In meeting the needs of a large and growing population with increasing affluence, industry
puts a significant stress on the environment. Thus there are demands on the corporate world to
decouple 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 the
daily operations of a business by integrating Life Cycle Costing (LCC) and Life Cycle
Analysis (LCA). These tools help decision makers in making environmentally and
economically motivated choices. However, LCA:s can generate life cycle inventory lists of
complex environmental data which decision makers often have limited time or knowledge to
interpret. Also the economic and environmental indicators need to be combined in a way so as
to facilitate sound eco-efficiency comparisons in decision making.
Therefore the purpose of this thesis was to investigate methods for aggregation and
communication 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 of
LCA and LCC data. Seven different weighting methods, and different ways of integrating
LCC and LCA data, were applied in an eco-efficiency analysis of a waste water treatment
plant at Akzo Nobel Site Stenungsund. In this case study the present process conditions are
scrutinized and compared to different scenarios representing other process settings.
Furthermore, two established principles for weighting were used to develop a set of weighting
indexes adapted to the environmental targets and preferences of the authorities in
Stenungsund municipality.
The results from the case study indicate that from an eco-efficiency perspective it is not
motivated to change the present process conditions. It also shows that different weighting
methods generate different results concerning what is the most environmentally benign
process setting. This is because different weighting methods are based on different
preferences towards nature and society. However, the study also identifies possibilities for
case and site specific weighting, i.e. weighting which is adapted to the environmental and
institutional context of the study. This proves the weighting to be meaningful in adding
information, and providing adequate and easy-to-interpret indicators, to assist in decision
processes.
The most appropriate way to aggregate LCA and LCC data will depend on the context of the
study. What is to be communicated and who is to take part of the information are important
aspects. The LCA and LCC data can be kept separate in a two-dimensional index, and be
presented in a graph, or they can be combined into a one-dimensional eco-efficiency index by
taking the ratio of the two. The study indicates that in general interpretation of a onedimensional
index requires more knowledge of the concept of eco-efficiency. This can be a
problem when applied in decision making. Simpler to grasp is a two-dimensional graph which
communicates the absolute and/or relative effectiveness of different alternatives. A onedimensional
index can however complement a two-dimensional index in also communicating
the 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 the
micro level on the macro level eco-efficiency will vary. For the global com...
