Methods and Tools: Explain the methodologies and tools used in the studies outlined in the paper.' 4.2 Process and Product Analysis for e-Production The variety of applications for commodities from e-Production, e.g., transportation and chemical industry, coupled with many uncertain parameters regarding technology, policy, and society (cf. Sect. 3.6) result in a high complexity for the selection of the most beneficial product and corresponding production process. For a fair comparison, two aspects are essential for decision-making: 1) consideration of the entire life cycle and interest groups and 2) consistent boundary conditions and assumptions. Regarding e-Production for transportation, Bongartz et al. [59] compared different e-fuels (H2, CH4, dimethyl ether (DME), and methanol) using diverse performance indicators: overall efficiency, energy/power density, infrastructure, pollutant formation, environmental impact, and handling/safety. The analyses were entirely based on the same boundary conditions (i.e., equal sources for H2, CO2, and electricity, steady-state operation), as well as methods (i.e., detailed simulations, equal cost models [69], DIN norms for LCA [70] and engine measurements). The respective experts evaluated each performance indicator in an interdisciplinary setting, before the indicators were weighted for a holistic comparison. The analyses showed that a steady-state operation of the e-Production processes with renewable H2 enables significant reduction of GHG emissions (up to 90 % [59]) and pollutant formation for all e-fuels, e.g., 95 % in particulate matter emissions for DME [59], compared to fossil fuels. However, cheap H2, e.g., below 5 € per kgH2 for DME production, and thus, a cheap average electricity price for cost competitiveness with fossil fuels need to be available. Apart from these clear results, most performance indicators turned out to be reverse for different fuels: in contrast to CH4, DME, and methanol, H2 is advantageous regarding fuel cost, emissions, and overall electricity consumption. The H2 infrastructure, however, is not given and its technology less advanced, which makes its fast implementation challenging. This example highlights the complexity in deciding how to utilize renewable electricity for e-Production in the most beneficial way: even a systematic assessment by quantification and weighting of performance indicators does not provide clear answers. In this regard, optimization-based methods considering multiple objective functions represent a powerful tool for supporting the decision-making process. Such a method is the reaction network flux analysis (RNFA) [71] that finds the most promising reaction pathway towards a fuel candidate. While this method is based on reaction stoichiometry and yield only, process network flux analysis [53, 72] also accounts for minimum energy demand for separation. ''