Preliminary results from simulating adoption of alternative ... - CiteSeerX

Paper for: DIME International Conference “Innovation, sustainability and policy” 11-13 September 2008, GREThA, University Montesquieu Bordeaux IV, France.

Preliminary results from simulating adoption of alternative fuels by heterogeneous motorists Oscar van Vlieta*, Bert de Vriesa, Wander Jagerb, Wim Turkenburga a

Copernicus Institute for Sustainable Development, Utrecht University Faculty of Economics and Business, University of Groningen * Heidelberglaan 2, 3584 CS Utrecht, The Netherlands, +31-30-2537646, [email protected] b

Abstract: We developed a multi-agent model to simulate the transition from diesel and petrol from crude oil to alternative fuels like biodiesel, ethanol and GTL. We used techno-economic data to parameterise 13 fuel production pathways and survey data to parameterise 11 subpopulations of agents that select fuels on the basis of cost, environmental impact, effects on vehicle performance and popularity. Our results show that consumer heterogeneity is very important, and that interventions other than reducing the price of alternative fuels can lead to market penetration through niche markets. However, this only works if the alternatives are priced similar to mainstream fuels.

Introduction The consumption of derived petrol and diesel derived from crude oil is can be considered problematic due to costs, lack of security of supply (1, 2), greenhouse gas (GHG) emissions (3), and air pollution (4). Many potential renewable alternatives are expensive and only pricecompetitive without subsidies with fossil fuels as long as oil prices remain above $75/barrel or so (5, 6), to which ethanol from sugar cane is currently the only exception (7, 8). Moreover, the consumption of biofuels can be unsustainable when impacts on food security, ecology and water consumption are not addressed (9). Alternative sources of fossil transport fuels, such as coal to liquids (CTL), gas to liquids (GTL), tar sands, and oil shale, are also expensive and in general more polluting than oil-derived fuels (5). A transition from our current fuels to low-cost, low-carbon alternatives is therefore needed, but the exact direction is unclear because the multiple goals of restraining price, improving security and reducing emissions seem difficult to reconcile. Moreover, our past experience with fuel transitions is limited: There has been a slow move from petrol-fuelled cars to dieselfuelled cars in Europe over many years (10) and a 30-year (ultimately successful) program to stimulate ethanol as a replacement for petrol in Brazil (11). There has also been a switch from leaded to unleaded petrol in Europe in the late 1980s to early 1990s. The current dilemma of policymakers is to encourage a transition to innovative fuels, given the limitations and uncertainties surrounding the available (and future) technologies and resources. In order to examine how such a transition may take place, we use our data on fuels and fuel production and combine it with a model of heterogeneous fuel consumers (motorists) to simulate the adoption of alternative fuels.

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For this study, we limit our scope by focussing on drop-in replacement fuels: as long as dropin fuel replacements are considered, or fuels that require only a minor change to a car (such as ethanol/petrol blends), vehicle producers and fuel distributors do not need to (invasively) adapt their facilities1 and can be disregarded as an exogenous and constant factor. We therefore limit our fuel options to petrol and diesel from crude oil, ethanol blends, biodiesel blends and Fischer-Tropsch (FT) synthetic fuels. For the sake of clarity and simplicity, we leave out petrol and diesel from heavy crude oil, LPG, natural gas and electricity. Furthermore, we focus on private motorists and leave out heavy-duty vehicles. With these boundaries, fuel producers cover the entire well-to-tank (WTT) part of the well-towheel (WTW) pathway and motorists cover the tank-to-wheel (TTW) consumption.

Model We consider, in a heavily stylized way, the entire well-to-wheel (WTW) system of transportation, regarding fuels from the resource base (oil, coal, natural gas, crops, residues) to use of the final product (transport fuels and fuel blends used in cars). The transition to from current to alternative fuels has several characteristics with implications for modelling: •

• •

A transition between alternative technologies is an intermediate, or meso-level research question (13, 14). It lies in between the micro level, which is characterised by strong heterogeneity and many inherited circumstances from the meso and macro level, and the macro level, which is characterised by many exogenous factors and interdependent (sub)systems. Motorists are a heterogeneous population. This is the result of variation in personal circumstances and preferences, bounded rationality (limited cognitive resources) and limited availability of information. There is a path dependence in the co-evolution of fuel technologies and circumstances. For a model, this means there exist multiple, dynamic equilibrium states. This is caused by non-linear dynamics like lock-in (due to capital stocks and vested interests), (technological) learning effects, supply limitations and increasing returns to scale of adoption (due to infrastructure and compatibility)2.

