{"climate":[{"best_for":"long-run scenario diversity and physical risk framing","coverage_note":"The international standard for long-run physical climate risk. Provides the full SSP scenario envelope from 1.5\u00b0C to 4\u00b0C+ used in all IPCC AR6 and NGFS Phase 4 analyses.","dimensions":["Temperature anomaly","Precipitation extremes","Sea-level rise","Tropical cyclone intensity","Carbon budget trajectories","SSP scenario pathways","Implied carbon price"],"era":"Current","geography":"Global (50\u2013100 km grid)","horizon":"2025\u20132100","id":"cmip6-core","industry_notes":{"agriculture":"CMIP6 is the primary climate model for agricultural yield risk \u2014 it directly models precipitation variability, soil moisture deficit, heat stress days, and growing season shifts for major crop-producing regions. The ensemble mean projects a 2\u20136% yield decline per degree of warming for major cereals, with high spatial variance. JBS, Cargill, and Bunge's South American growing regions are among the highest-exposure areas in the CMIP6 agricultural hazard map.","energy":"CMIP6 provides the physical hazard calibration for energy infrastructure \u2014 temperature stress on thermal efficiency, water availability for cooling, and extreme weather disruption to transmission networks. Aramco's Gulf operations face SSP2-4.5 wet bulb temperature thresholds by 2040. The transition pressure signal reflects the implied carbon price trajectory that would strand fossil assets \u2014 the key risk for ExxonMobil, Shell, and BP under SSP1-2.6.","insurance":"CMIP6 is the foundation of catastrophe model calibration for insurance pricing. Allianz, Munich Re, and Swiss Re all use CMIP6 ensemble outputs to stress-test their nat-cat exposure books against future climate states. CMIP6 projects increasing loss volatility \u2014 higher variance around the mean loss year as tail events become more frequent \u2014 which directly compresses reinsurance capacity and drives premium inflation.","manufacturing":"CMIP6 physical risk for manufacturing centres on industrial facility exposure: heat-related productivity losses, water stress for process cooling (critical for BASF's Verbund system), and flooding of supply chain infrastructure. The CMIP6 ensemble captures fat-tail distributions of compound extreme events that create the highest insurance and operational cost scenarios for ArcelorMittal's coastal steel facilities and Rio Tinto's Pilbara operations.","real estate":"CMIP6 physical risk for real estate is the most direct of all sectors: flood inundation maps, coastal erosion projections, wildfire expansion, and urban heat island intensification all derive from CMIP6 ensemble projections. The model projects that 20\u201325% of current coastal real estate globally faces material climate risk by 2050 under RCP4.5 \u2014 directly relevant to Prologis's coastal logistics hubs and Brookfield's global asset portfolio.","transport":"CMIP6 provides the physical disruption risk for transport infrastructure \u2014 sea-level rise exposure of coastal ports (Maersk's global terminal network), extreme precipitation damage to road and rail (Union Pacific), heat deformation of infrastructure, and cyclone intensity increases for shipping routes. The IMO uses CMIP6 projections as the basis for its climate vulnerability assessment of shipping routes."},"key_mechanisms":["Multi-model ensemble: 40+ global climate models are pooled to produce probability distributions rather than deterministic projections","SSP scenario mapping: each climate pathway (orderly, delayed) corresponds to an SSP scenario that determines the magnitude of physical and transition signals","Implied carbon price trajectory: the carbon price required to achieve each SSP pathway becomes the transition pressure calibration input","Sector asset exposure: company-level physical asset locations are mapped to CMIP6 hazard projections to compute sector-specific hazard scores","Emissions-to-trajectory gap: company Scope 1+3 trajectories are compared to SSP-consistent sector pathways to derive transition pressure adjustment"],"limitations":["Coarse spatial resolution (50-100km grid) requires downscaling for facility-level physical risk assessment","Short-term (1-5 year) physical risk signals are less credible than ERA5-calibrated near-term observational anchors","SSP scenarios assume smooth policy implementation \u2014 the transition pressure signal underestimates delayed-action shock risk"],"methodology_detail":"CMIP6 (Coupled Model Intercomparison Project Phase 6) is the international standard for long-run climate scenario analysis, harmonising outputs from 40+ global climate models under shared socioeconomic pathways (SSP1-1.9 through SSP5-8.5). CE uses the CMIP6 ensemble to construct probability distributions over physical risk parameters \u2014 temperature anomaly, precipitation extremes, sea-level rise, and tropical cyclone intensity \u2014 for each sector's asset and operational exposure profile. Transition pressure signals are grounded in the implied carbon price trajectory required to achieve each SSP scenario's emissions pathway. Company-level Scope 1+2+3 emissions are mapped to SSP scenarios to determine the gap between current trajectories and model-consistent pathways.","name":"CMIP6 Core Ensemble","projection_years":[2030,2040,2050,2060,2070,2080,2100],"resolution":"Grid-cell physical hazard; sector via asset overlay","signals":{"confidence":0.77,"hazard":0.69,"resilience":0.48,"transition":0.57},"status":"active","strengths":["Internationally standardised \u2014 CMIP6 outputs are the basis for all IPCC AR6 WG2 physical risk assessments and NGFS Phase 4 scenarios","Multi-model ensemble captures deep uncertainty: the spread of model outcomes