This dataset is about countries per year in Australia. It has 64 rows. It features 3 columns: country, and fossil fuel energy consumption.

Electricity consumption benchmarks – Survey responses matched with household consumption data for 25 households \r \r The AER is required to update electricity consumption benchmarks (available on www.energymadeeasy.gov.au) at least every three years. The benchmarks were initially developed in 2011. The update of the benchmarks is currently being undertaken, and this is a small subset of the data. Once the study is finalised, the whole dataset will be made available via www.data.gov.au. \r \r This data is made up of two elements:\r \r 1.\tResponses to a survey from 25 Victorian householders about their energy consumption (shown in the tab ‘questionnaire responses’).\r \r 2.\tEnergy consumption data (in Watt Hours (WH)) for each household in the sample from 1 April 2012 to 31 March 2014 (or such time as data are available after the installation of a smart meter). E_0000_WH refers to WH usage in the half hour commencing 12am. The column TYPE shows the type of usage. There are three types – general, controlled load (where the household has a dedicated circuit for a specific appliance, such as hot water) and generation (where the household has solar panels, this shows the WH exported to the grid from the solar panels - note that electricity generated and used within the house is not measured). \r \r There is also a word document titled ‘questionnaire’, which shows the survey questions. The corresponding question number in the spreadsheet shows the data for that question. \r

Australia Renewable Energy Consumption: % of Total Final Energy Consumption data was reported at 12.300 % in 2021. This records an increase from the previous number of 11.200 % for 2020. Australia Renewable Energy Consumption: % of Total Final Energy Consumption data is updated yearly, averaging 8.400 % from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 12.300 % in 2021 and a record low of 6.700 % in 2005. Australia Renewable Energy Consumption: % of Total Final Energy Consumption data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Environmental: Energy Production and Consumption. Renewable energy consumption is the share of renewables energy in total final energy consumption.;IEA, IRENA, UNSD, World Bank, WHO. 2023. Tracking SDG 7: The Energy Progress Report. World Bank, Washington DC. © World Bank. License: Creative Commons Attribution—NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO).;Weighted average;

This dataset is about countries per year in Australia. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, central government debt, and renewable energy consumption.

This dataset features Australian hydrogen projects that are active in the development, construction, or operating phase, and meet renewable hydrogen or carbon capture and storage (CCS) hydrogen production methods outlined in Australia's National Hydrogen Strategy. This dataset aims is to provide a detailed snapshot of hydrogen activity across Australia. It includes location data, proponent details, and descriptions for all hydrogen projects listed. Additional data is included, such as the energy source for hydrogen production, the method of hydrogen production, and the amount of hydrogen to be produced per year. This dataset is the basis of the point-location map of active Australian hydrogen projects featured on the Australia Hydrogen Opportunities Tool (AusH2.ga.gov.au). AusH2 aims to attract investment in Australia’s hydrogen industry, providing high quality, free, online geospatial analysis tools and data for mapping and understanding Australia’s hydrogen potential. It hosts key national-scale datasets, such as locations of wind and solar resources and distribution of infrastructure, as well as the Hydrogen Economic Fairways Tool (HEFT) that maps the economic viability of hydrogen production in Australia. The user can examine both hydrogen production by electrolysis using renewable energy sources and fossil fuel produced hydrogen coupled with CCS. AusH2 was produced by Geoscience Australia for the Council of Australian Governments (COAG) Energy Council’s Hydrogen Working Group in 2019. Updates to this dataset since September 2020 are coordinated with research.csiro.au/HyResource

This data set models the IEEE 14-bus system for studies on P2P electricity markets, including real data of consumption, solar and wind power from Australia. This data set is characterized by 30 minutes time-step over one year, i.e. from July 2012 to June 2013.

The transmission system comprises 14 buses and 20 lines, and its characteristics are based on [1]. The original number of generators was increased to 8 generators, i.e. 1 coal-based generator, 2 gas-based generators, 3 wind turbines and 2 PV plants. The data set uses the original number of 11 loads.

