that HDVs account for up to 30% of the CO2 emissions of the truck stock (Muncrief and Sharpe,2015). Fig. 1 shows the CO2emissions in different world regions in million tons as well as additional assumptions about annual mileages and CO2emissions per kilometre based on (IEA,2017). In February 2019, the European Union (EU) therefore agreed to introduce CO2emission standards for heavy-duty vehicles of −30% until 2030 following Canada, China, India, Japan and the United States (European Commission,2019). As shown in Fig. 2, the EU is the last major market to install such mandatory regulations. VECTO (Vehicle Energy Consumption Calculation Tool) is applied throughout the EU to determine, monitor and report the CO2emissions and fuel consumption of each manufacturer.1 In order to reach the ambitious EU targets, a significant reduction of CO2emissions in the HDV sector is necessary. According to (Eiband and Hohaus,2018), the CO2reduction potential of current diesel technologies is estimated at less than 15%.2Hence, the European regulatory objective incentivizes the use of alternative fuels and powertrains (AFP) for HDVs. AFPs therefore represent an important alternative to diesel-fuelled HDVs, which make up nearly 100% of the stock at present (Muncrief and Sharpe,2015). Extensive research has been done on AFPs in passenger vehicles since the beginning of this century because of their comparatively low CO2abatement costs (Hein et al.,2007). However, the use of AFPs in passenger or light-duty-vehicles (LDVs) differs significantly from their use in HDVs in terms of technology requirements, total-cost-of-ownership (TCO) and infrastructure use. Research on AFPs in HDVs is currently an emerging field in the mobility sector, because it offers lower abatement costs than introducing AFPs in the shipping or aviation sectors (Hein et al., 2007). However, the research discusses various AFPs without painting a clear picture of their market diffusion or contribution to CO2reductions. The authors identified two groups of technologies to decarbonize HDVs based on den Boer et al. (2013). The first group ‘‘alternative fuels’’ comprises six different types of fuel, while the second group contains four electrified powertrains. 1.2. Alternative fuels and powertrains Alternative fuels minimize the specific CO2emissions of ICEs and are based on fossil fuels or renewables. Liquefied petroleum gas (LPG) contains mainly propane and butane, which are liquefied at comparatively low pressures of around 5 to 10 bar. Liquefied natural gas (LNG) has a similar state of aggregation, but contains mainly methane and is liquefied by cooling the gas down to −160 ◦C. In contrast, compressed natural gas (CNG) is stored as a gas in the tank at 200 bar. Renewable fuels include e-methane (eMET, gaseous, 200 bar) and e-synfuels (eSYN, liquid 1Each regulated market regulatory scheme is different and uses a different standard type measurement for HDVs. For example, the USA applies both fuel consumption and CO2emission standards (e.g. the Federal Test Procedure Transient tool), China has a fuel consumption regulation, and the EU focusses on CO2emissions (using the VECTO tool). 2The engine optimization potentials considered for heavy-duty vehicles are heat recovery systems (potential between 1.5% and 2.5%), reduction of friction losses (up to 4%), improvement of auxiliary equipment (up to 5%), exhaust gas aftertreatment (up to 3%), and downspeeding (up to 0.8%). 1012 P. Kluschke, T. Gnann, P. Plötz et al. / Energy Reports 5 (2019) 1010–1024 Fig. 1. Global well-to-wheel CO2emissions of road freight vehicles in 2015 based on IEA (2017). Fig. 2. Heavy-duty vehicle CO2standards (coloured dots) for different world regions including Canada, China, EU, India, Japan and USA and linear trend line until 2050 based on current policies (dotted lines), illustration based on Rodriguez (2019). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) at atmospheric pressure), which are produced using electricity in power-to-gas and power-to-liquid applications. Biofuels (BIO) are liquid or gaseous fuels produced from biomass such as plant or animal waste. Electrified powertrains use electric motors for propulsion. Battery-electric vehicles (BEV) store the electric energy in onboard battery packs, which can be recharged conductively or inductively at charging stations. Catenary electric vehicles (CAT), also called ‘‘e-roads’’, use a similar technology but rely on overhead lines providing continuous power and have a second powertrain (e.g. an ICE or a larger battery like