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Enregistrement W7112409330

Race, Land and Climate Change: Equitable Resilience in California

2023· article· en· W7112409330 sur OpenAlexaboutno aff

Notice bibliographique

RevueDigital Access to Scholarship at Harvard (DASH) (Harvard University) · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEnvironmental Justice and Health Disparities
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésClimate changeResilience (materials science)Ecosystem servicesPolitical economy of climate changeGreenhouse gasClimate resilienceConvention on Biological DiversityUnited Nations Framework Convention on Climate ChangeNatural disaster
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Black, Indigenous, and People of Color (BIPOC) experience extreme climate change vulnerability, which is complicated by historical, environmental, and social factors (Sims et al., 2022). The spatial distribution of BIPOC and their degrees of exposure to extreme climate change impacts are crises that demand attention from cross-disciplinary research, conservation leaders, and public and private policy makers (IPCC, 2022). Reducing the effects of climate change on vulnerable communities with natural climate solutions should be a critical component of future efforts to build equitable and resilient communities. The Intergovernmental Panel on Climate Change (IPCC) defines climate change resilience as “the ability of a system to adjust to the unpredictability and extremes of climate change to lessen potential damages, to take advantage of opportunities, or to cope with the consequences.” (IPCC, 2001, as cited in Newsham and Bhagwat, 2016). Natural climate solutions, including conservation and land management actions that increase carbon sequestration or reduce greenhouse gas emissions, have the potential to contribute to climate resilience (Griscom et al., 2017). National and international efforts to improve resiliency through natural climate solutions include the Inflation Reduction Act of 2022 (United States), the IPCC, the United Nations Sustainable Development Goals (Global), and more recently, the Kunming-Montreal Global Biodiversity Framework (White House Council on Environmental Quality, 2022; IPCC, 2022; Convention on Biological Diversity, 2022). Protecting resilient land conserves vital ecosystem services and biodiversity that buffer negative climate change impacts (Anderson et al., 2016). By implementing land-specific land management strategies that focus on climate resilience, communities have better access to ecosystem services that buffer climate change impacts and reduce vulnerability to hazards. Spatial analysis at a variety of geographic scales and across demographic factors can help inform which areas to prioritize. This research explored the relationship between race, climate-resilient land, and protected land in California. Two linear regression analyses assessed the correlations between these variables and were informed by a detailed, historical overview of the injustices BIPOC experience related to climate change hazards. Colonization and slavery both contributed to present-day injustices and the conservation movement, either inadvertently or directly, has reinforced some of these inequalities. My two main research questions were: 1) Do census tracts with higher percentages of people of color have less climate-resilient land? 2) Do census tracts with higher percentages of BIPOC have less protected land? I hypothesized that census tracts with higher populations of BIPOC would have less climate-resilient land and less formally protected or conserved land due to their increased vulnerability to climate change hazards cited by previous research. Both hypotheses were confirmed by the analyses, showing a correlation between race and climate resilience and race and land protection. A linear regression analysis of hypothesis 1 performed in ArcGIS Pro 3.0.2 revealed a negative but weak relationship between climate resilience and census tracts with higher BIPOC populations. This indicates that the vulnerability experienced by BIPOC is not strongly correlated with how resilient the land in their communities is, or that other factors predict their vulnerability better. While race plays a factor, it is not the only factor contributing to vulnerability; climate change vulnerability is highly complex. Hypothesis 2 had a similarly weak and negative relationship between BIPOC communities and the percentage of protected land at the census tract level. While the results did not indicate a strong preference for land protection mechanisms in less racially diverse areas, it also suggests that conservation strategies lack prioritization based on social factors like race and climate vulnerability. While the results of this analysis do not have the same degree of correlation as prior studies, the linear regression model nonetheless identified evidence of racial inequalities related to climate-resilient land and land protection status. These results provide further validation of methods for inequality screening and conservation prioritization. Further research should continue to test screening methods for identifying racial inequities related to climate change vulnerability to create refined and well-informed climate action responses. Refining the development and implementation of natural climate solutions at all scales is crucial for supporting social equity for future generations and reducing climate change impacts. Policy makers and conservation leaders will need to expand their focus beyond traditional conservation criteria to incorporate social factors and historical disadvantages of BIPOC communities concerning access to open space, biodiversity, and ecosystem services. Perhaps the most important shift will be to include BIPOC communities in the planning and decision-making processes of these strategies so that their knowledge and expertise can inform equitable social and ecological systems.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,418
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,006
Science ouverte0,0010,002
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,005

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 Opus0,049
Tête enseignante GPT0,308
Écart entre enseignants0,259 · 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'étudeObservationnel
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

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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