Given 8 billion people distributed across the Earth's surface, what arrangement of sovereign structures maximizes aggregate welfare subject to the constraint that every population unit meets a minimum dignity threshold? The Dignity Floor Index (DFI) measures whether populations meet minimum thresholds across seven dimensions — health systems, political voice, housing, education, income, environmental safety, and social connection — not just income alone. Where the World Bank counts 3.5B people below $6.85/day, the DFI captures populations that income measures miss: people above the poverty line who lack adequate governance, health infrastructure, or environmental safety. Baseline figures reflect 2024 data; archetype scenarios model structural outcomes if implemented, not time-bound projections. Climate trajectories (216M internal climate migrants by 2050, per World Bank Groundswell) are modeled separately and suggest the baseline worsens without structural intervention.
| Rank | Country | DFI Score | Status | Binding Constraint | Confidence | Population |
|---|
Each archetype represents a distinct approach to reorganizing sovereign structures to maximize dignity floor compliance, ranging from minimal disruption to full structural transformation.
Explore the trade-off space between archetype configurations. Each slider blends between competing priorities, producing interpolated metrics from the five structural archetypes.
After governance propagation, what genuinely remains intractable? This tab shows the real remaining wall — which countries are stuck due to structural barriers vs. which were only stuck because the model ignored governance restructuring. Speculative extrapolations are clearly labeled.
Color intensity = gap severity under Full Optimization. Dark = no gap. Red = large remaining gap.
Per-entity dignity audit: dimension breakdown, binding constraints, ranked intervention options, and residual human capital exposure.
What would unprecedented global cooperation actually require? Not the aspiration — the org chart, the money, the timeline, and the honest feasibility of each component. Every claim below has either a citation, a calculation, or a stated assumption.
| Source | Mechanism | Optimistic | Pessimistic | Precedent | Feasibility |
|---|---|---|---|---|---|
| Carbon Tax | $75/ton on 50% of global emissions (36.8 Gt) | $1.38T/yr | $0.69T/yr | EU ETS avg $92/ton (2023) | MODERATE |
| Wealth Tax | Graduated 1–3% on wealth above $1M (US+EU+JP+AU+CA+KR) | $1.70T/yr | $0.85T/yr | France ISF (1982–2017): capital flight of €200B over 15yr | LOW |
| FTT | 0.1% equities + 0.01% FX + 0.005% derivatives | $0.35T/yr | $0.15T/yr | EU-11 FTT proposed 2013, still not implemented | LOW |
| Fossil Fuel Subsidy Reallocation | 50% of explicit subsidies ($1.3T/yr) over 10yr phase | $0.65T/yr | $0.30T/yr | Indonesia redirected $15.6B (2015); IMF total: $7T/yr incl. implicit | MODERATE |
| Sovereign Wealth Transfer | 1% of GDP from nations w/ GDP/cap >$30K (37 nations, ~$60T GDP) | $0.60T/yr | $0.30T/yr | Current ODA: $211B/yr (0.33% of donor GNI). Only 5 nations meet 0.7% UN target | LOW |
| TOTAL | $4.68T/yr | $2.29T/yr |
| Precedent | Cost (% GDP) | Nations | Compliance | Why It’s Inadequate |
|---|---|---|---|---|
| Montreal Protocol (1987) | 0.003% | 198 | 99% | Cost fell on 20 firms, not 8B people. Chemical substitutes existed. Science had short lag. |
| Paris Agreement (2015) | ~0.3% (pledged) | 196 | 30–40% | Non-binding by design. No enforcement. Global emissions rose 1.1% in 2023. |
| EU Acquis (1957–present) | ~1.0% of EU GDP | 27 | ~95% | GDP/cap ratio 10:1 (Bulgaria–Luxembourg). Global ratio: 450:1 (Burundi–Luxembourg). 70 years to build. |
| Marshall Plan (1948–52) | 2.5% of US GDP | 16 | High | Rebuilt ALREADY-INDUSTRIALIZED nations with EXISTING governance. 4 years, not 30. |
| PEPFAR (2003–present) | 0.01% of US GDP | 60 | High | Single-disease intervention. 20.1M on treatment. Cannot scale to all 7 DFI dimensions. |
| Required (Dignity Floor) | 4.5% | 150+ | 70–80% | 10–70× larger than any precedent. Sustained 30 years. Requires adversarial great powers to cooperate. |
Across our 16 archetypes, the dimension that most frequently prevents countries from meeting the dignity floor is political participation — which depends on electoral democracy, civil liberties, rule of law, and corruption control. These are not purchasable.
