发表机构
University of British Columbia; University of Connecticut; Harvard University（不列颠哥伦比亚大学; 康涅狄格大学; 哈佛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究结合机器学习与理论，以海事CO2排放为例，比较工程计算、结构回归、混合模型和机器学习，发现纯机器学习速度响应衰减，混合模型保留结构响应，减速成本效益分析显示衰减响应可翻转净收益符号，强调准确预测不能替代对反事实限制的审查。
AI 中文摘要
机器学习能很好地预测结果，但预测准确性并不能确保反事实响应的可靠性。我们研究如何将机器学习与理论相结合，用于测量和反事实分析，以海事二氧化碳排放为例，其中物理学提供了基准速度响应。将干散货船和集装箱船的每小时跟踪数据与欧盟法规下报告的年度燃料消耗相匹配，我们比较了工程计算、结构回归、混合模型和机器学习。在样本外，所有估计模型对报告总量的预测误差均在几个百分点以内，优于标准工程计算。然而，纯机器学习和无限制的结构回归暗示了衰减的速度响应。混合模型通过从机器学习组件中排除与速度相关的输入，保留了结构组件的速度响应。对减速的成本效益分析说明了政策利害关系：衰减的速度响应可能翻转净收益的符号。因此，准确的总体预测不能替代对决定反事实响应的限制条件的审查。
英文摘要
Machine learning predicts outcomes well, but predictive accuracy does not ensure reliable counterfactual responses. We examine how to combine machine learning and theory for measurement and counterfactual analysis, using maritime CO2 emissions where physics provides a benchmark speed response. Matching hourly tracking data for dry bulk and container ships to annual fuel consumption reported under EU regulations, we compare engineering calculations, structural regressions, hybrid models, and machine learning. Out of sample, all estimated models predict within a few percent of reported totals, outperforming standard engineering calculations. Yet pure machine learning and unrestricted structural regression imply attenuated speed responses. Hybrids preserve the structural component's speed response by excluding speed-related inputs from the machine learning component. A cost-benefit analysis of speed reductions illustrates the policy stakes: an attenuated speed response can flip the sign of net benefits. Accurate aggregate predictions therefore cannot substitute for scrutiny of the restrictions determining counterfactual responses.
