The European Union has implemented Sustainable Aviation Fuel (SAF) blend mandates in its member states, starting for bio-SAF in 2025 and for synthetic SAF (e-SAF) in 2030. Various e-kerosene plant projects have taken off in the last years in Europe. However, due to the high investment costs and dependency on feedstock availability, no plant has reached final investment decision yet. In the Netherlands, two full scale e-kerosene plants have been announced to be build in the next decade. This paper estimates the net present value of one of these in the current market conditions of the Dutch aviation fuel market. For this, a real option tree model is created to represent the current risks, investment costs and market state in the Dutch geographical context. In addition, the impact of various policy measures are added to the model to create different policy scenarios. The objective of this paper is to find which policy scenario's yields a positive net present value for the analyzed PtL plant in the Netherlands.

The real option decision tree was composed in various steps. The model is based on an e-jet fuel plant based in the Netherlands with annual jet-fuel production capacity of 50,000 tonnes. The e-fuel mix contains 75% jet fuel, 12.5% diesel and 12.5% naphtha. This plant sources green hydrogen and CO2 externally, therefore does not require investment in direct air capture systems or an electrolyzer. First, the project stages and options were defined as in other energy projects. The length of each is approximated based on the status and expected deployment of current PtL e-kerosene plants. Next, the project investment and value was determined following the findings of previous works. The CAPEX was split up over the investment stages determined previously, and adjusted for inflation. Likewise, the OPEX found in various literature sources was inflation adjusted and averaged. The selling price is modeled to decrease at the same rate as the projected electrolyzer costs because of technology maturity. Market conditions were based on both fuel demand projections in the Netherlands and the European blend mandates for synthetic fuels until 2050. From this, two market condition scenarios were modeled. These were based on whether the modeled plant or its smaller competitor reaches market first. This makes a difference, as the fuel demand in the first years of operation is limited because of lower blend-mandates. Next, the abandon options were modeled by determining the salvage value. The salvage value was defined as the current replacement costs minus the depreciation. For this, the depreciation rates for each investment during both testing and operation were determined. After finding the values, the probability distribution for the options in the different project stages were determined. This was done using the probability ranges as defined in the classical risk matrix. The current and forecasted status of most prominent project and market risks were described, where after the risks were allocated to a probability range. The main value of each was used in the probability distribution. Lastly, the different policy incentives and scenarios were defined.

Results indicate that the blend-mandates imposed by the European Union are insufficient for an large scale e-kerosene plant to yield a positive NPV in the market landscape within the Netherlands. Optimizing market conditions for these plants increases the NPV, however, not sufficiently. Instead, the risks of green hydrogen, grid electricity availability and the high CAPEX investment costs early in the project significantly impact the NPV, resulting in an unfeasible business case. Therefore, measures to reduce CAPEX and ensure feedstock supply will be most beneficial to increase the project value. Scenario's imposing combinations of certain policy incentives resulted in a positive NPV, hence, financial viability. Those with the most effect combined CAPEX lowering with either optimizing market conditions or increasing cash flow through a tax cut. In addition, a scenario was modeled that included a waiting period before the construction phase to increase the probability of hydrogen and green grid electricity availability. As the markets of each are projected to grow annually, there is a trade-off between risk reduction during construction and increased cost of capital and asset depreciation by waiting. It was found that a waiting period of 3 years before starting the construction phase resulted in the highest NPV.

This study found that more is required than currently incentivized by the European Union for PtL projects to become operational and successful long-term within the Netherlands. The focus of the Dutch and European policy makers needs to shift towards lowering capital expenditures for PtL plant construction and reducing feedstock supply risk in the following years. Only then can the first two full scale plant in the Netherlands continue to the next phases of their projects and eventually cover the market demand of the first years following 2030. The real option tree model composed in this study combined with the policy scenario's contribute to the economic analysis performed on PtL e-fuel plants. The decision tree model incorporates managerial flexibility and both project and external risk within the valuation within the Dutch context specifically. As the presence of both risk and options significantly affects the project NPV, this method adds to results found in previous techno-economic analysis works. The model provides an adjustable framework for policy makers, to test under what market conditions and within which policy scenarios investing in these plants would be profitable. The challenge lies in finding the right combination of incentives that allows the financial viability within the right time frame, while avoiding to put the entire financial burden on one group of stakeholders. Large scale deployment of (e-)SAF is the best pathway to meet European climate goals for aviation in 2030 and beyond. A trade-off needs to be made between sustainability and economic prosperity to enable this. Achieving this balance will be necessary to ensure the both environmental and economic objectives are met, enabling a sustainable and prosperous future for the Dutch aviation industry.

While many researchers have researched the factors affecting standard consortium success, the size and diversity of a consortium were given the most importance. However, investigating the combined effect of these two factors on standard consortium success using specific indicators of success was limited. Past research required action on applying a new approach to determine the extent to which these variables work together toward the success of standard consortia. It was also important to investigate the network configurations of consortia given the multiple factors that come into play in the process of standardization over the years. This required a configurational approach with huge consortium-level data to test. The objective of this research is to find the configurations of size and diversity that lead to standard consortium success, in the ICT and telecommunications industry, success being represented by specific indicators. The data was collected from the websites ‘consortiuminfo.org’ and LinkedIn using Python packages and was analyzed using Crisp-set Qualitative Comparative Analysis (csQCA). This analysis was done considering two indicators of success individually and combined as a single variable, summing up to three analyses. The analysis revealed that the size and diversity of consortia are necessary conditions, but only diversity is found to be a sufficient condition for standard consortium success in this sample dataset of ICT consortia. The findings suggest that researchers should continue to explore the involvement of other factors along with the size and diversity as combinations instead of only analyzing the variables’ effects individually. Furthermore, by doing this the scope of the research broadens by breadth and depth as more data and more insights about factors of standard consortium success can be gathered.