We propose a new approach to estimate the causal effects of maritime traffic when natural or policy experiments are not available. We apply this method to the case of Marseille, a large Mediterranean port city, where air pollution emitted by cruise vessels is a growing concern. Using a recent matching algorithm designed for time series data, we create hypothetical randomized experiments to estimate the change in local air pollution caused by a short-term increase in cruise traffic. We then rely on randomization inference to compute nonparametric 95\% uncertainty intervals. We find that cruise vessels' arrivals have large impacts on city-level hourly concentrations of nitrogen dioxide, particulate matter, and sulfur dioxide. At the daily level, road traffic seems however to have a much larger impact than cruise traffic. Our procedure also helps assess in a transparent manner the identification challenges specific to this type of high-frequency time series data.

翻译：我们提出一种新的方法,在没有自然或政策试验时估计海上交通的因果关系。我们将这种方法应用于地中海一个大型港口城市马赛的情况,因为那里的游轮排放的空气污染日益引起人们的关注。我们利用最近为时间序列数据设计的匹配算法,创建了假设随机实验来估计游轮交通短期增加造成的当地空气污染的变化。然后,我们依靠随机推论来计算非对数95 ⁇ 不确定的间隔。我们发现游轮的抵达对城市一级的二氧化碳、微粒物质和二氧化硫的每小时浓度有重大影响。然而,在日常层面上,道路交通的影响似乎比游轮交通大得多。我们的程序还有助于以透明的方式评估这类高频时间序列数据特有的识别挑战。