Uncertain and unstable policy support has often been claimed to be a major cause of the slower than expected deployment of technologies for production of advanced biofuels. We investigate the economic rationale of this claim by applying a real options framework incorporating uncertainties regarding energy prices, investment costs, and prevalence of policy support, which was linked to the greenhouse gas (GHG) performance of the produced biofuel. Six industri- ally relevant forest-based technologies for production of drop-in biofuels were evaluated. The technologies were integrated with a pulp mill and an oil reﬁnery and are at diﬀerent stages of their technical development. The results show that there is a limited economic rationale behind the claim that policy uncertainties are a major source for the stalled deployment of forest-based bioreﬁnery tech- nologies. Rather, the stalled deployment is mainly related to the uncertainties regarding investment costs and future energy prices, resulting in technologies with lower sensitivity with respect to these uncertainties have a larger chance of becoming commercially relevant investment options. If policy support is in- tended to promote investment in technologies with high GHG performance, it must be directed speciﬁcally to these technologies, otherwise, it is more bene- ﬁcial to invest in technologies with more favourable conditions for investment and operational costs, but lower GHG performance. Keywords: Real options; Drop-in biofuels, Pulp mill, Integration, Uncertainty Acronyms BLGblack liquor gasiﬁcation-catalytic synthesis EU ETS EU Emissions Trading System Corresponding author, jonas.zetterholm@ltu.se Preprint submitted to Energy Policy July 2, 2021 FCCﬂuidised catalytic cracking GHGgreenhouse gas HDOhydrodeoxygenation Hydropyr catalytic hydropyrolysis-hydroconversion LPGliqueﬁed petroleum gas LSHlignin separation-hydrotreatment with natural gas derived hydrogen LSH-E lignin separation-hydrotreatment with electrolysis derived hydrogen Pyr-FCC fast pyrolysis with upgrading via ﬂuidised catalytic cracking Pyr-HDO fast pyrolysis with upgrading via hydrodeoxygenation REDRenewable Energy Directive (2009/28/EC) RED II Revised Renewable Energy Directive (2018/2001/EU) TRLtechnology readiness level 1. Introduction 1 Bioreﬁneries have been highlighted as a key to reach fossil fuel reduction 2 targets as they have the potential to produce both upgraded biofuels, as well as 3 chemicals and materials (Fulton et al., 2015; Connolly et al., 2014). However, 4 concernshavebeenraisedregardingland-usechange, competitionforfoodcrops, 5 and greenhouse gas (GHG) mitigation potential (Stattman et al., 2018; Ziegler, 6 2008), making it important that the biomass feedstock is sustainably sourced. 7 To address these concerns the EU Renewable Energy Directive (2009/28/EC) 8 (RED) was introduced setting a target for use of biofuels, speciﬁed a minimum 9 GHG performance for the biofuels, and prohibited the use of sensitive land areas 10 and capped the use of agricultural crops for biomass feedstock sourcing (Euro- 11 pean Commission, 2009). However, the directive failed at promoting biofuels 12 with higher environmental performance (Stattman et al., 2018). The Revised 13 Renewable Energy Directive (2018/2001/EU) (RED II) introduced to partly 14 address sustainability concerns with previous legislation with higher targets for 15 sustainability, whereforexampleusageofwasteandresiduesarepromoted(Par- 16 liament and Council of the European Union, 2018; Mai-Moulin et al., 2021), 17 encouraging the development of advanced biofuels (Stattman et al., 2018). 