# The Economic Impact of Spacecraft Down-Mass and Orbital Reentry Operations on the U.S. National Airspace System **Candidate:** DOWNMASS-01 **Program:** COLLEGIUM 1st Battalion **NORTH STAR / JPL category:** Space Infrastructure Systems **Methodological anchors (Hall of Shoulders):** Rao (orbital-economy externalities), Angrist and Pischke (research design and identification), North (institutions and transaction costs) **Date:** 2026-06-15 --- ## Abstract Commercial space launch and reentry operations are managed today by closing large, static volumes of the U.S. National Airspace System (NAS) around the planned trajectory. Each closure forces nearby flights to delay, hold, or reroute, and each of those responses consumes fuel, extends flight time, and raises direct operating cost. As reentry frequency rises with the growth of large constellations and the emergence of a commercial down-mass sector, the cumulative cost of these closures is expected to scale faster than the safety benefit that the closures provide. This dissertation states and is designed to test a single falsifiable contribution: spacecraft reentry and down-mass operations impose a measurable disruption cost on the NAS, expressed in delay, reroute, and closure, and improved reentry prediction combined with dynamic airspace management yields a quantifiable avoided cost. The null hypothesis is that reentry operations impose no measurable NAS cost. The study uses an event-study and staggered difference-in-differences design. The treatment is a reentry-driven Aircraft Hazard Area (AHA) activation over a defined airspace sector and time window. The outcome is the change in realized flight-level cost (delay minutes, added distance, fuel burn, and direct operating cost) for flights exposed to that sector relative to comparable unexposed flights. The named data are the EU SST reentry catalog (timing, location, and prediction-uncertainty bounds of reentry events), FAA NAS and System Wide Information Management (SWIM) operations data (flight tracks, delay, and reroute records), the four completed IAC-26 down-mass PRISMA systematic reviews (the parameterized evidence base), and the ReentryFlow reentry-to-airspace economic model (which maps a reentry trajectory and its dispersion to an affected-flight set and a cost). The identification strategy and the modern difference-in-differences corrections of Goodman-Bacon, Callaway and Sant'Anna, and de Chaisemartin and D'Haultfoeuille are applied to handle staggered, heterogeneous treatment timing. This dissertation is presented at the design stage. The estimating equations, the variable construction, and the threats to validity are specified in full. Where results appear, they are labeled as expected or illustrative and are not presented as executed estimates on the full dataset. The contribution is the measurable-cost claim and the avoided-cost claim, both stated so that a near-zero, statistically insignificant treatment effect would falsify them. --- ## 1. Introduction and Contribution ### 1.1 The problem The commercial space sector has moved within one decade from an episodic, government-dominated enterprise to a high-frequency commercial one. Reusable launch vehicles reduced the cost of reaching low Earth orbit by roughly a factor of twenty, from approximately 54,500 US dollars per kilogram in the Space Shuttle era to approximately 2,720 US dollars per kilogram with Falcon 9 in 2017 dollars [@jones2018; @kang2025]. The binding constraint on the next stage of growth has shifted downstream of propulsion. For the emerging down-mass sector, defined as the commercial return to Earth of objects mined, refined, or manufactured in space, the value of the returned product depends on delivering it through occupied airspace to a point near its insertion into a terrestrial supply chain. The return leg is therefore integral to the activity rather than incidental to it. The mechanism that converts reentry into an economic problem for aviation is well documented. The FAA manages launch and reentry integration chiefly by closing large, predetermined Aircraft Hazard Areas (AHAs) around the planned trajectory, for windows historically measured in hours [@srivastava2015; @young2017]. Static segregation is conservative by design and has preserved a strong safety record. The cost of that conservatism scales with the size and duration of the withheld volume and with the density of the traffic that would otherwise have used it. At one event per month the cost is small. At one event per week or per day the same policy becomes a structural drag on the NAS. The down-mass case sharpens this because reentry is not launch played in reverse. Reentry trajectories carry greater inherent prediction uncertainty than ascent, disperse hazard over larger geographic areas, and intersect aircraft cruise altitudes differently [@rabu2024; @young2017]. Recent analysis of airspace closures caused by reentering space objects frames these closures