These three properties point towards a model that is tailored to domain-specific knowledge of technologies, preferences, markets and barriers. A multi-agent evolutionary modelling approach seems to fit the properties of our problem (c.f. 13, 16). We also use observable realworld data to make our simulation more applicable to the specific task of simulating a transition to alternative (drop-in replacement) fuels. In this way, we aim to combine the advantages of a multi-agent model with those of a bottom-up techno-economic model (such as MARKAL or TIMER, see 17, 18). The interactions between the fuel producers and motorists are described by means of a vintage model for fuel producers, which explicitly takes into account the large investments required, 1

We must assume that pump options in filling stations do not meaningfully limit the distribution of fuels, other than by limiting the total number of different fuels on offer. Limited availability of hydrogen in filling stations was indicated as a limiting factor in adoption by consumers (see 12). 2 Multiple equilibrium states can be visualized as a (fitness) landscape in which a local optimum is a valley. Each optimum has a basin of attraction, a collection of model states for which the preferred heading of change is towards that optimum, much like a drainage basin of a river (c.f. 15). However, due to learning, supply curves and returns to scale, the contours of the landscape are constantly changing.

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tied to a heterogeneous set of motorist agents, which explicitly takes into account the variety in consumer preferences. Motorists’ choices, that lead to a demand for various fuels, then determine the evolution of the production capacity of the these fuels. Outside (government) interventions are implemented by modifying properties of the fuel production pathways and public perception of the fuels. An overview of our model is depicted in figure 1. Fuel plant retirement

Available conversion processes Fuel plant construction Available resources

(Input) Parameter

Fuel production and sales

Perceived fuel properties

Fuel supply capacity

Consumer preferences Available vehicles

Fuel demand

Fuel choice

Modelled Variable

Figure 1: General structure of our model

We implemented the model in the Laboratory for Simulation Development (LSD) environment (19), with the equations making up some 2000 lines of C++ code, including comments. The supply side and demand side are linked only through macro-scale variables such as supply capacity and fuel demand. We use bottom-up technical data from literature for energy technologies, fuel properties and vehicles (5, 6, 7, 10, 20, 21, 22, 23) and survey data from literature to construct a population of agents (24, 25, 26, 27, 28). We limit our model to the next 25 years, to reflect the time horizon of our technological data. For this study, we use six fuels: diesel, rapeseed ethyl ester (REE) biodiesel, FT diesel, petrol, ethanol and FT petrol. There are also six blends of alternative and regular fuels: B20 (20% biodiesel, 80% regular diesel), B99, E10 (10% ethanol, 90% regular petrol), E85, FT17D (1/6th FT diesel and 5/6th normal diesel, much like a premium brand diesel) and FT17P (idem, with petrol instead of diesel).

Demand formalisation Our motorist agents must choose between fuels and fuel blends. The model has the agents narrow down the fuel options by eliminating fuels in a series of filtering rounds, until there is only one choice left. The model first checks what fuels the motorist’s current car can use, to avoid having a car with a diesel engine drive on petrol and such. The motorists then determine their preferred fuels in four evaluation rounds. We assume motorists use heuristics to choose preferred fuels, based on perceptions of 4 different fuel attributes: cost, performance (effect on vehicle), emissions (environmental impact), and popularity. We assume that motorists, unlike fuel producers, do not use shadow prices in decision making (not explicitly, at least), or even make decisions in compensatory way. The cost, performance and emissions are static, perceived properties of each fuel, and the popularity of a fuel is equal to its market share, forming a feedback loop. A ‘buzz factor’ can be added to popularity to simulate the effects of marketing. In each round, the fuel attribute filter eliminates fuel options that do not score within X% of the best-rated fuel on one of the four attributes, where the X% tolerance can be different for

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every attribute for every motorist. If all fuels are eliminated in an evaluation round, the fuels left remaining after the previous round are used. Different motorists can have a different order in which they use perceived fuel attributes to evaluate fuels. For each motorist, the highest-ranked attribute is evaluated first, then if more than one fuel is left, the second attribute is evaluated, and so on. This attribute-based filter algorithm is similar to ‘Take-The-Best’ and ‘LEXSEMI’ algorithms described by other authors (29) and an example is given in figure 2. (Groups of) motorists are differentiated by having a different order in which they use attributes to evaluate fuels, and/or by different tolerance values. The heuristic of comparing to the best for each separate attribute (which, for example, has a motorist compare the cost of biodiesel with cheap CTL diesel and compare the emissions of that same biodiesel with BTL diesel made with CCS) rather than a ‘package deal’ (for example, compare both cost and emissions of biodiesel with those of CTL diesel) was inspired by observations of this behaviour in non-scientists in a workshop on sustainable biomass and biofuels supply (30) and later in media coverage of biofuels. It seems apples are routinely compared to oranges. The effects of these attributes will complement and overlap in the aggregated demand. For instance, one motorist could choose to drive on E85 instead of petrol because it is better for the environment, another because it makes his car drive faster, and a third because two of his friends use it. As this example shows, it is possible for a fuel to satisfy several niche markets, building a significant total market share from several multiple small market shares. After the attribute filters, the model checks which of the preferred fuels are still available (ensure supply was not used up by other motorists that were evaluated earlier). For blends, the model checks if the ingredients are still available. If none of the chosen fuels are still available, the fuels that were eliminated earlier in the attribute filters are considered, in the order in which they were eliminated by the attribute filters. Current