18 Although there have been some deployments of bioreﬁneries in the EU, a 19 majority of projects are based on so-called ﬁrst-generation feedstock (such as 20 2 sugars and starch), and few are based on second generation feedstock (such as 21 forest residues) (Hassan et al., 2019). The hitherto slow deployment and un- 22 derdeveloped production capacity of second-generation bioreﬁneries have been 23 accredited to low energy prices, uncertain market conditions, and lack of long- 24 term stable legislation (European Commission et al., 2018; Fallde et al., 2017; 25 European Commission et al., 2017). The uncertainty of future policy condi- 26 tions, in particular, has been highlighted in previous research as a major point 27 that must be addressed to facilitate an environment where new bioreﬁnery tech- 28 nologies can be deployed (Palgan and McCormick, 2016; Hellsmark et al., 2016; 29 Huenteleretal.,2014),asthecurrentenergyandenvironmentalpolicylandscape 30 is neither long-term nor predictable (Näyhä and Pesonen, 2012). However, while 31 this could be the main reason for industrial actors being unwilling to invest, it 32 is not necessarily true from an economic rationale point of view. 33 Using traditional discounted cash ﬂow analysis to analyse investments usu- 34 ally constitutes a "now or never" approach (Gazheli and van den Bergh, 2018) 35 since the investor does not have the option to adapt their investment strat- 36 egy in face of future uncertainties. This approach may thus be unsuitable for 37 investment analysis that includes time-based uncertainties. By instead using 38 real options analysis for the economic evaluation, inclusion of the value of the 39 decision-makers ﬂexibility to adapt, postpone or abandon the investment in re- 40 spect to changing market conditions is enabled (Trigeorgis, 1996). This means 41 that if the investment decision has been made, the option of waiting for more 42 information regarding the market development is forfeited (Dixit et al., 1994), 43 which creates an opportunity cost associated with the investment decision. Al- 44 though real options analysis has been suggested as a complementary tool to 45 discounted cash ﬂow, its usage within the industry has been limited, which can 46 be explained by that it constitutes a more problem-speciﬁc tool that requires 47 more advanced mathematics (Sandahl and Sjögren, 2003). 48 For the case of evaluation of bioreﬁnery technologies, also academic appli- 49 cation of real options analysis has to date been relatively limited. Real options 50 analyses of biofuel projects have shown that the risk of policy shift can induce 51 both a postponement and a speedup of the investment decision, depending on 52 the direction of the change (Liu et al., 2018; Hassett and Metcalf, 1999). How- 53 ever, it is not only policy support that is uncertain, but also other factors which 54 can result in postponed investment, such as future energy market prices (Li 55 et al., 2015). The future market uncertainties can result in that a signiﬁcantly 56 higher biofuel selling price is required, compared to the breakeven biofuel selling 57 price, for the investment to be economically rational according to real options 58 theory (McCarty and Sesmero, 2015). This can partly be mitigated with a ﬂexi- 59 bleproductionstrategy, whichcanresultinimprovedeconomicperformancedue 60 to uncertainties in biofuel selling price (Ghoddusi, 2017). In order to properly 61 examine if there is an economically rational argument that policy uncertainty 62 is the main contributor to the lack of investments in advanced biofuel pro- 63 duction technologies, it is crucial to also investigate how emerging bioreﬁnery 64 technologies are aﬀected by the interactions between policy and energy market 65 uncertainties. To the best of our knowledge, previous work has to date not yet 66 3 explored these interactions. 67 To investigate the validity of the claim that policy uncertainty is a major 68 hindrance to the deployment of bioreﬁnery technologies, we here develop and 69 apply a real options framework. The real options framework incorporates the 70 option for a given decision-maker (investor or plant-owner) to postpone a biore- 71 ﬁneryinvestmentinfaceoffuturemarketuncertaintiesandinteractionsbetween 72 policy support level and policy uncertainty. Additional objectives of this work 73 are to identify how investment cost uncertainties aﬀect the technologies, and 74 to assess how policy price and policy uncertainty aﬀect early GHG emissions 75 reduction. 