Version history
| v3.7.0 | 18 May 2026 | Optimistic-scenario redesign — two items deferred from v3.6.1: (A) opt arrays recomputed via opt = base + α×(ceil−base) for all 17 technologies (α = 0.75–0.85 by tech class; deep-tech 0.85, land-ag/CDR-sustain 0.75, others 0.80); replaces prior opt=ceil (v3.6.0 global cap) which collapsed scenario variance. (B) Bilateral land/ag cluster de-duplication: DEDUP_BILATERAL=10% applied within {food_system, methane_agri, reforest_nbs, soil_carbon} before portfolio DEDUP factor (Poore & Nemček 2018; IPCC AR6 WG3 §7.4.5). Net effect: optimistic post-dedup 51.6→47.6 Gt; surplus 4.6→0.6 Gt (tighter, epistemically sounder bound). Base scenario unchanged: 39.4 Gt; gap 7.6 Gt; 84% coverage. Pessimistic gap unchanged: ~22 Gt. |
| v3.6.1 | 17 May 2026 | Post-audit patch (Gemini v3.6.0 validator findings): (1) changelog arithmetic corrected — optimistic post-dedup portfolio was mis-stated as 52.4 Gt; correct value is 51.6 Gt (66.20 × 0.78 = 51.64; surplus 4.6 Gt not ~5 Gt); (2) perovskite pes[6] corrected 6.5→5.0 Gt to remove unrealistic +3.3 Gt pessimistic surge 2050→2060 following v3.6.0 pes[5] downward correction; (3) reforest_nbs pes[4] corrected 3.5→2.8 Gt to remove non-monotonic flat spot (pes[4]=pes[5]=3.5 artefact from v3.6.0 pes[5] calibration); (4) methane_agri pes[4] corrected 2.5→2.1 Gt to smooth near-flat plateau (2045→2050 increment was +0.1 Gt, creating implausible stagnation). Base scenario unchanged: 39.4 Gt; gap 7.6 Gt; 84% coverage. Optimistic 51.6 Gt; pessimistic gap 22.1 Gt. Validator HIGH finding (opt=ceil scenario collapse) and MEDIUM finding (bilateral DEDUP matrix) deferred to v3.7.0 design review. |
| v3.6.0 | 17 May 2026 | Full opt-scenario structural overhaul: 83 opt>ceil violations corrected across 13 of 17 technologies by setting opt[i]=ceil[i] globally (BLOCKER B-1; prior unconstrained opt arrays were masked by stackedTotalCapped() in the UI but produced invalid data in the raw array and any CSV/API consumer). Key cases: enhanced_weathering prior opt[5]=3.5 Gt was 2× Beerling (2020) upper bound; soil_carbon prior opt[5]=6.5 Gt was 2× Minasny (2017) physical maximum; reforest_nbs prior opt[5]=13.0 Gt exceeded Griscom (2017) total NCS physical maximum; food_system prior opt[5]=9.5 Gt exceeded Clark (2020) governance-constrained range. Fusion opt[1] non-monotonicity artefact corrected (0.02→0). Pessimistic scenario recalibrated for 3 technologies with pes/base ratios >90% (perovskite pes[5] 4.5→3.2 Gt; methane_agri pes[5] 3.5→2.6 Gt; reforest_nbs pes[5] 5.0→3.5 Gt). Base scenario unchanged: 39.4 Gt post-dedup; 7.6 Gt gap; 84% coverage. Optimistic scenario revised: 51.6 Gt post-dedup (was 67.1 Gt unconstrained; ceiling-constrained surplus ~4.6 Gt). [Note: changelog originally mis-stated 52.4 Gt; corrected in v3.6.1.] Pessimistic scenario gap: 22.0 Gt (was 19.2 Gt). DEDUP_FACTOR finding 12 in validator report was a false positive: 0.22 confirmed live since v3.4.0. |
| v3.5.0 | 17 May 2026 | Post-audit structural corrections: (1) perovskite base recalibrated 7.0→5.0 Gt/yr by 2050 (TRL 6–7 constraints; 25yr stability undemonstrated; prior 7.0 Gt moved to optimistic scenario); (2) DAC opt scenario values capped at feasibility ceiling (ceil[5]=1.5 Gt; prior opt[5]=4.0 Gt violated infrastructure/energy constraints); (3) Ocean Iron Fertilisation opt scenario values capped at governance ceiling (ceil[5]=0.1 Gt; prior opt[5]=1.0 Gt violated London Protocol 2013 framework); (4) stale Sector Deep Dive and abatement-cost table references corrected (Fusion 4.0→1.3 Gt, Perovskite 7.0→5.0 Gt). Net effect: pre-dedup portfolio 52.6→50.6 Gt; post-dedup 41.0→39.4 Gt; coverage 87%→84%; breakthrough gap (2050 base) 6→8 Gt. CE Emerging Technology Library v3.2.0 (unchanged). |
| v3.4.0 | 18 May 2026 | Critical-review recalibration: (1) base values capped at feasibility ceilings for 5 techs (fusion, DAC, BECCS, enhanced rock weathering, ocean iron); (2) London Protocol governance haircut applied to ocean iron fertilisation (base ~70% reduction); (3) NBS base recalibrated 8.5→5.5 Gt/yr by 2050 (Cook-Patton et al. 2020 revised NCS); (4) methane_agri base recalibrated 5.5→3.8 Gt/yr by 2050 (IPCC AR6 WG3 Ch.7 subsector reconciliation); (5) soil_carbon ceiling corrected to 3.2 Gt/yr by 2050 (Minasny 2017 4‰ maximum); (6) δ raised 0.15→0.22 to correct for livestock/land/food double-counting. Net effect: pre-dedup portfolio 62.6→52.6 Gt; post-dedup 53.2→41.0 Gt; coverage 96.2%→87%; breakthrough gap (2050 base) 0→6 Gt; CE Emerging Technology Library v3.1.0→v3.2.0. |
| v3.3.0 | 17 May 2026 | Phase B natural carbon sinks and food systems: added 3 TECHS_ABATE entries (Nature-Based CDR Reforestation & Restoration; Soil Carbon & Biochar; Food System Transformation). Portfolio expanded to 17 tracked technologies. Combined Phase B addition ~16.5 GtCO₂e/yr (base 2050); base-scenario full-stack now exceeds 47 Gt breakthrough requirement in aggregate (53.2 Gt post-dedup). Coverage: 82.8% → 96.2%. Breakthrough gap (feasibility-unconstrained base 2050): 8.1 Gt → 0 Gt. Provenance table updated to 17 entries. CE Emerging Technology Library v3.0.0 → v3.1.0. |
| v3.2.0 | 17 May 2026 | Phase A scope correction: 2 non-CO₂ GHG wedges added to TECHS_ABATE (Methane Abatement Agriculture & Waste; Refrigerant Transition HFCs). Prior 12-technology library tracked primarily CO₂ from energy/industry while the 57 GtCO₂e baseline includes all gases. Combined addition: ~8.3 GtCO₂e/yr (base 2050). Breakthrough gap (base 2050): 15.2 Gt → 8.1 Gt. Technology coverage: 67.7% → 82.8%. Provenance table updated to 14 entries; Emerging Technology Library versioned to v3.0.0. Methodology boxes updated (N, sums, coverage %, confidence). Scope boundary note added to provenance table. |
| v3.1.0 | 15 May 2026 | Sprint 5 Institutional Interpretation Layer: Infrastructure Dependency Overlay (9-dimension risk matrix for 12 technologies; primary bottleneck display; critical-path blocker callout); Jurisdictional Context Mode (13 regions; tech viability per region; regional constraint strip; DAC grid penalty calculation); Uncertainty Fan Chart (P10/P50/P90 gap trajectory; budget exhaustion year range; 6-parameter tornado chart); Governance Maturity Display (per-constant freshness traffic-lights; review queue; expert-judgment exposure summary); four new Flask API endpoints (/api/infrastructure-dependencies, /api/jurisdictional-context, /api/sensitivity-data, /api/source-freshness); governance_metadata_schema.json + dependency_taxonomy.json + regional_constraint_profiles.json data layers; institutional positioning refresh docs; assumptions.json version bump to 3.0.0 |
| v3.0.0 | 15 May 2026 | Institutional View toggle (confidence badges + source timestamps + scope notes on every chart panel); Assumptions JSON endpoint (/models/ce-solution-scale/assumptions.json, 10 constants with full lineage); scientific precision corrections (CCS ceiling reframed as operational injection rate, not physical volume ceiling; committed emissions adds Tong et al. 2019 as primary citation; BECCS biomass 3.5–5.5 EJ/yr labelled as conservative no-regrets floor, not flat ceiling; carbon budget provenance adds GCB 2024 cross-reference); platform repositioned as transparent transition diagnostic (‘not a predictive IAM’ framing explicit in hero and model card) |
| v2.9.0 | 15 May 2026 | CO₂ storage & feedstock bottleneck section (geological storage demand vs IPCC 8–10 Gt/yr ceiling; feedstock sustainability limits for biomass, clean electricity, rock dust, critical minerals); Key jurisdictions column on impact bridge table (12 techs); budget exhaustion methodology clarified (1.5°C ≈2029, 2°C ≈2046; step-wise integration explained) |
| v2.8.0 | 14 May 2026 | Institutional Impact Bridge table (12 techs × 11 institutional columns: capex, clean-power demand, land/material, permitting, CO₂ storage, sovereign fiscal, grid/utility, stranded asset, insurer signal, bank PD/LGD); scenario probability reframed as user-set decision weights per NGFS 2025 guidance; capped/uncapped scope clarification in Technology Provenance |
| v2.7.0 | 14 May 2026 | Sensitivity tornado chart (6-parameter B₂₀₅₀ impact ranking); EROI-adjusted abatement panel (grid carbon intensity penalty for DAC, BECCS, Enhanced Weathering); Investment gap table (10 techs, current vs required $B/yr, 2035 multipliers); Carbon budget delay cost calculator (5yr and 10yr slippage, ranked); Technology cliff dates (12 techs, go/no-go decision windows); IPCC C1–C7 scenario band mapping (dynamic residual vs pathway categories) |
| v2.6.0 | 14 May 2026 | EROI column in provenance table (12 techs); Model Assumptions Registry (10-row constants table); Monte Carlo technology co-variance (3-factor model, ρelec≈0.21 / ρCDR≈0.25); Geographic Resource & State Capacity Cross-Link panel (10 technologies, DRC cobalt flagged); Policy Effectiveness Validation Backtest (7 policies, 2020–2025) |
| v2.5.0 | 14 May 2026 | Infrastructure sequencing Gantt chart (9 critical-path items, urgency tiers, Chart.js floating bars); State Capacity Index (20-country WGI table, 3 tier KPI boxes); Workforce Impact table restored (12-tech employment, 3-KPI summary) |
| v2.4.0 | 14 May 2026 | Workforce impact table: per-technology direct employment (peak deploy, ops 2050, displaced, net) + fossil at-risk framing; 3-KPI summary; just-transition footnotes |
| v2.3.0 | 14 May 2026 | Scenario probability weights (P=0.25/0.50/0.25); expected value KPI; Monte Carlo 80% CI on breakthrough gap; CSV export; version history panel |
| v2.2.0 | 14 May 2026 | Deployment constraints panel (permitting, grid queue, political continuity, capital stress); transition economics table; expanded backtests (wind, BEV); sector deep dives (6 expandable panels) |
| v2.1.3 | May 2026 | Feasibility ceilings for all 12 technologies; stackedTotalCapped(); orange dashed line on gap chart; feasibility KPI; 10th provenance column |
| v2.1.2 | May 2026 | Row-level provenance corrections: SAF TTW/WTW scope; BEV WTW fleet-level; DAC↔SAF double-count; fusion/perovskite expert-judgment labels |
| v2.1.0 | May 2026 | Decision Questions stakeholder panel; scenario uncertainty envelope in KPI strip; Predictive Skill Benchmarks |
| v2.0.0 | May 2026 | Initial launch: 12-technology portfolio, policy simulator, sector decomposition, mathematical specification, technology provenance table |
CE Climate Solution Scale Model
A transparent technology-transition diagnostic platform that sizes the total climate action required to achieve net-zero, stacks every tracked technology’s contribution against the gap, and computes the required breakthrough dimension — the minimum scale any transformative solution must deliver to close what the known technology portfolio cannot.
CE is a bottom-up gap-accounting model — in the same methodology class as the UNEP Emissions Gap Report and IEA NZE scenario accounting. It is not a predictive Integrated Assessment Model (IAM) such as DICE, PAGE, or MESSAGE-GLOBIOM. It does not generate equilibrium forecasts. It makes transition dependencies visible so that institutions can ask better questions.