BEVs) to cover small distances without overhead lines. Hybrid electric vehicles (HYB) also operate with two powertrains, and are classified as an interim stage between diesel-powered ICE and BEV technology. There are two types of HYB technology: with or without on-board charging equipment. Without the equipment, the HYB charges only by recuperating energy while driving. Fuel cell electric vehicles (FCEV) use on-board hydrogen storage to generate electricity P. Kluschke, T. Gnann, P. Plötz et al. / Energy Reports 5 (2019) 1010–1024 1013 within a fuel cell. The hydrogen is usually stored at 350 or 700 bar. 1.3. Research on alternative fuels and powertrains In general, the research on AFPs for HDVs currently comprises two types of studies. The first category focusses on vehicle design (Kast et al.,2017;Gangloff et al.,2017;Macauley et al., 2016;Ridjan et al.,2013) and the economic viability (Connolly, 2017;Zhao et al.,2013;Gnann et al.,2017;Sen et al.,2017; Mareev et al.,2018;Jordbakke et al.,2018) of HDVs with AFPs. This literature review examines the second category, which deals with the diffusion of AFPs in the HDV market. As the market diffusion of AFPs in HDVs is a potential lever for large CO2reductions and as the current research does not point to an unambiguous path towards HDV decarbonization, an overview of the existing research findings is beneficial for future research. The authors provide such an overview of AFP market diffusion studies for HDVs, synthesize the state of research and derive recommendations for future research. To the best of our knowledge, this paper is the first to summarize the approaches and key findings of research on AFP market diffusion in the HDV sector. This paper differs from others concerning the transport segment (HDVs), analysis criteria (research questions, design of market diffusion models and their output) and technologies (AFPs) examined. 1.4. Objective and research questions The aim of this review is to present the current state of research on the market diffusion of HDVs with alternative fuels and powertrains to decarbonize global heavy-duty traffic. This work presents a summary of market diffusion studies of AFPs for HDVs, their methods, main findings and recommendations. The authors focus on research questions, modelling design and market diffusion outcomes when generalizing the findings from current research. The authors compare and synthesize the results of these studies to address four research questions. First, the authors want to understand the impact of AFPs on the future HDV fleet and therefore analyse the market diffusion scenarios (output) of the reviewed studies and identify existing models and results regarding the market diffusion of alternative fuels and powertrains in HDVs. Second, the authors are interested in a better understanding of the modelling results and therefore examine the model design. Transparent model designs support us in interpreting the market diffusion outcomes. Third, the authors identify those AFP technologies that are considered when developing future scenarios in the current literature. Differentiating the AFPs considered in the studies gives insights into the convergence or divergence of particular AFP technologies in the HDV segment. Finally, the authors want to understand what influences market diffusion (drivers and barriers) in the HDV segment. Obtaining an overview of the levers for AFPs in HDVs helps us to understand how to promote AFPs and reduce CO2emissions in the transport sector. The structure of this paper is as follows: The authors describe the data sources, data collection and procedure used for the literature review, as well as briefly reviewing key studies in Section 2. Section 3contains the findings from the specific models, the market diffusion results for AFPs in HDVs (Section 3.1), and the synthesis regarding strengths (Section 3.2) and weaknesses (Section 3.3). A discussion of the literature is presented in Section 4and the authors close with conclusions and suggestions for further research in Section 5. 2. Material and method This section describes how the authors selected the studies, data sources and data collection for this review. The authors present the review method and an overview of all the studies analysed. 