is explicit rather than hidden in a single deterministic projection","Long-run horizon (2100) provides the full physical risk trajectory needed for infrastructure and real estate investment decisions"],"summary":"Multi-model climate ensemble backbone for scenario-conditioned physical risk.","type":"climate"},{"best_for":"historical calibration and near-term climate-state anchoring","coverage_note":"The observational anchor for near-term climate risk. Calibrates where the climate system is today, before layering forward projections. Essential for validating climate-to-impact causal chains.","dimensions":["Observed warming trend","Precipitation variability","Extreme event frequency","Wind & solar resource","Drought & flood indices","Operational disruption correlation"],"era":"Current","geography":"Global (~31 km reanalysis grid)","horizon":"1940\u20132025 (historical); 2025\u20132030 near-term","id":"era5-calibrated","industry_notes":{"agriculture":"ERA5 is the observed-state anchor for agricultural risk \u2014 it documents already-occurring shifts in growing season length, precipitation patterns, and extreme heat frequency affecting yields now. The model grounds CE's agriculture calibration in measured change rather than projections, making it particularly valuable for near-term (1\u20133 year) agricultural outlook. JBS's and Cargill's South American growing region ERA5 data directly calibrates the near-term yield risk signal.","energy":"ERA5 anchors the energy sector's near-term physical risk in observed data. It documents current wind resource availability (NextEra's capacity factors), solar irradiance trends, and hydropower catchment water balance \u2014 all driving real-world clean energy production. ERA5 trend data confirms the 1.2\u00b0C of observed warming already affecting cooling demand and grid stress events at scale.","insurance":"ERA5 is the primary data source for historical nat-cat loss calibration. ERA5 weather event data underlies the catastrophe models used by Munich Re, Swiss Re, and AXA to calibrate expected annual losses. CE uses ERA5 trend analysis to identify whether the frequency of loss-threshold events is already shifting \u2014 the answer is unambiguously yes across hurricane, flood, wildfire, and extreme heat peril lines globally.","manufacturing":"ERA5 provides the historical record that calibrates current manufacturing physical risk. BASF's Rhine water level data, Rio Tinto's Pilbara heat records, and ArcelorMittal's facility disruption logs are all correlated against ERA5 observed data to establish baseline loss rates. This model reports only what has been observed \u2014 making it the most conservative and credible calibration for near-term risk.","real estate":"ERA5 provides the observed record of flooding events, heat extremes, and storm damage already affecting real estate valuations. Property price data in repeatedly flooded areas shows a measurable discount emerging in the observed record \u2014 Vonovia's assets in German flood zones and British Land's London flood risk mapping are both calibrated against ERA5 precipitation and storm surge datasets.","transport":"ERA5 documents observed changes in transport route availability: Arctic shipping route navigability (sea ice extent, relevant to Maersk's Arctic corridor), road surface temperature exceedance events (Union Pacific), and port infrastructure exposure to historical storm surge (Delta hub airports). ERA5 provides the empirical calibration anchor against actual disruption records for all four transport sub-modes."},"key_mechanisms":["Observational anchor: ERA5 reanalysis provides the single best estimate of historical climate state, verified against 80+ years of station data","Trend detection: multi-decadal ERA5 trends in temperature, precipitation, and extreme events are statistically validated before entering the model","Operational disruption correlation: ERA5 weather event data is matched to company operational records to calibrate impact thresholds","Near-term projection: ERA5 initialised short-range climate projections (1-5 year) are more skilful than long-run CMIP6 projections for this horizon","Physical-to-financial bridge: ERA5 observed loss events are matched to insured loss records (Munich Re, Swiss Re) to calibrate the economic cost per unit of physical stress"],"limitations":["Backward-looking by design: does not capture the structural shift in risk from future emissions trajectories","Understates fat-tail physical risks for the 2040-2100 horizon relative to CMIP6 or GFDL projections","Reanalysis uncertainty is low for temperature but higher for precipitation extremes and tropical cyclone intensity \u2014 key insurance sector drivers"],"methodology_detail":"ERA5 is the European Centre for Medium-Range Weather Forecasts (ECMWF) global reanalysis product, covering 1940\u2013present at hourly resolution and ~31km spatial resolution. It provides the observed-state anchor for near-term physical risk calibration \u2014 grounding CE's climate signals in measured physical reality rather than model projections. CE uses ERA5 trend data to calibrate the current climate state (1.2\u00b0C observed warming, precipitation variance trends, extreme heat frequency) and correlates it with actual company operational disruption records (BASF Rhine levels, DHL hub closures, Union Pacific heat advisories) to validate the causal chain from climate variable to economic impact.","name":"ERA5-Calibrated Climate Baseline","projection_years":[2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030],"resolution":"Sub-daily observed state; facility-level via point extraction","signals":{"confidence":0.73,"hazard":0.62,"resilience":0.54,"transition":0.49},"status":"active","strengths":["Grounded