{"climate_forecast":{"emissions_contribution":0.0,"hazard_pressure":{"confidence":0.7,"direction":"up","name":"Physical hazard pressure","unit":"index","value":0.66},"narrative":"Consensus climate view derived from the selected model set.","physical_vars":null,"policy_contribution":{},"resilience":{"confidence":0.59,"direction":"up","name":"Adaptive resilience","unit":"index","value":0.4},"shock_contribution":{},"transition_pressure":{"confidence":0.65,"direction":"up","name":"Transition pressure","unit":"index","value":0.97}},"climate_model_forecasts":[{"forecast":{"emissions_contribution":34.0,"hazard_pressure":{"confidence":0.7,"direction":"up","name":"Physical hazard pressure","unit":"index","value":0.66},"narrative":"CMIP6 ensemble summary with CE near-term pathway overlays anchors the climate risk lens for North America. Under delayed transition conditions, long-run scenario diversity and physical risk framing is most relevant for energy exposure.","physical_vars":null,"policy_contribution":{"hazard":0.0,"resilience":0.0,"transition":0.0},"resilience":{"confidence":0.59,"direction":"up","name":"Adaptive resilience","unit":"index","value":0.4},"shock_contribution":{"hazard":0.0,"resilience":0.0,"transition":0.0},"transition_pressure":{"confidence":0.65,"direction":"up","name":"Transition pressure","unit":"index","value":0.97}},"model":{"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"}}],"climate_models":[{"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"}],"combined_model_profiles":[{"best_for":"balanced climate-economy integration with transition and resilience weighting","component_weights":{"climate":0.42,"economics":0.26,"transmission":0.32},"model_id":"ce-balanced-transition","name":"CE Balanced Transition Synthesizer","narrative":"CE integrated snapshot feed derives an integrated climate-economy profile for energy under delayed transition and fragmented policy conditions.","source":"CE integrated snapshot feed","summary":"Data-derived combined model overlay blending macro, physical, and transmission conditions.","targets":{"confidence_index":0.75,"opportunity_index":0.76,"pressure_index":0.79,"resilience_index":0.52}}],"combined_models":[{"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"}],"economic_forecast":{"growth":{"confidence":0.66,"direction":"up","name":"GDP growth","unit":"%","value":1.47},"inflation":{"confidence":0.61,"direction":"flat","name":"Inflation","unit":"%","value":4.53},"investment":{"confidence":0.58,"direction":"up","name":"Capital formation","unit":"%","value":3.84},"labor":{"confidence":0.56,"direction":"flat","name":"Labor tightness","unit":"index","value":0.66},"narrative":"Consensus economic view derived from the selected model set.","policy_contribution":{},"sector_indicators":{},"shock_contribution":{}},"economic_model_forecasts":[{"forecast":{"growth":{"confidence":0.66,"direction":"up","name":"GDP growth","unit":"%","value":1.47},"inflation":{"confidence":0.61,"direction":"flat","name":"Inflation","unit":"%","value":4.53},"investment":{"confidence":0.58,"direction":"up","name":"Capital formation","unit":"%","value":3.84},"labor":{"confidence":0.56,"direction":"flat","name":"Labor tightness","unit":"index","value":0.66},"narrative":"IMF WEO baseline with CE industry adjustments anchors the economic baseline for North America. For energy, global baseline growth, inflation, and policy context under fragmented policy conditions over the 12-24 months horizon.","policy_contribution":{"growth":0.0,"inflation":0.0,"investment":0.0,"labor":0.0},"sector_indicators":{"capex_shift_to_clean_pct":64,"fossil_fuel_revenue_dependency":0.62,"grid_investment_index":0.84,"power_price_volatility_score":0.69,"renewable_penetration_pct":38.4,"stranded_asset_risk_score":0.71},"shock_contribution":{"growth":0.0,"inflation":0.0,"investment":0.0,"labor":0.0}},"model":{"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"}}],"economic_models":[{"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"}],"guidance":{"industry":"energy","priorities":["Model a late-escalation carbon price shock in portfolio valuations \u2014 delayed transition carries binary transition-risk tail.","Initiate managed wind-down of high-carbon generation assets to limit stranded-asset exposure.","Deploy balance-sheet capacity into grid modernisation and offshore wind to capture transition revenue.","Engage regulators proactively to shape cost-recovery frameworks for early movers."],"rationale":["For energy in North America, climate stress matters economically through operations, financing, and supplier reliability rather than through a single aggregate damage number.","Primary operating pressure: 0.690","Primary financing pressure: 0.990","Composite pressure index: 0.810 (high band)","Climate pathway: Delayed transition \u2192 delayed profile"],"recommended_horizon":"12-24 months","watch_items":["Stranded-asset write-down triggers and accounting treatment","Regulatory forbearance timelines for thermal decommissioning","Social