Route Optimisation Emissions in 2026: Why Efficiency is a Stock, Not a Rate

Routing headroom is the distance an operation could remove from its plans by sequencing and clustering them better, expressed as a percentage of the distance it runs today. It is a finite quantity, because there is a shortest feasible set of routes for any given demand and no optimiser can go below it, which makes route optimisation a one-time stock of emissions savings rather than an annual rate of improvement. Volume growth, by contrast, compounds without limit, so every operation has a computable year in which efficiency stops being able to hold absolute emissions down. Locus, the world’s first Decision-Intelligent, Agentic TMS, is built to keep working past that point, because sequencing is only one of the decisions it makes.

Why Routing Headroom Runs Out: The Business Case

The growth side of the arithmetic is well documented. The World Economic Forum projects that without intervention the number of delivery vehicles in the top 100 cities globally will increase by 36% until 2030, emissions from delivery traffic will increase by 32% and congestion will rise by over 21%. Those are compound trends. Nothing in them decays.

The efficiency side does decay, and that asymmetry is the whole problem. An optimiser closes the gap between the plan you run and the shortest feasible plan for the same demand. Each year of improvement leaves less gap than the year before, and the sequence converges on a floor set by geometry rather than by software.

The usual explanation for savings that fail to appear is the rebound effect, where efficiency gains get consumed by increased activity. In European road freight that effect is real but modest. Llorca and Jamasb, writing in Transportation Research Part A, examined fifteen European countries and obtained on average a fuel efficiency of 89% and a rebound effect of 4%. A 4% leak does not explain an operation whose emissions rose while its routing improved.

What makes this hard to see from inside a programme is that the first year is always the best one. The opening pass takes the most obvious structure and returns the largest percentage, so the natural inference is that the same rate will continue. It cannot, because every subsequent pass works on a smaller remainder. A three-year plan built by extrapolating year one will overshoot its cumulative target by a wide margin, and the gap usually surfaces in the third year, when the structural alternatives are too slow to rescue it.

The stakes are worth stating, because this is not an argument for optimising less. With last mile running 60% to 70% of total parcel delivery cost, the headroom is the single largest self-funded efficiency available to most operations. The argument is about what it can be expected to deliver, and for how long.

How the Stock Depletes

1. A shortest feasible plan exists for any given demand

For a fixed set of stops, vehicles and constraints there is a minimum total distance. It is not usually computable exactly at enterprise scale, but it is a real quantity and it bounds every plan from below.

2. Today’s plan sits some distance above that floor

The gap between the two is the headroom. It is a property of how the operation currently plans rather than of the optimiser, which is why a well-run operation has less of it to find than a poorly run one.

3. Each year of improvement closes part of the remaining gap

Optimisation projects capture the easy structure first: obvious clustering, sequencing errors, depot assignment. What remains after each pass is harder and smaller than what was taken.

4. The series converges rather than continuing

Because each pass works on a smaller remainder, annual percentage gains fall even when the team and the tooling improve. The cumulative total is bounded by the original headroom no matter how many passes are run.

5. Demand growth applies to whatever efficiency has been reached

Absolute emissions are distance per delivery multiplied by deliveries. The first term converges downward to a floor. The second compounds upward without one.

6. The crossover year is computable in advance

Given a headroom estimate and a growth rate, the year in which absolute emissions return to today’s level can be calculated before the programme starts. That makes it a planning input rather than a surprise, and it is the date by which the slower structural work needs to be already under way rather than under consideration.

What the Model Shows

The following is an illustrative model with stated inputs rather than observed customer data. It simulates 60 operating days of depot-based regional distribution, roughly 118 stops a day across an urban core and a periphery, with capacity-constrained routing under a fixed vehicle capacity. Headroom is measured as the distance difference between a competent manual-style plan, built by sweeping the service area by angle and sequencing each route nearest-neighbour from the depot, and an optimised plan built with a savings heuristic and local search. Emission factors vary linearly with load following the structure published in the UK conversion factors methodology.

Headroom is large but bounded. Across modelled days the optimised plan ran 20.7% less distance than the manual-style plan on average, with a median of 20.3% and a 10th to 90th percentile range of 15.7% to 26.9%. That is a substantial one-off prize and it is consistent with what route optimisation is generally understood to deliver. It is also the entire prize.

The sustainable growth ceiling is roughly headroom divided by five. Spreading the headroom across a five-year horizon against compounding demand, the maximum growth rate that routing alone can absorb came out at 1.9% a year for 10% headroom, 2.9% for 15%, 4.0% for 20% and 5.1% for 25%. An operation with typical headroom growing at 6% sees absolute emissions exceed today’s level in year three. At 8% growth it happens in year one.

