A methodology for separating uncertainty and variability in the life cycle greenhouse gas emissions of coal-fueled power generation in the USA
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Results of life cycle assessments (LCAs) of power generation technologies are increasingly reported in terms of typical values and possible ranges. Extents of these ranges result from both variability and uncertainty. Uncertainty may be reduced via additional research. However, variability is a characteristic of supply chains as they exist; as such, it cannot be reduced without modifying existing systems. The goal of this study is to separately quantify uncertainty and variability in LCA.
In this paper, we present a novel method for differentiating uncertainty from variability in life cycle assessments of coal-fueled power generation, with a specific focus on greenhouse gas emissions. Individual coal supply chains were analyzed for 364 US coal power plants. Uncertainty in CO2 and CH4 emissions throughout these supply chains was quantified via Monte Carlo simulation. The method may be used to identify key factors that drive the range of life cycle emissions as well as the limits of precision of an LCA.
Results and discussion
Using this method, we statistically characterized the carbon footprint of coal power in the USA in 2009. Our method reveals that the average carbon footprint of coal power (100 year time horizon) ranges from 0.97 to 1.69 kg CO2eq/kWh of generated electricity (95 % confidence interval), primarily due to variability in plant efficiency. Uncertainty in the carbon footprints of individual plants spans a factor of 1.04 for the least uncertain plant footprint to a factor of 1.2 for the most uncertain plant footprint (95 % uncertainty intervals). The uncertainty in the total carbon footprint of all US coal power plants spans a factor of 1.05.
We have developed and successfully implemented a framework for separating uncertainty and variability in the carbon footprint of coal-fired power plants. Reduction of uncertainty will not substantially reduce the range of predicted emissions. The range can only be reduced via substantial changes to the US coal power infrastructure. The finding that variability is larger than uncertainty can obviously not be generalized to other product systems and impact categories. Our framework can, however, be used to assess the relative influence of uncertainty and variability for a whole range of product systems and environmental impacts.
KeywordsCarbon footprint Coal Electricity generation Life cycle assessment Monte Carlo simulation Uncertainty Variability
The authors thank ExxonMobil Research and Engineering for partially funding this research project.
- Dones R, Bauer C, Roeder A (2007) Kohle. Final report. Sachbilanzen von Energiesystemen: Grundlagen fuer den oekologischen Vergleich von Energiesystemen und den Einbezug von Energiesystemen in Oekobilanzen fuer die Schweiz. Paul Scherrer Institute Villingen, Swiss Centre for Life Cycle Inventories, Duebendorf, SwitzerlandGoogle Scholar
- EIA (2010) Coal production and number of mines by state, county, and mine type. http://www.eia.gov/cneaf/coal/page/acr/table 2.html. Accessed 9 August 2011
- EIA (2011a) Annual energy outlook 2011. U.S. Energy Information Administration, WashingtonGoogle Scholar
- EIA (2011b) Form EIA-923 detailed data. U.S. Department of Energy. http://www.eia.gov/electricity/data/eia923/. Accessed 30 April 2011
- EIA (2011c) Form EIA-860 2009 http://www.eia.gov/electricity/data/eia860/index.html. Accessed 30 April 2011
- EPA (1989) Exposure factors handbook. Washington DCGoogle Scholar
- EPA (2010) eGRID2010 version 1.1 year 2007 GHG annual output emission rates http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2010V1_1_year07_GHGOutputrates.pdf. Accessed 30 Oct 2011
- EPA (2011a) Inventory of U.S. greenhouse gas emissions and sinks: 1990–2009. U.S. Environmental Protection Agency, WashingtonGoogle Scholar
- EPA (2011b) Inventory of U.S. greenhouse gas emissions and sinks: 1990–2009. Annex 3 Methodological descriptions for additional source or sink categories. U.S. Environmental Protection Agency, WashingtonGoogle Scholar
- Heijungs R, Huijbregts MAJ (2004) A review of approaches to treat uncertainty in LCA. In: 2nd biennial meeting of the International Environmental Modelling and Software Society (IEMSS), Manno, Switzerland, 2004Google Scholar
- Hong BD, Slatick ER (1994) Carbon dioxide emission factors for coal quarterly coal report. EIA, Washington, DCGoogle Scholar
- IPCC (2007) Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. IPCC, Cambridge, New YorkGoogle Scholar
- Littlefield J, Bhander R, Bennett B, Davis T, Draucker L, Eckard R, Ellis W, Kauffman J, Malone A, Munson R, Nippert M, Ramezan M, Bromiley R (2010) Life cycle analysis: existing pulverized coal (EXPC) power plant. National Energy Technology LaboratoryGoogle Scholar
- McCollum DL (2007) Future impacts of coal distribution constrains on coal cost. University of California, DavisGoogle Scholar
- MIT (2011) The future of natural gas. MITGoogle Scholar
- Spielmann M, Bauer C, Dones R (2007) Transport services: Ecoinvent report no. 14. Swiss Center for Life Cycle Inventories, Dübendorf, SwitzerlandGoogle Scholar
- US Census Bureau (2010) 2010 census gazetteer files. http://www.census.gov/geo/www/gazetteer/gazetteer2010.html. Accessed 1 May 2012
- US Census Bureau (2011) American fact finder. http://factfinder.census.gov/home/saff/main.html?_lang=en. Accessed 28 July 2011
- Venkatesh A, Jaramillo P, Griffin WM, Matthews HS (2012a) Implications of near-term coal power plant retirement for SO2 and NOX and life cycle GHG emissions. Environ Sci Technol 46(18):9838–9845Google Scholar