76 The developed real options framework is applied to a case study for Swedish 77 market conditions. Sweden is chosen as a case study because of its high supply 78 potential of residual biomass (de Jong et al., 2017; Hamelin et al., 2019), and 79 well-developed biomass markets which are a result of the large presence of tra- 80 ditional forest industry (i.e., sawmills, and pulp and paper mills). In addition, 81 the recent introduction of a reduction obligation in Sweden can be expected to 82 create both a market for blend-in biofuels, and a biofuel market price which 83 is contingent on the fuel’s GHG performance (Furusjö and Lundgren, 2017). 84 The reduction obligation, a policy support mechanism that targets biofuels in 85 the Swedish road transport sector, enforces a penalty if a fuel supplier fails to 86 meet GHG emission reduction targets via blend-in of biofuels (Swedish energy 87 agency, 2019). While this mechanism is aimed at promoting biofuels with high 88 GHG performance, it is also subject to the uncertainty that the policy support 89 will disappear. Finally, Sweden has seen signiﬁcant interest from industrial 90 actors with several promising ongoing research and development projects for 91 bioreﬁnery technologies at diﬀerent technology readiness level (TRL). Particu- 92 lar interest is placed on technologies that combine integration with pulp and 93 paper mills and oil reﬁneries for the production of drop-in biofuels (Jafri et al., 94 2019a,b), which enables beneﬁts regarding both heat and material integration, 95 and utilisation of already existing industrial infrastructure. 96 The paper is structured as follows: Firstly, the investigated technologies are 97 presented in section 2. This is followed by a description of the developed real 98 options framework in section 3.1, and the input data and methods to calculate 99 input data in section 3.2 - 3.6. Section 3.7 summarises the evaluated scenarios 100 and the key performance indicators used for the evaluation. The results are 101 presented for simulated prices and investment costs in section 4.1, and the the 102 results according to the investment strategy from the real options framework 103 are presented in section 4.2 - 4.4, followed by overall conclusions in section 5. 104 2. Technology descriptions 105 The chosen technologies represent a selection of industrially relevant tech- 106 nologies for the production of advanced drop-in biofuels. The technologies are 107 integrated with pulp mills and oil reﬁneries in order to be able to beneﬁt from 108 both heat and material integration, and to enable the use of existing infrastruc- 109 4 ture to produce transport range fuels. The feedstocks are either based on forest 110 residues, or black liquor (a by-product in the kraft pulping process). 111 The technologies included in the study are summarised in table 1, with their 112 respective average TRL, used to adjust the literature assessed investment cost, 113 see section 3.5. A summary of the assessed TRL of each processing step in the 114 technologies is found in Appendix A. 115 Table 1: Outline of technologies included in study. Feedstock TechnologyAverage TRL1Abbreviation Lignin separation-hydrotreatment with natural gas derived hydrogen5.2 LSH Black liquor Lignin separation-hydrotreatment with electrolysis derived hydrogen5.0 LSH-E Back liquor gasiﬁcation-catalytic syn- thesis7.0 BLG Fast pyrolysis with upgrading via hy- drodeoxygenation6.0 Pyr-HDO Forest residues Fast pyrolysis with upgrading via ﬂu- idised catalytic cracking6.5 Pyr-FCC Catalytic hydropyrolysis- hydroconversion4.8 Hydropyr 1For details on TRL-level of each speciﬁc processing step, see Appendix A. 2.1. Black liquor-based technologies 116 Black liquor is a lignin-rich by-product from the kraft pulping process, which 117 currently is combusted in a recovery boiler for the production of process steam 118 and recovery of the pulping chemicals. In the black liquor-based biofuel produc- 119 tion pathways considered here, a part-stream of the black liquor is diverted from 120 the ﬂow to the recovery boiler to be used as feedstock for biofuel production. In 121 this way, a pulp mill can increase the pulp production capacity if the recovery 122 boiler is otherwise a bottleneck in the production1. The two technology options 123 examined in this work are lignin separation followed by hydrotreatment (LSH 124 and LSH-E), and black liquor gasiﬁcation followed by catalytic synthesis (BLG). 