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Enhanced Geospatial Validity for Meta-analysis and Environmental Benefit Transfer: An Application to Water Quality Improvements

机译：荟萃分析和环境效益转移的增强地理空间有效性：改善水质的应用

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摘要

Meta-regression models are commonly used within benefit transfer to estimate willingness to pay (WTP) for environmental quality improvements. Theory suggests that these estimates should be sensitive to geospatial factors including resource scale, market extent, and the availability of substitutes and complements. Valuation meta-regression models addressing the quantity of non-market commodities sometimes incorporate spatial variables that proxy for a subset of these effects. However, meta-analyses of WTP for environmental quality generally omit geospatial factors such as these, leading to benefit transfers that are invariant to these factors. This paper reports on a meta-regression model for water quality benefit transfer that incorporates spatially explicit factors predicted by theory to influence WTP. The metadata are drawn from stated preference studies that estimate per household WTP for water quality changes in United States water bodies, and combine primary study information with extensive geospatial data from external sources. Results find that geospatial variables are associated with significant WTP variations as predicted by theory, and that inclusion of these variables reduces transfer errors.
机译：在收益转移中通常使用元回归模型来估计改善环境质量的支付意愿（WTP）。理论表明，这些估计应对地理空间因素敏感，包括资源规模，市场范围以及替代品和补品的可用性。评估非市场商品数量的评估元回归模型有时会合并代表这些影响子集的空间变量。但是，对WTP的环境质量进行荟萃分析通常会忽略诸如此类的地理空间因素，从而导致收益转移不受这些因素的影响。本文报告了一种水质收益转移的元回归模型，该模型结合了理论预测的影响WTP的空间明确因素。元数据来自陈述的偏好研究，这些偏好研究估计了每个家庭的WTP对美国水体中水质变化的估计，并将主要研究信息与来自外部来源的大量地理空间数据结合在一起。结果发现，地理空间变量与理论上预测的显着WTP变化相关，并且包含这些变量可减少传输误差。

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