Assessment of energy technologies in electricity and transport sectors based on carbon intensity and costs.
Streimikiene, Dalia
Introduction
The current development process in energy policy is boosted by
Commission's Second Strategic Energy Review package in 2008. With
regard to the EU energy security and solidarity action plan the
Commission figured out five key areas:
--Infrastructure needs and the diversification of energy supplies;
--External energy relations;
--Oil and gas stocks and crisis response mechanisms;
--Energy efficiency;
--Making the best use of the EU's indigenous and renewable
energy resources. The substantial change in the European energy system
is expected over the next decades until 2050. All over Europe the
Commission estimates challenges and fundamental changes for the energy
system between 2020 and 2050. The EU's new energy and environment
policy-agreed by government leaders in their Council meeting in March
2007--established a political agenda to tackle three core energy
objectives: sustainability, economic competitiveness and security of
supply. European leaders reached a historic agreement for the first time
to create a common European energy policy.
A triad of specific policies addresses these challenges: first, the
20/20/20 targets of the EU; then, the Second Strategic Energy Review of
the European Commission; and finally, plans to liberalise energy
markets. In December 2008 the European Parliament adopted a set of
legislative documents (the so called EU climate and energy package) for
transforming Europe gradually into a low-carbon economy and increasing
energy security. An agreement has been reached on legally binding
targets, by 2020:
--to cut GHG emissions by 20% compared to 1990;
--to establish a 20% share for renewable energy in final energy
consumption and the share of biofuels up to 10% in transport fuels; and
--to achieve a 20% reduction in energy consumption by 2020 (to
improve energy efficiency).
Regarding the reduction of GHG emissions, the package contains an
offer to go further and commit to a 30% cut in the event of a
satisfactory international agreement being reached. The European Union
is making huge efforts to reduce GHG emissions. EU has prepared the
Roadmap for moving to a competitive low-carbon economy in 2050. In this
plan the European Commission is looking beyond these 2020 objectives and
setting out a plan to meet the long-term target of reducing domestic
emissions by 80 to 95% by mid-century. The EU set targets: to reduce the
EU's greenhouse emissions by 80% by 2050 (compared with 1990
levels) entirely through measures taken within Europe. Intermediate cuts
of 25% by 2020, 40% by 2030 and 60% by 2040 would be needed. Improving
energy efficiency, for instance by investing in energy-efficient
buildings and transport, can make the biggest contribution to reducing
emissions. Clean electricity--produced almost entirely without
greenhouse emissions --will also have a major role to play, partly
replacing fossil fuels for heating and transport (e.g. electric cars or
hybrid cars). Therefore there are 3 pillows for achieving low carbon
economy in EU by 2050: clean electricity, clean cars and energy
efficiency improvements.
Another important document--Energy efficiency plan 2011 was adopted
by EC. The plan states that the greatest energy saving potential lies in
buildings and focuses on instruments to trigger the renovation process
in public and private buildings and to improve the energy performance of
the components and appliances used in them.
According to the Plan Transport has the second largest potential.
The White Paper on Transport is under preparation. Energy efficiency in
industry will be tackled through energy efficiency requirements for
industrial equipment, improved information provision for SMEs and
measures to introduce energy audits and energy management systems. The
Commission therefore proposes a two step approach to target setting. As
a first stage, Member States are currently setting national energy
efficiency targets and programmes. These indicative targets and the
individual efforts of each Member State will be evaluated to assess
likely achievement of the overall EU target and the extent to which the
individual efforts meet the common goal.
A low-carbon economy would have a much greater need for renewable
sources of energy, energy-efficient building materials, hybrid and
electric cars, 'smart grid' equipment, low-carbon power
generation and carbon capture and storage technologies. To make the
transition to a low-carbon economy and to reap its benefits such as a
lower oil bill the EU would need to invest an additional 270 billion
[euro] or 1.5% of its GDP annually, on average, over the next four
decades. The short-term priorities of Roadmap towards low carbon economy
and Energy Roadmap 2050: energy efficiency, low carbon technologies in
electricity generation and transport.
