Every waste infrastructure decision rests on a set of assumptions: this fee holds, that feedstock shows up, the permit renews, the competitor stays out. What-if analysis is the discipline of asking, one assumption at a time, “what happens to this deal if I’m wrong?” It is the cheapest form of risk management available to an investor or developer, and the most commonly skipped.
Unlike a full Monte Carlo simulation, what-if analysis does not require probability distributions or thousands of iterations. It is deliberately simple: change one input, observe the effect, and decide whether the deal survives. That simplicity is its strength. You can run a meaningful what-if pass on a facility acquisition in an afternoon, and it will tell you which assumptions are load-bearing and which are noise.
This article covers how what-if analysis works, the specific scenarios that matter most in waste deals, and how it fits alongside the more quantitative tools in an underwriting toolkit.
What What-If Analysis Is, and Isn’t
What-if analysis explores the outcome of a decision by changing input values and observing how results move. In a waste facility model, that means taking a variable you are uncertain about, tipping fee, feedstock volume, operating cost, discount rate, and asking what the return looks like under a different value.
It is not forecasting. You are not predicting that fees will fall 15%; you are asking what happens to the deal if they do. That framing removes the temptation to argue about which future is most likely and focuses attention on resilience: does this investment still clear its hurdle when a key assumption breaks?
One Variable at a Time
The core move is isolation. Hold everything constant except the one input you are testing. If dropping the tipping fee from $65 to $55 turns a 14% IRR into a 6% IRR, that fee is a load-bearing assumption and deserves serious diligence. If the same $10 swing barely moves the return, you can stop worrying about it. Testing variables one at a time is what separates the assumptions that matter from the ones that don’t.
Best Case, Base Case, Worst Case
The most useful what-if structure is the three-scenario spread. For each critical variable, define a plausible best case, the base case you are underwriting to, and a defensible worst case. Run the model under each. A deal that only works in the best case is not an investment; it is a bet. A deal that survives the worst case on every critical variable is one you can size and price with confidence.
The What-If Scenarios That Matter in Waste Deals
Generic what-if analysis is a spreadsheet exercise. Useful what-if analysis is grounded in the specific ways waste infrastructure deals actually go wrong. Four scenarios recur across landfill, transfer, MRF, and biogas investments.
What If the Tipping Fee Moves?
Disposal revenue is the largest and most volatile line in most waste models. The fee moves with regional capacity and competition, so the essential what-if is: what happens if a competitor opens nearby airspace and fees compress? Grounding the downside case in real tipping-fee dynamics for the specific market, rather than a round-number haircut, is what makes this test credible. If the deal breaks on a fee move that the market has demonstrably produced before, that is a finding, not a hypothetical.
What If the Feedstock Falls Short?
For biogas, composting, and biomass projects, the mirror question is feedstock. What if the contracted supply churns, the herd shrinks, or a competing facility bids away the tons? A digester or biomass plant underwritten to its maximum feedstock volume every year is fragile. The what-if pass, dropping supply to a realistic low, exposes whether the project still services its debt when the tons run light, the same failure mode that sinks otherwise attractive biomass and organics deals.
What If a Better Site Exists?
Site selection is itself a what-if exercise. For a new facility, the question is not “does this location work?” but “does this location work better than the alternatives?” Running the same model across candidate sites, each with its own fee environment, feedstock radius, and competitive context, turns a gut call into a comparison. Wastenaut’s scenario comparison workflow is built for exactly this: evaluating locations side by side against the same underlying data.
What If Regulation Changes?
Organics landfill bans, per-ton environmental surcharges, and extended producer responsibility laws all reshape facility economics. The what-if here is directional: if an organics ban diverts a slice of your inbound tonnage, or a new surcharge raises your cost per ton, does the deal still hold? Regulation rarely arrives without warning, so this scenario is often the most foreseeable, and the most neglected.
Turning What-If Findings Into Decisions
A what-if pass produces a list of load-bearing assumptions. The next step is deciding what to do about each one. Broadly, there are three responses: verify it, structure around it, or walk.
Verification is the first resort. If the deal hinges on a tipping-fee assumption, the answer is not to hope, it is to validate that assumption against independent data before committing. Many what-if findings dissolve once you replace an estimate with a verified number. Others survive, and those are the ones to structure around, through contract terms, reserves, or price, or to treat as a reason to pass.
Where What-If Ends and Monte Carlo Begins
What-if analysis tells you which variables matter and roughly how much. When several of those variables are uncertain at once and interact, one-at-a-time testing understates the combined risk, because the bad cases tend to arrive together. That is the point to graduate from what-if to Monte Carlo simulation, which varies all the uncertain inputs simultaneously and returns the probability of the downside rather than a single alternative case. What-if is the fast first pass; Monte Carlo is the rigorous second.
Base your scenarios on real market conditions. A what-if is only as good as the numbers you plug into it. Wastenaut maps facility-level fees, capacity, and feedstock across the US so your best-, base-, and worst-case inputs reflect the actual market. Open Nexus to pull the data, or feed it into your own models via Stream API.
Common Pitfalls
Three mistakes undermine most what-if analysis. The first is testing implausible values, a downside case so extreme it never happens tells you nothing, and one so mild it never breaks the deal tells you less. Ground every case in what the market has actually produced. The second is confirmation bias: choosing the scenarios that support the deal you already want to do. Discipline means testing the assumptions you least want to be wrong about. The third is stopping at the analysis, a what-if pass that identifies a load-bearing assumption and then does nothing to verify or structure around it is wasted effort.
Done well, what-if analysis is the highest-leverage hour in waste-deal underwriting. It costs almost nothing and routinely surfaces the one assumption that would have turned a good investment into a bad one.
Frequently Asked Questions
What is the difference between what-if analysis and scenario modeling?
What-if analysis is a component of scenario modeling. Scenario modeling is the broader practice of representing possible futures and their impact on a decision; what-if analysis is the specific technique of changing input values to see how outcomes respond. In practice, a scenario-modeling exercise for a waste deal is largely a structured series of what-if tests across the variables that drive the return.
How is what-if analysis different from Monte Carlo simulation?
What-if analysis changes one variable at a time to a specific alternative value and observes the result. Monte Carlo simulation varies all uncertain inputs simultaneously according to probability distributions and produces a full distribution of outcomes. What-if is faster and simpler and identifies which assumptions matter; Monte Carlo is more rigorous and quantifies the combined probability of downside outcomes. Most disciplined underwriting uses both, what-if first, Monte Carlo when the interactions warrant it.
Which variables should I test first in a waste facility deal?
Start with the ones that drive the largest revenue and cost lines: tipping fee (or feedstock volume, for a processing facility), throughput or utilization, and operating cost inflation. Test each against a realistic downside grounded in market data. The variables that turn an acceptable return into an unacceptable one under a plausible move are your load-bearing assumptions, and they should absorb the bulk of your diligence effort.
Can what-if analysis replace due diligence?
No. What-if analysis tells you which assumptions are critical; it does not verify them. The natural output of a good what-if pass is a diligence list: the specific facts a deal depends on that now need to be confirmed against independent data. Treat what-if analysis as the tool that focuses due diligence, not a substitute for it.