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Record W7112409330

Race, Land and Climate Change: Equitable Resilience in California

2023· article· en· W7112409330 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Access to Scholarship at Harvard (DASH) (Harvard University) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeResilience (materials science)Ecosystem servicesPolitical economy of climate changeGreenhouse gasClimate resilienceConvention on Biological DiversityUnited Nations Framework Convention on Climate ChangeNatural disaster
DOInot available

Abstract

fetched live from 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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.006
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.005

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 head0.049
GPT teacher head0.308
Teacher spread0.259 · 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.

Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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