The main problem is addressing EU 20/20/20 targets by selecting the
best technologies in power and transport sector able to help in
achieving these targets with the lowest costs. Therefore the main aim of
the paper is to address the EU policy for achieving low carbon economy
by assessing energy technologies in electricity and road transport
sector based on costs and GHG emission reduction potential.
The main tasks of the paper are: to develop the multi-criteria
framework for comparative assessment of energy technologies by applying
MCDM methods by taking into account the EU energy policy priorities and
to apply developed framework for electricity and transport technologies
assessment.
1. The framework for energy technologies assessment
As there is a wide range of road transport technologies between
biofuels and hybrid cars each of them specific with different fuels,
selection of the most promising technology becomes very important issue
in development of the transport policy. Indeed, it is the selection of
the best transport technologies that should be promoted by policy tools
in terms of GHG emission and cost reduction. As GHG emissions from road
transport are usually provided as the range of values comparative
assessment of road transport technologies needs some sophisticated MCDA
tools (Hwang, Yoon 1981; Loken 2007; Zavadskas, Turskis 2011; Bauers,
Zavadskas 2010; Balezentis et al. 2012a, b; Streimikiene et al. 2011;
Kaplinski, Tupenaite 2011; Zvirblis, Buracas 2012).
The following description of TOPSIS for interval data is presented
according to Jahanshahloo et al. (2006). Let us assume there are i =
1,2, ...,m alternatives evaluated according to j = 1,2, ...,n criteria.
Each criterion can be assigned with respective weight [w.sub.j] such
that [[summation].sub.j][w.sub.j] = 1. The uncertain response of the
i-th alternative on the j-th criterion is expressed in interval number
[[??].sub.ij] = [[x.sup.l.sub.ij], [x.sup.u.sub.ij]], where
[x.sup.l.sub.ij] and [x.sup.u.sub.ij] are the lower and the upper bounds
respectively of respective response.
The initial decision matrix [MATHEMATICAL EXPRESSION NOT
REPRODUCIBLE IN ASCII] is turned into normalized decision matrix
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] in the following
way:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (1)
From now on, the ranges of normalized interval numbers belong to
[0, 1]. Furthermore, at this stage they can be multiplied by respective
weights.
Consequently, the positive ideal solution [A.sup.+] as well as the
negative ideal solution [A.sup.-] is obtained as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (2)
where I and J stand for sets of benefit and cost criteria,
respectively.
Thereafter, the Euclidean distances from the latter two ideal
alternatives are calculated for each i-th alternative:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. (3)
Finally, each alternative is given a closeness coefficient, which
is measured as a relative proximity from the negative ideal solution:
C[C.sub.i], = [S.sup.-.sub.i]/[S.sup.+.sub.i] + [S.sup.-.sub.i],
[for all]i. (4)
Alternatives with higher values of the closeness coefficient
(C[C.sub.i]) are attributed with higher ranks.
2. MCDA of electricity generation technologies
The main indicators or criteria for energy technologies assessment
will be private costs of energy generation and life cycle GHG emissions.
The main electricity generation technologies will be compared and ranked
according these criteria. The following energy technologies were
selected for assessment in power and heat generation sector: hard coal,
natural gas, oil, nuclear and biomass. In power sector just base load
technologies were assessed. In this chapter based on recent scientific
literature review and results of various EU funded projects the range of
life cycle GHG emissions and private costs for the selected electricity
generation technologies will be derived.
The principle factors determining the GHG emissions from a fossil
fuel power plant is the type of technology (and hence choice of fuel)
and its thermal efficiency. In addition, thermal efficiency increases
with the load factor (although efficiency reductions can be observed
towards achieving full load operation) and therefore GHG emissions from
a particular fossil fuel technology will depend on the mode of its
operation (e.g. peak load management, base load supply, combined heat
and power supply, etc.).