125 In the two LSH pathways, the lignin is separated from the black liquor and used 126 as feedstock for the biofuel production, while BLG utilises the pure black liquor 127 directly as feedstock. 128 1No economic consideration is received from value creation from eventual debottlenecking of pulp production. 5 2.1.1. Seperation-hydrotreatment 129 The lignin is membrane-separated from the black liquor, returning the pulp- 130 ing chemicals to the pulp mill (Anheden et al., 2017). The separated lignin is 131 puriﬁed and stabilised in the form of lignin oil before it is sent to the oil re- 132 ﬁnery, where it is hydrotreated and upgraded to diesel and petrol (Suncarbon 133 AB, 2020). The technology has relatively low technological maturity and oper- 134 ational experience, compared to fast pyrolysis and BLG (Anheden et al., 2017), 135 but beneﬁts from not requiring facilities with high production capacity to be 136 proﬁtable, due to low speciﬁc investment cost (Jafri et al., 2019b). 137 The source of hydrogen will heavily inﬂuence the GHG performance of the 138 process, and two options for the technology are studied. The ﬁrst option con- 139 siders that the hydrogen is produced at the reﬁnery from natural gas, LSH, 140 while the second option considers the investment in an electrolyser to produce 141 hydrogen from electricity and water, LSH-E. 142 2.1.2. Gasiﬁcation-catalytic synthesis 143 The pure black liquor is gasiﬁed in an entrained ﬂow gasiﬁer to produce 144 syngas and green liquor, where the latter contains the pulping chemicals and 145 is returned to the pulp mill. The syngas is synthesised to methanol which is 146 transported to the oil reﬁnery, where it is upgraded to the main product petrol, 147 with some co-production of LPG, via the methanol-to-gasoline process. 148 The gasiﬁcation of black liquor has been demonstrated as a viable route to 149 simultaneously produce biofuels and recover pulping chemicals, and has been 150 successfully demonstrated in pilot-scale (Landälv et al., 2014). The pathway 151 has a relatively high TRL and has been suggested to be economically favourable 152 (Akbari et al., 2018; Andersson et al., 2016). Although BLG has a relatively 153 high TRL, it is also associated with a relatively high speciﬁc investment cost. 154 2.2. Forest residues-based technologies 155 The technologies relying on forest residues as feedstock are based on fast py- 156 rolysis (Pyr-HDO and Pyr-FCC) and catalytic hydropyrolysis-hydroconversion 157 (Hydropyr), respectively. In these pathways, forest residues are converted to 158 pyrolysis liquids, which are subsequently upgraded to petrol and diesel blends. 159 The pyrolysis step is integrated with a pulp mill and the upgrading takes place 160 at an oil reﬁnery. While the pyrolysis pathways do not necessarily have to be 161 integrated with pulp mills, this design was selected in order to make the supply 162 chains directly comparable with the black liquor-based pathways. Additionally, 163 heat integration with pulp mills has been shown to be economically favourable 164 (Zetterholm et al., 2018), and the integration can provide beneﬁts in terms of 165 logistics and know-how from the experience of the pulp mill in operating large- 166 scale biomass supply chains2. 167 2No economic beneﬁt from this is considered in this work, as the beneﬁt is diﬃcult to quantify. 6 2.2.1. Fast pyrolysis 168 The fast pyrolysis facility is heat integrated at the pulp mill and forest 169 residues are imported as feedstock to produce pyrolysis liquids, which is subse- 170 quently transported to the oil reﬁnery for upgrading to diesel and petrol. The 171 fast pyrolysis technology is at a relatively high TRL (Anheden et al., 2017). 172 Several technology options for upgrading the pyrolysis liquids into diesel and 173 petrol have been previously investigated (Sorunmu et al., 2020), of which this 174 work includes two options: hydrodeoxygenation (HDO), and ﬂuidised catalytic 175 cracking (FCC), respectively. 