Hidden patterns of sustainable development in Asia with underlying global change correlations
Notice bibliographique
Résumé
<p>As the most populous continent, and its dominant role in the global economy, <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/asia" target="_blank">Asia</a> is arguably the most important region for understanding global change. To evaluate and guide humanity’s growth toward a more sustainable future, indicators and their composite indices have been adopted as key tools resulting in a paralyzing amount for decision-makers, practitioners, and researchers to choose from. Although research has improved understanding of development metrics for evaluating and monitoring global change, making progress toward sustainability remains as open as ever. Building from previous work, 44 Asian nations were studied using four guiding research questions: (<em>i</em>) What are the hidden dimensions within a collection of known sustainable development indices, and what differentiates winning locations from losing ones? (<em>ii</em>) Are the three major divisions of sustainability (economic growth, social equity, environmental integrity) equally supported by these development measuring initiatives? (<em>iii</em>) How do common global change indicators statistically respond to the canonical development dimensions? (<em>iv</em>) Do recent population growth and urbanization trends move humanity closer to planetary sustainability? Those questions were explored using four amassing methodological stages. First, six hidden development dimensions (factor axes) were revealed while maintaining over 80% of 35 known sustainable development indices’ variation. The dimensions expressed: (F1) human well-being synergies; (F2) environmentally efficient happiness; (F3) ecological integrity to economic performance trade-off; (F4) peace, prosperity, and <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/natural-resource" target="_blank">natural resources</a> protection; (F5) economic and political liberty; and (F6) generosity. Second, a mega-index of sustainable development (MISD) was created by combining the six latent dimensions. Third, spatial patterns of the hidden development axes, MISD, and nine common global change metrics were explored. Fourth, using global and local inferential tests, associations between the canonical development dimensions, MISD, and global change indicators were made. The human well-being synergies dimension (F1) explained over one-third of the total variance, and positively clustered in northern Asia and negatively in <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/southern-asia" target="_blank">southern Asia</a>. The MISD ranked Singapore best, followed by Cyprus, Sri Lanka, Bhutan, Kyrgyzstan, and Malaysia; Afghanistan ranked worst, then China, Syria, Russia, Turkmenistan, and India. Overall, improved sustainable development position came through increased population density, decreased country area, lower latitude, and a greater proportion of urban land cover. This cross-country analysis reiterates an underrepresentation of biogeochemical (ecosphere) conditions across development indices; moreover, spatial patterns of favorable development were rarely found simultaneous. Trade-offs and the lack of spatial concordance will make achieving sustainability a very difficult task in an urbanizing world without limits.</p>
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».