Valuing Water Quality as a Function of Water Quality Measures

Egan, Kevin J.; Herriges, Joseph A.; Kling, Catherine L.; Downing, John A.

doi:10.1111/j.1467-8276.2008.01182.x

Cited by 124 publications

(127 citation statements)

References 29 publications

(26 reference statements)

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“…In contrast to comparing models based on the likelihood values with unknown sampling properties (as in, e.g., Egan et al, 2009), we propose a model that draws on the Bayesian paradigm to integrate the variable selection process into the model and reflect the accompanying uncertainty about which is the "best" specification used for counterfactual predictions.…”

Section: October 8 2010mentioning

confidence: 99%

“…That is Q j,2 = Q j,1 + U (0, 1). This is to test the performance of our model when high correlation exists between the two 7 We also use the generated data as a guide to know how many draws will be needed for our application to achieve the same level of precision under independent and identical distribution (iid ) sampling.…”

Section: Generated Data Experimentsmentioning

confidence: 99%

“…7 Specifically, we generated a pseudo-data set consisting of N = 3000 individuals who are assumed to choose among J = 10 sites and the "stay at home" option on each of T = 52 choice occasions. The vector of individual characteristics in equation (1) (i.e., Z i ) consisted of a uniform random variable that signifies the age of the individual and a gender dummy variable generated from a Bernoulli distribution with equal probability of success and failure.…”

Section: Generated Data Experimentsmentioning

confidence: 99%

“…In a recent paper, Egan et al (2009) attempt to ameliorate this problem by employing a split sample approach, using separate portions of the available data for model specification, estimation, and evaluation. They isolated one third of their sample in order to consider alternative models and functional forms, using a likelihood dominance criteria to pick their final model, which is in turn estimated using a separate sample.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, and we would argue preferably, the model can be used as part of the process of employing a Bayesian model averaging, integrating competing models into a single structure that can be used for welfare analysis and counterfactual predictions. The model is applied using data from the 2002 survey of Iowa Lakes Project, the same data underlying both the Egan et al (2009) and the Abidoye, Herriges and Tobias (2010) analyses. We use our model to contrast our findings with those obtained from these earlier, highlighting the benefits of integrating model uncertainty into a unified framework.…”

Section: Introductionmentioning

confidence: 99%

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Model Uncertainty in Characterizing Recreation Demand

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Environ Resource Econ

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A Bayesian variable selection procedure is used to control for uncertainty in the specification of a recreational demand model. Specifically, we propose a model that draws on the Bayesian paradigm to integrate the variable selection process into the model and reflect the accompanying uncertainty about which is the "best" specification used for counterfactual predictions. The advantage of this procedure over previous non-Bayesian approaches is that overcomes the problem of pre-testing in specification searches. Keywords October 8, 2010Abstract A Bayesian variable selection procedure is used to control for uncertainty in the specification of a recreational demand model. In contrast to comparing models based on the likelihood values with unknown sampling properties (as in, e.g., Egan et al, 2009), we propose a model that draws on the Bayesian paradigm to integrate the variable selection process into the model and reflect the accompanying uncertainty about which is the "best" specification used for counterfactual predictions.

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Section: October 8 2010mentioning

confidence: 99%

Section: Generated Data Experimentsmentioning

confidence: 99%

Section: Generated Data Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model Uncertainty in Characterizing Recreation Demand

Abidoye

Herriges

2012

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Data‐driven predictions of summertime visits to lakes across 17 US states

et al. 2023

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Using a dataset of more than 51,000 US lakes, we estimated the relationship between summertime lake visits, lake water clarity, landscape features, and other amenities, where visits were estimated with counts of geo-located photographs. Given the size and complexity of our dataset, we used a combination of machine learning techniques, imputation techniques, and a Poisson count model to estimate these relationships. We found that every additional meter of average summertime Secchi depth was associated with at least 7% more summertime lake visits, all else equal. Second, we found that lake amenities, such as beaches, boat launches, and public toilets, were more powerful predictors of visits than water clarity. Third, we found that visits to a lake were strongly influenced by the lake's accessibility and its distance to nearby lakes and the amenities the nearby lakes offered. Our research highlights the need for (1) a better understanding of how representative social media data are of actual recreational behavior, (2) the development of best practices to account for nonrandom patterns in missing natural feature data, and (3) a better understanding of the potential endogeneity in the lake visit-water quality relationships.

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Optical water quality and human perceptions of rivers: an ethnohydrology study

West

Nolan

Scott³

2016

Ecosyst Health Sustain

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Rivers are revered worldwide for their ecologic, scenic, and recreational value. The capacity to communicate effectively among human groups with vested interest in rivers hinges on understanding the nature of human perceptions of water quality and the extent to which they vary intraculturally. Recognizing the intersection between measured water quality and the characteristics of rivers that influence human perceptions facilitates potential for better communication across disciplines and among stakeholders. We conducted interviews and a pile‐sort task with water quality experts and nonexperts. Our analysis suggested human evaluation of water quality is guided by culturally constructed criteria, regardless of respondent expertise, experience, or demographics. Cluster analysis results implied that measured physical and chemical parameters of rivers were directly related to the visible attributes used in human judgments. We suggest that, regardless of variability among individual stakeholders, observable characteristics may be the foundation for a common understanding of water quality in rivers.

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Valuing Water Quality as a Function of Water Quality Measures

Cited by 124 publications

References 29 publications

Model Uncertainty in Characterizing Recreation Demand

Model Uncertainty in Characterizing Recreation Demand

Data‐driven predictions of summertime visits to lakes across 17 US states

Optical water quality and human perceptions of rivers: an ethnohydrology study

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