Compilation of data related to Australian coal-mine fugitive emissions

Brief description

This dataset quantifies characteristics of 62 Australian coal mines, with particular focus on quantities associated with fugitive emissions during the 2020-2021 Australian financial year.

The dataset is a compilation of data from 305 publicly-available data sources, which are mostly mine annual reviews, mine environmental impact assessments and government sources. Mining companies use methods specified in the Australian Government's National Greenhouse Accounts and National Inventory Reports to estimate their emissions. The 62 coal mines in the database are estimated to collectively produce 90% of fugitive emissions from currently-operating Australian coal mines.

The data are contained in the single SQL file combined_data.db. That database contains a data table for each mine with columns defining: (1) the parameters; (2) their values; (3) units; (4) notes related to the quantities; and (5) reference information. The database also contains a references table with information concerning the 305 primary data sources.

Two types of data are contained in combined_data.db: data directly sourced from the 305 publicly-available data sources; and quantities inferred from that data, which may be useful for fugitive-emissions modelling. The data sourced directly from the data sources are always provided with reference information. The inferred data are always provided with a brief description in the “note” column, and more details and calculations are found in the script analyse_data.py.

CSIRO makes no claims concerning the accuracy of the data. Consumers of the data must decide whether the data are fit for their purpose. For the data directly sourced from the publicly-available data sources, the "note" column often contains remarks concerning supporting data (along with references) and sometimes contains remarks concerning apparently conflicting data (along with references) and the consumer of the data is encouraged to read the primary sources and consider the relevant notes. For the inferred data, consumers are encouraged to carefully consider whether the algorithms and models used are fit for their purpose.

The python script analyse_data.py may be used to produce various summaries (such as are presented in the data_description.docx file) as well as to combine the individual databases nsw_oc.db, nsw_ug.db, qld_oc.db and qld_ug.db (relating to opencut (OC) and underground (UG) mines in the Australian states of New South Wales (NSW) and Queensland (QLD)) into the single file combined_data.db. Most consumers of the data will not need to access these individual databases.

The data compares well with aggregated emissions in Australia’s National Greenhouse Accounts and National Inventory Reports, as well as the Safeguard Data published by the Australia's Clean Energy Regulator. The uncertainties in emissions are estimated in the National Inventory Reports to be ±10% for underground coal mines, and ±33% for opencut coalmines. Most other quantities in the database are also subject to uncertainty. For instance, the mining depth varies from year-to-year, from pit-to-pit, and longwall-panel-to-longwall-panel, but such detailed information is not usually provided in public data sources, and “mine depth” is a single number for each mine in the database. Similarly, coal-seam permeability is highly heterogeneous.

Lineage: Quantities from 305 publicly-available data sources, such as mine annual reviews, mine environmental impact assessments and government sources, were extracted to form the core of this dataset. Reference information is provided for all quantities.
Secondly, the python script, analyse_data.py, uses those core numbers to infer various other, potentially useful, numbers. Each of these is provided with a note explaining their provenance, and the script analyse_data.py may be inspected to determine the algorithms used.