All trial results
Twelve documented trials across road transport, buses, taxis, pharmaceutical logistics, stationary boilers and telecom generators, in Germany, Denmark, Japan and Nigeria. Results range from a small, inconclusive result to 28% better fuel efficiency — and the method behind each one explains most of that spread. Below them sit 8 operator records from our Japanese master dataset: real named fleets, 43 vehicles, but no trial protocol behind them — a weaker class of evidence that we keep separate rather than blend in.
| Trial | Where | Sample | Method | Measure | Result |
|---|---|---|---|---|---|
| Waste Services | Germany · refuse trucks | 5 vehicles · 21,830 km | Own baseline · blinded · routes verified · load fell 10.5% | Consumption, l/100 km | −18.2% |
| Municipality of Frederikshavn | Denmark · municipal truck | 1 vehicle · 1,484 km | Mode-corrected model, with Scania | Consumption, l/100 km | ≈5% (modelled) |
| Alfresa (A・L Plus) | Japan · pharma logistics | 2 vehicles · 4 measurements | Year-on-year, same calendar months | Efficiency, km/L | +8.4% … +20.0% |
| Sanden Kotsu | Japan · service buses | 2 buses · 3 months | Against untreated control group | Efficiency, km/L | +7.8% |
| Toyama Transport | Japan · logistics | 1 truck · 20,422 km | Own baseline · 3 applications | Efficiency, km/L | +13.1% |
| Chuo Taxi | Japan · taxi | 1 hybrid · 13,257 km | Own baseline · 2 applications | Efficiency, km/L | +28.3% |
| Hot-spring hotel boilers | Japan · heavy-oil boilers | 2 boilers · 6 months | Year-on-year, same calendar months | Consumption, litres/month | −10.7% … −16.0% |
| IHS Towers | Nigeria · tower generators | 11 sites · 5 months | Own baseline | Saving vs own baseline | +1.7% median — no clear effect established |
| Maruun Transport | Japan · mixed haulage | 9 vehicles · 2022 → 2023 | Year-on-year, same calendar month | Efficiency, km/L | +2.9% median · 3 of 9 negative |
| Toto Kanko Bus | Japan · coaches | 4 coaches · 12 measurements | Own baseline · 2 buses new | Efficiency, km/L | −3.4% … +44.0% · no common trend |
| Nakamoto Transport | Japan · distribution | 3 trucks · 3 months | Own baseline · single application | Efficiency, km/L | +7.2% median |
| Nishitetsu | Japan · route buses | 3 buses · Feb–Jun 2020 | 12-month rolling baseline — pandemic window | Efficiency, km/L | +7.8% median — not claimed |
Each one with its own page
Waste Services
−18.2%Drivers blinded and routes verified as unchanged — but the trucks also ran 10.5% lighter during the trial.
Read the trial →Municipality of Frederikshavn
≈5%Carried out with Scania. The raw figures are not comparable — the result comes from a drive-mode correction.
Read the trial →Alfresa (A・L Plus)
+8.4% … +20.0%Year-on-year comparison of the same months. The same figures appear in three independent records.
Read the trial →Sanden Kotsu
+7.8%The only trial measured against untreated control vehicles running in the same period.
Read the trial →Toyama Transport
+13.1%Three applications over 66 days, with the measured effect rising each time — 7.7%, 9.7%, 19.0%.
Read the trial →Chuo Taxi
+28.3%One hybrid taxi over 106 days. A large result on a single vehicle — shown with its aggregate.
Read the trial →Hot-spring hotel boilers
−10.7% … −16.0%Six monthly measurements, each against the same month a year earlier. Evidence beyond vehicle engines.
Read the trial →IHS Towers
+1.7% median · limitedPublished in full because a small, inconclusive result tells you where the boundary is too.
Read the trial →Maruun Transport
+2.9% medianThree of nine vehicles consumed more after treatment. The widest spread in our Japanese records.
Read the trial →Toto Kanko Bus
−3.4% … +44.0%Twelve measurements that do not agree with each other — and two buses that were new at the time.
Read the trial →Nakamoto Transport
+7.2% medianThe newest truck responded most — the opposite of how we usually explain the mechanism.
