Contrail Accounting: Flight Data Beats CO₂ Multipliers

Aviation climate reporting has a precision problem: many companies still estimate non-CO₂ impacts (especially contrails) by multiplying CO₂ emissions by 1.7. It’s tidy. It’s fast. And it’s often wrong.

RMI’s 2024 corporate pilots on contrail accounting found two numbers that should change how sustainability and logistics teams think about business travel emissions: flight-specific modeling averaged ~1.3× (about 1.32×) instead of 1.7×, and roughly 2% of flight bookings drove 80% of contrail impact. That’s not a rounding error—that’s a signal that the real climate drivers sit in the details.

This post is part of our AI in Transportation & Logistics series, where the theme is consistent: when outcomes depend on complex conditions (weather, routing, timing, equipment), averages and static factors underperform. The same analytics mindset that improves warehouse forecasting and route optimization can also improve aviation emissions accounting—and it can do it quickly.

Why contrail accounting is becoming a reporting priority

Contrails are not a niche issue anymore; they’re an avoidable warming lever. The International Council on Clean Transportation’s 2025 Aviation Vision 2050 outlook estimates contrail avoidance could represent more than 40% of total avoidable warming from aviation by 2050. If your organization flies a lot—consulting, sales, field service, global procurement, freight forwarding—contrails can dominate the climate story even when your CO₂ footprint looks stable.

There’s also a practical reason this is rising now: Europe is formalizing non-CO₂ monitoring. Under the amended EU ETS monitoring, reporting, and verification (MRV) regulation, airlines operating jet flights within the EEA must monitor and report non-CO₂ effects annually (starting 2025 through 2026). By December 31, 2027, the European Commission is expected to report results and may propose legislation expanding the ETS scope to include non-CO₂ effects.

Even if corporates aren’t directly required to report contrails yet for Scope 3, their suppliers will be producing higher-fidelity data, and that tends to flow downstream. Once data becomes available, investors, customers, and assurance teams will ask why a company still reports with a blanket multiplier.

The myth: “A multiplier is conservative, so it’s fine”

It’s not fine—because conservative isn’t the same as accurate, and bad estimates create bad decisions.

• If you overestimate contrail impact, you may over-purchase compensation instruments, misprice internal carbon, and divert budget from reductions that actually work.
• If you underestimate it, you may report progress you didn’t earn and miss high-impact travel policy changes.

RMI’s pilots show the bigger issue: contrail impacts are highly uneven across flights, so applying one number to everything misrepresents reality and hides the best reduction opportunities.

The three contrail accounting methods (and what they get you)

Answer first: You can model non-CO₂ aviation effects at three levels—static, tiered, and flight-specific—and the right choice depends on how much accuracy you need and how ready your data pipeline is.

RMI evaluated three approaches with corporate partners and a flight analytics provider:

1) Standard multiplier (baseline)

This is the common method: CO₂ × 1.7 to represent non-CO₂ effects (contrails plus other impacts). It’s simple, but it assumes every flight has roughly the same non-CO₂ effect. That assumption breaks immediately when you factor in:

• time of day
• season
• region and route
• aircraft type and performance
• altitude profile and trajectory
• actual weather conditions

If you’re using this method, you’re not really “managing contrails.” You’re just labeling them.

2) Tiered multiplier (differentiated default)

Tiered multipliers improve on the baseline by grouping flights into low / medium / high contrail risk based on characteristics like departure time, season, and region. This is useful because it lets you do something operationally valuable: make lower-impact choices during booking.

But tiers still compress variability. Two flights can fall into the same bucket while having very different contrail outcomes.

3) Flight-specific modeling (bottom-up)

Flight-specific modeling is the high-fidelity option: calculate impact per flight using detailed operational inputs (aircraft/engine type, trajectory/altitude, and weather conditions). RMI’s pilots found that this approach:

• improved accuracy for non-CO₂ effects and CO₂
• produced an average ~1.3× non-CO₂ effect (not 1.7×)
• revealed that ~2% of bookings drove 80% of contrail impact

This is the approach that most resembles what regulators are pushing toward (for airlines) and what assurance teams will increasingly prefer (for corporate reporting).

Snippet-worthy rule: “If 2% of flights create 80% of contrail impact, policy should target the 2%—not punish the 98%.”

What the pilots reveal: contrails behave like a logistics optimization problem

Answer first: Contrail impact is condition-dependent and non-linear—more like congestion, spoilage risk, or grid peaks than like fuel burn per mile.

If you work in transportation and logistics, you already live in systems where averages mislead. Two deliveries of identical distance can have completely different cost and emissions outcomes because of timing, route constraints, idling, temperature control, and network load.

Contrails behave similarly:

• Winter months showed higher mean contrail impact per distance than summer.
• Morning departures tended to have lower mean contrail impact per distance than late afternoon.
• Medium- and long-haul routes often concentrate more impact.

The takeaway isn’t “never fly in winter” or “ban afternoon flights.” It’s that the climate impact function is spiky, and spiky functions beg for modeling.

Why AI belongs in contrail accounting (and why this matters to utilities)

AI isn’t a buzzword here; it’s a scaling mechanism.