Alternative fuel options

Filter stage

Fuel 1

Fuel 2

Fuel 3

Fuel 4

Fuel 5

Fuel 6

Fuel 7

All available fuels

Fuel 1

Fuel 2

Fuel 3

Fuel 4

Fuel 5

Fuel 6

Fuel 7

Filter by suitability

Fuel 1

Fuel 2

Fuel 3

Fuel 4

Fuel 5

Fuel 6

Fuel 1

Fuel 2

Fuel 3

Fuel 4

Fuel 5

Filter by attribute #2

Fuel 2

Fuel 3

Fuel 4

Fuel 5

Filter by attribute #3

Fuel 2

Fuel 3

Fuel 4

Fuel 5

Filter by availability

Fuel 3

Fuel 4

Filter to current fuel if remaining

Fuel 3

Fuel 4

Select at random from remaining fuels

Fuel 1

Filter by attribute #1

Figure 2: Example of a motorists’ choices as the filters are applied to fuel options

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For the fuels that were preferred fuels, but are passed over as unavailable, the demand of this motorist is added to a shadow demand of a randomly chosen preferred fuel. In such a case, the shadow demand of the fuel that was eventually settled upon is reduced by an equal amount. If more than one fuel is left at the end of this procedure, the last filter can check if the fuel of last year is among the remaining fuel options. If so, it chooses the same fuel and if not, one fuel is selected at random from those remaining after the previous filters.

Demand parameterisation The preferences of motorists are difficult to obtain. Market shares of individual fuel brands are closely guarded commercial secrets, especially in relation to demographic data. Faced with a lack of international data, we extrapolated attribute priorities, tolerance and agent populations from the Dutch situation. Nijhuis et al. (25) found that in choosing cars, customers on average rank price > brand/image ≈ performance > environment. In addition, MNP (26) and Veldkamp (27) have conducted surveys to gauge the Dutch consumer’s value dispositions within the TNS-NIPO WiN™ model. We combine this with data on premium fuel sales from BOVAG-RAI (10). We derive that the majority (76%) of the Dutch public cares most about cost, another 11% care most about popularity, 8% care most about performance and 5% care most about environment. We use data on car distributions and fuels sales from BOVAG-RAI (10) and TNO-Inro (31) to determine the types of cars in use. We derive that 83% of motorists drive private cars on petrol, 3% drive leased cars on petrol, 9% drive private cars on diesel and 5% drive leased cars on diesel. Many of the leased car drivers do not pay for their own fuel and are therefore less strict about the price of the fuel they choose (32, 24). From this data, we arrive at 11 populations of agents, each given a (caricature) nickname for sake of easy reference, with properties as shown in table 1. Each agent starts out by using either normal petrol or normal diesel, depending on their type of car. Group nickname Mondeo Man Conformist P Golf Man Green P Petrolhead Big Spender D Traveller D Big Spender P Traveller P Conformist D Green D

Size 64,0% 10,3% 7,5% 4,5% 3,8% 2,7% 2,7% 1,5% 1,5% 1,1% 0,5%

Agents 398 64 47 28 23 17 17 9 9 7 3

Car type petrol petrol diesel petrol petrol diesel diesel petrol petrol diesel diesel

Primary Secondary Cost: 3% Pop: 80% Pop: 70% Cost: 3% Cost: 3% Pop: 80% Env: 20% Cost: 3% Perf: 10% Cost: 3% Cost: 10% Pop: 80% Perf: 10% Cost: 10% Cost: 10% Pop: 80% Perf: 10% Cost: 10% Pop: 70% Cost: 3% Env: 20% Cost: 3%

Tertiary Perf: 10% Perf: 10% Perf: 10% Pop: 80% Pop: 90% Perf: 10% Pop: 90% Perf: 10% Pop: 90% Perf: 10% Pop: 80%

Last Env: 20% Env: 20% Env: 20% Perf: 10% Env: 20% Env: 20% Env: 20% Env: 20% Env: 20% Env: 20% Perf: 10%

WiN Constituents others Conservatives others Broad minded Materialists Professionals Materialists Professionals Materialists Conservatives Broad minded

Table 1: Agent nicknames, populations, cars used, preferences and tolerances, and value dispositions

Due to a lack of data on emissions when driving on various blends, the fuel consumption and GHG emissions are linear combinations of the fuel consumption and emissions when driving on fuel ingredients. The data used to determine fuel choices are summarized in table 3. The public perceptions of environmental impact and performance are rendered as fractions of the baseline fuel, petrol or diesel, and based on expert judgement. Note that the absolute values of fuel properties are not relevant to motorists, only the relative values.

