Reaching No Net Loss: policy recommendations to improve Canada’s federal biodiversity offsetting policy
Bibliographic record
Abstract
Biodiversity, the variability among living organisms, is at risk across the globe. The threat of biodiversity loss does not get the same level of attention as topics such as climate change but will have severely detrimental impacts if not addressed. The relative lack of recognition that biodiversity loss receives could be because biodiversity is incredibly complex, making it difficult to fully understand and protect. Another reason could be that some people do not see the value that biodiversity brings to their lives and the lives of others. Regardless of why biodiversity loss is not front-page news, it poses a major threat to our planet, but few policy options exist that sufficiently address biodiversity loss, particularly the losses caused by economic project developments. The often-massive economic developments have direct negative impacts on large areas, resulting in major losses of biodiversity. There is no indication that the rate of economic project developments will slow, so what few policy options to address biodiversity loss exist must be as effective as possible. The leading policy mechanism used around the world to slow biodiversity loss is the use of biodiversity offsetting. Biodiversity offsetting requires that proponents of economic project developments implement measures that address, or offset, the negative impacts caused by the project. The intended outcome of biodiversity offsetting is that there is no net loss of biodiversity. While it may appear simple in theory, biodiversity offsetting in practice is as complex as biodiversity itself. It requires careful planning, extensive data collection, long-term commitment, stakeholder engagement, and much more to be successful, and these factors do not guarantee success. Organizations such as the Business and Biodiversity Offsets Programme have developed principles and best practices that are intended to bring greater rates of success to biodiversity offsetting. Measures similar to biodiversity offsetting have been used in Canada through different federal and provincial policies. The 2012 Operational Framework for Use of Conservation Allowances currently stands as the federal government’s key policy to address biodiversity losses caused by economic project developments. An analysis of this policy demonstrated that the policy does not meet the standards that are considered best practices in the international community, putting Canada’s biodiversity at risk of greater losses. Recommendations are provided throughout this paper based on these international best practices. By implementing the recommendations through an updated or new policy on biodiversity offsetting, Canada has an opportunity to become a leader in preventing biodiversity loss.
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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 | 0.025 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.043 | 0.021 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".