explicitly as a growing burden on aviation rather than a rare contingency, and ties their expected growth to the expansion of large constellations and the associated rise in reentry events [@wright2025]. ### 1.2 The gap in the literature Three literatures bear on this problem and none of them closes it. First, the airspace-impact literature quantifies the disruption of launch closures using fast-time simulation and historical traffic. It establishes that international carriers account for on the order of 8 to 10 percent of flights affected by a closure at a major coastal site and that general aviation accounts for roughly one third, with the precise figures sensitive to site, geometry, and timing [@srivastava2015; @tinoco2021]. This literature is largely descriptive and simulation-based; it does not estimate a causal treatment effect of a reentry event on realized flight cost using a credible identification strategy, and it concentrates on launch rather than reentry. Second, the air-transportation economics literature has a mature account of the cost of delay, including the congestion externality that one operator's use of scarce airspace imposes on others [@mayer2003] and the statistical characterization and prediction of delay propagation through the NAS [@rebollo2014]. This literature does not treat space reentry as a source of delay at all. Third, the space-economy literature treats market formation as governed by institutional design and the pricing of externalities rather than by technology readiness alone [@weinzierl2018; @peeters2024; @adilov2026]. It identifies authorization predictability and corridor access as first-order economic variables but does not measure the airspace cost that those variables are meant to manage. The gap is the intersection: no study estimates, with a defensible causal design, the marginal NAS cost imposed by a spacecraft reentry event, and no study estimates the cost that improved reentry prediction plus dynamic airspace management would avoid. This dissertation occupies that intersection. ### 1.3 The falsifiable contribution The contribution is a single, falsifiable, two-part claim, stated as competing hypotheses. **H0 (null):** Spacecraft reentry and down-mass operations impose no measurable cost on the NAS. The average treatment effect of a reentry-driven AHA activation on exposed-flight cost is statistically indistinguishable from zero. **H1 (alternative):** Spacecraft reentry and down-mass operations impose a measurable, positive disruption cost on the NAS in delay, reroute, and closure, and improved reentry prediction combined with dynamic airspace management yields a quantifiable avoided cost that is positive and statistically distinguishable from zero. H1 decomposes into two estimable parameters. The first is the disruption parameter: the average treatment effect of a reentry-driven closure on the cost borne by exposed flights, denoted the per-event NAS cost. The second is the avoided-cost parameter: the difference in that cost between a static-closure regime and a prediction-informed dynamic-closure regime that narrows the hazard volume in time and space as the reentry prediction tightens. A finding that the disruption parameter is near zero and statistically insignificant falsifies the first part. A finding that the avoided-cost parameter is near zero falsifies the second part and would imply that better prediction does not pay. ### 1.4 Why it matters for NASA and JPL This sits in the Space Infrastructure Systems category because the NAS is shared national infrastructure that commercial reentry must use, and because the question is one of system-level capacity and cost rather than vehicle design. NASA Langley and Analytical Mechanics Associates have an active line of work on airspace modeling for reentry authorization, and the joint IAC-26 study that this dissertation draws on argues that government can act as an economic enabler of the down-mass sector by adapting launch-era airspace capabilities to reentry rather than building new infrastructure. A credible estimate of the per-event NAS cost and the avoided cost of better prediction is the evidence base for that enabling role. It tells a regulator how much airspace disruption a given reentry cadence will cause, and it tells a prediction-and-modeling investment such as ReentryFlow what avoided cost it must clear to be worth funding. For JPL and the broader civil-space enterprise, the same estimate sets a defensible price on the externality that reentry imposes on aviation, which is the quantity that any future corridor-authorization or fee regime must reference. --- ## 2. Background and Literature ### 2.1 The airspace-disruption baseline The empirical anchor for the disruption claim is the launch-era record. Srivastava and colleagues assessed the impact of space launch and reentry operations on the NAS using historical traffic patterns and established the method of translating a closure geometry into