Beyond Outcome Variance: Addressing Ranking Instability via an RSSI-SA-ADF Framework for Adaptive Ecological Restoration Decision-Making

This dataset contains the full Supplementary Materials (S1–S17) for the research article “Beyond Outcome Variance: Addressing Ranking Instability via an RSSI-SA-ADF Framework for Adaptive Ecological Restoration Decision-Making.”
Research Hypothesis
We hypothesize that parameters driving outcome variance (e.g., costs) are statistically distinct from those causing ranking reversals (e.g., stakeholder preferences). The Ranking Stability Sensitivity Index (RSSI) captures this structural instability directly, including interaction effects (S3.3). Figure S4 empirically demonstrates this decoupling.
Data Content and Methodology
The document includes: the Multi-dimensional Lifecycle Evaluation (MD-LEA) model (S2); mathematical definition and computational algorithm of RSSI with bootstrap confidence intervals (S3); AHP-assisted threshold calibration protocol (τ=0.15) and stationarity tests for bounded preference uncertainty (S4); exploratory LSTM forecasting analysis (S5); TOPSIS-based Pareto solution selection (S6); probability distributions and data sources for 12 key parameters across Nanyang, Shangqiu, and Jiaozuo projects (S7, Table S4); computational efficiency benchmarks (S8); convergence analysis of RSSI and Sobol’ indices (S9, Figures S3–S4); cross-site comparison of ranking reversal events and critical thresholds (S10, Tables S6–S7); failure mode analysis under policy shocks and ecological time-lags (S11); stakeholder feedback and comparison with traditional decision frameworks (S12, Table S8); reproducibility resources (S13); glossary (S14); detailed ROI calculation for the Nanyang case (S15, Table S15-B); cost assumptions for the adaptive-baseline experiment (S16); and sensitivity of RSSI to baseline specification (S17). Code is available in the linked GitHub repository.
Notable Findings
1.Social preference parameters (βESV, βcarbon) dominate ranking reversals, while technical parameters (e.g., unit cost) drive outcome variance (Figure S4, S10).
2.The RSSI alert threshold τ=0.15 minimized total error (FPR+FNR=13.3%) with stable cross-validation (S4.3).
3.In the Shangqiu project, SA-ADF achieved 0% cost overrun, 6.6% replanting rate, and 109.0% ROI (Table S8); the Nanyang case achieved 108.8% conservative and 277.8% full ROI (S15).
4.RSSI estimates are insensitive to the choice between median and stakeholder best-estimate baselines (maximum difference 0.01; S17).
Interpretation and Usage
Use the parameter distributions in S7 for model testing. Recalibrate the alert threshold τ via the protocol in S4.1–S4.3, considering project-specific cost ratios. Consult S10 for context-dependent risk structures; do not apply a universal threshold. Distinguish the ex-post ROI in S15 from the ex-ante cost ratio (10:1) used in S4.2 for threshold calibration. Framework limitations under abrupt policy shifts and ecological time-lags are documented in S11.
Data format: .docx (Microsoft Word)
Study sites: Nanyang, Shangqiu, Jiaozuo (Henan Province, China)