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Carbon footprint of the nuclear
energy using IO-LCA
Jorge E. Zafrillaa, María Ángeles Cadarsoa, Fabio Monsalvea y Cristina de la Rúab
a
Universidad de Castilla-La Mancha, Albacete
b Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
E-mail: [email protected]
IX Congreso Asociación Española para la
Economía Energética
Madrid, 3 y 4 de febrero de 2014
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
Outline
1. Introduction
2.
2.1. Data
2.2. Methodology
2.3. Scenarios
3. Main results
4. Conclusions
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
1. Introduction
Energy Roadmap 2050 establishes an 80-95% GHG reduction by
2050 within the EU.
One of the main targets is to turn the electricity sector in a
quasi-zero emissions sector.
 Nuclear power could play an important role in the fulfilling of
environmental commitments.
But, how clean is nuclear power from an IO-LCA
perspective?
The main objective is to develop a MRIO-LCA model for a
nulcear power plant in Spain under different scenarios
analyzing the carbon footprint of the facility
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
1. Introduction
Contributions
a) The data set and the methodology allow us to estimate the variability and
the uncertainties of the analysis via multiple scenarios.
SRIO / MRIO / hMRIO-LCA
Methodological
Construction period
Lifetime
SCENARIOS
Technical
Load Factor
Political
GHG intensities
Consumption patterns
Financial
Uranium prices
Financial comparisons
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
1. Introduction
Contributions
b) The use of the MRIO-LCA model to face the problems related to emissions
linked to production processes of imported goods.
The case of nuclear power is special
because Spain does not have a
covering industry behind the
nuclear power. It has to import a
big amount of inputs in the
production process.
c) This is the first study of nuclear power carbon footprint in Spain.
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
2.1. Data
 The MRIO model is based on WIOD.
 The technical and sectorial data set is provided by Spanish
Nuclear Industry Forum.
 Ruesga, S.M. (2008). “Análisis económico de un proyecto
de ampliación de la producción eléctrica nuclear”
 Nucler power LCA phases differentiation:
1) Construction/Investment
2) Nuclear Fuel cycle (fuel decommissioning included)
3) Operation & Maintenance (facility dismantle phase
is included)
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
2.1. Data
Table 1. Investments function of a nuclear power plant (2450 M€UROS, constant 2009)
Years
1
2
3
4
5
6
7
8
Investment activity
allowance
allowance
civil engineering
equipments + ce
equipments + ce
equipments + ce
equipments + ce
end works + tests
Total
%/Total
M€uros
Allowances
(7513)
2%
2%
10%
18%
20%
25%
15%
8%
100%
49,00
49,00
245,00
441,00
490,00
612,50
367,50
196,00
2.450,00
49,00
49,00
0,00
0,00
0,00
0,00
0,00
8,17
106,17
EG
Civil
Buildings Insuaran
Mechan EG Electric
Enineering
and CE
ces
IMPORTS
ical
(311)
(35500)
(452)
(66031)
(291)
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
163,33
40,83
40,83
0,00
0,00
0,00
114,33
40,83
40,83
0,00
0,00
245,00
114,33
81,67
81,67
40,83
0,00
212,33
40,83
81,67
81,67
36,75
4,08
367,50
40,83
122,50
81,67
32,67
8,17
102,08
4,08
0,00
40,83
0,00
0,00
81,67
477,75
367,50 367,50
110,25
12,25 1.008,58
NOTE: Construction simulation of a 1.000 MW Advance Boiling Water Reactor (ABWR)
(similar to the one installed in Cofrentes Nuclear Power Plant)