Standard models answer only part of the picture. Physical climate models (CMIP6, ERA5) project temperature and hazard under fixed scenarios but never ask: how much CO₂ is already locked into the infrastructure we’ve built? Macro-economic models (IMF WEO, NiGEM, FRB-US) project growth and inflation but don’t account for the ~680 GtCO₂ of committed emissions embedded in today’s coal plants, oil pipelines, and ICE vehicle fleets — or the fact that proven fossil reserves contain 3,500 Gt of burnable carbon, 14× the remaining 1.5°C budget. And no standard model stacks the deployment ceiling of commercially mature technologies to show how much of the gap we can close right now, before any unknown breakthrough is needed.
Which policy instrument closes how much of the 2035 gap, at what fiscal and political cost? · When does the budget exhaust under current policy vs. announced pledges?
What is the maximum ceiling for solar/wind/storage to close the energy-sector gap? · At what year does the energy sector reach full coverage, and what storage and transmission capacity does that require?
What is the annual welfare cost of failing to close the gap (by scenario)? · How does the breakthrough gap change from optimistic to pessimistic deployment — and what does that spread imply for physical-risk claims?
Which technologies carry the widest opt/pes uncertainty band (technology-readiness risk)? · What is the expected portfolio abatement weighted across scenarios, and how does a 200bps capital cost rise shift it?
What minimum breakthrough scale must a single technology deliver by 2050? · How does a 3-year permitting delay shift the 2050 breakthrough gap, and what does that imply for materials and workforce lead times?
What is the scenario range for 2050 breakthrough gap — and the implied stranded-asset exposure if the optimistic pathway fails? · How sensitive is the gap to deduplication and deployment assumptions (transition-risk sensitivity)?
Even if every government in the world agreed today to stop all new fossil fuel projects, the coal plants, gas furnaces, and gasoline-powered cars that already exist would continue burning fuels for their entire working lives — releasing roughly 680 billion tonnes of CO₂ before they retire. That's nearly 3 times the entire carbon budget remaining before we hit 1.5°C of warming.
The bars show how much CO₂ is already "baked in" to existing infrastructure, broken down by type. The green and orange horizontal lines are the 1.5°C and 2°C budgets we have left. When the bars tower above those lines, it means retiring existing fossil infrastructure early isn't optional — it may be mathematically required just to stay within budget, even before accounting for any new emissions.
This chart answers a specific question: "If we deployed every technology we already have — as fast as physically possible — how much of the problem could we solve right now?" Solar panels, wind turbines, electric cars, nuclear reactors, heat pumps, and geothermal plants are all real, commercially available, and getting cheaper every year. These aren't future bets — they exist today.
The bars show the maximum annual emissions reduction each technology could achieve at full deployment. The green line shows the total reduction needed each year to hit net-zero. When the combined bars approach or exceed the green line, it means deployment speed — not invention — is the binding constraint. When they fall short, new technology or major policy action is also required. Use the scenario buttons above to see how much this picture changes between optimistic and pessimistic deployment assumptions.
The red line is where the world is headed under today's policies — largely flat, because new fossil fuel consumption roughly keeps pace with clean energy additions. The green line is where emissions need to be each year to stay on the IPCC's 1.5°C pathway.
The blue shaded area shows how much the combined portfolio of emerging and near-commercial technologies (hydrogen fuel cells, carbon capture, next-gen nuclear, advanced biofuels, etc.) could contribute under the selected scenario. These are technologies that work but aren't yet fully deployed at scale. The purple gap above the blue area is the portion that no currently tracked technology addresses — this model calls it the breakthrough dimension. Switch to the Optimistic scenario to see how much faster deployment shrinks it; Pessimistic shows how much worse the picture gets if deployment lags.
Think of the carbon budget like a bank account with a fixed balance. Every year we emit CO₂ under current policies, we withdraw from that account. The chart tracks cumulative spending — the running total of all CO₂ emitted — and marks when it hits the 1.5°C and 2°C limits.
Under current policies, the 1.5°C budget is exhausted in the early 2030s. That's not a distant future problem — it's this decade. Once the budget is spent, additional warming is essentially committed. Reducing emissions after the budget runs out can slow the rate of further warming but cannot undo the overshoot without pulling CO₂ back out of the atmosphere — a far more expensive and uncertain proposition.
Climate change isn't caused by one industry — it comes from power generation, manufacturing, driving, heating buildings, farming, and land use all at once. This chart shows the relative size of each sector's contribution to the 52-billion-tonne gap we need to close.
The energy sector (power generation) is the largest single piece, which is why solar and wind receive so much attention. But notice that transport, industry, and buildings together are roughly as large as the energy sector. A climate plan that only focuses on electricity generation leaves more than half the problem unsolved. Any serious net-zero strategy requires action across all six sectors simultaneously.
Each sector's share of the 52 Gt gap, the CE portfolio technologies assigned to it, decision-relevant metrics, and the primary structural constraint. Expand any sector for detail. Sector shares from IPCC AR6 WG3 Ch2 & Ch3 (2022).
~38% Energy & Power Generation ~18 Gt/yr by 2050 — CE portfolio: Perovskite Solar, Fusion, Green H₂ (upstream)
The energy sector is the largest single opportunity: perovskite at $15–$40/t abated and fusion (if commercial) can together cover nearly half the sector gap. The binding constraint is not technology readiness but grid infrastructure — interconnection queues are now 4+ years in the US and EU, meaning every year of permitting reform is worth ~1 Gt of near-term abatement headroom.
~20% Transport ~10 Gt/yr by 2050 — CE portfolio: BEV, SAF, Green H₂ (trucks / shipping)
Transport's biggest leverage point is the fleet turnover cycle — once an ICE vehicle rolls off the line, it locks in 12–15 years of tailpipe emissions. BEV mandates and scrappage incentives determine how fast that cycle turns. SAF is critical for aviation where electrification is not viable by 2050 at scale; the hard constraint is sustainable bio-feedstock, not the conversion technology.
~20% Heavy Industry ~10 Gt/yr by 2050 — CE portfolio: Green Steel/Cement, Green H₂, Advanced Recycling, DAC (residual)
Industry is structurally the hardest sector: assets last decades, operating margins are thin, and green premiums require either regulation or a sufficiently high carbon price to close. The critical insight is that the replacement cycle opportunity is now — blast furnaces being built today will operate to 2060+. Green hydrogen cost must reach ~$2/kg (from ~$5–$8 today) to make DRI cost-competitive without a carbon price.
~10% Buildings ~5 Gt/yr by 2050 — CE portfolio: High-Albedo Surfaces; heat pump electrification in mature-tech baseline
Buildings are unusual: 80% of the 2050 global building stock already exists — so new-build standards are insufficient. The critical lever is retrofit pace. The split-incentive problem (landlord pays, tenant benefits) is the structural barrier; targeted fiscal mechanisms (green mortgages, renovation grants, minimum efficiency standards) are the primary policy tool. High-albedo surfaces offer the highest benefit-cost ratio in the CE emerging portfolio at $5–$20/tCO₂.
~12% Agriculture, Forestry & Land Use ~6 Gt/yr by 2050 — CE portfolio: Enhanced Weathering, BECCS (biomass), Ocean Iron Fert.
Agriculture is unique: methane and N₂O emissions don't follow the same technology-learning pathway as CO₂. Enhanced weathering can sequester CO₂ with an agricultural co-benefit (crop yield improvement of 10–20%), making it unusual in having a negative net MAC in some conditions. The dominant structural barrier is measurement — robust monitoring, reporting, and verification (MRV) systems don't yet exist for soil carbon or ocean-based CDR at scale.
cross-sector Carbon Dioxide Removal (CDR) ~4–8 Gt/yr needed by 2050 for net-zero — CE portfolio: DAC, BECCS, Enhanced Weathering, Ocean Iron
CDR is the most critical wildcard in the CE model: engineered CDR today is ~0.01 Gt/yr, and the IPCC requires ~4–8 Gt/yr by 2050 — a 400–800× scale-up. This gap is why the feasibility ceiling for DAC and BECCS falls below the base scenario in CE's feasibility layer (orange dashed line on the gap chart). The model correctly flags this as a breakthrough requirement, not just a deployment problem. Key unknowns: DAC energy demand at scale (currently 1.5–2.5 MWh/tCO₂ — significant grid burden) and BECCS biomass sustainability limits.
Each colored layer in this chart represents a different emerging or near-commercial technology — things like green hydrogen, enhanced geothermal, direct air capture, bioenergy with carbon capture (BECCS), and advanced nuclear. These aren't fully commercial yet at the scale needed, but they are real and being actively developed. Stacked together, they show the combined abatement potential year by year through 2060.
The purple band at the top is the gap none of these technologies currently covers — the "breakthrough dimension." Key takeaway: even under the Optimistic scenario, the stack doesn't reach the net-zero line — some combination of faster policy action and/or a genuine breakthrough technology is still required. Note: this chart shows only emerging technology. Fully commercial technologies (solar, wind, EVs) are in the Mature Technology chart above and should not be added to this stack.
This converts the technology stack into a simple percentage: out of the total emissions reduction we need, how much can our current pipeline of emerging technologies actually deliver? 100% would mean technology alone solves the problem. Anything below 100% is the share that depends on something else — policy changes, behavior shifts, or a technology that doesn't yet exist at scale.
Notice how coverage builds over time as technologies scale up, but often dips or plateaus mid-period before recovering — this reflects ramp-up time. Compare this across the three scenarios: the Optimistic scenario likely shows much higher coverage. The gap between Optimistic and Pessimistic coverage is essentially the deployment risk — the range of outcomes depending on how quickly industry and governments execute.
Imagine describing the climate problem as a single engineering challenge: "Build one technology that removes X billion tonnes of CO₂ per year by year Y." This chart shows what X has to be, year by year, if one breakthrough solution had to close the entire gap the known portfolio can't handle.
Use this as a reality check. If someone claims a new technology — nuclear fusion, direct air capture, enhanced weathering — will "solve climate change," look at this chart and ask: can it plausibly operate at this scale in the required timeframe? The earlier a breakthrough technology is deployed, the smaller the peak scale it needs to reach, because existing technologies will have already closed part of the gap. This is why deployment speed of known technologies and breakthrough investment are complementary, not alternatives.
Every technology on this page comes with uncertainty. Experts have a range of views on how much green hydrogen, carbon capture, advanced biofuels, or next-gen nuclear can realistically deliver by 2050. This chart shows that range for each technology — the left end of each bar is the pessimistic estimate; the right end is the optimistic estimate. The middle dot is the base case.
Wide bars are "wild card" technologies — they could be transformative or could underperform significantly. Narrow bars mean experts are more aligned, giving higher confidence in those estimates. Technologies with wide ranges are the highest-stakes investment decisions — the upside is large but so is the risk of disappointment. Note that these estimates are for emerging technology only; mature technologies like solar and wind have much narrower uncertainty ranges.
Policy changes can happen fast — sometimes in months — unlike new technology development which takes decades. This means the policy levers on this page could move faster than the technology stack, if governments choose to use them. The scatter chart on the right shows which instruments deliver the most abatement at the lowest economic cost: these are the priority levers that offer the best starting point for any government's climate policy package.
Sources: Carbon price and mandate effects calibrated from IMF (2019) carbon pricing elasticity analysis, IPCC AR6 WG3 Chapter 13, IEA NZE 2023, and OECD (2021) CBAM analysis. Fuel subsidy and transport measures from IEA World Energy Subsidies 2023 and ICCT Road Transport 2023. Land use from FAO SOFA 2023. All estimates are approximate order-of-magnitude guidance.
| Instrument | Category | Primary Sector | Max Abatement Potential (GtCO₂/yr at full deployment, 2035) |
GDP Cost (% of GDP at full deployment) |
Co-benefits | Implementation risk |
|---|
The SCC is the foundational number that connects climate policy to welfare economics. A carbon tax set equal to the SCC would — in theory — price every tonne of CO₂ at its full social cost, making fossil fuels bear the damage they impose. Today's EU ETS (~$72/t) sits between the Nordhaus and Ramsey estimates. The current US carbon price is effectively $0 at the federal level. The 52 Gt abatement gap has an annual economic damage value ranging from $4.3T (Nordhaus) to $25T+ (Stern) per year — a range driven almost entirely by the choice of discount rate.
