Analysis Code and Simulation Results for MimiCIAMRegret

This repository contains the simulation results supporting the manuscript on robust coastal adaptation under deep uncertainty using the regret framework implemented on MimiCIAM. The datasets correspond to the full ensemble of model outputs used to generate the figures, tables, and quantitative analyses presented in the manuscript. The simulations were performed using the MimiCIAMRegret model, an extension of the MimiCIAM integrated assessment framework that implements regret-based decision criteria for coastal adaptation planning under uncertain local sea-level rise (LSLR), different socioeconomic pathways, and long-term climate scenarios. The model evaluates alternative adaptation strategies (protection, retreat, and no adaptation) across different stages-of-the-world. The complete model implementation is publicly available via the MimiCIAMRegret GitHub repository (it will be public after publication), which allows full reproducibility of the simulations. Researchers can clone the GitHub repository and run the model directly to reproduce or extend the analysis. This Zenodo archive specifically contains the processed simulation outputs used in the manuscript, including aggregated cost metrics, adaptation decisions, regret measures, and summary statistics. These results are provided to ensure transparency, reproducibility, and long-term archival stability of the computational experiments underlying the study. The repository does not contain the model source code. For model execution and documentation, please refer to the MimiCIAMRegret GitHub repository.本仓库包含支撑相关研究手稿的模拟结果，该手稿围绕深不确定性下采用遗憾框架（regret framework）开展的稳健沿海适应性研究展开，相关实现基于MimiCIAM平台。本数据集对应手稿中用于生成全部图表、表格与定量分析的完整模型输出合集。 本次模拟通过MimiCIAMRegret模型完成，该模型是MimiCIAM综合评估框架的扩展版本，针对不确定的局地海平面上升（local sea-level rise, LSLR）、不同社会经济路径及长期气候情景下的沿海适应性规划，实现了基于遗憾准则的决策判定。模型将针对不同世界情景阶段，评估防护、撤离及无适应性措施三类备选适应性策略。 完整的模型实现目前可通过MimiCIAMRegret GitHub仓库获取（将于论文正式发表后完全公开），可实现本次模拟过程的全复现。研究人员可克隆该GitHub仓库并直接运行模型，以复现或拓展本次分析工作。 本Zenodo存档专门收录了手稿中使用的经过预处理的模拟输出结果，包括聚合成本指标、适应性决策结果、遗憾测度以及汇总统计量。提供此类结果旨在保障本研究相关计算实验的透明度、可复现性与长期归档稳定性。 本仓库未包含模型源代码。如需获取模型运行方法与相关文档，请查阅MimiCIAMRegret GitHub仓库。