The complex links between governance and biodiversity

The complex links between governance and biodiversity
复制标题

DOI:
10.1111/j.1523-1739.2006.00521.x
复制
发表时间:
2006-10-01
影响因子:
6.3
通讯作者:
McCubbins, Mathew D.
McCubbins, Mathew D.
中科院分区:
环境科学与生态学1区
文献类型:
--
作者:
Barrett, Christopher B.;Gibson, Ciark C.;McCubbins, Mathew D.

文献摘要

被引文献

相似文献

我们认为,有两个问题削弱了那些谁链接腐败和自然资源的开采索赔,第一个是概念,第二个是方法。使用国家一级腐败指标的研究没有注意到,腐败有多种形式,在多个层面,可能对资源使用产生非常不同的影响:消极影响、积极影响或根本不影响。在腐败影响资源的机制没有明确的因果模型的情况下,应谨慎对待腐败与自然资源状况之间的任何估计关系。将腐败措施与自然资源使用联系在一起的简单、非理论模型通常不考虑对人与自然资源之间的关系至关重要的其他重要控制变量。为了说明这两个普遍关注的问题,我们使用统计方法来证明,最近一项著名研究的结果表明,腐败与森林和大象减少之间的联系并不足以通过简单的概念和方法改进来实现。特别是,一旦我们控制了一些合理的人为因素和生物物理条件因素,估计了变化的影响而不是水平,以便不混淆横截面和纵向变化,并纳入来自相同数据源的额外观察结果,腐败水平不再有任何解释力。
We argue that two problems weaken the claims of those who link corruption and the exploitation of natural resources, The first is conceptual and the second is methodological. Studies that use national-level indicators of corruption fail to note that corruption comes in many forms, at multiple levels, that may affect resource use quite differently: negatively, positively, or not at all. Without a clear causal model of the mechanism by which corruption affects resources, one should treat with caution any estimated relationship between corruption and the state of natural resources. Simple, atheoretical models linking corruption measures and natural resource use typically do not account for other important control variables pivotal to the relationship between humans and natural resources. By way of illustration of these two general concerns, we used statistical methods to demonstrate that the findings of a recent, well-known study that posits a link between corruption and decreases in forests and elephants are not robust to simple conceptual and methodological refinements. In particular, once we controlled for a few plausible anthropogenic and biophysical conditioning factors, estimated the effects in changes rather than levels so as not to confound cross-sectional and longitudinal variation, and incorporated additional observations from the same data sources, corruption levels no longer bad any explanatory power.