Speed of extraction barely matters. This was the result that changed the shape of the argument. Moving from closing 20% of the remaining gap each year to closing 70%, a fourfold increase in the pace of improvement, raised the sustainable growth ceiling only from 3.1% to 4.8% at 21% headroom. The constraint is the size of the stock, not the rate at which it is drawn down, which means a faster optimisation programme buys a later crossover rather than a different outcome.

A well-run operation reaches the wall sooner. This follows from the structure rather than from a separate simulation, and it is worth stating because it inverts the usual assumption. Headroom is the gap between current practice and the floor, so an operation that already plans well has less of it left. Two competitors growing at the same rate, one with 12% headroom and one with 26%, have sustainable growth ceilings of roughly 2.4% and 5.2% respectively. The better operator is closer to the point where routing stops being able to help, and is therefore the one who needs the structural programme sooner.

The rebound mechanism usually blamed is not the culprit. The intuitive story is that released capacity gets absorbed: dispatchers spread work across the vehicles they have rather than standing any down, and the saving evaporates. The model does not support it. Dispatching five vehicles more than the minimum feasible raised total distance by 0.8% and emissions by 0.3%. Ten more raised distance by 3.0% and emissions by 1.7%. The extra stem mileage is largely offset by the payload penalty running in reverse, because vehicles carrying less burn less per kilometre. Overcapacity is an expensive cost problem, since each vehicle carries a full driver day, but it is a minor carbon problem.

Efficiency Levers and Structural Levers: Key Differences

What to Look for in a Platform Once Sequencing Reaches Its Floor

Mode and carrier selection inside the same decision

When distance reduction is exhausted, the next lever is what carries the load rather than the order it is carried in. A platform that optimises sequence but treats mode and carrier as fixed inputs cannot help past the floor.

Consolidation across orders, days and channels

Density is the one lever that improves both terms of the equation at once, because it lowers distance per delivery and raises load factor. It requires the system to reason across orders that arrived separately rather than within a single day’s batch.

Load-aware emission factors

A flat factor per kilometre will misprice every consolidation and fleet-mix decision, since fuel burn varies with mass. The UK Government’s greenhouse gas conversion factors methodology, drawing on the EU ARTEMIS project, shows the largest articulated vehicles emitting up to 25% more CO2 per kilometre at full load than at half load, with the same magnitude of reduction when empty and the effect linear in between. A platform applying a single figure per kilometre will credit consolidation with savings that largely transfer to the vehicles that remain.

Headroom measurement rather than savings reporting

A platform should be able to say how much distance remains recoverable, not only how much it has recovered. Without that figure a programme cannot know whether it is early in the stock or nearly through it.

Fleet and network decisions informed by the same model

The crossover year is the trigger for structural investment, and the investment case depends on the same demand and constraint data the planner already holds. Splitting those into separate systems is what causes the structural work to start late.

Depleting the Stock in Practice

A global FMCG distribution network. Operations across ten Asian countries with more than 1,000 distributors and 5,000+ riders, where 12,000+ trips a month were eliminated against $4B+ in optimised orders. Trip elimination attacks both terms at once, because a trip that does not run contributes neither distance nor emissions, which is why it outperforms pure sequencing work.

A beverage distributor with depot-based mixed fleets. Vans, trucks and motorbikes serving thousands of small retail points a day, previously planned in spreadsheets. Fuel consumption fell 37% and orders per delivery trip rose 22%. The second number is the durable one: raising drops per trip changes the density of the network rather than the order of the stops.

A Fortune 50 parcel and logistics network. More than a million freight shipments a year across 51 sites, with each site planning independently. Centralising the decision raised weekly execution from 75% to 92% and surfaced more than $14M in unused capacity, including $565K at a single site. Cross-site pooling is a structural lever rather than a sequencing one, which is why it produced a result no amount of local optimisation had found. Each site had already optimised its own plans and each had exhausted its own headroom, yet the capacity sat there because no site could see beyond its own boundary. Widening the decision boundary is how an operation creates new headroom rather than merely extracting the stock it already has.

Common Mistakes in Planning a Routing Decarbonisation Programme

Extrapolating the first year’s gain. The first pass takes the easiest structure and produces the largest percentage. Treating it as an annual run rate builds a trajectory that the geometry cannot deliver, and commits the organisation to a target it will miss in year three.

Treating the rebound effect as the explanation. Documented rebound in European road freight is around 4%, and the operational version modelled here is smaller still. An operation whose emissions rose while routing improved is almost always looking at volume growth, not leakage.

Never measuring the remaining headroom. Reporting cumulative savings says nothing about what is left. Two operations reporting identical savings can be at completely different points in the stock, and only one of them has a routing strategy that will still work next year. The figure to put in front of a board is the remaining gap to the floor, not the distance removed so far.

