Economic and environmental impacts of integrated drone delivery systems

Rapid advances in unmanned aerial systems have intensified interest in drone-based last-mile delivery, yet their economic and environmental benefits remain unclear. This study develops a unified modeling framework that integrates continuous approximation with integer programming to optimize delivery system design across three modes: drone-only (DO), truck–drone hybrid (TD), and truck-only (TO). The framework extends prior work by (i) embedding emissions in a total-cost objective and (ii) allowing multi-stop drone tours to capture key operational realities. Pareto frontiers are generated to examine trade-offs between cost and emissions across a broad spectrum of technical, regional and policy scenarios, including variations in drone energy use, labor cost, carbon intensity of electricity, and payload capacity. We also analyze how carbon-pricing reshapes optimal delivery system designs. Results reveal that cost-emissions tradeoffs are often asymmetric and highly context-dependent. DO outperforms TD when drones are energy-efficient or powered by clean electricity, yielding up to 40% cost savings and 92% emissions reductions. In contrast, high labor or regulatory costs suppress DO adoption even at carbon prices above $1,000 per ton CO2e. Furthermore, large percentage reductions in emissions may translate into only modest absolute gains, highlighting the need to evaluate both absolute and relative metrics in policy and operational decisions.