Published January 31, 2025 | Version v1
Dataset Open

Global sand and gravel stocks and flows from 1900 to 2016

  • 1. ROR icon BOKU University
  • 2. University of Natural Resources and Life Sciences, Vienna

Description

This dataset complements the scientific article by Watari, T., Böcher, C., Baumgart, A., & Wiedenhofer, D. (2025). Mapping sand flows and stocks. One Earth.

For further information, please consult the cited publications or contact Dominik Wiedenhofer (dominik.wiedenhofer@boku.ac.at). 


Data

This dataset includes the following information:

  • Economy-wide global stocks and flows of sand and gravel for the year 2016, from raw material extraction, trade and processing to its accumulation as material stocks, as well as resulting waste management. Sand and gravel stocks and flows are further differentiated by end-use product groups for gross additions to stocks, in-use stocks, and end-of-life flows
  • Global timeseries of sand stocks and flows, for domestic extraction, gross-additions to stocks, and end-of-life flows from 1900 to 2016, by material category
  • Global timeseries of sand stocks from 1900 to 2016, by end-use product groups

The dataset combines information from the MAT_STOCKS database (Wiedenhofer et al., 2024a) derived from the MISO2 model (Streeck et al., 2024; Wiedenhofer et al., 2024b), and the UNEP IRP Global Material Flows database (TCCC.3.8.2) for global extraction and raw material trade.

Sand stocks and flows are based on estimates of sand, gravel and aggregates used for asphalt, concrete and sub-base layers thereof, as well as downcycled and virgin aggregates. Note that the prefix 'aggregates_for' denotes aggregates that are to be used in asphalt and concrete before they the final products are manufactured, whereas the prefix 'aggregates_in' denotes aggregates contained in the final products. The aggregates content in asphalt is based on a 1:20 bitumen to aggregates ratio, and a 1:6.64 cement to aggregates ratio for concrete. 

This dataset contains a summarized version of the more detailed MAT_STOCKS database as derived from the MISO2 model. Consequently, these flows are simplified in the sankey diagram and the data presented herein.

Further information

Please refer to the MAT_STOCKS database repository for detailed information on individual material processes and flows (Wiedenhofer et al., 2024a), as well as the documentation on GitHub for details on the MISO2 model (Streeck et al., 2024; Wiedenhofer et al., 2024b). Further information on economy-wide material flow accounting can be found in (UNEP, 2023), and the derived global country-level database in (UNEP – IRP 2024).

References

Streeck, J., Grammer, B., Wieland, H., Wiedenhofer, D., 2024. socialecologyboku/MISO2: v1.0.0. https://doi.org/10.5281/zenodo.12795906

UNEP, 2023. The Use of Natural Resources in the Economy: A Global Manual on Economy Wide Material Flow Accounting (Manual). United Nations Environment Programme, Nairobi, Kenya.

UNEP, & IRP. (2024). Global Material Flows Databasehttps://www.resourcepanel.org/global-material-flows-database

Wiedenhofer, D., Streeck, J., Wieland, H., Grammer, B., Baumgart, A., Plank, B., 2024a. From Extraction to End-uses and Waste Management: Modelling Economy-wide Material Cycles and Stock Dynamics Around the World. https://doi.org/10.5281/ZENODO.12794253

Wiedenhofer, D., Streeck, J., Wieland, H., Grammer, B., Baumgart, A., Plank, B., Helbig, C., Pauliuk, S., Haberl, H., Krausmann, F., 2024b. From extraction to end‐uses and waste management: Modeling economy‐wide material cycles and stock dynamics around the world. J. Ind. Ecol. jiec.13575. https://doi.org/10.1111/jiec.13575

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Additional details

Related works

Is derived from
Dataset: 10.5281/zenodo.12794253 (DOI)

Funding

European Commission
CircEUlar – Developing circular pathways for a EU low-carbon transition 101056810
European Commission
MAT_STOCKS – Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society 741950

Dates

Available
2025-01-31

Software

Repository URL
https://zenodo.org/records/12795906
Programming language
Python, R