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
2.1. Data
Table 2: Nuclear Fuel Cycle estimation costs and O&M (€/MWh) (2009 prices)
Combustible
O&M
O&M (replacement)
Decommissioning and final
disposal
Insurances
Total
%/Total
Uranium
Enrichment
Fabrication
1,925
1,225
0,35
1,925
12,18%
1,225
7,75%
0,35
2,21%
Fuel Cycle phase
Equipments
Own
Personnel
External
Personnel
1,11
2,21
2,59
0,34
0,85
3,50
3,70
3,40
1
0,5
3,5
5,00
4,32
27,32%
3,43
21,70%
4,35
27,51%
0,21
15,81
100,00%
Total
Operation & Maintenance phase
BASELINE SCENARIO:
- LIFETIME: 60 years  Production = 467.164 GWh (~ 4% of electrcity demand per year).
- Load factor assumption = 81,54%  Cofrentes NPP production in the last 25 years.
- Disscount rate = from 5 to 6% (Ruesga, 2008).
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
2.1. Data
Construction/Investment
DFd (FBKF)
DFm (FBKF)
Operation & Maintenance
DFd
DFmT
1 Agriculture, Hunting, Forestry and Fishing
2 Mining and Quarrying
899,29
from 3 to 7
8 Coke, Refined Petroleum and Nuclear Fuel
from 9 to 12
13 Machinery, Nec
14 Electrical and Optical Equipment
from 15 to 17
18 Construction
from 19 to 28
28 Financial Intermediation
29
30 Renting of M&Eq and Other Business Activities
31 Public Admin and Defence; Compulsory Social Security
from 32 to 35
Total
735,78
367,50
367,50
482,39
521,22
588,00
0,00
12,25
4,98
559,95
605,02
98,10
0,00
106,17
1.441
426,59
426,59
2.032,17
1.009
2.983,45
2.800,05
NOTE: Sectorial structure  WIOD (35 sectors).
Fuel Cycle phase
Table 3: Final demand (domestic and imported) vectors for the whole life cycle.
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
2.1. Data
Figure 1: Spain´s Nuclear Fuel Cycle (IAEA, 2005).
Uranium (U3O8)
Australia
Canada
Russia
Namibia
Niger
Portugal
South Africa
Conversion (UF6)
Cameco (CANADA)
Comurhex (FRANCE)
Convedyn (USA)
Minatom (RUSSIA)
WH Springfields (UK)
Reprocessing
BNFL
Reactors
LWR
Enrichment
Eurodif (FRANCE)
Minatom (RUSSIA)
Urenco (GER/NETHERLANDS)
USEC Inc. (USA)
Fabrication
Belgonucléaire (BELGIUM)
Framatome ANP (GERMANY)
GNF (USA)
WH Columbia (USA)
WH Springfields (UK)
WH Västeräs (SWEDEN)
NOTE: Investment phase and rest of O&M distribution between domestic and imported
goods comes from WIOD structure.
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
2.1. Data
Table 4: Imported Final Demand by region.
DFm (FBKF)
RUE
Construction/Investment
NAFTA
China
EastAsia
BRIIAT
RoW
DFmT
RUE
Operation & Maintenance
NAFTA
China
EastAsia
BRIIAT
RoW
356,13
326,18
216,98
248,22
1
2
899,29
8
735,78
265,24
222,33
559,95
605,02
428,93
372,33
21,09
26,16
2.800,05
1.066
from 3 to 8
from 9 to 12
10
11
12
13
14
from 15 to 17
18
from 19 to 35
Total
482,39
521,22
369,51
320,75
18,17
22,54
39,41
121,79
15,91
26,00
7,93
13,63
31,44
16,50
4,98
4,88
0,00
0,00
0,00
0,01
0,09
1.009
SPAIN
695
41
Rest of UE
161
42
NAFTA
22
48
CHINA
626
EAST ASIA
45,75
141,37
18,47
30,18
187
BRIIAT
49
9,21
15,82
599
36,50
19,16
273
RoW
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
2.2. Methodology
Figure 2: WIOT, aggregation to 7 regions valued in Euros (2009).
1 to 35
1 to 35
1 to 35
1 to 35
1 to 35
1 to 35
1 to 35
1 to 35
SPAIN
Ad
Am
Am
Am
Am
Am
Am
SPAIN
RUE
NAFTA
China
EastAsia
BRIIAT
RoW
1 to 35
RUE
Am
Ad
Am
Am
Am
Am
Am
1 to 35
NAFTA
Am
Am
Ad
Am
Am
Am
Am
1 to 35
China
Am
Am
Am
Ad
Am
Am
Am
1 to 35
EastAsia
Am
Am
Am
Am
Ad
Am
Am
1 to 35
BRIIAT
Am
Am
Am
Am
Am
Ad
Am
1 to 35
RoW
Am
Am
Am
Am
Am
Am
Ad
(245x245)
 x1   A11
 2   21
x  A
 3    31
x  A
    