176 Using the HDO upgrading pathway, the pyrolysis liquids undergo conversion 177 in a two-step catalytic hydrodeoxygenation process, followed by hydrocracking 178 to petrol and diesel. The upgrading of pyrolysis liquids to transport fuels has 179 been subject to signiﬁcant research (Zacher et al., 2014) but operational data 180 from long-term/sustained pilot demonstration is not widely available in the 181 scientiﬁc literature. In the FCC upgrading pathway, the pyrolysis liquids are 182 co-processed with fossil feedstock in the FCC unit at the reﬁnery to produce 183 diesel and petrol (Pinho et al., 2017). Compared to the HDO pathway, the FCC 184 upgrading of pyrolysis liquids has signiﬁcant lower hydrogen requirements. It 185 should, however, be noted that the technical limit of blended-in pyrolysis liquids 186 in the fossil feedstock amounts to a maximum of 10wt% percent (Pinho et al., 187 2017). 188 2.2.2. Hydropyrolysis 189 The catalytic hydropyrolysis-hydroconversion facility also is heat integrated 190 with the pulp mill. In this pathway, forest residues are imported as feedstock 191 to the pulp mill to directly produce unreﬁned diesel and petrol, which is trans- 192 ported to the oil reﬁnery for blending and ﬁnal upgrading. While the technology 193 iscurrentlyatalowTRL(Jafrietal.,2019b),ithasamajoradvantagecompared 194 to the other pyrolysis-based technologies in that the deoxygenation is occurring 195 directly within the hydropyrolysis process (Dabros et al., 2018; Marker et al., 196 2013). This results in a low requirement of integration at the oil reﬁnery. A 197 majority of the product is petrol (also true for the BLG pathway), which could 198 make it interesting for future drop-in biofuel markets, which otherwise typically 199 are dominated by diesel type fuels, as well as in future scenarios with relatively 200 high petrol prices compared to diesel. 201 3. Methods and data 202 Fundamental for the real options framework developed and applied for this 203 study is that the decision-maker (investor or plant-owner) has the option to (1) 204 invest now, (2) postpone the investment decision, or (3) decide not to invest 205 (section 3.1). The framework was implemented using Monte-Carlo simulations 206 to simulate future uncertain market conditions. Future energy prices were sim- 207 ulated assuming a Geometric Brownian motion (section 3.2), policy uncertainty 208 was simulated assuming a Poisson jump process (section 3.3), the GHG foot- 209 print of the technologies was assessed using two methods (section 3.4), and the 210 7 TRL-adjusted investment costs were based on an empirically developed correla- 211 tion between projected and actual investment costs in pioneering process plants 212 (section 3.5). The developed real options framework was applied and evaluated 213 based on various performance indicators, as presented in section 3.7. 214 The model was implemented in Python (Millman and Aivazis, 2011) relying 215 on Numpy (Harris et al., 2020) and Pandas (McKinney et al., 2011, 2010) for 216 improved maths and data handling functionality, and Matplotlib (Hunter, 2007) 217 and Seaborn (Waskom et al., 2020) for visualisations. 218 3.1. Real options framework 219 In our real options framework, the investor is in each time-step (set to 1 220 year) of each speciﬁc simulation faced with the decision options to either invest 221 directly, or postpone the investment decision to the following time-step. This 222 is iterated for each time-step until the end of the investment horizon (set to 10 223 years) where the investor no longer has the option of postponing the investment 224 decision and instead is faced with the decision to either invest immediately or 225 abandon the investment. The end of the investment horizon thus represents the 226 end of the viability to invest in the selected technologies. 