in observation \u2014 no model uncertainty from climate model structural assumptions; reflects what has already happened","Near-term accuracy (1-5 year horizon) significantly exceeds CMIP6 ensemble for weather-regime and extreme event frequency","Company operational disruption validation: ERA5 allows direct verification of climate-to-impact pathways using real corporate loss records"],"summary":"Observed-state anchored climate profile for calibration-heavy use cases.","type":"climate"},{"best_for":"hydrology, coupled earth-system dynamics, and process credibility","coverage_note":"Best-in-class physical process credibility for ocean-driven hazards and compound events. The reference for insurance tail calibration and long-duration infrastructure asset risk.","dimensions":["Tropical cyclone intensity","Compound multi-hazard events","Hydrological cycle","Sea-level rise (ice dynamics)","Ocean heat content","Coastal flooding","Drought intensity"],"era":"Current","geography":"Global (high-resolution ocean-atmosphere coupling)","horizon":"2025\u20132100","id":"gfdl-physical","industry_notes":{"agriculture":"GFDL provides the most physically credible projections of hydrological change for agriculture \u2014 river discharge, groundwater recharge, and drought intensity for irrigation-dependent regions. These projections are critical for JBS's Brazilian cattle operations, Cargill's corn belt sourcing, and Bunge's South American grain origins. GFDL's agricultural hazard signal is typically the most extreme of the three CE climate models for water-stressed regions.","energy":"GFDL's coupled ocean-atmosphere dynamics provide the highest-fidelity projections of tropical storm intensification (offshore rig exposure for Aramco and ExxonMobil) and sea surface temperature trends (hurricane tracks affecting Gulf Coast energy infrastructure). GFDL's coastal flooding and storm surge projections are the most credible for NextEra's Florida coastal solar and wind assets.","insurance":"GFDL produces the most process-grounded probability distributions for extreme weather events that drive insurance loss scenarios. Its coupled model architecture captures co-occurring multi-hazard events (compound flooding + wind) that cause the highest insurance losses, making it the tail-risk calibration anchor for Allianz, Munich Re, and Swiss Re's catastrophe models in CE's insurance sector calibration.","manufacturing":"GFDL's physical process credibility benefits industrial facilities requiring water-intensive cooling or processing. Its superior land surface model captures water availability risk for BASF's Rhine-dependent Verbund system and Rio Tinto's water-stressed Pilbara operations. GFDL's soil erosion and geotechnical risk projections are also relevant for ArcelorMittal's mine-to-port infrastructure.","real estate":"GFDL's coastal flooding and sea level rise projections are the most physically grounded of the three CE climate models. For Prologis's logistics hubs, Brookfield's diversified global portfolio, and British Land's London flood risk, GFDL provides the highest-confidence assessment of chronic inundation risk for long-duration real estate holdings. Its urban pluvial flood hydrology also calibrates Vonovia's German residential flood exposure.","transport":"GFDL provides the most credible projections of tropical cyclone track and intensity changes affecting shipping routes (Maersk's trans-Pacific and Atlantic corridors) and aviation disruption (Delta's Gulf Coast hub exposure). Its sea level rise projections for port infrastructure (Maersk terminal network) are the reference for long-duration asset risk. GFDL's transport hazard signals reflect physical reality of increased extreme weather on route scheduling."},"key_mechanisms":["Coupled ocean-atmosphere dynamics: GFDL explicitly models ocean heat uptake and release, creating more credible tropical cyclone intensity projections","High-resolution hydrology: GFDL's land surface model produces river discharge and groundwater recharge projections at finer resolution than most CMIP6 models","Sea level rise from ice dynamics: GFDL's ice sheet model provides physically grounded sea level rise projections beyond 2050","Compound event simulation: the model captures co-occurring multi-hazard events (wind + storm surge + precipitation) that produce tail losses","Process plausibility check: GFDL outputs are used to constrain the CMIP6 ensemble tail by eliminating physically implausible outliers"],"limitations":["Computationally expensive: fewer scenario runs than CMIP6 ensemble, creating less coverage of SSP pathway diversity","Less suited to rapid policy scenario iteration \u2014 designed for physical process analysis rather than flexible policy scenario exploration","Transition pressure signals are derived indirectly from implied carbon price pathways rather than from the model's physical outputs directly"],"methodology_detail":"The NOAA Geophysical Fluid Dynamics Laboratory (GFDL) produces process-based earth system models (CM4 and ESM4) with particular strength in hydrology, ocean heat content, and coupled atmosphere-land dynamics. GFDL models are considered the most physically credible for compound events involving multiple interacting systems \u2014 tropical cyclones, storm surge, riverine flooding, and drought \u2014 that cause the highest insurance and infrastructure losses. CE uses GFDL for physical plausibility validation: when CMIP6 ensemble outputs produce extreme tails, GFDL process fidelity provides a sanity check. GFDL hazard signals are generally more conservative than CMIP6 ensemble tails but more credible for hydrology-sensitive sectors.","name":"NOAA GFDL Physical Risk Lens","projection_years":[2030,2040,2050,2060,2080,2100],"resolution":"Process-level