The bus 1 represents the upstream connection to the main grid, where the generator assumes an infinite power. The market price from the Australian Energy Market Operator is used in this generator. It is assumed the same period from July 2012 to June 2013 [4]. This data set supposes a tariff of 10$/MWh for using the main grid. The energy imported and exported in bus 1 has to account this extra cost. Thus, the exportation price is equal to the market price minus this grid tariff. On the other hand, the importation price is equal to the market price plus this grid tariff.

The wind production has been based on the data set from [2]. The time resolution has been converted from 5 minutes to 30 minutes. The authors would like to acknowledge that the data set in [2] was processed by Stefanos Delikaraoglou and Jethro Dowell. The solar production and load consumption are taken from [3]. The load consumption is split into fixed and flexible consumption per time-step. Since there is no access to the total capacity of the flexible consumption, we split the daily flexible consumption over each time-step. In this way, the maximum consumption is equal to the fixed consumption plus twice this flexible consumption per time-step. The minimum consumption is equal to the fixed consumption in each time-step.

The wind, solar and load data sets have been normalized, i.e. values relative to rated power. Then, these normalized sequences were multiplied by the capacity of each element. The data is intended for use in studies related to consumer-centric electricity markets, e.g.:

Validate new market designs or business models;

Assess the impact of new grid operation strategies;

Test the effect of strategic behavior by producers or consumers.

This dataset presents the footprint of the number of installations of small generation unit (SGU) solar panel systems and their rated output in kilowatts (kW). The data spans the months of 2018 and includes a total for the year. The data is aggregated to Postal Areas (POAs) from the 2016 Australian Statistical Geography Standard (ASGS). Every month the Clean Energy Regulator (CER) publishes small-scale renewable energy installation data files. These data files provide a list of SGU (small-scale solar panel, wind and hydro systems) and kW capacity by installed postcode, and solar water heaters (SWH) and air source heat pumps by installed postcode. The data represents all systems that have had certificates validly created against them. The data includes new installations, upgrades to existing systems and stand-alone (off-grid) systems. This data is not publicly available via the Renewable Energy Certificate (REC) Registry and does not include systems that are pending registration or have been failed by the CER. For further information about this dataset, visit the data source:Clean Energy Regulator - Postcode Data for Small-scale Installations. Please note: A 12 month creation period for registered persons to create small-scale technology certificates applies under the Renewable Energy (Electricity) Act 2000. Small-scale technology certificates are calculated based on the life of the system. This differs for solar hot water heaters and small generation units (solar panels, wind and hydro). This data is current as at 28-02-2021. Further information can be found at theRenewable Energy Target FAQ. Capacity data has been revised to account for past data entry errors which caused significant inconsistencies. These systems were installed between 2001 and 2003. As a result of this revision, the cumulative installed capacity from 2004 onwards has been reduced by 5.517MW. AURIN has made the following change to the original data:

This dataset is as presented in the paper titled "Data article: Distributed PV power data for three cities in Australia." in the Journal of Renewable and Sustainable Energy, volume 11 by Jamie M, Bright, Sven Killinger and Nicholas A. Engerer.

Abstract:
We present a publicly available dataset containing photovoltaic (PV) system power measurements and metadata from 1,287 residential installations across three states/territories in Australia--- though mainly for the cities of Canberra, Perth and Adelaide.
The data is recorded between September 2016 and March 2017 at 10-min temporal resolution and consists of real inverter reported power measurements from PV systems that are well distributed throughout each city. The dataset represents a considerably valuable resource as public access to spatio-temporal PV power data is almost non-existent; this dataset has been used in numerous articles already by the authors. The PV power data is free to download and is available in its raw, quality controlled (QC) and `tuned' formats. Each PV system is accompanied by individual metadata including geolocation, user reported metadata and simulated parameterisation. Data provenance,download, usage rights and example usage are detailed within.
Researchers are encouraged to leverage this rich spatio-temporal dataset of distributed PV power data in their research.

Further information is available at ANU Data Commons and Solcast.
This dataset has an embargo period for 3 years after the ARENA funded ANU project closure, though data is always available through Solcast.

