SIXTH FRAMEWORK PROGRAMME
Project no: 502687
NEEDS
New Energy Externalities Developments for Sustainability
INTEGRATED PROJECT
Priority 6.1: Sustainable Energy Systems and, more specifically,
Sub-priority 6.1.3.2.5: Socio-economic tools and concepts for energy strategy.
Deliverable n° 8.5 - RS 1b
“Integration of External cost data and LCA data in
TIMES and application to two policy issues”
Denise Van Regemorter (CES K.U.Leuven) and F. Pietrapertosa, S. Di Leo and C. Cosmi (CNR-IMAA)
I.
INTRODUCTION
The objective of this WP was to develop an approach for the integration of external cost and LCA data, as
derived in stream RS1a and RS1b, into the TIMES modelling framework and to evaluate the expanded
modelling framework with policy cases. The integration of external cost allows taking into account in the
analysis of policy options the benefits/costs coming from the mitigation of emissions of pollutants. In
section 2 the integration approach is briefly described1. In section 3 two groups of policy scenarios are
examined: one with the Italian TIMES model on the impact of internalization of the cost of local pollutant
and of greenhouse gas emissions (IMAA) and one with the Pan European TIMES model on the impact of
the internalization of external cost of local pollutant (CES K.U.Leuven). These scenarios are meant to test
the modelling approach and to illustrate what can be done with the fully integrated Pan European TIMES
model but there is still a need for the further development of the emission data and the abatement
technologies.
II. INTEGRATION OF EXTERNAL COST AND LCA DATA IN TIMES
A.
External cost integration
1.
TIMES modelling framework
Within TIMES two approaches are modelled for the integration of damages from pollutants:
1. the environmental damage are computed ex-post, without feedback into the optimisation process
2. the environmental damages are part of the objective function and therefore taken into account in the
optimisation process.
The damage per pollutant is modelled as follows:
DAM (env)  EV coef (env) * EM (env) EV ( env )
where EV (env) reflects the relation between the damage cost and the level of emissions;
EVcoef(env) is computed through calibration from the marginal cost, EVcost(env) , i.e. the derivative
of the damage function w.r.t. emissions, and EVREFLEV(env), the emissions in the reference year, for which
the marginal cost has been evaluated.:
EV coef (env)  EV cos t (env) /( EV  (env) * EVREFLEV (env)
( EV  ( env ) 1)
)
In the first approach the damage function is used to compute ex-post the damage associated with the
model solution. In the second approach, a term is added to the objective function, which contains the sum
of the damage-functions per pollutant.
The link with stream RS1b is through the marginal cost EVcost(env) derived from RS1b data. It is the
marginal damage generated by the emission of a pollutant. There are several complex intermediate steps
between the emission of a pollutant and the damages caused by it. These steps are not explicitly and
separately modelled in TIMES, but the different steps are aggregated in the marginal damage figure used.
This raises the question of the constancy of the marginal damage cost. From discussion with RS1b it
seems that the linear approximation of the dose response function could be justified, but the
dispersion/transformation function cannot be considered as linear. The linear approximation can be used
when the scenario does not imply drastic changes compared to the reference case. However when the
changes are important (e.g. when a 50% reduction in CO2 emissions are imposed) this is not justified and
therefore this should be integrated in the data from RS1b or could be modelled through a specific value
1
See Integration report of Stream Integration for a more detailed overview
2
for EV (env), reflecting the non linearity between emission and air quality. At this stage, this was not
possible within the timeframe of the project.
As the computation are based on dose response functions which give the incremental damage from air
pollution, the results should also be interpreted in these terms, i.e. in terms of the change in total damage
compared to a reference year (the base year).
The damages considered for TIMES are those linked to air quality, acid deposition and climate change
generated from the use of energy; other categories linked to the use of a fuel or a technology within the
energy system can also be considered, e.g. damage linked to the nuclear technology, if data are available.
Ideally, it should be possible to link the region causing the damage and the region suffering the damage
through dispersion/transformation matrices, though this is only needed for the ex-post evaluation of the
damage per country. Within the optimization, it is sufficient to associate a damage figure in monetary
terms to the emissions or to the activity/technology generating the damage within a country (the damage
can be national, within the EU or outside the EU).
The damage generated abroad because of the import of a fuel within the EU can also be taken into
account through the association of a damage figure to the import category considered.
At this stage the possibility of internalisation of external cost is modelled as a YES/NO option for all
pollutant for which an external cost is given in the database. In a next version of the model code it will be
possible to include or not each pollutant separately for internalisation, the ex-post computation covering
the pollutant not included.
2.
The external cost data from RS1b
a)
External cost of local air pollutant
The local air pollutants considered for integration in TIMES were NMVOC, NO x, PPMcoarse (PM10),
PPM2.5 and SO2. The damage factors for the local and regional pollutants derived by RS1b for TIMES are
distinguished by region, by height of release (average and high) and by period (2010 and 2020) to allow
for a certain variation in the damage costs caused by source characteristics and background emissions. At
this stage, the background emissions in the RS1b data correspond to a BAU scenario. The damage
categories considered by RS1b are impacts on human health, crops yield loss, damage to building
materials and loss of biodiversity. The data from RS1b represent the damage generated by a country
emission to Europe as a whole.
