Schade, Burkhard, "Future Development of 2nd generation Biofuels in Transport considering Learning Rates", 2012 July 22-2012 July 26

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FuTURE DEVELOPMENT OF 2“° GENERATION BIOFUELS IN TRANSPORT CONSIDERING
LEARNING RATES

Burkhard Schade’, European Commission, JRC-IPTS, Edificio Expo; C/ Inca Garcilaso, 3;
E-41092 Sevilla, Spain, tel: + 34 95 4488475; fax: + 34 95 4488279;

burkhard.schade@ec.europa.eu

ABSTRACT

The aim of this paper is to illustrate the biofuel model BioPOL and its new developments, to describe
a set of scenarios, in which BioPOL was applied and to discuss the results of the scenarios.

BioPOL was developed and applied within the several European projects, among them TRIAS,
PREMIA, HOP!, iTREN-2030 and GHG TransPoRD (Schade et al., 2008, Wiesenthal et al., 2009,
Schade et al., 2007, Schade et al., 2010). This paper refers to the latter project GHG TransPoRD. The
BioPOL model is a system dynamics model that is constructed on the VENSIM modelling platform. A
detailed description of the BIOPOL model can be found in Schade& Wiesenthal (2011).

The BioPOL model is a recursive dynamic model that is constructed in the VENSIM modelling
platform. It is based on a year-by-year simulation of biofuel production, production cost and biofuel
demand until 2030. The model delivers detailed outcomes for the different types of biofuels with
regard to production capacity and produced volumes, costs and well-to-wheel emissions of greenhouse
gases. It considers the main production pathways of biofuels, namely first generation biodiesel with
rapeseed and sunflower and first generation ethanol with cereals and sugar beet. Furthermore, it
includes advanced 2"! generation pathways from ligno-cellulosic feedstock. An important issue of
BioPOL is the improved way in which learning for 2” generation is considered.

The paper refers to the work carried out in the GHG TransPoRD project. The main objective of GHG-
TransPoRD was to support the EU in defining a feasible research and policy strategy for GHG
reductions of transport that fits to the overall GHG reduction targets of the EU. As part of this strategy,
the project developed a reference scenario and a set of GHG emission scenarios. A set of GHG
emission reduction scenarios were developed varying the technical measures to reduce GHG
emissions. The technical measures refer to all transport modes including new vehicle technologies like
electric vehicles and hydrogen vehicles. In addition, different biofuel types were pushed into the
market according to the definition of the GHG emission reduction scenarios.

In these scenarios, BioPOL was applied together with energy model POLES and the transport model
ASTRA. The model set derives detailed results on transport performance, economic indicators (e.g.
GDP), vehicle stocks, energy demand, fuel consumption and GHG emission.

This paper focuses on the energy demand, the fuel consumption and the consumption of different
biofuel types.

TABLE OF CONTENTS

1. Introduction
2. Approach.
2.1. Basic structure of BIOPOL .
2.2. Main model equations
2.3. | Main input parameters and output variables
2.4. GHG Emission Reduction
2.5. Learning
2.6. — Interlinkages with other models

' The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official
position of the European Commission
3. Scenarios .
3.1. | Reference Scenario
3.2. GHG emission reduction scenarios.

4. Result:
4.1. Transport energy demand in the Reference and AMB_TP scenarios
4.2. Fuel consumption in the Reference and the AMB_TP scenario
43. 1 and 24 generation biofuels in the reference and the AMB_TP scenario
4.4. Results of the GHG scenarios

5. Conclusions

6. References

1. INTRODUCTION

The aim of this paper is to illustrate the biofuel model BioPOL and its new developments, to describe
a set of scenarios, in which BioPOL was applied and to discuss the results of the scenarios.

This paper focuses on the energy demand, the fuel consumption and the consumption of different
biofuel types.

2. APPROACH

2.1. Basic structure of BIOPOL

The model focuses on the main production pathways of biofuels, namely conventional biodiesel based
on the two feedstocks rapeseed and sunflower and conventional ethanol based on cereals and sugar
beet. Furthermore, it includes advanced 2" generation pathways from ligno-cellulosic feedstock (i.e.
ethanol and synthetic diesel BtL). The model does not assess the direct use of vegetable oils as
transport fuels, which in the year 2008 accounted for only 4% of the total biofuel consumption (in
energy content; Eurobserver, 2009). Also the use of biogas as transport fuel was not included as the
uptake of biogas is mainly driven by the deployment of gas-fuelled vehicles; yet, the modelling of
changes in the vehicle fleet go beyond the scope of the BioPOL model.

Figure | illustrates the main feedback loops of the BioPOL model. Feedback loop | 'feedstock prices!
describes the main feedback loop between feedstock prices, biofuel demand and biofuel production. If
biofuel demand increases then the (domestic) biofuel production capacity and biofuel production
increase. The related increase in feedstock demand means that feedstock prices, and with them biofuel
production costs, also increase, resulting in higher market prices of biofuel. The latter are then
compared with the market prices of fossil fuels to determine the biofuel demand which triggers biofuel
capacity and biofuel production.

+ +
Biofuel Production pi fol Demand Fossil Fuel
Capacity 4 Market Price
+
Biofiel Market
Price
Domestic Biofuel
Production
+ r +

Crop Production

Feedstock Prices —= Biofuel Additional Cost

Production Cost 4
Goh
+ Total Transport Fuel
Consumption

Biofuel Import,
Supply Curve

Biofuel

+ +
a hae _———_»
Biofuel Import Consumption

Figure 1: Basic feedback loop structure of the BioPOL model; Source: based on Schade& Wiesenthal
(2011)

Feedbackloop 2 ‘biofuel imports' describes the relation between domestic biofuel production cost,
biofuel imports, biofuel demand and domestic biofuel production. Rising domestic biofuel production
leads to higher biofuel production cost which in turn increase the amount of imported biofuels. The
biofuel imports are modelled with an exogenous biofuel import supply curve. Note that for imported
biofuels, no production costs are being calculated as it is reasonable to assume that imported biofuels
will not be sold at the production costs in the EU. This is due both to import duties of the WTO
(protecting the domestic market) and a motivation of producers to sell their biofuels at the highest
price possible, thus equaling the lowest price of domestically produced biofuels. For that reason, one
could estimate for imported biofuel to take the lower end of the EU domestic biofuel market prices.
We assumed some strategic pricing so that the costs of imports are slightly (5%) below that of
domestically produced biofuels. The resulting volumes of biofuels that are imported into the EU at that
price are determined based on cost-supply curves, which are taken from Resch et al. (2009).

Feedback loop 3 'technical adaptations! focuses on additional costs — reflecting technical adaptations —
related to certain levels of biofuel consumption. Once biofuel consumption (equalling domestically
produced biofuels and imports) exceeds certain share and passes from low blends to higher blends or
pure biofuels, additional costs occur due to distribution and blending and potentially adaptation of car
engines. These lead to additional costs that form part of the market prices of biofuels.

The market prices of fossil fuels and the total transport fuel consumption are treated exogenous in
BioPOL in order to reduce complexity and to carry out the sensitivity analysis in this paper. However,
it has been shown (Schade et al., 2007; Fiorello et al., 2009) that the BioPOL model can be linked to
the energy model POLES, which then enables the additional analysis of the impact of biofuel
consumption on the price of fossil fuels and transport energy demand.

