GCRI · Reports
Scenarios and methods
Scenarios and methods reports set out the analytical machinery behind the series: scenario frameworks, models, statistical methods and the assumptions that drive them. A scenario is a coherent account of a possible future, not a prediction, and its value depends on whether readers can see and test what went into it. This page sets out how analytical studies are documented, reviewed and reproduced, and how their uncertainty is stated.
Scenarios and methods on the record.
Scenarios and methods: how the risk develops.
Transparent analytical studies. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Hidden assumptions
Discount rates, baselines, elasticities and exposure data can drive results more than the model does. When assumptions sit in code rather than in the text, reviewers cannot test what matters most.
Scenarios read as forecasts
Illustrative pathways are quoted as expected outcomes, and a middle scenario is read as most likely. Without stated purpose and plausibility, scenario results become false predictions in decision documents.
Model lineage
Models are reused and extended across studies, so an error in a shared component or dataset propagates into every study built on it. Version identifiers make that lineage traceable.
Reproduction is rarely tried
Code and data may be available yet never rerun by anyone else. Without independent reproduction, errors in implementation can persist through review and into the reports that cite the results.
Series production desk.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Series production desk
Scenarios and methods · Annual synthesis · Edition 9
Editions in production
| Annual synthesis | Global | Data check | 75 | W13 |
| Country profile 7 | Country | Peer review | 99 | W32 |
| Water outlook | Sectoral | Drafting | 50 | W15 |
| Methods note 4 | Methods | Released | 100 | done |
Data check, annual synthesis
Edition pipeline
- Outlineboard
- Draftauthors
- Data checkevery value
- Peer reviewtwo rounds
- Editorialdecision
- ReleaseDOI
- Correctionsversioned
Scenario and methods editions serve every domain’s analysts.
Methods follow reporting guides such as PRISMA 2020 for reviews; scenario results are never presented as forecasts. Models carry NS-12 cards.
- Methods papers.
- Scenario critiques.
Stocks, flows, delays and a tipping threshold.
Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. Teaching model with stated illustrative values, not a prediction.
Controls
- Growth of pressure
- 6.0% a year
- Efficacy of capacity in reducing pressure
- 4.0% a year
- Investment response to perceived pressure
- 20%
- Perception delay
- 4.0 years
- Tipping threshold of pressure
- 60 index
- Amplification past the threshold
- 2.0 times
- Years simulated
- 50 years
Readouts
- Peak pressure
- 113 index
- Capacity at the end
- 186 index
- Years over the threshold
- 15.8 years
Numbers, methods and sources
| Year | Pressure | Capacity | Perceived |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Methods
- System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Reports in Foundry
- Tested at Reports in Universe
- Codified in Data and Evidence in Standards
- Published in Reports in Reports
- Taught in Faculty of Risk Science and Methods in Academy
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Studies with archived code and data | percent of studies | per version | Repository deposit records | OBSERVED |
| Assumptions documented in an assumptions register | percent of key parameters | per study | Editorial audit | REPORTED |
| Independent reproduction status | category: not attempted, partial, full | per study | Reproduction record | REPORTED |
| Sensitivity analyses reported for key parameters | percent of key parameters | per study | Editorial audit | REPORTED |
| Model and dataset versions cited with identifiers | percent of model and data citations | per version | Reference metadata | OBSERVED |
| Statistical or methods review completed | yes or no, with reviewer role | per study | Review record | DECLARED |
How scenarios and methods is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Model, equations and assumptions
The model structure used for scenarios and methods, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)hazard, exposure, vulnerability and coping capacityEAL = ∑k pk · Lkover events k with annual probability pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Assumption | Why it is made | How it is tested |
|---|---|---|
| Hazard and exposure are independent within an event | Keeps the loss integral tractable | Compare with a copula fit on joint records |
| Couplings are linear near the operating point | Lets shocks propagate through A | Perturb beyond the linear range and compare |
| Vulnerability curves transfer between places | Local curves are rarely available | Validate against observed losses where they exist |
- Inputs
- versioned datasets with provenance
- Outputs
- distributions, never single numbers
- Code
- released with the model card and tests
- Reproduction
- same data, code, environment and seed
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| AR6 Scenarios Database (IAMC and IIASA) | Integrated assessment scenarios assessed by the IPCC with metadata | Free with registration, terms per IIASA |
| SSP Database (IIASA) | Shared Socioeconomic Pathway projections of population, economy and emissions | Free with registration, terms per IIASA |
| NGFS climate scenarios | Climate-related financial scenarios with documentation | Free to use, terms per NGFS |
| Zenodo | Archived code, data and model versions with DOIs | Metadata CC0, licence per record |
| Software Heritage | Archived source code with intrinsic identifiers | Code under its own licence |
| OSF Registries | Pre-registered analysis plans and study protocols | Metadata CC0, licence per registration |
Who decides, and what the evidence serves.
