Large-scale randomized control trials of incentive-based conservation: What have we learned?
Large-scale randomized control trials of incentive-based conservation: What have we learned?
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基于激励的保护的大规模随机对照试验：我们学到了什么？
DOI：
10.1016/j.worlddev.2019.104785
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发表时间：
2020
影响因子：
6.9
通讯作者：
Asquith N
中科院分区：
文献类型：
--
作者：
Asquith N
景观规模的保护计划具有挑战性的实施，甚至更难以评估。玻利维亚自然基金会和相关研究人员在过去十年中对玻利维亚的一项激励性保护计划进行了一系列随机对照试验。大型随机对照试验是复杂的，在保守性方面可能更复杂，因为它们需要测量在不同时间尺度上运行的多种结果。我们已经了解到，成功的保护干预措施的随机对照试验需要项目实施者表现出七个特征，即他们能够并愿意：大规模复制已证实的干预措施，定义和测量结果，冒着声誉风险，有耐心，获得世界一流的技术研究支持，灌输紧密的研究人员/从业者合作，并根据评估结果调整干预措施。重要的是，我们已经证明，大规模的强大的RCT为基础的评估是可能的保护。学习如何使用这种评估工具是至关重要的，如果保护从业人员要证明归因于他们的干预措施的影响。
Landscape-scale conservation programs are challenging to implement, and even more difficult to evaluate. Fundación Natura Bolivia and associated researchers have spent the last decade undertaking a series of randomized control trials (RCTs) of an incentive-based conservation program in Bolivia. Large RCTs are complex, perhaps more so in conservation, as they require measurement of multiple kinds of outcomes operating on different timescales. We have learned that successful RCTs of conservation interventions require that program implementers demonstrate seven characteristics, namely that they are able and willing to: replicate a proven intervention at scale, define and measure outcomes, risk their reputation, have patience, access world-class technical research support, inculcate a tight researcher/practitioner collaboration and adapt the intervention based on evaluation results. Importantly, we have shown that large-scale robust RCT-based evaluations are possible in conservation. Learning how to use such evaluation tools is critical if conservation practitioners are to demonstrate attributable impact of their interventions.
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