+ +
Biofuel Production <p io fixe Demand ———— Fossil Fuel
Capacity . Market Price

Biofuel Market

GC Price
Domestic Biofuel a,

Production ———__.. Emission Factors ———# Carbon Taxes

SEEN

Crop Production

+
Feedstock Prices > Biofuel Additional Cost

Production Cost =
wy
a Total Transport Fuel
Consumption

Biofuel Import
Supply Curve

Biofuel

p> Biofie! import ——__p>
~~ Consumption

Figure 2: Further feedback loops within the BioPOL model

Besides the three main feedbackloops BioPOL contains further feedbackloops as illustrated in Figure
2. Feedback loop 4 ‘crop production’ determines the share of different crop types (e.g. wheat and
sugarbeet). An increasing domestic biofuel production leads to rising production of the crops they are
consisting of according to the share of crops in one biofuel. For example bioethanol is produced by
75% of the feedstock coming from wheat and 25% by sugarbeet. The induced increase of crops leads
in turn to higher feedstock prices of both crop types applying. As there increase might be different due
to different feedstock elasticities the price relation might alter. The new feedstock prices have an
impact on the share of wheat and sugarbeet with which bioethanol is produced in the next year.

Feedbackloop 5 'credits' describes the relation between domestic biofuel production, credits and
biofuel market cost. The price obtained for by-products need to be considered in the net biofuel
production costs. The way in which by-products are used (e.g. as chemical or animal feed; as energy
use or feed) can have a significant impact on the net costs. In order to not over-estimate the benefits
and be more realistic vis-a-vis a saturation of by-product markets, it is assumed that in the case of
glycerine from biodiesel production by-products will be used for animal feed rather than as chemical
substitute. DDGS (distiller's dried grains with solubles) from ethanol production will primarily be used
as animal feed (80% of total volume) until production levels reach around 5000 toe. With increasing
production volumes, the energetic use of DDGS increases up to a share of 80% of all by-products at
production levels of 12000 toe. Similar to this, feedback loop 6 ‘emission factors’ focuses on the
impact of how by-products are used on the emission factors of biofuels. Emission factors change
whether the by-products are used as chemical or animal feed. In scenarios in which a carbon tax is
applied the tax level is affected by the use of the by-products which then changes the biofuel market
price.

It has to be clarified that the shown feedback structures are not relevant for all types of biofuel and all
types of crops. Table | Illustrates on which biofuel or a crop type a feedbackloop is referring to. While
in the feedbackloop | the biofuel production of all biofuel types is affecting all feedstock types other
feedbackloops only refer to sub-set of biofuel or feedstocks. Especially feedbackloop 2, 5 and 6 refers
only to biofuels of the 1“ generation. The feedbackloop 3 instead considers bioethanol, biodiesel and
ligno-cellulosic but not BTL. For BTL no technical adaptations are required as and truck engines
can use BTL without adaptations, while in all other cases smaller technical adaptations are required
once they exceed a certain blend (e.g. diesel with a 7% blend of biodiesel).

2.2. Main model equations

The model equations can be grouped into three blocks:
e the biofuel production cost and the feedstock prices
e the market prices of biofuels, fossil fuels and the incentive to increase biofuel production
capacity
e the biofuel production capacity and the domestic biofuel production
In this paper we explain only a couple of important equations. A more detailed explanation on the
equations can be found in Schade& Wiesenthal (2011).
In a first step, the model calculates the production cost ‘cbf,’ per unit of tonne of oil equivalent
(toe) for each type of domestically produced biofuel (see equation 1). cbf, depends on capital costs,
fixed operational costs, energy costs and feedstock minus the price obtained for by-products. The way
how the production cost and its components are derived is shown in the following equations:
cbf, = cap, + opf, + ope, + fsb, —crd, (1)
With cbf: cost of biofuels per toe
cap: capital cost of biofuels per toe
opf: fixed operational cost of biofuels per toe
ope: costs of the energy input for biofuels per toe
fsb tock cost of biofuels per toe
crd: credits cost of biofuels per toe
index b: bioethanol, biodiesel, ligno-cellulosic, BTL

In a second step, the model calculates an equilibrium point for the penetration of biofuels as a
function of final price of biofuels relative to the pump price of fossil fuels. It first determines the
final market price of biofuels (per litre) based on the production costs ‘cbf’ (see equation 2), the prices
of imported biofuels and the applicable tax ‘thf’. This is included through a proxy ‘xbf’. The incentive
'bfi' is determined through the relation of biofuel prices to fossil prices; its level depends on the
distance to the equilibrium point and the profit margin (see equation 3).

pof, = (Le +.1bf, + xbf,) Q)
Ipt,
off
(ee sass 1)
oft = Pee (3)
& ebg

With _ pbf: market price of biofuels per liter
cbf: cost of biofuels per toe
tbf: tax of biofuels per liter
xbf: extra cost (like cost for adaption of vehicles) of biofuels per liter
onversion of toe into liter
bfi: incentive for increasing biofuel capacities
pff: market price of il fuels per liter
pbf: market price of biofuels per liter
ebg: elasticity of biofuel production second generation on feedstock cost
index b: bioethanol, biodiesel, ligno-cellulosic, BTL

In a third step, the model derives the domestic biofuel production. The amount of biofuels
produced is basically determined by the installed production capacities, which in return depend on the
incentive for producers to invest in additional capacities for each type of biofuel. This means that the
trend in the annual biofuel production tends to converge towards the equilibrium point where the final
price of biofuels equals that of the fossil substitutes.

2.3. Main input parameters and output variables

The previous description focused on the main variables and the main equations. Besides those
variables the model provides certain relevant output variables like additional production costs, avoided
GHG emissions and the net benefit.

The additional costs are derived multiplying the biofuel consumption with the cost differences
between biofuels and fossil fuels. The avoided GHG emissions are calculated on the basis of the
biofuel and fossil fuel consumption and their GHG emission factors. To receive the net benefit the
avoided GHG emissions are multiplied with the carbon value and additional production costs are
subtracted.

Figure 3 gives an overview of the use of the exogenous input parameters and of endogenous auxiliary
variables which were not explained in detail in the previous sections such as the fossil fuel production
costs and the operational energy cost.

Input Parameters

Techno- Capital + Credits for Annual

Energy
Oil Profit = Fuel Carbon Max
logical ~—fixcosts Costs. _ by-product fon capacity
leaming inyeart, | inyeart, use (function). Price Margin’ «Taxes Value Depreciation constraint
=] ty
en Fossil Fuel
Capital + || Operational] [F"2cHS fo! Fossil Fuel Market
Fix costs of || Energy y-Product) |p roduction| Price
biofuels ee. Cost
Biofuel Biofuel
Biofuel Market fm! Biofuel Production|
Demand Feedeibck Biofuel ¥ Price Demand Capacity
in year tt Price, Ppp] Production J.,.4| Production ry in yeart in year
Cost Cost tH
Difference Additional
4 v Cost
Vv Domestic (adapt)
Biofuel Prices of ‘Additional
Biofuel | JProduction| imported Pa ieee
Production} | in year biofuels ae
Capacity Output
in year t | Bieta Biofuel Variables
Con- Avoided
Import
Bs sumption GHG Bet
emissions poo
— . I I
ay Biofuel” r i (3
jofuel
| Feedstock Food Transport GHG
ie Ptice demand nPot Fuel emission ae
p elasticities. wheat etc. ae Consumption _ factors.

Input Parameter’ | Main Variable —— Main feedbackloop ——— Impact of input parameters
snae=@> Main feedbackloop with = Other impacts
Auxiliary Output Variable atime delay

Figure 3: Interaction of factors affecting supply and demand of biofuels in BioPOL; Source:
Schade& Wiesenthal (2011)

The BioPOL model depends on a number of exogenous parameters. In order to ensure consistency
between inherently interlinked parameters such as the production processes and emissions that are
specific for every biofuel production pathway and are sensitive to the way of accounting for e.g. by-
products, an effort has been made to stay close to a limited number of studies only. Here, the Well-to-
Wheel Analysis from JEC (JRC/EUCAR/CONCAWE, 2007, 2008) was chosen as a reference work.