This is not merely academic: the SCC directly drives cost-benefit assessments of regulations, infrastructure decisions, and litigation damages. Shell's Milieudefensie ruling implicitly required a high-SCC framing to justify mandating Scope 3 reductions. The IRA's social cost methodology at $190/t allowed EPA to justify far broader regulation than the prior $51/t estimate permitted. The discount rate choice is, at its core, a statement about how much we value the welfare of future generations relative to the present — a normative choice dressed in mathematical clothing.
1. Abatement requirement — the annual gap between the current-policy trajectory and the net-zero pathway at year t:
2. Technology portfolio coverage — sum of abatement contributions across all \(N = 17\) tracked technologies in scenario \(s\), with a flat de-duplication discount \(\delta\):
3. Breakthrough gap — the residual abatement not covered by any technology in the portfolio:
4. Carbon budget exhaustion year — the year \(\tau\) at which cumulative emissions under current policy consume the remaining budget \(C\):
5. Sector decomposition — each sector \(j\) receives a fixed share \(w_j\) of the total abatement gap:
Model class: This is a bottom-up accounting / gap model, not a predictive econometric or differential-equation model. It does not produce probabilistic forecasts. Outputs are structural scenarios conditioned on IPCC pathway assumptions. The appropriate comparators are IEA NZE scenario accounting and UNEP Emissions Gap Report, not DICE/PAGE/FUND IAMs.
- Non-CO₂ GHGs partially addressed: CH₄ (agriculture & waste) and HFCs are now tracked as separate wedges (v3.2.0); agricultural N₂O dynamics and coal-mine/O&G methane beyond the 1.5 Gt policy estimate remain simplified as CO₂e
- Technology abatement estimates have wide uncertainty — do not imply precision
- Double-counting correction is approximate; real-world overlaps are complex
- Deployment feasibility ceilings integrated for all 14 technologies (build-rate, asset-turnover, feedstock, and governance limits); see provenance table and orange-dashed feasibility line on gap chart
- Negative emissions accounting (CDR) included in tech stack but not independently verified
- IPCC AR6 WG3 Summary for Policymakers (2022)
- IPCC AR6 WG1 Table SPM.2 — Carbon budgets (2021)
- UNEP Emissions Gap Report 2024
- IEA Net Zero by 2050 — A Roadmap (2023 update)
- CE Emerging Technology Library — internal dataset
- NGFS Phase 4 Climate Scenarios (2023)
| Variable / Series | Source | Edition / Version | Resolution | Uncertainty | Last accessed |
|---|---|---|---|---|---|
| Global GHG emissions baseline (57 GtCO₂e/yr) | UNEP Emissions Gap Report | EGR 2024 | Annual · global total | ±2–3 GtCO₂e/yr (5th–95th pct) | Jan 2025 |
| Net-zero pathway (C1 median) | IPCC AR6 WG3 SPM Fig. SPM.4 / Table 3.2 | AR6 (2022) | 5-year intervals · global | C1 range shown as scenario spread | Mar 2025 |
| Carbon budget — 1.5°C (250 Gt) | IPCC AR6 WG1 Table SPM.2; cross-ref GCB 2024 | AR6 (2021); GCB Dec 2024 | 2020 ref. point; CE adjusts to 2025 via GCB 2024 drawdown (−14 GtCO₂/yr × 5 yr) | ±220 Gt (likely range); GCB 2024 gives ~235 Gt at 50% prob. — CE uses 250 Gt at 67% (different threshold, both consistent with AR6) | May 2026 |
| Carbon budget — 2°C (1,150 Gt) | IPCC AR6 WG1 Table SPM.2 | AR6 (2021) | 2020 ref. point; CE does not apply annual drawdown adjustment for 2°C budget (large residual renders adjustment immaterial) | ±300 Gt (likely range) | May 2026 |
| Sector abatement shares | IPCC AR6 WG3 Chapter 6 | AR6 (2022) | Sector · global | ±5 ppt per sector | Mar 2025 |
| Committed emissions from infrastructure | Tong et al. 2019 (Nature); IEA World Energy Outlook; Carbon Brief 2022 | Tong (2019); WEO 2022; CB 2022 | Operating infrastructure only; excludes planned/permitted assets | Tong (2019): 658 Gt; IEA WEO 2022: ~700 Gt; range ±50–100 GtCO₂ depending on asset-life assumptions | May 2026 |
| Proven fossil fuel reserves (3,500 Gt) | Global Registry of Fossil Fuels / Carbon Brief | 2022 edition | Asset type · country | ±10–15% (reserve classification) | Apr 2025 |
| Mature tech abatement potential (solar, wind, EVs…) | IEA Net Zero Emissions by 2050 | NZE 2023 update | Technology · 5-yr · global | Scenario spread (NZE vs. STEPS) | Apr 2025 |
| Technology abatement library (17 techs) | CE Emerging Technology Library | Public v3.2.0 | Technology · 5-yr · global | 3-scenario range (opt/base/pess) | May 2026 |
| Social Cost of Carbon presets | Nordhaus DICE-2023; EPA 2023; Stern (2006/2022) | 2023 editions | Global mean · single value | Scenario spread across discount rates | May 2026 |
| Carbon price / policy lever abatement | IMF (2019/2023); IPCC AR6 WG3 Ch13; OECD | 2023 editions | Global aggregate | Order-of-magnitude; policy-dependent | Apr 2025 |
Missing data handling: Technology abatement estimates for years between projection points are linearly interpolated. No imputation is applied to source data; missing country-level data is not extrapolated. Temporal alignment: All sources are harmonised to a 2025 baseline year; sources with 2020 or 2019 base years are forward-adjusted using UNEP EGR 2024 trend data. Geographic harmonisation: All figures are global aggregates; no sub-national disaggregation is performed.
Each row shows the effect of a ±1 standard deviation perturbation to the named parameter on the 2050 breakthrough gap \(B_{2050}^{\text{base}}\), holding all other parameters fixed. Results characterise the model's structural sensitivity, not probabilistic confidence intervals.
| Parameter | Base value | Shock (+1σ) | Gap Δ (Gt/yr) | Direction | Stability |
|---|---|---|---|---|---|
| Global emissions baseline (2025) | 57 GtCO₂e/yr | +3 GtCO₂e/yr (+5%) | +3.0 | worsens | high — near 1:1 pass-through |
| Technology de-duplication discount (δ) | 15% | +5 ppt (→ 20%) | +2.0 | worsens | moderate |
| Net-zero pathway 2030 waypoint | 36 GtCO₂e/yr | −3 GtCO₂e/yr (stricter) | +3.0 | worsens | high — structural |
| Solar/wind abatement potential (base) | ~12 Gt combined | −20% deployment | +2.4 | worsens | moderate |
| Carbon capture (DAC + BECCS) | ~4 Gt base | −50% deployment | +2.0 | worsens | high — very uncertain |
| Green hydrogen adoption rate | ~3 Gt base | −30% deployment | +0.9 | worsens | moderate |
| Fusion energy (2050 contribution) | ~1.5 Gt base | −100% (no contribution) | +1.5 | worsens | low — speculative |
| Sector weight — Energy vs. Industry | Energy 38% / Industry 24% | Swap 2 ppt to Industry | ~0 | neutral | very high — redistributive only |
| 1.5°C carbon budget | 250 GtCO₂ | −50 Gt (tighter IPCC estimate) | no effect on gap | neutral | very high — budget exhaustion year shifts, not gap |
Key finding: The model is most sensitive to the emissions baseline and the net-zero pathway assumption — both inherited directly from IPCC/UNEP; the model does not estimate them. The technology stack uncertainty (optimistic vs. pessimistic) spans from a ~0.6 Gt surplus (optimistic, formula-computed α-headroom + bilateral DEDUP; v3.7.0) to a ~22 Gt gap (pessimistic) by 2050 — the scenario selector above reflects this range. Carbon capture and green hydrogen are the highest-variance individual technologies.
This is a structural accounting / gap model, not a predictive forecasting model. It does not generate probabilistic forecasts of future emissions and therefore cannot be evaluated with conventional hindcast metrics (MAE, RMSE, interval coverage). The appropriate validation question is: are the IPCC source inputs correctly applied?
The model passes that test: sector shares, carbon budgets, and pathway waypoints are reproduced exactly from cited source tables. Computation logic is open in the browser's JavaScript source.
- 2015–2024 emissions trajectory: Global GHG rose from ~52 Gt in 2015 to ~57 Gt in 2024 (+10%). The model's near-flat current-policy assumption is consistent with observed data from UNEP EGR 2024.
- Carbon budget drawdown: At ~57 Gt/yr, the 250 Gt 1.5°C budget has consumed ~100 Gt since 2020. Budget exhaustion year ~2031 is consistent with Carbon Brief and Global Carbon Project (2024) calculations.
- Solar/wind delivery: IEA Global Energy Review 2025 reports solar PV ~1.4 GtCO₂ + wind ~0.9 GtCO₂ = ~2.3 GtCO₂ avoided in 2023. CE tracks emerging tech abatement beyond this commercial baseline.
- Wind LCOE 2010–2024: IRENA Renewable Power Generation Costs 2023 reports global-average onshore wind LCOE fell from ~$183/MWh (2010) to ~$33/MWh (2023) — a −82% decline over 13 years. CE uses the IEA NZE 2023 wind cost-deployment trajectory as the mature-technology baseline, consistent with observed IRENA learning rates. IEA Global Energy Review 2025 reports wind avoided ~0.9 GtCO₂ in 2023, consistent with CE MATURE_TECHS wind base[2025] = 0.8 Gt. ✓
- BEV deployment trajectory 2020–2023: IEA Global EV Outlook 2024 reports ~40 million BEVs on road globally by end of 2023, avoiding ~0.1–0.15 GtCO₂/yr vs. ICE baseline. CE MATURE_TECHS BEV base[2025] = 0.5 Gt represents fleet-level WTW displacement across the accumulated global fleet and is directionally consistent with the observed trajectory. CE TECHS_ABATE BEV (incremental above the committed commercial baseline) starts at 0.5 Gt — this represents the additional abatement from accelerated and extended deployment beyond the IEA NZE commercial baseline. ✓ with note.
- Limits: Technology ramp rates for nascent technologies (DAC, fusion, enhanced weathering) cannot be backtested — no historical track record exists at scale.
- Solar PV costs: IEA NZE learning-curve projection ($350→$25/MWh by 2030) matches IRENA 2024 actual ($350→$24/MWh 2010–2024); CE optimistic scenario consistent with observed outperformance. (Input consistency check, not a CE model output.)
- Global emissions 2020–2024: CE current-policy slope (−0.2 Gt/yr) tracks observed UNEP EGR 2024 data; post-COVID rebound correctly represented as near-flat recovery to ~57 Gt.
- Carbon budget drawdown: CE exhaustion calculation yields ~2029–2031 for 1.5°C; Global Carbon Project 2024 gives 2029–2031 independently — consistent. Note: GCB 2024 estimates ~235 GtCO₂ remaining from Jan 2025 at 50% probability; CE uses 250 GtCO₂ at 67% probability (different threshold), which is consistent with AR6 WG1 Table SPM.2.
- Solar + wind avoided emissions 2023: IEA Global Energy Review 2025 reports solar PV ~1.4 GtCO₂ + wind ~0.9 GtCO₂ = ~2.3 GtCO₂ combined in 2023. This is a plausibility check on the scale of emerging tech contributions; CE tracks additional abatement from the emerging tech portfolio beyond this commercial baseline.
- Limits: These are input-consistency and partial plausibility checks, not predictive skill benchmarks in an econometric sense. CE is a structural accounting model, not a predictive model. Nascent technologies (DAC, fusion, enhanced weathering, ocean iron) have no at-scale track record; CE ranges for these are forward projections only.
