The economics of conservation debt: a natural capital approach to revealed valuation of ecological dynamics
Bibliographic record
Abstract
Some species are valued for their direct usefulness to society, through immediate financial returns from market activities such as harvesting or ecotourism. But many are valued for their passive usefulness, i.e., their mere existence contributes to supporting, regulating or cultural environmental services that support human well-being. Hence, there is inherent social value to conserving such species as natural assets. However, such species are seldom priced as natural assets, and thus not accounted for in sustainability wealth measures because deriving non-market prices is challenging. We overcome this limitation by presenting a new approach for natural asset pricing of species with passive value that can be incorporated into national sustainability wealth accounting. We explicitly consider the relationship between prevailing institutions, species interactions, and ecosystem dynamics. Our approach is illustrated with the case of threatened woodland caribou in the Alberta Oil Sands. We show that conservation can be considered an investment while destructive activities can lead to a loss or conservation debt; and forgoing destructive activities can be considered a capital gain, increasing future wealth. Our approach reveals that caribou conservation in Alberta is leading to a conservation debt on the order of CA$800 million.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".