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The document presents a methodology for mapping traditional industrial classification (SIC) (RSIC) codes with alternative industrial classifications (RTIC, real-time industrial classifications) using machine learning techniques to finally the 8-growth sectors included in the recent green paper of the UK Industrial Strategy.Sectors included in the industrial conversion tables are:Advanced ManufacturingDefenceDigital and TechnologyLife SciencesClean Energy IndustriesFinancial Services.In particular, the methodological approach presented here finds significant concentrations of companies within specific traditional SICs, enabling us to identify overrepresented SICs in any RTIC. To do this, we compare the proportion of the SICs (RSICs) found nationally with the proportion found in the RTIC. Additionally, we consider the raw percentage of companies associated with each SIC (RSIC). To ensure accuracy, we normalise the data and highlight the SICs that show significant representation.
The UK Department for Business, Energy and Industrial Strategy (BEIS) emitted over 92,100 metric tons of carbon dioxide equivalent in the 2021-22 financial year. This was an increase of roughly two percent when compared to the previous financial year.
The Department for Business, Energy and Industrial Strategy (BEIS) publishes details about staff numbers and payroll costs for payroll and non-payroll staff on a monthly basis.
This data is also available on data.gov.uk:
In February 2023, BEIS was replaced by the Department for Energy Security and Net Zero, the Department for Science, Innovation and Technology, and the Department for Business and Trade.
We will continue to report BEIS staff numbers and costs until later in the year.
The Department for Business, Energy and Industrial Strategy (BEIS) publishes details of all spending over £500 using an electronic purchasing card solution (ePCS) on a monthly basis. The ePCS has replaced the government procurement card (GPC).
This data is also available on data.gov.uk:
In February 2023, BEIS was replaced by the Department for Energy Security and Net Zero (DESNZ), the Department for Science, Innovation and Technology (DSIT), and the Department for Business and Trade (DBT).
From August 2023 onwards, we reported spending as:
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This publication provides the only official government statistics that attempt to the estimate the total number of UK private sector businesses in the UK and their contribution to employment and turnover. Also provided is an estimate of the total number of businesses in the UK whole economy. The publication supercedes the publication 'Small and Medium-sized Enterprise Statistics for the UK and Regions'.
Source agency: Business, Energy & Industrial Strategy
Designation: National Statistics
Language: English
Alternative title: BPE for the UK and Regions
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Underlying data and charts supporting the publication BIS Economics Paper no 10a: Manufacturing in the UK: an economic analysis of the sector [URN 10/1333]
COVID-19 business support was the area that received the greatest spending by the UK Department for Business, Energy & Industrial Strategy. In its 2020 fiscal year, nearly 11 billion British pounds were directed towards offering aid to businesses suffering as a result of the coronavirus pandemic. Total spending amounted to 33.8 billion British pounds that year.
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The National Household Model (NHM) is delivered by the Department for Business, Energy and Industrial Strategy (BEIS). It was developed on behalf of BEIS by the Centre for Sustainable Energy (CSE) as an open-source tool for projecting the effects of policy and other legislative changes on the energy and emissions of the UK domestic housing stock by the Department of Energy and Climate Change (DECC). In order to be able to set up scenarios model users need to learn a specialist scenario language based on S-expressions (such as those used in the Lisp language) using the instruction manual found on the BEIS GitHub site. This will allow them to run the model which acts on the data in the various UK housing surveys. UK Data Archive stores the datasets for this model on behalf of the Department for Communities and Local Government (DCLG). Users need to register at UK Data Archive in order to access the raw data and run a conversion program to create a full dataset. The NHM team encourages feedback on all aspects of the model and documentation.
Summary of some of the key developments in the UK energy system: how energy is produced and used and the way in which energy use influences greenhouse gas emissions.
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Employees receiving job-related training Source: Department for Business, Innovation and Skills (BIS), Annual Population Survey (APS) Publisher: Department for Business, Innovation and Skills (BIS) Geographies: Local Authority District (LAD), County/Unitary Authority, Government Office Region (GOR), Learnings and Skills Council (LSC) Geographic coverage: England Time coverage: 2000 to 2008 Type of data: Survey.
Estimates of total final energy consumption by sector.
