Electronic supplementary materials for the manuscript "Prospective life cycle assessment of circular energy and waste systems in university buildings: a scalable workflow using openLCA and Brightway2"

This Zenodo record provides the electronic supplementary resources for the manuscript: “Prospective life cycle assessment of circular energy and waste systems in university buildings: a scalable workflow using openLCA and Brightway2.” The package contains five files: 1. Online Resource 2 — Guide.txt A README and navigation guide. It maps each online resource to the corresponding tables and figures in the manuscript, and clarifies the respective roles of openLCA (Monte Carlo baselines) and the algebraic metamodel (diagnostics and cross-tool consistency). 2. Online Resource 3 — openLCA outputs.xlsx - `MC_Summary`: scenario means, P5, P50, P95, standard deviations and sample sizes from openLCA Monte Carlo runs (independent sampling, n = 2,000 per scenario; unit: kg CO₂-eq·yr⁻¹, rounded to 10³). Used for Table 1 and Figure 2. - `Deltas`: two grid-exchange deltas (Queensland-2050 vs current grid at 64 % and 77 % diversion). Used for the grid rows in Table 2. 3. Online Resource 4 — Metamodel and Sensitivity.xlsx - `Summary_GUI_aligned` and `Consistency_S4`: GUI-aligned metamodel statistics and cross-tool consistency checks (openLCA vs algebraic metamodel, agreement within ±0.5 %). - `CRN_Paired_Contrasts`: CRN-paired metamodel deltas for diversion (77 % vs 64 %, seed = 2025; n = 3,000). Used for the diversion rows in Table 2 and for sensitivity analysis (Figure 3). 4. Online Resource 5 — Code.py A minimal algebraic metamodel (Brightway2-style) implementing closed-form annual GWP calculations, CRN pairing and GUI-factor-aligned consistency checks. Running this script generates per-scenario samples, summary CSV/PNG outputs and consistency tables. Reported values remain anchored to openLCA GUI factors. 5. Online Resource 6 — Requirements.txt Minimal Python environment needed to run `Code.py` (Python ≥ 3.10; numpy, pandas, scipy, statsmodels, matplotlib). Reproduction (quick start) - Install environment: `pip install -r "Online Resource 6 — Requirements.txt"` - Run: `python "Online Resource 5 — Code.py"` (outputs are written to an `outputs/` directory).