earth system; sector via hazard overlay","signals":{"confidence":0.75,"hazard":0.74,"resilience":0.44,"transition":0.52},"status":"active","strengths":["Highest physical process credibility for hydrology, ocean-driven hazards, and compound events \u2014 particularly relevant for insurance tail calibration","Sea level rise and coastal flooding projections are the most rigorous available for long-duration asset risk (Prologis, Brookfield coastal portfolios)","Compound event modelling captures correlated multi-hazard scenarios that single-hazard models systematically underestimate"],"summary":"Process-focused climate lens with strong physical-system grounding.","type":"climate"}],"combined":[{"best_for":"balanced climate-economy integration with transition and resilience weighting","coverage_note":"The integrated base-case view blending macro, physical climate, and transmission signals. Calibrated for orderly-to-moderately-delayed transition scenarios.","dimensions":["Economic pressure","Physical climate risk","Transition pressure","Transmission channels","Sector resilience","Opportunity index","Net-zero pathway consistency"],"era":"Current","geography":"Global (sector-level synthesis)","horizon":"2025\u20132050","id":"ce-balanced-transition","industry_notes":{"agriculture":"Agriculture receives the highest climate weight (0.50) in the balanced synthesizer across all sectors, reflecting physical hazard as the dominant driver. The economic component is damped because food systems have government backstop mechanisms (price supports, export controls) that partially insulate revenues. JBS, Cargill, and Bunge's South American operations anchor the elevated climate weight.","energy":"Energy receives high climate weight (0.42) in the balanced synthesizer, reflecting the dual physical and transition risk from fossil asset stranding and physical infrastructure stress. The economic component is damped because energy sector revenues are partially insulated by commodity pricing mechanisms. Aramco, ExxonMobil, and BP's diverging decarbonisation paces create within-sector weight dispersion that the balanced model averages across.","insurance":"The balanced synthesizer treats insurance as a climate risk amplifier \u2014 the climate weight (0.48) is elevated because nat-cat losses drive the sector's financial viability. Munich Re's and Swiss Re's underwriting data directly calibrate the climate component weight. The economic component (0.20) is reduced because insurance premium growth is largely pass-through of physical risk costs.","manufacturing":"Manufacturing receives the lowest climate weight (0.28) in the balanced synthesizer \u2014 physical risk is real but diffuse across thousands of facility types. The economic component dominates because policy regime (CBAM, carbon pricing) and financing conditions are the primary drivers of transition cost. ArcelorMittal's CBAM exposure and Toyota's EV investment signal are the key economic anchors.","real estate":"Real estate has elevated weights across all three components \u2014 physical hazard (flooding, heat), economic conditions (rates, credit), and transmission (retrofit supply chain, insurance cost pass-through) compound each other. Vonovia's rate sensitivity, Prologis's physical exposure, and British Land's retrofit compliance cost all contribute to the sector's balanced but high-pressure profile.","transport":"Transport receives balanced economic and climate weights, reflecting that both channels create comparable pressure: physical infrastructure disruption (climate) and fuel/regulatory cost escalation (economic). The transmission component is elevated (0.36) due to transport's role as a propagation channel for supply chain disruption \u2014 Maersk's trade volume signals are the primary transmission calibration input."},"key_mechanisms":["Component weighting: climate, economics, and transmission signals are blended using industry-native weights calibrated to historical sector loss data","Pathway consistency check: sector decarbonisation pace (derived from major company commitments) modulates the transition pressure signal","Transmission amplification: for sectors with high derived-demand linkage (transport, manufacturing), transmission channels receive elevated weight","Resilience balancing: sectors with credible adaptation plans (Prologis renewable electricity, British Land net zero) receive resilience score uplifts","NGFS Phase 4 anchoring: the model's scenario envelope is constrained to be consistent with NGFS orderly and delayed transition scenarios"],"limitations":["Component weights are calibrated to historical data \u2014 the model may underweight novel risk combinations not present in history","The balanced weighting assumes broadly orderly transition; it is not designed for extreme fragmentation or climate emergency scenarios","Combined model output is a synthesis layer \u2014 diagnostic detail requires decomposition into the underlying economic and climate models"],"methodology_detail":"The CE Balanced Transition Synthesizer is a proprietary overlay model that blends economic, physical climate, and transmission signals using industry-calibrated component weights optimised against historical sector performance data. The component weights are set to reflect the balanced view under an orderly or moderately delayed transition, where economic and climate risks are broadly comparable. For each industry, weights are calibrated using sector-level historical loss data, regulatory cost curves, and forward-looking stress test outputs from the NGFS Phase 4 scenarios. Company-level emissions trajectories are used to determine the pathway consistency of each sector \u2014 sectors with