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.