Assessing the Social Dimension in Strategic Network Design: The Case of Bioethanol Production in the EU

Main Presenter: Lukas Messmann 

Co-Authors: Lars Wietschel Andrea Thorenz Axel Tuma

Unlike product-specific or site-specific assessments, corporate or political sustainable decision-making on a strategic and multi-regional level, by nature, relies heavily on aggregated and often generic data. In contrast to the environmental dimension, the complexity of social indicators, their subjective and often qualitative nature, and a lack of data render the inclusion of social indicators into quantitative optimization models for strategic supply chain decision-making difficult. First, this work presents a structured process for including a comprehensive set of social aspects by selecting applicable quantitative and regionalized social indicators. This approach is applied to the case of lignocellulosic, second-generation bioethanol (2G EtOH) production in the EU, which is a promising substitute for fossil and food crop-based fuels. In total, we compile 10 maximizable objective functions and 25 categories for hotspot identification with respective quantifiable social indicators, based on i.a. the Guidelines for Social Life Cycle Assessment of Products, the Social Hotspots Database, literature, and previous work. Second, we evaluate impacts and benefits of a large-scale 2G EtOH production in the EU by integrating these 35 social categories as well as economic and 21 environmental LCA-based objective functions into a model for mixed-integer linear programming. We identify optimal strategic decisions (regional biorefinery locations and capacities in the EU, feedstock collection, EtOH transportation, and substitution of either fossil petrol or first-generation EtOH) and resulting optimal objective values for each of the social, environmental, and economic objectives. Key results show that social optimization mostly leads to large, labour-intensive networks, but value creation and the substitution of the two substituted reference products shift regionally, depending on the social objective. This approach allows for uncovering hidden relationships between the different objectives in this application case, for identifying Pareto-optimal trade-offs between multiple objectives, and for assessing the network’s impact on the level of the overarching Sustainable Development Goals (SDGs). The approach in the study at hand is novel in its depth both in the fields strategic supply chain design and the European bioeconomy, contributing to a more holistic life cycle sustainability optimization.

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