RS1b could not yet evaluate the impact on the damage cost of a drastic reduction of the background
emissions as would occur with a climate policy. Thus in the scenarios with TIMES, the same damage
figures were used in all scenarios. The ‘high release’ figures were applied to the electricity sector, while
the ‘average height’ for the other sectors. The table below report the data for the damage generated in
EU27 as illustration, for the implementation in TIMES the country data were used. For each emission, a
damage figure equal to the sum of damage for the different categories was associated to each pollutant
emission, with equal weight for each category.
Table 1: Damage generated in the EU (€2000/ton)
Average Height
2010
2020
Health+Biodiversity
NMVOC
516
190
NOX
6493
7487
PPMcoarse
1325
1381
PPM25
24412
24103
3
High Height
2010
2020
516
4945
490
12256
190
6186
481
11929
SO2
Crops+Materials
NMVOC
NOX
SO2
b)
6247
6866
5708
5952
189
398
219
103
506
205
189
251
204
103
389
192
External cost of greenhouse gasses
The evaluation of the marginal cost of greenhouse gases emissions and climate change is a very
contentious issue, with high inherent uncertainties. Since the EU-funded “External costs of EnergyExternE” project began in 1990 (Externalities of Energy, 2005), the discussion on CO2 damage costs
evaluation has been intense because of the very high variability of cost estimations related to assumptions
behind the data. It was also an important point of discussion in the NEEDS project.
An overview of CO2 external cost estimations done by different experts within and outside the project is
reported in Table 4 (R. Friedrich, 2008).
Table 2: Overview of the existing values of CO2 external cost, with and without equity
weighting (EW).
Source/ Explanation
Kuik, Avoidance Costs GHG; in €2005
Target
MNP, EU-targets until 2020; Marginal
Abatement Costs; abatement compared
to 1990; prices 2000
target -20% unilateral without CDM
target -20% unilateral with CDM
target -30% multilateral regimes
for emission target 500ppm CO2e
NEEDS, RS1B; Marginal Abatement
Costs; converted with Methodex
Conversion Tool in €2005
Watkiss: existing SCC central
Watkiss: SCC average (all)
Watkiss: Marginal Costs, until 2050 60% (only UK), prices 2005, converted
with Methodex Conversion Tool in
€2005
4
year
2010
2020
2025
2030
2040
2050
2020
2020
2020
2005
2020
2030
2050
2000
2010
2020
2030
2040
2050
2060
2000
2010
2020
2030
2040
2050
2030
2040
2050
Euro/t CO2
19
19
23
30
46
61
96
23
74
22
46
74
198
32
37
41
46
51
55
60
26
31
37
45
64
98
0-67
6-105
1-247
PAGE (in Watkiss); in year of
emission; only mean; SCC; converted
with Methodex Conversion Tool in
€2005
with EW
without EW
FUND (in Watkiss); in year of emission;
SCC; trimmed mean (trimmed 5%);
converted with Methodex Conversion
Tool in €2005
with EW
no EW, average 1% trimmed
Anthoff: Marginal
costs, €2000
external
damage
WeuEW, average 1% trimmed
Nordhaus: optimal carbon price, €2005
globally harmonized
550ppm CO2-eq.
IPCC AR4
550ppm CO2-eq., technology integrated
2000
2010
2020
2040
2060
2000
2010
2020
2030
2040
2060
2000
2010
2020
2030
2040
2060
2005
2015
2025
2035
2045
2055
2005
2015
2025
2035
2045
2055
2005
2015
2025
2035
2045
2055
2065
2075
2085
2095
2105
2030
2050
2030
2050
21
28
35
58
86
11
13
17
20
21
26
21
26
31
34
37
46
7
11
14
15
17
27
97
122
148
137
143
196
6
10
12
15
17
23
27
32
37
43
50
15-62
23-119
4-50
12-100
After an exhaustive literature review and a lively internal debate, two set of reference values (scenarios)
were identified (Table 3):
5


Ambitious scenario, a combination of the values proposed by RS1b (more ambitious scenario”)
and RS1a ( “preferred scenario”) [Ricci A., NEEDS Internal Communication], taking in the
recent policy decisions.
Realistic scenario, which represent an average between the values proposed by Anthoff in the
World EW scenario and Watkiss P.
Table 3: GHG external costs (Euro/t of CO2). Ricci proposal (D 1.1 - RS3a, 2008).
Scenarios
Ambitious
CO2
CH4
N2O
Realistic
CO2
CH4
N2O
B.
2010
2015
2020
2025
2030
2035
2040
2045
2050
23.5
493.5
7285
31
651
9610
46
966
14260
51
1071
15810
74
1554
22940
87
1827
26970
110
2310
34100
146
3066
45260
198
4158
61380
23.5
493.5
7285
27
493.5
7285
29
567
8370
32
609
8990
34
672
9920
37
714
10540
50
777
11470
66
1050
15500
77
1386
20460
LCA data integration
The TIMES model has the capability to represent energy consumed/produced and emissions caused by
the operation of technologies, but also those attached to the construction of technologies, as well as
energy and emission releases at dismantling time. It is also possible to model materials sunk at
construction and released at dismantling, but only if these materials are represented in the TIMES model.
The key problem in integrating LCA and energy modelling is to avoid double counting of energy and
emissions.
