## 2 Materials and Methods  
### LCA and Methodological Choices  
Life cycle assessment (LCA) methodology is largely applied to determine the GHG saving potential of alternative fuels [6; 18]. When applied to the use of these fuels in specific powertrains, LCA is usually down-tuned to the well-to-wheel (WTW) approach. WTW modelling includes so-called well-to-tank (WTT) [19] and tank-to-wheel/wake/wing (TTW) parts [20]. WTT emission intensity can be considered as the sum of individual contributions needed to convert a specific feedstock in a certain fuel, therefore encompassing downstream and upstream emissions. In the case of fossil feedstock, WTT includes crude oil production (upstream) and refining emissions. Figure 1 shows a diagrammatic representation of well-to-tank (WTT) and tank-to-wheel/wake/wing (TTW) emissions, respectively, along the fuel supply chains.  
The main contributors to upstream greenhouse gas emissions are:  
* Energy use to get the oil out of the formation and for pre-processing before shipment;
* Fugitive emissions of methane from process equipment via seals, purges, etc.;
* Deliberate venting of produced gases, where it is considered uneconomic to collect them;
* Flaring as an alternative to venting, as well as flaring at start-up/shutdown or as a part of the pressure relief system.  
The energy use in crude production is time- and location-dependent: EU refineries receive a mixture of crudes from different regions that have their own production characteristics, and this impacts upstream emissions. To determine the GHG intensity for fossil-derived kerosene it is needed to first properly characterize the crude feedstock. The physical properties of crude oil vary both by region and within a region, arising from differences in geology, operating factors and history. A review of the supply of crude to the EU and the GHG intensity of regional crudes supplying the EU [21; 22] shows a substantial variation between different oil field countries that reflects fundamental differences in crude and the geology but also operational practices.  
A refinery can be modelled by linear programming (LP). These kinds of models provide the distribution of energy and hydrogen supplies, allowing the overall GHG emission to be calculated as the sum of the individual contributions each multiplied by their characteristic GHG intensities. The refinery's GHG intensity is then calculated as the emission as gCO\({}_{\text{2 eq}}\) per tonne of crude input. For this study the functional unit is represented by the gCO\({}_{\text{2 eq}}\) per MJ of finished kerosene.  
Given the goal and scope of this paper, a simpler refinery model was defined, with the specific target of focusing on looking at the various independent levers that could reduce the GHG of crude refining. In this paper, emission savings have been allocated on an energy basis; therefore, the GHG emission savings benefitted all refinery products. However, it could be argued that if the GHG savings rise because of LCAF incentives, then GHG emission savings should be attributed solely to the jet fuel (free allocation). As the differences in the results could be relevant, this raises the question about what an appropriate allocation methodology could be for the additional GHG savings arising from LCAF-specific incentives. For this study, energy allocation has been chosen as the ICAO document [11] states that energy allocation should be used to assign emissions burdens to all co-products in the system, this would apply to both LCAF and sustainable aviation fuels (SAF). This approach is also in line with the EU REDII GHG methodology for renewable fuels.  
Figure 1: Well-to-wheel/wake/wing emissions for fuel supply chains.