The urgency to develop policy strategies to support clean energy investments to tackle the current climate and socio-economic challenges is significantly increasing the interest in energy system models, which aim to assess the effectiveness of possible energy transition policies in pursuing climate targets. Among these tools, energy system optimization models (ESOMs) are widely used to find the least-cost configuration of the technological network which allows to satisfy the final energy service demands, in a certain region and along a certain time horizon. Bottom-up ESOMs rely on a detailed techno-economic characterization of the technologies included in the energy system. While the cost trajectories for both well-established and innovative technologies are defined according to the available literature, technology-specific discount rates, also called hurdle rates, are often based on educated guesses.