Methods for aggregation and communication of life cycle inventory data within the framework of eco-efficiency analysis - A case study at Akzo Nobel

Abstract: In meeting the needs of a large and growing population with increasing affluence, industryputs a significant stress on the environment. Thus there are demands on the corporate world todecouple economic activity from environmental impact, i.e. to become more eco-efficient.Eco-efficiency analysis (EEA) is a tool that implements the concept of eco-efficiency into thedaily operations of a business by integrating Life Cycle Costing (LCC) and Life CycleAnalysis (LCA). These tools help decision makers in making environmentally andeconomically motivated choices. However, LCA:s can generate life cycle inventory lists ofcomplex environmental data which decision makers often have limited time or knowledge tointerpret. Also the economic and environmental indicators need to be combined in a way so asto facilitate sound eco-efficiency comparisons in decision making.Therefore the purpose of this thesis was to investigate methods for aggregation andcommunication of life cycle inventory data within the framework of eco-efficiency analysis,i.e. weighting methods for aggregation of LCA inventory data, and methods for integration ofLCA and LCC data. Seven different weighting methods, and different ways of integratingLCC and LCA data, were applied in an eco-efficiency analysis of a waste water treatmentplant at Akzo Nobel Site Stenungsund. In this case study the present process conditions arescrutinized and compared to different scenarios representing other process settings.Furthermore, two established principles for weighting were used to develop a set of weightingindexes adapted to the environmental targets and preferences of the authorities inStenungsund municipality.The results from the case study indicate that from an eco-efficiency perspective it is notmotivated to change the present process conditions. It also shows that different weightingmethods generate different results concerning what is the most environmentally benignprocess setting. This is because different weighting methods are based on differentpreferences towards nature and society. However, the study also identifies possibilities forcase and site specific weighting, i.e. weighting which is adapted to the environmental andinstitutional context of the study. This proves the weighting to be meaningful in addinginformation, and providing adequate and easy-to-interpret indicators, to assist in decisionprocesses.The most appropriate way to aggregate LCA and LCC data will depend on the context of thestudy. What is to be communicated and who is to take part of the information are importantaspects. The LCA and LCC data can be kept separate in a two-dimensional index, and bepresented in a graph, or they can be combined into a one-dimensional eco-efficiency index bytaking the ratio of the two. The study indicates that in general interpretation of a onedimensionalindex requires more knowledge of the concept of eco-efficiency. This can be aproblem when applied in decision making. Simpler to grasp is a two-dimensional graph whichcommunicates the absolute and/or relative effectiveness of different alternatives. A onedimensionalindex can however complement a two-dimensional index in also communicatingthe efficiency in terms of a benefit over costs incurred to generate that benefit.Moreover, depending on which interpretation key that is used, the effects of choices at themicro level on the macro level eco-efficiency will vary. For the global community to becomemore eco-efficient all actors in society need to take responsibility for becoming more ecoefficientin their actions. For corporations this means that it could be wise to measure the ecoefficiencyat the corporate level. The measure should then be in the form of a one-dimensionalindex with e.g. value added on the nominator and environmental impact on the denominator.