The LCA data distinguishes different phases for a technology, construction, operation and dismantling
and can generate for each phase the associated energy consumption and emissions release. The energy
consumed and produced at phases 2 and 3 are fully modelled in TIMES (including fuel supply), and
require no additional transfer of data from RS1a to TIMES. There remains the issue of accounting for
energy, materials and emissions attached to phases 1 and 4 (construction and dismantling).
The general approach followed for the integration of LCA data into TIMES was to exclude the material
and energy flows included in the LCA datasets for construction and dismantling from the material and
energy flows modelled in TIMES. LCA data would then provide the cumulative emissions (and other
externalities such as radioactive emissions, land use, etc) attached to phase 1 (respectively phase 4). By
cumulative emissions, we mean the emissions actually released during construction (respectively
dismantling) plus the emissions produced during the upstream production of fuels and materials required
for construction (respectively dismantling), including fuels produced outside EU25. These emissions are
provided by RS1a per unit of capacity of the plant.
It should however be realized that it is very difficult to isolate the flows of materials and fuels linked to
construction and dismantling of the technologies. Those flows are not explicitly represented in TIMES,
but included in the total flows modelled. Moreover, part of the construction of a technology does not
necessarily occur in the country where it is implemented. Therefore to take into account the difference in
external cost linked to the technologies and at the same time avoid possible double counting, the
implementation in TIMES was simplified. Only the difference compared to a reference was implemented
in TIMES, i.e. the difference from the emissions derived from an average of existing technologies, this
average being implicitly reproduced in the reference scenario. It is expected that the approximation will
be acceptable, since the amounts of materials and fuels involved are small relative to the overall
6
consumption of fuels and materials by the economy. Then TIMES attach this coefficient to the
technologies and combine them with external cost coefficients provided by RS1b to endogenize the
external cost for the construction and dismantling phases. The data implemented in TIMES for some
typical power technologies are given in the table below;
Table 4: Emission coefficient for typical technologies
(difference compared to average existing technologies)
Coal PP
CO2
NOx
NMVOC
PM2.5-10
PM2.5
SO2
ton/MW
kg/MW
kg/MW
kg/MW
kg/MW
kg/MW
70
22
27
119
197
213
Coal PP
with CC
362
554
186
425
490
745
Nuclear
100
83
29
22
72
114
Wind
offshore
0
0
0
6
0
101
Fuel cell
on gas
294
482
120
172
136
3357
PV
162
281
625
0
0
1060
At this stage, only data for power generation technologies are provided by RS1a and this might introduce
a bias in the choice of technologies in the TIMES model. The additional emissions remaining rather
small, the bias will also remain small.
III. THE POLICY SCENARIOS
A.
The Italian policy scenario
1.
Basic modelling enhancements
The NEEDS Italy model, carried out in the framework of RS2a activities (D. 5.14, RS2a, 2006) was
improved to enable a detailed representation of renewable energy cycles and their potential development
as well as to allow a full characterisation of the pollutants associated to the different fuels and energyrelated technologies. This mainly led to an in depth technical review of the model in order to associate to
energy flows by sector additional emission factors for local air pollutants (SO2, NOx, PM10, PM2.5,
VOC) as well as for GHGs (CO2, CH4, N2O). A calibration of the model on UNFCCC data (APAT, 2007)
was also made, to verify the consistency of the model’s outcomes with the official national inventories.
The improved representation of pollutants emissions allows to account for 94% of SO2, 84% of NOx, 95%
of VOC, and for 92% of the total GHG emissions for year 2000 (about 448 Mton of CO2eq, where the
remaining 8% could be imputed to a different fuel aggregation in the supply sector).
The external costs for local pollutant as derived by RS1b for Italy were implemented and are given in the
table below.
Table 5: External costs of LAP – Italy
(€2000/ton)
NMVOC
2001
2005
2010
2015
2020
639
670
712
343
364
High height of release
NOX
PPM10 PPM25
6602
7034
7617
9175
9933
650
696
757
827
900
13404
14339
15600
16977
18470
SO2
NMVOC
6566
7019
7629
8773
9540
639
670
712
343
364
7
NOX
Unknown height of release
PPM10
PPM25
8362
8907
9640
11411
12353
1730
1851
2014
2190
2382
29303
31347
34104
35305
38409
SO2
7434
7947
8640
9938
10808
2025
2030
2035
2040
2045
2050
386
411
424
438
452
467
10757
11653
12133
12633
13155
13699
979
1065
1111
1159
1209
1261
20094
21861
22806
23792
24821
25894
10375
11284
11770
12277
12805
13357
386
411
424
438
452
467
13377
14492
15089
15711
16359
17036
2592
2820
2941
3069
3201
3340
41787
45462
47427
49477
51616
53847
11755
12785
13335
13910
14510
15135
For GHG cost, the higher values of the ambitious scenario values were used to perform the Italy test runs
in order to highlight their influence on the model’s choice in terms of technology and fuel mix.
2.
The Scenario analysis
a)
Scenario assumptions
The scenario analysis was mainly addressed to emphasise the policy implications of external costs in
terms of model’s choices. Therefore, two reference scenarios and several case variants were defined,
whose basic assumptions are coherent with the ones adopted for the policy scenarios selected at Pan
European level (Kypreos & Van Regemorter, 2006; Kypreos et al., 2006; Cosmi et Al.,2007):


BAU (Business as Usual): the baseline scenario, whose macroeconomic and energy price
background assumptions are in line with DG TREN 2005 projections (Mantzos et al., 2005) and
all the exogenous assumptions around drivers, energy prices and policies follow a rather business
as usual trend. No climate policy is considered.
