[{"affected_models":["ClimateModelService"],"affected_scenarios":["all"],"alternative_assumptions":["Use ECS with explicit ocean heat uptake parameterisation","Use TCRE (transient climate response to cumulative emissions) directly"],"category":"Climate","counterarguments":"For projections beyond 2100, ECS becomes more relevant; TCR underestimates long-run commitment. Some models show TCR/ECS ratio departing from 0.6 standard assumption.","failure_conditions":"Would fail if rapid warming commitment (zero-emission commitment) substantially exceeds current IPCC estimate of 0.1\u20130.5\u00b0C","id":"A-CLM-001","last_reviewed":"2026-01-15","rationale":"TCR reflects transient (realised) warming over the policy-relevant horizon. ECS (equilibrium) overestimates near-term warming since ocean heat uptake is slow.","review_owner":"Climate Science Team","sensitivity_level":"high","statement":"TCR is the appropriate sensitivity metric for near-term warming projections (2025\u20132060).","supporting_evidence":"IPCC AR6 WG1 Section 4.3; Gillett et al. (2021) Geophysical Research Letters","validation_status":"validated_against_cmip6"},{"affected_models":["ClimateModelService","GapAccountingEngine"],"affected_scenarios":["all"],"alternative_assumptions":["Use GCP 2024 estimate of 54\u201356 GtCO\u2082 (CO\u2082-only); note non-CO\u2082 gases add ~10 GtCO\u2082e"],"category":"Climate","counterarguments":"Methane emissions may be underestimated by 40\u201360% per recent atmospheric measurements (Worden et al. 2017). Land use emissions highly uncertain (\u00b12 GtCO\u2082e).","failure_conditions":"If methane inventory corrections materially revise total; if COVID rebound was larger/smaller than estimated","id":"A-CLM-002","last_reviewed":"2026-02-01","rationale":"UNEP Emissions Gap Report 2024 is the most current and comprehensive global GHG accounting.","review_owner":"Climate Science Team","sensitivity_level":"high","statement":"The 2025 baseline emissions are 57.4 GtCO\u2082e/yr under current policies.","supporting_evidence":"UNEP EGR 2024 (Nov 2024); Global Carbon Project 2024; Climate Watch GHG data","validation_status":"annually_updated"},{"affected_models":["ClimateModelService","GapAccountingEngine"],"affected_scenarios":["all"],"alternative_assumptions":["Logistic (S-curve) deployment for each technology sub-sector"],"category":"Climate","counterarguments":"Technological deployment follows S-curves, not linear ramps. Linear interpolation underestimates early-decade inertia and late-decade acceleration.","failure_conditions":"Causes systematic over-optimism in 2025\u20132030 window; use S-curve deployment for technology-specific projections","id":"A-CLM-003","last_reviewed":"2026-01-15","rationale":"Linear interpolation is standard in scenario planning tools and avoids spurious precision between anchor years.","review_owner":"Climate Science Team","sensitivity_level":"medium","statement":"Linear interpolation between 2025, 2030, 2040, 2050 emissions waypoints is sufficient for pathway modelling.","supporting_evidence":"IPCC AR6 WG3 scenario methodology (similar approach in C1 pathway representation)","validation_status":"partially_validated"},{"affected_models":["DamageModelService"],"affected_scenarios":["all"],"alternative_assumptions":["Burke et al. (2015) non-linear damage function \u2014 gives ~23% global GDP loss at 3\u00b0C","Weitzman (2010) tipping-point adjusted damages","META model (Dietz et al. 2021) \u2014 explicit tipping cascade"],"category":"Physical Damage","counterarguments":"Quadratic form dramatically underestimates high-temperature damage. Weitzman 'Dismal Theorem' suggests heavy tails dominate expected utility. Burke et al. (2015) non-linear function gives 3\u20135\u00d7 higher damage at 4\u00b0C.","failure_conditions":"Fails at temperatures above ~3.5\u00b0C where tipping points, migration, and conflict become dominant damage channels not in DICE","id":"A-DAM-001","last_reviewed":"2026-01-20","rationale":"DICE is the most widely cited integrated assessment model damage function, enabling comparison with published literature.","review_owner":"Economics & Damage Team","sensitivity_level":"critical","statement":"The quadratic DICE damage function is used as the central damage estimate.","supporting_evidence":"Nordhaus (2023) DICE-2023; meta-analysis in Howard & Sterner (2017) with 27 studies","validation_status":"calibrated_to_literature"},{"affected_models":["CatastropheModel","DamageModelService"],"affected_scenarios":["SSP3-7.0","SSP5-8.5"],"alternative_assumptions":["Fully dynamic hazard modelling using CMIP6 GCM outputs at peril-level"],"category":"Physical