Usage rights:

There is a non-standard data usage rights agreement for this data. In the uploads is a 'license and metadata.txt' file that details the usage rights and metadata of the data. The exact agreement is reproduced here:

The data is released with bespoke terms. We state the crucial elements of these terms here. The dataset is freely provided to researchers as is with no guarantee of support. The dataset is not for commercial usage, but for research only. You are empowered to use this dataset however you wish in your research, through direct usage, adaptation, or improvements to the data itself. The data must not be redistributed, the access point for the data is exclusively through the website as described in Sec.III of the manuscript. Should you make significant changes to the data and wish to redistribute the new data, explicit permission must be obtained from the authors. Finally, appropriate accreditation to the creators must be made in all publications and outputs that arise from using this dataset in any way. To appropriately accredit the creators, we require that this exact data article (Bright et al., 2019) is referenced alongside its DOI: https://dx.doi.org/10.25911/5ca6a0640869a. Additionally, if using the QC version of the data, we also require a citation for the original papers detailing QCPV (Killinger et al., 2016a, 2016a). Furthermore, if using the tuned PV version of this data, we also require a citation for both the QCPV papers above and the PV tuning papers (Killinger et al., 2016b, 2017b) for full visibility of the data provenance. Lastly, the original hosts of this data PVoutput.org should be recognised for their efforts.

References:

Bright, Jamie M.; Killinger, Sven; and Engerer, Nicholas A. 2019. Data article: Distributed PV power data for three cities in Australia. Journal of Renewable and Sustainable Energy. Vol 11. See online for full details.

Killinger, Sven; Braam, Felix; Muller, Bjorn; Wille-Haussmann, Bernhard and McKenna, Russell, 2016a. Projection of power generation between differently-oriented PV systems. Solar Energy. 136, 153-165.

Killinger, Sven; Muller, Bjorn; Saint-Drenan, Yves Marie and McKenna, Russell. 2016b. Towards an improved nowcasting method by evaluating power profiles of PV systems to detect apparently atypical behavior. Conference Record of the IEEE Photovoltaic specialists Conference, pages 980-985.10.1109/PVSC.2016.7749757

Killinger, Sven; Engerer, Nicholas and Müller, Björn. 2017a. QCPV: A quality control algorithm for distributed photovoltaic array power output. Solar Energy. 143, 120-131.

Killinger, Sven; Bright, Jamie M.; Lingfors, David and Engerer, Nicholas A. 2017b. A tuning routine to correct systematic influences in reference PV systems’ power outputs. Solar Energy. 157, 6.

This dataset provides values for ELECTRICITY PRICE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Solar energy from rooftop photovoltaic (PV) systems in Australia’s National Electricity Market (NEM) has been continuously increasing during the last decade. How much this change has affected power demand from electricity networks is an important question for both regulators and utility investors. This study aims to quantify the impact of rooftop solar energy generation on spot electricity demand and also to forecast power system load in the post-covid-19 era. Using half-hourly data from 2009 to 2019, we develop a novel approach to estimate rooftop solar energy generation before building regression models for wholesale electricity demand of each state. We find that the adoption of solar PV systems has significantly changed the levels and intra-day patterns of power demand, especially by reducing daytime power consumption from the grid and creating a “duck curve”. The results also show that most states in the NEM would see decreased electricity demand during 2019–2034.

This data is from the customer trial conducted as part of the Smart Grid Smart City (SGSC) project (2010-2014). It provides one of the few linked sets of customer time of use (half hour increments) and demographic data for Australia, as well as detailed information on appliance use, climate, retail and distributor product offers, and other related factors. The project was jointly funded by the Australian Government and an industry consortium, led by Ausgrid.\r Data resources that comprise this Customer Trial Data may be linked by the Customer ID number. Details of data resources for this dataset are listed below and include: electricity use interval readings; home area network plug readings; peak events; peak events response; and offer and acceptance. \r The SGSC project was a commercial scale demonstration of smart grids, their associated technologies and other factors. Information about the project, including final reports, can be found at the archived [Department of Industry, Innovation and Science] website. (http://content.webarchive.nla.gov.au/gov/wayback/20160615043539/http://www.industry.gov.au/Energy/Programmes/SmartGridSmartCity/Pages/default.aspx).\r