Energy consumption from domestic, transport and industrial source broken down by key energy type (coal, gas, petroleum products, manufactured fuels, renewables and electricity). Data is measured in Gwh
Ktoe - Kilotonnes of Oil Equivalent.
GWh - Gigawatt Hours.
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Underlying data for a report that explores the likely economic effectiveness of the UK Innovation Investment Fund (UKIIF). Set up to invest in technology based businesses with high growth potential, the UKIIF will focus on investing in growing small businesses, start ups and spin outs, in digital and life sciences, clean technology and advanced manufacturing. This report provides an early assessment of the lessons learned in implementing and delivering the UKIIF early indications of the extent to which it is addressing market failures in the UK venture capital market and contributing to business growth.
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A ten-year industrial strategy for Britain is a book. It was written by Tony Benn and published by Institute for Workers Control in 1975.
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Financial and management accounting information for BIS and partners. Will be expanded to cover key performance indicators (KPIs) and financial planning information in the next 12 months.
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The UK Innovation Survey is part of a wider Community Innovation Survey (CIS) covering EU countries. The survey is based on a core questionnaire developed by Eurostat and Member States, and results form part of various EU benchmarking exercises. The UK Innovation Survey is funded by BEIS and carried out by the Office for National Statistics with assistance from the Northern Ireland Department of Enterprise, Trade and Investment. More historic information prior to 2009 is available from the National Archives website. For access to granular microdata, please contact Official for National Statistics (ONS) Secure Research Service or UK Data Service.
Source agency: Business, Energy & Industrial Strategy
Designation: Official Statistics not designated as National Statistics
Language: English
Alternative title: Community Innovation Survey
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BEIS analysis of innovation activities by UK businesses, from UK innovation survey (UKIS) 2015, 2017 and 2019 data, by NUTS2 geographic boundaries and Local Enterprise Partnership (LEP) area. The information provided is experimental, as the sample was not selected to be representative at this geographical level of detail. The following innovation activities are : innovation active businesses product innovators process innovators undertaking strategic and marketing innovation activities performing internal research and development collaboration within business group new to market goods and services as a per cent of turnover.
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Further Education success rate on the Train to Gain programme Source: Department for Business, Innovation and Skills (BIS) Publisher: The Data Service Geographies: County/Unitary Authority, Government Office Region (GOR), National, Parliamentary Constituency Geographic coverage: England Time coverage: 2005/06 to 2008/09 Type of data: Administrative data.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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A list of the main analytical models deployed by BEIS to support policies as of 30 March 2022. Recommendation 4 of the Review of quality assurance of government models (Macpherson Review) asked departments to make a list of their business critical models publicly available - to encourage good practice in their quality assurance. In 2022 the National Audit Office report Financial Modelling in Government re-emphasised the importance of these lists.
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Data underlying the report of a study that assesses and quantifes the impacts of the financial crisis and subsequent global economic recession on the growth and performance of UK SME employers. Analyses existing data from two previous survey sources on SME employers in the pre-recession and recessionary periods. Covers how the problems in the banking sector have affected the supply of finance to the SME sector, and whether this has depressed business performance and investment. Looks at the impact of the recession has been more serious for particular types of entrepreneurs and businesses.
The fuel poverty supplementary tables 2022 provide additional data relating to fuel poverty for various dwelling and household characteristics under the Low Income Low Energy Efficiency (LILEE) indicator.
If you have questions about these statistics, please email: fuelpoverty@beis.gov.uk.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The document presents a methodology for mapping traditional industrial classification (SIC) (RSIC) codes with alternative industrial classifications (RTIC, real-time industrial classifications) using machine learning techniques to finally the 8-growth sectors included in the recent green paper of the UK Industrial Strategy.Sectors included in the industrial conversion tables are:Advanced ManufacturingDefenceDigital and TechnologyLife SciencesClean Energy IndustriesFinancial Services.In particular, the methodological approach presented here finds significant concentrations of companies within specific traditional SICs, enabling us to identify overrepresented SICs in any RTIC. To do this, we compare the proportion of the SICs (RSICs) found nationally with the proportion found in the RTIC. Additionally, we consider the raw percentage of companies associated with each SIC (RSIC). To ensure accuracy, we normalise the data and highlight the SICs that show significant representation.