delay, distance, and fuel-burn estimates for the affected flight population [@srivastava2015]. Tinoco and colleagues extended this with fast-time simulation of commercial space operations, reporting that in a four-dimensional closure analysis at Cape Canaveral international carriers accounted for 9.5 percent of flights impacted by vertical launches and 8.3 percent by horizontal spaceplane landings, while general aviation represented 33 percent of impacted flights [@tinoco2021]. Young projected the effects of future launch and reentry operations on the NAS and is one of the few sources that couples launch and reentry demand explicitly [@young2017]. Robson, Bolic, and Cook examined air traffic management strategies for, and impacts of, space launches from the European side, finding that closure impact varies by aircraft type and flight profile and that the coordination load on air navigation service providers rises with cadence [@robson2024]. Wright and colleagues quantified airspace closures caused by reentering space objects and framed them as a forward-looking and growing burden tied to constellation deorbit rates [@wright2025]. These studies converge on direction and order of magnitude, and they share a limitation. They are descriptive or simulation-based. They report how many flights a closure touches and what the modeled fuel and time penalty is, but they do not estimate the causal effect of a reentry event on realized flight cost against a credible counterfactual. The present design treats that body of work as the source of expected effect sizes and prior parameter ranges, and supplies the identification it lacks. The architectural direction of this literature also matters for the second hypothesis. Hilton and colleagues argue that space operations must be integrated within the existing air traffic management network through advanced communication, navigation, and surveillance capabilities, and that such integration is a precondition for space transport to be technically and commercially viable rather than merely a safety refinement [@hilton2019]. Thangavel and colleagues develop a multi-domain traffic management architecture that brings conventional air traffic management, higher-altitude operations, and space traffic management together with interoperable separation assurance, and that names reentry as a first-class object on equal standing with launch [@thangavel2025]. Weitz and colleagues derive the predicted-trajectory accuracy requirements that let automation decide which flights clear a hazard area before activation, which is the technical content of a dynamic closure [@weitz2024]. Kaltenhaeuser and colleagues test a launch coordination center concept in European airspace and begin to quantify how dynamic handling reduces the closure footprint relative to static policy [@kaltenhaeuser2024]. Read together, these sources establish that the dynamic-closure regime whose avoided cost this dissertation estimates is not hypothetical: its components exist in mature or maturing form for launch, and the open work is their disciplined adaptation to reentry. The avoided-cost parameter is therefore an estimate of the value of completing an adaptation that the field has already begun, not the value of inventing a capability. ### 2.2 The cost of delay and the congestion externality The air-transportation economics literature supplies the cost framework. Mayer and Sinai showed that air traffic delay is in part a congestion externality: when one user consumes scarce airspace or runway capacity, it imposes delay on others that the consuming user does not bear, which is the textbook condition under which a market under-provides the avoidance of disruption [@mayer2003]. Rebollo and Balakrishnan characterized and predicted air traffic delays statistically, documenting how a localized disruption propagates through the network and how delay at one node raises delay downstream [@rebollo2014]. These two results matter for the reentry case in a direct way. A reentry-driven closure is a capacity shock imposed on the NAS by an operator (the space operator) who does not bear the resulting aviation cost. That is exactly the externality structure that Rao's orbital-economy work analyzes for the on-orbit environment, where a launching party imposes collision and debris risk on others without pricing it [@rao2023]. The airspace case is the terrestrial analogue: the reentry operator imposes a delay-and-reroute cost on aviation users, and the absence of a price on that cost is why a measurement of it is a precondition for any corrective instrument. ### 2.3 Institutions, transaction costs, and why the measurement is load-bearing (North) Douglass North's framework distinguishes institutions, the rules of the game, from organizations, the players, and holds that institutions exist to lower the transaction costs of impersonal exchange [@north1990]. Applied here, the authorization regime that governs reentry through shared airspace