Abstract
Purpose
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.
Methods
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.
Conclusions
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.
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Acknowledgments
The authors thank ExxonMobil Research and Engineering for partially funding this research project.
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Responsible editor: Andreas Ciroth
Electronic Supplementary Material
Supplemental material is available online. Additional information for the mining, transportation and use phase and background parameters, Table S1 Parameter values and uncertainty ranges for all uncertain foreground parameters, Table S2 Coal methane and carbon content by state and basin, Table S3 Parameters and distributions for background processes, Equation S1 Coal loss during rail transport, Equation S2 Calculation of transport distance uncertainty, Equations S3-S8 Calculation of mining emissions, Figure S1 Upstream greenhouse gas emissions expressed as a fraction of total life cycle greenhouse gas emissions, Figure S2 Uncertainty and variability in the carbon footprint per NERC region, Figure S3 Efficiencies of U.S. coal fired power plants calculated from the EIA 923 Electricity Data File, Figure S4 Life cycle greenhouse gas emissions for plant-mine pairs. (DOCX 176 kb)
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Steinmann, Z.J.N., Hauck, M., Karuppiah, R. et al. A methodology for separating uncertainty and variability in the life cycle greenhouse gas emissions of coal-fueled power generation in the USA. Int J Life Cycle Assess 19, 1146–1155 (2014). https://doi.org/10.1007/s11367-014-0717-2
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DOI: https://doi.org/10.1007/s11367-014-0717-2