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Fuel name diesel FT diesel biodiesel petrol FT petrol ethanol* electric FT17D B20 B99 FT17P E10 E85

Producer Consumer Performance Emissions price (€/GJ) price (€/l) higher is better lower is better 23 1,2 1,0 1,0 36 1,6 1,3 1,0 21 1,1 1,1 0,6 27 1,5 1,0 1,0 38 1,8 1,0 1,0 40 1,5 1,2 0,6 9 0,2 0,9 0,5 25 1,3 1,2 1,0 23 1,2 1,0 0,9 23 1,1 1,1 0,6 29 1,6 1,2 1,0 28 1,5 1,0 1,0 38 1,5 1,2 0,6

Real emissions (gCO2eq/MJ) 88 -89 to 159 56 88 -89 to 159 12 varies 59 to 100 81 52 59 to 100 81 24

Table 3: Fuel properties as perceived by motorists. Prices are in €/GJ, emissions and performance in ratio to baseline fuel (petrol or diesel), and based on expert judgement.

Supply formalisation Fuels are produced in discrete size installations, or plants, which include upstream supply systems from the raw resource. A plant takes in one or more raw materials (expressed in GJ or ha of land) and produces one or more types of fuels (in GJ). The sales from a single plant are derived from total demand and the plant’s capacity to supply the various fuels it can produce, as well as an industry-wide parameter of keeping production in reserve. Blended fuels (e.g. an E85 ethanol/petrol mix) are handled by converting motorist demand of blended fuels to production demand for its ingredients. On the feedstock side, there are several types of feedstock, each representing an initial form (coal, biomass, crude oil) and a source location (e.g. South America, Eastern Europe). Each feedstock is divided into segments of a given size and price, with each segment representing a volume of feedstock that is available every year at that price. When sorted by price, the segments form a supply curve (c.f. 33). The model uses the marginal unit cost at the feedstock price for all plants that need this resource. Because supply is limited by the available production capacity, a motorist’s preferred fuel (see section 4.3) is not necessarily available. Two types of demand are therefore used: a real demand of how much fuel motorists actually buy is used for production, and a shadow demand of how much fuel motorists would want to use is used for deciding on new capacity. The producers thus have clear knowledge of current aggregated motorist preferences, but no foresight into the development of demand. We assume fuel plants are evaluated only on the basis of profit made in the previous year. The profit per plant is calculated as a Rate of Return (ROR), equal to sales divided by costs. A plant that is insufficiently profitable is retired. New plants are constructed if they are projected to attain a sufficiently high ROR. The projected ROR is calculated using the shortfalls in supply vs. the shadow demand and the costs of available feedstocks. If calculations for any plant types suggest a sufficiently high ROR, the plant with the highest projected ROR is chosen, and another round of evaluation is started until fuel shortfalls are covered or no profitable plants can be found. For a few years after the decision to have a new plant, a plant is considered to be under construction and inoperative for the first few years. The capacity under construction is taken into account when determining the viability of additional plants. A new plant also has a ‘grace period’ in which to attain a stable production and become profitable.

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We implement technological innovation as a progressive series of discrete well-to-tank pathways of fuel production. Each pathway incorporates a coherent set of production technologies and intermediate transport. When preset levels of cumulative production are reached, the model allows the producer to build new (more profitable) production pathways, like a discrete version of an experience curve approach (see 34, 35). Pathways can also be made inaccessible in the same way. A similar system for enabling or disabling pathways is available for feedstock. This was inspired by the notion that problems with any product or resource generally become evident only after some amount of production has been completed. The speed of the development of plants (and therefore change in the supply mix) is limited by a maximum construction and retirement capacity per year. If the model exceeds either limit, further construction or destruction is not possible in that year. Because of the sorting mechanism, the most profitable possible plant is built first, and of the unprofitable ones, the plant producing the most expensive fuels (per MJ) is retired first.

Supply parameterisation The data on feedstocks is summarized in table 4. We do not change segment sizes over time and use a static price for each segment. Supply curves are given in figure 5. Resource y size (PJ/year) min. price (€/GJ) max. price (€/GJ) crude oil 21876 12,9 19,1 coal enough 2,0 2,0 ME NG 1345 1,0 1,1 EE biomass 4299 1,7 4,2 LA sugarcane enough 3,9 8,1 EU rapeseed 368 6,0 17,5

References UNdata.org WEO (2006 / 2007) WEO (2007) REFUEL (2008) Smeets (2008) Chacón (2004)

Notes EU oil trade size, base 120 $/bbl Maximum licensed amount for FT Real supply curve low price segment is 98 PJ/year Real supply curve

Table 4: Fuel supply data summaries from our model.