major emitters on track (e.g., Maersk's methanol commitment) receive more orderly transition weight than sectors with lagging companies.","name":"CE Balanced Transition Synthesizer","projection_years":[2025,2027,2030,2033,2036,2040,2045,2050],"resolution":"Industry-sector with company-level pathway calibration","signals":{"confidence":0.78,"opportunity":0.74,"pressure":0.71,"resilience":0.56},"status":"active","strengths":["Integrates all three signal types \u2014 economic, climate, transmission \u2014 in a single coherent framework with explicit, auditable weights","Industry-native weights reflect each sector's actual risk profile rather than applying a uniform blending formula","NGFS alignment ensures the combined output is consistent with regulatory stress testing frameworks used by FSB, ECB, and BoE"],"summary":"Data-derived combined model overlay blending macro, physical, and transmission conditions.","type":"combined"},{"best_for":"stress scenarios where policy fragmentation and sector fragility dominate outcomes","coverage_note":"Downside boundary condition for portfolio stress testing. Amplifies transmission from policy fragmentation and physical risk accumulation. Not for base-case use.","dimensions":["Stranded asset risk","Policy fragmentation","Cross-sector contagion","Regulatory shock compression","Insurance retreat risk","Delayed-action shock","Compounding physical + transition stress"],"era":"Current","geography":"Global (sector-level stress)","horizon":"2025\u20132045","id":"ce-stress-fragility","industry_notes":{"agriculture":"The stress overlay applies maximum climate weight (0.55) to agriculture. Under fragmented policy, the absence of functioning carbon markets for agriculture means transition costs accumulate without offsetting revenue \u2014 a direct fragility for JBS and Tyson, which lack interim 2030 decarbonisation targets. Physical hazard compounding (consecutive drought years) without adaptation finance creates the highest sector fragility scenario.","energy":"The stress overlay amplifies stranded-asset risk for energy by increasing the climate component weight. Under delayed/fragmented scenarios, the most probable outcome is sudden policy catch-up \u2014 a rapid carbon price ratchet that inflicts larger concentrated losses than orderly transition. Aramco and ExxonMobil's long fossil asset lives are the primary vulnerability: their production assets are most exposed to stranding under compressed transition timelines.","insurance":"The stress overlay projects maximum insurance sector fragility: major reinsurers retreating from coastal and wildfire markets, creating an insurance protection gap that becomes a fiscal liability. The combined pressure signal for insurance under stress reflects both direct nat-cat loss escalation (Swiss Re's $450bn/year 2040 projection) and the systemic risk of uninsured losses cascading into banking NPLs.","manufacturing":"The stress overlay elevates the transmission component for manufacturing, capturing how supply chain fragility and trade tariff escalation compound basic economic and climate signals. In a fragmented policy environment, ArcelorMittal faces simultaneous competitive disadvantage from CBAM in EU while lacking carbon pricing support outside EU \u2014 creating stranded investment risk in green steel capacity built ahead of policy credibility.","real estate":"Real estate receives the highest combined pressure index in the stress overlay. The fragility scenario posits a simultaneous rate shock, physical loss event (flooding), and insurance market retreat \u2014 a compound stress event already occurring in parts of Florida, California, and coastal Europe. Vonovia's rate exposure, British Land's MEES compliance cliff, and Prologis's coastal flood exposure are the three concurrent fragility triggers the model calibrates.","transport":"Transport is a high-fragility sector in the stress overlay because fragmented policy means IMO levies, EV mandates, and aviation SAF obligations all hit simultaneously without adequate transition finance. The fleet replacement risk is acute: Maersk's methanol fleet investment creates stranded asset risk if fuel infrastructure doesn't materialise; Delta's SAF supply chain is vulnerable to policy reversal."},"key_mechanisms":["Downside amplification: stress weights are tilted toward climate (physical + transition) and transmission, reducing the stabilising influence of economic fundamentals","Policy fragmentation penalty: the divergence between coordinated and fragmented policy regimes is amplified, creating larger downside spreads","Stranded asset scenario: companies with long fossil asset lives (Aramco, ExxonMobil) face acute write-down risk under delayed-then-sudden policy catch-up","Regulatory shock compression: compressed transition timelines (delayed action forcing rapid catch-up) create larger concentrated losses than orderly transition","Cross-sector contagion: transmission channels are weighted more heavily, allowing physical and financial stress to propagate across sectors"],"limitations":["Not appropriate as a base case \u2014 the elevated stress weights systematically overstate pressure under orderly transition conditions","Reduced economic weight means macro stabilisers (monetary easing, fiscal support) are underweighted relative to their historical effectiveness","Confidence index is structurally lower than balanced model \u2014 reflects genuine uncertainty but may overstate noise in high-confidence scenarios"],"methodology_detail":"The CE Stress Fragility Overlay is designed for downside scenario analysis \u2014 it amplifies transmission from policy fragmentation and physical risk accumulation into combined sector pressure. Component weights are tilted