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Methods of the systematic review We identified appropriate studies based on a comprehensive literature search. We began by conducting three queries in Scopus: 1 forest AND (displace* OR substitut*) AND carbon AND product* 2 lca AND wood AND substit* 3 compar* and LCA and wood These queries identified 488 studies, 87 and 377 studies, respectively. We screened the documents for relevance based on information provided in titles, abstracts, keywords and results, and shortened the list to 81 studies in the first query, 22 studies in the second query and 12 studies in the third query. We also reviewed the reference lists of the identified studies to identify additional relevant references and considered the studies by Rüter et al. (2016) and Valada et al. (2016). Reviewed studies were limited to those published in English, Swedish, Finnish, German or French.

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Summary Life‐cycle impact assessments (LCIAs) are complex because they almost always involve uncertain consequences relative to multiple criteria. Several authors have noticed that this is precisely the sort of problem addressed by methods of decision analysis. Despite several experiences of using multipleattribute decision analysis (MADA) methods in LCIA, the possibilities of MADA methods in LCIA are rather poorly elaborated in the field of life‐cycle assessment. In this article we provide an overview of the commonly used MADA methods and discuss LCIA in relation to them. The article also presents how different frames and tools developed by the MADA community can be applied in conducting LCIAs. Although the exact framing of LCIA using decision analysis still merits debate, we show that the similarities between generic decision analysis steps and their LCIA counterparts are clear. Structuring of an assessment problem according to a value tree offers a basis for the definition of impact categories and classification. Value trees can thus be used to ensure that all relevant impact categories and interventions are taken into account in the appropriate manner. The similarities between multiattribute value theory (MAVT) and the current calculation rule applied in LCIA mean that techniques, knowledge, and experiences derived from MAVT can be applied to LCIA. For example, MAVT offers a general solution for the calculation of overall impact values and it can be applied to help discern sound from unsound approaches to value measurement, normalization, weighting, and aggregation in the LCIA model. In addition, the MAVT framework can assist in the methodological development of LCIA because of its well‐established theoretical foundation. The relationship between MAVT and the current LCIA methodology does not preclude application of other MADA methods in the context of LCIA. A need exists to analyze the weaknesses and the strengths of different multiple‐criteria decision analysis methods in order to identify those methods most appropriate for different LCIA applications.

Background, aim and scope The methodological choices and framework to assess environmental impacts in life cycle assessment are still under discussion. Despite intensive developments worldwide, few attempts have been made hitherto to systematically present the role of different factors of characterisation models in life cycle impact assessment (LCIA). The aim of this study is to show how European average and country-dependent characterisation factors for acidifying and eutrophying emissions differ when using (a) acidifying and eutrophying potentials alone, (b) depositions from an atmospheric dispersion model or (c) critical loads in conjunction with those depositions. Furthermore, in the latter case, the contributions of emissions, an atmospheric transport model and critical loads to changes in characterisation factors of NO 2 are studied. In addition, the new characterisation factors based on the accumulated exceedance (AE) method are presented using updated emissions, a new atmospheric transport model and the latest critical loads. Materials and methods In this study, characterisation factors for acidifying and eutrophying emissions are calculated by three different methods. In the 'no fate' (NF) methods, acidifying and eutrophying potentials alone are considered as characterisation factors. In the 'only above terrestrial environment' (OT) approach, characterisation factors are based on the deposition of the acidifying or eutrophying substances to terrestrial land surfaces. The third method is the so-called AE method in which critical loads are used in conjunction with depositions. The results of the methods are compared both at the European and the country level using weighted mean, weighted standard deviation, minimum and maximum values. To illustrate the sensitivity of the AE method, changes in European emissions, employed atmospheric dispersion model and the critical loads database are conducted step-by-step, and the differences between the results are analysed. Results and discussion For European average characterisation factors, the three characterisation methods of acidification produce results in which the contributions of NH 3 , NO 2 and SO 2 to the acidification indicator do not differ much within each method when 1 kg of each acidifying substance is emitted. However, the NF methods cannot describe any spatial aspects of environmental Int J Life Cycle Assess (2008) 13:477-486

Greening the economy has been widely discussed as a new strategy for simultaneously reducing environmental pressures, promoting economic growth and enhancing social well-being. Indicators are one tool that can be used to describe the development of green growth. This paper presents and evaluates the process of attempting to build a set of policy-relevant key indicators of green growth for Finland. The challenges of developing a cross-scale indicator set integrating different sectors and levels of society are identified and discussed. It is argued that both the experts preparing the indicators and the potential users will benefit from a collaborative process that aims not only to build a shared awareness of the key issues of green growth but also to foster a realistic understanding of the strengths and weaknesses of the indicator approach. Key challenges include data availability, right balance between different indicator selection criteria, systemic understanding of the relationships between indicators, and the variable usage contexts of the indicators.