Abstract

The U.S. inland waterway system has played a critical role in promoting economic growth by efficiently transporting agricultural and manufacturing commodities along major riverine systems, such as the Mississippi River. However, the system faces escalating challenges due to the growing disruptions caused by climate change. For instance, the drought of the Mississippi River during the fall of 2022 resulted in an unprecedented increase in barge shipping rates. While the industry generally recognizes that climate change can significantly impact agricultural supply chains, it remains unclear how various environmental factors, including changes in water levels and temperature, affect inland waterway operations. This study aims to fill this research gap by conducting an empirical assessment of the impact of climate change-related factors on the operations of the inland waterway system in the Upper Mississippi—Illinois River Region. Unlike previous studies using conventional regression analysis, this study investigates the nonlinear relationship between changes in barge shipping rates and various environmental factors using the Gradient Boosting Decision Trees machine-learning method. Our analysis identifies the 29 ft gage height as a pivotal threshold influencing barge rates; rates tend to rise as the gage height decreases, and surpassing this threshold can also lead to increased shipping rates. This new approach enhances the model’s predictive capacity, allowing for a better understanding of the nonlinear effects of climate change on barge rates and productivity. It also enables planners and operational agencies to better understand the uncertainty of environmental conditions on the variations in barge rates and productivity performance. These findings provide valuable insights for decision-makers to understand the threshold effect of environmental conditions on inland waterway operations and facilitate the creation of effective adaptive strategies to mitigate future risks and consequences of climate change-induced disruptions.

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