227 Theinvestmentdecisionisbasedontheexpectednetpresentvalue( E[NPV ]), 228 calculated from the known investment cost in the speciﬁc simulation (the TRL- 229 adjusted investment cost), the energy balance of the technology, and the ex- 230 pected future energy prices. If the expected net present value is equal to or 231 greater than the value to postpone the investment ( Waitvalue ), the investment 232 is made, and the investor no longer has the option to invest in a later time step. 233 The decisions follow the rules: 234 t<10( E[NPV ]n;t>Waitvalue n;tinvest E[NPV ]n;tWaitvalue n;tpostpone t= 10( E[NPV ]n;100invest E[NPV ]n;10<0abandon TheWaitvalue for each speciﬁc simulation was calculated according to: 235 Waitvalue n;t=PN nw=1(max(E[NPV ]nw;t+1;0)) N(1 +r)(1) whereNis the total number of (nested) simulations, nis the speciﬁc Monte 236 Carlo simulation where the Waitvalue is compared against the expected NPV 237 (E[NPV ]n;t).nwis a speciﬁc (nested) simulation used to represent the stochas- 238 tic nature of the possible future developments to determine the waitvalue for 239 scenarion,tis the current time-step, E[NPV ]is the expected net present 240 value, andris the discount rate. For each speciﬁc scenario n, there are thus N 241 simulations to determine the waitvalue. 242 In total, the real options framework utilised 2500Monte-Carlo simulations, 243 and the calculation of the waitwalue utilised 2500(nested) Monte Carlo simu- 244 lations. 245 8 19921996200020042008201220162020 Date1020304050607080Price [EUR/MWh]Commodity Electricity Biomass Natural gas Diesel PetrolFigure 1: Historic price series based on the lower heating value. 3.2. Energy prices 246 Future energy prices were simulated assuming that they follow a Geometric 247 Brownian motion, which has also previously been applied to simulate future 248 energy prices (De Giovanni and Massabò, 2018; Li et al., 2015). The price in 249 time-steptwas calculated from: 250 Pt=Pt1(1 +dt+dW ) (2) wheretis the current time-step, Pthe price,the drift,dtthe size of 251 the time-step, the volatility, and dWthe increment of a standard Wiener 252 process. At any speciﬁc time-step and scenario where the decision maker knows 253 the current prices P0;n, the expected future prices at time-step t,E[Pt], can be 254 calculated as described by Murto (2007): 255 E[Pt] =P0et(3) 3.2.1. Energy price data 256 The future energy prices were simulated using equation 2 which relies on the 257 parameters describing the energy price drift (describing long-term price trends), 258 and the price volatility (random price disturbances). The parameters used to 259 simulate future energy prices are shown in table 2, and the historic prices used 260 to estimate these parameters in ﬁgure 1. 261 3.3. Policy support 262 Two components of the policy support were considered; the risk of policy 263 switching ( policy uncertainty ), and the future price for the GHG emission per- 264 formance of the biofuel ( policy price ). 265 9 Table 2: Estimated parameters describing future commodity prices. Initial price1Drift Volatility Commodity P0   Source Biomass 19.3 0.0166 0.00408 (Swedish Energy Agency, 2020) Electricity230.1 0 0.147 (Nordpool, 2018) Natural gas 36.7 0.0257 0.0155 (SCB, 2019) Hydrogen358.8 0.0257 0.0155 – Diesel 64.7 0.0498 0.0360 (European Commission, 2019) Petrol 49.2 0.0305 0.0258 (European Commission, 2019) 1EUR/MWh LHV2The drift for the electricity price was assumed to 0, as the price developmenthashistoricallybeengovernedbythevolatility.3Initialpriceforhydrogen was calculated as a factor 1.6 larger than the price for natural gas (Jafri et al., 2019b) and the drift and volatility was assumed the same as for natural gas. As the recently introduced reduction obligation has been implemented with 266 a penalty if the fuel supplier fails to fulﬁl the reduction obligation, it is natu- 267 ral to assume that a market will develop which depends on the GHG emission 268 reduction potential of the biofuel in comparison with the emissions of the cor- 269 responding fossil fuel. The price of the biofuel will thus be dependent on the 270 simulated price of the fossil alternative, and the market price for GHG emission 271 reduction, where the latter constitutes the policy price . The market price of the 272 blend-in biofuel, Pblend in, was calculated according to: 273 Pblend in=Pfossil +PGHG reduction (GHGbiofuel GHGfossil )(4) wherePfossilis the simulated fossil fuel price, PGHG reduction the policy 274 price,GHGbiofuelthe GHG footprint of the biofuel, and GHGfossilthe GHG 275 footprint of the fossil counterpart. 