Research and version note This is a version 0.1 research draft in The Future Has No Representative. It examines how climate scenarios bring future populations into current decisions and makes no new climate projection. IPCC Sixth Assessment Cycle material was reviewed to August 2026. The Seventh Assessment is in progress, so later versions should be revised as new assessments appear.
The Future Has No Representative · Article 9
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A climate model does not discover a particular person who will be born in 2090. Physical climate models use emissions or concentration pathways to simulate changes in temperature, precipitation, sea level, and other systems. Impact models combine those outputs with assumptions about population, land, infrastructure, and vulnerability to estimate exposure or loss. “Future population” in a table is a set of conditional model variables, not testimony from future people.
This does not make climate modelling a political invention. Atmospheric physics, observations, historical climate, and model evaluation impose strong empirical constraints. The IPCC Sixth Assessment finds unequivocal human influence on the warming of atmosphere, ocean, and land, and reports possible climate futures under different emissions scenarios. A future outcome depends on human action that has not yet occurred, so a scenario is not a forecast. It asks what physical and social consequences follow if emissions and social conditions develop in the stated way.
Political judgment enters elsewhere. Researchers and governments choose which scenarios to show, how population and economy are estimated, and what weights deaths, displacement, cultural loss, and ecosystems receive. Global mean temperature is comparable and important but hides regional and social difference. An assumption of wealthy, effective adaptation can reduce modelled loss while granting the future an institutional capacity that has not yet been built.
Moving from global projections to local policy also requires an explicit account of scale. A finer-looking number is not necessarily more certain local knowledge.
Nor are modelled people political subjects. They cannot reject an assumption, correct data, or express attitudes to risk. A model can reveal how a kind of future life may be affected without authorising present decision-makers to choose everything on its behalf. Policy has to combine model output with rights, distribution, local knowledge, and reversibility.
Climate models give future interests conditional visibility; they do not obtain future consent. Responsible use discloses scenario and value assumptions, compares several pathways, reveals group and regional differences, and applies safety margins under important uncertainty. It would be wrong to ignore a model because it is not future fact, and equally wrong to transfer political responsibility to it because its numbers are precise.
What does a map of loss in 2100 depict?
A climate-risk map may show the number of people exposed to extreme heat, flooding, or rising seas in a region in 2100. Its colours are clear and the population counted in millions, making it easy to view as a photograph of future reality. The construction contains several conditional layers.
It begins with a greenhouse-gas emissions or concentration pathway. Earth-system models simulate climate response to that forcing and produce variables including temperature, precipitation, oceans, and extremes. Downscaling converts global results into finer spatial information. Impact assessment then adds future population distribution, buildings, land use, health relationships, and adaptive capacity to derive exposure or damage.
Each layer answers a different question. Physical models estimate how a climate system responds under conditions. Population and economic scenarios describe a possible society. Damage functions connect climatic variables with consequences. Policy appraisal decides which consequences matter and how much. Calling the final map simply “what science says 2100 will be like” hides this conditional architecture.
Separating layers should not be used to dismiss science. Some relations are strongly constrained by evidence. The connection between cumulative carbon dioxide emissions and global warming is not an arbitrary storyline. A range among models does not imply that all outcomes are equally possible or erase observed human influence.
Facts, projections, and scenarios
The IPCC Working Group I Sixth Assessment headline statements describe human influence on the warming of the atmosphere, ocean, and land as unequivocal. That is an assessment of change already experienced, based on observation, theory, and attribution.
Future temperatures are expressed as conditional projections. The IPCC uses illustrative scenarios including SSP1-1.9, SSP2-4.5, and SSP5-8.5 to examine different greenhouse-gas paths. The AR6 Synthesis Report reports best estimates for average warming in 2081–2100 ranging from about 1.4°C under the very low scenario, through 2.7°C under an intermediate scenario, to 4.4°C under the very high scenario, with uncertainty ranges.
These are not three competing weather forecasts for one date. Emissions will be shaped by policy, technology, economy, and behaviour. A scenario explores results under an internally consistent set of assumptions and is not automatically assigned a probability of social occurrence. In IPCC usage, scenarios are plausible descriptions of possible future development, neither predictions nor forecasts.
There is physical uncertainty within a projection as well. With the same emissions pathway, climate sensitivity, natural variability, and model structure produce a range. Policy users need to know whether a range comes from scenario differences or physical response. The first can be changed through mitigation choices; the second gives a stronger reason for resilience and safety margins.
The calibrated uncertainty language of the IPCC should survive policy communication. “Confidence” and “likelihood” are not casual expressions that scientists happen to feel moderately certain; they organise judgment from evidence and agreement. A media or policy document that extracts only an upper bound, lower bound, or best estimate may change what the assessment says. Responsible use states the condition, reference period, and range together.
Attribution requires an additional distinction. A model can estimate the contribution of human influence to an observed climatic trend without attributing every consequence of one local disaster to one emitter. Legal and political responsibility may use cumulative contribution, capacity, and control, but that is a further institutional step. Scientific attribution narrows causal uncertainty; it does not allocate compensation by itself.
What society enters through the Shared Socioeconomic Pathways?
The SSPs are more than emissions curves. They construct socio-economic conditions through narratives and assumptions about population, economy, urbanisation, education, technology, and international cooperation. Integrated assessment models study energy, land, and mitigation within those conditions and provide emissions for physical climate modelling.
People initially appear through aggregated variables such as population, income, residence, and consumption. They have no names and express no conception of justice. Two pathways could reach a similar global temperature through different land, technology, and distributional arrangements, with different consequences for particular groups.
Aggregation is necessary for large-scale analysis. A global model cannot represent every household, and common variables enable comparison. The error begins when an aggregate is treated as everyone’s experience. Average GDP growth does not say who has cooling, insurance, mobility, or political protection. A count of coastal population does not express a cultural relationship to country.