The data for fuel production are summarized in table 5. The WTT pathways include distribution, conversion to fuels, conversion to intermediates where applicable and transport of raw materials and intermediates. Uncertainties in costs and/or GHG balances of all fuels are considerable (see van Vliet et al.(5) for details). Input (PJ/year) EE salix pellets to 400M 15 EE salix pellets to 2G 77 77 EE salix pellets to 2GC EE salix to 2GC EE 77 64 ME NG to 2G ATR/SM ME NG to 2G 64 ME NG to 2GC 64 Mixed EE salix pellets 66 67 Mixed EE TOPs & coa Large oil refinery 678 Small oil refinery 226 EU REE biodiesel 6 LA cane to ethanol 10

WTT plant unit

Output WTW GHG emis. (PJ/year) (gCO2/MJfuel) 7 25 37 25 35 -87 35 -89 33 110 38 97 37 65 37 159 36 3 608 88 203 88 3 56 4 12

Conversion & transport (M€/yr) 102 424 393 385 241 229 221 292 377 2563 870 7 25

Maximum ROR 1,9 2,2 2,3 2,3 2,6 3,3 3,4 2,9 2,4 1,3 1,3 1,3 2,3

Derived from van Vliet (2008)

JRC (2006), Shell Netherlands (2008) JRC (2006), Chacón (2004)

Macedo (2004)

Table 5: Selected properties of plants based on WTT analyses.

The model initially has 24 large refineries, 3 small refineries, 16 ME NG units, 5 ethanol plants and 22 biodiesel plant to mimic the existing EU supply situation. The produced fuels are not (necessarily) the same as the ones used in cars. Biodiesel and ethanol are available to motorists only as ingredients in blends with regular petrol or diesel3. 3

The alternative fuels are frequently used as additives to improve the properties of the fuels, both physical, such as with a higher octane number and lower particulate emissions by blending petrol with a few percent of ethanol, as well as perceptual, such as with labelling a blend containing biofuel as ‘green’ regardless of the feedstock origin.

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to consumer fuel (TankO)

The mix ratios of the blends are listed in table 6. The ethanol ingredient of B20 and B99 is the ethanol needed to produce rapeseed ethyl ester, which requires some 10% (energy/energy) of ethanol in its production. The ethanol input for REE was factored out of production and included in the blending stage to facilitate calculations. Mix matrix (BlendFraction) diesel FT diesel biodiesel petrol FT petrol ethanol electric FT17D B20 B99 FT17P E10 E85

diesel FT diesel 100% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 83% 17% 80% 0% 1% 0% 0% 0% 0% 0% 0% 0%

from producer fuel (TankBlendO) biodiesel petrol FT petrol 0% 0% 0% 0% 0% 0% 90% 0% 0% 0% 100% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0% 0% 0% 0% 18% 0% 0% 89% 0% 0% 0% 83% 17% 0% 90% 0% 0% 15% 0%

ethanol 0% 0% 10% 0% 0% 100% 0% 0% 2% 10% 0% 10% 85%

electric 0% 0% 0% 0% 0% 0% 100% 0% 0% 0% 0% 0% 0%

Table 6: Mix ratios of producer fuels to motorist fuels.

Results Below are several graphs of simulation results, Maximum build capacity 800 PJ/year using the data as described above and runMaximum destroy capacity 800 PJ/year specific parameters as described in table 7. The ROR for new plant 1.4 maximum capacity that can be built or destroyed ROR for existing plant 1.0 in a year is 800 PJ/year, which is equivalent to Time to production 5 years Time to profitability 8 years the size of one large and one small oil refinery. Spare capacity 5% The minimum ROR is set at a 40% profit margin Table 7: Base case model properties for new plants and at break-even for existing plants. The delay from the decision to construct to production is 5 years, which is reasonable for small plants, but extremely fast for a large oil refinery. The grace period to attain a profit is 3 years longer than the construction delay. Using only the base case parameters, with our 11 subpopulations of motorists, we observe limited changes in the fuel mix, as visible in figure 3. 7656

Petrol Demand (PJ)

5742

3828

Diesel

FT17P

E85

FT17D

FT diesel

1914

0 0

6

12

18

25

Time (year) Figure 3: Fuel demand (PJ/year) using base case parameters.