The ranges of life cycle GHG emissions for power and heat
generation technologies are presented in Table 1. Life cycle GHG
emission ranges (from minimal to maximal values) were presented based on
information provided by various sources (Gluch, Baumann 2004; Ekvall
1999; Fritsche, Lim 2006; Weiser 2008; Rhode 2005; Streimikiene 2010,
2013; Streimikiene, Balezentiene 2012). The range of direct C[O.sub.2]
emissions from combustion and total life cycle GHG emissions per
technology were calculated in kg/MWh (Table 1).
As one can see from information provided in Table 1 biomass wood
chips gasification technologies have the lowest life cycle GHG emissions
followed by wood chips CHP large scale. Hard coal technologies have the
highest life cycle GHG emissions followed by oil and natural gas
technologies. Hard coal IGCC with C[O.sub.2] capture technologies have
quite low life cycle GHG emission comparable even with Large scale wood
chips gasification technologies. Nuclear technologies have lower life
cycle GHG emission than some biomass technologies for example large
scale straw combustion technologies and large scale wood chips
combustion technologies. Biomass technologies with C[O.sub.2] capture
have negative life cycle GHG emissions. Especially high negative GHG
emissions are during combustion processes of Biomass IGCC with
C[O.sub.2] capture.
The private costs in EURcnt/kWh are based on the Average Levelised
Generating Costs (ALLGC) methodology. The methodology calculates the
generation costs (in EuroCents/kWh) on the basis of net power supplied
to the station busbar, where electricity is fed to the grid. This cost
estimation methodology discounts the time series of expenditures to
their present values in 2005, which is the specified base year, by
applying a discount rate. According to the methodology used in the IEA
study in 2005, the levelised lifetime cost per GWh of electricity
generated is the ratio of total lifetime expenses versus total expected
outputs, expressed in terms of present value equivalent.
The range of current and long-term private costs (ALLGC) for the
same power generating technologies were selected from various
information sources (PSI 2003; EUSUSTEL 2007; Mollersten et al. 2003;
CASES 2007; PLANETS 2009; Streimikiene 2010). In Table 2 the range of
current private costs of the selected power generation technologies is
presented.
As one see from information provided in Table 2 the cheapest
technologies in long-term perspective are: nuclear and hard coal
technologies followed by large scale biomass combustion and biomass
CHPs. The most expensive technologies in terms of private costs are: oil
and natural gas technologies. Therefore the energy technologies having
the lowest life cycle GHG emissions are not the most expensive but not
the cheapest one in terms of private costs.
The multi-criteria assessment of energy technologies in electricity
generation sector was performed based on data from Tables 1 and 2. As
one can note, data in table are rather uncertain. Hence, we employed
interval TOPSIS method. Note that both of the criteria (life cycle
C[O.sub.2] emissions and costs) are needed to be minimized. Firstly, Eq.
(1) was employed to normalize the data (Table 3). Thereafter the ideal
solutions, [A.sup.+] and [A.sup.-], were found (cf. Eq. (2)). The
distances of each alternative were found with respect to Eq. (3),
whereas closeness coefficients were obtained by employing Eq. (4). Thus,
the considered energy generation technologies were prioritized with
respect to decreasing value of the closeness coefficient.
The electricity generation technologies were assessed in terms of
the three weight sets, with first treating both criteria equally, second
putting the most of significance on GHG mitigation, and third--on the
private costs. Accordingly, the ranking was reiterated three times with
different weight sets viz. (0.5, 0.5), (0.75, 0.25), and (0.25, 0.75).
Table 4 presents the final results.
As one can note, the best option according to holistic (equal
weights) and economic approach is large scale wood chips combustion
(rank - 1), whereas large scale biomass IGCC with C[O.sub.2] capture is
the most preferable under the environmental approach thanks to
C[O.sub.2] storage. Nuclear technology was the second best option in
terms of holistic and economic approaches, though environmental approach
attributed rank of 6 to the latter technology. However, the conventional
energy sources--oil, natural gas, and hard coal--received the lowest
ranks with respect to all of approaches. Fig. 1 depicts the shifts in
closeness coefficients due to weights' alterations.