Movement between scales can create boundary effects. A national mean precipitation trend is not the same variable as flood behaviour in one catchment, and a global temperature goal does not directly specify the design of one building. Users should explain how global, regional, and local evidence are connected; a finer-looking number does not necessarily contain finer local knowledge.
Social assumptions can also pre-empt questions the model should expose. High future income may imply stronger adaptation and lower damage relative to GDP. Whether growth reaches affected people and whether governments create public adaptation are institutional questions. Including them as smooth assumptions can make future people appear more capable of rescuing themselves than current policy warrants.
When do future people become values?
Physical models calculate heat and sea level; policy models have to compare human consequences. Mortality risk, illness, labour loss, and property can receive monetary values. Ecosystems and cultural places are harder to price. A discount rate then determines how much distant harm weighs today.
This is not science covertly becoming politics. Public decisions necessarily contain valuation. The concern is whether it is visible. A model reporting one “optimal carbon price” may conceal population, growth, damage functions, risk preference, and discounting.
Article 3 of this series showed how a rate changes present value over a century. Climate appraisal adds distribution. The same average income loss means something different to a poor farmer and a wealthy urban household. Cultural and ecological damage may not be compensated by greater future consumption.
Future life should not appear solely as an expected monetary damage. Quantitative models compare scale and timing, but non-monetary indicators, rights thresholds, and regional analysis should stand alongside them. The more aggregated the model, the more external judgment has to restore differences compressed by it.
Can a model represent unborn people?
A model performs a representative-like function: it brings consequences for people unable to speak into the room. Without long projections, sea-level rise and heat a century away more readily vanish from a short budget. Modelling extends the causal chain beyond the decision-maker’s direct observation.
It lacks several properties of political representation. Future people did not authorise it, cannot dismiss its builders, and cannot state what they value. Scientific method can test accuracy; predictive performance alone cannot establish the legitimacy of a representative relationship.
Calling a model “the voice of the future” therefore goes too far. It is better understood as conditional evidence about the types of risk imposed by a path. Evidence constrains arbitrary political denial, but does not complete choices about distribution and rights.
Conversely, a lack of democratic authorisation is no reason to ignore it. A bridge calculation does not need an election before it has epistemic authority within engineering. Democratic institutions decide how evidence enters action and remain accountable for values; they do not vote the physical relation into being.
Why several scenarios are necessary
Showing only the optimistic pathway may treat policies not yet implemented as facts. Showing only a very high emissions pathway may make it seem like a forecast. Multiple scenarios distinguish a future altered by action from physical uncertainty that cannot be eliminated.
They should not be averaged mechanically into one central future. A weighted mean without credible probabilities creates false precision. Planners can seek measures effective across several paths and establish triggers for escalation under high-consequence developments.
Low-likelihood, high-impact outcomes should not disappear because they lie outside the most likely interval. The IPCC states that ice-sheet collapse, abrupt ocean-circulation change, and other low-likelihood outcomes cannot be ruled out and form part of risk assessment. Public safety normally considers consequence together with probability.
Scenario choice should fit purpose. A short-term asset needs regional extremes and service-life information. Global negotiations address cumulative emissions and equity. A local adaptation plan needs situated experience. Applying one global mean graphic to every problem gives a model authority at the wrong scale.
Who else has to enter the decision?
Communities experiencing climate risk now possess observations and values, not merely samples for model validation. Indigenous knowledge of country, farmers’ seasonal experience, clinicians’ heat-health information, and local maintenance records can disclose relations below model resolution. They do not replace physical models; they change how risk becomes understood and acted upon.
Youth participation has distinct relevance. Young people are present rights-holders who will experience policy for longer, but they still cannot represent every later person. Their involvement can correct an age imbalance without proving that “the future has consented”.
Model governance needs methodological disclosure, version records, reproducibility, and conflict-of-interest information. Where a commercial climate model changes insurance or credit, affected people also need access to significant assumptions. A model about future hazard is then creating present effects on housing and opportunity.
An accountable institution must ultimately make policy. Scientists can describe evidence and scenarios without being made responsible for every political value choice. Office-holders cannot avoid distributional reasons by saying, “the model required it”.
Provisional judgment: models make future consequences visible without making future people present
The future population in a climate model is neither an existing fact nor an arbitrary fiction. It is constructed through demographic and social assumptions and experiences conditional consequences within a climate projection constrained by physical evidence. It is not a political subject because a variable cannot exercise rights, consent, or objection.
That limitation does not weaken the value of modelling in intergenerational governance. Present emissions alter cumulative conditions, and many effects endure. Scenarios reveal boundaries being shaped for successors and compare policy paths. Without that knowledge, the absence of future people is easier for present interests to exploit.
Responsible use preserves the layers. Observed fact, physical projection, social scenario, damage valuation, and normative decision should be stated separately. Results should show ranges and regional distribution, with important conclusions tested across scenarios. Irreversible and high-consequence hazards need protections outside a single expected net value.
The future did not authorise us through a model. Modelling makes it harder to claim ignorance of long-term consequence. It does not yield one policy, but increases the burden of justification on present decision-makers. If serious risks can be foreseen, transferring them to people unable to participate requires a fuller reason than a precise chart can supply.
Primary sources and further reading
- IPCC, Climate Change 2021: The Physical Science Basis, especially Chapter 4 and the Summary for Policymakers.
- IPCC, Working Group I Summary for Policymakers headline statements.
- IPCC, Climate Change 2022: Mitigation of Climate Change — Summary for Policymakers, including its explanation of modelled pathways.
- IPCC, AR6 Synthesis Report: Summary for Policymakers, 2023.
Series navigation: The Future Has No Representative — series overview
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