276 The risk of policy switching, the policy uncertainty , was simulated assuming 277 a Poisson jump process, see for example (Hassett and Metcalf, 1999; Liu et al., 278 2018). This is in line with the actual tax policy behaviour, which gives an 279 expected duration, but not the actual duration, of a tax policy (Hassett and 280 Metcalf, 1999). For each time-step, there is a probability that policy switching 281 will occur to the following time-step. Policy switching means that if the policy 282 support exists, it can be removed, or, if the policy support is not in eﬀect, it 283 can be implemented. 284 Inourframework, thepolicyscenariosaredescribedbyboththepolicyprice, 285 which is implemented as a ﬁxed price for the GHG emission reduction compared 286 with the fossil reference for each scenario, and the policy uncertainty, which is 287 implemented as a probability (in percent) that there will be a policy switching 288 in the following year. 289 10 3.3.1. Policy price data 290 It is to date unknown how the future market for biofuels will develop given 291 the reduction obligation. However, the price for the GHG emission performance 292 ofthebiofuelswillnotexceedthatofthesetpenaltyforfailingtomeettheblend- 293 in requirements, which currently amounts to 660 EUR/ton CO 2-equivalent3. 294 Given the option to purchase a biofuel with a GHG emission reduction cost 295 lower than the penalty, that biofuel would likely be purchased. The result 296 would be a market price for the GHG emission reduction potential which would 297 be lower than the penalty. 298 The question is thus how the market for biofuels will develop in the fu- 299 ture. Firstly, we can compare with the historic prices in the EU Emissions 300 Trading System (EU ETS), which have been in the range of 5–30 EUR/ton 301 CO2-equivalent. Next, we can also compare with the current CO 2tax in Swe- 302 den, which aﬀects heat-generating facilities not included in the EU-ETS trad- 303 ing scheme, and which amounts to 105 EUR/ton CO 2-equivalent(World Bank, 304 2020). 305 Given the uncertainties of the future CO 2price, we varied the policy price 306 in the simulations with a CO 2price of 0–700 EUR/ton CO 2-equivalent (using 307 50 EUR/ton increments). The higher end of the range thus represents a failure 308 of the market to deliver biofuels with a lower price for the GHG emission reduc- 309 tion potential, compared with the set penalty for failing to meet the blend-in 310 requirements. 311 3.3.2. Policy uncertainty data 312 The policy uncertainty, expressed as a probability of a policy switching in 313 the following year, was varied from 0–45% in 5% increments. The 0% scenario 314 means that there exists no uncertainty and that the policy support is active 315 during the entire time horizon. In reality, it would not be possible to achieve a 316 0% uncertainty, as policy support is dependent on political support. The upper 317 limit of 45% uncertainty was set based on observed policy switching for biofuel 318 tax exemptions in the US, which amounted to 44% between 2005–2017 (Liu 319 et al., 2018). 320 3.4. GHG footprint evaluation 321 WeappliedtwodiﬀerentapproachesforexpressingtheGHGfootprint. Firstly, 322 we applied a simpliﬁed approach based on the RED guidelines (European Com- 323 mission, 2009). Secondly, we also applied an approach based on system expan- 324 sion as described in ISO-14044 (ISO, 2006). The reason for complementing with 325 the ISO-14044 approach (hereafter termed simply ISO) is that the RED guide- 326 lines prohibit the allocation of emissions to heat co-products, and thus fails to 327 capture the beneﬁts of heat integration, where heat replaces or reduces the need 328 for another primary fuel. 329 37 SEK/kg CO 2-equivalent (Drivmedelslagen, (2011:319, 27 §), converted with 10.6 SEK/EUR (average of 2019) (European Central Bank, 2020) 11 The GHG footprints for the diﬀerent technologies are shown together with 330 the fossil fuel reference emissions in table 3. The fossil fuel