| Component | Technical | Institutional | Political | Timeline | Net Assessment |
|---|---|---|---|---|---|
| Carbon tax ($75/ton, coalition) | HIGH | HIGH | MOD | MOD | Achievable with coalition |
| Wealth tax (coordinated) | HIGH | LOW | LOW | LOW | Very unlikely at scale |
| Financial transaction tax | HIGH | MOD | LOW | MOD | Modest revenue likely |
| Subsidy reallocation | HIGH | MOD | LOW | MOD | Partial reallocation plausible |
| Global Dignity Fund | HIGH | MOD | LOW | MOD | Pilot likely; full uncertain |
| UN Charter reform | N/A | HIGH | V.LOW | LOW | Not achievable |
| Governance reform (57 countries) | HIGH | MOD | V.LOW | LOW | The binding constraint |
| Health worker training | HIGH | MOD | HIGH | MOD | Achievable but slow (8yr pipeline) |
| Climate adaptation infra | HIGH | MOD | MOD | MOD | Achievable with funding |
Could a technological breakthrough change the trajectory? We assess 15 technologies against the structural finding: the binding constraint on the DFI is political, not technical. A technology that requires functioning institutions to deploy cannot help populations living under failed governance — regardless of how transformative the technology is.
| Technology | TRL | At Scale | What It Solves | What It Doesn’t Solve | DFI Impact |
|---|---|---|---|---|---|
| Energy | |||||
| Nuclear Fusion | 5–6 | 2035–2040 | Unlimited clean baseload. CFS SPARC targeting Q>1 in 2027. Helion claims 2028 grid power for Microsoft. | Arrives after critical 2025–2035 window. Requires institutional capacity to build and operate. ITER delayed to 2039. | NEGLIGIBLE |
| SMRs (TerraPower, NuScale) | 6–7 | 2031–2040 | Carbon-free baseload in OECD. TerraPower Natrium on grid by 2031. | NuScale costs doubled to $89/MWh. Requires nuclear regulatory capacity. HALEU fuel supply dependent on Russia. Below-floor countries can’t deploy. | MODEST |
| Enhanced Geothermal (Fervo, Quaise) | 7 | 2028–2035 | Baseload anywhere on Earth. Reuses oil/gas drilling expertise. Fervo Cape Station: 500 MW by 2028. 70% drilling time reduction year-over-year. | Quaise deep drilling (millimeter wave) still at 100m — commercial needs 10+ km. Induced seismicity risk. Scaling to hundreds of GW takes decades. | SIGNIFICANT if deep drilling works |
| Grid Storage (Li-ion, Na-ion, Fe-air) | 7–8 | NOW–2028 | Enables solar+storage as baseload. Li-ion at $70/kWh. Form Energy iron-air at $20/kWh for 100-hour storage. Na-ion at ~$40/kWh (CATL). | Manufacturing/supply chain concentrated in China. Doesn’t exist in most vulnerable nations. Battery materials have their own extraction footprint. | SIGNIFICANT enabler |
| Climate Intervention | |||||
| Stratospheric Aerosol Injection | 3–4 | 2–5 yrs | Only tech that reduces global temp by 1–2°C within 1–2 years. $2–8B/yr. Fleet of ~100 modified tanker aircraft. | Ocean acidification untouched. Monsoon disruption in South Asia. Termination shock: if stopped, temps snap back at 5–10× rate. No governance framework. ~30 nations could deploy unilaterally. Harvard SCoPEx cancelled 2024. | HIGH on temp / DESTABILIZING on governance |
| Direct Air Capture (Climeworks, Oxy) | 7 | Gt-scale: 2045+ | CO2 removal from atmosphere. Climeworks Mammoth: 36K tons/yr. Oxy Stratos: 500K tons/yr. | $1,000–1,300/ton. Current global capacity is ~0.04 Mt/yr vs. 40 Gt/yr emissions — 1,000,000× too small. Energy-intensive. Gt-scale requires trillions. | NEGLIGIBLE in timeframe |