is an institution. Static, case-by-case AHA closure is a high-transaction-cost rule: every event is adjudicated conservatively, and the cost falls on aviation users who are not party to the authorization decision. A repeatable, prediction-informed, dynamic-closure rule is a lower-transaction-cost institution, but moving to it requires a measured price for the disruption that the current rule imposes, because without that price the regulator cannot weigh a narrower closure's residual risk against its cost saving. Weinzierl's account of the space economy makes the same point at sector scale: the transition from public to commercial space activity is paced by institutional design and the correct pricing of externalities, not by engineering milestones alone [@weinzierl2018]. Peeters and Adilov and Alexander locate market formation in business-model and institutional design, with authorization frameworks and corridor access among the operative levers [@peeters2024; @adilov2026]. North's contribution to this dissertation is the argument that the measurement is not an academic exercise: it is the input that lets the rule change, and the rule change is where the economic value is. ### 2.4 The research-design lens (Angrist and Pischke) The methodological core follows the design-based tradition of Angrist and Pischke: identify a credible source of variation, define treatment and control explicitly, and let the design rather than functional-form assumptions carry the causal claim. The natural experiment here is the reentry event itself. A reentry-driven AHA activation occurs at a time and place set by orbital mechanics and operator scheduling, which is plausibly unrelated to the contemporaneous demand for the affected airspace sector, conditional on controls. Flights exposed to the closed sector during its active window are treated; comparable flights in the same sector at other times, or in adjacent sectors at the same time, are controls. Because reentry events arrive at staggered times across sectors and dates, the appropriate estimator is a staggered difference-in-differences or event-study specification. The recent econometric literature shows that the conventional two-way fixed-effects (TWFE) difference-in-differences estimator is biased under staggered timing and heterogeneous treatment effects, because it forms forbidden comparisons that use already-treated units as controls and can weight some unit-level effects negatively [@goodmanbacon2021; @dechaisemartin2023]. Callaway and Sant'Anna provide an estimator that defines group-time average treatment effects and aggregates them with valid weights, using not-yet-treated or never-treated units as clean controls [@callaway2020]. This dissertation adopts the Callaway and Sant'Anna estimator as the primary specification and reports the de Chaisemartin and D'Haultfoeuille and Goodman-Bacon diagnostics to demonstrate robustness, exactly as the modern frontier requires. ### 2.5 The reentry-prediction surface The avoided-cost claim depends on the fact that reentry prediction uncertainty is large and improvable. The magnitude of a hazard area, and therefore of the disruption, is driven by the uncertainty in the reentry prediction, which is in turn driven by atmospheric density uncertainty, ballistic coefficient, and reentry angle. Atmospheric density uncertainty during disturbed conditions can reach 20 to 30 percent and propagate to along-track position errors approaching 100 kilometers per day [@hayes2024]. Short-term reentry prediction studies document and decompose these uncertainties and show that they tighten as the object approaches reentry [@reentrypred2018]. The implication is that a closure sized to a worst-case envelope fixed hours in advance is much larger than a closure sized to the prediction available minutes before, which is the precise lever that a dynamic, prediction-informed closure regime pulls and that the avoided-cost parameter is designed to measure. --- ## 3. Data ### 3.1 EU SST reentry catalog The EU Space Surveillance and Tracking (EU SST) service issues reentry predictions and post-event records for uncontrolled and controlled reentries of tracked space objects. The dataset supplies, per reentry event, the predicted reentry epoch and its uncertainty window, the predicted ground-track and dispersion footprint, the object identity and physical characteristics where known, and the realized reentry time and location after the event. Access is via the EU SST portal and its reentry-bulletin feed, for which the candidate program holds vault credentials. The unit of analysis at this layer is the reentry event. The catalog defines the treatment: each event with a predicted footprint intersecting U.S. airspace, and each window over which an AHA was or would be active, is a candidate treatment occurrence. The principal variables constructed from it are the event timestamp and window, the footprint polygon, and the prediction-uncertainty bound (the