Damage","counterarguments":"Hazard non-stationarity may be discontinuous (tipping points, jet stream shifts). Multiplicative factors may be insufficient for extreme tail events.","failure_conditions":"Fails for perils with strong non-linear hazard amplification under warming (tropical cyclone intensification at high warming levels)","id":"A-DAM-002","last_reviewed":"2026-01-20","rationale":"Current cat model vendor hazard datasets are calibrated to historical periods. A multiplicative adjustment factor scales AAL for projected climate change.","review_owner":"Catastrophe Risk Team","sensitivity_level":"high","statement":"Catastrophe model hazard distributions are treated as stationary with a climate non-stationarity adjustment factor applied.","supporting_evidence":"RMS (2022) climate change adjustment methodology; Swiss Re Institute climate risk reports","validation_status":"partially_validated"},{"affected_models":["EconomicModelService"],"affected_scenarios":["high_warming"],"alternative_assumptions":["Additive damage formulation with floor constraint at 0"],"category":"Economics","counterarguments":"Additive formulation (Stern-style) can result in GDP below zero at extreme warming \u2014 Weitzman argues this is realistic for civilisational collapse scenarios.","failure_conditions":"Results in underestimation of damage at extreme warming; cannot represent true tipping-point economic collapse","id":"A-ECO-001","last_reviewed":"2026-02-01","rationale":"Multiplicative entry avoids GDP going negative at high damage levels; consistent with most IAM literature.","review_owner":"Economics Team","sensitivity_level":"medium","statement":"Climate damages enter the production function multiplicatively (\u03a9 scaling), not additively.","supporting_evidence":"Dietz & Stern (2015); DICE model family convention","validation_status":"convention_based"},{"affected_models":["EconomicModelService","GapAccountingEngine"],"affected_scenarios":["all"],"alternative_assumptions":["Step-function MAC with technology-specific discontinuities"],"category":"Economics","counterarguments":"Real technology deployment follows lumpy investment decisions and has lock-in effects. The MAC curve is empirically non-convex around major technology transitions.","failure_conditions":"Fails around major technology thresholds (e.g. when grid-scale storage crosses parity \u2014 cost drops discontinuously)","id":"A-ECO-002","last_reviewed":"2026-02-01","rationale":"Smooth MAC curves are standard in economic optimisation models and allow analytical treatment.","review_owner":"Economics Team","sensitivity_level":"medium","statement":"The MAC curve is smooth and convex \u2014 no technology step discontinuities.","supporting_evidence":"McKinsey MAC curve (2020); IPCC AR6 WG3 Chapter 3","validation_status":"stylised_fact"},{"affected_models":["FiscalModelService","EconomicModelService"],"affected_scenarios":["policy_optimistic"],"alternative_assumptions":["Partial efficiency with explicit coverage rate and leakage parameters"],"category":"Economics","counterarguments":"Real carbon markets have significant transaction costs, banking/borrowing rules, exemptions, price floors/ceilings, and political interference. Coverage rates remain low globally (~23% of emissions, World Bank 2024).","failure_conditions":"Overestimates carbon revenue and price signal effectiveness in jurisdictions with weak carbon market institutions","id":"A-ECO-003","last_reviewed":"2026-02-01","rationale":"Efficient carbon markets are the standard assumption in policy optimisation literature.","review_owner":"Policy & Finance Team","sensitivity_level":"high","statement":"Carbon markets function efficiently with full price signal transmission to emitters.","supporting_evidence":"Theoretically grounded; supported by EU-ETS empirical studies","validation_status":"debated"},{"affected_models":["GapAccountingEngine"],"affected_scenarios":["optimistic"],"alternative_assumptions":["Conservative scenario: only TRL\u22657 technologies achieve significant deployment by 2040"],"category":"Technology","counterarguments":"Historical technology scale-up timelines rarely match policy projections. Nuclear fusion, advanced geothermal, and DAC have repeatedly missed deployment milestones. Technology-valley-of-death between demonstration and commercial scale is empirically persistent.","failure_conditions":"Most scenarios relying on >5 Gt from currently sub-commercial technologies have no historical precedent of comparable scale-up","id":"A-TECH-001","last_reviewed":"2026-02-05","rationale":"IEA NZE and IPCC AR6 WG3 scenarios require significant