Weighting factors for LCA—a new set from a global survey

Abstract

Abstract Purpose This paper provides global weights (weighting factors) for the three endpoint impact categories (areas of protection (AoPs)) of the United Nations Environment Programme (UNEP) Life Cycle Initiative’s “Global Guidance for Life Cycle Impact Assessment Indicators and Methods” (GLAM) project, namely human health, ecosystem quality, and natural resources and ecosystem services. Methods A discrete choice experiment (DCE) was conducted to elicit the preferences of respondents on the GLAM AoPs, and they were then used to calculate the respective weights. Responses were obtained from a subset of countries pertaining to each income level defined by the World Bank (i.e. low, lower-middle, upper-middle, and high). The adimensional (between 0 and 1) weights were derived using two different approaches: econometric and multiple criteria decision analysis (MCDA). The econometric approach obtained weights by transforming the estimated preference parameters from a multinomial logit model. The MCDA approach obtained weights representing the vectors that best reconstitute the choices of each individual, using linear programming to fit an additive value function. Results When considering responses from all income groups, the weights from the econometric approach are 0.42, 0.31, and 0.26 for human health, ecosystem quality, and natural resources and ecosystem services, respectively. Following the same order for the AoPs, the weights from the MCDA approach are 0.41, 0.32, and 0.27. For high-income countries, ecosystem quality has the highest weight; for upper-middle-income countries, ecosystem quality and human health have the same weights using the econometric approach, while in the MCDA approach, human health is weighted higher than ecosystem quality. For the two lower income country groups, the priority is given to human health with both approaches. Recommendations for the use of these weights are also provided, as well as a comparison with other existing weights. Conclusion The two methods obtained similar weights overall, although with some differences when disaggregated by income groups. The weights proposed in this paper are suitable for decision-makers or users who want to use survey-derived weights for endpoint-based LCA when using the AoPs of GLAM. These weights can be used in projects where the decision-makers do not want to or have no resources to identify a set of weights themselves, or when decision-makers are not involved.