- Does not predict which technologies will succeed — only sizes what would need to
- Deployment feasibility ceilings applied for all 17 technologies (build-rate, asset-turnover, feedstock, governance); feasibility-constrained total shown on gap chart (orange dashed) and KPI strip
- Does not produce country-level or company-level estimates
- Does not model feedback loops between climate damage and economic capacity to invest
- Does not replace integrated assessment models (DICE, PAGE, MESSAGE) for policy optimisation
Every structural constant used in the model, its value, the tested sensitivity range, and the effect on B2050 (2050 abatement gap).
| Assumption | Value used | Range tested | Sensitivity on B2050 | Source |
|---|---|---|---|---|
| Baseline emissions | 57 GtCO₂e/yr | 55–59 Gt | ±2 Gt → ±2 Gt on B2050 | UNEP EGR 2024, p. vi |
| Net-zero residual target | 5 GtCO₂e/yr | 4–6 Gt | ±1 Gt → ±1 Gt on required abatement | IPCC AR6 WG3 C1 scenarios |
| 1.5°C carbon budget (from 2025) | 250 Gt | 200–300 Gt | ±50 Gt → ±1.5 yr on budget exhaustion | IPCC AR6 WG1 Table SPM.2; GCP 2024 |
| De-duplication factor (δ) | 0.22 (22% overlap discount) | 0.10–0.25 | ±5 pp → ±2 Gt on B2050 | CE expert judgment; IPCC AR6 WG3 overlap analysis |
| Scenario probabilities P(opt/base/pes) | 0.25 / 0.50 / 0.25 | 0.15/0.70/0.15 – 0.35/0.40/0.25 | P(opt)=0.35 → E[B] shifts −1.5 Gt | User-set decision weights; explore scenarios, not objective likelihoods — NGFS 2025 guidance p.6 |
| MC sigma — high-uncertainty techs | σ = 0.30 (fusion, DAC, ocean iron) | 0.20–0.40 | ±0.10 → ±2 Gt on P90 CI | IPCC AR6 WG3 uncertainty ranges |
| MC sigma — near-commercial techs | σ = 0.15 (other 9 techs) | 0.10–0.20 | ±0.05 → ±1 Gt on P90 CI | IEA WEO 2024 technology cost bands |
| MC technology co-variance | ρelec≈0.21 · ρCDR≈0.25 | ρ=0 (independent) to ρ=0.4 | ρ=0 → CI ~8 Gt; ρ=0.25 → CI ~11 Gt | CE v2.6.0 factor model; IPCC AR6 scenario spread |
| Social cost of carbon (SCC) | $190 / tCO₂ | $50–$300 | $50 → NPV ~$20T; $300 → NPV ~$90T | US EPA Supplemental Guidance 2023 |
| Discount rate (NPV base case) | 5% | 2–10% | 2% → NPV ≈$54T; 10% → NPV ≈$29T | Stern (2006); Nordhaus (2008); Weitzman (2013) |
Impact on B2050 (the 2050 abatement gap, Gt) when each assumption is moved from its base value to its tested extreme. Bars left of zero = gap improves (optimistic extreme); bars right = gap worsens (pessimistic extreme). Sorted largest to smallest impact range.
Technology abatement spread (opt vs pes portfolio) dominates all other structural uncertainties by 4× or more. Reducing technology uncertainty — through early deployment commitments, R&D investment, and pilot-scale validation — is the highest-value action for narrowing B2050.
Source traceability for each of the 17 Ai terms in the technology portfolio equation (12 CO₂/energy-system technologies + 2 non-CO₂ GHG wedges + 3 NBS/land-use/food-system entries). All abatement values are GtCO₂e/yr. Emission intensities track the IEA NZE 2023 grid decarbonisation trajectory. TRL scale: 1–9 (1 = basic research; 9 = commercial deployment at scale).
| Technology | TRL | EROI / Energy ratio or kWh/tCO₂ |
Primary sources | Ai definition | Counterfactual baseline | 2030 | 2040 | 2050 base | 2050 opt/pes | Overlap with | Deployment ceiling · binding constraint |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Nuclear Fusion Energy |
TRL 3–4 Pilot experiments |
~50–100:1 Projected; theoretical; ITER design basis (Murphy & Hall 2010) |
ITER Organization (2023); IEA ETP 2023; CFS SPARC design basis (2023); IPCC AR6 WG3 Box 6.7 ⚠ 2040/2050 values are expert scenario estimates; no published literature provides deployment trajectories at these scales |
Displaced fossil electricity generation → avoided GtCO₂/yr at IEA grid carbon intensity trajectory | Current-policy electricity mix (coal/gas grid) | 0.0 | 0.6 | 4.0 | opt: 7.0 · pes: 1.5 | perovskite, green_h2 (shared electricity sector); bev, albedo (demand-side electrification compresses marginal avoided emissions per kWh) | 1.5 Gt (2050) No commercial reactor before 2035; first-of-kind scale-up from zero; tritium breeding unresolved (CFS SPARC / ITER Org. 2023) |
| Perovskite Solar Energy |
TRL 6–7 Demonstration |
~10–20:1 Si-PV analogue; Carbajales-Dale et al. 2014 |
IRENA Renewable Power Generation Costs 2023; NREL Best Research-Cell Efficiency Chart 2024; IEA Solar PV Supply Chains 2022; IPCC AR6 WG3 Ch.6 ⚠ 2040/2050 values derived from learning-rate extrapolation; treat as expert-judgment ranges pending commercial-scale data |
Incremental abatement beyond IEA NZE baseline silicon-PV trajectory, at grid decarbonisation carbon intensity | IEA NZE 2023 silicon-PV baseline (already in MATURE_TECHS) | 0.5 | 2.5 | 7.0 | opt: 9.0 · pes: 4.5 | fusion (electricity sector), green_h2 (solar-powered electrolysis); bev, green_h2 (demand-side electrification increases electricity demand, compressing marginal abatement per kWh) | 8.0 Gt (2050) 25yr outdoor module stability not demonstrated; tandem-cell manufacturing scale-up lags silicon PV (NREL 2024; IRENA 2023) |
| Green Hydrogen Energy / Industry |
TRL 4–6 Early commercial |
~0.5–0.8:1 Energy vector, not generator; IEA GHR 2023 |
IEA Global Hydrogen Review 2023; IRENA Global Hydrogen Trade 2022; BloombergNEF Hydrogen Outlook 2023; IPCC AR6 WG3 Ch.11 | Displaced fossil fuels (grey H₂, HFO, coking coal) in steel, shipping, heavy transport, and industry by green electrolytic H₂ | Grey H₂ (SMR), heavy fuel oil, and coking coal at IPCC Tier 1 emission factors | 0.1 | 1.5 | 5.0 | opt: 7.0 · pes: 2.8 | saf (PtL-H₂ feedstock), green_steel (DRI-H₂), perovskite (electrolysis power) | 6.0 Gt (2050) Electrolyzer capacity ~3 GW/yr today vs. ~100 GW/yr needed (IEA NZE); +15,000 TWh/yr additional clean power required (IEA GHR 2023) |
| Direct Air Capture Carbon removal |
TRL 4–5 Small commercial |
~2,000 kWh/tCO₂ Solid sorbent; IEA DAC 2022; Fasihi 2019 |
IEA Direct Air Capture Special Report 2022; Fasihi et al. (2019) Joule; IPCC AR6 WG3 Ch.12; Carbon180 State of CDR 2023 | Net permanent CO₂ removal from atmosphere at scale (GtCO₂/yr), net of energy penalty using low-carbon electricity | Atmosphere (negative emissions; no fossil displacement counterfactual) | 0.0 | 0.3 | 1.8 | opt: 4.0 · pes: 0.6 | beccs (CDR accounting), enhanced_weathering (CDR budget); saf (PtL-SAF uses DAC-captured CO₂ as feedstock — double-count risk if both technologies fully credited) | 1.5 Gt (2050) ~2,000 kWh / tCO₂ energy demand; geological CO₂ storage siting; water ~3.7 t H₂O / tCO₂; cost ~$1,000/t today (IEA DAC 2022; Fasihi 2019) |
| BECCS Carbon removal |
TRL 5–6 Pilot / demo |
3–5:1 (power) Bioenergy yield minus CCS energy penalty |
IPCC AR6 WG3 Ch.12; IEA CCUS in Clean Energy Transitions 2020; Fajardy & Mac Dowell (2017) Nature Energy; Royal Society GGR (2018) | Bioenergy electricity generation with CCS applied to flue gas; net negative = fossil displacement + carbon capture | Fossil electricity generation at current grid emission intensity (IPCC AR6 WG3 Table 12.2) | 0.3 | 2.0 | 5.0 | opt: 7.0 · pes: 2.8 | dac (CDR accounting), enhanced_weathering (land use competition) | 4.5 Gt (2050) IPCC AR6 sustainability ceiling; land competition (food, biodiversity); geological CCS storage availability (IPCC AR6 WG3 Ch.12) |
| Enhanced Rock Weathering Carbon removal |
TRL 3–4 Field trials |
~80–200 kWh/tCO₂ Mining/grinding logistics; Beerling 2020 |
Beerling et al. (2020) Nature; Hartmann et al. (2022) Nature Rev. Earth & Env.; IPCC AR6 WG3 Ch.12; Amann & Hartmann (2019) | Net CO₂ sequestration via crushed silicate rock alkalinity addition to agricultural soils (GtCO₂/yr); excludes co-benefits | Unweathered baseline (natural weathering flux not accelerated; net is fully incremental) | 0.02 | 0.4 | 1.8 | opt: 3.5 · pes: 0.8 | beccs (land use), ocean_iron (ocean alkalinity overlap) | 1.5 Gt (2050) Mine / crush / spread logistics at Gt scale untested; monitoring & verification protocols not yet standardised (Beerling 2020; Hartmann 2022) |
| Ocean Iron Fertilisation Carbon removal |
TRL 2–3 Small field experiments |
~20–50 kWh/tCO₂ Vessel operations; uncertain at scale |
Buesseler et al. (2004) Science; Nat. Academies Ocean-Based CDR (2022); IPCC AR6 WG3 Ch.12; Aumont & Bopp (2006) Global Biogeochem. Cycles | Net CO₂ drawdown from phytoplankton bloom stimulation and particle export to deep ocean; permanence highly uncertain | Natural ocean carbon flux (increment above pre-industrial biological pump baseline) | 0.0 | 0.05 | 0.35 | opt: 1.0 · pes: 0.12 | enhanced_weathering (ocean alkalinity mechanism overlap) | 0.25 Gt (2050) London Protocol (LC/LP 2013) governance barrier; ecological hypoxia risk; deep-ocean permanence unverified (Nat. Academies Ocean CDR 2022) |
| Battery Electric Vehicles Transport |
TRL 9 Commercial at scale |
~3–5× vs ICE Drivetrain efficiency gain; IEA GEO 2023 |
IEA Global EV Outlook 2023; IEA NZE 2023 (transport sector); IRENA Electric Mobility 2022; IEA Transport Technology Perspectives 2023 | Fleet-level WTW CO₂ displacement from ICE→BEV transition; BEV upstream electricity emissions tracked at IEA grid decarbonisation trajectory; ICE at WTW per IPCC AR6; road transport only | ICE fleet at IPCC AR6 WG3 WTW road-transport emission factors (gCO₂/km by vehicle class, incl. upstream fuel production); same WTW boundary as BEV side | 1.0 | 3.0 | 5.2 | opt: 6.5 · pes: 4.0 | green_h2 (H₂ fuel-cell vehicles compete for same transport abatement); perovskite, fusion (BEV electricity demand reduces marginal avoided emissions per kWh from clean generation) | 6.0 Gt (2050) Fleet turnover: ~1.5B ICE vehicles × ~15yr avg life; +3,000–8,000 TWh/yr additional clean electricity required (IEA GEO 2023; IEA NZE 2023) |
| Sustainable Aviation Fuel Transport |
TRL 7–8 HEFA commercial; PtL demo |
~0.8–1.3:1 HEFA near-parity; PtL ~0.4:1; IEA ETP 2023 |
IEA Aviation ETP 2023; IRENA Bioenergy Hard-to-Abate Sectors 2021; ReFuelEU Regulation (EU 2023/2405); ICAO CORSIA 2022 | Displaced fossil jet kerosene CO₂ via blended SAF; scope = commercial aviation TTW (tank-to-wake, combustion only); upstream fuel-production emissions excluded from both SAF and fossil sides for consistent boundary | Fossil jet-A kerosene at 2.54 kgCO₂/kg fuel TTW (IPCC 2006 Vol.2 Ch.3 Table 3.3.1); consistent TTW boundary — upstream crude/refining excluded from both sides | 0.1 | 0.7 | 1.8 | opt: 2.8 · pes: 1.1 | green_h2 (PtL-SAF uses electrolytic H₂), beccs (bio-based SAF pathway); dac (PtL-SAF uses DAC-captured CO₂ as carbon feedstock — double-count risk if both credited) | 2.0 Gt (2050) Sustainable bio-feedstock limited ~1–2 EJ/yr (food / BECCS competition); PtL-SAF requires Gt-scale green H₂; ICAO CORSIA cap (ICAO 2022; IEA ETP 2023) |
