The steadily increasing demand for battery technologies and their widespread use raise concerns about their sustainability, safety, and efficiency. Batteries are repeatedly criticized, for example, for their high energy consumption during production, the use of critical materials, the highly flammable electrolyte, or their limited power range. To overcome these drawbacks, new battery technologies are being developed, which in turn require a prospective assessment of their potential environmental impacts, e.g., through life cycle assessments. Life cycle assessment (LCA) is a standardized method to evaluate the environmental impacts of a system, product, or service throughout its life cycle, from raw material extraction to end-of- life. When conducting LCA, all material and energy flows entering and leaving a system are recorded in a life cycle inventory and converted into potential environmental impacts (e.g., global warming potential, acidification).

However, this is where the Collingridge dilemma arises: In the early development phases (TRL 1-4) of emerging technologies, the results of a comprehensive life cycle assessment can have the greatest impact because there is greater design freedom. Therefore, it is imperative to prospectively assess potential environmental impacts as early as possible. At the same time, the lack and uncertainty of input-output data on the novel technology and its future use phase affect the robustness of the results and severely limit decision support. Although LCA is a standardized and well-established method, there is a lack of approaches to performing prospective LCA, especially for batteries.

In this context, uncertainties about the functioning and framework of the new technology, uncertainties about future external developments, limited availability, and poor quality of life cycle inventory data are major challenges. Where inventory data are available, they usually represent laboratory-scale production. But lab-scale processes are subject to greater uncertainty and variability, reflected in low throughput. In contrast, the industrial production level is characterized by its efficiency orientation. Its goal is to produce an existing and mature technology under already known parameters in maximum quantity at minimum cost. Consequently, the energy and material consumption determined at laboratory scale is multiple orders of magnitude higher than at industrial level, which ultimately leads to higher environmental impacts. In order to increase the validity of the LCA results, the lab-scale datasets need to be scaled up. This presents another challenge, as there is no established scaling method for inventory data of emerging battery technologies to date.

Therefore, the central question of this dissertation is how to increase the robustness and reliability of prospective LCAs for emerging battery technologies in early stages of development (TRL < 4).

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