2.4. GHG Emission Reduction

The well-to-wheel emissions of biofuels are largely influenced by the use of the primary feedstock and
the use of the by-products and the related credits calculated for them. Also potential land-use change
can largely influence the total greenhouse gas emissions; however, this is usually not been included in
the well-to-wheel emissions provided.

Figure 4 below provides an indication of the potential emission savings when replacing one energy
unit fossil fuels with biofuels. 1* generation biofuels turn out tor reduce GHG emission rather in the
range of 20 to 70%, while GHG emission reductions of 2"' generation biofuels are higher (80 to 95%).
The only exemption to this pattern are the GHG emission savings of 1° generation biogas, which are
in the range of 70-80%.

However, Gameson (2010) points out that further improvements of GHG emission reductions can be
realised by using cleaner energy sources and by adding new enzymes and microbes which enhance the
conversion efficiency.

Note that the specific emission reductions as shown above in Figure 4 do not take into account the
effects of indirect land use changes. These can largely influence the net emissions as shown for

example in WBGU (2010), in Al —Raffai et al. (2010) and in (Croezen, 2010). Moreover, Crutzen et
al. (2008) pointed out that that the N2O emissions caused by fertilizer use should be well above the
default values of the IPCC approach. Applying a higher conversion factor from N to N20 could lead
to higher specific emissions of biofuels.

0
-25

_
a3 &
25
3:
25
ao &
£3 .
3 ¢
as % 3
e =
- Q wo
BE g a $

a a
3~ £ ¥ = 4 wii
3 3 (8 5 5 e & #
3 & 8 7 8 i

5s 2 B
Q & 3 | o
FA -100 3
£ \ J \ J A
Lg Y Y
VY y.
gasoline substitutes diesel substitutes kerosene substitutes gas substitutes

Figure 4: GHG emission reduction

2.5. Learning

Learning curves are a major vehicle to describe the relation between RD input and technological
development. It is taken in to consideration that market activities influencing technological
developments of e.g. biofuels take place EU and non-EU. Therefore, trends for RD investment and
biofuel production outside of EU have been developed and kept fix for all scenarios, while they vary
in the EU depending on the scenario. The trend of the investment cost is then derived on the global RD
investments and the global cumulative production of a specific biofuel technology. In the case of
biofuel the most relevant technologies were investment cost decrease significantly due to learning
were ligno-cellulosic ethanol, BTL and DME.

A specific issue related to the learning curve approach is the valley of the death. Due to a lack of
competitiveness of new technologies they do not enter the market. As they do not enter the market the
cumulative production doesn't rise and they do not learn sufficiently to gain the necessary level of
competitiveness. To overcome the valley of death in some cases investment programs are assumed,
which push some of the new technologies in the market.

1.1.1. Biofuel production plants

The estimation of learning rates is based on a time series of biofuel production plants. The information
on biofuel production plants stem from databases from IEA/OECD ((IEA bioenergy, 2008; IEA
bioenergy, 2010) and biofueldigest (biofueldigest, 2011). They were completed with company
information to fill the gap when investment figures or the status of a specific plant was missing. In
several cases financial data were missing and had to be completed by company information gained
from internet or company brochures.

Table 2 shows a selection of the resulting database on biofuel production plants that was used to
estimate the learning rates. The database contains information on the location, raw material, pathway,
type of facility, capacity, plant type, private investment, public funding, status of the plant and starting
year. The database considers plants producing biobutanol, Fischer-Tropsch diesel (BTL), dymethyl
ether (DME), methanol, ammonia, biogas (SNG), ligno-cellulosic ethanol, algae-based biofuels,
biodiesel from starch and hydrotreated vegetable oils (HVO). No 1° generation biofuels are
considered. It contains information on operational and planned power plants between 1990 and 2016.
It is distinguished between pilot, demonstration and commercial production plants.

Table 2: Biofuel production plants

?
2 H 2 g j i
H i 5 5 i 2
HI 2 | 2a i]iladlelil adel £ FEL € fa} ete
4 i :/e]/¢e]¢) 2 )4] 2 |a]o2 |e] aya
g g 3 E 2/8 a | a 3 2 g s| 2 4
F i i} ae}; el] Ey) ele} g [a] bP le] @ |e
s é é g g — ri é
E g 5 z i E
E
aaron ral na cfr ——_Jua ganar [wn fanaa] alo 7 amet foo
Any ace fms Usk [Enea inked Sotsfemetolrone farce nt fcaton [aoe
nr Gyaaes mr pi lcaiae Jaret —_[emeratfgurcane yar] allt rr coefino
rs Gye ne cenmeStanerto asi [emenabe igre ere] Woon] 100000000] Usb panes so
ae pie [ona etal carmany ication wf rraqaae] ofp feat [00
eronen acannon —[retag —[oxmany mca wood Tagua] Sa brn on
Kern Uavasiy fF pe —[aissing ust [locals reais] ope sat [nos
ae nc oa Teor ayaa ars wrangell pleas oa ro aan rate] —Jeatert_foos
amen Reawafearotaey aofouram ——_umte sgntalftaueed igus] Sod | —sooonoluso — | een] 05>—eprtna_[za
Rise actu Oyen ——ataus nant —ignctafiea edict | leno fotos
oti cas Tecwotper [bes anes maa geal we red 2 Tobe 95 nds ane fo30
[ronan Fat Fee pat Feta —[owrany [roc ny wont rraqade| 00a] eon OR nr comma
ew Pagetiotuee Lk | wicons United St dgnocll Foret rel Figs] — 16000] demo | 84000000] uso | 3000000050 planned [2012
ants ve offroma Toe _Pak Fae [mie Stegner eed Taquc] —aeoo0 ice | —avonaaaa|iso —| swoon para [ia
Reseach Teresita poate Fanfuniad i ignakeses [ergata] 2a) 2ozou eo | avonluso owed

Source: IEA/OECD, biofueldigest, gap-filled with company information

In total, we investigate the data of some 80 power plants out of which the major part are pilot power
plants. In general pilot plant shave a rather small capacity below 1000 t/a, demonstration facilities are
bigger and most of the commercial plants are designed for more than 100000 t/a. Commercial plants
became operational after 2007 starting with HVO (2007) and DME (2009). The first amounts of ligno-
cellulosic ethanol from commercial plants were produced in 2010 (Range Fuels, 2010).

The number of pilot plants is increasing very strongly. While there were only 6 pilot plants in 2005,
the number of pilot plants increased to 24 in 2010 and is expected to increase further. During the same
time period the cumulative amount of investment quadrupled (seeFigure 5).
Cumulative investment in pilot plants

Millions Euro

160
140 ve
120
100
80 A
. rae
4 a
20 ES
Se
0

2001 «2002, 2003S 2004 )= 2005 2006) 2007 2008 )39 2009 2010 2011S 012
year

Figure 5: Cumulative investment in pilot plants
Source: Own calculation based on data from IEA/OECD, biofueldigest, company information

While HVO is already produced in large quantities ligno-cellulosic ethanol production facilities form
with 50 plants the biggest part of the plant database followed by BTL plants with 14 facilities.
Especially, ligno-cellulosic and BTL are expected to have much higher cost reduction compared to
other types of biofuels e.g. methanol (OECD, 2008b). Figure 6 shows the development of the
production capacity of different biofuel production pathways. HVO and DME plants are already in a
commercial phase, but a high number lingo-cellulosic, BTL and other 2" generation biofuel plants are
expected to become operational in the coming years.