KYOTO_FOREVER: a climate policy scenario aimed to achieve the national Kyoto Protocol’s
target (-6.5% of GHGs in the period 2008-2012 compared to the 1990 values2). Thus, starting
from the values of the reference scenario, a reduction of 448 Mton of CO2eq was imposed from
2010 to 2050 to model the GHGs stabilisation on the full time horizon. No tradable permits or
flexible mechanisms are allowed to achieve the prefixed -6.5% target.
Starting from these reference scenarios, the following case variants were defined to analyse the behaviour
of the model due to the integration of the external costs:




b)
BAU_GHG: Baseline scenario assumptions with the internalisation of the externalities related to
CO2, CH4 and N2O only (the “Ambitious scenario” values are considered) on BAU.
BAU_LAP: Baseline scenario assumptions with the internalisation of the externalities related to
local air pollutants only (SO2, NOx, NMVOC, PM10 and PM2.5).
BAU_LAP-GHG: Baseline scenario assumptions with the internalisation of the externalities on
both local and global air pollutants, including CO2 (the “Ambitious scenario” values are
considered).
Kyoto_LAP: KYOTO_FOREVER scenario assumption with the internalisation of the
externalities on local air pollutants (SO2, NOx, NMVOC, PM10 and PM2.5).
Scenarios comparison
In order to compare the effects of the internalisation of damage costs on the total system cost, all the case
variants were analysed with both the damage included in the optimization (ex-ante) and damage
computed ex-post (Table 6).
Table 6: Total discounted system cost with and without the internalization of external
costs
2
Total emission WITHOUT land-use, land-use change and forestry
8
Total discounted system cost (MEuro)
Case variants
ex post
energy system cost
ex ante
damage
total
energy system cost
damage
total
BAU_GHG
5159
GHG dam
608
5767
5196
531 5728
BAU_LAP
5159
LAP dam
584
5744
5224
454 5679
BAU_LAP_GHG
5159
GHG and LAPdam 1192
6351
5246
999 6245
Kyoto_LAP
5244
LAP dam
5774
5273
475 5748
531
The internalization of the external cost (ex-ante approach) induces a general increase of energy system
costs (excluding damage cost) due to new investments in technologies more efficient and with lower
environmental impacts. On the total discounted system cost, including damage cost, the effect is a
reduction in all cases, up to 1.7% in BAU_LAP_GHG case.
In terms of avoided damage costs, the combined policy turns out to be less beneficial: the reduction is 193
MEuro in the BAU_LAP_GHG case, against 206 MEuro summing up the reduction of BAU_LAP and
BAU_GHG cases (respectively 130 and 77 MEuro). This is due to the lower LAP emissions (that have
the higher external costs per unit of pollutants) in BAU_LAP case compared to BAU_LAP_GHG (Figure
3) with a reduction of respectively -26.5% and -21.4% over entire time horizon.
Nevertheless, the effectiveness of combined policies on LAP and GHGs, is clear in terms of GHG
emissions trends (Figure 1, Figure 2). The BAU_LAP case variant shows a strong increase of GHGs
(+25%), whereas in the BAU_LAP_GHG case (-17%) the internalization of both externalities fosters a
reduction of GHGs stronger than in the BAU_GHG case (-15%).
Figure 1: Total GHG emissions (Mton CO2eq).
BAU
BAU_GHG
BAU_LAP
BAU_LAP_GHG
KYOTO
KYOTO_LAP
650
GHG emissions (MtonCO2eq)
600
550
500
450
400
350
300
2000
2010
2020
2030
9
2040
2050
Concerning CO2 emissions the internalisation of externalities (BAU_GHG scenario) is effective on long
term but not in the short term, highlighting that only the higher values of CO 2 external costs have an
influence on the energy system configuration changes.
As shown in Figure 2, a higher CO2 reduction is obtained in the conversion sector, through carbon capture
processes that give their maximum contribution (about 23%) in BAU_GHG case, showing the lowest CO2
emissions in 2050. In fact, in this case, the highest contribution to CO2 emissions reduction is due to
Conversion and Transport (respectively about -78% and -37% respect to the reference emissions of year
2000), obtained besides the use of carbon capture processes by fuel switching to less carbon intensive
fuels (in particular, biofuels use in Transport).
Households, Commercial and Agriculture that shows a 4% reduction of CO2 emissions in the BAU
scenario and about 13% in presence of a Kyoto target, reduce their CO2 emissions about 17% in the
BAU_LAP_GHG case.
On the other hand, Industry increases its CO2 emissions in every scenario and case variant (from 95% of
BAU scenario to 43% of BAU_GHG_case variant) because of a larger use of natural gas consequent to an
increase of demand. In particular, natural gas consumption in industry sector doubled in all the case
variants, reaching the higher value in Kyoto_LAP case (from 906 PJ in 2000 to 2412 PJ in 2050)
compared with a 72% increase of energy intensive industry’s demand and a 32% demand increase for non
energy intensive industry.
Figure 2: Carbon emissions per sector (Mton/year).
