Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025

Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025Dataset OverviewThis dataset contains comprehensive spatial and analytical data supporting the research on balancing biodiversity conservation with renewable energy infrastructure development in Queensland, Australia. The materials include energy system modeling results, conservation priority analyses using Zonation software, species and ecological community data. The code to analyse this data can be found here: https://github.com/amrogers/Biodiversity_and_energy_system_planning_QLD_2025<b>Study Area</b>: Queensland, Australia<br><b>Temporal Scope</b>: 2030, 2040, 2050 projection years<br><b>Data Volume</b>: ~7.8 GB total<br><b>Coordinate System</b>: GDA2020 / MGA Zone 56 (EPSG:7856)Dataset ContentsEnergy System Analysis Data<b>QLD_v202412_eplus_tx1.gdb.zip</b> (1.0 GB): Geodatabase containing renewable energy infrastructure scenarios under transmission development option 1. Includes solar photovoltaic, onshore wind, and offshore wind potential development areas under different biodiversity protection thresholds (0%, 10%, 30%, 50%, 70%, 90%).<b>QLD_v202412_eplus_tx2.gdb.zip</b> (2.7 GB): Geodatabase for transmission development option 2, containing the same renewable energy technologies and protection scenarios as tx1 but under alternative transmission infrastructure assumptions.<b>cost_increase_results.csv</b>: Economic analysis results showing percentage cost increases for residential and industrial energy consumers under different High Biodiversity Value Area (HBVA) exclusion scenarios.<b>eplus_Domestic_NPV_2025.xlsx</b>: Net Present Value calculations for domestic renewable energy projects across different protection thresholds and projection years (2030, 2040, 2050).Conservation Priority Analysis<b>Zonation_output/250m_SNES_ECNES_red_zones_weighted_QLD/</b>: Complete Zonation conservation prioritization analysis results at 250m resolution, including:<b>feature_curves.csv</b> (17.7 MB): Performance curves for 524+ conservation features showing coverage across priority ranks<b>feature_coverage_summary_with_CI.csv</b>: Summary statistics with confidence intervals for feature coverage at different protection thresholds<b>rankmap.tif</b> (47.5 MB): Spatial priority ranking map<b>MNES_2019_prioritisation_QLD.tif</b> (47.5 MB): Matters of National Environmental Significance prioritization layerConfiguration files, analysis logs, and metadataBiodiversity Data<b>Species_files_weights_table.xlsx</b>: Weighting schemes applied to individual species in conservation planning, including rationale for differential weighting based on threat status and endemism.<b>Table 8_The 524 species and their associated threat status.xls</b>: Comprehensive list of fauna species included in the analysis with IUCN Red List categories, national conservation status, and state-level classifications.<b>Table 9_The 22 ecological communities and their threat status.xlsx</b>: Threatened ecological communities included in conservation planning with threat classifications and distribution information.Spatial Constraints<b>Supplementary table_other spatial exclusions.xlsx</b>: Non-biodiversity spatial exclusion layers used in energy system modeling, including urban areas, protected areas, infrastructure corridors, and other development constraints.Analysis ScriptsComplete set of R scripts for reproducing all analyses:<b>percent cost increase_line plot.R</b>: Creates visualizations of energy cost impacts under different conservation scenarios<b>Zonation curves.R</b>: Generates conservation performance curves and coverage statistics<b>NPV_bar_plot.R</b>: Produces economic analysis plots with Net Present Value breakdowns<b>domestic_export_map_iterations.R</b>: Creates spatial maps of renewable energy infrastructure for domestic and export scenariosTechnical SpecificationsData Formats<b>Spatial Data</b>: ESRI Geodatabase (.gdb), Shapefile (.shp), GeoTIFF (.tif)<b>Tabular Data</b>: CSV, Microsoft Excel (.xlsx, .xls)<b>Analysis Code</b>: R scripts (.R)Software Requirements<b>R</b> (≥4.0.0) with packages: sf, dplyr, ggplot2, readr, readxl, tidyr, furrr, ozmaps, ggpattern<b>ESRI ArcGIS</b> or <b>QGIS</b> for geodatabase access and spatial analysis<b>Zonation</b> conservation planning software (for methodology understanding)Hardware Recommendations<b>RAM</b>: 16GB minimum (32GB recommended for full spatial analysis)<b>Storage</b>: 15GB free space for data extraction and processing<b>CPU</b>: Multi-core processor recommended for parallel processing scriptsDetailed Description of the VRE Siting and Cost-Minimization Model<br>This section provides an in-depth description of the Variable Renewable Energy (VRE) siting model, including the software, the core algorithm, and the optimisation process used to determine the least-cost, spatially constrained development trajectory for VRE infrastructure in Queensland, Australia.Software and Spatial ResolutionThe VRE siting model is implemented using Python and relies heavily on ArcGIS for comprehensive spatial data handling and analysis.Spatial Resolution: The analysis uses a working spatial resolution of 250-meter grid cells to generate Candidate Project Areas (CPAs).Core Tool: CPAs are generated using a custom fork of the source code (released with this Article) supplied by the Multi-criteria Analysis for Planning Renewable Energy (MapRE) initiative.2. Model Inputs and Exclusion CriteriaThe overall methodology is based on a prior economy-wide energy system modeling framework, which we modified to incorporate detailed spatial land-use data.A. Static Exclusion LayersThe model begins by applying a common set of static and predetermined land-use norms over the entire 40-year transition period. These permanent exclusions prevent VRE development in specific areas based on economic, technical, and environmental factors:Existing Development: Built-up or remote communities, defence areas.Infrastructure: Transport infrastructure, existing energy infrastructure.Economic/Technical: Active mines, irrigated areas, areas with low VRE resources.Topography: Slope.Offshore: Offshore shipping lanes.B. Biodiversity and Natural Capital CasesIn addition to the static exclusions, the analysis considers increasing biodiversity protection that apply different levels of exclusion thresholds for natural capital layers. These biodiversity exclusions are combined with the common exclusion criteria to generate aggregate exclusion maps for each VRE resource type (solar PV, onshore wind, and offshore wind).3. VRE Siting Algorithm and Optimization<br>The VRE siting model uses a cost-minimization optimization approach to select the most cost-efficient project sites to meet a projected energy mix target.A. Least-Cost, Sequential OptimizationThe model simulates a realistic development trajectory by selecting projects in sequential five-year periods from 2025 to 2050.