Multi-quality hydrogen e-methanol supply chain: Appendix and datasets

Description This dataset and appendix support the study on distributionally robust optimization of multi-quality hydrogen e-methanol supply chains for net-zero mobility in carbon-intensive industrial clusters. The package includes four components. First, a technical appendix (Appendices A–D) detailing the linearization techniques for non-linear constraints, the column-and-constraint generation algorithm with convergence proof, comprehensive parameter tables (by-product hydrogen composition, full LCA emission factors, PSA operating costs), and extended sensitivity analyses on Wasserstein radius tuning, carbon price trajectories, and technology learning versus regulatory tightening. Second, baseline techno-economic input data covering facility-specific capital and operating costs (PSA purification, PEM electrolyzers, methanol synthesis, CCUS, storage), hydrogen source compositions, renewable curtailment characteristics by season, monthly transport demand with seasonality, carbon intensity thresholds, and uncertainty parameters for the distributionally robust optimization model. Third, 60 historical monthly uncertainty samples (2020–2024) capturing by-product hydrogen availability and renewable power curtailment in the Guizhou phosphate chemical cluster, including maintenance events and shortfall risk indicators, with descriptive statistics and seasonal breakdowns. Fourth, optimization results including optimal capacity deployment across three hubs, cost-carbon Pareto frontier comparisons across scenarios (Business-as-Usual, Pure Green, Hybrid Transitional), the non-linear “fast-then-slow” transition pathway (2025–2035), robustness validation against extreme dual-source disruptions, cooperative game symbiosis rent distribution via Shapley values, monthly operational profiles for 2025, and comprehensive sensitivity analysis. All data are provided in Excel format with multiple sheets, enabling full reproducibility of the optimization model and case study. Cost parameters are expressed in 2024 constant RMB. The Wasserstein radius is set to 0.12, with risk aversion coefficient 0.3, consistent with moderate ambiguity aversion under data-scarce conditions (N = 60 samples). These materials are intended to support researchers and practitioners working on hydrogen supply chains, e-fuel systems, industrial symbiosis, and robust optimization under deep uncertainty. They may be reused under open license terms, with appropriate citation of the associated publication.