Production capacity

Algae
BTL (FT)
[Biodiesel (other routes)

Biogas
mBiobutanol

CLigno-cellulosic
CDME, Methanol
mHVO

year

Figure 6: Development of biofuel production capacity
Source: Own calculation based on data from IEA/OECD, biofueldigest, company information

1.1.2. Learning rate of biofuel production
The learning rates of biofuel production were estimated based on the concept of a One-Factor-
Learning Curve (OFLC) and — if possible — on a Two-Factor-Learning Curve (TFLC). In both cases
the capital cost per capacity forms the independent variable. For the OFLC the capital cost per unit
production depends on the development of the cumulative capacity:

C,, =mQ,, (4)

with C = Capital costs per capacity, €/(t/a)

= Cumulative Capacity, t/a

Elasticity of learning (learning index)

= normalisation parameter with respect to initial conditions
Technology

= Period (year)

The parameters of the OFLC were estimated for ligno-cellulosic ethanol, BTL and DME. Figure 7
illustrates the decrease of capital cost, while the cumulative capacity of pilot, demonstration and
commercial plants increased for ligno-cellulosic. Based on 38 data points we derived an elasticity of
learning of -0.36 which equals a learning rate of 0.22.

<7" 370
I

Ligno-cellulosic ethanol
(pilot, demo, commercial)

1000000
2 y = 354691x°%%
g R? = 0.8801
@ 100000
:
°
oO
= 10000
2 .
¢
oO
& 1000
£
$
&

100

100
1000
10000
100000
1000000
10000000

cum Production Capacity

Figure 7: Learning curve for Ligno-cellulosic ethanol
Source: Own calculation based on data from IEA/OECD, biofiueldigest, company information

With respect to BTL we have 11 data points (see Figure 8). We estimated an elasticity of learning of -
0.14 which equals a learning rate of 0.09. In the case of DME the estimation is based on only 2 data
points (see Figure 9). We derived an elasticity of learning of -0.26 which equals a learning rate of
0.17.
BTL
(pilot, demo, commmercial)

1000000
y= 25353x°%25
2 R’ = 0.8561
8 100000
8
8
§
3
7
8
oo
B 10000 ne
a
s
oO
rs
a
£ 1000
3
&
100

- é 8 g g

100000

4000000
40000000

cum Production Capacity

Figure 8: Learning curve for BTL
Source: Own calculation based on data from IEA/OECD, biofueldigest, company information

DME
(pilot, demo, commmercial)

1000000
y = 5841x063
2 R?=14
S 100000
Q
&
2
8 10000
3° ae ee
®
a
4 1000
>
&
100

2
8

1000
10000
100000
1000000

cum Production Capacity

Figure 9: Learning curve for DME
Source: Own calculation based on data from IEA/OECD, biofueldigest, company information

However, estimating the factors that drive the learning of a given technology is a multi-dimensional
problem (for wider discussion see Wiesenthal et al. 2010). In general, several factors like spillover
effects, scaling, cost of material inputs and data availability incur some uncertainties. In this very
specific estimation with the exception of ligno-cellulosic ethanol the number of data points is low and
that the time series are rather short (2003-2014) to estimate a learning curve with three estimated
parameters. Especially in the case of DME one might argue that the cost decrease most probably rather
reflect scale effects than learning effects as we have here only two data points which differ
significantly in the capacity of the DME plants. Furthermore, the investments in pilot plants form only
one part of the RD investment. E.g. RD investment undertaken at universities undertaken with public
funding are not considered. In addition, some of the cost information refers to biofuel production
plants which are under construction and cost figures might change until the construction is finished.

Table 3: Overview on learning rates for biofuels
Measure Learning rate | Dependent Independent Area Source
variable variable
Bioethanol 0.13—0.22 | Sales price Cumulative Brazil, USA Goldemberg
production (1975 — 2005) | (1996), Van den
Wall Bake (2009),
Hettinga (2009),
de Wit (2010)
0.07 Brazil, (1980 — | Goldemberg
1985) (2004)
0.29 Brazil, (1985 Goldemberg (2004
2002)
Biodiesel 0.10 Investment/ | Cumulative EU25 De Wit (2010)
operating production (1993 — 2004)
cost
Ligno- 0.10 assumption IEA (2008)
cellulosic 0.22 Investment | Cumulative World Own estimate
cost production (2003 — 2014)
capacity (all
plants)
BTL 0.10 assumption IEA (2008)
0.09 Investment | Cumulative World Own estimate
cost production (2003 — 2014)
capacity
DME 0.17 Investment | Cumulative World Own estimate
cost production (2003 — 2014)
capacity
Biogas 0.12 Investment | Cumulative Denmark Junginger (2006)
cost production (1984 — 2001)
0.15 Biogas pro- | capacity (1984 — 1990)
0.00 duction cost (1991 — 2001)

Source: Own calculation and various studies

However, the estimated are broadly in line with learning rates of other studies (see Table 3). Several
studies investigated the learning rates of 1“ generation bioethanol and biodiesel which were in the
range of 0.07 to 0.29, the learning rates for bioethanol being at the higher end of the range.

With respect to 2" generation biofuels, the IEA assumed learning rates of 0.10 for ligno-cellulosic
ethanol and BTL. Junginger (2006) derived learning rates for biogas in the range of 0 and 0.15
depending on the time span and cost function.

As main outcome we receive learning rates for ligno-cellulosic ethanol and BTL of 0.22 and 0.09
respectively applying a One-Factor-Learning-Curve approach. The range of the learning rate is in line
with the estimations and/or assumptions of other studies. The learning rate of ligno-cellulosic ethanol
is higher than of BTL which is supported by the higher activity (number of projects) in this sector.

The implementation of learning leads to a new feedback loop. Feedback loop 8, which is a positive
feedback loop, is illustrated in Figure 10. Rising biofuel production capacity leads to an increase of the
cumulative production capacity, which in turn reduces the biofuel capital costs. Lower biofuel
production cost lead to lower biofuel market prices and higher biofuel demand. Higher biofuel demand
leads to an increase of biofuel production capacity.

Feedback loop 8 is reinforcing feedback. It means on one hand, ones a certain biofuel type overcomes
market barriers and enters the market, it will further reduce its production cost and, therefore, will
come into the market even stronger. On the other hand, it hints, that if a certain biofuel type doesn't

enter the market, the production cost won't change and it might never enter the market. As most of the
feedback loops were dampening the impact of the reinforcing feedback loop is limited.

"
Biofiel Production Rie Demand Fossil Fuel
Canscty. . Market Price
+
¢
Cumulative
Production Capacity Biofuel Market
7+ Price
Domestic Biofuel
Production
Biofuel Capital “g
Cost 4

aE
Crop Production

+
Feedstock Priees——f>_—_—Biofirel Additional Cost

Production Cost
.
wy
. Total Transport Fuel
Consumption

Biofuel Import + Gbiclimedt 4 Biofitel
_—— —
Supply Curve oer DON Consumption

Figure 10: Implementation of learning rates

2.6. Interlinkages with other models

In GHG TransPoRD BIOPOL was applied together with a set of models. During the simulation
BIOPOL was interlinked with the energy model POLES and the transport model ASTRA. ASTRA.
(Assessment of Transport Strategies) is applied for Integrated Assessment of policy strategies. The
model is implemented as System Dynamics model. The ASTRA model has been developed and
applied in a sequence of European research and consultancy projects for more than 10 years now by
three Institutions: Fraunhofer-ISI, IWW and TRT. Applications included analysis of transport policy
(e.g. TIPMAC, TRIAS), climate policy (e.g. ADAM project) or renewables policy (e.g. Employ-RES
project). The ASTRA model consists of nine modules that are all implemented within one Vensim©
system dynamics software file.

