Introduction
Most of the statistics in this report are based on information from the Data City platform. This uses the Companies House register of businesses as a basis but additionally uses website text and machine learning to provide detailed insights. In particular, the platform aims to improve the accuracy of the classification of the industry sector(s) that a company has activities within.
Each Companies House registration includes at least one Standard Industrial Classification (SIC) code that the business has used to classify what they do. The Data City platform classifies companies using Real-Time Industrial Classifications (RTICs) which allow many more sectors to be identified, particularly those that have developed since the last SIC update in 2007. The platform also highlights all relevant sectors rather than the limited and often quickly outdated choices in the original registration.
A business can be classified in the Data City platform within more than one RTIC and sub-categories of RTICs are also used, known as RTIC verticals. The Companies House register is known to include many shell companies with no active operations. This analysis, therefore, focuses on companies with at least one employee to ensure that these and other inactive businesses are excluded.
The Companies House register includes employee numbers for each business but not for each site. Data City estimates initially divided total employment equally for each site of a multi-site company. Recent enhancements have used vacancy data to model employee numbers more realistically in many of these companies but these improvements are still in progress.
This report also includes statistics from a one-off regional breakdown of the ONS annual UK estimate of the number of businesses that are part of the UK's low carbon and renewable energy economy (LCREE). This is based on responses to a survey of companies. Those reporting activity in one or more of 17 defined sectors are considered part of the LCREE. The regional breakdown for 2020 has not been repeated.