How fuel shortages were secretly building at petrol pumps before crisis

Country/area: United Kingdom

Organisation: NationalWorld.com

Organisation size: Small

Publication date: 07/10/2021

Credit: Claire Wilde


Claire Wilde is data and investigations editor for JPIMedia. She was previously crime correspondent at The Yorkshire Post and has written for the BBC, i and The Scotsman.

Project description:

The fuel crisis that engulfed Britain in September and October was one of the biggest stories of 2021 in the UK.

A shortage of petrol (gas) and diesel deliveries had sparked panic-buying and caused mass closures of forecourts around the country. 

This article used new data and an interactive map to reveal how the crisis developed, showing how supplies at forecourts had been dwindling in secret for three weeks before becoming public.

It provided an important counter to the narrative being used by Government ministers that the crisis was a “manufactured situation” caused by panic-buying rather than any underlying shortages. 

Impact reached:

At the time of my report, a Government narrative was emerging that the fuel crisis was caused by panic-buying – stoked by media reporting – rather than because of underlying shortages. 

In an interview with Sky News, transport minister Grant Shapps called the crisis a “manufactured situation” caused by a leak to the media by a road haulage association.

A YouGov poll in September revealed that 47% of people believed the fuel shortages had been caused by the media.

Indeed, a cursory look at experimental data released for the first time by the Department for Business, Energy & Industrial Strategy initially seemed to back up this theory, and this is how the story was reported by other outlets at the time

But a more in-depth look at the data revealed a different story and provided an important fact-check to this narrative, instead finding that shortages had indeed been building for some weeks at forecourts. Stocks had fallen to abnormally low levels – one-third of capacity – before panic-buying took off.

My article – published on the day the dataset was released – was the top story on the NationalWorld.com website, the flagship national title of the JPIMedia news group.

The same day, I also shared my analysis, copy and visualisations with the wider JPIMedia group, which consists of about 150 local and regional news brands including The Scotsman, The Yorkshire Post and the Lancashire Evening Post.

This allowed local titles big and small to report my findings, giving their readers regionalised insights into how the crisis developed in their area.

Titles in London, Manchester, Liverpool, Milton Keynes and Buckinghamshire were among those to run localised versions of the story. 

For example, here is coverage of the story on the website ManchesterWorld, serving the whole of the Manchester city region:


Techniques/technologies used:

The Government dataset was released as an MS Excel file. I used Excel to conduct my analysis before turning it onto a Google Sheets file to enable me to share my analysis with colleagues at other JPIMedia titles.

My key question was whether an underlying supply problem had existed before the crisis, or whether it was indeed caused by consumer panic-buying. 

A cursory look at the dataset’s information page seemed to suggest the latter. It said that sales had rocketed by 80% from Friday, September 24 onwards (the date shortages were first reported in the media), adding: “As a result of this increased demand, stock levels in the sampled filling stations dropped significantly”.

The volume of deliveries, it said, had been “relatively constant” before being “ramped up considerably in response to increased demand”.

The dataset included a line chart of deliveries over time, which appeared to show they had indeed fallen only slightly before the crisis – the line reported by other news organisations.

However, there was other data showing the stocks of fuel being stored at forecourts. 

This is where I found my story: analysis showed that average stocks at British forecourts had been below pre-pandemic levels since May and had then dwindled steadily from August 30, hitting just a third of capacity before the crisis began.

I also analysed this data at a regional level, finding that London and the South East had seen stocks fall first. 

I created an interactive map showing how the secret crisis had built up, using the data visualisation tool Flourish. I made a series of choropleth maps showing the change over time, then layered them as a ‘story’ that readers could click through.

I also created a line chart showing how average supplies at forecourts had fallen.

What was the hardest part of this project?

The biggest challenge with this project was the speed at which it had to be turned around. I had to avoid the Government spin to find and present a strong and novel news story. This had to be done within a few hours from a complex dataset I was unfamiliar with.

There was intense media interest in the fuel crisis so I knew other news organisations would be publishing stories based on the dataset and I had to move fast. 

The Government dataset was released as an Excel file, with 16 tabs, some of which ran to more than 20,000 rows. 

It was experimental data and the first weekly release of its kind, so there was no way I could familiarise myself with the format beforehand by looking at previous releases. Instead, I had to spend considerable time familiarising myself with the dataset once it had been released.

Please note: This article was published on Thursday 7 October, 2021, the same day that the Government dataset was released. The date which appears on the main article, Friday 8 October, is incorrect and was caused by the later addition of a hyperlink to the story.

What can others learn from this project?

There is a good lesson here: journalists should take the Governmental ‘introduction’ to a dataset with a healthy dose of scepticism. Often it contains useful guidance and information, but sometimes it is steering you away from the real story.

Also, it pays to really dig around a Governmental dataset when it is published. Your story may not be the first to report on its contents but it may well be the most insightful.

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