| Marine Cloud Brightening | 3–4 | 2030s | Localized cooling. Potential reef/Arctic protection. Less commitment risk than SAI. | Not scalable to global temp reduction. San Francisco field test cancelled. Regional precipitation effects poorly understood. | MINIMAL |
| Food & Water | |||||
| Solar Desalination | 9 | NOW | Directly addresses water stress for 1.96B people. $0.50–1.50/m³ with solar PV at <$0.03/kWh. 60% cost reduction in 10 years. | Only coastal/brackish. Brine disposal damages marine ecology. Capital-intensive ($100M–$2B/plant). Distribution requires governance. | SIGNIFICANT for coastal pop. |
| CRISPR Heat/Drought Crops | 5–7 | 2030–2040 | Directly helps below-floor populations. Heat-tolerant staples could maintain yields where our model predicts agricultural collapse. | Regulatory fragmentation (US permissive, EU restrictive). Seed distribution requires agricultural extension services. Off-target genetic effects. | SIGNIFICANT for food dim. |
| Precision Fermentation | 7–8 | 2030–2035 | Protein without land. 90% land reduction. Price parity projected 2027–2029 for some products. $5.8B → $151B market by 2034. | Produces protein, not the cheap calories (rice, wheat, maize) that 2B people need. 10× cost gap vs. conventional. Energy-intensive bioreactors. | MODERATE (rich world) |
| Vertical Farming | 8 | — | Microgreens and herbs in urban areas. | Industry collapsed. Plenty ($2.3B valuation) bankrupt March 2025. Bowery ($2.3B) shut down fall 2024. Physics makes caloric staples prohibitive — LED photosynthesis vastly less efficient than sunlight. | ZERO |
| Governance & Logistics | |||||
| Digital Identity (Aadhaar model) | 8–9 | NOW | Bypasses corrupt intermediaries. 1.31B enrolled in India. Saved $39B. ~$1/enrollment. 850M globally still lack ID. | Requires minimum institutional capacity. Surveillance/weaponization risk under authoritarian regimes. Doesn’t create economic opportunity. | SIGNIFICANT enabler |
| Satellite Monitoring (Planet, Maxar) | 8–9 | NOW | Near-real-time compliance verification. Deforestation >1ha auto-detected. 200+ satellites, 100M+ km²/day. | Detection is not enforcement. Cannot observe governance quality or indoor conditions. 30% non-compliance is a political will problem, not an information problem. | MODERATE (infra layer) |
| AI Resource Allocation | 6–7 | 2025–2030 | Supply chain optimization. Predictive healthcare. Administrative efficiency. | 70% of US hospital AI pilots failed (weak endpoints, workflow misalignment, data gaps). If it fails in American hospitals, what about South Sudan? Requires data infra that doesn’t exist. | MODERATE (OECD only) |
SAI deserves special attention because it is the only technology that operates on the right timescale and cost. At $2–8B/year (roughly the cost of a single aircraft carrier), a fleet of ~100 modified tanker aircraft could reduce global temperature by 1–2°C within 1–2 years. No other technology comes close to this cost-effectiveness ratio on temperature.
Buys 20–30 years of time for every other solution to deploy. Directly reduces heat stress, slows Arctic ice loss, preserves some agricultural yields. The atmospheric science is well-understood (volcanic eruptions are natural analogs — Pinatubo 1991 cooled the planet 0.5°C for 2 years). Deployable within 2–5 years of a political decision.
