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Testing household preferences for the importance of the frequency and severity of water quality impairment
Canadian Water Resources Journal ( IF 1.7 ) Pub Date : 2021-09-01 , DOI: 10.1080/07011784.2021.1957718
Steven Renzetti 1 , James I. Price 2 , Diane Dupont 1 , Asit Mazumder 3
Affiliation  

Abstract

Water quality indices are employed by governments largely as a means of communicating the multifaceted nature of water quality and aquatic ecosystem health to the general public. Given the complexity of responsibility for oversight of freshwater quality in Canada, the Canadian Council of Ministers of the Environment (CCME) has developed an index based on the severity, frequency, and scope of water impairment. An important feature, and potential shortcoming, of this approach is that the three attributes of water quality are weighted equally. If households, however, weight these attributes differently, then the index’s ability to convey information to the public may be weakened. This issue is examined by eliciting household preferences for a hypothetical water quality protection program that reduces the severity and frequency of impairment using a discrete choice experiment (issues of scope are not included in the analysis). Latent class and mixed logit models are estimated. The latent class models, which outperform the mixed logit, indicate the presence of two preference classes that hold dramatically different preferences for benefits of the protection program. While one group of respondents is unresponsive to the severity and frequency of impairment, there is evidence that the other group may assign different weights to the attributes. These findings suggest the CCME index could convey different information to the two groups.



中文翻译:

测试家庭偏好对水质损害频率和严重程度的重要性

摘要

政府采用水质指数主要是作为向公众传达水质和水生生态系统健康的多方面性质的一种手段。鉴于加拿大淡水质量监督责任的复杂性,加拿大环境部长理事会 (CCME) 已根据水损害的严重程度、频率和范围制定了一个指数。这种方法的一个重要特征和潜在缺点是水质的三个属性的权重相等。但是,如果住户对这些属性的权重不同,则该指数向公众传达信息的能力可能会减弱。通过使用离散选择实验(分析不包括范围问题),通过引发家庭对假设的水质保护计划的偏好来检查该问题,该计划可降低损害的严重程度和频率。估计潜在类和混合 logit 模型。潜在类别模型优于混合逻辑模型,表明存在两个偏好类别,它们对保护计划的好处持有截然不同的偏好。虽然一组受访者对损伤的严重程度和频率没有反应,但有证据表明另一组可能会为这些属性分配不同的权重。这些发现表明 CCME 指数可以向两组传达不同的信息。估计潜在类和混合 logit 模型。潜在类别模型优于混合逻辑模型,表明存在两个偏好类别,它们对保护计划的好处持有截然不同的偏好。虽然一组受访者对损伤的严重程度和频率没有反应,但有证据表明另一组可能会为这些属性分配不同的权重。这些发现表明 CCME 指数可以向两组传达不同的信息。估计潜在类和混合 logit 模型。潜在类别模型优于混合逻辑模型,表明存在两个偏好类别,它们对保护计划的好处持有截然不同的偏好。虽然一组受访者对损伤的严重程度和频率没有反应,但有证据表明另一组可能会为这些属性分配不同的权重。这些发现表明 CCME 指数可以向两组传达不同的信息。有证据表明另一组可能会为这些属性分配不同的权重。这些发现表明 CCME 指数可以向两组传达不同的信息。有证据表明另一组可能会为这些属性分配不同的权重。这些发现表明 CCME 指数可以向两组传达不同的信息。

更新日期:2021-09-01
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