FRITS.B: A Python Tool for Transparent and Reproducible Modification of LCI Data for Prospective Life Cycle Assessment of Energy Scenarios

Abstract

To drastically reduce future GHG emissions and fight climate change, an energy transition is imperative. Energy system models (ESMs) are used to develop energy transition pathways to a more renewable energy supply. They usually focus on the reduction of future CO2 emissions while minimizing costs of the energy system. However, ESMs cannot provide a complete assessment of environmental impacts of energy scenarios, as they neglect other environmental impact categories, such as human toxicity or metal depletion. To assess future impacts of our energy systems beyond CO2 emissions, a coupling of energy system models (ESMs) with prospective LCA (pLCA) is needed. Such a coupling is unfortunately challenging, as data and processes in ESMs are structured differently than in pLCA. Differences apply for instance to the general model structure, technological granularity, assumed parameters, e.g., energy or material efficiency, life times, full load hours, etc. Especially the distinction between life cycle phases, i.e. of construction and operation, pose a challenge. ESMs model capacity building of technologies and their operation temporally individually. Construction occurs once, while operation takes place dynamically over the life time of a technology depending on, e.g., full load hours. Yet, LCA models mostly aggregate these two phases into one average process, e.g. average construction requirements and average operation to generate 1 kWh of electricity. Thus, standard LCA results cannot represent the temporal dimension of construction and operation correctly, nor do they allow to vary operation parameters, such as annual full load hours. Hence, to assign future impacts temporally correctly and represent the assumed technology parameters as well as scenario specifications from the ESM, the LCI processes need to be modified and aligned to the structure of the ESM. This primarily means splitting aggregated LCA processes into sub-processes of construction and operation. The separation of processes in already existing databases, such as ecoinvent, is not trivial and very technology dependent, as processes are not structured or named in a consistent way. Existing studies coupled ESMs with LCA models, but process modifications in the LCA model are mostly done manually. Manual modifications are not only error prone, very time intensive, and often poorly documented, but also inhibit transparency and reproducibility. To address this issue, we developed a python tool called FRITS.B, a new component within FRITS, the FRamework for the assessment of environmental Impacts of Transformation Scenarios (Junne et al. 2020, Naegler et al. 2022). FRITS.B allows to systematically modify LCI processes in an automatized way using python and brightway2. It comprises the following features: a) Importing LCI databases; b) Adaptation of LCI data, e.g., to separate construction and operation processes, or to delete flows from LCI data which are explicitly represented in another technology within the ESM; c) Documentation of all modifications at flow and dataset level; d) Exporting new databases; e) Relinking databases to different background databases; f) Integration of background scenarios using premise; g) Calculation of prospective environmental impacts using the superstructure-approach. FRITS.B enables a more transparent and reproducible environmental impact assessment of not only energy scenarios but scenarios in general, thus contributing to Open Science. For scenarios optimized solely for climate change impacts, it offers a means to avoid a carbon tunnel vision and burden shifting to impacts beyond climate change, thereby facilitating an environmentally more informed decision-making for policy-makers.