| High-Albedo Surfaces Buildings / Geo |
TRL 8–9 Commercial (cool roofs) |
~50–200:1 Cooling saved / install energy; Akbari 2012 |
Akbari et al. (2012) Energy; Santamouris (2013) Build. Environ.; IPCC AR6 WG3 Ch.12 (urban albedo); Sleeter et al. (2019) | Avoided cooling-energy CO₂ from urban heat-island reduction via roof/pavement albedo increase; excludes stratospheric SRM | Dark urban surfaces at current albedo with fossil/mixed grid electricity (IEA building-sector baseline) | 0.2 | 0.7 | 1.3 | opt: 1.8 · pes: 0.9 | recycling (roofing materials supply chain) | 1.4 Gt (2050) Building stock replacement ~2–3%/yr; policy mandates and urban planning cycles 10–30 yr (Akbari 2012; Santamouris 2013) |
| Advanced Recycling Circular economy |
TRL 6–8 Chemical recycling: demo; mechanical: commercial |
~5–15:1 vs virgin IEA Mat. Effic. 2019; circular economy savings |
IEA Material Efficiency 2019; Ellen MacArthur Foundation CE 2023; IPCC AR6 WG3 Ch.11; Circle Economy Circularity Gap 2023 | Avoided industrial process emissions from closed-loop recycling (plastics, metals, construction) replacing virgin production | Linear production model at current material throughput and IPCC AR6 WG3 Ch.11 sectoral emission intensities | 0.25 | 1.0 | 2.1 | opt: 3.0 · pes: 1.3 | green_steel (steel scrap EAF subset of recycling) | 2.5 Gt (2050) EPR policy mandates needed; sorting contamination rates; waste collection infrastructure investment (IEA 2019; Circle Economy 2023) |
| Green Steel & Cement Industry |
TRL 5–7 H₂-DRI pilots; CCUS cement demo |
~2–5:1 vs BF-BOF H₂-DRI pathway; IEA Steel 2020 |
IEA Iron & Steel Technology Roadmap 2020; Mission Possible Partnership Net-Zero Steel 2022; GCCA 2050 Roadmap 2021; IPCC AR6 WG3 Ch.11 | Displaced blast-furnace BOF steel (H₂-DRI route) and conventional Portland cement (CCUS retrofit or calcined clay substitute) | BF-BOF steel at 1.85 tCO₂/t steel; Portland cement at 0.83 tCO₂/t cement (IPCC AR6 WG3 Table 11.1) | 0.1 | 0.8 | 2.1 | opt: 3.2 · pes: 1.3 | green_h2 (H₂ feedstock), recycling (scrap EAF steel) | 2.5 Gt (2050) BF-BOF asset lifetimes 20–40 yr; H₂-DRI cost competitiveness needed; CCS required for cement process CO₂ (IEA Steel 2020; GCCA 2021) |
| Methane Abatement (Agri & Waste) Non-CO₂ GHG |
TRL 7–9 Feed additives commercial; AD/landfill deployed |
~50 kWh/tCO₂e Low overhead; inhibitor synthesis + capture energy |
UNEP Global Methane Assessment 2021; IEA Global Methane Tracker 2023; FAO Enteric Fermentation Assessment 2022; IPCC AR6 WG3 Ch.7 | Avoided CH₄ from enteric fermentation (3-NOP/Bovaer feed additives), manure management, landfill gas capture, and wastewater anaerobic treatment — converted at GWP100 = 27 (IPCC AR6 WG1) | Current-policy GHG trajectory; 57 GtCO₂e/yr baseline already includes these CH₄ sources at today’s emission rates | 0.8 | 3.0 | 5.5 | opt: 8.5 · pes: 3.5 | beccs (biogas AD feedstock overlap), enhanced_weathering (agricultural co-benefit) | 7.0 Gt (2050) Structural limit: enteric fermentation cannot be fully eliminated while livestock sector persists; MRV for soil/manure CH₄ still developing; rice paddy water management covers ~10% of rice-sector emissions (FAO 2022; IEA GMTk 2023) |
| Refrigerant Transition (HFCs) Non-CO₂ GHG |
TRL 8–9 HFO/CO₂/natural refrigerants commercial |
<10 kWh/tCO₂e Drop-in substitution; minimal energy penalty |
UNEP TEAP 2022; Kigali Amendment (2016); EU F-Gas Regulation 2023/517; EPA HFC Emissions Inventory 2023; IPCC AR6 WG3 Ch.11 | Avoided HFC/F-gas emissions from refrigerant substitution (HFOs, CO₂ transcritical systems, natural refrigerants) under Kigali Amendment phase-down schedule; includes leak prevention and end-of-life recovery | BAU HFC growth trajectory absent Kigali: UNEP TEAP 2022 projects HFC emissions at ~5–9 GtCO₂e/yr by 2050 without controls; CE baseline already credits current Kigali progress, so values here are incremental to remaining phase-down | 0.9 | 2.0 | 2.8 | opt: 3.6 · pes: 2.0 | minimal; small overlap with green_steel (industrial refrigeration HFC use) | 3.5 Gt (2050) Full Kigali phase-down plus additional F-gas containment; ceiling constrained by residual use in hard-to-switch applications and compliance rate in developing economies (UNEP TEAP 2022) |
| Nature-Based CDR (Reforestation & Restoration) Carbon removal |
TRL 5–7 Restoration deploying; ARR pipeline early-stage |
≈100–400 kWh/tCO₂ Land management; seedling production; monitoring |
Griscom et al. 2017 (NCS); IPCC AR6 WG3 Ch.7; Cook-Patton et al. 2020; Bonn Challenge roadmap; WRI Global Restoration Initiative; FAO FRA 2020 | Carbon removal via afforestation/reforestation (ARR), ecosystem restoration (peatlands, mangroves, grasslands), improved forest management (IFM), and avoided deforestation (REDD+) — converted to tCO₂e using IPCC Tier 1/2 carbon stock change factors | Land-use change baseline from FAO FAOSTAT; comparison with IEA AFOLU sectoral pathway | 1.5 | 5.0 | 8.5 | opt: 13.0 · pes: 5.0 | soil_carbon (land management overlap), methane_agri (co-benefit land transitions), beccs (land competition) | 10.0 Gt (2050) Land availability constrained by food/feed demand; permanence uncertain under climate feedbacks; additionality and leakage in REDD+ under active debate (Griscom et al. 2017; Cook-Patton et al. 2020) |
| Soil Carbon & Biochar Carbon removal |
TRL 4–7 Biochar commercial; soil management practices widespread |
≈500–1 500 kWh/tCO₂ Pyrolysis + agri integration; logistics |
Minasny et al. 2017 (4 per mille initiative); Lehmann et al. 2021 (biochar review); IPCC AR6 WG3 Ch.7; FAO 2021 (soil carbon management); Smith et al. 2020 (NBS review) | Soil organic carbon enhancement (cover crops, no-till, compost, biochar application), wetland soil restoration, and permanent biochar carbon removal; MRV via soil sampling frameworks | BAU soil carbon trajectory from FAO/IPCC AFOLU sector; comparison against Minasny 4-per-mille ceiling scenario | 0.5 | 1.8 | 3.5 | opt: 6.5 · pes: 2.0 | reforest_nbs (land management overlap), methane_agri (agricultural co-benefits) | 4.5 Gt (2050) Saturation dynamics constrain long-term sequestration; MRV at scale unproven; biochar permanence >100 yr well-established but pyrolysis feedstock limited; peatland climate-feedback risk (Lehmann et al. 2021) |
| Food System Transformation Non-CO₂ GHG + land use |
TRL 4–6 Dietary shift + waste reduction; systemic early-stage |
Low (≈demand-side) Primarily behavioural/policy; precision fermentation moderate |
Springmann et al. 2018 (EAT-Lancet); Clark et al. 2020; IPCC AR6 WG3 Ch.7; Project Drawdown food sector 2023; Willett et al. 2019; Searchinger et al. 2021 | Abatement via protein transition (ruminant livestock reduction, plant-based/alternative proteins), food loss & waste reduction, rice cultivation water management, and precision fermentation reducing agricultural CH₄/N₂O at source | BAU food system from FAO/FAOSTAT and IPCC AFOLU; Springmann et al. 2018 reference trajectories | 0.8 | 2.5 | 4.5 | opt: 9.5 · pes: 2.5 | methane_agri (enteric fermentation overlap with protein transition), reforest_nbs (land freed by dietary shift) | 6.0 Gt (2050) Behavioural/cultural barriers to dietary transition are primary constraint; food waste ceiling well-understood; precision fermentation still emerging; additionality vs methane_agri complex for enteric fermentation (Clark et al. 2020; Springmann et al. 2018) |
Scope boundary: These values represent the emerging technology contribution — incremental abatement beyond the IEA NZE 2023 commercial technology baseline (solar PV, wind, conventional BEV, energy efficiency, nuclear fission, heat pumps, geothermal — modelled separately in the Mature Technology Ceiling chart). Abatement ranges are expert-consensus-based scenario estimates, not optimisation outputs. Opt and pes bounds represent the outer range of published literature forecasts at the cited sources. Capped vs uncapped: The base/opt/pes columns are uncapped scenario trajectories; the Deployment Ceiling column shows the literature-sourced physical feasibility cap applied separately when computing the aggregate feasibility KPI (orange dashed line on gap chart). Headline abatement totals use capped values; per-row trajectory values are uncapped.
Scope expansion — v3.3.0 (Phase B): Three nature-based and land-use wedges have been added, bringing the portfolio to 17 tracked technologies. Nature-based CDR (reforestation & restoration) and soil carbon & biochar address carbon removal; food system transformation addresses demand-side agricultural GHG emissions. Combined Phase B addition: ~16.5 GtCO₂e/yr (base, 2050). The base-scenario 17-tech aggregate now exceeds the 47 Gt breakthrough requirement; feasibility ceilings (orange dashed line) reflect real-world deployment constraints. See the Architecture Note in the Methodology section for full scope-gap history (v3.2.0 added methane abatement + HFC refrigerant transition; v3.3.0 adds NBS + food systems).
Technologies with high energy overhead per tCO2 removed have their claimed abatement eroded when powered by a carbon-intensive grid. The table shows gross base-case 2050 abatement vs net abatement under three grid scenarios. Only technologies with >1% energy penalty are shown; others (<50 kWh/tCO2 equivalent) are negligible.
| Technology | Energy overhead | Gross 2050 (Gt) | Current grid net 0.42 kgCO₂/kWh |
2035 grid net 0.15 kgCO₂/kWh |
2050 clean grid net 0.02 kgCO₂/kWh |
|---|---|---|---|---|---|
| Direct Air Capture | 2,000 kWh/tCO₂ | 1.80 | 0.36 (−80%) | 1.53 (−15%) | 1.73 (−4%) |
| BECCS (CCS energy penalty) | ~200 kWh/tCO₂ | 5.00 | 4.58 (−8%) | 4.85 (−3%) | 5.00 (0%) |
| Enhanced Rock Weathering | ~140 kWh/tCO₂ | 1.80 | 1.69 (−6%) | 1.77 (−2%) | 1.80 (0%) |
| Green Steel (H₂-DRI energy) | ~50 kWh/tCO₂ | 2.10 | 2.08 (−1%) | 2.10 (0%) | 2.10 (0%) |
Critical finding: DAC deployed before 2035 on today's global grid (0.42 kgCO₂/kWh average) achieves only ~20% of claimed net removal — requiring 5× the clean electricity to deliver the modelled 1.8 Gt/yr by 2050. The model's base-case values assume deployment is timed with grid decarbonisation. Grid carbon intensity assumptions: IEA World Energy Statistics 2024; IEA NZE 2023 trajectory. Energy overhead: IEA DAC 2022 (2,000 kWh/tCO₂); IPCC AR6 WG3 Ch.12 (BECCS ~200 kWh/tCO₂, weathering ~80–200 kWh/tCO₂).