Termination shock: If stopped abruptly, temperatures snap back at 5–10× the rate of gradual warming. Once started, it essentially cannot be stopped. Monsoon disruption: Models show reduced rainfall in South Asia and altered Sahel precipitation — potentially harming the very populations most at risk. No governance: ~30 nations could deploy unilaterally. There is no treaty, no framework, no agreement on who controls the thermostat. Harvard’s SCoPEx field test was cancelled in 2024 under pressure from civil society and Indigenous groups.
If unprecedented cooperation is impossible and all human life has equal value, the rational response is planned mass migration — moving people out of regions where the DFI will collapse. This tab lifts the immobility constraint and asks: what does Plan B actually look like?
Climate risks are multiplicative, not additive. Bangladesh does not face flooding OR heat OR agricultural collapse — it faces all three simultaneously. Countries with 3+ overlapping climate risks require departure by 2040.
| Country | Population | Current DFI | Heat | Sea Level | Water | Agriculture | Compound Score |
|---|---|---|---|---|---|---|---|
| Bangladesh | 174M | 0.498 | ● | ● | ● | ● | 0.92 |
| Pakistan | 247M | 0.390 | ● | ● | ● | ● | 0.90 |
| India (north) | ~500M | 0.547 | ● | ● | ● | ● | 0.89 |
| Yemen | 39M | 0.199 | ● | ● | ● | ● | 0.95 |
| Sudan | 50M | 0.174 | ● | ○ | ● | ● | 0.88 |
| Egypt | 114M | 0.567 | ● | ● | ● | ● | 0.85 |
| Iraq | 45M | 0.248 | ● | ○ | ● | ● | 0.82 |
| Niger | 26M | 0.207 | ● | ○ | ● | ● | 0.80 |
| Chad | 19M | 0.222 | ● | ○ | ● | ● | 0.82 |
| Somalia | 18M | 0.198 | ● | ● | ● | ● | 0.88 |
| Vietnam | 98M | 0.682 | ● | ● | ● | ● | 0.78 |
● Severe ● Moderate ○ Low/None. Sources: IPCC AR6 WG2, CMIP6 wet-bulb projections, ND-GAIN, World Bank Groundswell.
Ranked by composite absorption score: physical capacity (land, water, climate stability), governance quality (DFI), infrastructure, and economic integration potential. The binding constraint is never physical — it is political.
| Rank | Country | Absorption Score | Max Absorption | Key Constraint |
|---|---|---|---|---|
| 1 | Canada | 0.92 | 40–80M | Infrastructure in north nonexistent; cold climate |
| 2 | United States | 0.88 | 80–150M | Political will; nativist backlash |
| 3 | Germany | 0.87 | 15–25M | Population density; existing housing crisis |
| 4 | France | 0.86 | 15–25M | Political backlash threshold |
| 5 | United Kingdom | 0.85 | 15–25M | Island geography; housing shortage |
| 6 | Australia | 0.84 | 15–30M | Water constraint; distance |
| 7 | Sweden | 0.83 | 5–10M | Small economy; cold |
| 8 | Norway | 0.82 | 3–5M | Very small capacity |
| 9 | New Zealand | 0.81 | 3–5M | Remote; small |
| 10 | Brazil | 0.72 | 30–60M | Governance gaps; Amazon constraint |
| 11 | Argentina | 0.68 | 15–30M | Economic instability |
| 12 | Russia | 0.55 | 50–100M | Governance; political barriers |
| TOTAL | 286–545M |
| Scenario | People | Duration | Rate/Year | Cost (mid est.) | % of Global GDP/yr |
|---|---|---|---|---|---|
| Conservative | 400M | 25 years | 16M/yr | $32T total | 1.3% |
| Moderate | 1B | 30 years | 33M/yr | $81T total | 2.7% |
| Comprehensive | 2B | 35 years | 57M/yr | $162T total | 4.6% |
The moderate scenario requires sustaining a migration rate 5–8× the peak of WWII displacement for thirty consecutive years. For context: current global migration stock is ~280M total (including voluntary). The largest single-year refugee crisis was WWII at ~10M/year. Cost per person relocated: ~$81K (transport, housing construction, infrastructure, integration, healthcare transition). At 1B people that’s $81T — roughly $2.7T/year, equivalent to global military spending.
