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Headline data

Source: Office for National Statistics (ONS)

Geographical Area: United Kingdom

Unit of Measurement: Million metric tonnes, Tonnes per capita, Tonnes per GDP (£m)

Footnote: The level of material footprint for the Eurostat approach is shown excluding imports and exports of precious metals. Precious metals (particularly gold) have a high RME factor, so can have distinctive impact on the results. This is particularly true for the UK, who are responsible for more than three quarters of the EU’s imports of gold. While the impact is minimal at the total level, it is more evident when looking at the material footprint of metal ores.

This table provides metadata for the actual indicator available from UK statistics closest to the corresponding global SDG indicator. Please note that even when the global SDG indicator is fully available from UK statistics, this table should be consulted for information on national methodology and other UK-specific metadata information.

Indicator available
Indicator description
Geographical coverage

United Kingdom

Unit of measurement

Million metric tonnes, Tonnes per capita, Tonnes per GDP (£m)


UK’s material footprint, or raw material consumption, captures the amount of domestic and foreign extraction of materials needed to produce the products used by households, governments and charities in the UK.

Material Footprint estimates provided are based on a Eurostat method and calculated as raw material equivalent of imports (RMEIM) plus domestic extraction (DE) minus raw material equivalents of exports (RMEEX).

Material footprint per capita is calculated as raw material consumption (material footprint)/UK population.

Material footprint per GDP is calculated as raw material consumption (material footprint)/GDP.

Available disaggregations
Other information

The choice of method used to calculate material footprint has an impact on the final estimates. In 2017, the Department for Environment, Food and Rural Affairs (Defra) consulted extensively with the University of Leeds about developing further environmentally relevant metrics for material use and resource efficiency, particularly in carbon terms. As a result, the University of Leeds have developed a multi-regional input-output (MRIO) approach, using a specifically derived UK MRIO database to underpin calculations. These estimates and further information on the Eurostat and University of Leeds approach are published in the Measuring material footprint report. While data is available from 2000, it is not reported here because data from 2008 onwards are not directly comparable to those in earlier years. In addition, the Eurostat method is more suitable for international comparisons. Data follows the UN specification for this indicator. This indicator has been identified in collaboration with topic experts.

Data last updated 03 December 2021
Metadata last updated 03 December 2021

This table provides information on metadata for SDG indicators as defined by the UN Statistical Commission. Complete global metadata is provided by the UN Statistics Division.

Indicator name

Material footprint, material footprint per capita, and material footprint per GDP

Target name

Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation, in accordance with the 10‐Year Framework of Programmes on Sustainable Consumption and Production, with developed countries taking the lead

Global indicator description
UN designated tier

Tier II

UN custodian agency

United Nations Environment Programme (UNEP)

Link to UN metadata United Nations Sustainable Development Goals metadata for target 8.4 opens in a new window

Office for National Statistics (ONS)



Earliest available data


Geographical coverage

United Kingdom

Link to data source Material Footprint in the UK opens in a new window
Release date 02 April 2020
Next release


Statistical classification

Official Statistic

Contact details

Other information

Data acquired is based on the Eurostat approach.