Marginal abatement cost (MAC) ranges for each emerging technology at 2040+ deployment scale, drawn from IEA, IRENA, IPCC AR6 WG3, and primary literature. Costs shown are incremental vs. the incumbent solution (i.e., the additional expenditure per tonne avoided, not system-level cost). Values narrow significantly as technologies scale along their respective learning curves. Fusion cost is pre-commercial — no MAC estimate is available.
| Technology | MAC range ($/tCO₂, 2040+) | Base 2050 abatement | Annual invest. needed by 2050 | Primary cost driver | Source |
|---|---|---|---|---|---|
| Nuclear Fusion | Pre-commercial | 1.3 Gt | — | FOAK capital; not yet estimable | IEA ETP 2023 |
| Perovskite Solar | $15–$40 | 5.0 Gt | ~$10–$28B | Module manufacturing; balance-of-system | IRENA 2023 |
| Green Hydrogen | $60–$150 | 5.0 Gt | ~$30–$75B | Electrolyser CAPEX; clean electricity cost | IEA GHR 2023 |
| Direct Air Capture | $150–$400 | 1.8 Gt | ~$27–$72B | Sorbent regeneration; CO₂ compression/storage | IEA DAC 2022 |
| BECCS | $80–$200 | 5.0 Gt | ~$40–$100B | Sustainable biomass supply; CCS transport & storage | IPCC AR6 WG3 |
| Enhanced Weathering | $50–$180 | 1.8 Gt | ~$9–$32B | Rock mining; grinding; logistics; MRV | Beerling 2020 |
| Ocean Iron Fertilisation | $20–$100 | 0.35 Gt | ~$0.7–$3.5B | Vessel operations; monitoring & verification | NAS 2022 |
| Battery Electric Vehicles | $20–$60 | 5.2 Gt | ~$10–$31B | Incremental vehicle premium; charging infra | IEA GEO 2024 |
| Sustainable Aviation Fuel | $80–$250 | 1.8 Gt | ~$14–$45B | Feedstock (bio or e-fuel); processing premium over Jet-A | IEA ETP 2023 |
| High-Albedo Surfaces | $5–$20 | 1.3 Gt | ~$0.7–$2.6B | Retrofit materials; urban heat island co-benefit offsets cost | Akbari 2012 |
| Advanced Recycling | $15–$50 | 2.1 Gt | ~$3–$11B | Collection logistics; processing facilities | IEA Mat. Effic. 2019 |
| Green Steel & Cement | $50–$150 | 2.1 Gt | ~$11–$32B | Green H₂ or electrification premium; kiln retrofit | IEA Steel 2020 |
| Portfolio total (excl. fusion) | — | ~30 Gt/yr (base, 2050) | ~$155–$432B/yr by 2050 | 3.5–10% of IEA WEO 2024 total clean-energy investment need ($4.5T/yr by 2030) | |
Cumulative abatement 2025–2050 (base): ~440 GtCO₂ — average ~17 Gt/yr over 26 years.
At US EPA 2023 Social Cost of Carbon ($190/tCO₂), avoided climate damage: ~$84 trillion (undiscounted).
Total emerging-tech transition investment (low–high): ~$4–$11 trillion cumulative 2025–2050.
Net benefit: ~$73–$80 trillion — positive at any reasonable discount rate.
Note: This is a partial equilibrium accounting estimate, not an IAM optimisation. Does not include co-benefits (air quality, energy security) which further improve the ratio. Source: US EPA Social Cost of Carbon 2023 update; IEA WEO 2024.
| Discount rate | PV of avoided damage | PV of investment | Net NPV |
|---|---|---|---|
| 2% (Stern / Ramsey) | ~$66T | ~$7T | +~$59T |
| 5% (consensus mid-range) | ~$48T | ~$6T | +~$42T |
| 10% (Nordhaus range) | ~$29T | ~$5T | +~$24T |
All estimates are strongly positive: the transition portfolio is cost-beneficial across the full range of discount rates used in mainstream climate economics. Methodology: SCC × cumulative abatement, continuously discounted from abatement year; investment discounted from deployment year. US EPA SCC $190/tCO₂ (2023 central estimate).
| Technology | Current 2024 $B/yr |
Required 2035 $B/yr (CE base) |
Gap multiple | Primary source |
|---|---|---|---|---|
| Enhanced Rock Weathering | ~0.1 | ~20–40 | 200–400× | Beerling 2020; Carbon180 2023 |
| Ocean Iron Fertilisation | ~0.05 | ~2–5 | 40–100× | Nat. Academies Ocean CDR 2022 |
| Perovskite Solar | ~5 | ~80–150 | 16–30× | IRENA 2023; BloombergNEF 2024 |
| Direct Air Capture | ~7 | ~100–200 | 14–28× | IEA DAC 2022; Carbon180 2023 |
| BECCS | ~5 | ~60–100 | 12–20× | IPCC AR6 WG3 Ch.12; IEA CCUS 2020 |
| Green Steel & Cement | ~10 | ~80–150 | 8–15× | IEA Steel 2020; MPP Net-Zero Steel 2022 |
| Sustainable Aviation Fuel | ~15 | ~80–150 | 5–10× | IEA ETP 2023; IRENA Bioenergy 2021 |
| Nuclear Fusion | ~10 | ~30–50 | 3–5× | ITER Org 2023; CFS $2.8B raise 2023 |
| Green Hydrogen | ~100 | ~300–500 | 3–5× | IEA GHR 2023; IRENA H₂ Trade 2022 |
| Battery Electric Vehicles | ~320 | ~500–700 | 1.5–2× | IEA GEO 2024; BloombergNEF EV 2024 |
| Portfolio total (excl. mature tech) | ~$470B | ~$1.3–2.0T | ~3–4× | IEA WEI 2024; BloombergNEF 2024 |
Methodology: Current investment data from IEA World Energy Investment 2024 (tracked public and private commitments); required estimates derived from CE base-scenario deployment volumes at IRENA/IEA technology cost-learning trajectories. Figures are directional estimates — ranges reflect cost uncertainty. BEV includes manufacturing capex and charging infrastructure. Gap multiple = required 2035 midpoint ÷ current 2024 central estimate.
Per-technology direct employment estimates at CE base-scenario deployment scale. Peak build jobs = annual new-hire rate at the technology's maximum construction/manufacturing ramp. Ops 2050 = permanent direct jobs at 2050 operating scale (O&M, monitoring, fuel supply where applicable). Displaced = direct jobs in incumbent industries made redundant. Net = Ops 2050 − Displaced.
| Technology | Peak build M/yr at peak |
Ops 2050 M direct |
Displaced M direct |
Net M direct |
|---|---|---|---|---|
| ● Nuclear Fusion | 0.3 | 0.6 | 0.1 | +0.5 |
| ● Perovskite Solar | 4.5 | 4.8 | 0.8 | +4.0 |
| ● Green Hydrogen | 2.0 | 2.3 | 0.3 | +2.0 |
| ● Direct Air Capture | 0.5 | 0.5 | 0 | +0.5 |
| ● BECCS | 1.0 | 0.9 | 0.1 | +0.8 |
| ● Enhanced Rock Weathering | 0.8 | 0.7 | 0 | +0.7 |
| ● Ocean Iron Fertilisation | 0.05 | 0.05 | 0 | +0.05 |
| ● Battery Electric Vehicles | 3.5 | 2.2 | 1.2 | +1.0 |
| ● Sustainable Aviation Fuel | 0.6 | 0.5 | 0.1 | +0.4 |
| ● High-Albedo Surfaces | 0.6 | 0.5 | 0 | +0.5 |
| ● Advanced Recycling | 0.8 | 0.8 | 0.1 | +0.7 |
| ● Green Steel & Cement | 0.7 | 0.7 | 0.35 | +0.35 |
| Portfolio total | 14.4 | 14.6 | 3.0 | +11.5 ≈ +12M |
Fossil at-risk note: The ~10M at-risk figure represents the global coal, oil & gas, and ICE auto assembly workforce — a broader category than the "displaced" column above, which counts only workers directly substituted by each emerging technology. Not all 10M will face job loss: reskilling pathways exist (e.g. gas turbine mechanics → electrolyser maintenance), but geographic concentration of at-risk workers (Appalachia, Ruhr, Silesia, Queensland, Jharkhand) means just-transition policy is the binding constraint.
Methodology: Employment intensities scaled from IRENA Renewable Energy and Jobs Annual Review 2023; ILO/IRENA Just Transition 2023; IEA WEO 2024 energy jobs annex; and technology-specific literature. Figures represent direct employment only; indirect (supply-chain) multipliers of 2–4× apply per ILO methodology. CE base-scenario deployment values used; opt/pes scenario employment scales proportionally with abatement volumes.
Technology deployment is constrained by prerequisite infrastructure. Each bar shows the window during which foundational investment must begin to remain on a 2050 net-zero critical path at CE base-scenario deployment pace. Hover bars for technology dependencies.
| Infrastructure | Technologies unlocked | Lead governance body | Key bottleneck |
|---|---|---|---|
| ● Permitting reform | Perovskite solar, wind, grid lines | National legislatures, planning agencies | Environmental review timelines 5–12 yr |
| ● Grid expansion | Green H₂ (electrolysis), BEV charging, DAC (power) | TSOs, FERC (US), ENTSO-E (EU) | ~1,300 GW in interconnection queues (US+EU 2024) |
| ● MRV standards (CDR) | BECCS, Enhanced rock weathering, Ocean iron | UNFCCC, ISO, London Protocol, ICAO | No agreed permanence accounting for CDR |
| ● Nuclear regulatory | Nuclear fusion, advanced SMR designs | NRC (US), GIF, IAEA, national regulators | Novel reactor licensing 10–15 yr standard timeline |
| ● Critical minerals | Perovskite (In, Te), BEV batteries (Li, Co, Ni), Green H₂ electrolyzers (Ir, Pt) | IEA CRMA, US DPA Title III, EU CRMA 2024 | Mine-to-refinery pipeline 7–10 yr; geographic concentration |
| ● Biomass supply | BECCS, SAF (bio-based feedstock) | FSC, RSPO, national forestry bodies, EU RED III | Land competition; 2–3× current certified sustainable supply |
| ● CO₂ network | DAC, BECCS, Green Steel & Cement (CCS) | National govts, pipeline regulators, IEA CCS initiative | Right-of-way acquisition; geological storage verification |
| ● Green H₂ hubs | SAF (power-to-liquid), Green Steel (H₂-DRI), NH₃ shipping | EU Hydrogen Bank, US DOE H₂ Hubs, H2Global | Electrolyzer scale-up: ~3 GW deployed vs ~800 GW needed by 2050 |
| ● Ocean governance | Ocean iron fertilisation (limited-scale CDR) | London Protocol, BBNJ Treaty (2024), UNEP | Ecosystem risk assessment; no agreed monitoring framework |
Methodology: Critical-path windows derived from IEA Net Zero by 2050 (2023), IPCC AR6 WG3 Ch.6 infrastructure deployment timelines, BloombergNEF Energy Transition Investment Trends 2024, and IRENA Renewable Power Generation Costs 2024. "Must start by" year = latest feasible initiation year to achieve CE base-scenario deployment at each milestone, accounting for typical project development, permitting, and construction lead times.
Cumulative GtCO₂ of additional carbon budget consumed if a technology's commercial-scale deployment is deferred by 5 or 10 years from the CE base-scenario ramp. Computed via trapezoid integration over the 2025–2050 YEARS grid with 22% de-duplication applied (δ = 0.22). Sorted by 5-year delay cost.
Formula: 5yr delay cost = (b[4] + b[5] − b[0]) × 2.5 × (1−δ); 10yr delay cost = (b[3] + 2·b[4] + b[5] − b[0]) × 2.5 × (1−δ). b[i] = base abatement at YEARS[i]; δ = 0.22 de-duplication factor; Budget % = delay cost ÷ 250 Gt (1.5°C carbon budget from 2025).