| Event | People | Duration | Rate/yr | Deaths | Lesson |
|---|---|---|---|---|---|
| Partition of India (1947) | 10–20M | ~6 months | ~40M/yr | 1–2M | Unplanned mass migration kills at scale. Trains arrived full of corpses. |
| Post-WWII Europe | 60M | 12 years | 5M/yr | — | Required Marshall Plan (2.5% US GDP). Took decades. Many never returned. |
| Syrian Crisis (2011–) | 13M | 10 years | 1.3M/yr | ~500K | 2M refugees triggered Europe’s far-right surge. AfD: 4.7% → 20.8%. Brexit. |
| Bangladesh Internal | 400K/yr | Ongoing | 400K/yr | — | Dhaka collapsing under weight. 40% in slums. Destination becomes next crisis. |
| US Dust Bowl (1930s) | 2.5M | 10 years | 250K/yr | — | Same nationality, language, culture. Still met with hostility and discrimination. |
| Planned Relocation (moderate) | 1B | 30 years | 33M/yr | — | 5–8× WWII rate sustained for 3 decades. No historical analog. |
No legal framework. The 1951 Refugee Convention doesn’t cover climate. No nation is obligated to accept climate migrants. The Global Compact on Migration (2018) is non-binding.
Nativism scales with numbers. Europe’s far-right surge was triggered by ~2M refugees (0.4% of EU population). At 33M/year, receiving countries absorb 5–20% of their population per decade. Every democracy that has faced immigration at 5%+/year has produced authoritarian backlash.
The colonial dimension. Most departure zones were impoverished by colonialism from the very nations that would need to receive migrants. This is simultaneously morally justified (climate debt + colonial debt) and politically explosive.
Aging nations need people. Japan loses 840K/year. Germany, Italy, South Korea, Spain all shrinking. Combined demographic deficit: ~1.16M/year and accelerating.
Departure zones are young. Median age in Sahel: ~15. In South Asia: ~28. In Japan: ~49. The matching algorithm writes itself — young workers to aging economies.
But the scale doesn’t match. Aging nations need 2–3M/year. The relocation demand is 33–57M/year. The demographic dividend absorbs <10% of the need.
| Source | Estimate | Year | Horizon | What It Counts |
|---|---|---|---|---|
| WHO | 250K/yr | 2014 | 2030–2050 | 4 direct pathways only: heat, malaria, diarrhoea, undernutrition |
| Lancet Countdown | ~700K/yr | 2025 | Observed (2024) | Heat deaths + wildfire PM2.5 — already 3× WHO’s projection from just two pathways |
| GBD / IHME | 8.1M/yr | 2024 | Baseline (2021) | Air pollution alone — now the 2nd leading risk factor for death globally |
| Zhao et al. / MCC | 5.0M/yr | 2021 | Baseline (2000–19) | All non-optimal temperature: 4.6M cold + 489K heat = 9.4% of all deaths |
| This Model (baseline) | 2025 | Projected | 4 mortality-relevant dimensions: material security, health access, environment, housing | |
| WEF / Oliver Wyman | 14.5M total | 2024 | By 2050 | 6 climate event categories at 2.5–2.9°C trajectory |
| Bressler (Columbia) | 83M total | 2021 | 2020–2100 | Heat-related mortality only, business-as-usual (4.1°C) |
| Climate Impact Lab | +73 per 100K | 2022 | By 2100 | Temperature-mortality only — equal to current rate from all infectious disease |
| 1,000-Ton Rule Synthesis | ~1B total | 2023 | Next 100–200 yrs | All pathways. Convergent estimate from economics, philosophy, climate science |
| This Model (collapse) | 2025 | Projected | Full state failure scenario — our worst-case archetype | |
| This Model (floor) | 2025 | Projected | Structural minimum — even perfect global cooperation cannot eliminate |
WHO’s widely-cited 250,000 deaths/year projection covers only four direct pathways: heat stress, malaria, diarrhoea, and child undernutrition. It excludes air pollution (8.1M/yr at baseline), flooding, drought, displacement, conflict, cardiovascular disease exacerbation, ecosystem collapse, and all cascade effects. WHO itself describes the figure as conservative.