Deferring structural decisions until routing stops working. Mode conversion, fleet renewal and network changes take quarters to years to deliver. Starting them in the crossover year means absolute emissions rise for the whole of the period in between.

How Locus Helps Once Sequencing Alone Stops Working

Locus, the world’s first Decision-Intelligent, Agentic TMS, is designed around the observation that sequencing is one decision among several. The route planning and dispatch layer solves routing, load allocation and carrier selection together rather than in sequence, with load-aware emission factors so consolidation and fleet-mix choices are priced on the real curve rather than on a flat rate per kilometre. Because the stock of sequencing headroom depletes, the levers that matter over a multi-year horizon are mode, carrier, consolidation and drop density, and these are held as first-class constraints in the same model rather than in separate systems that meet only in a spreadsheet. Six governance mechanisms covering explainability, traceability, evaluation, autonomy levels, execution sandbox and human-in-the-loop keep each of those decisions traceable to the state and logic that produced it. Measuring remaining headroom also requires the executed record rather than the plan, since realised distance and planned distance diverge daily, and the Control Tower carries that comparison so the gap to the floor is calculated against what actually ran.

The platform reasons across more than 250 real-world operating constraints over 1.5B+ deliveries for 360+ enterprise customers in 30+ countries at 99.99% uptime, with $320M+ in aggregate logistics cost savings, 800M+ miles reduced and 17M+ kg of CO2 avoided. Locus has been recognised by Gartner for seven consecutive years, including the 2026 Gartner Hype Cycle for Supply Chain Execution and Logistics Technologies and the 2026 Gartner Market Guide for Multicarrier Parcel Management Solutions, where ShipFlex is featured as a Representative Vendor. Locus holds Leader designation in the QKS SPARK Matrix for Transportation Management Systems 2025 and the #1 position for Route Planning in G2’s 2026 Best Software Awards. In October 2025, Ingka Investments, the investment arm of Ingka Group, the world’s largest IKEA retailer, acquired Locus. Locus continues to operate independently.

Two deployments show the difference between drawing down the stock and changing the structure. A global FMCG distribution network running across ten Asian countries with 1,000+ distributors and 5,000+ riders eliminated 12,000+ trips a month against $4B+ in optimised orders, at 3X ROI, reaching 1.8M+ retail outlets. Eliminating a trip removes its distance and its emissions in full, which is a different class of result from shortening it. A beverage distributor running vans, trucks and motorbikes from depots to thousands of small retail points cut fuel consumption 37% and raised orders per delivery trip 22%, with route planning time down 35% and end-of-day reconciliation down 60%. The fuel number is the one-time stock being collected. The orders-per-trip number is the density change that keeps paying as volume grows.

Route optimisation reduces emissions, and the reduction is real, bounded and one-time. Modelling puts typical headroom near 21% of distance, enough to hold absolute emissions flat against roughly 4% annual growth over five years and no more, and extracting it faster moves the crossover year without changing the destination. The practical conclusion is that the routing programme should be run hard and treated as a window rather than a strategy, with mode, fleet, consolidation and density work started inside it. Locus holds all of those decisions in one constraint model so the structural levers are available before the sequencing floor is reached. Request a Locus routing headroom assessment to find out where your operation sits in the stock.

Frequently Asked Questions

What is routing headroom? It is the distance an operation could remove from its plans through better sequencing and clustering, measured against the shortest feasible plan for the same demand. It is finite, because geometry sets a floor that no optimiser can pass. In the model described here it averaged 20.7% of distance against a competent manual-style plan across 60 simulated operating days.

How much volume growth can route optimisation absorb? Over a five-year horizon, roughly headroom divided by five. At 21% headroom that is about 4% a year. Above that rate absolute emissions rise despite genuine and continuing improvement in the plans.

Does optimising faster help? Only marginally. Quadrupling the annual rate at which the remaining gap is closed raised the sustainable growth ceiling from 3.1% to 4.8% in the model. The binding constraint is the total size of the headroom rather than the speed of extraction, so a faster programme buys a later crossover rather than a different one.

Is the rebound effect why routing savings disappear? Usually not. Published work on European road freight puts the rebound at around 4%, and the operational version modelled here, where dispatchers spread work across spare vehicles, cost only 1.7% in emissions for ten extra vehicles. Volume growth is almost always the larger explanation.

Why do emissions rise when routing has genuinely improved? Because an emissions inventory is an absolute figure. It records deliveries multiplied by distance per delivery, and only the second term is falling. In any year where volume growth outpaces the efficiency gain, the disclosure rises while the underlying work was sound.

What should an operation do once routing headroom is spent? Move to the levers that do not converge: mode selection, fleet composition, consolidation across orders and days, and drop density. These take quarters to years to deliver, which is why the crossover year should be calculated at the start of the routing programme rather than discovered at the end of it.