pre-commercial technology deployment.","review_owner":"Technology Team","sensitivity_level":"critical","statement":"Breakthrough technologies scale from current TRL to commercial deployment by 2040 under optimistic scenario.","supporting_evidence":"IEA NZE 2050 technology deployment milestones; IRENA breakthrough technologies assessment","validation_status":"historically_not_validated"},{"affected_models":["GapAccountingEngine","LandValuationService"],"affected_scenarios":["all"],"alternative_assumptions":["No-BECCS scenario (Dooley et al. 2018)","High-BECCS scenario (IEA NZE low-land-use variant)"],"category":"Technology","counterarguments":"Some integrated assessment models use up to 200 EJ/yr BECCS. Realistic sustainable biomass potential is highly contested.","failure_conditions":"If BECCS ceiling is set too high, net-zero scenario becomes unachievable in practice due to competing land uses (food, biodiversity, carbon sinks)","id":"A-TECH-002","last_reviewed":"2026-02-05","rationale":"Land use and water constraints from IPCC AR6 WG3 Chapter 12 and dedicated land use analysis.","review_owner":"Land & Natural Capital Team","sensitivity_level":"critical","statement":"BECCS ceiling is 4.5 EJ/yr biomass feedstock (approximately 2.5 Gt CDR/yr).","supporting_evidence":"IPCC AR6 WG3 Chapter 12.3; Fajardy et al. (2018) Nature Climate Change","validation_status":"literature_range"},{"affected_models":["MonteCarloEngine"],"affected_scenarios":["uncertainty_analysis"],"alternative_assumptions":["Correlated sampling using Cholesky decomposition; pairwise correlations from IPCC scenario ensemble"],"category":"Uncertainty","counterarguments":"Technology abatement potential, policy effectiveness, and carbon budget are positively correlated \u2014 all are high under optimistic scenarios. Ignoring correlation underestimates tail risk in pessimistic scenarios.","failure_conditions":"Underestimates P5 (pessimistic tail) by approximately 15\u201325% based on CE sensitivity test","id":"A-UNC-001","last_reviewed":"2026-01-10","rationale":"Independence simplifies implementation and is the default in most MC scenario tools.","review_owner":"Quantitative Methods Team","sensitivity_level":"high","statement":"Parameter distributions are independent (zero covariance) in the Monte Carlo simulation.","supporting_evidence":"CE internal implementation; standard practice in ensemble climate-economic modelling","validation_status":"known_limitation"},{"affected_models":["MonteCarloEngine"],"affected_scenarios":["uncertainty_analysis"],"alternative_assumptions":["Log-normal for damage parameters","Pareto tails for catastrophic risk parameters"],"category":"Uncertainty","counterarguments":"Many climate-economic parameters have heavy right tails (damage function, carbon budget) that are poorly captured by normal distributions. Damage costs may follow power-law tails.","failure_conditions":"Underestimates catastrophic tail risk; power-law tails for damage would materially shift P95","id":"A-UNC-002","last_reviewed":"2026-01-10","rationale":"These distributions are analytically tractable and well-understood. They match the symmetric-unimodal character of most climate parameter posteriors.","review_owner":"Quantitative Methods Team","sensitivity_level":"medium","statement":"Normal and triangular distributions adequately represent parameter uncertainty.","supporting_evidence":"IPCC AR6 uncertainty characterisation guidance; CE parameter calibration","validation_status":"known_limitation"},{"affected_models":["ClimateModelService","EconomicModelService","FiscalModelService"],"affected_scenarios":["policy_optimistic","orderly_transition"],"alternative_assumptions":["Realistic implementation scenario with 60\u201370% NDC delivery rate"],"category":"Governance","counterarguments":"Historical NDC implementation rates are significantly below stated commitments. Climate Action Tracker currently rates 75% of NDCs as 'insufficient'. Policy reversal risk is not quantified.","failure_conditions":"If policy implementation remains at current rates, 2\u00b0C pathway becomes infeasible with existing policies by 2028 (Climate Action Tracker)","id":"A-GOV-001","last_reviewed":"2026-02-10","rationale":"NGFS scenarios are the institutional standard for financial sector climate risk assessment.","review_owner":"Policy Team","sensitivity_level":"critical","statement":"NDC commitments stated by governments will be implemented with the effectiveness rates assumed in NGFS Phase IV scenarios.","supporting_evidence":"NGFS Phase IV Technical Documentation (2023); Climate Action Tracker NDC assessment","validation_status":"contested"}]