The Lancet Countdown’s observed 2024 data — not a projection — already shows ~700,000 annual deaths from heat and wildfire smoke alone, nearly 3× what WHO projected for all four pathways combined. Twelve of twenty health-threat indicators reached record levels in 2025. There is no observed inflection point.
Our mortality model uses a convex curve (deficit1.5) across four dimensions that kill people: material security, health access, environmental safety, and housing adequacy. Three DFI dimensions — political participation, education, and social connection — matter for dignity but have weak direct mortality links.
Our baseline archetypes produce ~12.7M excess deaths/year. This is consistent with the GBD’s 8.1M from air pollution alone plus additional mortality from malnutrition, preventable disease, and inadequate sanitation that our material, health, and housing dimensions capture. It is more pessimistic than WHO (which undercounts) but less extreme than the broadest estimates (which project over longer horizons and include indirect cascades our model doesn’t attempt).
Our structural floor of exists because meeting DFI minimum-dignity thresholds (0.35–0.40 per dimension) does not eliminate excess mortality — the “safe level” above which a dimension stops contributing to death is higher (0.60–0.70). Minimum dignity and minimum safety are not the same thing.
Proprietary satellite + mobility data — Cell phone location data for 5B+ people, tracking migration patterns in real time at district level. We model departure zones at country level; they see it at village level, month by month.
Reinsurance catastrophe models — Swiss Re, Munich Re, and Lloyd’s have the largest proprietary catastrophe databases on Earth, combining satellite data, machine learning, and decades of claims history. Insured losses reached $107B in 2025. These firms are actively declaring certain regions “uninsurable” — a stronger statement than any public climate model makes. When an insurer says a region is uninsurable, they are saying their proprietary models show expected losses exceed any premium the market will bear.
Hedge fund climate intelligence — Citadel hired PhD meteorologists and built in-house weather forecasting that earned $16B in 2022. Bridgewater has built “a top-down understanding of the net zero transition.” These firms treat climate risk as a tradable information asymmetry. They are not building models that show a more optimistic picture — they are building models that show where the damage hits first and positioning accordingly.
Military threat assessments — The US DoD’s Climate Risk Analysis labels climate change a “threat multiplier.” The classified versions almost certainly contain specific timeline estimates for state failure in several of the 57 countries our model identifies, with higher-resolution migration flow predictions combining satellite and cell phone data for billions of people.
Almost certainly not. Proprietary data adds precision (which district, which month, which supply chain node), but every entity with access to this data is behaving as if the picture is as bad or worse than our model shows. Reinsurers are pulling coverage. Hedge funds are building private climate intelligence. Military doctrine is shifting. Wealthy individuals are building bunkers. The revealed preferences of the best-informed actors align with our model.
There is one documented case where proprietary data led public models: the insurance industry’s catastrophe models began signaling the severity of secondary perils (severe convective storms, wildfire, flood) 5–10 years before public climate models adequately captured them. The 2017–2023 period of “unexpected” insurance losses was not unexpected to the reinsurers. This suggests our model’s mortality estimates may be conservative for insurance-linked dimensions.
The one area where proprietary data might diverge: adaptation effectiveness for wealthy populations. Firms and wealthy nations may have data showing adaptation works better than public models suggest for the populations they serve. This makes the wealthy-world experience better but says nothing about the 3.8B below the dignity floor. If anything, it widens the gap.