along-track and cross-track dispersion at the time the closure decision is made). Coverage is global for tracked objects above the catalog's size threshold; small or untracked objects are not represented, which biases the sample toward larger, better-characterized reentries. The uncertainty bounds are themselves model outputs and carry their own error, which is recorded and propagated rather than treated as exact. ### 3.2 FAA NAS and SWIM operations data FAA System Wide Information Management (SWIM) feeds and the associated NAS operations archives provide flight-level operational records: filed and flown trajectories, departure and arrival times against schedule, ground and airborne delay, reroute advisories and traffic management initiatives, and the activation records of special-use and hazard airspace. Access is through the SWIM subscription channels and the FAA operations data archives. The unit of analysis at this layer is the flight-segment-by-time-window observation. From these records the outcome variables are constructed: realized delay minutes (relative to schedule and to an undisrupted baseline), added flown distance relative to the great-circle or filed route, modeled fuel burn from the distance and aircraft type, and direct operating cost from standard per-block-hour and per-nautical-mile cost factors by aircraft class. Exposure is constructed by intersecting each flight's filed route and time with the AHA polygons and windows derived from the EU SST layer. Coverage is comprehensive for instrument-flight-rules traffic in U.S.-controlled airspace; general aviation under visual flight rules is under-represented, which is relevant because the launch-era evidence shows general aviation bears a disproportionate share of closure impact [@tinoco2021]. Reroute advisories attribute cause imperfectly, so weather and other concurrent traffic management initiatives are controlled for explicitly and used to test for confounding. ### 3.3 The four IAC-26 down-mass PRISMA reviews The four completed systematic reviews at the IAC-26 down-mass paper repository constitute the parameterized evidence base and are read and cited directly. P1 (business case) supplies the launch-cost trajectory, the constraint-tier structure, and the return-leg economics that make airspace access an economic variable. P2 (airspace integration) supplies the disruption baseline, the affected-flight percentages, the static-to-dynamic management transition, and the transferability assessment of launch-era tools to reentry. P3 (reentry modeling) supplies the trajectory, footprint, and demise tool lineage (ORSAT, DAS, SCARAB, DRAMA, STELA) and the dominant uncertainty drivers. P4 (governance) supplies the authorization-architecture options and the digital-licensing processing-time reductions reported at 67 to 84 percent. Collectively the four reviews carry 502 included references with PRISMA 2020 documentation. Their role in this dissertation is to set prior ranges for the model parameters (affected-flight fractions, per-flight cost penalties, prediction-uncertainty magnitudes, and dynamic-closure efficiency gains) and to bound the plausibility of the estimates. The unit of analysis here is the review-extracted parameter. The limitation is that these are secondary syntheses and several anchor figures are simulation-derived or analogy-derived rather than measured on reentry, which is recorded in the certainty assessment. ### 3.4 ReentryFlow model ReentryFlow is the reentry-to-airspace economic model maintained in the project's GitHub repository, with the concept documented at the MITRE project directory. It ingests a reentry trajectory and dispersion footprint, intersects it with an airspace traffic scenario, and returns the affected-flight set and an associated cost in delay, distance, fuel, and direct operating cost. In this design ReentryFlow plays two roles. First, it is the deterministic mapping from a treatment (a reentry event and its footprint) to a predicted exposed-flight set and a predicted cost, which is the bridge between the EU SST layer and the FAA layer. Second, it is the simulator used to generate the counterfactual dynamic-closure regime: for each real event, ReentryFlow recomputes the closure under a prediction-informed dynamic policy and returns the implied cost, so that the avoided-cost parameter can be formed as the difference between the realized static-closure cost and the simulated dynamic-closure cost. The model is itself an object of validation: its predicted exposed-flight sets are checked against the realized FAA exposure records before its counterfactual outputs are trusted. --- ## 4. Research Design and Identification ### 4.1 Estimator and identification strategy The primary estimator is a staggered difference-in-differences event study following Callaway and Sant'Anna [@callaway2020]. The treatment is a reentry-driven AHA activation over an airspace sector and time window. Units are airspace-sector-by-time-window