Carbon footprint of the nuclear energy using IO-LCA Jorge E. Zafrilla a, María Ángeles Cadarso a, Fabio Monsalve a y Cristina de la Rúa b a Universidad.

Presentación del tema: "Carbon footprint of the nuclear energy using IO-LCA Jorge E. Zafrilla a, María Ángeles Cadarso a, Fabio Monsalve a y Cristina de la Rúa b a Universidad."— Transcripción de la presentación:

1 Carbon footprint of the nuclear energy using IO-LCA Jorge E. Zafrilla a, María Ángeles Cadarso a, Fabio Monsalve a y Cristina de la Rúa b a Universidad de Castilla-La Mancha, Albacete b Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas E-mail: Jorge.Zafrilla@uclm.es IX Congreso Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

2 IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014 Outline Outline 1.Introduction 2.2.1. Data 2.2. Methodology 2.3. Scenarios 3. Main results 4. Conclusions 1.Introduction 2.2.1. Data 2.2. Methodology 2.3. Scenarios 3. Main results 4. Conclusions

3 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. 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 But, how clean is nuclear power from an IO-LCA perspective? IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

4 Financial comparisons SRIO / MRIO / hMRIO-LCA Construction period Lifetime Load Factor GHG intensities Consumption patterns Uranium prices 1. Introduction a) The data set and the methodology allow us to estimate the variability and the uncertainties of the analysis via multiple scenarios. ContributionsContributions Methodological Technical Political Financial SCENARIOS IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

5 1. Introduction b) The use of the MRIO-LCA model to face the problems related to emissions linked to production processes of imported goods. ContributionsContributions c) This is the first study of nuclear power carbon footprint in Spain. 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. IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

6 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)  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

7 2.1. Data YearsInvestment activity%/TotalM€uros Allowances (7513) Civil Enineering (35500) EG Mechan ical (291) EG Electric (311) Buildings and CE (452) Insuaran ces (66031) IMPORTS 1allowance2%49,00 0,00 2allowance2%49,00 0,00 3civil engineering10%245,000,00163,3340,83 0,00 4equipments + ce18%441,000,00114,3340,83 0,00 245,00 5equipments + ce20%490,000,00114,3381,67 40,830,00212,33 6equipments + ce25%612,500,0040,8381,67 36,754,08367,50 7equipments + ce15%367,500,0040,83122,5081,6732,678,17102,08 8end works + tests8%196,008,174,080,0040,830,00 81,67 Total100%2.450,00106,17477,75367,50 110,2512,251.008,58 Table 1. Investments function of a nuclear power plant (2450 M€UROS, constant 2009) 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

8 2.1. Data UraniumEnrichmentFabricationEquipments Own Personnel External Personnel Total Combustible1,9251,2250,353,50 O&M1,112,593,70 O&M (replacement)2,210,340,853,40 Decommissioning and final disposal 10,53,55,00 Insurances0,21 Total1,9251,2250,354,323,434,3515,81 %/Total12,18%7,75%2,21%27,32%21,70%27,51%100,00% Table 2: Nuclear Fuel Cycle estimation costs and O&M (€/MWh) (2009 prices) Fuel Cycle phaseOperation & 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

9 2.1. Data Table 3: Final demand (domestic and imported) vectors for the whole life cycle. NOTE: Sectorial structure  WIOD (35 sectors). Fuel Cycle phase IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

10 2.1. Data Figure 1: Spain´s Nuclear Fuel Cycle (IAEA, 2005). 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

11 2.1. Data Table 4: Imported Final Demand by region. SPAIN Rest of UE NAFTA CHINA BRIIAT EAST ASIA RoW IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

12 2.2. Methodology Figure 2: WIOT, aggregation to 7 regions valued in Euros (2009). (245x245) (245x35) Also expressed in compact form by: Through Leontief Inverse [L= (I – A) -1 ]: IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

13 2.2. Methodology Figure 3: Matrix of emissions coefficients “e” (ktCO 2 –eq per million of euros) [WIOD] NOTE: Matrix e is combined with ECO-INVENT data for the development of the hybrid MRIO-LCA model ECO-INVENT data for Mining and Quarrying and Coke, Refined Petroleum and Nuclear Fuel IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014 hMRIO- LCA

14 2.2. Methodology Summary of models used: Hybrid Multi-Regional IO model (HMRIO-LCA) ECO-INVENT emissions coefficients and WIOD emissions coefficients Multi-Regional IO model (MRIO-LCA) where e is the WIOD emissions coefficients Single Region IO model (SRIO-LCA) where e SP is the Spanish (DTA) IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

15 2.3. Scenarios Table 5: Summary of scenarios. IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

16 3. Main Results Table 6: CO2 emissions of nuclear electricity by origin of inputs and life cycle stage (g CO2 –eq /Kwh). Graph 1: CO2 –eq emissions of nuclear electricity by origin of inputs and life cycle stage (g CO2/Kwh). IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

17 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

18 3. Main Results Graph 2: hMRIO-LCA Carbon footprint (gCO 2 –eq /KWh) by country. 7,6% 21,46% 19,86% 11,21% 27,5% 11,08% 1,28% IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

19 3. Main Results Table 7: Scenarios results IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

20 3. Main Results Table 7 (excerpt): Methodological scenarios. Comparison with other studies. Comparison with other technologies. IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

21 3. Main Results Table 7 (excerpt): Construction phase, Load factor and GHG intensities scenarios. 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. IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

22 3. Main Results Table 7 (excerpt): Lifetime scenarios. 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 IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

23 Table 7 (excerpt): World consumption patterns scenarios. 3. Main Results 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

24 3. Main Results Table 7 (excerpt): Uranium prices, Financial comparison and Bests and Worts scenarios. 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

25 4. Conclusions  The Hybrid MRIO-LCA is the most accurate model to evaluate the Nuclear Power plant Carbon Footprint. The consideration of uncertainties and variability is crucial. Most of the studies presents very different results depending on the variables and assumptions considered. Nuclear Spanish industry was dismantled because of the nuclear moratorium since eighties.  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. IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

26 Baseline 19,70 gCO2 –eq /kWh Baseline 19,70 gCO2 –eq /kWh 4. Conclusions The scenarios with a higher influence over emissions are:  METHOLODGY  LIFETIME  CONSUMPTION PATTERNS  URANIUM PRICES  FINANCIAL DATA SETS The scenarios with a higher influence over emissions are:  METHOLODGY  LIFETIME  CONSUMPTION PATTERNS  URANIUM PRICES  FINANCIAL DATA SETS Fuel cycle is the most pollutant phase. Most of emissions are produced in BRIIAT. Uncertainties captured by the multiple scenarios simulated. Results are coherent with other reference studies. Low Carbon Footprint compared to other technologies. BEST CASE: 9,06 gCO2 –eq /kWh WORST CASE: 83,58 gCO2 –eq /kWh IX Congreso de la Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014

27 THANKS FOR YOUR ATTENTION! Carbon footprint of the nuclear energy using IO-LCA Jorge E. Zafrilla a, María Ángeles Cadarso a, Fabio Monsalve a y Cristina de la Rúa b a Universidad de Castilla-La Mancha, Albacete b Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas E-mail: Jorge.Zafrilla@uclm.es IX Congreso Asociación Española para la Economía Energética Madrid, 3 y 4 de febrero de 2014