600
CO2 sequestration
Emissions of CO2 [Mio t]
500
Transport
400
300
Households,
commercial, AGR
200
Industry
100
Conversion,
production
2010
2030
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
Kyoto_LAP
Kyoto_forever
BAU_LAP
BAU_LAP_GHG
BAU
BAU_GHG
2000
0
2050
The strong increase of CO2 emissions that can be noticed in the BAU_LAP case, is mainly caused by an
increase in natural gas consumption used in a Fischer-Tropsch process to produce synthetic diesel fuel
(Fischer-Tropsch diesel) characterised by a very low sulphur and aromatic content (Norton, 1998) and by
the absence of the carbon capture processes that, without specific constraints on GHGs emissions, are not
activated.
Table 7 reports the CO2 marginal abatement costs in Kyoto_forever scenario and BAU_GHG case and
the percentages CO2 emission reductions obtained compared to base year. As can be seen, on the long
10
term in the BAU_GHG the CO2 damage is much higher than in the KYOTO scenario and induces thus a
higher CO2 reduction respect to Kyoto cap scenario. In the short term, the CO2 damage figure used here
is not sufficient to achieve the Italian Kyoto target.
Table 7: CO2 costs and reduction.
2010
2030
2050
marginal cost (Euro/ton)
CO2 reduction
230
12%
230
14%
100
14%
marginal cost (Euro/ton)
CO2 reduction
20
2%
70
3%
200
21%
Kyoto_forever
BAU_GHG
An in-depth examination of the local air emission pollutants (NOx, SO2, NMVOC and particulates (Figure
3) shows that apart from the decreasing trend due to technical improvement already in the BAU, the
internalization of external costs is effective to obtain a further reduction with or without an explicit
constraint on GHGs. In particular, the reduction percentage is the highest (about -42%) in the
BAU_LAP_GHG case, with a remarkable reduction of NMVOC (-55%). As no specific abatement
technologies were inserted in the model (e.g. end of pipe technologies), the abatement of LAP emissions
is achieved by fuel switching and technology substitution. The cost of the policy or the percentage
reduction could therefore change with the model improvements.
Figure 3: Local air pollutants
Nox
NMVOC
Particulates
SO2
Emissions of LAP [Mio t]
3500
3000
2500
2000
1500
1000
500
2010
2030
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
2000
0
2050
Table 8 summarizes the situation by scenario relatively to the emissions of the main pollutants on the
overall time horizon.
Table 8: Pollutant emissions by scenario from 2000 to 2050.
11
Scenario
Total emissions (Mt)
Case variants
CO2
BAU
BAU_GHG
BAU_LAP
BAU_LAP_GHG
VOC
Particulates
24.70
68.40
40.93
14.60
21934
24.17
63.15
35.52
13.42
(-10.6%)
(-3.3%)
(-8.8%) (-14.2%)
(-7.1%)
27041
22.97
(+10.3%)
21039
25.98
13.27
(-8.1%) (-30.7%) (-37.2%)
(-8.1%)
11.66
(-14.2%) (-10.7%) (-23.3%) (-25.3%)
(-19.2)
21101
22.32
47.97
30.93
(-14.0%)
Kyoto_LAP
NOx
24523
21101
KYOTO_FOREVER
SO2
23.41
53.09
33.40
13.14
(-6.3%) (-12.2%) (-19.3%)
(-9.0%)
22.33
60.75
51.43
28.85
11.50
(-14.0%) (-10.6%) (-25.7%) (-30.3%)
(-20.4%)
To assess the effects of internalization of external costs, it is also worth investigating the behaviour of the
energy system in terms of energy supply and consumption. According to demand projections, primary
energy consumption increases in all scenarios (Figure 4), from the initial 6799 PJ of the reference year to
7329 PJ in 2050 for the BAU_LAP_GHG case and 8063 PJ for Kyoto_LAP.
The increased energy demand is fulfilled mainly by renewable and natural gas. In particular, there is a
huge increase of renewable in the Kyoto_LAP case (about 21% in 2050), whereas natural gas
consumption is more or less doubled in the BAU_LAP case, to compensate a drastic decrease of oil
consumption (quite halved as expected).
Figure 4: Primary energy consumption.
12
Electricity
import
8000
Waste
7000
6000
Other
renewables
5000
4000
3000
Hydro, wind,
photovoltaic
2000
Natural gas
1000
0
2010
2030
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
Oil
2000
Primary Energy Consumption [PJ]
9000
Lignite
Coal
2050
This behaviour is emphasized by the electricity production modes (Figure 5). In particular, the
endogenous production increases from 254 TWh of year 2000 to 343.6 TWh in 2050 for the Kyoto_LAP
case. Electricity from fossil fuels is produced mainly by coal, enabling the use of technologies provided
with sequestration processes to diminish the CO2 emissions, and natural gas (that increases about 74%
from 2000 to 2050 without any constraint whereas internalizing external costs of GHGs and LAP
emissions this percentage decrease up to 49% in the BAU_LAP_GHG case). Among renewables, it can
be noted, a remarkable increase of wind energy (from 0.6 TWh in 2000 to a maximum of 27.8 TWh
without the internalization of externalities on LAP and 21.5 TWh with the internalization of externalities
on LAP). Photovoltaic also increases (+68% comparing the share in 2050 of all the constrained cases with
BAU) even if its contribution is still very low (about 2.2 TWh).