cells. A cell is treated in the window during which an AHA from a reentry event is active and in the defined event window around it; it is a clean control if it is never treated in the sample or not yet treated at the comparison time. The identification rests on the conditional independence of reentry timing from contemporaneous sector-specific demand shocks. Reentry epochs for uncontrolled objects are set by orbital decay and are not chosen with reference to U.S. airspace demand; controlled reentries are scheduled by operators, and for those the design conditions on schedule and tests sensitivity to excluding them. The maintained assumption is parallel trends: absent the reentry closure, treated and control cells would have followed the same path in the outcome, conditional on covariates. ### 4.2 Specification Let \(Y_{i,t}\) be the outcome (delay minutes, added distance, fuel burn, or direct operating cost) for cell \(i\) in window \(t\). The group-time average treatment effect is \[ ATT(g,t) = E\big[\,Y_{i,t}(g) - Y_{i,t}(0)\ \big|\ G_i = g\,\big], \] where \(G_i = g\) denotes the window in which cell \(i\) first receives a reentry closure and \(Y_{i,t}(0)\) is the never-treated potential outcome. The \(ATT(g,t)\) are estimated against not-yet-treated and never-treated controls and aggregated into an overall average treatment effect on the treated and into an event-study profile by time-since-closure. The disruption parameter is this aggregated effect on the cost outcomes. The avoided-cost parameter is estimated by replacing the realized closure with the ReentryFlow dynamic-closure counterfactual and forming the per-event difference, then averaging; it is also estimated directly where the data contain natural variation in closure tightness across events with differing prediction uncertainty, using prediction uncertainty as a continuous treatment intensity. ### 4.3 Variables The dependent variables are the four cost measures in Section 3.2. The treatment indicator is AHA exposure. The treatment intensity is the prediction-uncertainty bound at closure-decision time from the EU SST layer, which scales the closure size. Covariates include sector traffic density at baseline, time-of-day and day-of-week, season, concurrent weather severity, concurrent non-space traffic management initiatives, aircraft class mix, and the great-circle distance of the affected routes. The aircraft-class mix is retained because the launch-era evidence shows the impact distribution differs sharply between general aviation and international carriers [@tinoco2021]. ### 4.4 Threats to validity **Internal validity.** The chief threat is confounding by concurrent disruptions, principally weather, which also closes or constrains airspace. This is addressed by controlling for weather severity explicitly, by exploiting the fact that reentry timing is independent of weather, and by placebo tests on sectors adjacent to but outside the closure footprint. A second threat is the TWFE bias under staggered, heterogeneous timing; this is addressed by using the Callaway and Sant'Anna estimator and reporting the Goodman-Bacon decomposition and the de Chaisemartin and D'Haultfoeuille robust estimator as diagnostics [@goodmanbacon2021; @callaway2020; @dechaisemartin2023]. A third threat is anticipation: if flights reroute before the closure activates, the pre-period is contaminated; the event-study leads are inspected for pre-trends and anticipation, and the treatment window is widened to capture pre-activation avoidance. **External validity.** Estimates from the current U.S. reentry mix and cadence may not extrapolate to a future high-cadence down-mass regime. This is the local-effect caution of the design-based tradition: the estimate is local to the events and margins observed. The design addresses it by estimating the cost as a function of cadence and prediction uncertainty rather than as a single scalar, so that extrapolation is explicit and conditional rather than assumed. **Construct validity.** The outcome construct is the economic cost of disruption, proxied by delay, distance, fuel, and direct operating cost. These proxies omit passenger time value and schedule-network knock-on effects; the network-propagation result of Rebollo and Balakrishnan is used to bound the omitted downstream cost [@rebollo2014]. The treatment construct, AHA exposure, is measured with error because footprint polygons are model outputs; the EU SST uncertainty bounds are propagated into the exposure measure. **Statistical-conclusion validity.** Cells are spatially and temporally correlated, so inference uses clustering at the sector level and, where event counts are low, the small-sample wild-cluster bootstrap. Power is a genuine concern because the number of U.S.