The POLES (Prospective Outlook for the Long term Energy System) model is a global sectoral
simulation model for the development of energy scenarios until 2050. POLES has been developed and
applied in a variety of EU projects, e.g. the WETO, WETO-H2, TRIAS, HOP! and GRP project. The
dynamics of the model is based on a recursive (year by year) simulation process of energy demand and
supply with lagged adjustments to prices and a feedback loop through international energy price.
BIOPOL POLES ASTRA

. “\ GS
A Rite
Market Price 2

Pe
Biofuel Production jp uel Denman

Capacity 7 3

i‘ an: Fuel Fuel consumption

Price per vehicle
Bios Ze
Price ha
Domestic Bi Big

Production m SK

. a > Fuel
a ‘Consumption
Crop Production
Fcauenewe™ Biofuel Additional YS wae

Production Cost

G) =e
Gs

Biofuel Import__* + Biofiel
Biotic! Import»
Supply Curve Biofie! Import ‘Consumption

Figure 11: Feedback loops including linkages between the models BioPOL, POLES and ASTRA

The connection establishes a feedback (8) on the oil price (determined in POLES) and the transport
fuel consumption (determined in ASTRA). Rising biofuel demand reduces fossil fuel demand, which
in turn reduces oil prices and transport fuel demand including fossil fuel and biofuel demand.

A positive feedback loop (9) is established related to the additional costs (vehicle adaptation) by
linking the models. Rising biofuel market prices lead to higher transport prices and to a lower
transport performance and lower transport fuel demand. This affects the biofuel share and the
increases the blending of biofuels in transport fuel, which has an impact on the required vehicle
adaptations to run transport vehicles with higher blends.

Both, POLES and ASTRA, contain a number of further feedback loops like e.g. on the biomass
potential, which are not further described in this article.
3. | SCENARIOS

3.1. Reference Scenario

The Reference Scenario is the scenario against which the GHG emission reduction scenarios were
tested in GHG TransPoRD. The Reference Scenario includes assumptions about exogenous trends
(e.g. economic growth) but also about the endogenous variables in GHG-TransPoRD such as e.g.
transport demand, energy supply and demand, transport emissions. Furthermore, the reference
scenario includes some transport policies.
The GHG-TransPoRD Reference Scenario is based on two main sources:
+ Until 2030 the Reference Scenario is taken from the PRIMES as defined in the document
“EU energy trends to 2030 — UPDATE 2009” (EC, 2010). This reference scenario is the
one used for assessment of the White Paper of the European Commission
+ From 2030 to 2050 the Reference Scenario is extended using the ADAM reference scenario.
Despite the PRIMES reference scenario can be considered a sort of forecasting exercise trying to
anticipate a possible future, the role of the GHG-TransPoRD Reference Scenario is just to provide a
benchmark. It does not imply any strong belief on the future development of the economy or of the
transport demand or of the energy sector.

3.1.1. Socioeconomic assumptions in the Reference Scenario

The PRIMES reference scenario assumes that the economic crisis has long lasting effects leading to a
permanent loss in GDP. At the same time, while the average EU-27 growth rate for the period 2000-
2010 is only 1.2% per year, the projected rate for 2010-2020 is recovering to 2.2%, similar to the
historical average growth rate between 1990 and 2000. This assumption is challenged by the economic
trend registered in 2010 and 2011. Short term forecasts (e.g. OECD 2011) for the incoming years are
also quite below 2% per year. Therefore, the PRIMES scenario can be considered on the optimistic
side. Between 2020-2030 the growth rate is slightly reduced to about 2% per year. Between 2030 and
2050 the growth rate, taken from the ADAM reference scenario, is further lowered to 1.8%.

The population projections for EU27 are based on the EUROPOP2008 convergence scenario
(EUROpean POpulation Projections, base year 2008) from Eurostat. The demographic projection
includes a dynamic immigration trend which helps keeping positive growth rates but is not sufficient
to sustain higher growth. Both total population and active population are assumed to grow at positive,
albeit very low, growth rates over the entire projection period; this contrasts past scenarios.

The assumptions concerning the energy prices trend was taken from POLES rather than from the
PRIMES scenario (also to get a consistent picture until 2050), however the two projections are quite
similar until 2030 as far as oil price is concerned. There is a general consensus among the experts that
the rise of energy prices should be regarded as a structural condition due to the foreseeable trend of
demand and supply. The rising demand from fast developing regions and uncertainty about the future
availability of cheap resources suggest that crude oil prices will not fall back to the low levels
observed before 2007. It was therefore assumed that they rise from present prices and then remain at
high levels at around 80 €2005/bbl in 2020, almost 90 €2005/bbl in 2030 and nearly 110 €2005/bbl in
2050. Gas prices are assumed to increase in a similar pattern but at a slower pace, reflecting the
dynamics of the inter-fuel competition and the rising supply costs. Coal prices increase by only one
third due to the ample reserves.

3.1.2. Policy content of the Reference Scenario

The PRIMES reference scenario includes assumptions on the policy content. Measures implemented
in the Member States by April 2009 and legislative provisions adopted by April 2009 that are defined
in such a way that there is almost no uncertainty how they should be implemented in the future are
within this scenario. As far as the transport sector is concerned, the main measures considered are:
+ Regulation on CO2 from cars 2009/443/EC (binding CO2 emission targets for cars: 135 g
CO2/km in 2015; 115 g CO2/km in 2020; 95 g CO2/km in 2025).

+ Labelling regulation for tyres 2009/1222/EC

+ Regulation Euro VI for heavy duty vehicles 2009/595/EC

+ RES directive 2009/28/EC on the promotion of the use of energy from renewable sources;
10% target for renewables in transport is achieved for EU27

With respect to biofuels we considered the Communication from the Commission on the practical
implementation of the EU biofuels, which sets minimum GHG emission savings for biofuels (EC
2010). The communication requires a greenhouse gas emission saving of 35 % (rising to 50 % in
January 2017, and 60 % in January 2018 for installations in which production started from 2017
onwards). It is assumed that biofuel plants will be replaced successively after a lifetime of 12 years.
This means that after 2030 all biofuel plants will fulfill the greenhouse gas emission savings of 60%.

3.1.3. Reference trends for endogenous variables

The Reference Scenario includes forecasts for several variables which are endogenous in GHG-
TransPoRD, e.g. transport demand, energy consumption in the transport sector and transport
emissions. For these variables the Reference Scenario is actually a reference, i.e. a comparison term
for the modelling results. Therefore models calibration was revised to be consistent with the reference
trends before to apply policy input.

Table 4 summarises the key trends of the Reference Scenario. In PRIMES transport demand is
expected to growth until the year 2030 but less than the GDP. Passenger and freight are expected to
have a very similar trend. In the ADAM projections, after 2030 passenger demand is expected to
decline slightly (at least partially for demographic reasons) while freight traffic should continue its
growth although at slower pace. The energy consumption in the transport sector is stagnating for the
whole period, while CO2 emissions from transport are expected to decrease slowly until 2030 and
restart a slight increase beyond that time. The transport sector should perform worse than other sectors
in terms of emissions reductions as in overall terms the PRIMES scenario assumes that CO2 emissions
are reduced faster (even if not so fast in absolute terms).

Table 4:Summary of key trends of endogenous variables in the reference scenario

Variable Average growth rates per year (%)
2010-2030 2030-2050

Passengers-km 1.2: -0.2
Tonnes-km (maritime excluded) 1.3 1.0
Energy demand (transport) 0.1 0.0
CO, Emissions (transport, tank to -0.2 0.2
wheel))
CO2 Emissions (total) -0.8

Source: EU energy trends to 2030 — UPDATE 2009, ADAM project

In summary, in the Reference Scenario the transport sector is very far from any emissions reduction
target. Despite some gains in energy efficiency, which allows stopping the growth of transport energy
demand, CO2 emissions in the year 2050 are above the 1990 level.
Fuel Consumption of Transport per Mode
250
200 +
B Road transport cars
150 4 m Road transport freight
o
2 g Rail
100 + m Aviation
Oo Other transport
50 + D
04
2000 2010 2020 2030 2040 2050

Figure 12: Fuel consumption per transport mode

With respect to fuel consumption, we identified strong growth of fuel consumption for aviation and
road freight, while only moderate growth is projected for road passenger and rail transport.

