Summary Recent developments within life‐cycle impact assessment (LCIA) allow direct modeling of changes to predefined safeguard subjects that represent the environment to be protected. These changes are often expressed in a few damage indicators and calculated for each product alternative to be compared. This article analyzes the correlation among damage indicators within one specific LCIA methodology, assesses the implications of such correlation for the use of weighting methods, and proposes a method for graphically displaying choices with respect to weighting.
When one models impact pathways due to stressors that are caused by the provision of product systems, it results in indicators for environmental damages. These indicators are incommensurable and cannot be compared per se. For example, the statistical life years lost for a human population cannot necessarily be compared with the potentially affected fraction of species within an ecosystem. However, some decision makers who use life‐cycle assessment (LCA) prefer a single index, because it facilitates interpretation better than a multi‐indicator system. This requires a method for aggregating environmental damages of differing types, thereby confronting LCA with a valuation problem. The article describes a nonmonetary approach to valuation in LCA that incorporates the findings of a survey among LCA practitioners and users. The survey focuses on the weighting of three safeguard subjects for Eco‐indicator 99, a damage‐oriented impact‐assessment method: human health, ecosystem quality, and resources. Of particular interest here is what influence the context provided in the survey (framing) and an individual's characteristics have on his or her weighting of environmental damages. The results indicate that damages on the European level are easier to compare than damages on a micro level. Additionally, although only half of the survey participants could be classified unequivocally into one of three cultural perspectives, each perspective rated the damage categories presented to them significantly differently from the others. Our conclusions were that framing effects need to be more carefully considered in weighting procedures and that weighting preferences vary significantly according to a group's archetypical attitudes.
This series of two papers discusses the elicitation of weights for damage categories in LCIA with the aid of panel surveys. The papers focus especially on methodological aspects in panel surveys. Part 1 discusses potential cognitive limits of the panel members to understand the reference that is weighted. Part 2 focuses on the influence of the question format and compares results from two different weighting tasks: discrete choice (between alternatives) and score allocation.
Background, aim, and scope Within life cycle impact assessment (LCIA), 'panel methods' has become a common term to denominate methods that elicit and measure stakeholders' stated preferences on environmental impact categories. Such panel procedures use different question formats to elicit information on weighting across impact categories from the stakeholders. The two most frequently used question formats are score allocation and choice between alternatives. The differences between these two question formats were analyzed in order to give advice on how to frame future panel procedures. Materials and methods A choice-based weighting procedure (choice experiment) for the three damage categories of human health, ecosystems quality, and resources was developed and executed. A logistic regression model was applied in order to estimate the weighting factors for the polled sample. Results from this choice-based procedure were compared to the results from an allocation-based procedure described in part 1 of this paper. Results When weighting factors are elicited by score allocation questions, panelists tend to distribute the scores more equally. A factor of 1.5 between the least and the most weighted damage category was found. Weighting factors from a choice experiment were more spread, i.e., the most important category was weighted considerably higher, whereas the other two categories were weighted less. Thus, for the choice experiment, the range between the most and the least weighted categories was considerably bigger-by about a factor of 4. Discussion A comparison of the two procedures revealed that the weighting of environmental damage categories is considerably influenced by the question of format. The reason for these variations may be different cognitive routines that are applied. In addition, several advantages and shortcomings of choice experiments are discussed. Conclusions The developed, choice-based procedure provided meaningful results. Thus, choice experiments, often used for the monetary valuation of environmental goods, can also be applied in LCIA to elicit nonmonetary weighting factors. Recommendations and perspectives Choice experiments form a new interesting approach for weighting procedures in the future as they have some advantages over the often used score allocation methods. They are simple and more realistic than other procedures, as panelists have practiced in choice tasks from everyday life. We, therefore, recommend such choice-based procedures for future panel studies.Keywords Choice experiments . Framing . Panel surveys . Stated preference . Weighting of damage categories in LCIA 1 Background, aim, and scopeIn general, life cycle impact assessment (LCIA) finishes up with a set of three to 12 impact category indicator results that describe the impact of a product system on the environment. Weighting across impact categories is often needed in order to interpret these category indicator results and to draw conclusions. Within LCIA, 'panel methods' Int J Life Cycle Assess (2008) 13:468-476
Summary An increasing number of software tools support designers and other decision makers in making design, production, and purchasing decisions. Some of these tools provide quantitative information on environmental impacts such as climate change, human toxicity, or resource use during the life cycle of these products. Very little is known, however, about how these tools are actually used, what kind of modeling and presentation approaches users really want, or whether the information provided is likely to be used the way the developers intended. A survey of users of one such software tool revealed that although users want more transparency, about half also want an easy‐to‐use tool and would accept built‐in assumptions; that most users prefer modeling of environmental impacts beyond the stressor level, and the largest group of respondents wants results simultaneously on the stressor, impact potential, and damage level; and that although many users look for aggregated information on impacts and costs, a majority do not trust that such an aggregation is valid or believe that there are tradeoffs among impacts. Further, our results show that the temporal and spatial scales of single impact categories explain only about 6% of the variation in the weights between impact categories set by respondents if the weights are set first. If the weights are set after respondents specify temporal and spatial scales, however, these scales explain about 24% of the variation. These results not only help method and tool developers to reconsider some previous assumptions, but also suggest a number of research questions that may need to be addressed in a more focused investigation.
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