licence risk from energy affordability pressures","Near-term regulatory announcement risk (COP outcomes, domestic carbon-price reviews)"]},"implications":["Energy faces critical climate-economic pressure under a delayed transition pathway \u2014 costs, asset values, and operating conditions are expected to reflect this over the 12-24 months horizon.","Adaptive capacity is moderate: the sector has room to absorb near-term shocks without major operational disruption.","A combined model overlay is applied; 1 overlay shift the integrated view toward a data-derived climate-economy consensus.","The primary route through which climate risk enters energy is grid resilience and peak load \u2014 this is where mitigation spending delivers the greatest near-term return.","Strategic repositioning opportunity is meaningful: acting now on low-carbon transitions can generate a durable competitive advantage."],"integrated_forecast":{"adaptation":null,"climate":{"emissions_contribution":0.0,"hazard_pressure":{"confidence":0.7,"direction":"up","name":"Physical hazard pressure","unit":"index","value":0.66},"narrative":"Consensus climate view derived from the selected model set.","physical_vars":null,"policy_contribution":{},"resilience":{"confidence":0.59,"direction":"up","name":"Adaptive resilience","unit":"index","value":0.4},"shock_contribution":{},"transition_pressure":{"confidence":0.65,"direction":"up","name":"Transition pressure","unit":"index","value":0.97}},"component_contributions":{"climate":0.61,"economics":0.58,"transmission":0.56},"confidence_index":0.71,"damage":null,"economic":{"growth":{"confidence":0.66,"direction":"up","name":"GDP growth","unit":"%","value":1.47},"inflation":{"confidence":0.61,"direction":"flat","name":"Inflation","unit":"%","value":4.53},"investment":{"confidence":0.58,"direction":"up","name":"Capital formation","unit":"%","value":3.84},"labor":{"confidence":0.56,"direction":"flat","name":"Labor tightness","unit":"index","value":0.66},"narrative":"Consensus economic view derived from the selected model set.","policy_contribution":{},"sector_indicators":{},"shock_contribution":{}},"fiscal":null,"natural_capital":{"critical_dependencies":["Freshwater availability","Coastal ecosystems","Wetlands"],"dependency_scores":{"Coastal ecosystems":0.48,"Fisheries":0.05,"Forest cover":0.22,"Freshwater availability":0.85,"Pollinators":0.04,"Soil health":0.12,"Wetlands":0.38},"depletion_risk":{"Coastal ecosystems":0.04,"Fisheries":0.04,"Forest cover":0.03,"Freshwater availability":0.04,"Pollinators":0.03,"Soil health":0.03,"Wetlands":0.05},"ecosystem_pressure_index":0.1834,"industry":"energy","narrative":"The energy sector has elevated ecosystem pressure under a delayed transition pathway. The two most critical natural capital dependencies are freshwater availability and coastal ecosystems. Under current or delayed policy the depletion trajectory continues at observed rates. Aggregate ecosystem pressure index: 0.183.","sources":["FAO (2020) Global Forest Resources Assessment. Rome.","FAO AQUASTAT (2023) Global freshwater withdrawal statistics.","Ramsar Convention Secretariat (2021) Global Wetland Outlook: Special Edition."]},"opportunity_index":0.72,"pressure_index":0.81,"provenance":[{"caveats":["Long-run climate scenarios are conditional, not unconditional forecasts."],"domain":"climate","methodology":"ensemble scenario framing with observed-state anchoring","source_family":"CMIP6 + reanalysis-informed"},{"caveats":["Sector-specific outcomes depend on transmission assumptions."],"domain":"economics","methodology":"scenario-based macro baseline with transition overlays","source_family":"IMF/NGFS/NiGEM-inspired"},{"caveats":["Combined models are scenario-conditional syntheses rather than structural forecasts."],"domain":"combined","methodology":"data-derived combined model overlays blended into fused output","source_family":"Integrated climate-economy feed"}],"resilience_index":0.53,"scenario":{"active_shocks":[],"assumptions":["Policy remains directionally tighter on emissions reporting.","Capital costs stay above the previous decade baseline.","Acute climate disruptions remain operationally relevant."],"climate_pathway":"Delayed transition","confidence_notes":["Near-term climate conditions retain substantial internal variability.","Industry transmission scores are scenario-conditional, not deterministic losses."],"energy_regime":"elevated volatility","geography":"North America","horizon":"12-24 months","industry":"energy","policy_instruments":{},"policy_regime":"Fragmented policy","shock_family":"compound transition","trade_regime":"selective fragmentation"},"summary":"energy in North America faces elevated climate-linked pressure, but still retains selective growth potential if capital is redirected toward resilience and supply-chain hardening. Combined model overlays from CE Balanced Transition Synthesizer shift the integrated profile toward a data-derived climate-economy