Out motorists do not respond to the B20 and E10 blends, because we set their positive properties insufficiently different from the baseline fuel to exceed the tolerance. Their market share remain below 1%. The FT17P blend initially has some clientele, but is crowded out as soon as E85 comes into the market in volume. FT diesel blends and E85 are the fuels of 8

choice, and 6.7% of the fuel volume are biofuels, slightly exceeding the EU biofuels target of 5.75% for 2010. However, this share is very dependent on niche market sizes (when using other agent populations, the biofuels share could easily drop below 6%). Closer examination of the results also showed that there is extensive demand for biodiesel, but this demand is never filled because biodiesel production seems insufficiently profitable. To check if heterogeneity in consumers matters, we did a simulation in which all motorists are converted to the homo economicus, with fuel costs homogeneously as the first priority and 0% tolerance on price. The results are visible in figure 4. Note that the rounded 1.2 €/l of ethanol in table 3 is actually slightly lower than the 1.2 €/l for petrol. Ethanol blends are therefore slightly cheaper than regular petrol. For diesel vehicles, regular diesel is the cheapest option. 7661.3

Petrol Demand (PJ)

5745.97

E85

3830.65

Diesel 1915.32

FT diesel

FT17D

FT17P 0 0

6

12

18

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Time (year) Figure 4: Fuel demand (PJ/year) using cost as first priority with 0% tolerance for all motorists.

We expect the cheapest fuels (regular diesel, and E85 for petrol cars) to become dominant. They do, and the production of petrol quickly tails off to the minimum to fill demand for 15% of the E85. Diesel production plummets along with it. Around year 18, the rapid collapse in refineries leaves some 10% of consumers without the diesel fuels they need, which would create a window of opportunity for alternatives. Heterogeneity certainly has a strong influence on the results. For an experiment with the effects of marketing, we added buzz factors (increasing the perceived popularity) to FT-, biodiesel- and ethanol blends. Biodiesel and FT refused to be propelled into large scale market penetration. Giving ethanol a buzz factor of 0.1 (equivalent to a free 10% market share of public awareness) however, resulted in a demand graph (figure 5) that resembles the base case on the left, and the homo economicus case on the right. 7661.3

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Demand (PJ)

5745.97

E85

3830.65

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FT diesel

1915.32

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FT17P 0 0

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Time (year) Figure 5: Fuel demand (PJ/year), with E10 and E85 given a 0.1 buzz factor to popularity.

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The ethanol market share grows to a point where it, due to the buzz, becomes acceptable to other mainstream motorists. Because the ethanol blends have properties that are indistinguishable from normal petrol or more favourable, the demand explodes after reaching a threshold when lack of familiarity is no longer an obstacle. This is even more clearly illustrated in the shadow demand (figure 6), which indicates demand for motorists’ preferred fuels, without the constraints of supply. Shadow demand (PJ)

7661.3

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5745.97

E85

3830.65

FT17P

Diesel

B99

FT17D

FT diesel

1915.32

0 1

7

13

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Time (year) Figure 6: Fuel shadow demand (PJ/year), with E10 and E85 given a 0.1 buzz factor.

The shadow demand graph clearly shows the different popularity thresholds. The majority of motorists switch at year 9, at which point the shadow demand becomes jittery because the ethanol is not yet available. A small group remains unconvinced until year 15, when the real market share of E85 has improved again. Figure 6 therefore shows three jumps in demand, except that the last one is somewhat obscured.

Discussion We sought to verify and validate our model with the steps Midgley et al. (16) recommend in their rigorous discussion on the assurance of agent-based models. We used dummy configuration throughout the model development to verify that our code was implementing the specified equations properly. Our code is also freely available for review. As for the validation, our simulation results show macro-behaviour that is explainable, given the input data, but we lack empirical data on fuel transitions to compare with. On a micro-level, we used stylized agents because of a lack of survey data. Complete empirical validation of our model requires much more work than intended for this demonstration. In this demonstration, the properties of the agents and perceived properties of the fuels were not based on targeted surveys. When such data becomes generally available (36), it should be possible to make more realistic motorist agents, using different tolerances and/or attributes. However, the definition of the subpopulations for a multi-agent simulation should not be overestimated: testing with different sizes of subpopulations did change the market shares of the various fuels, but the dynamics involved were strikingly constant. The focus should be on using salient attributes. Now that we have demonstrated how a model for a fuel transition can be created, we can explore its further. In the future, we will use this model to further examine the dynamics emerging from the interactions of fuel producers and motorists, and the effect of varying some of their properties on these dynamics (robustness/ indifference). Our results from the buzz factor experiment also indicate that is much room for experimenting with consumer motivations other than price. It also indicates that, for a substitutable product 10

like a drop-in replacement fuel, the price must be acceptable to mainstream consumers to allow for these other motivations to come into play; an alternative fuel doesn’t need to be much cheaper, but it does need to be on par with the mainstream fuel. Building on innovation system theory (37), we will experiment with interventions in our model system to mimic the effect of some possible policy instruments to stimulate breakthrough of alternative fuels. At the time of writing, our model, data and results are still in development, and we intend to publish a more comprehensive description and more results in the near future.

