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How can we value an environmental asset that very few have visited or heard of? Lessons learned from applying contingent and inferred valuation in an Australian wetlands case study

机译:我们如何评估很少有人去过或听说过的环境资产?在澳大利亚湿地案例研究中运用或有和推定估值获得的经验教训

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To date, the majority of environmental assets studied in the economic valuation literature clearly have high amenity and recreational use values. However there are many cases where small, but nevertheless unique and important, ecosystems survive as islands amongst large areas of modified, productive, or urban, landscapes. Development encroaches on the landscape and as urban landscapes become more concentrated these types of conservation islands will become increasingly more important. Previous experience with economic valuation suggests that lower total values for smaller contributions to conservation are more liable to be swamped by survey and hypothetical bias measures. Hence there needs to be more understanding of approaches to economic valuation for small and isolated environmental assets, in particular regarding controlling stated preference biases. This study applied the recently developed method of Inferred Valuation (IV) to a small private wetland in South-East Australia, and compared willingness to pay values with estimates from a standard Contingent Valuation (CV) approach. We found that hypothetical bias did seem to be slightly lower with the IV method. However, other methods such as the use of log-normal transformations and median measures, significantly mitigate apparent hypothetical biases and are easier to apply and allow use of the well-tested CV method. (C) 2018 Elsevier Ltd. All rights reserved.
机译:迄今为止,经济估价文献中研究的大多数环境资产显然具有较高的舒适度和娱乐用途价值。然而,在许多情况下,规模虽小但却独特而又重要的生态系统仍作为岛屿在大面积的改良,生产或城市景观中生存。发展侵占了景观,并且随着城市景观变得更加集中,这些类型的保护岛将变得越来越重要。先前的经济估值经验表明,对保护的较小贡献的较低总价值更容易被调查和假设性的偏见措施所淹没。因此,需要对小型和孤立的环境资产进行经济评估的方法有更多的了解,尤其是在控制陈述的偏好偏差方面。这项研究将最新开发的推定估值(IV)方法应用于澳大利亚东南部的一个小型私人湿地,并将支付价值的意愿与标准偶然估值(CV)方法的估计值进行了比较。我们发现,使用IV方法的假设偏见似乎稍低。但是,其他方法(例如,使用对数正态变换和中值度量)可以显着减轻明显的假设偏差,并且更易于应用并允许使用经过良好测试的CV方法。 (C)2018 Elsevier Ltd.保留所有权利。

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