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Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025 Andrew Rogers (17623239) Climate change impacts and adaptation not elsewhere classified Ecological applications not elsewhere classified renewable energy planning biodiversity conservation systematic conservation planning energy infrastructure Queensland Australia energy transition SUPPLEMENTARY DATA: BIODIVERSITY AND ENERGY SYSTEM PLANNING - QUEENSLAND 2025DATASET OVERVIEWThis dataset contains the spatial and analytical data supporting research on balancing biodiversity conservation with renewable energy infrastructure development in Queensland, Australia. The materials include energy system modelling results, conservation priority analyses using Zonation software, species and ecological community data, and pre-computed outputs for every figure and table in the associated publication.Associated manuscript: Rogers et al., "Joint infrastructure and biodiversity optimisation reveals favourable cost-protection trade-offs in a carefully planned renewable energy transition."The code to analyse this data can be found here: https://github.com/amrogers/Biodiversity_and_energy_system_planning_QLD_2025Study Area: Queensland, AustraliaTemporal Scope: 2030, 2040, 2050 projection yearsData Volume: approximately 11.2 GB unzipped (3.7 GB as two zip archives)Coordinate System: GDA2020 / MGA Zone 56 (EPSG:7856)DEPOSIT STRUCTUREThis deposit consists of two zip archives, both of which extract into a clone of the GitHub repository linked above.BESP_data_qld_2025.zip (3.7 GB) extracts to /BESP_data_qld_2025/ and contains the raw input data: energy system model outputs, transmission line data, and Zonation conservation-priority analysis outputs.results.zip (11.1 MB) extracts to /results/ and contains every figure and table from the manuscript, pre-generated.results.zip ships pre-populated with the figures and tables as published. The analysis pipeline detects these and redraws them without recomputation; emptying the folder forces a full recompute from the deposited inputs. DATASET CONTENTSEnergy System Analysis DataLocated under Energy_system_model_outputs/.QLD_v202412_eplus_tx1.gdb.zip (1.1 GB): Geodatabase containing renewable energy infrastructure scenarios under transmission development option 1 (reference transmission costs). Includes solar photovoltaic, onshore wind and offshore wind development areas under six biodiversity protection thresholds (0%, 10%, 30%, 50%, 70%, 90%). The analysis pipeline extracts this archive automatically on first use; no manual unzipping required.QLD_v202412_eplus_tx2.gdb.zip (2.7 GB): As above, under transmission development option 2 (doubled transmission costs). Also auto-extracted by the pipeline.cost_increase_results.csv: Percentage cost increases for residential and industrial energy consumers under each High Biodiversity Value Area exclusion scenario.eplus_Domestic_NPV_figure.csv and eplus_Domestic_NPV_2025.xlsx: Net Present Value calculations for domestic renewable energy projects across protection thresholds and projection years. The CSV is the processed version read directly by the analysis code; the spreadsheet is the full source dataset it was derived from.Electricity_Transmission_Lines/: Existing Queensland transmission network shapefiles (raw, buffered and simplified variants), plus a Tx_outputs/ subfolder containing modelled new-build transmission length summaries by biodiversity protection threshold, for both transmission scenarios.Conservation Priority AnalysisLocated under Zonation_analysis/Zonation_output/250m_QLD_2024/out_example1/. Complete Zonation conservation prioritisation results at 250 m resolution, including:feature_curves.csv (18 MB): Performance curves for all 545 conservation features, showing distribution coverage across priority ranks.rankmap.tif (48 MB): Spatial conservation priority ranking map.feature_coverage_summary_with_CI.csv and feature_coverage_summary_with_CI.xlsx: Per-feature coverage summaries with 95% confidence intervals at each protection threshold.Zonation configuration files, feature lists, analysis logs and metadata.Biodiversity DataThe prioritisation covers 545 conservation features.Zonation_MNES_shapefiles/shapefiles/: Per-species and per-community distribution shapefiles used as Zonation input features.species_attributes.csv: Per-feature lookup table with shapefile ID, species name, common name and threat status, extracted from the distribution shapefiles above.species_weights.csv: Weighting applied to each feature in the prioritisation, based on threat status, with the source filename recorded for each.Supplementary table_EPBC listed species and ecological communities and their associated threat status.xlsx: Species and ecological communities list with threat status under the EPBC Act.Zonation_QLD_biodiversity_feature_rasters.zip (78 MB): Rasterised feature layers used as Zonation inputs. Must be extracted manually to re-run the prioritisation; the analysis pipeline does not use this archive directly.QLD_CAPAD/: Collaborative Australian Protected Areas Database boundaries for Queensland, used as a reference layer in figure production.Spatial ConstraintsSuplementary table_other spatial exclusions.xlsx: Non-biodiversity spatial exclusion layers used in energy system modelling, including built-up areas, defence land, transport and energy infrastructure, active mines, irrigated