# CE Balanced Transition Synthesizer — Methodology
# Model ID: ce-balanced-transition
# Version: 3.7.0
# Last updated: 2026-05-17
# Type: combined
# Geography: Global (sector-level synthesis)
# Horizon: 2025–2050

## Summary
CE's primary combined model for investment-decision-grade sector analysis. Synthesizes physical climate hazard (IPCC AR6 WG2), economic transition risk (NGFS Phase 4), and sector transmission signals into industry-native composite scores — pressure, resilience, and opportunity — for six industry sectors. The default counterpart to the CE Solution Scale Model: where the scale model sizes the challenge, the Synthesizer navigates the response.

## Methodology Detail
The CE Balanced Transition Synthesizer is CE's primary combined model for sector-level climate-economy analysis. It operates as a three-component signal fusion engine: (1) a physical climate component derived from IPCC AR6 WG2 industry-sector hazard exposure bands; (2) an economic transition component anchored to NGFS Phase 4 Orderly Transition scenario families; and (3) a transmission channel component capturing cross-sector derived-demand linkages. Component weights are industry-specific, determined by maximum-likelihood calibration against 15 years (2008–2023) of sector-level financial loss and drawdown data from Munich Re NatCatSERVICE, IMF Global Financial Stability Reports, and CDP sector emissions trajectory data. The calibration procedure minimises mean absolute directional error (MADE) between model pressure signals and observed sector financial drawdowns across six industry groups. A pathway consistency adjustment is applied by sector using CDP Science-Based Targets initiative (SBTi) company trajectory data: sectors where more than 30% of sector emissions come from companies without SBTi-aligned 2030 targets receive a transition pressure uplift; sectors with demonstrated first-mover commitments (Maersk methanol, Prologis renewable electricity, British Land MEES compliance) receive resilience uplifts. The three composite signals — pressure (P), resilience (R), and opportunity (O) — are normalised to a [0,1] scale within the NGFS Phase 4 scenario envelope, making cross-sector comparison directly interpretable at any projection year.

## Key Mechanisms
- Three-component fusion: physical climate (IPCC AR6 WG2), economic transition (NGFS Phase 4), and sector transmission signals are blended using industry-native, separately calibrated component weights — not a universal formula applied across all sectors
- Industry-specific weight calibration: component weights are determined by maximum-likelihood estimation (MADE minimisation) across 15 years of Munich Re NatCatSERVICE physical loss data, IMF GFSR sector drawdown data, and CDP SBTi trajectory data for each of six industry groups independently
- Pathway consistency adjustment: CDP Science-Based Targets initiative (SBTi) company commitment data modulates transition pressure by sector — sectors with >30% of emissions from companies lacking SBTi-aligned 2030 targets receive a transition pressure uplift; sectors with verified first-mover commitments receive resilience uplifts
- Transmission channel amplification: sectors with high derived-demand linkage (transport, manufacturing) receive elevated transmission component weight, capturing how supply chain disruption propagates physical and economic stress cross-sectorally
- Resilience scoring: company-level adaptation commitments (independently verified) generate sector resilience uplifts above the baseline — Prologis renewable electricity, British Land MEES compliance, and Maersk's methanol fleet transition are the three primary calibration anchors
- NGFS Phase 4 scenario envelope anchoring: the model's scenario range is constrained to be consistent with NGFS Orderly Transition and Net Zero 2050 scenario families, ensuring outputs are directly compatible with ECB, BoE, and FSB regulatory stress test frameworks
- Signal normalisation to [0,1] within the NGFS Phase 4 scenario envelope: P=0 is minimum pressure under the best-case supported NGFS scenario, P=1 is maximum under the worst-case supported scenario — cross-sector comparison is directly interpretable
- Temporal commitment decay: company pathway consistency data is weighted by commitment vintage and verification status — recent, quantified, independently verified commitments receive full weight; older or unverified pledges are partially discounted in the sector adjustment

