Wildfire Risk-Reduction Guidance for European Critical Infrastructure: Case Study of an Electrical Substation in Austria
Bibliographic record
Abstract
Wildfires are growing in intensity due to land-use changes and climate change impacts. 2023 ranked as the most destructive year for wildfires in the European Union since 2000, with over 500,000 hectares burned. As wildfire hazard increases, so does the need to prevent and mitigate wildfire risk, especially in interface areas where communities and the built environment are adjacent to or intermixed with wildlands. An emerging field of research focus is the Wildland-Industrial Interface (WII), where industrial buildings and infrastructures are at risk of potential wildfire exposure and damage.In an effort to implement practical wildfire risk-reduction measures in Europe, the DG-ECHO-funded project FIREPRIME is working to deploy wildfire risk-reduction guidelines in three European interface pilot sites: in Austria, Sweden, and Spain. Each pilot site includes a critical infrastructure (an electrical substation, train rail network, and chemical storage facility, respectively) to consider in the risk-reduction guidance. Critical infrastructures, defined as facilities that provide essential services to society, are expected to face a tenfold increase in damages due to climate change by the end of the century, highlighting the urgent need for tailored risk reduction measures against natural hazards. The power grid has had numerous hazardous interactions with wildfires, both through igniting highly destructive wildfires and by experiencing significant damage and subsequent power supply disruptions. Recent examples include the highly destructive 2023 Maui wildfire, ignited by fallen power lines, and the largest wildfire in Texas history, which occurred in 2024, when a damaged utility pole caused power lines to ignite vegetation.The Austrian critical infrastructure analyzed in this project is an electrical substation operated by the Austrian Power Grid, located in Haiming and nearly completely surrounded by an Alpine forest. Risk-reduction guidance and risk assessments methodologies for the electric power grid are reviewed to identify the most significant wildfire exposure mechanisms and damage modalities. The FireSmart Guidelines for Oil and Gas Industry from Canada are adapted and applied as an initial vulnerability assessment considering the local wildfire threat conditions, defensible space conditions, and location of vulnerable equipment. Available vulnerability assessments methodologies and preventative guidance are outlined to inform further risk-reduction measures.The FIREPRIME project focuses on implementing already-developed mitigation measures for wildfire risk, by adapting them to European realities; this work is a contribution to increase the European power grid resilience against increasing wildfire threats in future years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".