Code, Notebooks and Dataset for "When to replace a PV panel? A prospective life cycle assessment of photovoltaic panels repowering considering component reuse.": v1.0.0
Bibliographic record
Abstract
This deposit contains the Python code and Jupyter notebooks used to produce the results, figures, and tables in “ "When to replace a PV panel? A prospective life cycle assessment of photovoltaic panels repowering considering component reuse.". It includes: Core model utilities for PV repowering, Brightway/premise scenario creation, LCA scoring, and avoided-impact calculations (PV_Repowering_Functions_for_Article.py), with all constants centralized in PV_Repowering_GLOBAL_VARIABLES_for_Article.py. Figure/graph helper functions tailored to IJLCA two-column layouts (Final_Graphs_for_Article_Generation.py). Result exploration and analysis helpers (results_explorer.py, results_function_analysis.py) and a full Sobol sensitivity pipeline (sobol_pipeline.py). Repro notebooks: main article run (PV_Repowering_Notebook_For_Article.ipynb), component reuse ratios (PV_Component_Reuse_Notebook_for_Article.ipynb), final figure generation (Final_Graphs_for_Article_Generation.ipynb), and a lightweight results explorer (PV_Repowering_Results_Exploration_Notebook.ipynb). A conda environment file for the notebooks that require Brightway/premise and plotting (PV_Repowering_Env.yaml). Not includedLicensed datasets (e.g., ecoinvent) and credentials are not redistributed. Users with valid licenses can reproduce results by following the instructions in the README and placing data as indicated. The exploration notebook reads a local results file (DATA_AVOIDED_IMPACT_ELEC) provided in the companion dataset deposit. DATA_AVOIDED_IMPACT_ELEC* files: per-year, per-source electricity amounts/impacts and avoided-impact structures used by the exploration and figure notebooks (see README for schema and units). How to reproduce (short) Create the conda environment (PV_Repowering_Env.yaml) and register the kernel (see README). Run the main notebook PV_Repowering_Notebook_For_Article.ipynb (long runtime) to compute results, or use the dataset deposit to skip to visualization. Build final figures via Final_Graphs_for_Article_Generation.ipynb.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.321 | 0.244 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".