<br>Demand Projection: At the beginning of each time step, the model determines the required VRE capacity for each technology based on projected energy demand. This aligns with the domestic energy generation scenarios considered, involving nearly full electrification by 2050.Site Identification: For that time step, the model identifies and maps candidate projects with the lowest Levelized Cost of Energy (LCOE) that are required to meet the capacity target within a given Queensland region.Capacity Allocation: If sufficient suitable sites are unavailable within the target region due to land-use constraints, the remaining required capacity is automatically allocated to the next nearest region with available resources.Land-Use Tracking: Once a site is selected, it is removed from the candidate pool until its projected end-of-life, ensuring no double-counting of the land used.B. Project Cost CalculationProject selection is driven by minimizing costs, specifically balancing generation and transmission costs^.Generator Costs: Capital cost projections incorporate significant reductions by 2050. Costs are sourced from the 2021 Australian Energy Market Operator (AEMO) Integrated System Plan (ISP) and the CSIRO GenCost Report.Transmission Costs: Transmission assets and their costs are based on AEMO’s transmission cost database.Cost Prorating: Costs for new transmission infrastructure are prorated based on the VRE project's capacity and the assumption that lines will serve multiple users, allocating only a portion of the bulk transmission costs to the specific VRE project.Financial Basis: All costs are shown in 2025 Australian dollars. Capital costs are annualised using a weighted average cost of capital. The total Net Present Value (NPV) of costs is cumulative since 2020 and discounted using a social discount rate of 2.7%.4. Transmission Line RoutingThe transmission routing model is integrated into the VRE siting process to ensure selection minimises the combined cost of generation and transport. The method identifies the least-cost path between VRE projects and load centres or between two loads.<br>Least-Cost Path: Transmission projects connected to CPAs are sited using established least-cost path methods. The specific tool is the Cost Path as a Polyline tool from ArcGIS Pro.Routing Surface: Routing is guided by a cost surface adjusted by multipliers that reflect the significance of obstacles (social, environmental, technical) or easements (e.g., preference for existing easements).Corridor Preference: The model prioritises augmentation of transmission in existing easements. Note that this approach prioritises existing right-of-way corridors without fully accounting for potential secondary impacts on surrounding natural capital.Conservation PlanningSystematic conservation prioritisation was conducted using Zonation software with 524 vertebrate species and 22 threatened ecological communities. Analysis incorporated species threat status, range size, and habitat specificity through differential weighting schemes.Economic AnalysisCost-benefit analysis quantified the economic implications of biodiversity protection on energy system development, including infrastructure costs, consumer price impacts, and Net Present Value calculations for different scenarios.Data Quality and LimitationsSpatial ResolutionEnergy infrastructure analysis: 250m grid resolutionConservation features: Variable resolution (10m-1km) depending on source dataEconomic modelling: Aggregated to energy market regionsTemporal ScopeBiodiversity data: Current distributions (2019-2024)Energy projections: 2030, 2040, 2050 scenariosEconomic analysis: 2025 Australian dollar valuesCoverage LimitationsStudy area limited to Queensland, AustraliaMarine environments included for offshore wind but limited for biodiversity featuresSome rare species distributions may be incomplete due to survey limitationsUsage GuidelinesGetting StartedDownload and extract all ZIP files to a local directoryInstall required R packages using provided scriptsSet working directory to the extracted data folderRun analysis scripts in the following order:Cost analysis scripts firstConservation analysis scriptsSpatial mapping scripts last (most computationally intensive)ReproducibilityAll analysis scripts use relative file paths and include comprehensive error handling. Scripts will automatically create output directories and provide progress feedback during execution.Citation RequirementsWhen using this dataset, please cite:The associated research paperIndividual data sources as listed in acknowledgmentsThis Figshare dataset DOISupport and DocumentationComplete documentation including setup instructions, troubleshooting guides, and methodology details is provided in the README.md file.For technical questions about data processing or analysis methods, contact the corresponding author.License and Usage RightsUsers are free to download, use, and build upon this work with appropriate attribution.<b>Keywords</b>: renewable energy, biodiversity conservation, spatial planning, Queensland, systematic conservation planning, energy economics, GIS analysis, Zonation, infrastructure development<b>Subject Categories</b>: Environmental Sciences, Energy Systems, Conservation Biology, Spatial Analysis, Environmental Economics