The South Australian Diesel Generation Database (Database) contains publicly available information on the ownership, use, location and configuration of electricity producing, diesel-fueled generation plants in South Australia. The database was compiled at the request of RenewablesSA with the intention of increasing the available information on diesel generation use in South Australia. A voluntary survey was carried out to obtain the information. Significant information was provided by the Government of South Australia through the Department of Planning Transport and Infrastructure through the Buildings Management section and through the former Department of Minerals, Industry, Trade, Resources and Energy (now the Department for Energy and Mining). The data set was updated in 2018 to include large and small diesel generation and includes projects under development and hybrid installations where there is more than one source of generation. Updated data has been sourced from the Department for Energy and Mining. The fields have been updated to be consistent with the SA Power Generation data set.

This dataset presents the location of point features representing entries into the Australian Building Energy Efficiency Register spanning the years of 2011 to 2019. The data includes entries related to NABERS ratings, Tenancy Lighting Assessments and Building Energy Efficiency Certificates under the Commercial Building Disclosure (CBD) program. Entries are based upon each functional space within a building, so multiples entries may be recorded for a single point location.

The CBD Program is an initiative of the Council of Australian Governments (COAG). It was established by the Building Energy Efficiency Disclosure Act 2010 and is managed by the Australian Government. The CBD Program is a regulatory program that requires energy efficiency information to be provided in most cases when commercial office space of 1,000 square metres or more is offered for sale or lease. The aim is to improve the energy efficiency of Australia's large office buildings and to ensure prospective buyers and tenants are informed. The CBD Downloadable Data Set is a compilation of the entire Building Energy Efficiency Register since its commencement in 2011 into a downloadable form updated on a weekly basis.

For more information, please visit:

• Commercial Building Disclosure - Downloadable Dataset.

• Commercial Building Disclosure - Frequently Asked Questions.

Please note:

• AURIN has spatially enabled this dataset.

• While the Australian Government uses reasonable endeavours to monitor the quality of information available on the CBD website and to update this information regularly, it does not guarantee, and accepts no legal liability whatsoever arising from, or in connection with, the accuracy, reliability, currency or completeness of any material contained on this website or on any linked site.

• The Australian Government recommends that users exercise their own skill and care with respect to their use of the CBD website, and the information contained in the CBD Downloadable Data Set.

Geoscience Australia and Monash University have produced a series of renewable energy capacity factor maps of Australia. Solar photovoltaic, concentrated solar power, wind (150 metre hub height) and hybrid wind and solar capacity factor maps are included in this dataset. All maps are available for download in geotiff format.

Solar Photovoltaic capacity factor map The minimum capacity factor is <10% and the maximum is 25%. The map is derived from Bureau of Meteorology (2020) data. The scientific colour map is sourced from Crameri (2018).

Concentrated Solar Power capacity factor map The minimum capacity factor is 52% and the maximum is 62%. The map is derived from Bureau of Meteorology (2020) data. Minimum exposure cut-off values used are from International Renewable Energy Agency (2012) and Wang (2019). The scientific colour map is sourced from Crameri (2018).

Wind (150 m hub height) capacity factor map The minimum capacity factor is <15% and the maximum is 42%. The map is derived from Global Modeling and Assimilation Office (2015) and DNV GL (2016) data. The scientific colour map is sourced from Crameri (2018).

Hybrid Wind and Solar capacity factor maps Nine hybrid wind and solar maps are available, divided into 10% intervals of wind to solar ratio (eg. (wind 40% : solar 60%), (wind 50% : solar 50%), (wind 60% : solar 40%) etc.). The maps show the capacity factor available for electrolysis. Wind and solar plants might be oversized to increase the overall running time of the hydrogen plant allowing the investor to reduce electrolyser capital expenditures for the same amount of output. Calculations also include curtailment (or capping) of excess electricity when more electricity is generated than required to operate the electrolyser. The minimum and maximum capacity factors vary relative to a map’s specified wind to solar ratio. A wind to solar ratio of 50:50 produces the highest available capacity factor of 64%. The maps are derived from Global Modeling and Assimilation Office (2015), DNV GL (2016) and Bureau of Meteorology (2020) data. The scientific colour map is sourced from Crameri (2018).

