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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- 作者：Zoran J. N. Steinmann (1)
Mara Hauck (1)
Ramkumar Karuppiah (2)
Ian J. Laurenzi (2)
Mark A. J. Huijbregts (1) - 关键词：Carbon footprint ; Coal ; Electricity generation ; Life cycle assessment ; Monte Carlo simulation ; Uncertainty ; Variability
- 刊名：The International Journal of Life Cycle Assessment
- 出版年：2014
- 出版时间：May 2014
- 年：2014
- 卷：19
- 期：5
- 页码：1146-1155
- 全文大小：
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Mara Hauck (1)
Ramkumar Karuppiah (2)
Ian J. Laurenzi (2)
Mark A. J. Huijbregts (1)
1. Department of Environmental Science, Radboud University Nijmegen, Toernooiveld 1, 6525 ED, Nijmegen, The Netherlands
2. ExxonMobil Research and Engineering Company, 1545 Route 22 East, Annandale, NJ, 08801-3059, USA - ISSN：1614-7502
文摘
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.