consensus.","supply_chain_risk":{"affected_nodes":["aviation","shipping","manufacturing","transport","agriculture","trucking"],"cost_amplification":3.57,"critical_path":["energy","aviation"],"industry":"energy","narrative":"A supply disruption originating in global affecting energy propagates primarily to aviation, shipping, manufacturing. Tier exposures \u2014 tier 1: 2.41; tier 2: 0.16. Overall cost amplification factor: 3.57x above direct shock cost. High geographic concentration and low substitutability on key upstream links are the dominant risk drivers.","shock_source_geography":"global","sources":["OECD TiVA 2023 \u2014 Trade in Value Added database","IMF Global Supply Chain Pressure Index 2024","ClimateWorks Foundation physical risk supply chain study 2023","UNCTAD Maritime Transport Review 2024"],"tier_exposures":{"tier_1":2.4118,"tier_2":0.1584},"time_to_impact_months":4.0},"tipping_risk":{"activated_tips":["west_antarctic_ice_sheet","permafrost_carbon_feedback","coral_bleaching_dieoff","greenland_ice_sheet"],"damage_amplifier":1.3045,"dominant_risk":"coral_bleaching_dieoff","narrative":"At +2.0\u00b0C, 4 tipping point(s) exceed the activation threshold \u2014 critical tipping risk for the energy sector. The dominant risk is Coral Reef Mass Die-off: Reef loss removes coastal buffering, increasing storm-surge insurance claims for tropical coastal property. Fisheries collapse disrupts food security in over 1 billion coastal-dependent people. Tourism sector losses cascade to hospitality and transport. Cascade compounding amplifies sector damage by 1.30\u00d7 relative to linear projections. Non-linearities at this temperature level mean adaptation windows may be shorter than scenario timelines suggest.","probabilities":{"amazon_dieback":0.1,"amoc_slowdown":0.22,"coral_bleaching_dieoff":0.75,"greenland_ice_sheet":0.4,"permafrost_carbon_feedback":0.45,"west_antarctic_ice_sheet":0.35},"sources":["Armstrong McKay et al. (2022) Science \u2014 Exceeding 1.5\u00b0C global warming could trigger multiple climate tipping points","Lenton et al. (2019) Nature \u2014 Climate tipping points \u2014 too risky to bet against","IPCC AR6 WG1 Chapter 4 (2021) \u2014 Short-lived Climate Forcers and Tipping Points"],"tip_count":4},"transmission":{"channels":[{"affected_metrics":["operating margin","capex timing","reliability"],"impact_score":0.76,"mechanism":"Heat and acute weather raise outage risk while investment costs shape asset replacement timing.","name":"Grid resilience and peak load"},{"affected_metrics":["power pricing","capital allocation","regulatory exposure"],"impact_score":0.72,"mechanism":"Transition policy and financing conditions reprice generation portfolios and interconnection decisions.","name":"Policy and generation mix"}],"financing_pressure":0.99,"industry":"energy","narrative":"For energy in North America, climate stress matters economically through operations, financing, and supplier reliability rather than through a single aggregate damage number.","operating_pressure":0.69,"supply_chain_pressure":0.7},"uncertainty_band":{"half_width":0.0348,"opportunity_hi":0.7548,"opportunity_lo":0.6852,"pressure_hi":0.8448,"pressure_lo":0.7752,"resilience_hi":0.5648,"resilience_lo":0.4952}},"steps":[{"detail":"For energy in North America, climate stress matters economically through operations, financing, and supplier reliability rather than through a single aggregate damage number.","title":"How climate risk reaches this sector"},{"detail":"Operating pressure: 0.69 / 1.0. Financing pressure: 0.99 / 1.0. Supply-chain pressure: 0.70 / 1.0. Higher values signal tighter margins, elevated cost of capital, and procurement risk.","title":"Operating and financing pressure"},{"detail":"This output models a Delayed transition pathway under Fragmented policy conditions over a 12-24 months horizon. Integration confidence: 71%.","title":"Scenario and policy context"},{"detail":"Pressure, resilience, and opportunity indices are integrated across economic, climate, and transmission layers \u2014 not drawn from a single model. They can be updated as policy signals, market conditions, or physical climate data change. Revisit quarterly or after major policy announcements.","title":"What this means for planning"}],"transmission":{"channels":[{"affected_metrics":["operating margin","capex timing","reliability"],"impact_score":0.76,"mechanism":"Heat and acute weather raise outage risk while investment costs shape asset replacement timing.","name":"Grid resilience and peak load"},{"affected_metrics":["power pricing","capital allocation","regulatory exposure"],"impact_score":0.72,"mechanism":"Transition policy and financing conditions reprice generation portfolios and interconnection decisions.","name":"Policy and generation mix"}],"financing_pressure":0.99,"industry":"energy","narrative":"For energy in North America, climate stress matters economically through operations, financing, and supplier reliability rather than through a single aggregate damage number.","operating_pressure":0.69,"supply_chain_pressure":0.7}}