2.1. Data collection In order to identify suitable research on the topic of AFP market diffusion in HDVs, the authors conducted a comprehensive search of publications in online libraries: namely Ebsco, Google Scholar and Science Direct. The authors used five dimensions to select the studies: Definition of HDV, scientific level, time horizon, search terms and languages. First, ‘‘heavy-duty vehicles’’ are our focus. HDVs are not uniformly defined by weight; there are different regional categorizations. Some countries, such as the US, define HDVs as single vehicles (‘vehicles’ or ‘trucks’). Others separate HDVs into vehicles and vehicles with trailers (‘trailers & semitrailers’ or ‘tractors’), e.g. the EU or China. Due to these heterogeneous HDV definitions, the authors based the definition of HDVs for this review on the international truck categories shown in Table 1: The US vehicle category 8, the EU vehicle category N3 and (semi- )trailer category Q4, as well as Chinese trucks with a weight above 16 tons and a tractor weight above 18 tons were considered. Second, the authors limited the studies to those in peer-reviewed journal papers and studies of renowned scientific institutions to ensure quality standards. Third, the authors focussed on literature from 2011 onwards to provide current research insights and ensure the comparison of up-to-date research. Fourth, the authors selected literature using combinations of the following search sets M1to M3in both English and German (no results were found using the French and Spanish equivalents): (a) M1(‘‘trucks’’ ∨‘‘heavy-duty’’ ∨‘‘long-haul’’) ∩ (b) M2(‘‘alternative fuels’’ ∨‘‘alternative powertrains’’ ∨ ‘‘decarbonization’’ ∨‘‘electrification’’ ∨‘‘electric road’’) ∩ (c) M3(‘‘market diffusion’’ ∨‘‘market penetration’’) The resulting literature set contains 46 studies without further filtering. These studies were then content crosschecked to narrow them down to the most relevant studies. The authors use three fulfilment criteria for the content crosscheck: The studies need to focus on the relevant HDV sizes (cf. chapter ‘Data collection’), contain market diffusion models and incorporate quantitative data regarding the market penetration of AFPs. This resulted in 19 studies for the review, comprising eight peer-reviewed journal publications, two PhD theses and nine scientific reports (see Table 2). The relatively low number of relevant studies already indicates the early research stage of this topic and the lack of research in some developed countries (e.g. France and Japan) and in most developing markets such as Africa, India, the Middle East and Latin America. 2.2. Review method This section presents the method used to analyse the data output of the previous section. As this review focusses on the diffusion of a particular innovation (AFPs) in socio-economic systems (HDV stock), the analysis criteria are set up along three categories based on the general modelling of social systems (Luhmann and Knodt,1995;Karnowski,2017): Environmental parameters (I), input and throughput parameters (II) and output parameters (III). In order to apply this approach to our work, the authors renamed the category ‘environment’ ‘model objective’ and consolidated the input and throughput parameters as ‘model design’ (see Fig. 3). 1014 P. Kluschke, T. Gnann, P. Plötz et al. / Energy Reports 5 (2019) 1010–1024 Table 1 Definition of international truck weight classes and classes considered in the review (IEA,2017). Table 2 Data collected as input for the literature review consisting of eight peer-reviewed journal publications, two PhD theses and nine scientific reports. Author Focus region Title Observation period Type of publication Ambel (2017) EU28 Roadmap to climate-friendly land freight and buses in Europe 2020 to 2050 Study Askin et al. (2015) USA The heavy-duty vehicle future in the US: A parametric analysis of technology and policy trade-offs 2030 to 2050 Peer-reviewed paper Bahn et al. (2013) Canada Electrification of the Canadian road transportation sector: A 2050 outlook with TIMES-Canada 2020 to 2050 Peer-reviewed paper Bründlinger et al. (2018) Germany Pilot Study Integrated Energy Turnaround: Impulses for the design of the energy system until 2050 2030 to 2050 Study Çabukoglu et al. (2018) Switzerland Battery electric propulsion: An option for heavy-duty vehicles? Results from a Swiss case study none (only potential) Peer-reviewed paper Capros et al. (2016) EU-28 EU Reference Scenario 2016: Energy, transport and GHG emissions trends to 2050 2030 to 2050 Study Gambhir et al. (2015) China Reducing China’s road transport sector CO2emissions to 2050: Technologies, costs and decomposition analysis 2050 Peer-reviewed paper Gerbert et al. (2018) Germany Climate paths for Germany 2020 to 2050 Study Kasten et al. (2016) Germany Development of a technical