Figure 5: Net electricity generation (TWh).
13
Others
Net electricity production [TWh]
400
350
300
Solar
photovoltaic
250
Wind
200
Hydro
150
100
Natural gas
50
Oil
2010
2030
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
2000
0
Lignite
Coal
2050
Similarly to primary energy consumption, the final energy consumption increases in all scenarios from
2000 to 2050 (Figure 6), with a maximum in the BAU scenario (+25%) a minimum in the BAU_GHG
and BAU_LAP_GHG cases (+21%).
The most used fossil fuels on long term in all scenarios are obviously natural gas and oil products, but
there are significant differences in their share along the examined cases. In particular, oil products share
in 2050, which is 36% in the BAU scenario, up to 18% in the BAU_LAP case and about 24% in the
Kyoto_LAP case. On the contrary, natural gas share is about 30% in the BAU scenario and increases up
to 38% in the Kyoto_LAP case, with an average increase of about 52% respect to base year.
There is also a remarkable increase of renewable use in all the scenarios, whose share in 2050 is more
than 14% in BAU_LAP_GHG and Kyoto_LAP cases, their consumption increasing from 81 PJ of the
base year to respectively 989 and 1028 PJ.
The strong increase of other fuels (“Others”) in the BAU_LAP case (about 938 PJ) is fostered by a large
use of FT-diesel in Transport sector.
Figure 6: Total final energy consumption.
14
Total final energy consumption [PJ]
8000
Others (Methanol,
Hydrogen)
7000
Waste
6000
5000
Renewables
4000
Heat
3000
2000
Electricity
1000
Gas
2010
2030
Kyoto_LAP
Kyoto_forever
BAU_LAP
BAU_LAP_GHG
BAU
BAU_GHG
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU_GHG
BAU
Kyoto_LAP
Kyoto_forever
BAU_LAP_GHG
BAU_LAP
BAU
BAU_GHG
2000
0
Petroleum
products
Coal
2050
The effects of internalization of externalities in terms of renewable share in final energy consumption are
summarized in Table 9.
The 2020 objective of 17% share of energy from renewable sources stated for Italy by the European
Directive on the promotion of the use of energy from renewable sources is nearly reached in the cases
with GHG cap (Kyoto_forever, Kyoto_LAP), whereas the externalities alone are not effective to give a
boost to use of renewables. In 2050, all the cases increase the RES share until 24% for BAU_LAP_GHG
case.
Table 9: Impact of internalization on renewable use – Final energy consumption.
Scenario
Case variants
BAU
BAU_GHG
BAU_LAP
BAU_LAP_GHG
KYOTO_FOREVER
Kyoto_LAP
B.
% of renewable in final energy consumption
2000
2010
2020
2050
4%
5%
7%
12%
5%
11%
22%
5%
6%
13%
5%
15%
24%
4%
10%
16%
20%
9%
16%
21%
The European policy scenario
The European policy3 evaluated with TIMES in this WP aims at internalizing the external cost linked to
local pollutant emissions from the energy system. This policy could be implemented through an emission
tax differentiated by country and equal to the external cost generated by the country emissions. The
results for this scenario are compared to the reference scenario of RS2a WP4, in which a small CO2 tax is
3
The Pan European TIMES model includes, besides the EU 27 countries, Norway, Switzerland and Iceland
15
implemented from 2010 onwards (5€2000/ton CO2)4. The internalisation is implemented from 2010
onwards. This scenario is meant to illustrate the integration of external cost in TIMES, other scenarios
with the European model combining different environmental targets are done in RS2a, WP4.
1.
System cost
The cost of the policy (without including the damage reduction) represents an increase of 0.6% compared
to the reference. When the damage reduction is included, the policy generates a benefit, 1.6% compared
to the reference, as can be seen in Table 10. The reduction in damage is already effective from the start of
the policy and discounted, it is around 35% over the entire horizon compared to the reference. It should be
mentioned that, except for transport and the power sector, no specific local air pollution is assumed in the
reference scenario. Also, the perfect foresight and optimisation approach in TIMES limit the adjustment
cost of such a policy, which would be observed in real life.
Table 10: Total EU discounted cost 2000-2050
(% difference compared to reference)
incl. damage
-1.6%
-10.2%
-0.5%
total discounted cost
difference as % GDP2000
annualized cost as % GDP2000
2.
excl. damage
0.6%
3.8%
0.2%
Emissions
The reduction in local pollutant emissions is rather drastic. The local air pollution policy has however
only a small impact on the CO2 emissions, except at the beginning and the end of the horizon.
Table 11: Air pollutant emissions in the EU
(% difference compared to reference)
NOX
PM
SO2
NMVOC
CO2
3.
2010
-29%
-70%
-58%
-41%
-9%
2020
-40%
-74%
-47%
-42%
1%
2030
-38%
-72%
-44%
-39%
-1%
2040
-30%
-70%
-38%
-34%
-5%
2050
-23%
-68%
-36%
-29%
-8%
Energy
The reductions in emissions occur through four mechanisms:




reduction in demand for energy services
energy saving through improved efficiency
fuel switching
end of pipe abatement technologies
The last option is however rather limited because the technology database of TIMES does not contain yet
many end of pipe technologies.