 m   m1
x  A
A12
A13

A 22
A 23

A 32
A 33




m2
m3

A
A
  y 1r 

A1m   x 1   r
2r 
 2 
y
2m


A  x   r

  
A 3m   x 3     y 3r 
r
      

  
A mm   x m   y mr 


 r

Final Demand
SPAIN
yd
ym RUE
ym NAFTA
ym China
ym EastAsia
ym BRIIAT
ym RoW
(245x35)
Also expressed in compact form by:
x  Ax  y
Through Leontief Inverse [L= (I – A)-1]:
x  ( I  A) 1 y
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
2.2. Methodology
Figure 3: Matrix of emissions coefficients “e” (ktCO2 –eq per million of euros) [WIOD]
1 to 35
SPAIN
1 to 35
RUE
1 to 35
NAFTA
1 to 35
China
1 to 35
EastAsia
1 to 35
BRIIAT
1 to 35
RoW
hMRIOLCA
1 to 35
SPAIN
eWIOD
1 to 35
RUE
1 to 35
NAFTA
1 to 35
China
1 to 35
EastAsia
1 to 35
BRIIAT
1 to 35
RoW
eWIOD
e WIOD
eWIOD
eWIOD
eWIOD
eWIOD
ECO-INVENT data for Mining and Quarrying and Coke,
Refined Petroleum and Nuclear Fuel
NOTE: Matrix e is combined with ECO-INVENT data for the
development of the hybrid MRIO-LCA model
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
2.2. Methodology
Summary of models used:
Single Region IO model (SRIO-LCA)
^
Ee
SP