3.1.4. Biofuel production, consumption and share

The biofuel consumption is expected to increase over time reaching a share of somewhat 7.5% in
2020. From the beginning until 2015 biodiesel is expected to have the highest share in biofuel
consumption, while bioethanol is becoming the dominant biofuel afterwards. 2™ generation biofuels
enter the market around 2020. 1* generation bioethanol reaches the highest share of biofuels in 2030
and declines thereafter. After 2030 2" generation bioethanol reaches the highest share and keeps its
level until 2050. The highest peak in the biofuel production is around 2030, due to the required GHG
emission reductions savings of 60%. It is assumed that biofuel plants will be replaced successively
after a lifetime of 12 years. This means that after 2030 all biofuel plants will fulfill the greenhouse gas
emission savings of 60%.

Reference Scenario

60
IB kerosene
1 gas 2nd gen
50 gas 1st gen
IB diesel 2nd gen
B diesel 1st gen
40 I. gaso 2nd gen
1 gaso 1st gen
30
20
10
0

Source: GHG transPoRD, BIOPOL

Figure 13: Biofuel consumption in the reference scenario Source: GHG TransPoRD
3.2. GHG emission reduction scenarios

Each scenario consists of a different bundle of measures, either technological or policies or both.
When packages of policies are defined, two main approaches can be used. One approach is to select
measures according to some criteria (e.g. by transport mode, by technological content, etc.) and then
measuring their impact. An alternative approach is to set impact targets and put together measures
potentially capable to meet the targets. This second approach — backcasting approach — has been
followed in GHG-TransPoRD.
Ambitious greenhouse gas reduction targets have been the guiding principle as this is in line with the
higher policy framework (e.g. the White Paper mentioning a 60% reduction as a goal of European
policy). Among all the measures analysed in the previous packages, the selection has been made based
on their effectiveness (potential amount of reduction provided) and efficiency (potential abatement
cost per tonne).
A first set of scenarios has been defined to make initial modelling simulations and analyse the
forecasted impacts in comparison to theoretical potential and abatement costs appraised previously.
Three main scenarios have been considered in this initial phase. The first was a “Maximum
Technology” scenario where basically all the technological measures are included. The second
scenario picked up a selection of technical measures which, according to the estimated potential, are
able to reduce greenhouse gas emissions by 60% at the horizon of the year 2050. The third scenario
added some policy measures — both universal and urban ones — still aiming at the same reduction
targets.
The results of the simulation of such scenarios and their discussion with stakeholder in a public
workshop provided some useful indications, namely:
+ Most of the technological instruments initially selected are needed to meet or get
close to emissions reduction targets.
+ Market penetration of innovative vehicles can be crowded out by efficiency
improvements of conventional vehicles.
+ A noticeable rebound effect on transport demand can be expected as result of
significantly more energy-efficient vehicles.
+ Ambitious targets for renewable energy sources are needed in addition to emissions
reduction targets.
+ Policy measures should be very strong to be effective and should change behavioural
habits.

These indications have been used to define the final set of scenarios for the techno-economic
assessment in WP4:

a) MAX _E&M: Maximum Efficiency at Market conditions. This scenario includes most
of the technological measures for all modes, including both conventional and
innovative cars. Neither the latter nor biofuels are supported by dedicated policy to
promote their penetration in the market.

b) EV: Electric Vehicles. In this scenario the technological effort is concentrated on
Electric Vehicles (although some technological development is assumed also for
conventional road vehicles and other modes). Furthermore, additional supporting
policies for Electric Vehicles (e.g. feebate schemes) are supposed to be in place to
promote the diffusion of Electric vehicles.

c) HFC: Hydrogen Fuel Cells vehicles. This scenario follows the same approach of the
EV scenario, but the technological effort and the supporting policies is concentrated on
Hydrogen Fuel Cell vehicles.

d) EV+HFC. This scenario is the combination of the EV and HFC scenarios. In particular,
supporting policies do not select in advance one of the two technologies, but are
applied to promote both (roughly with the same amount of resources split between the
two).
e) AMB_TP: Ambitious Technology and Policy. This scenario shares the same
technological measures as in the MAX_E&M scenario plus the additional supporting
policies for Electric and Hydrogen Fuel Cells vehicles. Additionally other policy
instruments are assumed at urban and universal level (including urban charges,
promotion of walking and cycling, promotion of efficient logistics. Last but not least, a
huge increase of fuel taxation (on average up to +200% with respect to 2010 value) is
assumed in order to contrast demand rebound effect and offset fuel taxation revenues
loss determined by more efficient vehicles.

Table 5 provides a summary of the scenarios and of their content.

Table 5: Summary of scenarios tested by GHG-TransPoRD

Policy bundles
2 3 a.
cd ry & a
a > x |
! a = + 2
c B
=
Technologies
Conventional road x x x x x
Electric vehicles x x x x
Fuel cells vehicles x x x
Non road x x x x x
Policies
Universal x
Urban x
Support innovative vehicles x x x x
Drastic fuel taxes x x
Biofuels xX x xX x xX
Renewables x x x x x

With respect to biofuels only a specific set of biofuels are supported by investment programs. The
choice of the biofuels being supported depends on the definition of the scenario. In the scenarios with
fuel cell vehicles, biogas production is supported

Table 6: Biofuel policies in the GHG emission reduction scenarios of GHG-TransPoRD
Policy bundles

=
3 & g &
4 > 2 = al
x EI x z
a &
3 a
Biofuel technologies
Biogas x x x
BTL x x xX
HVO xX x x
4. RESULTS

The section on results is separated in a comparison of results where we compared the reference
scenario with a specific GHG emission scenario and section in which we compare all scenarios. For
the specific comparison we have chosen the AMB_TP scenario out of the GHG emission scenarios.

4.1. Transport energy demand in the Reference and AMB_TP scenarios

In the GHG scenarios we derive a break in trend for the energy demand of road passenger transport
and road freight transport. After a strong reduction until 2030 the development of energy demand
remains stable around 125 mtoe and 40 mtoe respectively. Energy demand of aviation is curbed and
remains at a level of 50 mtoe, while it was experiencing strong growth in the reference scenario.
Energy demand of rail transport is quite similar in all scenarios.

Reference Scenario AMB_TP Scenario
Fuel Consumption of Transport per Fu Fuel Consumption of Transport per Fuel Type
250 250
200 200
@ Road transport cars
450 150 m Road transport freight
2 ®
2 2 a Rail
100 100 Aviation
50 50 Other transport
oO 0
2000 2010 2020 2030 2040 2050 2000 2010 2020 2030 2040 2050

Source: GHG TransPoRD
Figure 14: Fuel consumption per transport mode

4.2. Fuel consumption in the Reference and the AMB_TP scenario

This affects of course the development of fuel consumption. While we were determining a strong
increase of diesel consumption in the reference scenario, diesel consumption remains at a level of
around 100 mtoe. Gasoline consumption drops to around 25 mtoe, while we derive an increase of
hydrogen consumption to about 20 mtoe.

Reference Scenario AMB_TP Scenario
Fuel Consumption of Transport per Fuel Type Fuel Consumption of Transport per Fuel Type
300 300
250 250 m Diesel
w Gasoline
200 200
m Kerosene
3 150 3 150 Electricity
= =
400 400 m Biofuels
tHydrogen
50 50 Gas
0 0
2000 =2010 §=2020 92030 += 2040 = 2050} 2000 2010 2020 2030 2040 2050

Source: GHG TransPoRD
Figure 15: Fuel consumption per Fuel Type
If we assign the biofuels to the fuel types they are blended with we receive Figure 16. While we have
in the reference scenario in 2050 mostly bioethanol, there is almost no bio-kerosene and only very
little biodiesel.

