Predicting the Financial Feasibility of Space Mining: A Quantitative Review of Lunar Oxygen Mining Cost Structure

Abstract

The first half of the 2023 has seen space mining becoming an achievable dream as it barged in the trending topics within the new space economy. AstroForge, one of the leading startups in the market niche, has announced two asteroid mining missions for the current year, demonstrating the possibility of running refinery activity in absence of gravity and scouting for potential target asteroids (AstroForge, 2023). Complementarily, an experiment run by NASA's Carbothermal Reduction Demonstration (CaRD) team developed using Johnson Space Center’s Dirty Thermal Vacuum Chamber has proven the feasibility of extracting oxygen from the lunar regolith. "This technology has the potential to produce several times its own weight in oxygen per year on the lunar surface, which will enable a sustained human presence and lunar economy”; those are the words with which Aaron Paz – JSC Senior Engineer – has commented on the successful outcome of the breakthrough test (Dinner, 2023).
The following pages have been drafted inspired by those words, by decomposing such a complex matter in its foundational bricks and elements, aiming to tackle what has been historically a common issue in disruptive innovation and space ventures: cost estimation and predictive modelling.
The dissertation starts with the first chapter proposing an overview of the youthful and explosive space business sector, introducing some of the inherent complexities tackled further in the current project management studies, reviewing how innovation takes place in this industry, and proceeding with a bridge that leads to the mining sphere, where the most prominent mission designs are pondered.
There, a concluding paragraph addresses the relevance of the study as it reviews the cost prediction methods and their goodness of fit, identifying a structural inappropriateness of the current literature and the associated methods, which result in a limited practical employability.
The second chapter develops following an abductive structure and is dedicated to carefully explain the research process, focusing on both the multiple iterative stages and the derivation of the two core building blocks: the PLS-SEM-reliant predictive model and the NPV equation.
A concluding paragraph comments on the complementary and concordant outcomes of the two models, providing a countable estimation of the project cost value. Further, it continues with the implications as well as the limitations of the work, suggesting openings for further research.