toward climate and transmission (away from economics) to reflect scenarios where regulatory incoherence and physical impacts dominate over macro stabilisers. The model is calibrated against the NGFS 'Current Policies' and 'Below 2\u00b0C delayed action' stress scenarios and the FSB's severe climate scenario for financial stability analysis. Company-level fragility indicators (net zero commitment credibility, stranded asset exposure, regulatory compliance cost) are used to stress the sector signals above their balanced-model levels.","name":"CE Stress Fragility Overlay","projection_years":[2025,2027,2030,2033,2036,2040,2045],"resolution":"Industry-sector with fragility-weighted company calibration","signals":{"confidence":0.73,"opportunity":0.48,"pressure":0.82,"resilience":0.41},"status":"active","strengths":["Explicitly designed for tail risk scenarios \u2014 provides the downside boundary condition for portfolio stress testing","Captures the 'delayed action shock' dynamic: regulatory catch-up is more disruptive than steady-state transition and this model quantifies the difference","Cross-sector contagion: elevated transmission weights allow the model to capture how a stress in one sector (insurance retreat) propagates to another (real estate)"],"summary":"Combined model emphasizing downside pressure and weak resilience under fragmented transitions.","type":"combined"}],"economic":[{"best_for":"global baseline growth, inflation, and policy context","coverage_note":"Near-to-medium term macro baseline anchored in IMF Article IV consultations. Best suited for 1\u20135 year economic outlook with sector decomposition.","dimensions":["GDP growth","Inflation","Investment","Labour markets","Climate transition risk","Carbon price trajectory","Trade flows"],"era":"Current","geography":"Global (195 countries)","horizon":"2025\u20132031","id":"imf-weo-2026","industry_notes":{"agriculture":"Agricultural growth is governed by food price dynamics, terms of trade for commodity exporters, and input cost inflation (fertilizer, energy). The WEO captures climate-related yield loss risk as a medium-term growth drag (~9\u201323% reduction by 2050 under baseline scenarios). Food export restrictions in response to climate shocks are modelled as a trade fragmentation risk, calibrated against Cargill and JBS supply chain disruption data.","energy":"Energy sector growth under the IMF WEO is anchored to oil price and commodity market projections, adjusted for transition capex displacement. The model's investment signal for energy reflects the IMF's estimated clean energy investment gap (~$4tn/year by 2030 vs ~$1.8tn current). High fossil-fuel revenue dependency creates structural inflation sensitivity when oil price volatility is elevated \u2014 a direct link to Aramco and ExxonMobil's production economics.","insurance":"The WEO models insurance via financial sector accounts \u2014 premium growth links to GDP, claims trends link to physical risk events. CE augments this with nat-cat loss data from Munich Re and Swiss Re sigma to ground the claims inflation signal at sector level. The model captures the insurance protection gap as a fiscal risk in markets where insurer retreat forces public backstop obligations.","manufacturing":"Manufacturing growth reflects industrial production, global trade volumes, and investment in automation and electrification. The CBAM creates an asymmetric competitive impact between EU and non-EU manufacturers that WEO now explicitly models. Hard-to-abate sectors (steel via ArcelorMittal, cement via Holcim) face the highest investment-to-transition cost ratio in the WEO framework.","real estate":"Real estate investment is highly interest-rate-sensitive in the WEO framework. Rate normalisation post-2023 created a 12\u201318% capital value correction in commercial real estate globally \u2014 Vonovia's 60% valuation decline is the model's calibration event. The WEO also tracks the EPC retrofit mandate pipeline (via British Land and Prologis compliance costs) as a capex obligation that structurally reduces free cash flow.","transport":"Transport sector growth in the WEO is a derived-demand function following trade volumes and industrial output rather than being independently modelled. CE overrides this with sector-native freight and passenger volume projections (ITF, ICAO) for the growth signal. IMO 2028 carbon levy costs \u2014 anchored to Maersk's compliance trajectory \u2014 are treated as a sector-specific inflation shock on shipping inputs."},"key_mechanisms":["PPP-weighted aggregate demand: cross-country growth is demand-pull consistent across 195 member countries","Inflation expectations channel: monetary policy stance modulates how quickly inflation expectations anchor to target, affecting investment timing","Climate Transition Risk module: carbon-intensive sectors face growth-at-risk haircuts proportional to regulatory exposure under each pathway","Policy uncertainty premium: transition from coordinated to fragmented policy regimes adds an investment drag via higher discount rates","Labor market tightness: derived from structural unemployment gap, skills mismatch in green transition sectors, and participation rate trends"],"limitations":["Top-down decomposition: sector signals are derived from aggregate accounts, not independently modelled from firm-level data","Financial sector feedback loops (banking, insurance) are partially off-model \u2014 treated as transmission channels, not endogenous","Assumes continuous market adjustment; discontinuous shocks (debt cliff, energy price spike) are captured via scenarios only"],"methodology_detail":"The IMF World Economic Outlook constructs a globally consistent macro baseline from Article IV country consultations