The latest year to make a binding commercial-scale deployment commitment (funding, policy mandate, or FID) to remain on the CE base-scenario ramp. After the cliff date, the delay costs above begin accruing.
| Technology | Cliff date | Binding decision required | Status (2026) |
|---|---|---|---|
| Green Hydrogen | 2026 | Electrolyzer factory orders and off-take agreements for 2030 deployment targets; ~100 GW/yr production capacity needed | 🔴 AT CLIFF NOW |
| High-Albedo Surfaces | 2026 | Building code mandates need 1–2 legislative cycles; 10+ yr rollout via 2–3% annual building stock replacement | 🔴 AT CLIFF NOW |
| Advanced Recycling | 2026 | Extended Producer Responsibility (EPR) regulation legislative cycle; waste collection infrastructure investment decisions | 🔴 AT CLIFF NOW |
| Perovskite Solar | 2027 | GW-scale tandem cell manufacturing investment + 25yr outdoor stability validation; factory lead time 3–5 yr | 🟡 1 YR WINDOW |
| Direct Air Capture | 2027 | Nth-of-kind commercial facility FIDs; geological CO₂ storage permitting pipeline (7–10 yr); grid clean-power supply agreements | 🟡 1 YR WINDOW |
| BECCS | 2027 | Certified sustainable biomass supply chain agreements + CO₂ storage network FID; 5–8 yr plant build | 🟡 1 YR WINDOW |
| Enhanced Rock Weathering | 2027 | ISO/UNFCCC MRV protocol ratification; Gt-scale logistics procurement (mining, crushing, spreading fleet) | 🟡 1 YR WINDOW |
| Sustainable Aviation Fuel | 2027 | HEFA biorefinery FIDs and PtL-SAF green H₂ supply chain decisions; ICAO CORSIA enforcement mechanism | 🟡 1 YR WINDOW |
| Green Steel & Cement | 2027 | H₂-DRI pilot plant go/no-go at commercial scale; CCS retrofit FID for cement; EU carbon border adjustment locking in EAF scrap premium | 🟡 1 YR WINDOW |
| Nuclear Fusion | 2028 | SPARC ignition demonstration → FOAK commercial reactor FID; NRC/IAEA novel-reactor licensing pathway (10–15 yr standard) | 🟡 2 YR WINDOW |
| Battery Electric Vehicles | Past | Manufacturing commitments made; binding constraint is now grid integration capacity and critical minerals supply chain | ✅ COMMITTED |
| Ocean Iron Fertilisation | 2030+ | London Protocol amendment enabling commercial-scale CDR research must precede any investment commitment | 🔵 GOVERNANCE FIRST |
| Country | CO₂e share % of global |
WGI effectiveness gov't eff. percentile |
Tier | Primary implementation constraint |
|---|---|---|---|---|
| China | 27.0% |
|
T2 Med | Coal fleet lock-in; grid expansion pace; political coordination at scale |
| USA | 13.5% |
|
T1 High | Political continuity risk; grid interconnection queue; permitting |
| India | 7.2% |
|
T2 Med | Capital cost ~9–12%; coal & LPG subsidies; grid reliability |
| Russia | 5.2% |
|
T3 Low | Political isolation; sanctions; institutional bottleneck |
| Japan | 3.0% |
|
T1 High | LNG contract lock-in; nuclear policy uncertainty |
| Indonesia | 2.3% |
|
T2 Med | Coal lock-in; archipelago grid; concessional financing gap |
| Germany | 2.1% |
|
T1 High | Grid congestion; permitting speed; H₂ import dependency |
| Iran | 1.9% |
|
T3 Low | US/EU sanctions; governance deficit; no capital access |
| S. Korea | 1.9% |
|
T1 High | Heavy industrial lock-in; offshore wind permitting |
| Saudi Arabia | 1.8% |
|
T2 Med | Oil revenue dependency; economic diversification lag |
| Canada | 1.5% |
|
T1 High | Oil sands transition; inter-provincial grid coordination |
| Brazil | 1.4% |
|
T2 Med | Amazon deforestation governance; grid transmission capacity |
| Mexico | 1.3% |
|
T2 Med | Grid reform reversal risk; CFE monopoly constraints |
| S. Africa | 1.2% |
|
T2 Med | Coal lock-in; Eskom grid instability; financing access |
| Australia | 1.2% |
|
T1 High | Mining lobby; state-level variation; LNG export contracts |
| Turkey | 1.1% |
|
T2 Med | Administrative capacity; CPI concerns; fossil dependency |
| UK | 0.9% |
|
T1 High | Grid capacity limited; North Sea CCS buildout timeline |
| France | 0.8% |
|
T1 High | Slow renewables build-out; permit delays; strong nuclear base |
| Poland | 0.8% |
|
T2 Med | Coal workforce transition; EU CAP compliance pace |
| Italy | 0.7% |
|
T2 Med | Permitting delays 3–7 yr; north-south grid imbalance |
Methodology: WGI Government Effectiveness percentile rank from World Bank 2022/2023 data. Tier thresholds: High ≥75, Medium 40–74, Low <40. Emissions shares from IEA World Energy Statistics 2023 + Global Carbon Budget 2024. These 20 countries represent ~77% of global GHG emissions. State capacity measures institutional readiness to plan and execute large-scale infrastructure deployment — not willingness or policy commitments. Source: World Bank WGI 2022; IEA 2023; GCB 2024.
Each technology's deployment ceiling is partly determined by access to critical resources concentrated in specific geographies. The table below cross-references State Capacity tier (above) against each technology's binding geographic constraint.
| Technology | Critical resource | Key geography | Cap. tier | Deployment risk signal |
|---|---|---|---|---|
| Perovskite Solar | Solar cell manufacturing (~85% global capacity) | China | T2 | High — export-control & supply-chain concentration risk; MENA solar resource (T2) adds offtake dependency |
| BEV — Cobalt | Battery-grade cobalt (~70% mine supply) | DRC (Congo)* | T3* | High — governance deficit; conflict-mineral risk; below top-20 emitters index; EU CRMA and US IRA battery rules increase strategic pressure |
| BEV — Lithium | Lithium triangle (55% of global reserves) | Chile (T2), Argentina (T2), Australia (T1) | T2 T1 | Moderate — Chile/Argentina T2 mining reform risk; Australia T1 stable; lithobrine water scarcity emerging |
| Green Hydrogen | Cheap renewable electricity + land for production | Saudi Arabia (T2), Australia (T1), Chile (T2) | T2 T1 | Moderate — MENA T2 capacity constraints; concessional finance or strategic offtake agreements needed to unlock low-cost sites |
| BECCS | Sustainable biomass supply + CO₂ storage geology | Brazil (T2), Indonesia (T2); USA/Canada for storage (T1) | T2 T1 | Moderate — deforestation governance and biomass certification risk in T2 supply countries; CO₂ storage in T1 countries is lower risk |
| Enhanced Weathering | Silicate rock deposits + agricultural access | India (T2), China (T2), Brazil (T2) | T2 | Moderate — monitoring & verification protocols across T2 governance; smallholder adoption requires extension services at scale |
| Ocean Iron Fertilisation | Southern Ocean access + multi-state governance | International waters (London Protocol jurisdiction) | N/A | High — London Protocol (LC/LP 2013) governance barrier; no single state can authorise at-scale deployment; multi-lateral treaty amendment required |
| Direct Air Capture | Low-cost clean electricity + CO₂ storage geology | USA (T1), Iceland (T1), Canada (T1) | T1 | Low — T1 governance; binding constraint is energy cost (~2,000 kWh/tCO₂) and permitting speed for Class VI CO₂ storage wells |
| SAF | Sustainable bio-feedstock supply | Brazil (T2), Indonesia (T2), USA (T1) | T2 T1 | Moderate — ICAO CORSIA sustainability certification across T2 jurisdictions; feedstock competition with BECCS; PtL-SAF pathway depends on green H₂ build-out |
| Green Steel | Iron ore + green hydrogen supply | Australia (T1, ore), Brazil (T2, ore), MENA (T2, H₂) | T1 T2 | Low-Moderate — Australia T1 ore stable; Brazil land-use-change risk; H₂ supply from MENA adds T2 dependency; BF-BOF asset-stranding political risk in China (T2) |
*DRC (Democratic Republic of Congo) is not in the State Capacity Index top-20 emitter list but is included here as a critical-mineral supplier. Available proxy data (World Bank WGI 2022: government effectiveness ~6th percentile) places DRC in Tier 3. | Tier codes match State Capacity Index above. Deployment risk signal is CE editorial judgment based on cited governance and resource literature.
The model does not assert economic outcomes — it sizes the physical gap. However, the gap values feed downstream economic risk models that do assert causal linkages. The table below documents each link, its mechanism, the empirical evidence for it, and the quantitative parameter used in CE's coupled modules.
| Step | From | To | Mechanism | CE parameter / value | Empirical anchor |
|---|---|---|---|---|---|
| 1 | Physical climate hazard | Infrastructure damage | Extreme weather events destroy or stress energy, transport, and water infrastructure proportionally to hazard intensity | damage_pct_gdp = α·ΔT^β; α=0.00236, β=2.00 | Burke et al. 2015 (Nature); Kalkuhl & Wenz 2020; IPCC AR6 WG2 Ch.16 |
| 2 | Infrastructure damage | Productive capacity | Reduced grid reliability and transport throughput lower total factor productivity; supply-chain disruptions amplify sectoral output loss | tfp_loss = 0.6 · infra_damage_pct | NGFS Phase IV (2023) macro scenarios; IMF WEO Ch.3 (2022) |
| 3 | Productive capacity | Employment | Output loss reduces labour demand; transition sectors shed fossil-fuel workers faster than clean-energy sectors absorb them in the short term | Okun coefficient = 0.4; transition mismatch lag = 5 yr | ILO (2023) Just Transition report; IEA WEO 2023 jobs annex |
| 4 | Employment | Climate migration | Unemployment combined with physical hazard (heat stress, flooding, drought) drives internal and cross-border migration flows | migration_M = 0.021 · unemployment_pct · exposed_pop | Rigaud et al. 2018 (World Bank Groundswell); Lustgarten 2020 |
| 5 | Climate migration | Political instability | Large-scale population displacement strains host-country services, increases social tension, and raises sovereign risk premiums in vulnerable geographies | instability_score += 0.15 per 1M displaced persons | Carleton & Hsiang 2016 (Science review); Mach et al. 2019 (Nature) |
| 6 | Political instability | Investment risk | Sovereign credit downgrades and policy uncertainty raise the cost of capital for clean-energy investment, slowing the transition and widening the breakthrough gap | cost_of_capital_bp += 8 · instability_score | NGFS (2023); S&P sovereign rating sensitivity studies; IMF GFSR 2022 |
| 7 | Investment risk | Economic contraction | Elevated cost of capital reduces clean-investment flows, increasing residual emissions, accelerating physical damage feedback (steps 1–6 loop), and reducing GDP growth | GDP_impact = −0.012 · Δcost_of_capital_pct · GDP_baseline | Dietz & Stern 2015; DICE-2016R; NGFS Phase IV orderly vs disorderly scenarios |
Note: The Solution Scale Model itself computes only the physical gap (steps before step 1). The parameters in steps 1–7 belong to CE's downstream damage and economic modules (ce-damage-model, ce-physical-climate). They are documented here for completeness of the systems coupling chain. All parameters are inspectable in the JavaScript source of their respective pages.
All published KPIs and derived indices are computed from first principles below. Every coefficient is declared as a named constant in the JavaScript source visible at page-source inspection.
TECHS_ABATE array in page source.
The model uses a 57 GtCO₂e/yr (2025) baseline drawn from all-gas GHG inventories (IPCC AR6 WG3, UNEP EGR 2024). This baseline includes all greenhouse gases — CO₂, CH, N₂O, F-gases, and others — expressed as CO₂-equivalent using GWP100 factors. However, the original 12-technology library (v1.0–v3.1.0) tracked primarily CO₂ from energy combustion, industrial processes, transport, and buildings.
This created a structural mismatch: the gap denominator (breakthrough gap) referenced a full-gas baseline while the technology stack numerator captured only ~72–75% of total GHG sources. The result was a systematic overstatement of the breakthrough gap by approximately 8–20 GtCO₂e/yr depending on year and scenario. The degree of overstatement varied by year because non-CO₂ gases have different trajectories.
Resolution across two phases:- v3.2.0 (Phase A): Added two non-CO₂ GHG abatement wedges — Methane Abatement (Agri & Waste) covering enteric fermentation, manure, landfill gas, and wastewater; and Refrigerant Transition (HFCs) covering the Kigali Amendment phase-down. Combined base-scenario addition: ~8.3 GtCO₂e/yr by 2050. Breakthrough gap: 15.2 Gt → 8.1 Gt.
- v3.3.0 (Phase B): Added three NBS/land-use/food wedges — Nature-Based CDR, Soil Carbon & Biochar, and Food System Transformation. These address AFOLU-sector CO₂ (land-use change), terrestrial carbon sinks, and agricultural CH/N₂O from food production and dietary change. Combined base-scenario addition: ~16.5 GtCO₂e/yr by 2050. Breakthrough gap: 8.1 Gt → ~0 Gt (feasibility-unconstrained, pre-v3.4.0 recalibration; revised to ~6 Gt in v3.4.0 after base recalibration and δ correction).
Note: the DEDUP factor (δ = 0.22) applies to the full 17-technology stack to correct for portfolio-level co-benefits and double-counting (raised from 0.15 in v3.4.0 to account for livestock/land-use/food-system bilateral overlaps). Per-technology overlap notes in the provenance table above identify the most material pairwise interactions.
CURRENT_POLICY array ≈ 54 Gt/yr).
Integration is step-wise over the YEARS grid (2025, 2030, … 2060) using
linear interpolation within each 5-year block.
1.5°C (250 Gt): First step = 57 Gt/yr × 5 yr = 285 Gt > budget;
remaining = 250 Gt ÷ 57 Gt/yr ≈ 4.4 yr → exhaustion ≈ 2029.
2°C (1,150 Gt): cumulative reaches 1,110 Gt at 2045; remaining 40 Gt ÷ 53 Gt/yr ≈ 0.75 yr → exhaustion ≈ 2046.
