Not in my backyard? Prospects, problems and perceptions of lithium extraction in Austria
Bibliographic record
Abstract
Abstract Background The European Green Deal has rekindled interest in the mining of critical raw materials within Europe’s borders. The Weinebene lithium deposit, near Wolfsberg (Austria), deemed uneconomic as late as the 2000s, has attracted interest from developers because of the widespread demand for the metal for battery technology and in the electro-mobility sector. Based on a multi-scalar analysis, the main objective of this study is to investigate local citizens’ and politicians’ perceptions of potential environmental and socio-economic impacts of the Wolfsberg project. We deploy an interdisciplinary political geology approach that assesses its geological feasibility, social acceptability and the associated power relations, in the light of European debates around so-called ‘green extractivism’. Results The exploitation of the lithium deposit seems to be promising from a geological point of view: the Weinebene hard rock, vein-type spodumene deposit was assessed at 12.9 Mt grading 1% Li2O, and the planned mine could provide 10,500 tpa LiOH/year for a period of 20 years, which would be around 4.5% of global production in 2021. However, the main results of the study show that conflicts are emerging around local environmental impacts, for example, the increase of traffic. Such environmental impacts resulting in greater CO2 emissions contradict decarbonisation objectives and ecological transitions. Local youth and politicians have highlighted the possibility of local mineral production, job creation and economic development. Nevertheless, politicians have criticized the company’s communication policy. Conclusions The geological analysis suggests adequate lithium resources. Otherwise, the Wolfsberg project is undermined by the lack of an open public dialogue on its future. Local residents and politicians are barely involved in the planning and permitting stages. The company European Lithium is confident of starting extraction soon, but in reality this is still uncertain. More widely, our results point towards the need for a strong degrowth strategy to generally reduce mineral consumption in Europe while also stopping destructive mining projects in the Global South. Local public perceptions have to be taken more into account when it comes to the future of lithium extraction in Europe’s ‘backyard’. Mechanisms need to be developed to fully integrate local residents into decision making processes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".