See the ‘Downloads' tab for the full list of references.

Disclaimer The capacity factor maps are derived from modelling output and not all locations are validated. Geoscience Australia does not guarantee the accuracy of the maps, data, and visualizations presented, and accepts no responsibility for any consequence of their use. Capacity factor values shown in the maps should not be relied upon in an absolute sense when making a commercial decision. Rather they should be strictly interpreted as indicative. Users are urged to exercise caution when using the information and data contained. If you have found an error in this dataset, please let us know by contacting clientservices@ga.gov.au.

This dataset is published with the permission of the CEO, Geoscience Australia.

Abstract The Electrical Infrastructure database presents the spatial locations of Major Power Stations, Electricity Transmission Substations and Electricity Transmission Lines; in point and line format respectively, for known major power stations, transmission substations and transmission lines within Australia.This dataset describes Electricity Transmission Lines; structures in which high voltage electricity supply is converted, controlled or transformed. Currency Date modified: 17 January 2025 Modification frequency: As needed Data extent Spatial extent North: -9.00° South: -44.00° East: 154.00° West: 112.00° Source information In addition to Esri World Imagery, the latest information sources used to identify and attribute the electricity transmission lines were publicly available publications from utility companies, engineering firms and government agencies. Catalog entry: National Electricity Infrastructure Lineage statement The release information for previous and current versions of this dataset is included below: Data download: Mar 2015: Public release of GA’s Electricity Infrastructure Database (separated into 3 parts: Major Power stations, Electricity Transmission line and Electricity Transmission Substations) – Version 1 Mar 2017: Public release of GA’s Electricity Infrastructure Database (separated into 3 parts: Major Power stations, Electricity Transmission line and Electricity Transmission Substations) – Version 2 Feb 2021: Public release of GA’s Electricity Infrastructure Database (separated into 3 parts: Major Power stations, Electricity Transmission line and Electricity Transmission Substations) – Version 3 Nov 2024: Public release of GA’s National Electricity Infrastructure Database – Version 4 Web Service: Feb 2016: Public release as a subset of GA’s Electricity Infrastructure separated into 3 parts: Major Power stations, Electricity Transmission line and Electricity Transmission Substations) web service – Version 1 July 2017: Public release as a subset of GA’s Electricity Infrastructure separated into 3 parts: Major Power stations, Electricity Transmission line and Electricity Transmission Substations) web service – Version 2 Feb 2021: Public release as a subset of GA’s Electricity Infrastructure separated into 3 parts: Major Power stations, Electricity Transmission line and Electricity Transmission Substations) web service – Version 3 Jan 2025: Public release as GA’s National Electricity Infrastructure web service – Version 4 Data dictionary All layers

Attribute name Description

OBJECTID* Automatically generated system ID

SHAPE* Geometry type (Polyline)

FEATURETYPE A singled feature type “Transmission Line” is the collective name of the different facility subtypes identified in the CLASS field

DESCRIPTION Brief description of the feature type

CLASS The feature type subtypes:OverheadUnderground

GA_GUID A global unique ID

NAME The name of each individual feature

OPERATIONALSTATUS A description of the feature’s status:Operational (functioning as an active transmission line)Non-Operational (no longer operational as an active transmission line)

CAPACITYKV Transmission voltage of the powerline - kilovolts

STATE The state where this feature is located

SPATIALCONFIDENCE Confidence rating of the accuracy of the feature’s spatial location (5 high – 1 low)

REVISED The date the feature was last revised

COMMENT A free text field for adding general comments about this feature to external users

LENGTH_M Length of the line in metres measured along the shortest distance with Earth curvature (geodesic line).

SHAPE_Length Automatically generated length in decimal degrees

Contact Geoscience Australia, clientservices@ga.gov.au

This dataset displays the locations of all operating renewable energy generators. The generators are classified by technology and by state. The renewables webmap contains locations of Australian renewable power stations that are greater than 3kW. Each power station has such information as fuel type, technology used, size (kW), ownership, latitude and longitude and data source. Web links and site photographs are provided where possible. A download feature is provided for clients who want the base data.