Conclusions We have formalized and parameterized a multi-agent model for the production of 6 fuels and 6 fuels blends from 6 resources through 13 different production pathways, and their adoption of by 11 distinct subpopulations of motorists. While our model is undoubtedly an incomplete description of the dynamics of a real-world transition to alternative fuels, we have shown that evolutionary, multi-agent modelling of fuel transitions can be done. Our preliminary results indicate that motorist heterogeneity is extremely important for model results, and motivations other than price at the pump could have important effects, especially in the creation and limits of niche markets. While alternative fuels could compete by occupying different niche markets, ‘seeding’ a sufficiently large (set of) niche market(s) with an alternative fuel may lead to a cascade of adoption. However, general adoption only seems possible if alternative fuels fall within the price range of established fuels, because price at the pump is the largest motivation in selecting a fuel across the whole motorist population. This seems very possible with the high oil prices seen in 2007-2008. If oil prices fall, existing import duties are likely to hamstring ethanol to a fairly low level of development in the EU, because outside production for the EU market becomes insufficiently profitable. Biodiesel is already in this situation, and would require feedstock subsidies or tax breaks to achieve market penetration.

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References [1] Mitchell JV. A new era for oil prices. Chatham House (Royal Institute of International Affairs); 2006, 32 pp. http://www.chathamhouse.org.uk/pdf/research/sdp/Oilprices0806.pdf [2] Campbell CJ. The Rimini Protocol an oil depletion protocol: Heading off economic chaos and political conflict during the second half of the age of oil. Energy Policy 2006;34:1319-25. http://dx.doi.org/10.1016/j.enpol.2006.02.005 [3] EEA. Greenhouse gas emission trends and projections in Europe 2004. European Environmental Agency; 2004. http://reports.eea.eu.int/eea_report_2004_5/en/GHG_emissions_and_trends_2004.pdf [4] EEA. Environmental consequences of transport. Website accessed on: 2005/07/19. http://themes.eea.eu.int/Sectors_and_activities/transport/indicators/index_html?treee=eJyLLWTUCOVxhIJiQ1/bQiY439XN1dO2kDlVDwC4dAmH#AAAAAAAEFEI= [5] van Vliet OPR. Fischer-Tropsch diesel production in a well-to-wheel perspective: a carbon, energy flow and cost analysis. submitted for publication to Energy Conversion & Management 2008. [6] Chacón FAT. Techno-Economic Assessment of Biofuel Production in the European Union. Karlsruhe: Fraunhofer Institut Systemtechnik und Innovationsforschung, department of Energy Policy and Energy Systems Department; 2004. [7] Macedo IdC, Leal MRLV, da Silva JEAR. Assessment of greenhouse gas emissions in the production and use of fuel ethanol in Brazil. Secretariat of the Environment, Gotsosp; 2004, 32 pp. http://www.eners.ch/plateforme/medias/macedo_2004.pdf [8] Goldemberg J, Coelho ST, Nastari PM, Lucon O. Ethanol learning curve — the Brazilian experience. Biomass and Bioenergy 2004;26:301-4 http://dx.doi.org/10.1016/S0961-9534(03)00125-9 [9] Fargione J, Hill J, Tilman D, Polasky S, Hawthorne P. Land Clearing and the Biofuel Carbon Debt. Science 2008:1152747. http://www.sciencemag.org/cgi/content/abstract/1152747v1 [10] BOVAG-RAI. Mobiliteit in cijfers auto's 2006 2006, 62 pp. http://www.nnmedia.nl/bovagrai.nl/forcedownload.php?file=pdf/autos2006.pdf [11] Goldemberg J. The ethanol program in Brazil. Environmental Research Letters 2006:014008. http://dx.doi.org/10.1088/1748-9326/1/1/014008 [12] Schwoon M. Simulating The Adoption of Fuel Cell Vehicles. Research Unit Sustainability and Global Change, Departments of Geosciences and Economics, Hamburg University: 2005. [13] Verspagen B. The use of modeling tools for policy in evolutionary environments. Technological Forecasting and Social Change 2008:art 2. http://dx.doi.org/10.1016/j.techfore.2007.10.006 [14] Rotmans J. Maatschappelijke innovatie: tussen droom en werkelijkheid staat complexiteit. Rotterdam: Erasmus Universiteit Rotterdam; 2005. [15] Schwoon M, Alkemade F, Frenken K, Hekkert MP. Flexible transition strategies towards future well-to-wheel chains: an evolutionary approach. 2006. [16] Midgley D, Marks R, Kunchamwar D. Building and assurance of agent-based models: An example and challenge to the field. Journal of Business Research 2007;60:884-93. http://dx.doi.org/10.1016/j.jbusres.2007.02.004 [17] van den Broek M, Faaij A, Turkenburg W. Planning for an electricity sector with carbon capture and storage: Case of the Netherlands. International Journal of Greenhouse Gas Control 2008;2:105-29. http://dx.doi.org/10.1016/S1750-5836(07)00113-2 [18] van Ruijven B, van Vuuren DP, de Vries HJM. The potential role of hydrogen in energy systems with and without climate policy. International Journal of Hydrogen Energy 2007;32:1655-72. http://dx.doi.org/10.1016/j.ijhydene.2006.08.036 [19] Valente M. Laboratory for Simulation Development. 5.7 ed. Rome: Italy; 2008.

