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Keywords:

  • computers;
  • electronics;
  • greenhouse gas (GHG) emissions accounting;
  • industrial ecology;
  • life cycle assessment (LCA);
  • Monte Carlo simulation

Summary

Recent years have seen increasing interest in life cycle greenhouse gas emissions accounting, also known as carbon footprinting, due to drivers such as transportation fuels policy and climate-related eco-labels, sometimes called carbon labels. However, it remains unclear whether applications of greenhouse gas accounting, such as carbon labels, are supportable given the level of precision that is possible with current methodology and data. The goal of this work is to further the understanding of quantitative uncertainty assessment in carbon footprinting through a case study of a rackmount electronic server. Production phase uncertainty was found to be moderate (±15%), though with a high likelihood of being significantly underestimated given the limitations in available data for assessing uncertainty associated with temporal variability and technological specificity. Individual components or subassemblies showed varying levels of uncertainty due to differences in parameter uncertainty (i.e., agreement between data sets) and variability between production or use regions. The use phase displayed a considerably higher uncertainty (±50%) than production due to uncertainty in the useful lifetime of the server, variability in electricity mixes in different market regions, and use profile uncertainty. Overall model uncertainty was found to be ±35% for the whole life cycle, a substantial amount given that the method is already being used to set policy and make comparative environmental product declarations. Future work should continue to combine the increasing volume of available data to ensure consistency and maximize the credibility of the methods of life cycle assessment (LCA) and carbon footprinting. However, for some energy-using products it may make more sense to increase focus on energy efficiency and use phase emissions reductions rather than attempting to quantify and reduce the uncertainty of the relatively small production phase.