-airspace-intersecting reentry events in the current record is modest; the analysis plan reports the minimum detectable effect at the available sample size and treats an underpowered null as inconclusive rather than as confirmation of H0. --- ## 5. Analysis Plan and Findings This section is a design-stage analysis plan. No estimates below have been executed on the full dataset. Numbers that appear are expected ranges derived from the four PRISMA reviews and the cited launch-era studies, or illustrative values used to demonstrate the procedure. They are labeled accordingly and must not be read as results. ### 5.1 Estimation procedure The procedure has five steps. First, assemble the panel by joining the EU SST event layer to the FAA flight layer through ReentryFlow exposure mapping, producing the sector-by-window cells with treatment indicators, intensities, outcomes, and covariates. Second, validate ReentryFlow by comparing its predicted exposed-flight sets to realized FAA exposure for past events and reporting precision and recall before any counterfactual is trusted. Third, estimate the disruption parameter with the Callaway and Sant'Anna estimator, report the event-study profile and the aggregated average treatment effect on the cost outcomes, and run the Goodman-Bacon and de Chaisemartin and D'Haultfoeuille diagnostics. Fourth, estimate the avoided-cost parameter by differencing realized static-closure cost against the ReentryFlow dynamic-closure counterfactual, and separately by using prediction uncertainty as continuous treatment intensity. Fifth, run the robustness and placebo battery: weather placebos, adjacent-sector placebos, controlled-reentry exclusion, anticipation leads, and alternative cost factors. ### 5.2 Expected findings, labeled as expected The launch-era evidence leads to the expectation, not the result, that the disruption parameter will be positive and statistically distinguishable from zero, with the cost concentrated in a minority of exposed flights and the impact share differing by aircraft class along the lines that Tinoco and colleagues report (international carriers on the order of 8 to 10 percent of affected flights, general aviation near one third) [@tinoco2021; @srivastava2015]. The expected sign is positive because every mechanism in the literature, larger withheld volume, longer window, denser displaced traffic, points the same way [@young2017; @robson2024; @wright2025]. For the avoided-cost parameter, the expectation is also positive, because the closure size scales with prediction uncertainty and that uncertainty falls sharply as the object nears reentry [@hayes2024; @reentrypred2018]. An illustrative calculation, presented only to show the arithmetic of the design and not as an estimate, would proceed as follows. Suppose a static closure withholds a volume sized to a worst-case dispersion envelope fixed several hours in advance, and suppose a prediction-informed dynamic closure withholds a volume sized to the uncertainty available shortly before reentry. If the along-track uncertainty that drives footprint length falls by a stated fraction between those two decision times, the withheld area, and the population of displaced flights, falls in rough proportion. The avoided cost per event is the realized static-closure cost minus the simulated dynamic-closure cost, and the program-level avoided cost is that per-event difference multiplied by the projected reentry cadence over the planning horizon. Every quantity in this calculation is left as a symbol here precisely because the dissertation does not yet have the executed estimates to fill them in. The governance review's reported 67 to 84 percent processing-time reductions from digital-licensing analogues are the order-of-magnitude prior for what process and modeling improvements can achieve, applied here to closure footprint rather than to paperwork. These figures are priors and analogues, not findings. The estimation also produces an event-study profile, which is informative beyond the single aggregated number. The profile plots the treatment effect by time relative to closure activation. A credible result shows effects near zero in the pre-activation leads (no anticipation and no pre-trend), a jump at activation, and a decay back toward zero as displaced traffic recovers. The shape of that decay is itself a finding, because the network-propagation evidence implies that a closure's cost outlives its active window as delay cascades downstream [@rebollo2014]. A profile in which the post-activation effect persists well beyond the closure window would indicate that the within-window cost understates the true cost, and would raise the estimated per-event NAS cost accordingly. A profile in which the leads are non-zero would indicate anticipation, relocating rather than eliminating the effect and requiring the treatment window to be widened. ### 5.3 What an executed result would have to show For H1 to survive, the executed disruption estimate must be positive, of non-trivial magnitude, and statistically distinguishable from zero under the Callaway and Sant'Anna specification and robust to the diagnostics, and the executed