SAI METHODOLOGY

Version 3.0.0 · April 2026 · AstraVeris Research

The AstraVeris Space Accessibility Index (SAI) measures humanity's progress toward making space access a routine part of the global economy. It produces a single composite score from 0 to 100, where 0 represents the pre-commercial era and 100 represents a future where space-derived services are as routine as air travel or internet connectivity.

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Most space industry metrics focus on headline numbers: launches per year, economy size, number of satellites. These capture what is happening but not why it's happening. The SAI tracks the full industrial supply chain that enables space access, from raw material suppliers and component manufacturers through system integrators to end users.

Our thesis: space becomes routine when the supply chain is deep enough that no single company or government is a critical bottleneck. 10 new companies making satellite components matters as much as one new rocket flying — because supply chain depth is what separates a specialist industry from a mature one.

The SAI tracks 17 factors across 6 supply chain tiers. Each factor is classified by causal role:

• Input — factors that cause accessibility to improve
• Throughput — factors that are both cause and effect (the flywheel)
• Output — factors that measure whether accessibility improved

Added in v3.0

Government Space Budgets (Tier 0, Input)

Total annual civil space agency budgets across the five largest agencies: NASA, ESA, CNSA, JAXA, and ISRO. This is the raw upstream fuel — government spending accounts for ~18% of the total space economy but seeds the technology and capability that makes the other 82% possible. Budget allocations precede launches by 3–5 years, making this a leading indicator.

Government Commercial Grants (Tier 3, Input)

Annual value of government grants and contracts specifically awarded to commercial space companies (NASA SBIR/STTR, COTS/CRS/Commercial Crew, ESA ARTES, etc.). Distinct from total budgets because most government spending is internal. The commercial grant portion is what directly catalyzes commercial capability — SpaceX, Rocket Lab, and Blue Origin all trace origins to these programs.

Data Sources for Government Factors

• NASA budgets: Congressional Budget Justifications (reported)
• ESA budgets: ESA Annual Reports (reported, EUR→USD conversion = derived)
• CNSA budgets: Euroconsult/CSIS estimates (estimated — China does not publish detailed space budgets)
• JAXA/ISRO budgets: National budget documents (derived)
• Commercial grants: USAspending.gov API (NASA awards, auto-updated), ESA contract announcements, OECD Space Forum

Five factors capture dimensions of space access not present in the original 16-factor model:

Reusability Rate (Tier 2, Input)

Percentage of orbital launches using previously-flown boosters. The cost revolution driver — reuse enables rapid cadence and dramatically lower marginal cost. SpaceX Falcon 9 drove this from 0% (pre-2017) to ~70% (2025).

Rideshare Missions (Tier 2, Throughput)

Missions carrying payloads from 2+ distinct operators. Rideshare democratizes access for small satellite operators who cannot fill a full launch.

Commercial Crew Missions (Tier 2, Throughput)

Crewed spaceflight events per year, including both launches to orbit and crewed reentry/landing missions. Measures human spaceflight accessibility beyond government-only programs.

Controlled Reentry Missions (Tier 2, Throughput)

Controlled reentry and landing missions per year, including crewed capsule returns, cargo returns, and targeted deorbit operations. Measures return-to-Earth capability critical for human spaceflight, in-space manufacturing, and on-orbit servicing.

Satellite Broadband Subscribers (Tier 4, Output)

Total global satellite broadband subscribers across LEO constellation providers (Starlink, OneWeb, Kuiper). The ultimate end-user adoption metric for space-based connectivity.

Weights are not set by editorial judgment. They are derived empirically from historical data (2010–2025) using two statistical methods:

Method 1: Explained Variance (R²)

For each input/throughput factor, we run a bivariate log-log regression against each output factor. The R² value tells us what fraction of the variance in the output that input explains. We average R² across all outputs to get each factor's explanatory power, then normalize to weights summing to 1.0.

Interpretation: Factors that statistically explain more of the observed growth in space economy, satellites, and nations participating get higher weights.

Method 2: Elasticity

The coefficient from a log-log regression is an elasticity: the percentage change in the output per 1% change in the input. We average the absolute elasticities across all outputs and normalize.

Interpretation: Factors where a small improvement produces a large downstream effect get higher weights.

Combined Weight

The final weight for each factor is the average of Method 1 and Method 2 weights, normalized to sum to 1.0. This balances “how much does this explain” (R²) with “how sensitive is the system to this” (elasticity).

Each factor is scored on a 0–100 scale using logarithmic interpolation between a defined floor (the pre-commercial baseline, roughly 2005–2010 values) and a ceiling (the “Routine” aspiration target).

Log scale is used because most factors grow exponentially. Doubling from 100 → 200 launches should count as the same progress as doubling from 1,000 → 2,000 launches.

For cost factors (where lower is better), the scale is inverted.

Composite Calculation

The composite SAI score is the weighted average of all factor scores:

SAI = Σ (factor_score_i × weight_i) / Σ weight_i

Every data point in the SAI has a named source and a confidence level:

Primary Data Sources

• Space Foundation Space Report (economy size, workforce)
• BryceTech annual reports (launches, satellites, investment)
• SIA State of the Satellite Industry Report (manufacturing revenue)
• SpaceStats Online / Ill-Defined Space (launch counts)
• UCS Satellite Database (active satellites)
• FAA AST (spaceport data, licensing)
• Novaspace Space Economy Report (economy projections)
• StartUs Insights (company counts, workforce)
• BEA Space Economy Statistics (US employment, GDP contribution)
• NASA Congressional Budget Justifications (government budgets)
• ESA Annual Reports (government budgets)
• Euroconsult Government Space Programs (aggregate budgets, CNSA estimates)
• OECD Space Forum (government space budgets by country)
• USAspending.gov (NASA commercial grants and contracts, auto-updated)

1. Small sample size. N=16 (2010–2025). Results will stabilize as the dataset grows. We rerun the analysis annually.
2. Multicollinearity. Input factors are correlated (e.g., more launches correlates with more providers). Individual weights should be interpreted as relative importance, not causal isolation.
3. Launch cost flatness. The Falcon 9 set the market at ~$2,720/kg in 2015 and it hasn't moved. This limits cost's explanatory power in regression. When Starship breaks through $1,000/kg, cost's weight will likely increase significantly.
4. Estimated data points. Some factors (workforce, ground stations) lack clean historical time series. These use interpolated estimates flagged with “estimated” confidence. As data collection improves, these will be replaced with reported values.
5. SpaceX concentration risk. SpaceX dominates multiple factors (launches, satellites, cost). The index may overstate “industry” maturity when much of the progress is attributable to one company.
6. CNSA budget opacity. China does not publish detailed civil space budgets. CNSA figures are estimates from Euroconsult and CSIS, marked with “estimated” confidence. Actual spending may be 20–50% higher when dual-use military programs are included.
7. Government grant attribution. Not all government contracts are “grants to commercial space.” Defense contracts, facility operations, and civil servant salaries are excluded from the commercial grants factor but included in total budgets. The boundary is imprecise for programs like SDA (defense but commercially procured).

The full regression results, confidence levels, and weight derivation are available in the published weights.json audit file. Anyone can reproduce the analysis by running the code against the published dataset.

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