This capstone project evaluates the environmental impact of autonomous drone-based inventory automation in a U.S. fulfillment warehouse operated by a global logistics company. The central research question is: What is the impact of warehouse inventory automation on greenhouse gas (GHG) emissions across Scope 1, 2, and 3? To address this, we developed a quantitative decision-making framework grounded in the GHG Protocol and applied it to compare pre- and post-implementation scenarios using activity-based emissions modeling and life cycle assessment (LCA). Drawing on operational data, drone usage logs, and structured interviews, we modeled emissions from labor, material handling equipment, energy consumption, and inventory waste. The results show a 49.5% reduction in total annual emissions (from 79,200 kg CO₂e to 40,008 kg CO₂e), driven largely by a 40% decrease in inventory write-offs and a 70% drop in forklift energy use. Expanding drone coverage from 64% to 90% yields an additional 33% reduction in emissions, though with diminishing marginal returns. The study concludes that droneenabled automation can significantly reduce indirect emissions—particularly Scope 3 sources such as employee commuting and inventory loss—while offering a scalable, data-driven tool for operational sustainability. The framework presented serves as a replicable model to support emissions-informed decision-making in warehouse automation initiatives.