Reference Scenario AMB_TP Scenario
@350
3
&
‘300 B Hydrogen
DB Electricity
250 @ Biofuel 2nd
@ Biofuel 1st
200 @ Fossil fuel
150
100
“ail
3 | Be ma |

Gasoline Diesel Kerosene Gas Electricity Hydrogen Gasoline Diesel Kerosene Gas Electricity Hydrogen

Source: GHG TransPoRD
Figure 16: Fuel consumption with biofuel blends in 2050

The picture is completely different for the AMB_TP scenario. The share of bioethanol and of biogas is
limited by the blending limits of vehicles 20% for gasoline; 80% for gas). In principle, it would be
possible to produce more bioethanol and biogas at competitive costs, but the limitation is here on the
demand side.

For biodiesel and bio-kerosene we have a different picture. Both only marginally entered the market in
the reference scenario. And as they didn't enter the market, they also didn't experience strong cost
reductions due to learning. But in the AMB_TP scenario they received an investment incentive at the
beginning of the simulation. Based on this they enter the market and experience cost reductions which
pushes them further into the market until higher biodiesel and bio-kerosene demand lead to an increase
of feedstock costs. The rise in feedstock cost limits the further market diffusion of biodiesel and bio-
kerosene. Both could be used with higher blends: for biodiesel in passenger cars we assume a blending
of 7% and for trucks with 20%. In aviation the blending could be significant higher at about 80%.
From this result we derive that bioethanol and biogas is limited from the demand side, while biodiesel
and bio-kerosene are limited from the supply side.

4.3. 1“ and 2" generation biofuels in the reference and the AMB_TP

scenario

Figure 17 provides a more detailed view on the development of specific biofuel pathways (see
description of reference scenario in 3.1.4). It shows that until 2020 the most important biofuels are the
1* generation biofuels. Biodiesel reaches its peak before 2015 and remains at this level, while
bioethanol picks up fast until 2020, followed by a lower increase until 2030. 2" generation biodiesel,
bioethanol and biogas enter the market around 2020 in both scenarios. Overall biofuel consumption
reaches its peak at around 2030, but the biofuel consumption decreases thereafter due to the
requirements of a certain level of GHG emission savings.
Reference Scenario AMB. TP Scenario

260
8
=
50
kerosene
40 @ gas 2nd gen
W gas tst gen
B diesel 2nd gen
30 1 diesel 1st gen
1 gaso 2nd gen
20 | [m-gaso Ast gen
10
0

°
3S
a

ice: GHG TransPoRD, BIOPOL

Figure 17: Ist and 2nd generation biofuels in the reference and the AMB_TP scenario

However, Figure 17 reveals also a number of differences: overall biofuel consumption remains on a
level of around 40 mtoe (AMB_TP) instead of 30 mtoe (reference). The biofuel consumption of 2"
generation biodiesel and biokerosene is much higher in the AMB_TP scenario, while 2’ generation
bioethanol is much lower. The main reason for this is that the reduction of gasoline demand limits the
consumption of 2"! generation bioethanol. Diesel demand remains at quite high levels and as in
AMB._TP bio-kerosene and 2™ generation biodiesel are fostered they enter the market and substitute
diesel and kerosene. Furthermore, the AMB_TP scenario reaches a higher level of 1“ and 2™
generation biogas due to the fact that in AMB_TP more gas vehicles diffuse into the market. In
combination with the support of biogas this leads to an elevated level of biogas consumption.

4.4. Overview on all GHG emission reduction scenarios

The comparison of the final energy demand per transport sector reveals that there are only
minor differences between the scenarios in the year 2020. In 2030, substantial differences
between the reference and the GHG emission reduction scenarios emerge. In the reference
scenario the overall final demand is around 480 mtoe. In the set of GHG emission reduction
scenarios with only technical measures, the final demand ranges between 260 and 340 mtoe.
If further policies are applied, the final energy demand could decrease to 180-220 mtoe.
Looking at the variation in consumption of different fuel types, the finding are that electricity
and hydrogen differ strongly between the scenarios as some of the scenarios bring electric
vehicles or hydrogen vehicles strongly in to the market. In contrast to this the biofuel
consumption looks rather similar and stays within a range of 26 to 37 mtoe. However, the
Table 7 has to be interpreted with care, when discussing the results on diesel, gasoline and
kerosene. The indicated energy demand for diesel, gasoline, gas and kerosene refers to pure
amount of these fuel types and does not reflect any blending. This means, to give a correct
picture biofuels have to be split into bioethanol, biodiesel, biogas and biokerosene and to be
added on the fossil fuel demand they refer to.

Table 7: Final energy demand in the reference scenario and the GHG emission reduction scenarios
(source: POLES)

Final 2 I g ; 2 5 8
energy faa} a faa} fac}
4 | & Z Oo + af o| F

aemmi..] 8/38) 2) 2) 2) 2| 2) 888) 2| 2) 2] 2 2

-.per fuel | 388 | 329 | 365 | 374 | 365 | 322 | 320 | 478 | 265 | 313 | 340 | 297 | 224 | 185
Diesel 219 | 178 | 205 | 209 | 203 | 170 | 168 | 305 | 152 | 196 | 169 | 131 | 121 | 44
Gasoline 70 65| 69| 73 72| 64| 63| 57| 23| 28] 21/ 16| 22 8
Kerosene 62 56 | 55| 56 55| 55| 55| 83| 45| 25] 47/ 25| 26/ 26
Electricity 5 5 8 5 7 5 6 6 4| 21 6| 16 4| 23
Biofuels 23 21| 24| 23 26/ 25/ 25/ 26| 31/ 35| 33| 35| 37| 32
Hydrogen 0 0 0 0 (e) ie) 0 0 9 6| 63/ 74] 14/ 51
Gas 7 4 4 7 2 2 2 1 1 2 1 0 ie) ie)
ea}

sein 388 | 329 | 365 | 374 | 365 | 322 | 320 | 478 | 265 | 313 | 340 | 297 | 224 | 185
Road

transport

cars 180 | 151 | 171 | 176 | 171 | 146 | 145 | 193 | 109 | 129 | 142 | 120| 86| 65
Road

transport

freight 125 | 105| 119 | 123 | 119 | 102 | 101 | 178 | 100 | 119 | 131 | 111 | 80| 60
Rail 11 8 9/10 10 9 9| 13 6| 10/ 10] 10 6 7
Aviation 63 57 | 57 | 57 57 | 57| 57| 84| 47| 47| 48/ 48| 49/ 49
Other

transport 8 8 8 8 9 8 8 9 3 8 8 8 3 3

Table 8 illustrates the biofuel consumption per biofuel type. While in 2020 overall biofuel
consumption and consumption per biofuel type is quite similar, the picture changes when
looking at different biofuel types in 2050.

The main difference between the scenarios is whether 2° generation biodiesel and bio-
kerosene enters the market or not. If these two biofuel types enter the market and become
competitive then they might reach together a level of around 23 mtoe or around 2/3 of the
overall biofuel consumption. In those scenarios the nd generation bioethanol consumption is
reduced as 2"! generation biodiesel and bioethanol are produced with the same feedstock.

A high variation of biofuel consumption can be identified for biogas. Main important factor
for the variation of biogas is the diffusion of gas vehicles into the market.

The avoided GHG emission reduction could be in the range of 80 to 110 mt CO2 eq. The
avoided GHG emission considers the GHG emission savings per biofuel type, but do not take
into account the effects of indirect land use changes (see discussion in section 2.4).

