Risk, Uncertainty and Tipping Points

Regime change and hysteresis

Content

TBD.

Transcript

Appendix

Learning objectives

After this chapter, you should be able to:

Distinguish risk from deep uncertainty.
Explain why many environmental problems involve irreversibility.
Describe what a tipping point is in ecological and climate systems.
Explain why standard cost–benefit logic struggles under deep uncertainty.
Describe how Earth–economy models handle risk, thresholds, and futures.
Explain why sustainability policy must be robust, not just “optimal.”

Risk versus uncertainty

In everyday language, “risk” means danger.
In economics, it has a more precise meaning:

Risk: outcomes are uncertain, but probabilities are known or estimable.
(e.g., a 1% chance of flood in a given year)
Deep uncertainty: we do not know:
- all possible outcomes,
- their probabilities,
- or sometimes even the structure of the system.

Climate change, biodiversity loss, and ecosystem collapse live in this second category.

We know:

the direction of change,
the broad mechanisms,
and that damages rise with pressure.

We do not know:

exact thresholds,
precise timing,
or full chains of consequence.

This is not ignorance that will simply disappear.
It is structural.

Irreversibility

Many environmental changes cannot be undone on human timescales:

species extinction,
ice sheet collapse,
soil degradation,
salinized aquifers,
coral reef death.

Irreversibility changes the logic of choice.

In a reversible world:

mistakes can be corrected,
policies can be tweaked,
learning can occur.

In an irreversible world:

Some mistakes close doors forever.

This makes “wait and see” strategies dangerous.

Tipping points

A tipping point is a threshold beyond which:

change accelerates,
feedbacks dominate,
and the system moves to a new state.

Examples:

ice sheet disintegration,
rainforest dieback,
coral reef collapse,
permafrost carbon release,
regime shifts in fisheries.

Before the threshold:

change is gradual.

After the threshold:

change is rapid and often self-reinforcing.

Tipping points mean:

damages are nonlinear,
small additional pressure can cause large jumps,
historical data may be misleading.

Why standard cost–benefit logic struggles

Traditional cost–benefit analysis assumes:

smooth damage functions,
known probabilities,
reversible choices.

Under deep uncertainty and tipping risk:

expected values hide tail risks,
discounting can trivialize catastrophe,
“optimal” paths may gamble with collapse.

A policy that is “efficient on average” can be disastrous in some states of the world.

When stakes include:

civilizational disruption,
planetary systems,
and intergenerational harm,

optimization alone is not enough.

Robustness instead of optimization

A different question becomes central:

Which policies perform acceptably well across many plausible futures?

This is the logic of robust decision-making.

Instead of:

choosing the best policy for one forecast,

we ask:

which strategies avoid catastrophe,
across a wide range of unknowns.

This shifts emphasis toward:

precaution,
diversity,
resilience,
and reversibility.

Risk inside Earth–economy models

Earth–economy models address uncertainty by:

running scenarios rather than single forecasts,
varying:
- climate sensitivity,
- technology costs,
- ecological response,
- policy effectiveness,
exploring distributions of outcomes.

They can represent:

threshold damages,
regime shifts,
and nonlinear feedbacks.

Outputs become:

ranges,
fan charts,
and robustness metrics.

This allows questions like:

Which policies avoid worst-case outcomes?
Which paths keep inclusive wealth positive under pessimistic assumptions?
Which strategies fail under plausible shocks?

The model becomes a stress-testing environment for the future.

A simple thought experiment

Suppose two climate strategies:

Strategy A: minimal action now, aggressive response later.
Strategy B: steady mitigation and adaptation now.

Under optimistic assumptions:

A looks cheaper.
B looks cautious.

Under pessimistic assumptions:

A crosses a tipping point.
B avoids it.

If probabilities are unknown, expected-value reasoning is fragile.

A robust strategy favors B—not because it is optimal in a single future,
but because it avoids irreversible failure across many.

The Doughnut perspective

The Doughnut defines boundaries:

falling below the social foundation,
overshooting ecological ceilings.

Tipping points make those boundaries sharp.

They remind us:

Some lines cannot be crossed and then negotiated back.

Risk-aware policy therefore aims to:

keep society well inside the safe-and-just space,
not merely graze its edges.

Earth–economy modeling gives us a way to test whether policies do that.

Open resources you can remix for this chapter

All are compatible with a CC BY-NC-SA Quarto book.

Natural Resources Sustainability: An Introductory Synthesis (CC BY-NC-SA)
Use for: sustainability, resilience, and systems framing.
https://uen.pressbooks.pub/naturalresourcessustainability/
Principles of Economics (UMN Libraries Publishing, CC BY-NC-SA)
Use for: risk, intertemporal choice, and uncertainty basics.
https://open.umn.edu/opentextbooks/textbooks/principles-of-economics
InTeGrate teaching materials (many CC BY-NC-SA)
Use for: climate risk, resilience, and scenario exercises.
https://serc.carleton.edu/integrate/teaching_materials/index.html

Exercises

Risk vs uncertainty.
Give one example of a risk and one example of deep uncertainty in environmental policy.
Irreversibility.
Describe an environmental change that cannot easily be undone.
How should this affect policy timing?
Robustness.
Choose a climate or conservation policy.
Describe how you would test whether it is robust across uncertain futures.