The Hidden Complexity Behind Emissions Projections

Projections often appear deceptively simple. A tidy emissions curve, an elegant bar chart, a waterfall of reductions—all conveying the illusion of order. But underneath these visuals lie a web of assumptions, relationships, and timing rules that reflect a far messier truth.

Real-world emissions modelling is not a linear exercise. It requires accounting for how different interventions interact, compound, or interfere with one another. Capturing this complexity is not optional—it’s essential for making sound decisions.

This post explores four key modelling concepts—nested effects, sequential modelling, compound transformations, and cascading transitions—and explains why they are crucial in forecasting greenhouse gas (GHG) emissions.

Nested Effects: When One Change Unlocks Another

Sometimes one intervention is only effective because another has occurred first. These are nested effects, and ignoring them can lead to wildly inaccurate projections.

Take the example of a company transitioning its vehicle fleet to electric. That move reduces tailpipe emissions—but not necessarily overall emissions. The impact depends heavily on the emissions intensity of the local electricity grid.

If the grid is still fossil-fuel dominated, the emissions reductions may be marginal. If it's mostly renewable, the benefit is significant. The grid transition must therefore be nested inside the EV transition. A flat model that treats them as separate actions risks overstating benefits—or worse, creating an illusion of progress where none exists.

Sequential Modelling: Order Matters

Transitions don’t happen all at once. Sequential modelling captures the idea that the order of interventions changes their impact.

For instance, if a company first reduces energy demand through retrofitting and then switches to green energy procurement, the second action will have a smaller emissions impact—because total energy use has already been reduced.

Modelling both interventions independently, or assuming they occur in reverse order, will lead to flawed results. The correct sequencing of events is essential for understanding the true cumulative effect.

Timing isn't everything—but it's close.

Compound Transformations: Multiplying, Not Adding

Not all interventions act in isolation. Some changes enable or amplify the impact of others. These are compound transformations—where effects multiply, rather than simply add.

Imagine a company adopts a hybrid work policy that reduces commuting emissions. Encouraged by this shift, it then downsizes its physical office footprint, cutting heating and electricity use. This compound effect—enabled by the first action—creates a non-linear reduction in emissions.

Models that assume one measure = one reduction miss this effect. Compound changes often yield greater-than-expected outcomes, but only if they are modelled with appropriate interdependencies.

Cascading Transitions: The Domino Effect—For Better or Worse

In complex systems, a single transition can ripple outward, triggering changes elsewhere. These cascading transitions are the system-wide knock-on effects that come from local decisions.

Take hydrogen infrastructure, often touted as a catch-all climate solution. In reality, hydrogen suffers from serious efficiency losses—particularly when used in sectors like transport or heating where direct electrification is far more effective.

Investment in hydrogen infrastructure can therefore delay electrification efforts by locking in complexity, diverting funds from more efficient alternatives, and prolonging reliance on legacy technologies.

In this case, a cascade has negative consequences. Delayed electrification in transport or heating means higher emissions for longer—even if the hydrogen is “green” on paper. What looks like progress may actually be a false carbon reduction.

Understanding these cascading effects is vital to avoid policy missteps and stranded investments.

Why This Complexity Matters

All of these concepts—nesting, sequencing, compounding, and cascading—reflect the same principle: emissions systems are interdependent. Actions are not interchangeable. They must be understood in context, in time, and in relation to each other.

At Viable Pathway, our emissions projection engine is designed to model these real-world complexities. Not because we expect the future to play out precisely as modelled—but because a flat, static, one-variable-at-a-time approach is no longer credible.

If your model doesn’t account for timing, interactivity, or system-wide effects, then your net-zero strategy is built on sand.

Conclusion: Modelling What Matters

The world is not linear. Neither is the path to decarbonisation.

As organisations build their climate transition plans, they must rely on quantitative models that capture the richness—and messiness—of the real world. That means going beyond tick-box reductions and simplistic trendlines.

It means recognising that electrification is more than a plug-and-play fix, that hydrogen is not always the hero it’s made out to be, and that actions must be sequenced, integrated, and stress-tested.

Climate ambition must be paired with modelling discipline. Only then can we avoid good intentions leading to bad outcomes—and ensure our strategies hold up when the future doesn’t follow the script.