4
The reference is slightly different from the reference used for the Italian model and coresponds to an update of the NEEDS reference in 2008.
16
a)
Impact on demand
The reductions in demand are concentrated in the industry and commercial sector. In the transport sector
the decrease is smaller because already in the reference more drastic emission controls are implemented
through the euro-norm and this limits the price increase through the internalisation. The high decrease in
agriculture can be explained by the lack of other reduction options in that sector, which is modelled until
now in a very generic way with no fuel or technology switch possibilities.
Table 12: Energy services/Material demand in the EU
(% difference compared to reference)
2010
-10.5%
-3.4%
-1.6%
-0.5%
-0.2%
-3.4%
-0.8%
-2.7%
-4.1%
-2.6%
-3.0%
-2.5%
-0.8%
0.0%
-1.2%
Agriculture (PJ)
Commercial (PJ)
Residential (PJ)
Freight transport (tkm)
Passenger transport (pkm)
Industry (PJ)
Industry Aluminium demand in ton
Industry Ammonia demand in ton
Industry Cement and Lime demand in ton
Industry Copper demand in ton
Industry Glass demand in ton
Industry Iron and Steel demand in ton
Industry Paper demand in ton
Aviation transport (PJ)
Navigation transport (PJ)
b)
2020
-11.4%
-2.2%
-0.6%
-0.7%
-0.2%
-3.4%
-0.3%
-2.3%
-4.3%
-2.8%
-1.8%
-2.7%
-0.5%
0.0%
-1.4%
2030
-12.0%
-2.0%
-0.7%
-0.6%
-0.3%
-3.4%
-0.3%
-2.6%
-5.1%
-1.8%
-2.5%
-2.8%
-0.6%
0.0%
-1.4%
2040
-12.5%
-1.5%
-0.6%
-0.6%
-0.2%
-3.9%
-0.2%
-2.3%
-6.2%
-3.5%
-2.3%
-2.7%
-0.9%
0.0%
-1.4%
2050
-12.0%
-1.5%
-0.5%
-0.6%
-0.1%
-3.9%
-0.3%
-2.4%
-6.4%
-2.6%
-2.0%
-2.6%
-0.9%
0.0%
-1.4%
Impact on final energy consumption
The decrease in final energy consumption lies between 5 and 7%, the higher decreases are observed
between 2020 and 2030.
Table 13: Final energy consumption by fuel and by sector in the EU
(difference compared to reference in PJ and in %)
By fuel in PJ
Bioenergy
Solid fuels
Electricity
Gas
Heat
Oil
Renewables
Synthetic fuels
Total
By fuel in %
Bioenergy
Solid fuels
Electricity
Gas
2010
-109
-1034
-23
-1595
28
-1593
0
1724
-2602
2020
-128
-1403
190
-1768
82
-5342
52
4958
-3358
2030
-327
-1761
238
-1527
-49
-4709
87
4617
-3431
2040
-304
-1763
198
-1532
-83
-1787
142
2272
-2857
2050
-341
-1699
223
-1344
-67
-298
42
711
-2772
-12%
-27%
0%
-12%
-8%
-33%
2%
-13%
-11%
-38%
2%
-13%
-8%
-35%
2%
-13%
-8%
-33%
2%
-11%
17
Heat
Oil
Renewables
Synthetic fuels
Total
By sector in PJ
Agriculture
Commercial
Industry
Residential
Transport
By sector in %
Agriculture
Commercial
Industry
Residential
Transport
1%
-8%
-1%
46236%
-5%
4%
-26%
249%
16804%
-6%
-2%
-24%
374%
369%
-6%
-4%
-11%
439%
50%
-5%
-3%
-2%
22%
9%
-5%
2010
-125
-1026
-824
-577
-50
2020
-140
-1350
-905
-862
-102
2030
-157
-1222
-1081
-828
-144
2040
-172
-1031
-1246
-285
-122
2050
-172
-1046
-1180
-226
-149
-11%
-17%
-5%
-4%
0%
-11%
-20%
-5%
-7%
-1%
-12%
-18%
-5%
-8%
-1%
-13%
-15%
-6%
-3%
-1%
-12%
-14%
-5%
-2%
-1%
The highest decrease is observed in the commercial sector, mainly through a shift towards heat pump on
electricity in place of bio and gas heating system. The same is observed in the residential sector but to a
lesser extent. There is also a shift towards LPG for coking and hot water.
In the transport sector synthetic fuels, here DME from coal, is penetrating more and much faster than in
the reference scenario, from 2020 onwards instead of 2040 and mostly for freight transport by truck.
In the industry, there is mainly a shift from solid fuels to bioenergy, triggered by their relative emission
coefficients.
The shifts between fuels in the different sectors are mainly driven by the differences in their emission
coefficients for local pollutant and this must clearly be further examined.
c)
Impact on electricity production and other supply
The overall production of electricity is approximately 2% higher over the entire horizon, except for the
first period (2010). This reflects the shift towards electricity in the final energy consumption.
Within the electricity sector there is a shift away from solid fuels towards gas, biomass (in CHP) and
renewables. Nuclear is penetrating more (+5%) at the end of horizon in the countries where the option is
available.