I  AdSP

1
^
y
^
e
d
SP

I  AtSP

1
 SP
 Am


I  AdSP

1
^
y

d
^ 
y m 
where eSP is the Spanish
(DTA)
Multi-Regional IO model (MRIO-LCA)
^
F e
I  A
1 ^
y
where e is the WIOD emissions coefficients
Hybrid Multi-Regional IO model (HMRIO-LCA)
L  ( I  A) 1  ( I  A  A 2  A3  ...)
LD  ( I  A)
LI  ( A 2  A3  ...)
F *  [eˆ * ( I  A)  eˆ( A2  A3  ...)] yˆ
ECO-INVENT emissions coefficients
and WIOD emissions coefficients
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
2.3. Scenarios
Table 5: Summary of scenarios.
Variable
Unit
Scenario
code Variation
SRIO-LCA
A1
g/CO2 MRIO-LCA
A2
Variable
hMRIO-LCA (baseline)
A3
fast built (5y) -10%
B1
5
B. CONSTRUCTION PERIOD years 3 years extension +10%
B2
11
6 years extension +20%
B3
14
early decommissioning 25Y
C1
25
C. LIFETIME
years intermediate 40Y F. WORLD CONSUMPTION
C2
40
long lifetime 100Y PATTERN C3
100
1980 USA (75%)
D1
75%
D. LOAD FACTOR
%
intermediate (85%)
D2
85%
near-full load
D3
95%
Paner Plan 2020
E1
-35%
E. GHG INTENSITY OF
g/CO2 90% renewables scenario
E2 PRICE-78%
G. URANIUM
ECONOMY
Coal Decree
E3
22,80%
A. METHODOLOGICAL
APPROACH
Unit
g/CO2
g/CO2
H. FINANCE COMPARISON
g/CO2
I. BEST AND WORST CASE
g/CO2
Scenario
EU-Fuel Cycle
NAFTA-Fuel Cycle
BRIIAT-Fuel Cycle
EU-O&M
China-O&M
BRIIAT-O&M
RUE-Construction
China-Construction
BRIIAT-Construction
Double price
Triple price
Report OCDE+IEA
Report MIT 2003
Best cases compiled
Worst cases compiled
code Variation
FA1
FA2
FA3
FB1
FB2
FB3
FC1
FC2
FC3
G1
G2
H1
H2
I1
I2
-----
100%
200%
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
3. Main Results
Table 6: CO2 emissions of nuclear electricity by origin of inputs
and life cycle stage (g CO2 –eq /Kwh).
Domestic
Foreign
Total
Construction
0,62
2,13
2,75
Graph 1: CO2 –eq emissions of nuclear electricity
by origin of inputs and life cycle stage (g
CO2/Kwh).
Fuel
0,01
13,13
13,14
O&M
0,87
2,94
3,81
Total
1,50
18,20
19,70
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
3. Main Results
Graph 2: CO2 –eq emissions of nuclear electricity by region (gCO2 –eq /Kwh intensities).
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
3. Main Results
Graph 2: hMRIO-LCA Carbon footprint (gCO2 –eq /KWh) by country.
11,08%
Rest of World
27,5%
BRIIAT
East Asia
1,28%
11,21%
China
19,86%
NAFTA
21,46%
Rest of EU
7,6%
Spain
0.00%
5.00%
10.00%
Construction
15.00%
Fuel
20.00%
O&M
25.00%
30.00%
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
3. Main Results
Table 7: Scenarios results
Variable
Scenario
SRIO-LCA
A.
METHODOLOGICAL MRIO-LCA
APPROACH
hMRIO-LCA (baseline)
fast built (5y) -10%
B. CONSTRUCTION
3 years extension +10%
PERIOD
6 years extension +20%
early decommissioning 25Y
C. LIFETIME
intermediate 40Y
long lifetime 100Y
1980 USA (75%)
D. LOAD FACTOR
intermediate (85%)
near-full load
Paner Plan 2020
E. GHG INTENSITY
90% renewables scenario
OF ECONOMY
Coal Decree
EU-Fuel Cycle
NAFTA-Fuel Cycle
BRIIAT-Fuel Cycle
EU-O&M
F. WORLD
CONSUMPTION
China-O&M
PATTERN
BRIIAT-O&M
RUE-Construction
China-Construction
BRIIAT-Construction
Double price
G. URANIUM PRICE
Triple price
H. FINANCE
Report OCDE+IEA
COMPARISON
Report MIT 2003
Best cases compiled
I. BEST AND WORST CASE
Worst cases compiled
code
A1
A2
A3
B1
B2
B3
C1
C2
C3
D1
D2
D3
E1
E2
E3
FA1
FA2
FA3
FB1
FB2
FB3
FC1
FC2
FC3
G1
G2
H1
H2
I1
I2
Variation
5
11
14
25
40
100
75%
85%
95%
-35%
-78%
22,80%
-----
100%
200%
Emissions
Emissions Fuel EmissionsO&M Total Emissions
Sensitivity
Construction
(gCO2eq/kWh) (gCO2eq/kWh) (gCO2eq/kWh) (total/baseline)
(gCO2eq/kWh)
1,47
3,81
2,02
7,30
-62,97%
2,69
6,86
3,73
13,28
-32,57%
2,75
13,14
3,81
19,70
----2,22
13,14
3,81
19,17
-2,67%
2,71
13,14
3,81
19,67
-0,16%
2,96
13,14
3,81
19,91
1,09%
6,59
13,14
3,81
23,54
19,51%
4,12
13,14
3,81
21,07
6,97%
1,65
13,14
3,81
18,60
-5,57%
2,98
13,14
3,81
19,94
1,21%
2,63
13,14
3,81
19,59
-0,57%
2,36
13,14
3,81
19,31
-1,97%
2,69
13,14
3,70
19,54
-0,82%
2,62
13,14
3,57
19,33
-1,85%
2,78
13,14
3,88
19,80
0,54%
2,75
12,94
3,81
19,49
-1,05%
2,75
12,72