[FIGURE 1 OMITTED]
The results of analysis imply that the ranking of electricity
generation technologies varies across the approaches employed in the
analysis. Therefore it is important to quantify the underlying factors
of discordance. Thus, Pearson correlation coefficients were obtained to
measure the linear relationships between closeness coefficients provided
by the three approaches as defined above. Similarly, Spearman rank
correlation coefficients were estimated for ranks attributed to the
electricity generation technologies according to the three approaches.
The results of correlation analysis are presented in Table 5.
It is evident that the holistic approach-based ranking is related
to the environmental approach-based one at a higher extent if compared
to the linkage between holistic approach and economic approach. The
lowest values of the correlation coefficients were observed between
environmental and economic approaches. This finding suggests that GHG
mitigation and energy costs are the two conflicting criteria. The
economic incentive schemes therefore are rather important for
sustainable energy development.
3. MCDA of transport technologies
The main indicators or criteria for energy technologies assessment
will be private costs of energy generation and life cycle GHG emissions.
The main transport technologies will be compared according these
indicators and ranked.
The data on life cycle GHG emissions for specific fuel cycles is
necessary seeking to assess external costs of GHG emissions for
different energy technologies using information about C[O.sub.2] prices
over the time and space delivered by various models by running policy
scenarios. Life cycle C[O.sub.2] emissions from power an transport
sector depend strongly upon details of supply chain, production
techniques, forestry and agriculture practices, transport distance, etc.
The range of life cycle GHG emissions of transport technologies in
g/vehicle km were obtained by gathering data on GHG emissions from
transport sector from various sources and evaluating direct C[O.sub.2]
emissions from combustion and total life cycle GHG emissions for
specific transport technologies (Litman 2008; Bauen 2007; Woods et al.
2005; Zah et al. 2007; Samaras, Meisterling 2008; Moawad et al. 2009;
Ecolane Transport Consultancy 2006; Maclean, Lave 2003; Rajagopal,
Zilberman 2008; EPA 2005; Harrington, McConnell 2003; Farell et al.
2006; EPRI 2007; Gallo 2011). The life cycle GHG emissions for road
transport technologies are presented in Table 6.
Fuel GHG intensity is the key factor which represents the net
lifecycle emissions impact associated with the consumption of a unit of
fuel. Sometimes termed a fuel's "carbon footprint", it
can be expressed in units of grams of carbon dioxide-equivalent per
megajoule (gC[O.sub.2]eq/MJ) of energy delivered to vehicles or other
transportation equipment.
The range of current private costs of road transport technologies
were evaluated in EURcnt/vehicle km based on information about costs of
fuels provided by various data sources (Bauen 2007; Woods et al. 2005;
Zah et al. 2007; Samaras, Meisterling 2008; Gross et al. 2009; Schipper
2011; Rajagopal, Zilberman 2008; Arslan et al. 2010; Harrington,
McConnell 2003; Lipman, Delucchi 2006; Moawad et al. 2009; MacLean, Lave
2003) are presented in Table 7. The price of gasoline and diesel is
based on cost of crude oil ca $50/barrel (FOB Gulf price). These costs
for biofuels vary widely depending on location for existing bioethanol
and biodiesel technologies.
The multi-criteria assessment of energy technologies for road
transport technologies was performed based on data from Tables 6 and 7.
As one can note, data in table are rather uncertain. Hence, we employed
interval TOPSIS method. Firstly, Eq. (1) was employed to normalize the
data. Thereafter the ideal solutions were found (cf. Eq. (2)). The
distances of each alternative were found with respect to Eq. (3),
whereas closeness coefficients were obtained by employing Eq. (4). Thus,
the considered road transport technologies were prioritized with respect
to decreasing value of the closeness coefficient.
The road transport technologies were assessed in terms of the three
weight sets, with first treating both criteria equally, second putting
the most of significance on GHG mitigation, and third--on the private
costs. Accordingly, the ranking was reiterated three times with
different weights. Table 8 presents the final results.
As one can note, the best option according to holistic (equal
weights) and environmental approach is biodiesel from waste vegetable
oil, whereas customers would prefer biodiesel from rapeseed. Indeed, the
first two approaches suggest bioethanol and biodiesel as the most
preferable fuels. The customer-first approach, however, graduated diesel
to the fourth place and subsequently put bioethanol from wheat into the
sixth place. Fig. 2 depicts the shifts in closeness coefficients due to
weights' alterations.
