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Record W3017368587 · doi:10.1002/eap.2132

The economics of conservation debt: a natural capital approach to revealed valuation of ecological dynamics

2020· article· en· W3017368587 on OpenAlexaffabout
Samantha Maher, Eli P. Fenichel, Oswald J. Schmitz, Wiktor Adamowicz

Bibliographic record

VenueEcological Applications · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNatural capitalNatural resource economicsSustainabilityValuation (finance)Ecological economicsEcosystem servicesThreatened speciesEconomicsDebtNatural resourceAsset (computer security)BusinessEcologyEnvironmental resource managementEcosystemFinanceHabitat

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.219
Teacher spread0.123 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2020
Admission routes2
Has abstractyes

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