## Strengths
- Industry-native weight calibration — the only major combined climate-economy model that derives component weights from sector-specific historical loss data rather than applying a uniform blending formula; directly comparable in scope to MSCI Climate Solutions and Bloomberg NEF sector outputs, with greater industry granularity
- Pathway consistency integration — CDP SBTi company-level commitment data is embedded in the transition pressure signal, making the model sensitive to real decarbonisation velocity and commitment quality, not just macro pathway assumptions
- Regulatory stress-testing compatibility — NGFS Phase 4 anchoring means outputs can be placed alongside ECB Climate Risk Stress Test, Bank of England CBES, and FSB TCFD scenario analysis with no translation layer required
- Three-signal composite architecture (P, R, O) enables genuine portfolio differentiation — separating transition pressure, adaptive resilience, and net opportunity allows hold/reduce/build decisions to be framed analytically rather than via a single ESG score
- Fully auditable weight structure — all industry component weights are documented in the CE Equation Registry with calibration inputs; analysts can trace any sector signal to the data sources that drive its magnitude
- Longitudinal comparability — normalisation to a consistent [0,1] scale across projection years (2025–2050) supports trend analysis and time-series portfolio rebalancing signals, not just single-point risk snapshots

## Limitations
- Historical-data-calibrated weights may underweight novel risk combinations without analogues in the 2008–2023 calibration period — particularly relevant for compound physical-economic stress events that have no close historical parallel
- Balanced-transition framing assumes broadly orderly conditions — for severe policy fragmentation, delayed-action-shock, or climate emergency pathways, the CE Stress Fragility Overlay should be used instead; it is explicitly calibrated for downside scenarios
- Within-sector company variance is averaged into a sector signal — the model outputs a sector mean plus calibration uncertainty band, not a distribution of company-level signals; individual company exposure analysis requires the company profiles layer in CE Workbench
- Cross-sector contagion is represented as a static transmission weight, not a dynamic network model — simultaneous compounding stress across three or more sectors is not captured; the CE Stress Fragility Overlay applies elevated transmission weights for this purpose
- Combined model output is a synthesis layer — decomposing a sector signal into its component contributions (physical vs. economic vs. transmission) requires querying the underlying CE economics and physical climate services separately

## Terminology Note
- '52 Gt total abatement required' (KPI): net reduction from 57 GtCO2e/yr baseline to 5 GtCO2e/yr net-zero residual.
- 'G_2050 = 47 Gt annual gap': annual policy-to-NZ gap at 2050, because under current policy
  the trajectory reaches only ~52 GtCO2e/yr by 2050 (not the 57 Gt baseline).
  G_t = CURRENT_POLICY[t] - NET_ZERO_PATH[t]; at t=2050: 52 - 5 = 47 Gt.

## Core Equations
G_t = E_t_policy - E_t_NZ                               (annual abatement gap)
T_t_s = sum(A_i_t_s for i in 1..N) * (1 - delta)       (tech coverage; delta=0.15)
B_t_s = max(G_t - T_t_s, 0)                            (breakthrough gap)
tau = min{t | sum(E_y_policy, y=2025..t) >= C}         (budget exhaustion year)
G_t_j = w_j * G_t                                       (sector decomposition)

## De-duplication Discount
delta=0.15 is a central estimate for cross-sector emission overlap.
Primary overlap sources: (i) green H2 and SAF both reduce transport fossil demand (~2-3%);
(ii) BECCS and enhanced weathering both draw on land-based biological carbon sinks (~3-4%);
(iii) ocean iron fertilisation and enhanced weathering compete for ocean sink capacity (~2%);
(iv) green steel and recycling address overlapping industrial-process emissions (~2-3%).
Estimated total overlap range: 13-18%; 15% used as central estimate.
Sensitivity: ±5pp change in delta shifts B_2050_base by approximately ±2 Gt.

## Data Sources
- UNEP Emissions Gap Report 2024 (baseline 57 GtCO2e/yr)
- IPCC AR6 WG3 SPM Table 3.2 (net-zero C1 pathway)
- IPCC AR6 WG1 Table SPM.2 (carbon budgets; original 2020 reference: 400 Gt for 1.5C at 67%;
  adjusted to ~250 Gt from 2025 by deducting ~150 Gt emitted 2020-2024; AR6-adjusted illustrative
  budget, uncertainty ±50 Gt. Independent check: GCB 2024 (ESSD 2025) gives ~235 Gt from Jan 2025
  at 50% probability — consistent within uncertainty bounds given different probability threshold.)
- IPCC AR6 WG3 Chapter 6 (sector abatement proportions)
- IEA Net Zero by 2050 NZE 2023 (mature technology ceilings)
- CE Emerging Technology Library v3.1.0 (12 technology abatement ranges; public provenance table
  at /models/ce-solution-scale — sources, TRL, EROI, counterfactuals, overlap deps, feasibility ceilings per technology)
  Machine-readable constants: /models/ce-solution-scale/assumptions.json

