DISE: A Dynamic Integrated Space-Economy Model for Orbital Debris Mitigation Policy Evaluation
Bibliographic record
Abstract
Abstract This paper presents the Dynamic Integrated Space-Economy (DISE) model, designed to study the economic implications of alternative policies to mitigate orbital debris. The DISE model combines a standard neoclassical growth model with a physical space model for orbital debris dynamics. The economic model categorizes capital assets into two types: Earth’s capital and Space’s capital (i.e., satellites). The orbital debris model describes the dynamic of three types of objects: derelict satellites, rocket bodies, and fragments. DISE is intended to calculate the cost of space debris and its impact on the global economy. The model is simulated for a horizon of 200 years, starting from 2024, under different scenarios, including a clean space environment, laissez-faire, derelict satellites de-orbiting policy, all intact objects de-orbiting policy, debris-free launch systems, a combination of de-orbiting and debris-free launch vehicles, and collision avoidance. We find that the implementation of de-orbiting and debris-free launch systems mitigation policies is not enough to ensure space environmental sustainability, as in the long run the main source of debris generation would be collisions. Without any debris mitigation intervention, the cost of orbital debris would be more than 0.5% of world GDP in the long-run.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".