As Fig. 2 exhibits, diesel, petrol, and HEV were those road
transport technologies preferred by customers due to lower private
costs. At the other end of spectrum, there were biodiesel from waste
vegetable oil, bioethanol from wheat, and PHEV 90 specific with lower
contribution to GHG emission.
[FIGURE 2 OMITTED]
Other options as eco-driving and better roads have also significant
impact on GHG emissions from road transport (Knudsen, Bang 2007; Raborn
2011) however these options were left out of the scope of this paper.
Conclusions
1. The framework for energy technologies assessment and ranking
based on EU energy policy priorities is developed. The main assessment
and ranking criteria for energy technologies are: life-cycle GHG
emissions and private costs. The transport and electricity generation
technologies were assessed by applying the same tools.
2. Multi-criteria analysis of electricity generation technologies
encompassing life cycle GHG emissions and private costs was carried out
in terms of the three different approaches: 1) holistic approach
considered both of the criteria were equally important; 2) environmental
approach placed the highest significance on GHG emission reduction; and
3) economic approach first of all aimed at cost mitigation.
3. The holistic approach suggests large scale wood chips
combustion, large scale wood chips gasification, and nuclear power, in
that order, are the three most sustainable technologies. Hard coal IGCC
with C[O.sub.2] capture was also supported by the economic approach. The
environmental approach identified large scale wood chips gasification,
large scale biomass IGCC with C[O.sub.2] capture, and small scale
biomass (wood chips gasification) CHP as the most suitable technologies.
The carried out analysis indicated that certain economic incentives are
needed to ensure sustainable energy development.
4. Analysis of life cycle GHG emissions and private costs of the
main road transport technologies performed in the paper derived that
road transport technologies based on biodiesel from waste vegetable oil
have the lowest life cycle GHG emission followed by technologies using
bioethanol from wheat. Petrol based transport technologies have the
highest life cycle GHG emissions followed by diesel technologies. The
most expensive in terms of fuel costs are bioethanol transport
technologies and the cheapest are transport technologies based on petrol
and diesel. Therefore the transport technologies having lowest life
cycle GHG emission are among the most expensive in terms of fuel costs.
Therefore the policy oriented ranking of transport technologies taking
into account these two main issues into account allow to develop new
transport policies and to promote the best ranked technologies.
5. The multi-criteria assessment of energy technologies for road
transport was carried out. Hence, road transport technologies were
ranked with respect to GHG emission and private costs. In order to check
the sensitivity of the results, the three weight sets were defined: 1)
both of the criteria were considered equally important (i.e. no weights
were defined); 2) for environmental approach the most of significance,
namely 80 per cent, was given to C[O.sub.2] emission reduction; 3) for
consumer-oriented approach the greatest significance of 80 per cent was
attributed to the private costs criterion. The analysis showed that
bioethanol from sugar beet, biodiesel from rapeseed, and biodiesel from
waste vegetable oil are the most preferable technologies and have to be
further promoted by policy tools.
6. Comparative assessment of road transport technologies based on
lifecycle GHG emission and private costs presents just one issue of
climate change mitigation policy related with promotion of advanced road
transport technologies having the lowest costs. Other policies, i.e.
improvement of road infrastructure, traffic management, eco-driving,
spatial planning etc. can significantly reduce GHG emissions from road
transport.
Caption: Fig. 1. Impact of different weighting schemes on
summarized assessment of energy technologies in electricity generation
Caption: Fig. 2. Impact of different weighting schemes on
summarized assessment of energy technologies
doi: 10.3846/20294913.2013.837113
Received 13 June 2012; accepted 08 December 2012
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Dalia STREIMIKIENE
Kaunas Faculty of Humanities, Vilnius University, Muitines g. 8,
44280 Kaunas, Lithuania
Corresponding E-mail:
dalia@mail.lei.lt
Dalia STREIMIKIENE is a senior research fellow at the Social
Cultural Institute of Kaunas Faculty of Humanities, Vilnius University.