Read the trial →Nishitetsu
+7.8% — not claimedThe best baseline design on this site, ruined by the months it happened to run in.
Read the trial →Operator records from the master dataset
These 8 operators come from our Japanese master test dataset: 43 vehicles at named fleets, with before-and-after fuel figures we can recalculate but no trial report, no measurement period and no control group behind them. They are not trials, and we do not present them as trials. They are published because we hold them, and because an operator judging us is entitled to see the ordinary records as well as the twelve we chose to write up.
| Operator | Where | Sample | Method as recorded | Result |
|---|---|---|---|---|
| ALSOK (Kanagawa) | Japan · security transport fleet | 14 vehicles | Year-on-year comparison + Before/after, no seasonal control | +8.8% … +30.2% |
| Daiwa Logistics (Misato branch) | Japan · regional freight, 4–6 t trucks | 6 vehicles | Against a reference figure | +9.3% |
| Bridgestone Cockpit store | Japan · tyre retail — customer vehicles | 5 vehicles | Not recorded | +14.1% |
| N Kotsu | Japan · lpg taxi fleet | 4 vehicles | Against a reference figure | −6.6% … +10.1% |
| Sanwa Kotsu | Japan · lpg taxi fleet | 4 vehicles | Against a reference figure | +2.5% … +8.6% |
| Inabe Logistics Service | Japan · tipper trucks and heavy haulage | 4 vehicles | Before/after, no seasonal control | +6.3% |
| Iwaida Taxi | Japan · lpg taxi fleet | 3 vehicles | Against a reference figure | +13.2% |
| Tamagoya | Japan · delivery vans | 3 vehicles | Not recorded | +19.1% |
ALSOK (Kanagawa)
+8.8% … +30.2%The largest operator group in our dataset — and the clearest illustration that the measurement method, not the vehicle, decides the size of the number.
Read the trial →Daiwa Logistics (Misato branch)
+9.3%Six diesel trucks measured against a reference consumption figure. The tightest group in the dataset — every vehicle between +4.2% and +17.6%.
Read the trial →Bridgestone Cockpit store
+14.1%Five unrelated private cars treated at a tyre retail store. No fleet, no duty cycle, no recorded method — the weakest group we publish.
Read the trial →N Kotsu
−6.6% … +10.1%Contains the only negative measurement in the entire 386-record dataset — a taxi that used 6.6% more fuel after treatment.
Read the trial →Sanwa Kotsu
+2.5% … +8.6%Holds the highest figure in the whole dataset, +75.7% — which we traced to an implausible baseline and do not use.
Read the trial →Inabe Logistics Service
+6.3%The narrowest result in the whole dataset: four heavy vehicles inside a 2.2-point band, and the lowest median of any operator here.
Read the trial →Iwaida Taxi
+13.2%Two conventional taxis and one hybrid. The hybrid returns the largest figure — as it does in every hybrid record we hold.
Read the trial →Tamagoya
+19.1%Three vans of the same model and near-identical age — and baselines that differ by a factor of two, which is the part worth reading.
Read the trial →All 386 individual measurements, including the 300 we cannot verify →
Why the spread is so wide
A range from “no effect” to “+28%” looks like noise until you sort the trials by how they were measured. What emerges is not that one method gives bigger numbers than another — it is that the trials measured against their own past scatter across the entire range, while the better-controlled ones land in a narrow band.
Method decides the spread, not the level
Of the trials plotted here, those measured against their own history run from 1.7% to 22.1% — the whole range. The two compared with the same months a year earlier sit at 11.6% and 13.0%, and the one with a parallel control group at 7.2%. Better control narrows the spread; it does not simply shrink the number.
Sample size matters more than percentage
A 28% result on one taxi is weaker evidence than an 8% result across a controlled group. We show sample size in the same table as the result so the two are never separated.
Duty cycle decides
Refuse collection, urban taxi work and boiler operation responded. Telecom generators running continuously at near-zero load did not. The application matters more than the engine.
Effect appears to build
Where trials ran several applications, later measurements were consistently higher than early ones — which also means a single early measurement will understate the result.
Run the trial that actually applies to you
None of these trials is a promise about your vehicles. A pilot on your own fleet, designed with a control group, is the only figure worth budgeting against.