strategy for the energy supply of transport up to the year 2050 2020 to 2050 Study Liimatainen et al. (2019) Finland & Switzerland The potential of electric trucks — An international commodity-level analysis none (only potential) Peer-reviewed paper Mai et al. (2018) USA Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States 2020 to 2050 Study Mulholl et al. (2018) Global The long haul towards decarbonizing road freight — A global assessment to 2050 2030 to 2050 Peer-reviewed paper Naceur et al. (2017) Global Energy Technology Perspectives: Catalysing Energy Technology Transformations 2060 Study Özdemir (2011) Germany The Future Role of Alternative Powertrains and Fuels in the German Transport Sector 2020 to 2030 PhD-Thesis Plötz et al. (2019) EU-28 Impact of Electric Trucks on the European Electricity System and CO2Emissions 2020 to 2040 Peer-reviewed paper Repenning et al. (2015) Germany Climate protection scenario 2050 2020 to 2050 Study Seitz (2015) Germany Diffusion innovativer Antriebstechnologien zur CO2-Reduktion von Nutzfahrzeugen 2020 to 2035 PhD-Thesis Siegemund et al. (2017) Germany The potential of electricity-based fuels for low-emission transport in the EU 2020 to 2050 Study Talebian et al. (2018) Canada Electrification of road freight transport: Policy implications in British Columbia 2040 Peer-reviewed paper In the model objectives category (I), high level information from the HDV market diffusion literature is analysed such as the research question(s), country of observation and, time horizon. The authors believe that these parameters provide insights into the motivation and objectives of the studies. In addition, the geographic and time-related parameters help to interpret the data more accurately. P. Kluschke, T. Gnann, P. Plötz et al. / Energy Reports 5 (2019) 1010–1024 1015 Fig. 3. General structure of considering market diffusion and parameters in this review. The second category covers the model design and its input for market diffusion models (II). The analysis criteria here are the type of model, number and type of scenarios and consideration of historical data. The authors consider demand attributes (such as transport activity or customer choice factors), supply attributes (vehicle attributes such as fuel, powertrain, range, power, etc.) and framework factors (such as government policies or infrastructure) as inputs. On the one hand, the authors consider understanding a model to be beneficial when interpreting its outputs. On the other hand, it is expected that the input parameters reveal insights into the baseline and assumptions concerning the HDV sector and enable a better understanding and comparison of the starting point of the studies. The third and final category combines analysis criteria for the model output factors (III), i.e. the results of modelling HDV market diffusion. Our focus is on AFP market penetration rates in HDVs for the different scenarios in the studies. To enable a comparison of scenarios across the various studies, the authors highlight the most optimistic scenario with a high AFP market share, and a pessimistic scenario with a low AFP market share per study. In addition, infrastructure and energy system implications are analysed as the authors consider them an important catalyst for any diffusion of AFPs. Finally, the data are also analysed regarding the main findings and recommendations. The authors expect insights into homogeneous statements, i.e. consistent opinions about the future of AFPs in HDVs, as well as heterogeneous statements. 2.3. Overview of market diffusion studies on HDV In this section, the authors briefly summarize the relevant studies that projected the market diffusion of AFPs in the HDV segment. Ambel (2017) focus on how to achieve zero GHG from road freight for Europe by 2050. Their bottom-up accounting tool EUTRM enables them to generate prognoses for traffic, energy and CO2emissions. Using the tool, Ambel (2017) define four scenarios from business-as-usual towards full electrification. Their analyses indicate an AFP share of up to 100% in the HDV stock by 2050. Askin et al. (2015) develop a model to analyse technology and policy trade-offs for HDVs in the US. They construct a bottom-up consumer choice model to investigate the drivers for and barriers to the market diffusion of efficiency technologies and AFPs in HDVs. Modelling the HDV market, Askin et al. (2015) focus on US class 7 and 8 HDVs, define 4 different fleet sizes, and focus only on alternative fuels because these could quickly replace diesel in current ICE