She holds PhD in Economics and is a Professor and a Leading Research
Associate at Vilnius University Kaunas Faculty of Humanities. She has
experience in various projects related to sustainable development,
environmental and climate change mitigation policies. She also has
experience in consumer behaviour and business ethics. The main area of
her research is sustainability assessment of policies, technologies and
products in energy field, development of indicator frameworks for
sustainability assessment.
Table 1. Life cycle GHG emissions of the main energy technologies in
power sector
Fuel or energy type Direct C[O.sub.2] emissions
from combustion
kg/GJ kg/MWh
Nuclear 2.5/30.3 9/110
Oil 126.9/300.7 460/1090
Natural gas 96.6/179.31 350/650
Hard coal 193.1/262.1 700/950
Hard coal IGCC 52.4/60.7 190/220
with C[O.sub.2] capture
Large scale wood chips -- --
combustion
Large scale wood chips -- --
gasification
Large scale biomass -39.4/-143.5 -505/-520
IGCC with C[O.sub.2] capture
Large scale straw combustion -- --
Biomass (wood chips) -- --
CHP large scale
Biomass (wood chips -- --
gasification) CHP small scale
Fuel or energy type Life cycle C[O.sub.2] emissions
kg/GJ kg/MWh
Nuclear 2.8/35.9 10/130
Oil 137.9/331.0 500/1200
Natural gas 110.3/215.2 400/780
Hard coal 206.9/344.8 750/1250
Hard coal IGCC 38.6/46.9 140/170
with C[O.sub.2] capture
Large scale wood chips 21.0/23.0 76.0/83.3
combustion
Large scale wood chips 6.0/8.0 21.6/29.0
gasification
Large scale biomass -35.9/-41.4 -130/-150
IGCC with C[O.sub.2] capture
Large scale straw combustion 62.0/70.0 223.2/252.0
Biomass (wood chips) 6/10 21.6/36.0
CHP large scale
Biomass (wood chips 3/6 10.8/21.6
gasification) CHP small scale
Table 2. Long-term private costs of power generation technologies
(2030-2050), EUR/MWh
Fuel or energy type Costs, EUR/MWh
Min Max
Nuclear 24 42
Oil 79 100
Natural gas 53 60
Hard coal 21 44
Hard coal IGCC with C[O.sub.2] capture 40 43
Large scale wood chips combustion 35 38
Large scale wood chips gasification 42 49
Large scale biomass IGCC with C[O.sub.2] capture 57 60
Large scale straw combustion 44 48
Biomass (wood chips) CHP large scale 37 60
Biomass (wood chips gasification) CHP small scale 37 60
Table 3. The normalized interval decision matrix
Fuel or energy type Life cycle C[O.sub.2] Costs
emissions
Nuclear [0.001, 0.015] [0.074, 0.13]
Oil [0.057, 0.137] [0.245, 0.31]
Natural gas [0.046, 0.089] [0.164, 0.186]
Hard coal [0.085, 0.142] [0.065, 0.136]
Hard coal IGCC with [0.016, 0.019] [0.124, 0.133]
C[O.sub.2] capture
Large scale wood chips [0.009, 0.009] [0.108, 0.118]
combustion
Large scale wood chips [0.002, 0.003] [0.13, 0.152]
gasification
Large scale biomass IGCC [-0.015, -0.017] [0.176, 0.186]
with C[O.sub.2] capture
Large scale straw combustion [0.025, 0.029] [0.136, 0.149]
Biomass (wood chips) CHP [0.002, 0.004] [0.115, 0.186]
large scale
Biomass (wood chips [0.001, 0.002] [0.115, 0.186]
gasification) CHP small
scale
[A.sup.+] -0.015 0.065
