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Enregistrement W2098815206 · doi:10.1371/journal.pbio.1001344

The Shifting Boundaries of Sustainability Science: Are We Doomed Yet?

2012· article· en· W2098815206 sur OpenAlexaboutno aff
John Matthews, Frederick Boltz

Notice bibliographique

RevuePLoS Biology · 2012
Typearticle
Langueen
DomaineEnergy
ThématiqueGlobal Energy and Sustainability Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSustainabilitySustainability scienceFraming (construction)EcologyBiosphereSystems ecologySustainable developmentEnvironmental ethicsSustainability organizationsApplied ecologySociologyBiologyBiodiversityGeography

Résumé

récupéré en direct d'OpenAlex

In this issue of PLoS Biology, Burger and colleagues make several important contributions to the discourse of sustainability science, recalling limits of human economic and population growth derived from macroecology and physical principles [1]. We agree with many of the points offered in their paper in this issue and with those in the paper by Brown and colleagues [2]. However, we also believe there is danger in a vision of sustainability that is overly deterministic and does not reflect the dynamic nature of the biosphere, its ecosystems, and economies. We are also concerned about the implications of framing sustainability in the language of physics rather than ecology. Recent policy discussions in preparation for the Rio+20 Convention emphasize the concept of “green economies.” Perhaps most cogently described by microbiologist Lynn Margulis, the term refers to any theory of economics that views human economic activity as embedded within ecosystems. Green economics is often used with or in place of the more widely used term of “sustainability” or “sustainability science.” Both terms reflect a new, evolving, and diffuse discipline—or perhaps a goal approached through many disciplines, including ecology, economics, engineering, and sociology. Given the central role of ecosystems in current paradigms for sustainable development, the science of ecology is a seemingly natural home for sustainability science. However, ecology may also present some operational limits to assessing or implementing sustainable strategies. Given how difficult it is to develop ecological experiments and test hypotheses, ecology has been described as having more in common with the earth sciences (such as geology) than other biological sciences (such as physiology or molecular biology), and much less with physical sciences such as chemistry and physics [3],[4]. Given the importance of observation and inference in ecology, making predictions about complex ecological interactions requires accepting their inherent uncertainty and thus a particular humility in drawing conclusions [5]. A reader of the Burger and colleagues paper [1], for instance, might assume that the logical endpoints for its arguments are either an imminent global economic collapse triggered by stringent natural resource scarcities or catastrophic human population decline in a forceful realignment with global carrying capacity. These are dire options, with no realistically actionable response, and a reader would be forced to either reject the initial assumptions or to despair, neither of which is a useful motivational force for positive change. Moreover, while we believe that heightened concern is warranted and that these endpoints are possible, we also believe there is evidence that they can be avoided or mitigated. Predictions made on similar first principles have been put forward repeatedly in the past (e.g., [6]–[8]), and rigidly materialist approaches to social and economic change often underestimate the flexibility and resilience of human economies and societies [9]. To date, technological advances such as increases in agricultural productivity spurred by the prospect or reality of scarce primary inputs (land, water, nutrients, energy), shifts in economic valuation, and policy-based human behavioral change, such as the actions under the Montreal Protocol to reduce tropospheric concentrations of ozone-depleting gases, have avoided or delayed our transgression of perceived thresholds in the Earth system [10],[11]. While we cannot assume that there is an equivalent to Moore's Law of semiconductor capacity for natural resource management [12] or have faith that efficiency and innovation alone will save us, we can credibly assume that the existential imperative for human adjustment and adaptation will prompt us to correct our seemingly disastrous course. As a result, we believe that sustainability itself must rest on a broader foundation, particularly if we posit that sustainability science encompasses socioeconomic development, which requires the mobilization of natural resources in new ways to sustain and improve human well-being. Here, we describe several potential gaps in sustainability science, as well as evidence for what we hope is useful optimism that emerging economic paradigms are becoming more ecologically sensitive.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,008
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,416
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,006
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,321
Écart entre enseignants0,293 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations21
Publié2012
Routes d'admission1
Résumé présentoui

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