Benefit Transfer of Environmental and Resource Values: Progress, Prospects and Challenges

Johnston, Robert J.; Rolfe, John; Zawojska, Ewa

doi:10.1561/101.00000102

Cited by 37 publications

(42 citation statements)

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“…The potential advantages of meta-analysis for benefit transfer are established in the literature (Rosenberger and Phipps 2007; Nelson and Kennedy 2009; Rosenberger and Johnston 2009; Johnston and Rosenberger 2010; Kaul et al 2013; Rolfe, Brouwer, and Johnston 2015; Boyle and Wooldridge 2018; Johnston, Rolfe, and Zawojska 2018). Meta-regression analyses synthesize the results of multiple prior studies into a single set of parametric predictors that can be used within benefit transfer, providing a means to ground transfers in a broad base of prior information 7 .…”

Section: Meta-analysis Properties and Benefit Transfer Validitymentioning

confidence: 99%

“…Benefit transfer is defined as the use of research results from preexisting primary studies at one or more sites or policy contexts (called study sites) to predict welfare estimates such as willingness to pay (WTP) or related information for other, typically unstudied sites or policy contexts (called policy sites) (Johnston et al 2015a). Among the different approaches to benefit transfer in the literature, 1 there is emerging consensus over the advantages of methods that synthesize data from multiple sources, such as meta-analysis (e.g., Rosenberger and Phipps 2007; Boyle et al 2009; Johnston and Rosenberger 2010; Kaul et al 2013; Rolfe, Brouwer, and Johnston 2015; Johnston, Rolfe, and Zawojska 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Concerns such as these are sometimes overlooked in the ecosystem services valuation literature, leading to many MRMs that—despite having superficially acceptable statistical performance—are poorly suited to credible benefit transfer applications. As noted by Johnston, Rolfe, and Zawojska (2018, p. 202), “Many applications [of meta-analysis in the ecosystem services] literature lack minimal properties necessary to promote valid and reliable [benefit transfer] estimates.” Even if the underlying MRM is suitable for this purpose, valid transfers also depend on the post-estimation procedures used to predict ESVs from the estimated benefit function. These issues are seldom discussed.…”

Section: Introductionmentioning

confidence: 99%

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Using Meta-Analysis for Large-Scale Ecosystem Service Valuation: Progress, Prospects, and Challenges

Johnston¹,

Bauer²

2019

Agric. Resour. Econom. Rev.

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This article discusses prospects and challenges related to the use of meta-regression models (MRMs) for ecosystem service benefit transfer, with an emphasis on validity criteria and post-estimation procedures given sparse attention in the ecosystem services literature. We illustrate these topics using a meta-analysis of willingness to pay for water quality changes that support aquatic ecosystem services and the application of this model to estimate water quality benefits under alternative riparian buffer restoration scenarios in New Hampshire's Great Bay Watershed. These illustrations highlight the advantages of MRM benefit transfers, together with the challenges and data needs encountered when quantifying ecosystem service values.

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Section: Meta-analysis Properties and Benefit Transfer Validitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Meta-Analysis for Large-Scale Ecosystem Service Valuation: Progress, Prospects, and Challenges

Johnston¹,

Bauer²

2019

Agric. Resour. Econom. Rev.

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“…Benefit transfer allows values to be estimated when primary valuation studies cannot be conducted, but are not as accurate as primary study valuation methods (see also Brouwer and Bateman 2005;Johnston and Rosenberger 2010;Kaul et al 2013;Johnston et al 2015aJohnston et al , b, 2018c. Transfers involving benefit functions and information synthesised from multiple studies are often-but not always-more acute than transfers of a single value from individual studies (Kaul et al 2013;Rosenberger 2015;Johnston et al 2018c).…”

Section: Benefit Transfermentioning

confidence: 99%

“…Benefit transfer should be an ordered process that begins with a clear definition of the impact or change to be valued and the scale and context of the changes occurring, as well as the relevant populations (Johnston et al 2018c). This should be followed by a systematic search to identify available studies that might provide source values or value functions, evaluation to select the most suitable of those studies and the transfer process that will be used, performance of any adjustments needed to extrapolate values or functions to the target site, and sensitivity testing to ensure that value estimates are reliable.…”

Section: Benefit Transfermentioning

confidence: 99%

Valuing non-market economic impacts from natural hazards

et al. 2019

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Prioritising investments to minimise or mitigate natural hazards such as wildfires and storms is of increasing importance to hazard managers. Prioritisation of this type can be strengthened by considering benefit and cost impacts. To evaluate benefits and costs, managers require an understanding of both the tangible economic benefits and costs of mitigation decisions, and the often intangible values associated with environmental, social and health-related outcomes. We review the state of non-market valuation studies that provide monetary equivalent estimates for the intangible benefits and costs that can be affected by natural hazard events or their mitigation. We discuss whether managers can usefully call upon these available estimates, with a view to using the benefit transfer approach to include non-market values in economic decision frameworks. Additional context-specific non-market valuation studies are required to provide a more accurate selection of value estimates for natural hazard decision making. Decision making would benefit from considering these values explicitly in prioritising natural hazard investments.

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Relative Versus Absolute Commodity Measurements in Benefit Transfer: Consequences for Validity and Reliability

Johnston¹,

Zawojska²

2020

American J Agri Economics

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Non‐market goods can be measured on cardinal or relative scales. Consider a marsh of 200 acres, of which 20 acres would be affected by a policy. The same affected area can be measured in cardinal terms (20 acres) or as a relative proportion (10% of the marsh). When relative units such as percentages are a scalar transformation of cardinal units, the units of measurement used for modeling are often inconsequential for single‐site econometric and welfare analysis. However, this seemingly inconsequential transformation can have significant implications for benefit transfer across sites—a simple observation that remains unacknowledged by the literature. This article provides a theoretical and empirical evaluation of variable measurement conventions within benefit transfer, deriving conditions under which different types of measurement scales are expected to enhance validity and reliability. Theoretical results are illustrated using an application of discrete choice experiments to coastal flood adaptation in two Connecticut (United States) communities. Empirical findings validate expectations from the theoretical model, suggesting that transfers over goods measured in relative units may often outperform transfers over goods measured in cardinal units. These findings imply that the outcome of benefit transfer convergent validity tests may hinge on whether goods are measured in cardinal or relative units.

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Benefit Transfer of Environmental and Resource Values: Progress, Prospects and Challenges

Cited by 37 publications

References 0 publications

Using Meta-Analysis for Large-Scale Ecosystem Service Valuation: Progress, Prospects, and Challenges

Using Meta-Analysis for Large-Scale Ecosystem Service Valuation: Progress, Prospects, and Challenges

Valuing non-market economic impacts from natural hazards

Relative Versus Absolute Commodity Measurements in Benefit Transfer: Consequences for Validity and Reliability

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