Emerging technologies for the reduction, capture, and utilization of CO₂ are crucial for achieving the 1.5°C target and thus reducing climate-related risks for humanity. A prerequisite for meeting this target is net negative CO₂ emissions from 2050 onwards, since reducing CO₂ alone will most likely not be sufficient. Photocatalytic and photoelectrochemical CO₂ reduction technologies, also known as Artificial Photosynthesis (AP), could be a promising option in this context. They enable the direct, potentially energy-efficient, and environmental impact mitigating production of chemicals and fuels by mimicking the process of natural photosynthesis. By utilizing CO₂, AP could contribute to a negative CO₂ footprint and potentially reduce further environmental impacts.

However, due to their early technology readiness level (TRL), emerging technologies such as AP systems are difficult to evaluate by standard methods such as Life Cycle Assessment (LCA). This is because, in addition to the existing uncertainties in LCA, new challenges arise that are associated with further uncertainties regarding the life cycle inventory (LCI) foreground data, the background database, the life cycle impact assessment (LCIA) methods, the normalization and weighting approaches, and the interpretation of the results.

While uncertainties in the foreground and background models are addressed in the concept of prospective or ex-ante LCA, uncertainties in LCIA, normalization and weighting as well as in the communication of results are not yet sufficiently reflected  with a prospective perspective. This applies in particular to the climate change impact category, which is a widely used method in LCA for decision making and for assessing the risks of climate change. For AP technology, which is intended to enable large-scale CO₂ utilization, only static models have been developed so far; a prospective modeling perspective has not yet been applied. It is therefore unclear whether the results of the assessments are robust enough and whether the environmental performance ranking will hold up when uncertainties are taken into account.

This raises the question of whether LCA is applicable in the early development stage of an emerging technology like AP, or whether the uncertainties are so great that they do not allow reliable comparisons between the technology and its reference product systems. Furthermore, it is necessary to investigate whether social science methods, such as the wisdom of crowds, can be applied to reduce the range of results.

Using a prospective LCA of AP in combination with further method development and application, the dissertation aims to contribute to the reduction of uncertainties in the evaluation of emerging technologies. Moreover, the prospective LCA of AP, taking into account the uncertainties of the foreground and background models as well as the impact assessment, is intended to provide information about the potential LCIA results and their probability of occurrence. Furthermore, the results of probabilistic simulations will be coupled with crowd wisdom methods in order to reduce the range of results. The main aim is to support technology developers and decision makers in conducting a transparent and detailed evaluation of Artificial Photosynthesis in comparison to reference and competing technologies.

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