Table 14: Net electricity production by fuel type in the EU
(difference compared to reference in PJ)
Solids
Gas
Oil
Nuclear
DME
Biogas
Biomass
Renewables
2010
-1030
757
-29
0
0
1
17
144
2020
-411
611
-31
-4
0
-1
47
-54
18
2030
-649
616
-13
-3
0
-1
71
168
2040
-687
639
0
0
0
-5
54
145
2050
-673
568
-1
131
0
-3
99
58
-139
-1.2%
Total
Total (%)
156
1.3%
189
1.4%
147
1.0%
179
1.2%
Coal and lignite steam power plants are replaced by IGCC power plants, CHP on coal are replaced by
CHP on gas and biomass.
Renewables: wind and hydro are penetrating more rapidly in 2010 but then less in 2020 to remain stable
thereafter. Solarthermal and wave powerplants see their share slightly increasing with 1% up to 5.5% at
the end of the horizon.
Combining the fuel shifts in final consumption and in the electricity production, the overall share of
renewables and bioenergy as computed for the EU renewable target5 increases with 1.3% reaching 14.1%
in 2020.
The carbon tax imposed in both scenarios induces carbon sequestration mostly through afforestation in
the reference scenario because it is the cheapest option but with a limited capacity in TIMES. With
internalisation of local pollutant and the production of DME from coal, the cost minimisation induces
more carbon sequestration. As the technology for DME is modelled without carbon sequestration, the
model chooses technologies in the electricity sector with carbon sequestration from 2020 onwards while
the supply sector sequesters carbon through afforestation. The electricity produced with these
technologies doubles compared to the reference but represent still only 6% of the total.
d)
Impact on primary energy consumption
The impact of the policy in the different energy consumption sectors is reflected in the impact on primary
energy consumption. There is a shift from oil to coal because of the fuel switch in the transport sector.
The increase in renewables reflects their penetration in the electricity sector and in the final energy
consumption. However overall, the reduction in the primary energy is not higher than 3%.
Table 15: Primary energy consumption in the EU
(difference compared to reference in PJ and in %)
in PJ
Bioenergy
Gas
Nuclear
Oil products
Solid fuels
Renewable
Total
in %
Bioenergy
Gas
Nuclear
Oil products
Solid fuels
Renewable
Total
5
2010
-107
69
0
-1642
-2149
144
-3685
2010
-8%
0%
0%
-5%
-17%
5%
-5%
2020
-105
-499
-11
-5400
4935
7
-1073
2020
-5%
-3%
0%
-17%
40%
0%
-1%
2030
-443
-267
-7
-4750
3700
255
-1513
2030
-14%
-2%
0%
-15%
23%
5%
-2%
2040
-525
-368
-1
-1817
105
289
-2316
2040
-13%
-3%
0%
-6%
0%
5%
-3%
2050
-395
-554
364
-320
-2227
105
-3027
2050
-9%
-4%
4%
-1%
-8%
2%
-3%
It is computed as the share of electricity from renewables and renewable final energy consumption in total final energy consumption (incl.
electricity losses).
19
The figure hereafter gives another view of the impact of the internalisation on the composition of the
primary energy.
Figure 7: Primary energy consumption in the reference and the internalisation scenario
(PJ)
100000
90000
80000
Renewable
70000
Solid fuels
60000
Oil products
50000
Nuclear
40000
Gas
30000
Bioenergy
20000
10000
2000
2010
2020
2030
2040
REF_decinterlocn
REF_decn
REF_decinterlocn
REF_decn
REF_decinterlocn
REF_decn
REF_decinterlocn
REF_decn
REF_decinterlocn
REF_decn
REF_decn
0
2050
IV. CONCLUSION
The analysis above is mainly meant to evaluate the development of the TIMES model regarding
externalities linked to energy consumption. From a methodological point of view it was important to set
up an integrated modeling framework covering all environmental dimensions linked to energy that allows
to exploit synergies and trade-offs between the various targets imposed on the energy system (climate, air
quality, energy security, import dependence). In particular, this modeling framework is particularly
suitable to explore the medium to long term effects of energy, environmental and resources use policies
on the energy system structure and on its cost.
With the Italian model, the focus was on the assessment of combining climate and air quality policies. It
showed that the internalisation of externalities of both GHGs and LAP simultaneously reduces the total
discounted system cost compared to the case where the internalization is done separately for the two
environmental issues because of the exploitation of the synergies. The results obtained so far emphasize
the role of renewable and carbon capture in the mix of options to reduce emissions.
With the European model the test was on the internalization of the external cost of local air pollution
alone. It shows that a policy internalizing the external cost of local pollution could be very effective for
local pollution but has only a small effect on CO2 emissions.
20
Both scenario exercises show the importance of having an integrated model covering the total energy
system because of the interaction between its different components and the synergies and trade-offs
between policies adressing issues faced by the energy system. But further improvements of the model
technology database are clearly needed to draw final conclusions from this scenario analysis. First, the
emission coefficients associated with the fuels and technologies have to be further examined because they
play a crucial role in the choice of technologies and fuel switching (e.g. in transport) for reducing
emissions. Secondly more end-of pipe abatement technologies for local pollutant have to be included for a
better trade-off between the different options.
V.
REFERENCES













The Italy NEEDS TIMES model – D5.14 RS2a NEEDS Project
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Norton P., Vertin K. D., Bailey B. K., Clark N., Lyons D. W., Goguen S. J., Eberhardt J. J.
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21