Uncertainties in a carbon footprint model for detergents; quantifying the confidence in a comparative result

Abstract

Background, aim, and scope A new trend driven by climate change concerns is the interest to label consumer products with a carbon footprint (CF) number. Here, we present a study that examines the uncertainty in the estimated CFs of a liquid and a compact powder detergent and how the uncertainty varies with the type of comparison one wishes to make. Materials and methods A simplified CF model for detergents, encompassing all life cycle stages, has been used for the calculation of CFs. The CFs for the two detergents were compared under three different cases: (1) a situation where most of life cycle assessment (LCA) system is similar, (2) a situation where the LCA background systems may be different but certain choices with regard to system boundaries are standardized, and (3) a situation where the LCA background system, choices of system boundaries, and foreground system may also be different. Uncertainty in the CFs was calculated for each of the three comparison situations using a stepwise sensitivity/uncertainty analysis approach. Results The stepwise approach makes it possible to obtain reliable uncertainty estimates without the need to have very good uncertainty descriptions for every input parameter. Only a few input parameters were found to drive the uncertainty of the CF values. For case 1, the uncertainties in the difference between the CF of the ultraliquid and compact powder products are very small. The CF of compact powder is always larger than that of the ultraliquid product. In case 3, the uncertainties become much larger, such that in 23% of the cases, a CF comparison would wrongly indicate that the compact powder product has a lower CF than the ultraliquid product. Case 2 falls between the extremes of cases 1 and 3. Discussion One of the challenges of developing userfriendly CF methods based on the ISO 14040 framework is to ensure a high level of comparability of CF values, such that misleading or oversimplified conclusions can be avoided. Our analysis shows how the uncertainty margins around the calculation of a CF for a set of given products will broaden as the assessment moves from an "internal" comparison to a comparison with data from third parties where there is no specific information how these data have been obtained. CF calculations based on internal comparisons can lead to very clear distinctions between products and illustrate the utility of a CF tool to optimize the environmental performance of products using difference analysis. Conclusions CF calculations for products can only provide a fair comparison if the LCA background system used for the two products is the same and exactly the same choices in the foreground system are made. In practice, this would

Uncertainties in the carbon footprint of refined wheat products: a case study on Swedish pasta

“…This study focused on data variability and uncertainty and, to some extent, model uncertainty in the methods used to quantify the emissions at farm level. However, model and data uncertainty cannot be clearly divided (Koning et al 2010). For example, the data from aggregated background processes, e.g.…”

The contribution of ISO 14067 to the evolution of global greenhouse gas standards—A review

“…Wu et al [43] compared three carbon labels from Singapore, the U.S., and UK and found that although these carbon labels are based on the LCA method, comparisons cannot be made because many transparency issues, such as system boundary, unit of measurement and accredited evaluation, vary significantly across the these three carbon labels. Koning et al [21] provided an example showing how increasing the discretion of choosing system boundaries in LCA studies can result in misleading results. Manufacturers can manipulate data in the operational stage to create "low carbon" products.…”

A Methodology for Robust Comparative Life Cycle Assessments Incorporating Uncertainty

“…For example, parameters may be used in scenarios or choices may be made in models. de Koning et al 21 have noted that these three types of uncertainty manifest themselves by contributing to the uncertainty of the final result of an aggregated cradle-to-gate LCA. They correctly point out that all forms of uncertainty are expressed as uncertainty in a parameter value, even though it is actually an aggregate of parameter, model, and scenario uncertainty.…”

“…although it is technically feasible and certainly has been done. 21,23 When the influence of these is convolved through weighting, important information about their impact is masked and lost.…”