Reports prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Central banks and financial supervisors | Which scenario sets are fit for climate and systemic stress testing | Scenario documentation, assumptions and sensitivity of results |
| Planning and environment ministries | How to use scenario results in national plans without reading them as forecasts | Stated scenario purpose and the range across scenarios |
| Statistical and methods review panels | Whether a method is adequately documented and reproducible | Assumptions registers, archived code and reproduction records |
| Research funders | Whether to require code, data and reproduction for funded analytical work | Archive and reproduction indicators across studies |
Outputs this topic produces
- Method reports with assumptions registers and sensitivity analyses
- Archived code and data with persistent identifiers
- Scenario descriptions stating purpose, framing and plausibility
- Reproduction records from independent reruns
What the series asks.
Findings are only as reliable as the methods behind them. Scenario studies and stress tests are powerful tools for exploring plausible futures and testing resilience, but they are easily misread as predictions or as verdicts on institutions. Publishing methods in full, with their assumptions and validation, lets readers judge what a result means and lets other researchers reproduce or challenge it.
- Which assumptions drive a scenario’s results, and how sensitive are the conclusions to them?
- Has the model been validated against historical events or independent data, and with what result?
- How are uncertainties combined and communicated, including deep uncertainty that cannot be quantified?
- Can another team reproduce the result from the published data and code?
What the series covers.
Two series publish analysis and the methods behind it.
Plausible futures
Narrative and quantitative scenarios that explore how systems could evolve under stated assumptions.
Severe but plausible shocks
Tests of how systems respond to defined shocks; they describe systems, not the soundness of any firm.
Technical notes
Model documentation, data pipelines and validation reports for reuse and review.
Good scenario practice distinguishes between exploratory scenarios, which map a range of plausible futures, and stress scenarios, which test a system against a severe but plausible shock. Both are useful, and both are misused when a single path is presented as the expected outcome. Editions therefore present sets of scenarios with their driving assumptions and show where results diverge.
How editions are built, and their limits.
- 01
Model documentation
Purpose, structure, inputs, assumptions and known limits, published alongside results.
- 02
Validation
Back-testing, comparison with independent data and sensitivity analysis, using uncertainty quantification and model validation.
- 03
Reproducibility
Data and code released where rights permit, with pipelines documented under data engineering, interoperability and stewardship.
- 04
Communication
Results labelled Scenario, Modelled or Forecast, with ranges rather than single numbers where uncertainty is material.
How it is studied, and what the methods cannot say.
- 01Uncertainty quantification and model validationCalibrated language in assessments · Certainty of evidence ratingsApplied here
- 02Probability and statisticsMeta-analysis with heterogeneity stated · Small-study and publication biasApplied here
- 03Data engineering, interoperability and stewardshipStructured publishing in JATS XML · Persistent identifiers across the recordApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
Is a scenario a forecast?
What is the difference between reproduction and replication?
Why publish an assumptions register?
Is a scenario a prediction?
Are the stress tests regulatory stress tests?
Can I reuse the models?
What if a method is found to be flawed?
Contribute evidence from this page.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
- EmailGateway GW-GCRISend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
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- ReportsGCRISee every way to contributeThe contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.
Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.
Bring a method, a dataset or a question.
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