The KPI strip displays whichever budget is active (toggle above). Note: naive flat-rate estimate 250/57 = 4.38 yr → 2029; the step-wise integration gives the same result because the budget is exhausted in the first 5-year block.
Consistent with Global Carbon Project 2024 (GCB 2024 gives 2029–2031 for 1.5°C at 50–67% probability thresholds) and Carbon Brief estimates.
Because this is a structural accounting model (not a forecast model), traditional RMSE/MAE backtesting does not apply. Instead, calibration is verified by checking that the model's constants reproduce the source values they claim to represent. The table below documents five independent checks.
| Event / Observable | Period | Model constant / assumption | Observed / published value | Discrepancy | Source |
|---|---|---|---|---|---|
| Global GHG emissions baseline | 2024 | BASELINE_EMISSIONS = 57 Gt | 57.1 GtCO₂e (UNEP EGR 2024, p. vi) | +0.2% ✓ | UNEP Emissions Gap Report 2024 |
| Remaining 1.5°C carbon budget | 2025 (from Jan 2025) | BUDGET_1P5 = 250 Gt | ~250 Gt (67% prob.; IPCC AR6 WG1 Table SPM.2 adjusted for 2020–2024 drawdown by Global Carbon Project 2024) | Exact ✓ | IPCC AR6 WG1; GCP 2024 |
| Solar + wind avoided emissions 2023 | 2023 | CE tracks emerging tech abatement beyond commercial solar/wind baseline | ~2.3 GtCO₂ solar+wind (IEA GER 2025: ~1.4 Gt solar + ~0.9 Gt wind) | Plausibility ✓ (different scope) | IEA Global Energy Review 2025 |
| Committed emissions from existing infrastructure | As of 2022 | COMMITTED_INFRA = 680 Gt (locked_in chart) | 682 Gt (IEA WEO 2022 Special Focus; Global Registry of Fossil Fuels) | −0.3% ✓ | IEA WEO 2022; GRFF (2022) |
| IPCC AR6 2030 net-zero waypoint | 2030 target | NET_ZERO_PATH[1] = 36 Gt (−37% from 57 Gt) | ~34–38 GtCO₂e (IPCC AR6 WG3 C1 scenarios, median ≈ −43% from 2019; adjusted for 2025 base) | Within C1 range ≈ | IPCC AR6 WG3 SPM Fig. SPM.4; Table 3.2 |
The 2030 waypoint (row 5) is within the IPCC C1 scenario range but uses a 2025 starting year rather than the IPCC reference year of 2019, producing a slightly less steep short-term decline slope. This is intentional and documented in the model methodology. All other constants reproduce their source values within measurement uncertainty.
The CE current-policy slope assumption (−0.2 Gt/yr) depends on real-world policy partially delivering. The table below benchmarks major climate policies against observed 2020–2025 deployment data to validate that the CE model's near-flat current-policy baseline is calibrated correctly. A delivery ratio <50% for systemic policies would require upward revision of the baseline emissions slope.
| Policy / mechanism | Jurisdiction | 2020–2025 target / commitment | Observed delivery (through 2024) | Delivery ratio | CE model treatment |
|---|---|---|---|---|---|
| Paris NDCs (1st round aggregate) | Global | −30% below BAU by 2030 (aggregated NDC pledges) | Tracking ≈−15% below BAU (Climate Action Tracker 2024) | ~50% | CE current-policy slope (−0.2 Gt/yr) reflects partial NDC compliance; consistent with observed half-delivery rate ✓ |
| EU Green Deal / Fit for 55 | European Union | −55% below 1990 GHG by 2030 | Tracking ≈−43% below 1990 by end-2024 (EEA 2024 projection) | ~78% | CE treats EU as Tier 1 high-capacity; partial delivery embedded in baseline; ~22% gap concentrated in industry and transport sectors that CE emerging techs address ✓ |
| US Inflation Reduction Act (IRA) | United States | $369B clean energy investment; 40% US emissions reduction by 2030 | ~$250B committed; ~120 GW clean capacity announced 2022–2024 (BloombergNEF 2024) | ~68% | CE optimistic scenario includes IRA-equivalent demand-side pull for solar, BEV, H₂; base scenario uses ~60% delivery fraction consistent with observed pace ✓ |
| China Dual Carbon Goals | China | Emissions peak before 2030; carbon neutrality 2060 | Coal capacity additions accelerating 2022–2024; emissions still rising ~1%/yr (GCP 2024); peak timing at risk | <50% | CE current-policy near-flat baseline consistent with China tracking below target; CE 2030 waypoint (36 Gt global) implicitly assumes China near-flat contribution ✓ |
| IEA NZE Solar 2030 target | Global | ~3,000 GW solar installed capacity by 2030 (IEA NZE 2023) | ~2,000 GW installed end-2024; ~500 GW added in 2024 alone (IEA GER 2025) — pace accelerating | ~67% | CE MATURE_TECHS solar baseline consistent with IEA NZE ramp; accelerating pace supports CE optimistic scenario for perovskite incremental abatement ✓ |
| Global EV 2030 targets | Major economies | ~60% of new passenger car sales to be BEV by 2030 (IEA NZE 2023) | ~18% of global new car sales were BEV in 2023; China ~35%, EU ~25%, US ~9% (IEA GEO 2024) | ~30% | CE BEV TECHS_ABATE base ramp reflects observed 2023 trajectory; optimistic scenario requires 2024–2030 acceleration consistent with current pace in leading markets ≈ |
| EU ETS carbon price | European Union | Sustained ≥€40/tCO₂ price signal needed to drive fuel switching | Averaged ~€60/tCO₂ 2022–2024; peaked €90 in Feb 2022; dropped to ~€55 end-2024 (EEX 2024) | >100% | CE SCC assumption ($190/tCO₂) is above EU ETS (~$65); trajectory consistent — carbon price signal is functioning and directionally validates the CE NPV framework ✓ |
Interpretation: Six of seven policies show 30–78% delivery ratios through 2024 — consistent with a near-flat current-policy baseline rather than either accelerating decline or accelerating growth. EU ETS exceeding its price target and solar exceeding pace are positive leading indicators for CE emerging-tech scale-up assumptions. The CE current-policy assumption would require upward revision only if aggregate NDC delivery falls below ~25% for multiple consecutive years. Sources: Climate Action Tracker 2024; EEA 2024; BloombergNEF 2024; IEA Global Energy Review 2025; IEA GEO 2024; GCP 2024; EEX 2024.
2050 residual emissions by CE scenario (57 GtCO₂e baseline minus emerging-tech portfolio net abatement; mature-tech ceiling not included) mapped to IPCC AR6 WG3 C1–C7 scenario categories. Values computed dynamically from current TECHS_ABATE at each scenario.
IPCC C-category 2050 net emission thresholds (GtCO₂e): C1 (≤5 Gt, 1.5°C no overshoot) · C2 (5–12 Gt, 1.5°C limited overshoot) · C3 (12–21 Gt, likely below 2°C) · C4 (21–35 Gt, below 2°C ~66%) · C5 (35–48 Gt, below 2.5°C) · C6 (48–55 Gt, below 3°C) · C7 (>55 Gt, above 3°C). Source: IPCC AR6 WG3 Table SPM.1 (2022). Note: adding the IEA NZE mature-technology baseline (solar, wind, efficiency, heat pumps, nuclear fission) reduces residuals by an additional 25–40 Gt in the optimistic scenario, potentially reaching C1 territory.
Technologies that depend on geological CO₂ storage (DAC, BECCS, cement CCS) share a common operational ceiling. IPCC AR6 WG3 Chapter 12 scenario modelling shows CCS deployment in C1 pathways reaching 4–15 Gt/yr by 2050 (wide range reflecting infrastructure investment assumptions); CE uses 8–10 Gt/yr as the mid-range operational injection ceiling. (Note: total physical geological storage volume is vast — estimated 8,000–55,000 Gt globally and not a constraint. The binding limit is annual injection infrastructure, verified site development, and monitoring capacity, not underground volume.) At the CE base scenario, combined storage demand from these three technologies approaches that ceiling in 2050. Under the optimistic scenario, demand exceeds the mid-range ceiling by approximately 2×, making injection capacity the binding constraint on engineered CDR. Feedstock sustainability limits create analogous ceilings for biomass-dependent technologies.
| Technology | Pes 2050 Gt/yr |
Base 2050 Gt/yr (capped) |
Opt 2050 Gt/yr (uncapped) |
Constraint |
|---|---|---|---|---|
| Direct Air Capture | 1.3 | 2.5 | 7.0 | All output must be stored; ~2,000 kWh/tCO₂ energy penalty |
| BECCS | 4.0 | 6.0 | 10.0 | All biogenic carbon captured must be geologically stored; biomass ceiling co-limits |
| Green Steel & Cement (CCS) | 0.3 | 0.5 | 0.7 | Cement process emissions only; ~30% of total Green Steel & Cement abatement |
| Total CCS demand | 5.6 | 9.0 | 17.7 | Sum of above |
| IPCC capacity ceiling | 8–10 Gt/yr mid-range operational ceiling (IPCC AR6 WG3 Ch.12 C1 scenario range: 4–15 Gt/yr; physical total storage volume is not the constraint) | Pes: within Base: at limit Opt: exceeds 1.8× | ||
Source: IPCC AR6 WG3 Ch.12 (Bui et al. 2018; Dooley 2013); IEA CCUS in Clean Energy Transitions 2023. Base values use feasibility-capped deployment (CE ceiling column); optimistic uses uncapped scenario trajectory.
| Feedstock | Technology | 2050 demand (base scenario) |
Sustainability ceiling |
Status |
|---|---|---|---|---|
| Sustainable biomass | BECCS, bio-SAF | ~3.5–4.5 EJ/yr | 3.5–5.5 EJ/yr | At limit (base) |
| Clean electricity | Green H₂, DAC, BEV charging | ~19,000 TWh/yr | Requires +11,000 GW new capacity | Infrastructure-limited |
| Bio-SAF feedstock | SAF (bio-route) | ~1.0 EJ/yr | ~1–2 EJ/yr | Approaching limit |
| Crushed silicate rock | Enhanced Rock Weathering | ~4–10 Bt/yr | Mining infrastructure lead time | Logistics-limited |
| Critical minerals | BEV (Li, Co, Ni), Perovskite (In, Sn) | Li: ~700 kt/yr; Co: ~250 kt/yr | IEA: 3–6× current production needed | Supply-constrained |
Sources: IPCC AR6 WG3 Ch.12 (biomass); IEA NZE 2023 (electricity); ICAO CORSIA (SAF); Beerling et al. 2020 (rock dust); IEA Critical Minerals 2023 (metals).
Source: IPCC AR6 WG3 Ch.12 (Dooley 2013; Bui et al. 2018); IEA CCUS in Clean Energy Transitions 2023; CE model TECHS_ABATE feasibility ceiling values (BECCS ceil=6 Gt, DAC ceil=2.5 Gt at 2060; interpolated to 2050).
Translates each CE emerging-technology deployment (base scenario, 2050) into the downstream metrics that governments, utilities, supervisors, insurers, and banks must quantify for stress testing and capital planning. Reference scenario shell: IEA Net Zero by 2050 (NZE 2023) / NGFS Net Zero 2050. Scenario deployment targets are decision weights for planning purposes, not objective probability forecasts — consistent with NGFS guidance that climate scenarios explore plausible futures rather than assign likelihoods (NGFS 2025 FAQ, p.6). All values are order-of-magnitude reference estimates for institutional scoping; they are not point forecasts. Row-level provenance shown in the Sources column.
Methodology: Capex ranges: IEA NZE 2023; BloombergNEF Energy Transition Investment Trends 2024; IRENA 2024. Clean power demand: IEA NZE 2023 sector chapters and IEA GHR 2023. Land and material constraints: IPCC AR6 WG3 Chs. 6, 11, 12. Permitting lead times: IEA NZE Ch. 4; BloombergNEF 2024. CO₂ storage requirements: IPCC AR6 WG3 Ch. 12; IEA CCUS in Clean Energy 2023. Sovereign fiscal signals: IEA NZE transition investment tables; IMF Fiscal Monitor 2023. Stranded asset exposure: IRENA World Energy Transitions Outlook 2023. Bank PD/LGD stress signals: NGFS 2025; ECB climate stress-test methodology 2025; IAIS supervisory guidance on climate risk. This table addresses the gap identified in NGFS guidance (2025) and ECB transition stress-test frameworks (2025): translating physical deployment gaps into balance-sheet and portfolio metrics for supervisory use.