Biodiversity Offsets in a Public Lands Context: A Romantic Concept or a Practical Tool to Balance Economic Development and Biodiversity Conservation Goals?
Bibliographic record
Abstract
Economic development through the exploitation of natural resources has led to biodiversity loss among other environmental issues around the world. The use of biodiversity offsets to balance economic development and biodiversity conservation goals has significantly increased during the last three decades. A recent report of the Organization for Economic Cooperation and Development (OECD) released in December of 2016 identified at least 56 countries with laws or policies requiring the use of these types of instruments worldwide. There are over 100 biodiversity offset programs operating in countries such as United States of America, France, New Zealand, Mexico, Australia and others, which are injecting over 3 USD billion per year into the world’s economy. Experiences of different jurisdictions indicate that biodiversity offsets can become a promising tool in addressing the biodiversity loss issue in their territories. Canada and some of its provinces such as Alberta and British Columbia, which have important oil and gas sectors, and are home to important wildlife species, have been part of the biodiversity offsets debate, and have been exploring their use. This research derives from the observation that although some of the international biodiversity offset experiences have been vastly studied, there is little experience analyzing the legal challenges of implementing biodiversity offset systems, including biodiversity banks (a type of biodiversity offset that creates biodiversity markets) on public lands. The very nature of public land, where multiple users may simultaneously access the land and conduct a variety of potentially incompatible activities, can create extra legal challenges with respect to the implementation of biodiversity offsets. Through an Alberta-focused case study, the thesis explores the characteristics that a planning and legal framework of a province with a majority of public lands would need to have in order to support the use of biodiversity offsets and a biodiversity banking system. It also identifies and analyzes the legal issues and challenges of implementing long lasting biodiversity offsets in that context. Under the system studied by this dissertation, the main users of Alberta’s public forests (forest operators and oil and gas developers) become the biodiversity bankers or suppliers, and buyers of biodiversity credits, respectively. This thesis is therefore a contribution to knowledge about how biodiversity offsets, specifically biodiversity banks, can be applied on provincial public lands, used by multiple users. It focuses on the legal frameworks, property right issues, permanence, and additionality needed for a potential biodiversity banking system for a province such as Alberta.
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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 | 0.000 | 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) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".