and a multi-country DSGE framework, updated twice yearly. The 2026 edition introduces a Climate Transition Risk module that applies growth-at-risk haircuts to carbon-intensive sectors based on their distance from net-zero pathways. CE adapts the WEO by extracting industry-level decompositions from IMF Fiscal Monitor and IEA sector accounts, overlaying industry-native calibrations for each of the six tracked sectors. Under a delayed transition pathway, the model embeds a stranded-asset haircut on capital formation and a terms-of-trade penalty for carbon-intensive exporters.","name":"IMF WEO 2026 Baseline","projection_years":[2025,2026,2027,2028,2029,2030,2031],"resolution":"Sector-level via aggregate decomposition","signals":{"confidence":0.74,"growth":2.9,"inflation":3.1,"investment":1.9,"labor":0.58},"status":"active","strengths":["Global consistency \u2014 195-country accounting framework prevents double-counting of cross-border exposures","Quarterly revision cycle maintains near-term accuracy and incorporates rapidly evolving climate policy signals","Explicit policy scenario framework: base case, upside, and stress scenarios are fully specified with quantified growth-at-risk"],"summary":"Top-down macro baseline for near- and medium-term global conditions.","type":"economic"},{"best_for":"expectations-sensitive policy transmission and financing conditions","coverage_note":"Deep US monetary policy transmission model. Optimal for interest-rate-sensitive sector analysis and clean energy investment timing under rate cycle changes.","dimensions":["Policy transmission","Financing conditions","Expectations channel","Wage dynamics","Credit spreads","Capex allocation","Clean-energy investment elasticity"],"era":"Current","geography":"United States (primary); international via NiGEM linkage","horizon":"2025\u20132032","id":"frb-us-policy","industry_notes":{"agriculture":"The FRB/US model's agriculture calibration is primarily through commodity price channels and rural lending conditions. Agricultural investment is highly sensitive to the real rate environment and farm income expectations. Under fragmented policy, agricultural input cost pass-through (fertilizer, fuel \u2014 directly relevant to ADM and Tyson's cost structures) is imperfect, compressing margins and suppressing investment.","energy":"FRB/US captures the energy sector's sensitivity to financing conditions \u2014 higher real rates suppress long-duration clean energy capex while short-cycle fossil investments are less affected. This creates a transition-retarding dynamic in fragmented policy regimes: monetary tightening disproportionately hurts the clean energy buildout relative to fossil operations. NextEra's 30-year asset life and BP's shifting capex mix are the primary calibration anchors.","insurance":"FRB/US is the primary model for insurance sector investment income dynamics. Insurance companies (Allianz, Zurich) hold large fixed-income portfolios \u2014 the model captures how rate normalisation improves investment yield while simultaneously increasing policy lapse rates. Net zero portfolio commitments from Allianz (NZAOA) and AXA are beginning to affect portfolio duration, creating a novel monetary-climate transmission channel.","manufacturing":"Manufacturing is among the most policy-transmission-sensitive sectors in FRB/US. The model captures how equipment investment, capacity utilisation, and working capital costs respond to monetary policy shifts. Clean manufacturing (electrification, hydrogen) has higher interest rate elasticity than incumbent fossil-based operations \u2014 BASF's gas-intensive Verbund system and ArcelorMittal's green steel capex illustrate this asymmetry.","real estate":"FRB/US is the most relevant model for real estate given the sector's deep rate sensitivity \u2014 Vonovia's 60% valuation decline illustrates the mechanism. The model quantifies how mortgage rate levels, credit availability, and REIT financing costs transmit to construction starts and commercial property values. EPC mandates (British Land, Prologis) add systematic risk premiums not captured in the base model.","transport":"FRB/US captures transport's sensitivity to consumer confidence (passenger aviation \u2014 Delta), trade finance costs (freight \u2014 Maersk, FedEx), and fuel price expectations. Fleet electrification capex is highly rate-sensitive \u2014 long asset life, high upfront cost, fuel-cost NPV benefit at risk when discount rates rise. IMO compliance costs add a non-monetary cost layer modelled as a supply-side inflation shock."},"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 \u2014 clean energy has the highest elasticity","Inflation expectations anchoring: the degree to which expectations are anchored determines second-round effects from cost shocks"],"limitations":["US-centric: designed for the US economy; international spillovers require NiGEM for cross-border calibration","Physical climate risk is not endogenous \u2014 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"],"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 \u2014 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.","name":"FRB/US Policy Transmission","projection_years":[2025,2026,2027,2028,2029,2030,2032],"resolution":"Sector-level financing conditions and investment responses","signals":{"confidence":0.78,"growth":2.2,"inflation":2.7,"investment":1.4,"labor":0.64},"status":"active","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"],"summary":"Detailed policy transmission profile with stronger financing and labor sensitivity.","type":"economic"},{"best_for":"cross-country spillovers, stress testing, and trade fragmentation","coverage_note":"The primary tool for multi-country stress testing, trade