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[20] Smeets E, Faaij APC, Lewandowski I. The economic and environmental performance of Miscanthus and Switchgrass production and supply chains in an European setting. In: Smeets, E, editor. Possibilities and limitations for sustainable bioenergy production systems, Utrecht: Utrecht University; 2008, pp. 223-61 (Chapter 7). [21] Edwards R, Larivé J-F, Mahieu V, Rouveirolles P. Well-to-Wheels Analysis of Future Automotive Fuels and Powertrains in the European Context. Joint Research Centre; 2006. [22] IEA. World Energy Outlook 2007 China India Insights. International Energy Agency; 2007. [23] Smeets E, Junginger M, Faaij A, Walter A, Dolzan P, Turkenburg W. The sustainability of Brazilian ethanol--An assessment of the possibilities of certified production. Biomass and Bioenergy 2008;32:781-813. http://dx.doi.org/10.1016/j.biombioe.2008.01.005 [24] Breunesse E, Calis HP. Telephone conversations on the social position of V-Power and alternative fuels. Personal communication to Van Vliet, Opr; 2007-03-15. [25] Nijhuis J, van den Burg S. Consumer-oriented strategies in new car purchasing: An analysis of environmental information tools and taxation schemes in the Netherlands. In: Paris: Cases in Sustainable Consumption and Production: Workshop of the Sustainable Consumption Research Exchange (SCORE!) Network; 2007. [26] Visser H, Aalbers T, Vringer K, Verhue D. Hoe Nederlandse burgers prioriteiten toekennen aan de sociale agenda. Een analyse van de 2003-, 2005- en 2006-enquetes. Milieu Natuur Planbureau; 2007, 71 pp. http://www.mnp.nl/bibliotheek/rapporten/500086002.pdf [27] Verhue D, Binnema H, Mulder S. Denken, doen en draagvlak. Amsterdam: Veldkamp; 2007, 87 pp. [28] EU. Report on the progress made in the use of biofuels and other renewable fuels in the Member States of the European Union. Commission, E: Official Journal of the European Communities; 2006, 1-16 pp. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2006:0845:FIN:EN:PDF [29] Todd PM. Fast and Frugal Heuristics for Environmentally Bounded Minds. In: Gigerenzer, G, Selten, R, editors. Berlin: Bounded Rationality: The Adaptive Toolbox; 1999. [30] Breukers S, Cuppen E, Bergsma E, Hisschemöller M. Analyse Workshop 1 Biomassadialoog. Amsterdam: Insitute for Environmental Studies, Vrije Universiteit; 2008, 18 pp. [31] Wilmink IR, Eijkelenbergh PLC, Korver W, Droppert-Zilver MN. De zakenautorijder aan het begin van de 21e eeuw. Delft, NL: TNO Instituut voor Verkeer en Vervoer, Logistiek en Ruimtelijke Ontwikkeling; 2002, 88 pp. [32] Graus W, Worrel E. The principal–agent problem and transport energy use: Case study of company lease cars in the Netherlands. Energy Policy 2008. http://dx.doi.org/10.1016/j.enpol.2008.07.005 [33] de Wit MP, Faaij APC. Biomass Resources Potential and Related Costs: Assessment of the EU27, Switzerland, Norway and Ukraine. ECN; 2008, 59 pp. [34] Wene C-O, IEA, OECD. Experience Curves for Energy Technology Policy. International Energy Agency; 2000. [35] Junginger M, de Visser E, Hjort-Gregersen K, Koornneef J, Raven R, Faaij APC et al. Technological learning in bioenergy systems. Energy Policy 2005;34:4024-41. http://dx.doi.org/10.1016/j.enpol.2005.09.012 [36] Fimereli E, Mourato S, Pearson P, Woods J. Consumer Preference for Flexi-fuel Cars and Bioethanol Blends. Valencia, Spain: 16th European Biomass Conference & Exhibition; 2008. [37] Hekkert MP, Suurs RAA, Negro SO, Kuhlmann S, Smits REHM. Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change 2007;74:413-32. http://dx.doi.org/10.1016/j.techfore.2006.03.002

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