areas, slope constraints and offshore shipping lanes.combined_wind.tif (26 MB) and combined_pv.tif (27 MB): Combined exclusion rasters for wind and solar development respectively, located under Energy_system_model_outputs/area_outside_exclusions/.Pre-computed Resultsresults.zip contains, as published:Figures: all manuscript and supplementary figure panels, including the VRE siting maps for both transmission scenarios and both domestic/export configurations, the biodiversity value map, transmission length and cost figures, and the zero-coverage species map.Tables: the species coverage summary underlying Table 1, the CE/EN coverage summary, the transmission build-length summary, and the TX1 vs TX2 spatial comparison summary.Intermediate spatial outputs: per-threshold shapefiles and summary CSVs from the transmission processing pipeline, and the overlap and co-occurrence shapefiles and statistics derived from them. TECHNICAL SPECIFICATIONSData FormatsSpatial Data: ESRI Geodatabase (.gdb), Shapefile (.shp), GeoTIFF (.tif)Tabular Data: CSV, Microsoft Excel (.xlsx, .xls)Software RequirementsR (version 4.4 or later) with packages: sf, dplyr, ggplot2, readr, readxl, tidyr, furrr, data.table, progress, ozmaps, purrr, ggpattern, gridExtra, here, scales, magick, extrafont (optional). Exact versions are pinned in the repository's renv.lock file.ESRI ArcGIS Pro or QGIS for geodatabase access and spatial analysis.Zonation 5 (Windows) to re-run the conservation prioritisation.ArcGIS Pro to compose the Figure 1 map panels.Hardware RecommendationsRAM: the standard analysis pipeline (_RUN_ALL.R) has no unusual RAM requirement. Re-running the Zonation prioritisation from scratch is memory-intensive - the original run used approximately 15 GB for feature data alone; 32 GB is recommended for that step.Storage: at least 15 GB free disk space for extraction and processing.CPU: Multi-core processor recommended for parallel processing scripts.Path LengthSome paths within the deposit are long. Windows users should extract to a short directory such as C:\besp, as exceeding the 260-character path limit causes files to appear absent when they are present. DETAILED DESCRIPTION OF THE VRE SITING AND COST-MINIMIZATION MODELThis 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.1. 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 OptimizationThe 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 OptimizatioThe model simulates a realistic development trajectory by selecting projects in sequential five-year periods from 2025 to 2050.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.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 (for example, 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 545 conservation features. 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: 250 m grid resolutionConservation features: variable resolution (10 m to 1 km) 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, Australia.Marine environments included for offshore wind but limited for biodiversity features.Some rare species distributions may be incomplete due to survey limitations.Biodiversity distributions are habitat-based and do not represent migratory corridors or movement pathways between areas of suitable habitat. USAGE GUIDELINESGetting Started 1. Clone the GitHub repository linked above.2. Download both zip archives from this deposit and extract them into the repository as described in Deposit Structure.3. Open the .Rproj file.4. Run source("_RUN_ALL.R").Expected runtime: well under a minute as downloaded, since results/ ships pre-populated and each step detects existing output and skips recomputation. A full recompute from an emptied results/ takes approximately 4 minutes. The repository README.md provides full setup instructions, platform requirements per analysis step, and a figure-to-script-to-input reference table.ReproducibilityBy default, each analysis step checks whether its output already exists in results/ and, if so, displays it without recomputing - this is why a run against the deposited results.zip finishes in under a minute. Deleting or emptying results/ forces every step to recompute from the deposited inputs in BESP_data_qld_2025/. Two steps cannot be regenerated by the code in this repository: Figure 1a (the biodiversity priority map) is composed in ArcGIS Pro from the deposited rankmap.tif, and re-running the Zonation prioritisation itself requires Zonation 5, run outside this pipeline.CITATION REQUIREMENTS When using this dataset, please cite: The associated research paper.Individual data sources as listed in the acknowledgements.This figshare deposit: https://doi.org/10.26188/29604590SUPPORT AND DOCUMENTATIONComplete documentation, including setup instructions, troubleshooting guidance and methodology details, is provided in the README.md file of the linked GitHub repository. 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.Keywords: renewable energy, biodiversity conservation, spatial planning, Queensland, systematic conservation planning, energy economics, GIS analysis, Zonation, infrastructure development, transmission planningSubject Categories: Environmental Sciences, Energy Systems, Conservation Biology, Spatial Analysis, Environmental Economics 2025-07-31T06:26:38Z Dataset Dataset 10.26188/29604590.v2 https://figshare.com/articles/dataset/Supplementary_Data_Biodiversity_and_Energy_System_Planning_-_Queensland_2025/29604590 CC BY 4.0