## Uncertainty Quantification
Scenario probabilities: P(optimistic)=0.25, P(base)=0.50, P(pessimistic)=0.25.
Expected value: E[B_2050] = 0.25*B_opt + 0.50*B_base + 0.25*B_pes.
Monte Carlo CI: delta~N(0.15,0.03), per-tech abatement perturbation drawn from a 3-factor co-variance model.
Factors: global transition momentum (bGlobal=0.35*sigma), electricity/grid sector (bElec=0.30*sigma),
CDR governance (bCDR=0.35*sigma). Variance-preserving: idiosyncratic sigma = sigma*sqrt(1-bG^2-bE^2-bC^2).
Implied cross-tech correlations: rho(elec pairs)~0.21, rho(CDR pairs)~0.25.
Positive co-variance widens CI vs independent draws (correct direction: shared policy/finance shocks
cause portfolio-level fat tails). sigma_i=0.30 for fusion/DAC/ocean_iron; 0.15 for other 9 techs. N=600.
Output: 80% CI on breakthrough gap (P10/P90).

## Deployment Constraints (v2.2.0+)
Interactive sliders model four institutional deployment barriers:
1. Permitting/build delay (0-10 yr): shifts each tech trajectory right in time.
2. Grid interconnection queue (0/3/6 yr): extra delay for grid-dependent techs.
3. Political continuity risk: post-reversal-year values switch to pessimistic scenario.
4. Cost-of-capital stress (+100/200/400 bps): global finance multiplier 0.95/0.88/0.78.

## Transition Economics (v2.2.0+)
Marginal Abatement Cost (MAC) ranges per technology at 2040+ deployment scale.
Sources: IEA WEO 2024, IRENA 2023, IEA GHR 2023, IEA DAC 2022, IPCC AR6 WG3.
NPV calculated at SCC=$190/tCO2 (US EPA 2023). Discount rates: 2%, 5%, 10%.
All NPV estimates positive across full range of mainstream discount rates.

## Workforce Impact (v2.4.0+)
Per-technology direct employment estimates at CE base-scenario 2050 deployment scale.
Sources: IRENA WESO 2024; IEA WEO 2024; ILO WESO 2022; IEA DAC 2022; IPCC AR6 WG3 Ch.17.
Peak deploy jobs (M): construction/manufacturing surge 2025-2040 (temporary).
Ops/mfg 2050 (M/yr): permanent direct ops, maintenance, and ongoing manufacturing.
Direct displaced (M): job losses in directly substituted incumbent sectors only.
Portfolio net: ~+12M direct ops jobs; separate fossil at-risk: ~10M (coal ~7M + oil/gas ~3M).
Economy-wide net (before supply-chain multipliers 1.5-3x): ~+9 to +12M by 2050.
All estimates carry +/-40-60% uncertainty at global scale.

## Infrastructure Sequencing (v2.5.0+)
9 foundational infrastructure investments mapped to must-start and must-complete years for 2050 critical path.
Urgency tiers: Critical (must start <=2026), Soon (2026-2028), Planned (2028+).
Critical: permitting reform, grid transmission expansion, MRV standards (CDR), nuclear regulatory pathway.
Soon: critical minerals supply chain, sustainable biomass supply, CO2 transport & storage network, green H2 hubs.
Planned: ocean governance framework (London Protocol+).
Sources: IEA NZE 2023; IPCC AR6 WG3 Ch.6; BloombergNEF ETI 2024; IRENA 2024.

## State Capacity Index (v2.5.0+)
Per-country implementation readiness for top 20 emitters (~77% of global GHG emissions).
WGI Government Effectiveness percentile rank (World Bank 2022/2023).
Tier 1 (>=75): USA, Germany, Japan, UK, France, Canada, Australia, S. Korea -- ~25% of emissions.
Tier 2 (40-74): China, India, Indonesia + 7 others -- ~45% of emissions.
Tier 3 (<40): Russia, Iran -- ~7% of emissions.
Source: World Bank WGI 2022; IEA 2023; Global Carbon Budget 2024.

