Data and Code for: Capital Uncertainty and the Efficiency Frontier of Climate Adaptation

Description This archive provides the complete data and computational resources supporting the study “Capital Uncertainty and the Efficiency Frontier of Climate Adaptation.” It is designed to ensure methodological transparency and enable full reproducibility of the stochastic frontier analysis used to evaluate climate adaptation interventions under capital cost uncertainty. The research assesses a portfolio of adaptation measures using an output-oriented Variable Returns to Scale (VRS) Data Envelopment Analysis framework combined with typology-specific Extreme Value modeling of cost overruns. Technical efficiency is evaluated within a Monte Carlo simulation environment to derive the Stochastic Robustness Index (SRI), a metric capturing the persistence of efficiency under conditions of epistemic volatility in capital expenditures. To strengthen decision-chain transparency, adaptation interventions were pre-selected through a Multi-Criteria Assessment (MCA) process. The anonymized MCA scoring matrix included in this archive enables independent verification of the pathway linking portfolio construction, stochastic cost generation, frontier estimation, and robustness diagnostics. Contents of the archive include: An anonymized intervention-level dataset for the adaptation portfolio MCA scoring data used for portfolio selection Typology-specific parameters for Extreme Value cost sampling A linear-programming implementation of an output-oriented VRS DEA frontier Monte Carlo simulation code for estimating the Stochastic Robustness Index Sensitivity-testing utilities examining volatility gradients and systemic cost correlation Posterior predictive validation scripts Supplementary technical documentation aligned with the manuscript Reproducibility All stochastic procedures use fixed random seeds to ensure computational reproducibility. The archive is self-contained and executable using standard scientific Python libraries without specialized hardware. Data Availability Note Datasets are anonymized to preserve confidentiality while maintaining the statistical properties required for replication. Original policy datasets may be available from the author upon reasonable request, subject to institutional data-sharing restrictions.# 数据集说明 本归档文件包含支撑《资本不确定性与气候适应效率前沿》（Capital Uncertainty and the Efficiency Frontier of Climate Adaptation）研究的完整数据与计算资源，旨在确保方法学透明度，并可完全复现用于评估资本成本不确定性下气候适应干预措施的随机前沿分析。 本研究采用面向产出的可变规模报酬（Variable Returns to Scale, VRS）数据包络分析（Data Envelopment Analysis, DEA）框架，结合针对成本超支的类型特异性极值建模，对一系列气候适应措施组合进行评估。技术效率的评估在蒙特卡洛（Monte Carlo）模拟环境中开展，以推导随机稳健性指数（Stochastic Robustness Index, SRI）——该指标用于刻画资本支出存在认知不确定性时，效率水平的持续稳定性。 为提升决策链条的透明度，本研究通过多准则评估（Multi-Criteria Assessment, MCA）流程预先筛选气候适应干预措施。归档文件中包含的匿名化MCA评分矩阵，可独立验证从组合构建、随机成本生成、前沿估计到稳健性诊断的完整研究路径。 本归档文件包含以下内容： - 适用于该气候适应措施组合的匿名化干预层面数据集 - 用于措施组合筛选的MCA评分数据 - 用于极值成本抽样的类型特异性参数 - 面向产出的VRS DEA前沿的线性规划实现代码 - 用于估计随机稳健性指数的蒙特卡洛模拟代码 - 用于检验波动梯度与系统性成本相关性的敏感性检验工具 - 后验预测验证脚本 - 与研究论文配套的补充技术文档 ## 可复现性说明 所有随机计算过程均使用固定随机种子，以确保结果可完全复现。本归档文件为独立完整的可执行包，仅需使用标准科学计算Python库即可运行，无需专用硬件设备。 ## 数据可用性说明 数据集已做匿名化处理，在保留复现研究所需统计特性的同时保护研究对象的机密性。原始政策数据集可在符合机构数据共享限制的前提下，经合理申请向作者获取。