avoided-cost estimate must be positive and distinguishable from zero. For H0 to be retained as more than an underpowered non-finding, the disruption estimate must be near zero with a confidence interval tight enough to exclude economically meaningful effects, which requires the minimum-detectable-effect check in Section 4.4 to pass. --- ## 6. Discussion ### 6.1 Implications If the design returns the expected signs, the implications are concrete. A measured per-event NAS cost gives a regulator the quantity it needs to weigh a reentry cadence against its aviation burden, and gives a prediction-and-modeling investment such as ReentryFlow a target avoided cost to clear. It supplies the price on the reentry-to-aviation externality that, in North's terms, lets the authorization institution move from high-transaction-cost case-by-case adjudication to a lower-cost repeatable rule [@north1990], and that, in Rao's terms, is the externality price without which the airspace common is over-consumed [@rao2023; @weinzierl2018]. ### 6.2 Rival explanations The leading rival explanation for any measured cost is that weather or unrelated traffic management initiatives, not the reentry closure, drive the disruption. The design confronts this directly through weather controls, the independence of reentry timing from weather, and adjacent-sector placebos. A second rival is reverse causation, that closures are scheduled into already-disrupted windows; this is implausible for uncontrolled reentries and is tested by excluding controlled reentries. A third rival is that operators fully anticipate and pre-absorb the closure so that no marginal cost appears at activation; the anticipation leads in the event study test for this, and finding pre-activation cost would relocate rather than eliminate the effect. ### 6.3 External validity and what would falsify the contribution The estimate is local to the observed cadence and event mix. The honest external-validity statement is that the cost is reported as a function of cadence and prediction uncertainty, so its extrapolation to a future down-mass regime is conditional and stated, not assumed. The contribution is falsified in its first part by a precisely estimated near-zero disruption effect, and in its second part by a near-zero avoided-cost effect, which would imply that better reentry prediction does not reduce airspace cost and that dynamic closure buys nothing over static closure. Either outcome is a publishable, decision-relevant result, which is the mark of a genuinely falsifiable design rather than a foregone conclusion. --- ## 7. Contribution and Conclusion This dissertation states and operationalizes one falsifiable contribution: spacecraft reentry and down-mass operations impose a measurable disruption cost on the U.S. National Airspace System in delay, reroute, and closure, and improved reentry prediction combined with dynamic airspace management yields a quantifiable avoided cost. The null is that reentry imposes no measurable NAS cost. The design joins the EU SST reentry catalog, FAA NAS and SWIM operations data, the four IAC-26 down-mass PRISMA reviews, and the ReentryFlow model in a staggered difference-in-differences event study, with treatment defined as a reentry-driven Aircraft Hazard Area activation, outcomes defined as flight-level cost, and identification built on the conditional independence of reentry timing and the modern difference-in-differences corrections for staggered, heterogeneous timing. The methodological anchors supply the three pillars: Angrist and Pischke for design-based identification, North for the institutional reason the measurement is load-bearing, and Rao for the externality structure that makes the measured cost a price rather than a curiosity. The work is presented honestly at the design stage. The estimating equations, variables, data construction, and threats to validity are specified in full, and no empirical estimate is claimed on the full dataset. The expected signs are positive on both parameters, but the design is built so that a near-zero, precisely estimated effect would falsify the contribution. The value to NASA, JPL, and the civil-space enterprise is the same whether the effect is large or small: a defensible, design-based number for the cost that reentry imposes on aviation, and for the cost that better prediction avoids, is the evidence base on which any reentry-authorization or corridor-pricing institution must rest. --- ## References 1. Wright, S., et al. "Airspace closures due to reentering space objects." *Scientific Reports*, 2025. DOI: [10.1038/s41598-024-84001-2](https://doi.org/10.1038/s41598-024-84001-2). 2. Srivastava, A., St. Clair, T. J., Zobell, S., and Fulmer, D. "Assessing impact of space launch and reentry operations on the national airspace system (NAS) using historical traffic patterns." *Symposium on Dependable, Autonomic and Secure Computing (DASC)*, 2015. 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