How online retailers are incentivised to wreck our roads

Take the example of highway maintenance. Politicians probably get more angry letters about potholes than any other issue – but somehow there’s never enough money for adequate repairs. One reason why is that the taxation of heavier vehicles doesn’t properly reflect the damage that they do to road surfaces.

It might be assumed that the heavier a vehicle the more damage it does – and indeed that’s the case. What isn’t as widely understood is just how disproportionate an impact that each unit of extra weight has – meaning a heavy goods vehicle does massively more damage than a car:

It’s a point explained by Philippa Edmunds in a piece for CityMetric:

“The standard 44 tonne HGV, which is the industry workhorse, causes 136,000 times more damage to road infrastructure than a Ford Focus because the damaging power rises exponentially as weight increases – a phenomenon known as the ‘Generalised Fourth Power Law’.

“Local and urban roads, unlike motorways, are not built to sustain the large volume of heavy goods now traffic using them.”

The Generalised Fourth Power Law, or Fourth Power Rule, means that with every doubling of weight, a vehicle does sixteen times (i.e. 2×2×2×2) as much damage.

There are a few complications to take into account, but that’s the basic physics.

Ms Edmunds represents the UK Campaign for Better Transport, which argues that road freight is significantly undertaxed – and not solely because the damage caused to roads:

“HGVs are almost seven times more likely than cars to be involved in fatal collisions on minor roads. They’re responsible for 17 per cent of greenhouse gas emissions and a fifth (21 per cent) of NOx emissions from road transport, despite making up just 5 per cent of vehicle miles. When the full costs of HGVs are taken into account that equates to a £6bn a year taxpayer subsidy.”

As with all hidden subsidies, the distortion of incentives causes further problems:

“This subsidy undermines the incentive for the haulage industry to be efficient. Only a third (34 per cent) of HGVs are full in terms of load volume. Almost another third (30 per cent) are driving around completely empty – a figure that’s been growing for some years. Partially loaded trucks result in unnecessary lorry journeys to feed our shopping habits.

“In contrast, the German distance-based charging system incentivises efficiency and has reduced empty lorries by a third, as well as reducing tonne kilometres because of better loading rates; prior to its introduction, Germany had similar empty lorry levels to the UK.”

Hidden subsidies often have hidden beneficiaries. A prime example, in this case, are the online retailers whose delivery vehicles trundle around minor roads and residential streets in ever-greater numbers.

If those such as Amazon had to shoulder the full costs of the congestion, pollution and damage done by their deliveries, then price competition would speed up the transition to more sustainable transport technologies.

Electric and self-driving vehicles are the key to change:

• Electric engines would massively cut pollution (including noise pollution)
• With dynamic road charging, delivery vehicles could be incentivised to travel where and when they cause the least congestion
• Without driver error, there’d be fewer accidents
• And without the need for a driver in each vehicle, it would be feasible to have larger fleets of smaller, lighter vehicles – which would do less damage to the roads

Door-to-door deliveries could be entrusted to very-much smaller vehicles such as the delivery ‘pods’ already being tested. These are small enough to share pavement space with pedestrians rather than road space with traffic. We might trip over them, but that’s better than being run over by a truck.

All of this is going to happen sooner or later, but to make it sooner we fast need a rational system of road taxation.

---

Peter Franklin is Associate Editor of UnHerd. He was previously a policy advisor and speechwriter on environmental and social issues.

peterfranklin_