SimDec Meets Sustainability: Exploring Carbon Footprints with Simulation Decomposition

When sustainability science met SimDec

Some of the most passionate early adopters of SimDec came not from finance or engineering, but from sustainability science. Researchers in that field instantly recognized its potential: a way to make uncertainty visible and interpretable.

The paper “Simulation Decomposition for Environmental Sustainability: Enhanced Decision-Making in Carbon Footprint Analysis” (Socio-Economic Planning Sciences, 2020) was the first to fully integrate SimDec into life-cycle assessment (LCA) — the standard method for measuring environmental impacts like carbon footprints.

The idea: seeing complexity instead of simplifying it

Traditional Monte Carlo simulations in LCA show uncertainty as wide histograms or confidence intervals. But that approach hides the effects of input factors — for instance, how one assumption amplifies or cancels another.

SimDec changes this. It decomposes simulation outputs into scenarios — visual slices of uncertainty that reveal the structure behind the outcomes. In this study, researchers used SimDec to analyze the carbon footprint of wooden pallets used in global logistics systems.

A case of wooden pallets and climate impact

Every year, more than a billion pallets move goods around the world. Their environmental impact depends on many factors:

• How long they are reused
• How they are disposed of (incinerated or landfilled)
• What kind of trucks transport them

By running Monte Carlo simulations and then decomposing the results, the team could see how these uncertain parameters interacted.
The visual decompositions (shown in Figure 4 of the paper) display the carbon footprint distribution as layered colors — each color representing a scenario: truck type, disposal method, or number of uses.

What the results revealed

The results surprised even the authors:

• Truck type didn’t matter as much as expected. Larger trucks did not guarantee lower emissions once system-wide uncertainty was considered.
• Disposal method mattered enormously — landfilling led to strong positive emissions, while incineration often produced net negative emissions due to energy recovery.
• Reusing pallets longer wasn’t always better. Under some conditions, fewer reuses combined with incineration actually lowered total emissions — an insight invisible to standard sensitivity analysis.

SimDec made these patterns visible at a glance. What used to require dozens of separate sensitivity runs emerged from a single simulation run, visualized in one chart.

Why it mattered

This study showed that SimDec could serve sustainability science — not just economics or engineering. It demonstrated how environmental models full of interacting uncertainties could be explored, understood, and communicated clearly to policymakers.

As the authors put it, “SD can readily facilitate more environmentally sustainable decision-making in situations characterized by high degrees of uncertainty”.

A new way of thinking

The sustainability community embraced SimDec because it does what decision scientists had long dreamed of: it turns uncertainty into insight. It doesn’t simplify reality — it visualizes its complexity.

For SimDec, this paper marked another expansion of its world — from finance to energy, and now to the realm of sustainability and carbon analysis.
For sustainability researchers, it became a tool to see uncertainty as structure, not noise.