applications. In this model, infrastructure availability is a prerequisite for customer decisions in favour of AFPs and therefore a deal-breaker if not available. However, they exclude vehicle availability explicitly from the model indicating a made-to-order situation for consumers. Within their exploratory reference scenario, Askin et al. (2015) project an AFP market share of 11% in 2050. Bahn et al. (2013) focus on the Canadian road sector and its potentials for electrification with AFPs. Their TIMES-Canada model is a bottom-up optimization model considering passenger vehicles as well as light, medium and heavy-duty freight vehicles. While FCEV and HYB AFP technologies are considered, Bahn et al. (2013) explicitly exclude BEV HDVs due to range limitations. They define two scenarios in addition to a reference scenario: One imposes targets for electric vehicle penetration and the other enforces targets for CO2emission reduction. Within the latter scenario, alternative fuels are assumed to dominate the HDV market by 2050. Bründlinger et al. (2018) focus on the German ‘Energiewende’ (transition towards a renewables-based energy system) and include the decarbonization of the national transport sector. Their ‘Dimension+’ optimization model considers different modes of transport including on-road heavy-duty freight vehicles. In addition to a reference scenario, Bründlinger et al. (2018) consider four scenarios based on a matrix of technologies (pure electrification or technology mix) and the degree of decarbonization (80% or 95%). In their most optimistic scenario, AFP will reach a market share of 95% in 2050. Çabukoglu et al. (2018) explore the maximum penetration depth for BEV–HDV under ideal conditions in Switzerland. Their bottom-up accounting model also considers total energy demand. Besides a current technology scenario, Çabukoglu et al. (2018) also use a maximum potential and battery swapping scenario. Their most optimistic scenario shows a share of up to 100% of AFPs in HDV stock. Capros et al. (2016) calculate a price-indicated market balance and combine technological and economic parameters for various sectors including transport in the European Union. Their main tool, PRIMES, enables them to generate prognoses for traffic, energy and CO2emissions. Using the tool, Capros et al. (2016) follow a single reference scenario extrapolating current policies. As a result, their extrapolative analysis indicates an AFP share of 3% in the HDV stock in 2030. Gambhir et al. (2015) analyse CO2emission and cost implications of AFPs for HDVs in the Chinese transport sector. Their bottom-up optimization model considers all types of road vehicles and clusters them into 9 classes, with HDV as one of them. Considering 5 AFPs in total, they explicitly include FCEV technology due to ‘‘growing interest’’. They also consider AFP infrastructure explicitly as an additional mark-up on fuel costs, and derive two scenarios: business-as-usual (BAU) and low-carbon. Their results present HYB as the predominant technology, accounting for up to 60% of the HDV stock in 2050. Gerbert et al. (2018) look for the minimum cost way to lower CO2emissions in Germany without considering additional measures. Their VIEW model features a cohort model for the transport sector including passenger cars and trucks. Besides a reference 1016 P. Kluschke, T. Gnann, P. Plötz et al. / Energy Reports 5 (2019) 1010–1024 scenario, Gerbert et al. (2018) also use a matrix logic for four additional scenarios covering two dimensions: regionality (national path or global path) and degree of decarbonization (80% or 95%). As a result, their most optimistic scenario shows an AFP share of up to 85% in HDV stock by 2040. Kasten et al. (2016) focus on deriving a strategy for the CO2- neutral energy supply of the transport sector in Germany until 2050. Their bottom-up TEMPS model covers multiple means of transport including trucks. The model sets the goal of carbonneutral AFPs and derives four normative scenarios: power-to- liquid fuels, direct electrification, power-to-gas methane and power-to-gas hydrogen. The result of Kasten et al. (2016) is an AFP share of up to 95% using direct electrification with CAT HDVs. Liimatainen et al. (2019) develop a methodology to estimate the potential of BEV–HDVs in Finland and Switzerland. They use a bottom-up accounting model to simulate four scenarios: current technology, improved vehicles, improved vehicles & charging, and towards full electrification. Within the most optimistic scenario, they forecast an AFP market share of