Life cycle assessment-based environmental product declarations (EPDs) require the inclusion of biodiversity impacts across the entire supply chain. The objective of this study is to test the applicability of a Scandinavian biodiversity assessment tool, developed specifically for use with EPD applications, in an African desert environment, linking the industry types power generation and mining. For this purpose, a GIS-based spatial analysis tool-the biotope method-was adapted to a framework approach which allowed the selection of more suitable, site-specific biodiversity indicators. The biotope method provides a step-by-step process of defining system boundaries, mapping biotopes, categorizing biotopes based on site-specific indicators, and evaluating change in biotope status "before" and "after" the impact. The development of site-specific indicators was piloted in this study and determined by the affected ecosystem and the status of knowledge on biodiversity in this geographic area. Thus plants were used as indicators for biodiversity, and red-list status and endemism constituted the prime criteria for conservation value of plants. This in turn represented the key criterion for classifying biotopes. The tested biodiversity assessment tool has potential for application in different environments and operational settings but leaves room for improvement by including secondary impacts in the assessment and using a wider range of taxa for indicators of biodiversity.
The biotope method was developed to provide biodiversity impact quantification in life-cycle inventory and assessment studies of energy generation. It is based on measurements of land-use induced biotope alterations, which are considered representative of the impact on biodiversity and facilitate measurements of, and comparisons among, different projects. It includes tools necessary for classifying and characterising the areas affected, and results in transparent, quantitative data. During the past five years, the method has been applied on various energy production systems. The results of these applications are analysed and compared, and suggestions for further methodology development are discussed.
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