Biodiversity damaging subsidies in Switzerland – an overview
Bibliographic record
Abstract
Through Aichi Target 3, all signatory states of the Biodiversity Convention were committed to eliminate, phase out or minimize biodiversity damaging subsidies by 2020. None of the signatory states has achieved this goal. It has therefore been again included in the Kunming-Montreal Agreement of 2022, in Target 18. Switzerland has included the intention of the Aichi Target 3 in its national biodiversity strategy (Bundesblatt, 2012). In 2020, a study was published by the Federal Research Institute for Forest, Snow and Landscape (WSL) that provides a broad overview of subsidies with negative effects on biodiversity (Gubler et al., 2020). The assessment covers subsidies of the following areas: transport; agriculture; forestry; settlement development; energy production and consumption; tourism; flood protection and wastewater disposal. The study is based on a broad understanding of the term subsidy, which also includes tax reductions and non-internalised external costs. The 162 subsidies identified were then categorised and assessed in terms of their level of damage. In 2022, the Federal Office for the Environment (FOEN) prioritized, based on the WSL study, eight subsidies. The relevant federal offices are now commissioned to examine the impact of these eight subsidies on biodiversity more deeply and to submit proposals for their reform by 2024. Beside targeting single subsidies, another way to minimise the negative effects on biodiversity would be to include biodiversity and environmental goals in the process of subsidy allocation or in the general increase of policy coherence between the individual sectoral policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".