about 60% in Finland and 68% in Switzerland. Mai et al. (2018) aim to build an understanding of how the potential for HDV electrification might influence demand in the USA. Their bottom-up accounting model EnergyPathways sets three scenarios until 2050: Reference, medium and high. Within their most optimistic scenario, Mai et al. (2018) forecast an AFP market share of 41% in 2050. Mulholl et al. (2018) assess the ‘‘long haul towards decarbonizing road freight’’ by calculating future energy needs and emissions in the respective sector on a global scale. Their Mobility Model, a bottom-up simulation model, focusses on the truck market, distinguishes light, medium and HDV and considers four AFPs for HDVs. Within their second – rather optimistic – scenario, a significant diffusion of CAT and HYB technologies is projected up to 2050. Mulholl et al. (2018) worked closely on study IEA (2017), ‘‘The Future of Trucks’’. Naceur et al. (2017) focus on a cost-related optimization of the technology portfolio used in various industries on a global level. They apply a model combination of ETP (sales model) and MoMo (stock model) to model the HDV market in detail. Three scenarios are defined within their analyses: ‘reference’ (average temperature increase of 2.7 ◦C until 2100), ‘2 ◦C scenario’ (maximum 2 ◦C increase) and ‘beyond 2 ◦C’ (maximum of 1.75 ◦C increase). The latter scenario results in an AFP market share of about 90% in HDV stock. Özdemir (2011) develops a model-based scenario analysis covering technical, economic and environmental aspects and focussing on road transport in Germany. He uses the TIMES-D model to simulate four scenarios until 2030: baseline, free market, CO2emission restriction and technology-based. Within the most optimistic scenario, he forecasts an AFP market share of about 3% in 2030. Plötz et al. (2019) evaluate the impact of CAT HDVs on European CO2emissions and its electricity system. They use a combined model (ALADIN, PERSEUS-EU) to model both the HDV sector and the electricity system. The study analyses four scenarios: a BAU energy system and a strong renewable energy system, both with and without the usage of CAT HDVs. Their scenarios result in comparatively similar AFP market diffusion rates of between 40% and 50% of the HDV stock in 2040. Repenning et al. (2015) focus on cost-related optimization to reach CO2targets in Germany. Combining two models, TIMES and ASTRA-D, they analyse different sectors including the transport sector and focus on heavy-duty-vehicles. Using the same scenario set-up as Gerbert et al. (2018), they predict an AFP share of 100% in HDV stock by 2040. Seitz (2015) analyses the diffusion of various innovative powertrain technologies to reduce the CO2emissions of freight vehicles in Germany. His bottom-up system dynamics model considers seven different types of heavy-duty vehicle and constructs four scenarios until 2030: baseline, CO2policy, e-mobility, and recession. Within his most optimistic scenario, AFPs reach a market share of 15% in 2030 with the HYB technology. Siegemund et al. (2017) compare the investments and energy demand of different technologies in the transport sector of the EU. Their model considers passenger vehicles as well as light, medium and heavy-duty freight vehicles. Siegemund et al. (2017) define three scenarios: power-to-liquid, power-to-gas, and e-drive (direct electrification). Within their most optimistic scenario, they forecast an AFP market share of 95% in the HDV stock in 2050 based on FCEV technology. Talebian et al. (2018) focus on the electrification of HDVs and the respective policy implications for one Canadian province. Their bottom-up accounting framework model considers class 8 HDVs and distinguishes them into nine sub-categories based on weight, roof height and cabin design. Within the two scenarios targeting the reduction of CO2emissions by more than 60%, only fully electrified powertrain options are considered. Since both scenarios set a large CO2reduction as an input parameter, they result in significant AFP market shares of more than 70% in 2040. 3. Results In this section, the authors present the results of the literature review and compare the model objectives, model designs and model outputs of the analysed studies. In addition, the authors compare the main results and policy recommendations of all the reviewed literature. 