RiskPorts: replication data and code for "Climate Variables and the Costs Generated for the Port Sector: The Case of a Port in Southern Brazil"

This dataset supports the article "Climate Variables and the Costs Generated for the Port Sector: The Case of a Port in Southern Brazil", which estimates the operational delays and the opportunity cost caused by weather-related stoppages at the Sao Francisco do Sul port complex, in Santa Catarina, Brazil. It covers 4,332 vessel calls recorded between 2018 and 2021 at three facilities of the complex. Each row combines port operations records (six operational time measures, cargo volume and profile, terminal, port ownership, revenue, and an indicator of weather-related stoppage) with the rainfall, humidity, wind and wave conditions observed during the vessel's stay. A codebook documenting all 79 columns is included. The accompanying R and Stata code reproduces the published results, which rely on propensity score matching followed by regression with meteorological controls. The port operations records were originally extracted from the public statistics of the Brazilian National Waterway Transportation Agency (ANTAQ). That extraction is no longer reproducible from the source: ANTAQ has restructured its statistical system and discontinued the port revenue variable used in the opportunity cost estimates. The meteorological series were provided by EPAGRI/CIRAM; the values matched to each vessel call are included here, while the original station-level series may be obtained from that institution on request. Code is maintained at https://github.com/AliFiorentin/RiskPorts.本数据集支撑论文《气候变量与港口部门的成本：以巴西圣卡塔琳娜州圣弗朗西斯科-杜苏尔港口综合体为例》，该研究估算了该港口综合体因天气相关停航引发的作业延误与机会成本。数据集涵盖2018年至2021年间该综合体下属3个码头的4332艘次船舶靠港记录。每条记录整合了港口作业数据（含6项作业时长指标、货物吞吐量与货种结构、码头信息、港口权属、营收以及天气相关停航标识）与船舶靠港期间观测到的降雨、湿度、风速与海浪状况。数据集附带一份代码手册，对全部79个字段进行了详细说明。随附的R语言与Stata代码可复现已发表的研究结果，该结果基于倾向得分匹配（propensity score matching）后结合气象控制变量的回归分析。 原始港口作业记录取自巴西国家水路运输管理局（ANTAQ）的公开统计数据，但当前已无法从原始数据源复现该提取过程：巴西国家水路运输管理局已重构其统计体系，并停用了本次机会成本估算中使用的港口营收字段。气象序列数据由EPAGRI/CIRAM提供；本数据集已包含与每艘次靠港船舶匹配的气象数据，而原始台站级气象序列可通过向该机构申请获取。相关代码托管于https://github.com/AliFiorentin/RiskPorts。