Moving beyond land use intensity types: assessing biodiversity impacts using fuzzy thinking

Abstract

Purpose The impact of land use on biodiversity is a topic that has received considerable attention in life cycle assessment (LCA). The methodology to assess biodiversity in LCA has been improved in the past decades. This paper contributes to this progress by building on the concept of conditions for maintained biodiversity. It describes the theory for the development of mathematical functions representing the impact of land uses and management practices on biodiversity. Methods The method proposed here describes the impact of land use on biodiversity as a decrease in biodiversity potential, capturing the impact of management practices. The method can be applied with weighting between regions, such as ecoregions. The biodiversity potential is calculated through functions that describe not only parameters which are relevant to biodiversity, for example, deadwood in a forest, but also the relationships between those parameters. For example, maximum biodiversity would hypothetically occur when the nutrient balance is ideal and no pesticide is applied. As these relationships may not be readily quantified, we propose the use of fuzzy thinking for biodiversity assessment, using AND/OR operators. The method allows the inclusion of context parameters that represent neither the management nor the land use practice being investigated, but are nevertheless relevant to biodiversity. The parameters and relationships can be defined by either literature or expert interviews. We give recommendations on how to create the biodiversity potential functions by providing the reader with a set of questions that can help build the functions and find the relationship between parameters. Results and discussion We present a simplified case study of paper production in the Scandinavian and Russian Taiga to demonstrate the applicability of the method. We apply the method to two scenarios, one representing an intensive forestry practice, and another representing lower intensity forestry management. The results communicate the differences between the two scenarios quantitatively, but more importantly, are able to provide guidance on improved management. We discuss the advantages of this condition-based approach compared to pre-defined intensity classes. The potential drawbacks of defining potential functions from industry-derived studies are pointed out. This method also provides a less strict approach to a reference situation, consequently allowing the adequate assessment of cases in which the most beneficial biodiversity state is achieved through management practices. Conclusions The originality of using fuzzy thinking is that it enables land use management practices to be accounted for in LCA without requiring sub-categories for different intensities to be explicitly established, thus moving beyond the classification of land use practices. The proposed method is another LCIA step toward closing the gap between land use management practices and biodiversity conservation goals.

Assessing the contribution of products to the United Nations’ Sustainable Development Goals: a methodological proposal

“…Other possible curve types described in Lindner et al (2021) were not considered suitable by the experts for the impact assessment of the identified 20 indicators discussed here. Since it would be beyond the scope of the paper to discuss the impact assessment for each of the 20 C2-indicators, in the following, an example is given for each of the five priorities defined in the systematic approach to setting the reference value for the impact assessment.…”

“…This has already been started by Kühnen et al (2019) for the assessment of positive sustainability impacts of products and has been developed further in this study. The approach permits both quantitative and qualitative knowledge to be transformed into a numerical representation (Lindner 2016;Lindner et al 2021). The method proposed by Lindner (2016) is used in biodiversity impact assessment (Lindner et al 2019a(Lindner et al , b, 2021 and uses fuzzy modelling, an approach that is used in a wide range of scientific fields, e.g.…”

“…The definition of the S-LCIA functions is based on the approach proposed by Lindner et al (2021) for biodiversity impact assessment. According to this approach, first, measurable input parameters for the defined indicators and reference points are identified and, second, a contribution function is defined.…”

“…Next, the relationship between the parameter and its contribution is defined following the approach proposed by Lindner et al (2021). The type of curve, describing the relationship, is based on expert knowledge and is furthermore discussed within an expert workshop (see Sect.…”

“…The other type is a simple linear curve. To identify which type fits the contribution of a given parameter, three questions have to be discussed with experts, in order to be able to define the S-LCIA-functions (Lindner et al 2021):…”