## Model Assumptions Registry (v2.6.0+)
All structural constants with tested range and B_2050 sensitivity documented in-page.
Key sensitivities: baseline +-2 Gt -> +-2 Gt; delta +-5pp -> +-2 Gt; sigma(high) +-0.10 -> +-2 Gt P90.
Full table at /models/ce-solution-scale (Model Assumptions Registry section).

## Geographic Resource & State Capacity Cross-Link (v2.6.0+)
10 technologies mapped to critical resource geographies and State Capacity tier.
Key findings: DRC cobalt (BEV batteries) is Tier 3 equivalent -- governance deficit flagged.
Perovskite solar: ~85% manufacturing in China (Tier 2) -- supply-chain concentration risk.
Ocean iron fertilisation: multi-jurisdictional governance (London Protocol) -- T3/N/A tier.
BECCS/SAF bio-feedstock: Brazil and Indonesia Tier 2 -- deforestation governance risk.

## Policy Effectiveness Validation Backtest (v2.6.0+)
7 major climate policies benchmarked against 2020-2025 observed delivery:
- Paris NDCs aggregate: ~50% delivery (15% vs 30% below BAU) -- consistent with CE near-flat baseline.
- EU Green Deal: ~78% delivery -- CE Tier 1 capacity assumption validated.
- US IRA: ~68% delivery -- consistent with CE optimistic scenario demand-side pull.
- China Dual Carbon: <50% delivery -- consistent with CE near-flat China baseline.
- IEA NZE solar target: ~67% delivery but pace accelerating -- supports CE optimistic perovskite ramp.
- Global EV targets: ~30% delivery -- CE BEV base scenario consistent with observed trajectory.
- EU ETS carbon price: >100% (exceeded target price) -- validates CE NPV framework direction.

## Sensitivity Tornado Chart (v2.7.0+)
6-parameter B_2050 impact ranking (Chart.js horizontal floating bars).
Technology opt-pes spread: +-8.5 Gt (dominant, 4x all others combined).
Baseline emissions +-2 Gt -> +-2 Gt; De-dup delta +-5pp -> +-2 Gt.
Scenario probs P(opt) +-0.10 -> +-1.5 Gt; MC co-variance rho 0->0.4 -> +1.5 Gt CI widening.
Net-zero residual +-1 Gt -> +-1 Gt.

## EROI-Adjusted Abatement (v2.7.0+)
Grid carbon intensity penalty for energy-intensive removal technologies.
DAC (2000 kWh/tCO2): current grid (0.42 kgCO2/kWh) reduces 1.8 Gt gross to 0.36 Gt net (-80%).
DAC at 2035 grid (0.15): net 1.53 Gt (-15%); at 2050 clean grid (0.02): net 1.73 Gt (-4%).
BECCS (~200 kWh/tCO2): current grid -8%; 2050 grid 0%. Enhanced Weathering ~140 kWh: current -6%.
Key finding: DAC only viable at scale on near-zero-carbon grid (post-2035 deployment preferred).

## Investment Gap Panel (v2.7.0+)
Current 2024 vs required 2035 capital deployment by technology ($B/yr).
Portfolio current: ~$470B/yr; required 2035: ~$1.3-2.0T/yr; whole-portfolio gap: ~3-4x.
Largest relative gaps: Enhanced Weathering 200-400x; Ocean Iron 40-100x; Perovskite 16-30x.
Sources: IEA WEI 2024; BloombergNEF 2024; IRENA 2023; IEA GHR 2023; IEA DAC 2022.

## Carbon Budget Delay Cost (v2.7.0+)
Cumulative GtCO2 consumed by 5yr or 10yr deployment slip per technology.
Formula: 5yr cost = (b[4]+b[5]-b[0])*2.5*(1-delta); sorted descending by 5yr cost.
Highest delay cost: Perovskite 24.4 Gt (5yr); BEV 18.9 Gt; BECCS 17.9 Gt; Green H2 17.0 Gt.

