Journal of the Association of Environmental and Resource Economists · 2026 · Robert J. Johnston、Klaus Moeltner
中文摘要
效益转移 (BT) 已发展为环境效益成本分析中占主导地位的估值方法,包括美国联邦机构被要求开展的分析。然而,即使是基于元回归模型 (MRM) 的最佳实践 BT 方法,通常也表现出较差的预测拟合度与样本外精度。本文引入随机森林 (RF) 对 MRM 进行非参数估计并构建 BT 预测。我们将不同 RF 模型的表现与当前 BT 最佳实践方法进行比较。我们发现,基于森林的模型大幅提高了福利预测的样本外准确度,并收窄了既定政策情景下预测效益的置信区间。表现最佳者属于局部线性森林 (LLF) 家族,这是一种结合随机森林与局部加权回归要素的混合方法。结果表明,这一新方法有潜力在不牺牲理论性质的前提下大幅提高环境政策制定中 BT 的准确度,同时相对于主要替代方法降低了计量与计算方面的困难。
Abstract
Benefit transfer (BT) has evolved as the dominant valuation method for environmental benefit-cost analyses, including those required of US federal agencies. Yet even best-practice approaches for BT based on meta-regression models (MRMs) typically exhibit poor predictive fit and out-of-sample precision. This article introduces random forests (RFs) for nonparametric estimation of MRMs and construction of BT predictions. We compare the performance of different RF models to current best-practice approaches for BT. We find that forest-based models substantially improve the out-of-sample accuracy of welfare predictions and tighten confidence intervals of predicted benefits for stipulated policy scenarios. The best performers reside within the family of local linear forests (LLFs), a hybrid approach that combines elements of RFs and locally weighted regression. Results suggest that this new approach has the potential to substantially improve BT accuracy for environmental policymaking without sacrificing theoretical properties, while simultaneously reducing econometric and computational difficulties relative to leading alternatives.