Environmental effects of emerging technologies in construction processes: A prospective LCA study for active drone use in building tasks

In the construction sector, drone technology is still mostly limited to monitoring construction progress, photogrammetry, and data acquisition. However, there is a high potential still to be explored, placing drones in an active role during the construction process. Indeed, several studies have been exploring the technical feasibility of active use of drones for automating construction processes, mostly in terms of the drone technology required, control and automation of multidrone systems, and construction performance and safety, but the consequences of this promising construction approach in terms of the environment are still to be assessed. In this context, this study consisted of a comparative Life Cycle Assessment (LCA) in order to estimate the environmental impacts of a hypothetical drone-based construction process. The LCA was conducted in four phases: goal and scope definition, Life Cycle Inventory (LCI), Life Cycle Impact Assessment (LCIA), and Interpretation. The scope was the construction of non-load-bearing brick walls, and 2 scenarios were used: drone construction and conventional manual construction. LCI was based on the Ecoinvent database for the upstream process, as well as previous studies providing metrics for drone-based construction. LCIA was carried out using the ImpactWorld+ methodology, as it provides a more encompassing overview in terms of the environmental impact categories included. As expected, the results indicate that in terms of construction material efficiency, the drone-based construction scenarios have a high potential to reduce environmental impacts. On the other hand, in other categories like abiotic depletion, drone systems present a higher demand on scarce materials to be manufactured, which is why they scored worse in related categories. Moreover, drone scenario results and comparison with conventional systems should be analyzed with caution, due to the high uncertainty in the operational indicators used for drone processes, as they are based on assumptions, simulation studies, or experimental tests.