3,81
19,27
-2,17%
2,75
13,75
3,81
20,30
3,07%
2,75
13,14
2,99
18,88
-4,14%
2,75
13,14
7,35
23,24
17,97%
2,75
13,14
5,53
21,41
8,71%
2,11
13,14
3,81
19,07
-3,20%
5,50
13,14
3,81
22,45
13,99%
4,08
13,14
3,81
21,03
6,78%
2,75
16,49
3,81
23,04
16,98%
2,75
19,83
3,81
26,39
33,97%
2,76
13,84
4,01
20,62
4,66%
3,22
46,23
13,40
62,86
219,12%
1,91
3,35
3,81
9,06
-54,00%
7,72
62,45
13,40
83,58
324,28%
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
3. Main Results
Table 7 (excerpt): Methodological scenarios.
Variable
Unit
Emissions
Sensitivity
Emissions Fuel EmissionsO&M Total Emissions
code Variation Construction
(total/basel
(gCO2eq/kWh) (gCO2eq/kWh) (gCO2eq/kWh)
(gCO2eq/kWh)
ine)
Scenario
SRIO-LCA
A. METHODOLOGICAL
g/CO2 MRIO-LCA
APPROACH
hMRIO-LCA (baseline)
A1
A2
A3
1,47
2,69
2,75
Comparison with other studies.
3,81
6,86
13,14
2,02
3,73
3,81
7,30
13,28
19,70
-62,97%
-32,57%
-----
Comparison with other technologies.
Survey: SOVACOOL, 2008
Paper: BARNABY AND KEMP, 2007
Survey: SOVACOOL, 2008
Frontend (Fuel)
Construction
O & M (Fuel)
Backend
Deommissioning
TOTAL
Estimated
(gCO2/Kwh)
25.09
8.2
11.58
9.29
12.01
66.08
%
37.97%
12.41%
17.52%
13.92%
18.17%
100.00%
OECD
ISA (Aus)
Extern-E (UK)
Estimated
(gCO2/Kwh)
25.09
8.2
11.58
2.5 Mw
1.5 MW
3.1 MW
80 MW
Various
Estimated
(gCO2/Kwh)
9
10
10
13
14-31
1000 MW
19.7
80 MW
Various
Various
Various
38
443
778
960-1050
Capacity
Wind offshore
Wind onshore
Hydro
Solar Thermal
Biomass
NUCLEAR
(hMRIO-LCA)
Geothermal
Natural Gas
Diesel
Coal
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
3. Main Results
Table 7 (excerpt): Construction phase, Load factor and GHG intensities scenarios.
Variable
B. CONSTRUCTION
PERIOD
D. LOAD FACTOR
E. GHG INTENSITY OF
ECONOMY
Unit
Scenario
fast built (5y) -10%
years 3 years extension +10%
6 years extension +20%
1980 USA (75%)
%
intermediate (85%)
near-full load
Paner Plan 2020
g/CO2 90% renewables scenario
Coal Decree
code Variation
B1
B2
B3
D1
D2
D3
E1
E2
E3
5
11
14
75%
85%
95%
-35%
-78%
22,80%
Emissions
Sensitivity
Emissions Fuel EmissionsO&M Total Emissions
Construction
(total/basel
(gCO2eq/kWh) (gCO2eq/kWh) (gCO2eq/kWh)
(gCO2eq/kWh)
ine)
2,22
2,71
2,96
2,98
2,63
2,36
2,69
2,62
2,78
13,14
13,14
13,14
13,14
13,14
13,14
13,14
13,14
13,14
3,81
3,81
3,81
3,81
3,81
3,81
3,70
3,57
3,88
19,17
19,67
19,91
19,94
19,59
19,31
19,54
19,33
19,80
MAIN IDEAS:
B and D  There are not big impacts over Carbon Footprint about uncertainties related to the
lenght of construction phase and to Load factor operation.
E  As Spain main emissions are produced outside Spain (internationalized nuclear industry)
changes in domestic energy mix has not too much impact over Carbon Footprint.
-2,67%
-0,16%
1,09%
1,21%
-0,57%
-1,97%
-0,82%
-1,85%
0,54%
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
3. Main Results
Table 7 (excerpt): Lifetime scenarios.
Variable
C. LIFETIME
Unit
Scenario
years
early decommissioning 25Y
intermediate 40Y
long lifetime 100Y
Emissions
Sensitivity
Emissions Fuel EmissionsO&M Total Emissions
code Variation Construction
(total/basel
(gCO2eq/kWh) (gCO2eq/kWh) (gCO2eq/kWh)
(gCO2eq/kWh)
ine)
C1
C2
C3
25
40
100
6,59
4,12
1,65
13,14
13,14
13,14
3,81
3,81
3,81
23,54
21,07
18,60
MAIN IDEAS:
• Early decommissioning scenarios, related to new and unexpected energy policies (like the
shutdown of nuclear energy generation in Germany after Fukushima) would generate an
increase on emissions by almost 20%.
• 40 years lifetime generates similar results.
• 100 years of operation do not reduce too much the amount of emissions.
The less lifetime the more emissions in construction phase
19,51%
6,97%
-5,57%
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
3. Main Results
Table 7 (excerpt): World consumption patterns scenarios.
Variable
F. WORLD
CONSUMPTION
PATTERN
Unit
Scenario
EU-Fuel Cycle
NAFTA-Fuel Cycle
BRIIAT-Fuel Cycle
EU-O&M
g/CO2 China-O&M
BRIIAT-O&M
RUE-Construction
China-Construction
BRIIAT-Construction
code Variation
FA1
FA2
