Speaker
Description
The rapid growth of Low Earth Orbit (LEO) activity has increased debris generation risk beyond the scope of existing mitigation regimes, which rely primarily on non-binding measures such as post-mission disposal (PMD) and fragmentation avoidance. While these measures provide a necessary baseline, they do not adequately capture the coupled, stochastic dynamics of debris propagation or the cumulative effects of decentralized operator behavior. As launch cadence increases and large constellations continue to expand, the orbital environment is increasingly characterized by system-level interactions that cannot be managed through isolated compliance measures alone. This paper advances the thesis that effective debris mitigation requires a transition to enforceable, quantitatively grounded capacity management strategies derived from simulation-based analysis and supported by operational policymaking tools.
This work presents the Enhanced Monte Carlo Orbital Capacity Assessment Tool (EMOCAT), developed within Embry-Riddle Aeronautical University’s XD Lab as an extension of the Massachusetts Institute of Technology’s Orbital Capacity Assessment Tool (MOCAT). EMOCAT is designed as a policy-oriented simulation environment for evaluating long-horizon debris evolution under variable traffic, compliance, and fragmentation conditions. The framework propagates object populations using Monte Carlo sampling of launch schedules, fragmentation events, and maneuver uncertainties, enabling estimation of collision rates, debris growth trajectories, and regime stability thresholds. Within this structure, orbital capacity is defined in terms of collision frequency and steady-state population behavior, allowing for direct assessment of system limits under different operational and regulatory scenarios.
Policy levers are explicitly parameterized within the model, including PMD compliance rates, launch rate constraints, and active debris removal (ADR) interventions. These inputs are evaluated across scenario ensembles to quantify their impact on long-term system behavior, providing a basis for comparing mitigation strategies in terms of effectiveness, scalability, and enforceability. This approach enables capacity to be treated as a measurable and policy-relevant quantity, supporting the development of governance mechanisms that are directly informed by physical system constraints.
To bridge the gap between simulation outputs and policy implementation, EMOCAT is coupled with a Large Language Model (LLM) that structures policy design as a constrained optimization problem over regulatory instruments. The LLM supports rapid generation and iteration of policy strategies, including adaptive licensing regimes, congestion-indexed launch controls, and compliance-linked incentive structures. These strategies are evaluated within the simulation environment, enabling closed-loop assessment of policy performance under uncertainty and across varying traffic conditions. This integration allows for systematic exploration of trade-offs between economic activity, operational flexibility, and long-term environmental stability.
An interactive graphical user interface (GUI) complements this architecture by providing real-time visualization of simulation outputs and policy impacts. The interface presents debris population evolution, collision probability distributions, and capacity threshold exceedance metrics through scenario-based dashboards, enabling policymakers to explore policy trade spaces without direct interaction with underlying model code. This reduces the technical barrier to entry while preserving analytical rigor, supporting more transparent and participatory decision-making processes.
Together, these components establish a unified framework in which high-fidelity simulation, policy design, and decision support are co-located within a single analytical environment. This architecture moves beyond the use of models as descriptive tools and positions them as active components of governance development. The results demonstrate that capacity-based governance, implemented through simulation-calibrated constraints and iteratively refined policy mechanisms, provides a tractable and scalable approach to debris mitigation. By aligning regulatory design with the dynamic limits of the orbital system, this framework offers a pathway toward more effective, enforceable, and sustainable space debris mitigation strategies in an increasingly congested LEO regime.
| Which section would you like to submit your abstract to? | Session 9: “Space debris mitigation policies” |
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