Environmental Decision-Making Using Life Cycle Impact Assessment and Stochastic Multiattribute Decision Analysis: A Case Study on Alternative Transportation Fuels

Abstract

Life cycle impact assessment (LCIA) involves weighing tradeoffs between multiple and incommensurate criteria. Current stateof-the-art LCIA tools typically compute an overall environmental score using a linear-weighted aggregation of characterized inventory data that has been normalized relative to total industry, regional, or national emissions. However, current normalization practices risk masking impacts that may be significant within the context of the decision, albeit small relative to the reference data (e.g., total U.S. emissions). Additionally, uncertainty associated with quantification of weights is generally very high. Partly for these reasons, many LCA studies truncate impact assessment at the inventory characterization step, rather than completing normalization and weighting steps. This paper describes a novel approach called stochastic multiattribute life cycle impact assessment (SMA-LCIA) that combines an outranking approach to normalization with stochastic exploration of weight spacessavoiding some of the drawbacks of current LCIA methods. To illustrate the new approach, SMA-LCIA is compared with a typical LCIA method for crop-based, fossil-based, and electric fuels using the Greenhouse gas Regulated Emissions and Energy Use in Transportation (GREET) model for inventory data and the Tool for the Reduction and Assessment of Chemical and other Environmental Impacts (TRACI) model for data characterization. In contrast to the typical LCIA case, in which results are dominated by fossil fuel depletion and global warming considerations regardless of criteria weights, the SMA-LCIA approachresultsinarankorderingthatismoresensitivetodecisionmaker preferences. The principal advantage of the SMA-LCIA method is the ability to facilitate exploration and construction of context-specific criteria preferences by simultaneously representing multiple weights spaces and the sensitivity of the rank ordering to uncertain stakeholder values.

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

“…Another issue in external normalization is that of inverse proportionality where impact categories with larger regional emissions generate a smaller normalized impact and are thereby identified as less relevant (Rogers and Seager 2009;White and Carty 2010). This inverse proportionality effect between normalization references and normalized impacts can introduce systematic biases.…”

“…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). Alternatively, in the absence of preference information, novel stochastic approaches in LCA provide a useful way to sample all possible weight values without favoring any single impact category thus enabling an inclusive view of the problem (Rogers and Seager 2009;Prado-Lopez et al 2014). However, regardless of the elicitation process, weighting can remain ineffective given a linear normalization step (Stewart 2008).…”

“…When the effects of the normalization step are too strong, the effects of weighting become negligible, leading to recommendations that are independent of stakeholder values. Previous LCA studies have already identified instances of weight insensitivity in external normalization (Rogers and Seager 2009;White and Carty 2010;Cortés-Borda et al 2013;Myllyviita et al 2014;Castellani et al 2016). For instance, Myllyviita et al (2014) evaluates different weight elicitation approaches and finds that most weights have little influence on the results given external normalization.…”

Princípios e ferramentas da produção mais limpa: um estudo exploratório em empresas brasileiras

“…Desta forma, as organizações adotam o princípio para reduzir o risco ambiental aos stakeholders para mitigar a poluição gerada na cadeia de suprimentos (Rogers & Seager, 2009), considerando a aceitação de princípios ambientais na gestão da cadeia de suprimentos com o objetivo de viabilizar a efetividade da prática de produção mais limpa por meio de seleção de materiais e de fornecedores ambientalmente corretos, processos produtivos, entrega de produtos finais aos consumidores e gestão do fim da vida útil dos produtos (Yüksel, 2008;Srivastava, 2007). No processo produtivo, ressalta-se o planejamento e controle da produção com foco na gestão da capacidade para evitar desperdícios de recursos produtivos e naturais e prover controle dos fluxos de materiais e energia (Chen & Monahan, 2010) e estruturação da logística reversa de pós-consumo e pós-venda para a gestão dos resíduos sólidos (Rogers & Tibben-Lembke, 1998).…”

“…O Fator 3 enfatiza sobre os relacionamentos entre os princípios 1,14 e 7, denotando como temática a oportunidade de estabelecer mudanças ecológicas incrementais no PL1-planejamento e controle da produção (PCP) com foco na redução da poluição e desperdícios de recursos no sistema produtivo por meio de planejamento da capacidade e controle da disposição final de resíduos por meio de ações de remanufatura, reuso e reciclagem (Chen & Monahan, 2010), permitindo PL14 -reduzir os riscos ambientais externos para responder e informar sobre prática de P+L (Rogers & Seager, 2009). Entretanto, é necessário PL7 -investimento em pesquisa e inovação para minimizar o uso de matérias-primas, energia e água no sistema de produção (UNEP, 1990b).…”