## Technology Cliff Dates (v2.7.0+)
Latest year to make binding go/no-go deployment commitment per technology.
At cliff now (2026): Green H2 (electrolyzer orders), High-Albedo (building codes), Recycling (EPR regs).
1yr window (2027): Perovskite, DAC, BECCS, Enhanced Weathering, SAF, Green Steel.
2yr window (2028): Nuclear Fusion (SPARC ignition -> FOAK decision).
Committed: BEV. Governance-gated: Ocean Iron (London Protocol amendment first).

## IPCC Scenario Band Mapping (v2.7.0+)
CE portfolio scenarios mapped to IPCC AR6 WG3 C1-C7 pathway categories.
CE Optimistic: ~9.6 Gt residual -> C2 (1.5C limited overshoot) -- with mature tech could reach C1.
CE Base: ~25.2 Gt residual -> C4 (below 2C ~66%).
CE Pessimistic: ~38.5 Gt residual -> C5 (below 2.5C).
Current policy (no emerging tech): 57 Gt -> C7 (above 3C median).
Sources: IPCC AR6 WG3 Table SPM.1 (2022) for C-category thresholds.

## Scientific Precision Corrections (v3.0.0+)
CCS injection ceiling: previously stated as '8-10 Gt/yr geological storage capacity (IPCC)'. Corrected:
  IPCC AR6 WG3 C1 scenario range is 4-15 Gt/yr for CO2 injection rates; CE uses 8-10 Gt/yr as mid-range.
  Physical geological storage volume (hundreds of Gt) is NOT the binding constraint -- injection rate infrastructure is.
Committed emissions: primary citation added -- Tong et al. 2019 (Nature 572, 373-377): 658 GtCO2 from 2018
  operating fossil-fuel infrastructure (operating assets only, excl. planned/permitted pipeline).
  CE 680 Gt figure adds ~22 Gt additional 2018-2025 committed build; consistent with Tong upper bound.
BECCS biomass: 3.5-5.5 EJ/yr is a conservative no-regrets floor (zero food/land conflict scenarios).
  Full IPCC AR6 WG3 Ch.7 sustainable bioenergy range: 50-250 EJ/yr (wide, heavily sustainability-constrained).
  CE does not use the upper end; 3.5-5.5 EJ/yr represents lowest-controversy deployment ceiling only.
Carbon budget: AR6 WG1 Table SPM.2 400 Gt (67% probability, 2020 reference) cross-checked against
  GCB 2024 (ESSD 2025) ~235 Gt from Jan 2025 at 50% probability. CE 250 Gt figure is consistent
  within stated uncertainty bounds given the different probability threshold (67% vs 50%).

## Assumptions API (v3.0.0+)
All 10 structural constants with source lineage, uncertainty ranges, and scope notes available at:
  GET /models/ce-solution-scale/assumptions.json
  Returns: model_id, version, generated date, epistemic_status, comparable_to / not_comparable_to lists,
  assumptions array (constant, value, unit, source, scope, uncertainty, last_reviewed per entry),
  scenario_probabilities, and reproducibility links.
Machine-readable; CORS open (*); suitable for programmatic audit by institutional users.

## Platform Positioning (v3.0.0+)
CE is a TRANSPARENT TRANSITION DIAGNOSTIC platform, not a predictive IAM.
Methodology class: bottom-up gap accounting -- same as UNEP Emissions Gap Report and IEA NZE scenario accounting.
CE does NOT produce: equilibrium temperature projections, macro-economic forecasts, probabilistic damage estimates.
CE DOES produce: technology portfolio coverage quantification, committed-emissions accounting,
  breakthrough gap sizing, deployment-ceiling analysis, and cross-sector de-duplication.
Appropriate use: institutional transition planning, policy gap analysis, technology prioritisation,
  portfolio stress-testing, and complementary analysis alongside NGFS scenarios.
Not appropriate as a standalone substitute for: NGFS scenario sets, IPCC AR6 physical science,
  probabilistic IAM runs (DICE, PAGE, MESSAGE-GLOBIOM, REMIND), or national GHG inventories.

Structural accounting / gap model. Not a probabilistic forecast.
Outputs are scenarios conditioned on IPCC pathway assumptions.
Comparable to IEA NZE scenario accounting and UNEP Emissions Gap Report methodology,
not to predictive IAMs (DICE, PAGE, FUND, MESSAGE).
Computation is client-side JavaScript; fully reproducible from cited sources.