What is IPGOD?\r

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.\r \r \r

How do I use IPGOD?\r

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.\r \r \r

IP Data Platform\r

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform\r \r

References\r

\r The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.\r \r * Patents\r * Trade Marks\r * Designs\r * Plant Breeder’s Rights\r \r \r

Updates\r

\r

Tables and columns\r

\r Due to the changes in our systems, some tables have been affected.\r \r * We have added IPGOD 225 and IPGOD 325 to the dataset!\r * The IPGOD 206 table is not available this year.\r * Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.\r \r

Data quality improvements\r

\r Data quality has been improved across all tables.\r \r * Null values are simply empty rather than '31/12/9999'.\r * All date columns are now in ISO format 'yyyy-mm-dd'.\r * All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.\r * All tables are encoded in UTF-8.\r * All tables use the backslash \ as the escape character.\r * The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

The global coal price index reached 156.46 index points in August 2025. This was an increase compared to the previous month, while the overall fuel energy price index decreased. The global coal index expresses trading of Australian and South African coal, as both countries are among the largest exporters of coal worldwide. How coal profited from the 2022 gas crunch Throughout 2022, coal prices saw a significant net increase. This was largely due to greater fuel and electricity demand as countries slowly exited more stringent coronavirus restrictions, as well as fallout from the Russia-Ukraine war. As many European countries moved to curtail gas imports from Russia, coal became the alternative to fill the power supply gap, more than doubling the annual average price index between 2021 and 2022. Main coal traders and receivers Although China makes up by far the largest share of worldwide coal production, it is among those countries consuming the majority of its extracted raw materials domestically. In terms of exports, Indonesia, the world's third-largest coal producer, trades more coal than any other country, followed by Australia and Russia. Meanwhile, Japan, China, and India are among the leading coal importers, as these countries rely heavily on coal for electricity and heat generation.

All the data for this dataset is provided from CARMA: Data from CARMA (www.carma.org) This dataset provides information about Power Plant emissions in Australia. Power Plant emissions from all power plants in Australia were obtained by CARMA for the past (2000 Annual Report), the present (2007 data), and the future. CARMA determine data presented for the future to reflect planned plant construction, expansion, and retirement. The dataset provides the name, company, parent company, city, state, metro area, lat/lon, and plant id for each individual power plant. Only Power Plants that had a listed longitude and latitude in CARMA's database were mapped. The dataset reports for the three time periods: Intensity: Pounds of CO2 emitted per megawatt-hour of electricity produced. Energy: Annual megawatt-hours of electricity produced. Carbon: Annual carbon dioxide (CO2) emissions. The units are short or U.S. tons. Multiply by 0.907 to get metric tons. Carbon Monitoring for Action (CARMA) is a massive database containing information on the carbon emissions of over 50,000 power plants and 4,000 power companies worldwide. Power generation accounts for 40% of all carbon emissions in the United States and about one-quarter of global emissions. CARMA is the first global inventory of a major, sector of the economy. The objective of CARMA.org is to equip individuals with the information they need to forge a cleaner, low-carbon future. By providing complete information for both clean and dirty power producers, CARMA hopes to influence the opinions and decisions of consumers, investors, shareholders, managers, workers, activists, and policymakers. CARMA builds on experience with public information disclosure techniques that have proven successful in reducing traditional pollutants. Please see carma.org for more information http://carma.org/region/detail/18

The Queensland based data for the Australian Biomass for Bioenergy Assessment (ABBA).

ABBA provides detailed information about biomass resources across Australia. This information will assist in project development and decision making for new bioenergy projects, and provide linkages between biomass supply, through the supply chain, to the end user. To achieve this, the project collects, on a state by state basis, data on the location, volumes and availability of biomass, for inclusion on the Australian Renewable Energy Mapping Infrastructure (AREMI) platform.

For detailed information about how this data was derived download the technical methods documents.