FA3
FB1
FB2
FB3
FC1
FC2
FC3
-----
Emissions
Sensitivity
Emissions Fuel EmissionsO&M Total Emissions
Construction
(total/basel
(gCO2eq/kWh) (gCO2eq/kWh) (gCO2eq/kWh)
(gCO2eq/kWh)
ine)
2,75
2,75
2,75
2,75
2,75
2,75
2,11
5,50
4,08
12,94
12,72
13,75
13,14
13,14
13,14
13,14
13,14
13,14
3,81
3,81
3,81
2,99
7,35
5,53
3,81
3,81
3,81
19,49
19,27
20,30
18,88
23,24
21,41
19,07
22,45
21,03
-1,05%
-2,17%
3,07%
-4,14%
17,97%
8,71%
-3,20%
13,99%
6,78%
MAIN IDEAS:
• Changes in consumption patterns of some phases are relevant.
• Fuel Cycle is a high pollutant phase, differences between patterns are not too different to Baseline.
• O&M  If China-Spain trade relationships would be higher and higher, total Carbon Footprint
would be almost a 18% higher.
• Construction  If the amount of Chinese construction phase inputs would be higher, Carbon
Footprint almost 14%.
• All the scenarios show a reduction of the Carbon Footpint if EU produce the inputs. REASON: The
cleaner energy mix.
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
3. Main Results
Table 7 (excerpt): Uranium prices, Financial comparison and Bests and Worts scenarios.
Variable
G. URANIUM PRICE
H. FINANCE
COMPARISON
I. BEST AND WORST
CASE
Unit
Scenario
Double price
Triple price
Report OCDE+IEA
g/CO2
Report MIT 2003
Best cases compiled
g/CO2
Worst cases compiled
g/CO2
Emissions
Sensitivity
Emissions Fuel EmissionsO&M Total Emissions
code Variation Construction
(total/basel
(gCO2eq/kWh) (gCO2eq/kWh) (gCO2eq/kWh)
(gCO2eq/kWh)
ine)
G1
G2
H1
H2
I1
I2
100%
200%
2,75
2,75
2,76
3,22
1,91
7,72
16,49
19,83
13,84
46,23
3,35
62,45
3,81
3,81
4,01
13,40
3,81
13,40
23,04
26,39
20,62
62,86
9,06
83,58
16,98%
33,97%
4,66%
219,12%
-54,00%
324,28%
MAIN IDEAS:
• The scarcity of Uranium in the world, and the subsequent increase of prices related to the exploit of
new mining deposists would increase the Carbon Footprint a 34% (x3) and a 17% (x2).
•
•
If we use financial data from OECD and IEA report, Carbon Footprint woul be similar.
If we use the MIT 2003 report data, Carbon Footprint would be a 220% higher (closer to fossil fuels).
•
Adding best and worst scenarios, the interval of emissions depending on the uncertainties
considered goes from 9 gCO2/kWh to 83,58 gCO2/Kwh (Fuel Cycle highest responsability).
o Taking into consideration the variability and uncertainties is crucial to estimate nuclear power
Carbon Footprint properly.
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
4. Conclusions
 The Hybrid MRIO-LCA is the most accurate model to evaluate the Nuclear Power plant
Carbon Footprint.
 The use of the MRIO model allow to enhance the role of imports in a Spanish Nuclear
Power plant life cycle (almost 90%), concentrated in the Fuel Cycle phase.
Nuclear Spanish industry was dismantled because of the nuclear
moratorium since eighties.
The consideration of uncertainties and variability is crucial. Most of the studies presents
very different results depending on the variables and assumptions considered.
IX Congreso de la Asociación Española para la Economía Energética
Madrid, 3 y 4 de febrero de 2014
4. Conclusions
Low Carbon Footprint compared to other technologies.
Uncertainties captured by the multiple scenarios simulated. Results are coherent
with other reference studies.
Fuel cycle is the most pollutant phase. Most of emissions are produced in BRIIAT.
The scenarios with a higher influence over emissions are:
 METHOLODGY
 LIFETIME
 CONSUMPTION PATTERNS
 URANIUM PRICES
 FINANCIAL DATA SETS
BEST CASE: 9,06 gCO2 –eq /kWh
Baseline
19,70 gCO2 –eq
/kWh
WORST CASE: 83,58 gCO2 –eq /kWh
THANKS FOR YOUR ATTENTION!
Carbon footprint of the nuclear
energy using IO-LCA
Jorge E. Zafrillaa, María Ángeles Cadarsoa, Fabio Monsalvea y Cristina de la Rúab
a
b
Universidad de Castilla-La Mancha, Albacete
Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas
E-mail: [email protected]
IX Congreso Asociación Española para la
Economía Energética
Madrid, 3 y 4 de febrero de 2014