Browsing by Author "Finkbeiner, Matthias"

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    A system thinking normative approach towards integrating the environment into value-added accounting : paving the way from carbon to environmental neutrality
    (2022) Miehe, Robert; Finkbeiner, Matthias; Sauer, Alexander; Bauernhansl, Thomas
    Life Cycle Assessment (LCA) is increasingly being applied in corporate accounting. Recently, especially carbon footprinting (CF) has been adopted as ‘LCA light’ in accordance with the Greenhouse Gas Protocol. According to the strategy ‘balance, reduce, substitute, compensate’, the approach is intended to provide the basis for optimization towards climate neutrality. However, two major problems arise: (1) due to the predominant focus on climate neutrality, other decisive life-cycle impact categories are often ignored, resulting in a misrecognition of potential trade-offs, and (2) LCA is not perceived as an equal method alongside cost and value-added accounting in everyday business, as it relies on a fundamentally different system understanding. In this paper, we present basic considerations for merging the business and life-cycle perspectives and introduce a novel accounting system that combines elements of traditional operational value-added accounting, process and material flow analysis as well as LCA. The method is based on an extended system thinking, a set of principles, a calculation system, and external cost factors for the impact categories climate change, stratospheric ozone depletion, air pollution, eutrophication and acidification. As a scientifically robust assessment method, the presented approach is intended to be applied in everyday operations in manufacturing companies, providing a foundation for a fundamental change in industrial thought patterns on the way to the total avoidance of negative environmental impacts (i.e., environmental neutrality). Therefore, this is validated in two application examples in the German special tools industry, proving its practicability and reproducibility as well as the suitability of specifically derived indicators for the selective optimization of production systems.