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Open AccessJournal ArticleDOI

A Cross‐National Analysis of How Economic Inequality Predicts Biodiversity Loss

Timothy G. Holland, +2 more
- 01 Oct 2009 - 
- Vol. 23, Iss: 5, pp 1304-1313
TLDR
Inequality was a significant predictor of biodiversity loss and significantly improved the fit of the models, confirming that socioeconomic inequality is an important factor to consider when predicting rates of anthropogenic biodiversity loss.
Abstract
We used socioeconomic models that included economic inequality to predict biodiversity loss, measured as the proportion of threatened plant and vertebrate species, across 50 countries. Our main goal was to evaluate whether economic inequality, measured as the Gini index of income distribution, improved the explanatory power of our statistical models. We compared four models that included the following: only population density, economic footprint (i.e., the size of the economy relative to the country area), economic footprint and income inequality (Gini index), and an index of environmental governance. We also tested the environmental Kuznets curve hypothesis, but it was not supported by the data. Statistical comparisons of the models revealed that the model including both economic footprint and inequality was the best predictor of threatened species. It significantly outperformed population density alone and the environmental governance model according to the Akaike information criterion. Inequality was a significant predictor of biodiversity loss and significantly improved the fit of our models. These results confirm that socioeconomic inequality is an important factor to consider when predicting rates of anthropogenic biodiversity loss.

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A Review of Footprint analysis tools for monitoring impacts on sustainability

TL;DR: An overview of the definitions and units of measurement associated with environmental, social, and economic footprints is presented in this paper, where composite footprints combining two or more individual footprints are also assessed.
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A social–ecological approach to conservation planning: embedding social considerations

TL;DR: In this article, the authors suggest that linking conservation planning to a social-ecological systems (SES) framework can lead to a more thorough understanding of human-environment interactions and more effective integration of social considerations.
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Income Inequality and Carbon Emissions in the United States: A State-level Analysis, 1997–2012

TL;DR: In this paper, the authors investigated the relationship between U.S. state-level CO 2 emissions and two measures of income inequality: the income share of the top 10% and the Gini coefficient.
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Half-Earth or Whole Earth? Radical ideas for conservation, and their implications

TL;DR: In this paper, the authors question whether the increasingly popular, radical idea of turning half the Earth into a network of protected areas is either feasible or just, and call instead for alternative radical action that is both effective and more equitable, focused directly on the main drivers of biodiversity loss by shifting the global economy from its current foundation in growth while simultaneously redressing inequality.
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The Haves, the Have-Nots, and the Health of Everyone: The Relationship Between Social Inequality and Environmental Quality

TL;DR: The mechanisms by which inequality can lead to environmental degradation and their relevance for public health are summarized, and common metrics of inequality and methodological considerations are synthesized in an effort to bring cohesion to such efforts.
References
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Journal ArticleDOI

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TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
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TL;DR: In this paper, an institutional approach to the study of self-organization and self-governance in CPR situations is presented, along with a framework for analysis of selforganizing and selfgoverning CPRs.
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TL;DR: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data is as discussed by the authors, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced.
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