Tradeoff Evaluation Improves Comparative Life Cycle Assessment: A Photovoltaic Case Study

Abstract

SummaryCurrent life cycle assessment (LCA) interpretation practices typically emphasize hotspot identification and improvement assessment. However, these interpretation practices fail in the context of a decision-driven comparative LCA where the goal is to select the best option from a set of dissimilar alternatives. Interpretation of comparative LCA results requires understanding of the trade-offs between alternatives-instances in which one alternative performs better or worse than another-to identify the environmental implications of a specific decision. In this case, analysis must elucidate relative trade-offs between decision alternatives, rather than absolute description of the alternatives individually. Here, typical practices fail. This article introduces a probability distribution-based approach to assess the significance of performance differences among alternatives that allows LCA practitioners to focus analyses on those aspects most influential to the decision, identify the areas that would benefit the most from data refinement given the level of uncertainty, and complement existing hotspot analyses. In a case study of a comparative LCA of five photovoltaic technologies, findings show that thin-film cadmium telluride and amorphous silicon cell panels are most likely to perform better than other alternatives. Additionally, the impact categories highlighted by the new approach are different than those highlighted by typical external normalization practices, suggesting that a decision-driven approach to interpretation would redirect environmental research efforts. Decision-Driven Comparative Life Cycle AssessmentsComparative life cycle assessments (LCAs) quantify the life cycle environmental impacts of equivalent products technologies or processes throughout all the life phases, from raw material extraction to final disposal (Prado et al. 2012). A decision-driven comparative LCA, where the goal is to identify the most environmentally viable alternative(s) among a set of options, can guide material and processing selection in industry, identify a best policy scenario to inform environmental regulations, and lead researchers toward the most promising areas for reduction of environmental impacts in technology development. However, the results are seldom conclusive, and when one alternative performs best in some aspects, it often also performs worse in others-these trade-offs among the different impact categories make it difficult to identify the most viable alternative. Because International Organization for Standardization (ISO) guidelines leave all steps after characterization as optional, the majority of comparative LCAs truncate analysis at characterization (ISO 2006). Thus, comparative LCA results

Interpretation of comparative LCAs: external normalization and a method of mutual differences

“…Unlike these approaches, the overlap area approach can generate one result per impact category regardless of the number of alternatives and thus facilitating communication of results (Prado-Lopez et al 2016). Therefore, the overlap area serves as a good example to illustrate a fundamentally distinct approach to that of external normalization.…”

“…That way, the decision is informed by the aspects with the best resolution and we can save data refinement efforts for those aspects where uncertainties are the largest. Tradeoff significance in a comparative LCA does not necessarily correlate to hotspots as identified by external normalization since each approach describes different aspects of the data (Prado-Lopez et al 2016). Uncertainties included in the overlap area can be propagated from inventory and characterization via Monte Carlo analysis.…”

“…Similar to normalization, weighting is an optional stage in LCIA that is avoided in most LCA studies. Given subjectivity concerns, and a general lack of information regarding decision-maker preferences, most LCAs truncate results at characterization, at external normalization, or apply Bequal weights^ (Prado-Lopez et al 2016). However, where analysis continues beyond these stages, weight values can be derived from a panel of experts in a professional field (Gloria et al 2007), through surveys (Schmidt et al 2002), monetization or willingness-to-pay techniques (Finnveden 1999), linear programming (Cortés-Borda et al 2013), and distance-to-target approaches (Seppälä and Hämäläinen 2001).…”

“…An alternative approach to identify the category indicators relevant to a decision in a comparative LCA is the overlap area approach (Prado-Lopez et al 2016). The overlap area approach applies to probabilistic characterized results of comparative LCAs in any of the LCIA methods.…”

“…This requires a shift in assessment from absolute impact of alternatives (as done by external normalization) to relative impact of the decision. Here, the aspects most pertinent to the decision are those which differentiate the alternatives (Prado-Lopez et al 2016). For example, alternatives A and B may both have large magnitudes for ozone depletion in comparison to a normalization reference.…”