3.1. Model objectives When comparing the reviewed studies, it becomes clear that all the authors aim to gain insights into the reduction of CO2 emissions in the HDV sector in the future and thus into the market diffusion of AFPs in HDV. Apart from this shared objective, some authors also target additional aspects, such as cost implications (Gambhir et al.,2015) or impacts on the energy system (Mai et al.,2018;Naceur et al.,2017;Plötz et al.,2019). Most studies are in line with the time horizon of global climate targets, e.g. Capros et al. (2016). The observed time horizon runs from 2020 up to 2050 (in 12 studies). Özdemir (2011) and Seitz (2015) observe up to 2030, while Plötz et al. (2019) and Talebian et al. (2018) stop at the year 2040. Only (Naceur et al.,2017) forecasts until 2060. Çabukoglu et al. (2018) and Liimatainen et al. (2019) decouple HDV decarbonization from a timeline and refer to feasible potentials. The studies cover different geographical scopes: These range from single countries, such as Canada (Bahn et al.,2013;Talebian et al.,2018), China (Gambhir et al.,2015), Germany (Bründlinger et al.,2018;Gerbert et al.,2018;Kasten et al.,2016;Özdemir, 2011;Repenning et al.,2015;Seitz,2015), Switzerland (Çabukoglu et al.,2018;Liimatainen et al.,2019) or the US (Askin et al.,2015;Mai et al.,2018), through regions such as the EU28 (Ambel,2017;Capros et al.,2016;Siegemund et al.,2017) up to a global perspective (Mulholl et al.,2018;Naceur et al.,2017). The German bias is probably due to the search languages used, even though the authors also tried other languages such as French or Spanish. In sum, the research questions indicate that the reviewed studies have a similar motivation for the research conducted: the reduction of CO2emissions in HDVs until 2050. However, there is still a noticeable lack of current research on global HDV markets such as Africa, India, Middle East and Latin America, which account for about 30% of today’s global HDV stock (IEA, 2017). P. Kluschke, T. Gnann, P. Plötz et al. / Energy Reports 5 (2019) 1010–1024 1017 3.2. Model designs Before comparing the outputs, the authors aimed to understand the structure of each model in order to address our second research question. According to Karnowski (2017), model design is separated into the two sub-sections ‘model parameters’ and ‘input parameters’. 3.2.1. Model parameters This section presents the modelling parameters of the existing market diffusion studies of AFPs in HDVs obtained by analysing the model type, modelled scenarios, sectoral scope and the economic perspective. In order to classify the model types used in the literature, the authors applied the framework developed by Gnann and Plötz (2015). This framework defines bottom-up models as a combination of individual assumptions to generate an aggregated outcome with a strong focus on technologies. All the models used in the analysed studies are bottom-up. As shown in Table 3, half of them use bottom-up simulation models to reconstruct behavioural processes based on either individual agents or systemic rules (system dynamics). The other half use either a bottomup optimization model, which optimizes supply and demand to reach an economic optimum, or a bottom-up accounting framework to determine sectoral outcomes (e.g. transport and industrial production sector). One of the non-peer-reviewed studies does not provide any information about the model used. All the models construct between one and five scenarios. The majority of models provide a reference scenario as a baseline and add scenarios with increasing CO2emission restriction. Ten of the models with at least two scenarios define the reference scenario as an exploratory scenario, while the other scenario(s) is (are) normative. Exploratory scenarios describe potential future developments based on known processes, current trends or causal dynamics and generate a forecast, while normative scenarios are prescriptive, using a future target and backcasting to develop scenarios (McCarthy et al.,2018). The normative scenarios mainly set single dimensional target fulfilment (CO2emission target) on different levels e.g. 80% or 95% CO2emission reduction in 2050. Table 4 shows the policies considered to reach the normative scenarios. Most authors do not specify the policy level needed to reduce CO2emissions; however, some researchers focus on sector-specific policies, e.g. vehicle efficiency standards or fuel taxes. Additionally, two studies considered existing restrictions regarding particulate matter (Askin et al.,2015;Mulholl et al., 2018). Six studies specifically model the truck transport sector (Ambel,2017;Askin et al.,2015;Mulholl et al.,2018;Plötz et al., 2019;Seitz,2015;Talebian et al.,2018), while all