Environmental projects—from R&D investments to renewable energy storage and industrial joint ventures—are filled with uncertainty. Yet many decision frameworks still rely on deterministic net-present-value (NPV) calculations that obscure risk, suppress “what-if” thinking, and miss the drivers that shape project success.

A new study compares two Monte-Carlo-based approaches used to evaluate such investments: the Datar–Mathews (DM) real options method, widely used for pricing investment flexibility, and Simulation Decomposition (SimDec), a modern global sensitivity analysis method designed to reveal how uncertain inputs shape outcomes.

The results highlight that while DM is useful for pricing flexibility, SimDec is far more informative for understanding uncertainty, identifying success drivers, and guiding strategic planning.

Why these methods were compared

Real options methods originate from finance theory and are designed to value flexibility under uncertainty. They condense uncertainty into a single valuation number. The DM method takes a simulated NPV distribution, discards negative outcomes, and computes the expected value of the positive portion (Fig. 2A, page 6).

SimDec, by contrast, comes from global sensitivity analysis. It decomposes the entire output distribution into segments driven by the most influential variables, revealing interactions, causal structures, and the ranges of outcomes associated with different input states (Fig. 2B, page 6).

Both approaches use the same Monte Carlo data. What differs is what they do with it.

Three environmental cases: R&D, hydro storage, and a joint venture

The study evaluates both methods on three Finnish environmental applications (Section 3):

1. A sustainable packaging R&D investment
2. A pumped hydroelectric storage expansion project
3. A joint venture for cathode active material production

Each case uses scenario-based inputs (Tables 1–3, pages 4–6), allowing direct comparison of how DM and SimDec process identical uncertainty structures.

What DM reveals

Across all three cases, DM provides:

• a single number—the “real option value”
• a histogram with the negative side shaded
• no visibility into which factors drive profitability
• no guidance on how to steer a project toward better outcomes

For example, in the hydro storage project, DM recommends investing (all simulated NPVs are positive), but cannot show whether profitability arises from electricity price spreads, operating costs, or scenario conditions (Fig. 3A, page 7).

What SimDec reveals

SimDec consistently provides deeper insights:

1. Identifies the dominant variables

For all cases, operating revenues explain 85–90% of NPV variation (Figures 2B, 3B, and 4B).
This immediately points decision-makers to the levers that matter.

2. Shows outcome ranges for different scenarios

SimDec partitions the output histogram into low, medium, and high states of the influential inputs.

Example: In the hydro storage case, only the high revenue segment of the distribution produces strong returns; low/medium levels shift outcomes downwards (Fig. 3B).

3. Reveals interactions and hidden risks

A more detailed analysis (Fig. 6, page 9) decomposes hydro storage profitability by both electricity price scenarios and night-time prices.
Insights include:

• Under nuclear-like price patterns, the project is never profitable.
• Under wind-heavy price patterns, it is almost always profitable.
• In the “as is” scenario, profitability depends entirely on securing low night-time electricity prices.
  

These findings directly support strategy and risk management—something real options valuations cannot do.

4. Enables follow-up redesign

SimDec can incorporate both internal (design) and external (market) variables, highlighting where flexibility—hedging, design changes, operational adjustments—has the greatest value.

When to use which method

Table 6 (page 10) summarizes the difference:

• Use SimDec for project exploration, planning, and understanding
  • Identifying high-impact drivers
  • Assessing interactions
  • Guiding design choices
  • Building robust investment strategies
  • Revealing where flexibility matters
• Use DM for explicit pricing tasks only
  • Contract clauses
  • Licenses
  • Premiums for embedded flexibility
  • Binary invest / do-not-invest decisions

SimDec provides the “value density” that real options analysis lacks, turning raw distributions into actionable insight.

Key conclusion

SimDec and the DM method answer different questions.
The DM method is useful when a single financial metric is required.
SimDec is essential when the goal is strategic decision-making under uncertainty.

The study concludes that for environmental and sustainability applications, SimDec should be the primary tool for exploration and planning, with real options used only as a supplementary pricing instrument.

Reference

Liukkonen, S., Kozlova, M., Stepanov, R., & Yeomans, J. S. (2025). Comparative analysis of the Datar-Mathews real options method and simulation decomposition for strategic environmental decision-making. Journal of Environmental Informatics Letters. https://doi.org/10.3808/jeil.202400156

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