Abstract Legislators in the European Union have long been concerned with the environmental impact of farming activities and introduced so-called agri-environment schemes (AES) to mitigate adverse environmental effects and foster desirable ecosystem services in agriculture. This study combines economic theory with a novel machine learning method to identify the environmental effectiveness of AES at the farm level. We develop a set of more than 130 contextual predictors to assess the individual impact of participating in AES. Results from our empirical application for Southeast Germany suggest the existence of heterogeneous, but limited effects of agri-environment measures in several environmental dimensions such as climate change mitigation, clean water and soil health. By making use of Shapley values, we demonstrate the importance of considering the individual farming context in agricultural policy evaluation and provide important insights into the improved targeting of AES along several domains.

中文翻译：

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使用机器学习识别欧盟农业环境计划的异构影响：案例研究

摘要 欧盟立法者长期以来一直关注农业活动对环境的影响，并引入了所谓的农业环境计划（AES）来减轻不利的环境影响并促进农业中理想的生态系统服务。本研究将经济理论与一种新的机器学习方法相结合，以确定 AES 在农场层面的环境有效性。我们开发了一组 130 多个上下文预测器来评估参与 AES 的个人影响。我们在德国东南部的实证应用结果表明，农业环境措施在减缓气候变化、清洁水和土壤健康等多个环境维度存在异质但有限的影响。通过利用 Shapley 值，