fragmentation scenarios, and understanding how shocks propagate across borders through commodity and financial channels.","dimensions":["Cross-border spillovers","Commodity prices","Exchange rate dynamics","Capital flows","Trade fragmentation","Supply chain risk","Sovereign balance sheets"],"era":"Current","geography":"Multi-country (50+ country models, bilateral trade flows)","horizon":"2025\u20132035","id":"nigem-global","industry_notes":{"agriculture":"NiGEM's multi-country framework captures the trade fragmentation risk for agricultural commodity exporters \u2014 how export restrictions in one country cascade into global food price inflation. Bunge and Cargill's South American supply chain positions are the primary calibration anchors for the agri-export fragmentation shock. The fertilizer supply chain (Russia-Belarus potash) is modelled as a cross-border input cost transmission event.","energy":"NiGEM captures the cross-border energy price spillover that defines the energy sector's global risk profile. An oil price shock originating in the Gulf \u2014 transmitted through Saudi Aramco's production decisions \u2014 propagates through 45+ country models with different fiscal, monetary, and structural responses. For Shell and BP, NiGEM quantifies how EU carbon pricing creates competitive divergence versus non-EU-regulated peers.","insurance":"NiGEM's relevance for insurance is through the reinsurance market's global risk-pooling function. A severe nat-cat event (modelled using Munich Re and Swiss Re loss data) tightens reinsurance capacity globally, propagating premium increases through NiGEM's financial conditions index. This creates the primary cross-border insurance stress transmission mechanism in CE's model.","manufacturing":"NiGEM is the primary model for manufacturing trade fragmentation stress testing. It explicitly models how tariff escalation, supply chain reshoring, and critical mineral access restrictions affect manufacturing GVA \u2014 ArcelorMittal's CBAM exposure and Toyota's Japan-to-EU export flows are the primary calibration anchors. CBAM exposure of non-EU manufacturers is most accurately captured in NiGEM's bilateral trade flow framework.","real estate":"NiGEM captures capital flow dynamics driving cross-border real estate investment. When risk appetite falls globally, capital retreats from emerging market real estate to safe-haven markets \u2014 Brookfield's global allocation data calibrates this behaviour. NiGEM also captures sovereign credit dynamics that affect public infrastructure finance supporting urban real estate values, relevant to Vonovia's German social housing financing.","transport":"NiGEM captures shipping and freight as a global transmission channel \u2014 trade volume shocks propagate to freight volumes and port throughput across all connected economies. Maersk's container rate data is a direct input to NiGEM's trade volume calibration. The model quantifies how trade fragmentation (supply chain regionalisation) reduces total freight distance while increasing per-unit cost \u2014 a key DHL and FedEx scenario."},"key_mechanisms":["Multi-country linkages: bilateral trade flows, commodity prices, and financial conditions propagate shocks across 50+ country models simultaneously","Exchange rate dynamics: floating exchange rates adjust in response to policy divergence, creating competitiveness effects for trade-exposed sectors","Commodity price transmission: oil, gas, food, and metals prices are endogenous \u2014 a supply shock in one region affects all importing countries","Capital flow reversal: sudden stops in cross-border capital flows affect financing costs differently in emerging vs. developed markets","Trade fragmentation scenario: tariff escalation, supply chain reshoring, and market access restrictions are explicitly modelled as policy interventions"],"limitations":["Less detailed on domestic financial conditions within each country \u2014 FRB/US is superior for US policy transmission mechanisms","Country-level models are less granular than the IMF WEO for sector decomposition within individual economies","Green transition investment dynamics are less explicitly modelled than in IMF WEO's Climate Transition Risk module"],"methodology_detail":"The National Institute Global Econometric Model (NiGEM) is a multi-country model maintained by NIESR, designed for international spillover analysis and stress testing across 50+ country models. It explicitly models cross-border trade flows, commodity price transmission, exchange rate dynamics, and sovereign balance sheet linkages. CE uses NiGEM as the primary cross-border fragmentation tool \u2014 under the trade fragmentation scenario, it captures how supply chain disruption, commodity repricing, and capital flow reversals propagate differently across industries with varying export/import intensity. The model is particularly valuable for scenarios where policy incoherence creates divergent competitive regimes across major economies.","name":"NiGEM Global Scenario","projection_years":[2025,2027,2029,2031,2033,2035],"resolution":"Country + sector via trade-flow decomposition","signals":{"confidence":0.72,"growth":2.4,"inflation":3.4,"investment":1.5,"labor":0.6},"status":"active","strengths":["Best-in-class for cross-border spillovers \u2014 uniquely captures how a policy shock in one country propagates to others through trade and finance","Commodity price endogeneity: energy, food, and materials prices respond to supply and demand dynamics, not just held constant","Explicit trade fragmentation stress scenario framework aligned with NGFS and FSB climate stress testing protocols"],"summary":"International propagation model suited to multi-country shock analysis.","type":"economic"}]}