首页> 美国卫生研究院文献>Proceedings of the National Academy of Sciences of the United States of America >Biodiversity Conservation and Poverty Traps Special Feature: Conditions associated with protected area success in conservation and poverty reduction
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Biodiversity Conservation and Poverty Traps Special Feature: Conditions associated with protected area success in conservation and poverty reduction

机译:生物多样性保护和贫困陷阱专题:保护区在保护和减贫方面取得成功的条件

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摘要

Protected areas are the dominant approach to protecting biodiversity and the supply of ecosystem services. Because these protected areas are often placed in regions with widespread poverty and because they can limit agricultural development and exploitation of natural resources, concerns have been raised about their potential to create or reinforce poverty traps. Previous studies suggest that the protected area systems in Costa Rica and Thailand, on average, reduced deforestation and alleviated poverty. We examine these results in more detail by characterizing the heterogeneity of responses to protection conditional on observable characteristics. We find no evidence that protected areas trap historically poorer areas in poverty. In fact, we find that poorer areas at baseline seem to have the greatest levels of poverty reduction as a result of protection. However, we do find that the spatial characteristics associated with the most poverty alleviation are not necessarily the characteristics associated with the most avoided deforestation. We show how an understanding of these spatially heterogeneous responses to protection can be used to generate suitability maps that identify locations in which both environmental and poverty alleviation goals are most likely to be achieved.
机译:保护区是保护生物多样性和生态系统服务供应的主要方法。由于这些保护区经常被置于贫困普遍的地区,并且由于它们可能限制农业发展和自然资源的开发,因此人们对它们创造或加强贫困陷阱的潜力提出了关切。先前的研究表明,哥斯达黎加和泰国的保护区系统平均减少了森林砍伐并减轻了贫困。我们通过表征以可观察特征为条件的保护响应的异质性来更详细地研究这些结果。我们找不到证据表明保护区使贫困地区陷入了历史上较贫困的地区。实际上,我们发现,由于受到保护,基线较贫穷的地区似乎具有最大程度的减贫水平。但是,我们确实发现,与减轻贫困程度最大相关的空间特征不一定与与避免森林砍伐最相关的特征。我们展示了如何将对保护的这些空间异质性反应的理解用于生成适合性地图,以识别最有可能实现环境和贫困目标的地点。

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