[A.sup.-] 0.142 0.310
Table 4. Closeness coefficients (CC) and ranks for energy technologies
Technologies Equally Environmental Economic
important approach approach
criteria
Nuclear 0.815 3 0.825 6 0.793 2
Oil 0.339 11 0.358 10 0.272 11
Natural gas 0.493 9 0.484 9 0.523 10
Hard coal 0.385 10 0.286 11 0.593 9
Hard coal IGCC with 0.774 7 0.785 7 0.747 3
C[O.sub.2] capture
Large scale wood chips 0.833 1 0.844 5 0.807 1
combustion
Large scale wood chips 0.817 2 0.873 2 0.720 4
gasification
Large scale biomass IGCC 0.802 4 0.921 1 0.630 8
with C[O.sub.2] capture
Large scale straw combustion 0.716 8 0.727 8 0.689 5
Biomass (wood chips) CHP 0.776 6 0.859 4 0.663 7
large scale
Biomass (wood chips 0.780 5 0.867 3 0.664 6
gasification) CHP small
scale
Table 5. Correlation between closeness coefficients (CCs) and
ranks across different approaches
Approaches Equally important Environmental approach
criteria
CCs Ranks CCs Ranks
Environmental 0.98 0.80
approach
Economic 0.84 0.77 0.72 0.39
approach
Table 6. Life cycle GHG emissions of road transport technologies
Life cycle GHG emissions of the in g/vehicle km
momotor vehicles
HEV 180-192
PHEV 30 126-183
PHEV 60 104-181
PHEV 90 96-183
Petrol 227.4-307.6
Diesel 243.0-251.7
Bioethanol from sugar beet 103.5-120.2
Bioethanol from wheat 43.5-75.5
Biodiesel from rapeseed 109.1-120.2
Biodiesel from waste vegetable oil 30.8-41.9
Table 7. Private costs of motor vehicle in 2020, EURcnt/kWh
Average fuel costs
EURcnt/ Litres/ EURcnt/
litre vehicle km vehicle km
HEV 50 0.057 2.85
PHEV 30 50 0.042 2.1
PHEV 60 50 0.03 1.5
PHEV 90 50 0.02 1.0
Petrol 50 0.08 4.0
Diesel 40 0.08 3.2
Bioethanol from sugar beet 70 0.08 5.6
Bioethanol from wheat 90 0.08 7.2
Biodiesel from rapeseed 60 0.08 4.8
Biodiesel from waste vegetable oil 80 0.08 6.4
Average electricity cost
EURcnt/ kWh/ EURcnt/
kWh vehicle km vehicle km
HEV -- -- --
PHEV 30 8 0.2 2.4
PHEV 60 8 0.25 3.0
PHEV 90 8 0.3 3.6
Petrol -- -- --
Diesel -- -- --
Bioethanol from sugar beet -- -- --
Bioethanol from wheat -- -- --
Biodiesel from rapeseed -- -- --
Biodiesel from waste vegetable oil -- -- --
Average Total average
vehicle private costs,
costs, EURcnt/
EURcnt/km vehicle km
HEV 9.0 11.9
PHEV 30 9.1 12.8
PHEV 60 9.9 13.4
PHEV 90 12.2 15.6
Petrol 7.2 11.2
Diesel 7.0 10.2
Bioethanol from sugar beet 7.2 12.8
Bioethanol from wheat 7.2 14.4
Biodiesel from rapeseed 7.0 11.8
Biodiesel from waste vegetable oil 7.0 13.4
Table 8. Closeness coefficients (CC) and ranks for energy technologies
Technologies Equally Environmental Customer-first
important approach approach
criteria
CC Rank CC Rank CC Rank
HEV 0.455 8 0.443 8 0.554 5
PHEV 30 0.543 7 0.544 7 0.534 7
PHEV 60 0.569 5 0.575 6 0.512 8
PHEV 90 0.559 6 0.580 5 0.402 10
Petrol 0.261 10 0.227 10 0.458 9
Diesel 0.299 9 0.232 9 0.561 4
Bioethanol from sugar 0.685 4 0.695 3 0.609 3
beet
Bioethanol from wheat 0.791 2 0.850 2 0.553 6
Biodiesel from 0.690 3 0.690 4 0.696 1
rapeseed
Biodiesel from waste 0.868 1 0.948 1 0.646 2
vegetable oil
