# FRB/US Policy Transmission — Methodology
# Model ID: frb-us-policy
# Version: 3.7.0
# Last updated: 2026-05-17
# Type: economic
# Geography: United States (primary); international via NiGEM linkage
# Horizon: 2025–2032

## Summary
Detailed policy transmission profile with stronger financing and labor sensitivity.

## Methodology Detail
The Federal Reserve Board's FRB/US model is a large-scale econometric model of the US economy emphasising rational expectations, policy transmission, and financial conditions. It is particularly sensitive to the expectations channel — forward guidance and rate path credibility directly affect investment and consumption timing. CE adapts the model's policy transmission architecture to the six-sector framework by mapping financing conditions, credit spreads, and investment sensitivity to each industry's balance sheet structure and leverage. The model is the primary tool for stress-testing how interest rate paths affect sector-level capex allocation and decarbonisation investment decisions.

## Key Mechanisms
- Rational expectations: agents use model-consistent expectations, making forward guidance and credibility central to outcomes
- Financial conditions index: credit spreads, equity risk premium, and mortgage rates transmit monetary policy to sector investment
- Labor market dynamics: wage bargaining, Phillips curve slope, and participation rate responses to policy are explicitly modelled
- Investment elasticity: each sector's capital expenditure responds differently to real interest rate changes — clean energy has the highest elasticity
- Inflation expectations anchoring: the degree to which expectations are anchored determines second-round effects from cost shocks
- Term structure dynamics: long-end rates respond to anticipated future short rates plus a time-varying term premium — affects clean energy project finance NPV through the full project lifetime discount rate
- Sectoral balance sheet evolution: household, corporate, and government balance sheets evolve endogenously, creating non-linear investment capacity effects when climate stress hits multiple sectors simultaneously
- Green bond and transition finance channel: Federal Reserve asset purchase programs and bank capital requirements affect relative financing costs for green versus brown investment — explicitly modelled as a policy transmission instrument

## Strengths
- Best-in-class policy transmission: captures how monetary policy affects investment timing and sector-level financing costs
- Expectations sensitivity allows accurate modelling of how forward guidance changes corporate investment planning cycles
- Labor market detail captures sectoral skills mismatch and wage pressure dynamics relevant to green transition workforce shifts
- Federal Reserve stress test alignment: Baseline / Adverse / Severely Adverse scenarios feed directly into DFAST and CCAR regulatory requirements — highest US regulatory authority
- Most detailed US financial sector transmission of any macro model: bank lending standards, equity risk premium, mortgage market, and insurance sector linkages are all endogenous
- Real sector–financial sector feedback loop: corporate distress from climate shocks propagates through bank balance sheets, tightening credit conditions and amplifying the initial shock

## Limitations
- US-centric: designed for the US economy; international spillovers require NiGEM for cross-border calibration
- Physical climate risk is not endogenous — it is treated as an external shock rather than a feedback mechanism
- Less suited to commodity-price-driven sectors (energy, agriculture) where global supply dynamics dominate over monetary policy
- Closed economy with limited international spillover modelling — requires NiGEM coupling to analyse cross-border transmission channels for non-US jurisdictions
- Climate physical risk module is externally specified as a shock rather than an endogenous feedback — the model cannot generate compound physical-financial stress scenarios from first principles

## 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.