others also model the passenger transport sector or even non-road transport sectors such as trains, planes and ships. Most models take a macro-economic perspective i.e. they determine an overall economic optimum. This perspective looks for a holistic optimum for the region analysed without considering controlling elements such as taxes or subsidiaries. In contrast, Askin et al. (2015), Repenning et al. (2015) do not take this perspective; their models consider taxes. In addition, there are no clear references to the perspective taken in Seitz (2015), Talebian et al. (2018). In summary, researchers use different types of bottom-up model (simulation, optimization, and accounting framework) to determine market diffusion, and generally between three and five scenarios. 3.2.2. Input parameters The authors look at two key input parameters: supply and demand. The technologies and their CO2emissions are common supply input parameters when modelling the market diffusion of AFPs in HDVs. As outlined in the Introduction, ten AFP technologies are considered in addition to today’s predominant diesel technology: six alternative fuels (LPG, LNG, CNG, eMET, eSYN, BIO) and four electrified powertrains (CAT, BEV, HYB, FCEV). CNG, HYB and FCEV receive the most attention, with a citation rate of about 60% (9/15), 53% (8/15) and 46% (7/15), respectively, as shown in Table 5. Studies published in 2013 or earlier have a stronger focus on alternative fuels as an option to reduce CO2emissions, while the literature from 2015 and later tends to focus more on electrified powertrains. Repenning et al. (2015) are the first to mention the CAT powertrain; all other studies dealing with CAT were published from 2017 onwards. A more recent emphasis in the research aiming to reduce CO2emissions from transport is on electrifying HDV powertrains. Besides considering the CO2emissions of technologies, vehicle range is frequently mentioned when evaluating AFPs (in most cases, the BEV powertrain is excluded for HDV applications due to its low range). Additional potential customer requirements, such as vehicle power or refuelling/recharging time, are not mentioned in more recent publications, but were before 2015 (Askin et al.,2015;Özdemir, 2011;Seitz,2015). Vehicle auxiliaries were considered heterogeneously and only mentioned in a minority of the reviewed studies. However, we could not find any connection between consideration of auxiliaries and the market diffusion of AFP or other reviewed criteria. On average, a single AFP technology was mentioned only in 50% of the studies or even less (cf. Table 6). The reviewed studies agree that future demand for HDVs will grow. While the literature before 2015 did not state specific vehicle numbers or ton kilometres (tkm), more recent publications expect the stock to grow by at least 20% until 2050 (Askin et al., 2015;Talebian et al.,2018). The framework parameters within the reviewed literature mainly concern currently implemented CO2emission policies. In contrast, consumer choice factors are generally disregarded (see Table 7). Most authors do not include range anxiety, vehicle availability, decision alternatives or technology improvements, even though these parameters are recommended by the research conducted on passenger vehicles (Gnann and Plötz,2015). Most studies mention the infrastructure for AFPs, but only four indicate its respective cost (Bründlinger et al.,2018;Gambhir et al.,2015; Gerbert et al.,2018;Kasten et al.,2016), mainly by applying a mark-up on fuel and electricity prices. None of the studies consider the physical ramping up of additional electricity provision to supply AFP-HDVs, i.e. power grid expansion. The interdependency of market diffusion and infrastructure is not explicitly modelled in any of the reviewed studies. In sum, all the reviewed studies project that the future HDV volume will grow significantly. However, other input factors vary strongly. The AFPs considered by researchers are manifold and not homogeneous. Further, the majority of studies do not consider AFP infrastructure and its energy supply. 3.3. Model outputs In this section, the authors review a specific output of the analysed models: the market diffusion of AFPs in HDVs. In order to be able to compare the studies and their scenarios, the authors categorize the scenario results into two clusters. All exploratory reference scenarios are categorized within the cluster ‘‘reference scenario’’. The most AFP-positive scenarios are 1024 P. Kluschke, T. Gnann, P. 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