Contrail accounting is essentially a prediction-and-attribution pipeline:

1. Ingest large volumes of flight activity data (booking + operational)
2. Enrich with weather and route/altitude context
3. Model non-CO₂ impacts flight by flight
4. Aggregate into corporate Scope 3 reporting
5. Feed insights back into travel policy and supplier engagement

That workflow mirrors what many energy and utilities teams already do:

• Demand forecasting (hourly variability beats annual averages)
• Grid optimization (peaks matter more than means)
• Predictive maintenance (a few assets drive most outages)

Contrails are the same pattern: a small share of events drives most risk, and you need analytics to identify them.

Practical playbook: how corporates can reduce contrail impact without freezing travel

Answer first: Start by finding your “high-warming flights,” then redesign policy to steer behavior—without breaking client delivery.

Here’s what works in practice, especially for organizations with meaningful business travel or air freight management.

Step 1: Upgrade your data foundation (before you touch policy)

If your travel data is mostly “city pair + spend,” you can’t do this well. A minimum viable dataset typically includes:

• flight number
• date/time of departure
• origin/destination
• cabin class
• (ideally) aircraft type and operator

If you can only get two improvements this quarter, I’d pick flight numbers and timestamps. They unlock route matching, schedule inference, and better modeling.

Step 2: Identify the 2% problem flights

RMI’s pilots showed extreme concentration: ~2% of bookings accounted for 80% of contrail impact. Your distribution won’t match exactly, but expect a similar “long tail.”

Run a quarterly analysis that answers:

• Which routes, departure windows, and seasons dominate non-CO₂ impact?
• Which business units or travel reasons correlate with high-impact flights?
• Which suppliers (airlines, booking channels) provide the best data quality?

This is where AI-driven analytics helps: the point isn’t only reporting—it’s finding patterns that humans won’t spot across thousands of segments.

Step 3: Build travel guidance that employees can actually follow

Policies fail when they’re abstract (“choose lower emissions”). They work when they’re specific (“choose option A when it meets constraints”).

Examples of implementable guidance:

• Prefer morning departures on certain routes when travel outcomes are equivalent.
• Avoid specific high-impact departure windows during winter for the top 10 routes.
• When two itineraries have similar cost and time, choose the one with lower modeled non-CO₂ impact.

Also: acknowledge reality. Sometimes the “cleaner” option requires an extra overnight stay, which adds hotel emissions and cost. Your policy should compare trip-level impact, not just flight-level impact.

Step 4: Use booking-tool nudges, not spreadsheets

If employees need to open a separate dashboard to find low-impact flights, they won’t.

Better options:

• integrate emissions scores into the booking flow
• default-sort by a combined metric (time + cost + emissions)
• require a short justification only for the highest-impact itineraries

That last one is a proven behavior design move in logistics, procurement, and energy usage programs: don’t block; add friction only where the impact is highest.

Step 5: Prepare for assurance and comparability

Non-CO₂ reporting is currently inconsistent across organizations. If you want your disclosure to survive assurance scrutiny and peer comparison, document:

• what model/method you used (multiplier, tiered, flight-specific)
• what data fields were available vs missing
• how you handled uncertainty and updates to emissions factors

A note that’s easy to miss: emissions factors can shift materially year to year. For example, the UK’s 2025 business air travel factors were adjusted downward 16% to 42% (depending on seating class) compared with 2024, partly due to post-pandemic demand normalization. If your footprint “improves” because factors changed, you need to be ready to explain it.

Where this is heading in 2026–2028

Answer first: Expect higher-fidelity non-CO₂ data from airlines, growing pressure for consistent corporate Scope 3 methods, and increasing use of AI to automate flight-level emissions measurement.

Three developments to watch over the next couple years:

1. EU-aligned tooling and data flows: As airline MRV reporting matures, corporate customers will ask for the same fidelity in supplier reporting and travel emissions exports.
2. Standards evolution: Major voluntary standards are in revision cycles through 2027, and the window to influence guidance on non-CO₂ accounting is open now.
3. Automation becomes table stakes: Once flight-level modeling is viable, stakeholders won’t accept “we didn’t have capacity” as a reason to stick with a static multiplier.

For AI in transportation & logistics teams, this is familiar territory: when regulators and customers demand traceability, the winning approach is to instrument the system and make reporting a byproduct of operations.

What to do next (if you want accuracy and action)

Contrail accounting is one of those rare sustainability topics that rewards operational thinking. If you measure it at flight level, you don’t just get a better report—you get a prioritized list of changes that won’t disrupt the whole travel program.

Start with a pilot:

• pick one region (or top 20 routes)
• run flight-specific modeling for the last 12 months
• quantify how far off your current multiplier-based reporting is
• identify the highest-impact 2–5% of flights
• test two policy nudges in the booking tool for one quarter

If your organization already applies AI to routing, network planning, energy management, or maintenance, you have the muscle to do this. The same discipline—good data, clear constraints, continuous improvement—works here too.

Where do you suspect your “2% flights” are hiding: winter evening long-hauls, premium-heavy routes, or something more surprising in your network?