Table 8: Comparison of biofuel consumption per biofuel type in the reference scenario and the GHG
emission reduction scenarios (source: BioPOL)

a| 8 a | 2

le zB) oi) & Ie E) o|®
a fy iS) = fo iS) e
pot! | $22] 2/2] 6[ 2) 2| Se2[ 2[ E| e| 2/2
consumption
[mtoe] 2020 2050
Ist gen
bioethanol 12| 11 | 11] 11] 12] 12| 12 5 6 5 6 5 5| 5
2nd gen
bioethanol 1 1 2 1 2 1 1| 13] 15 5 | 12 4 3| 4
Ist gen
biodiesel 10 9 9} 9/ 10 9 9 5 5 1 5 1 1 1
2nd gen
biodiesel 0 0 o| 0 0 0 0 0 0 8 0 8 8| 8
Biokerosene 1 1 2 1 2 2 2 1 2| 15 2| 15 | 15 | 15
Ist gen 0 ie) ie) 1 1 1 1 0 0 2 3 1 2/0

5. CONCLUSIONS

The paper described the model BioPOL, a set of scenarios that were developed and their results with
respect to energy demand, fuel consumption and biofuel consumption per biofuel type.

The BioPOL model is a recursive dynamic model that is constructed in the VENSIM modelling
platform. It is based on a year-by-year simulation of biofuel production, production cost and biofuel
demand until 2030. The model delivers detailed outcomes for the types of biofuels considered with
regard to production capacity and produced volumes, costs and well-to-wheel emissions of greenhouse
gases. The description of the BioPOL model focuses on the feedback structure were we identified
seven feedbackloops, three of them with a mayor impact on the development of biofuel production and
consumption.

An important issue of BioPOL is the improved way in which learning for 2" generation is considered.
Based on a database on biofuel plants learning rates for 2"! generation biofuels (Ligno-cellulosic
ethanol, BTL, DME) were estimated. The resulting learning rates are considered in the BioPOL
model, which means that the cumulative production of 2" generation bioethanol, biodiesel, biogas and
HVO affects their production costs. The decrease of capital cost due to learning leads to a further
increase of these biofuel types and shifts their consumption on a higher level.

A Reference Scenario has been developed. It is the scenario against which the GHG emission
reduction scenarios were tested in GHG TransPoRD. The Reference Scenario includes assumptions
about exogenous trends (e.g. economic growth) but also about the endogenous variables in GHG-
TransPoRD such as e.g. transport demand, energy supply and demand, transport emissions.
Furthermore, the reference scenario includes some transport policies.

A set of GHG emission reduction scenarios were developed varying the technical measures to reduce
GHG emissions. The technical measures refer to all transport modes including new vehicle
technologies like electric vehicles and hydrogen vehicles. In addition, different biofuel types were
pushed into the market according to the definition of the GHG emission reduction scenarios.

As result we derive a break in trend for the energy demand of road passenger transport and road
freight transport. After a strong reduction until 2030 the development of energy demand remains
stable around 125 mtoe and 40 mtoe respectively. Energy demand of aviation is curbed and remains at
a level of 50 mtoe, while it was experiencing strong growth in the reference scenario.

With respect to fuel types we identify an increase of alternative fuel types like hydrogen depending on
the definition of the scenarios. For biofuels we derive that overall biofuel consumption and
consumption per biofuel type is quite similar in 2020, but the picture changes when looking at
different biofuel types in 2050.

The main difference between the scenarios is whether 2" generation biodiesel and bio-kerosene enters
the market or not. If these two biofuel types enter the market and become competitive then they might
reach together a level of around 23 mtoe or around 2/3 of the overall biofuel consumption. In those
scenarios the 2" generation bioethanol consumption is reduced as 2"! generation biodiesel and
bioethanol are produced with the same feedstock.

A high variation of biofuel consumption can be identified for biogas. Main important factor for the
variation of biogas is the diffusion of gas vehicles into the market.

The avoided GHG emission reduction could be in the range of 80 to 110 mt CO2 eq. The avoided
GHG emission considers the GHG emission savings per biofuel type, but do not take into account the
effects of indirect land use changes.

One of the outcomes is that bioethanol and biogas is limited by the blending limits of vehicles (20%
for gasoline; 80% for gas). In principle, it would be possible to produce more bioethanol and biogas at
competitive costs, but the limitation is here on the demand side. In contrast to this, higher biodiesel
and bio-kerosene demand lead to an increase of feedstock costs. The rise in feedstock cost limits the
further market diffusion of biodiesel and bio-kerosene. Both could be used with higher blends in road
fright transport and aviation. From this result we derive that bioethanol and biogas is limited from the
demand side, while biodiesel and bio-kerosene are limited from the supply side.
6. REFERENCES

EC (2010): Communication from the Commission on the practical implementation of the EU biofuels and
bioliquids sustainability scheme and on counting rules for biofuels. COM(2010/C 160/02).

EU (2003): Directive 2003/30/EC of the European Parliament and of the Council of 8 May 2003 on the
promotion of the use of biofuels or other renewable fuels for transport.

EU (2009): Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the
promotion of the use of energy from renewable sources and amending and subsequently repealing Directives
2001/77/EC and 2003/30/ECber States’ Reports in the frame of Directive 2003/30/EC, 2004, 2005 and 2006
reports.

EurObserv'ER (2008): Biofuels Barometer June 2008.
EurObserv'ER (2009): Biofuels Barometer July 2009.
IEA (2008a): Energy Technology Perspectives 2008. IEA/OECD, Paris.

IEA (2008b): From Ist to 2nd generation biofuel technologies. An Overview of current industry and RD&D
activities. IEA and IEA Bioenergy. IEA/OECD, Paris.

JEC (JRC, EUCAR, CONCAWE) (2007): Well-to-Wheels analysis of future automotive fuels and powertrains
in the European context WELL-TO-WHEELS Report Version 2c.

JEC (2008): Well-to-Wheels analysis of future automotive fuels and powertrains in the European context.
WELL-TO-TANK Report Version 3.0 November 2009. WITT App.2 - Description and detailed energy and
GHG balance of individual pathways

Resch, G., Ragwitz, M., Panzer, C., Faber, T., Toro, F., Held, A., Hass, R. (2009): Beyond 2020 -
Characterization of Alternative Renewable Energy Scenarios for the EU. Final report of a project contracted
by JRC IPTS. Unpublished.

Schade B., Casamassima, G., Fiorello, D., Martino, A., Schade W., Walz, R. (2007): High Oil Prices: Scenarios
assumptions and model interfaces. Deliverable 2 of HOP! (Macro-economic impact of high oil price in
Europe). Funded by European Commission 6th RTD Programme. Seville, Spain.

Schade, B., Wiesenthal, T. (2011): Biofuels: a model based assessment under uncertainty applying the Monte
Carlo method. Journal of Policy Modeling, Vol. 33, No. 1, pp. 92-126.

Schade, W., Fiorello, D., Kéhler, J., Krail, M., Martino, A., Schade, B., Wiesenthal, T. (2008): Final Report of
the TRIAS Project: SIA of Strategies Integrating Transport, Technology and EnergyScenarios. Deliverable 5
of TRIAS. Funded by European Commission 6" RTD Programme. Karlsruhe.

Schade, W., Krail, M., Fiorello, D., Helfrich, N., Kohler, J., Kraft, M., Maurer, H., Meijeren, J., Newton, S.,
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of iTREN-2030. Project co-funded by European Commission 6" RTD Programme. Fraunhofer-ISI,
Karlsruhe.

Wiesenthal, T., Leduc, G., Christidis, P., Schade, B., Pelkmans, L., Govaerts, L., Georgopoulos, P. (2009):
Biofuel Support Policies in Europe: Lessons Learnt for the Long Way Ahead. Renewable and Sustainable
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Metadata

Resource Type:: Document
Description:: The aim of this paper is to illustrate the biofuel model BioPOL and its new developments, to describe a set of scenarios, in which BioPOL was applied and to discuss the results of the scenarios.
Rights:: CC BY-NC-SA 4.0
Date Uploaded:: January 1, 2020

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System Dynamic Society Records, 1978-2016

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Schade, Burkhard, "Future Development of 2nd generation Biofuels in Transport considering Learning Rates", 2012 July 22-2012 July 26

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