Epd Parser

Product Identity (columns A-H)

EPD Metadata (columns I-P)

Impact Indicators — Product Stage A1-A3 (columns Q-V)

Impact Indicators — Other Stages (columns W-AB)

Resource Use (columns AC-AH)

Tracking (columns AI-AP)

Material Category Vocabulary

Use ONE normalized term: Concrete, Steel, Aluminum, Wood/Timber, Insulation, Gypsum, Glass, Ceramic/Tile, Carpet, Resilient Flooring, Roofing Membrane, Sealant, Paint/Coating, Masonry, Stone, Composite Panel, Acoustic, Cladding, Rebar, Cement, Aggregate, Furniture, Other.

EPD-specific data in Notes (col AO)

EPDs contain fields that don't have dedicated columns. Append these to Notes:

• EPD Type: Type: Product-specific or Type: Industry-average or Type: Sector
• Verification: Verified by: [verifier name]
• EN version: EN 15804+A1 or EN 15804+A2 (important for comparability)
• Source File: Source: holcim-readymix-epd.pdf
• LCA Software: LCA: GaBi or LCA: SimaPro or LCA: openLCA
• Biogenic carbon note: If the EPD reports biogenic carbon storage separately

Example Notes cell: Type: Product-specific | Verified by: Underwriters Laboratories | EN 15804+A2 | LCA: GaBi | Source: holcim-readymix-epd.pdf

LEED Eligibility Logic (col AI)

• Yes: Product-specific, third-party verified EPD conforming to ISO 14025
• Partial: Industry-average or sector EPD (counts for LEED Option 1 but not Option 2)
• No: Self-declared, unverified, or non-conforming

Workflow

Step 1: Get input

Parse the user's input to identify PDF file(s) and output preferences.

• If given a folder, list all .pdf files and report count
• If no PDFs found or path is invalid, ask the user
• Report: "Found N EPD PDF(s) to process."

Step 2: Extract text from PDF

Use PyMuPDF (fitz) to extract text from each PDF. Run this Python script via Bash:

import fitz
import sys
import json

pdf_path = sys.argv[1]
doc = fitz.open(pdf_path)
pages = []
for i, page in enumerate(doc):
    text = page.get_text()
    pages.append({"page": i + 1, "text": text})
doc.close()

print(json.dumps({"filename": pdf_path.split("/")[-1], "total_pages": len(pages), "pages": pages}))

For each PDF, extract all pages and save the JSON output.

Step 3: Parse EPD data

Read the extracted text and identify all environmental impact data. This is the core intelligence step.

For small EPDs (<=30 pages): Process all pages at once.

For large EPDs (>30 pages): Process in chunks of 15 pages. Carry forward context between chunks.

Parsing instructions:

1. Identify the EPD structure — EPDs typically have these sections:

   • General information (pages 1-2): manufacturer, product, declared unit, program operator, validity
   • Product description (pages 2-3): materials, manufacturing process, application
   • LCA information: system boundary, data sources, allocation rules
   • Impact indicator tables: the core data — usually 1-3 pages of tables
   • Resource use and waste tables
   • Additional information: scenarios, biogenic carbon, recycled content

2. Extract product identity first — manufacturer, product name, declared unit, functional unit. These are always on page 1-2.

3. Extract EPD metadata — registration number, program operator, PCR, standard, dates, system boundary. Usually on page 1 or in a header/sidebar.

4. Find and parse impact indicator tables — This is the most critical step:

   • Look for tables with life cycle stage columns (A1, A2, A3 or A1-A3 combined, A4, A5, B1-B7, C1-C4, D)
   • Impact categories are rows: GWP, ODP, AP, EP, POCP, ADP (or ADPE/ADPF)
   • EN 15804+A2 EPDs split GWP into: GWP-total, GWP-fossil, GWP-biogenic, GWP-luluc. Capture all that exist.
   • EN 15804+A1 EPDs report a single GWP row. Put this value in GWP-total (A1-A3).
   • If A1, A2, A3 are reported separately (not combined), sum them for the A1-A3 total.
   • Units are standardized: GWP = kg CO2e, ODP = kg CFC-11e, AP = kg SO2e (or mol H+ eq for +A2), EP = kg PO4e (or mol N eq / kg P eq for +A2), POCP = kg C2H4e (or kg NMVOC eq for +A2).

5. Extract resource use — PERE, PENRE, fresh water, waste. Usually in a separate table following the impact indicators.

6. Extract additional data — recycled content %, manufacturing plant, country of origin, LCA software, verifier name.

7. Determine LEED eligibility — based on EPD type and verification status.

8. Leave fields blank rather than guessing — if a field isn't in the EPD, leave it empty.

Multi-product EPDs

Some EPDs declare impacts for multiple products, product groups, or concrete mix designs. Create one row per product/variant:

• If the EPD covers "Product A" and "Product B" with separate impact tables, create two rows
• If the EPD covers multiple concrete mixes (e.g., 3000 PSI, 4000 PSI, 5000 PSI), create one row per mix
• The product name should distinguish variants: "ReadyMix Concrete — 4000 PSI"

Step 4: Present results

Show a summary table for each parsed EPD:

## EPD Parse Results

### holcim-readymix-epd.pdf
| Field | Value |
|-------|-------|
| Product | ReadyMix Concrete — 4000 PSI |
| Manufacturer | Holcim |
| Declared Unit | 1 m3 |
| GWP (A1-A3) | 312 kg CO2e |
| System Boundary | Cradle-to-gate |
| Program Operator | NSF |
| Valid | 2024-01-15 to 2029-01-15 |
| LEED Eligible | Yes |

Products extracted: 3 (3000 PSI, 4000 PSI, 5000 PSI)

Ask: "Does this look correct? Should I adjust anything before saving?"

Step 5: Write output

Ask the user (if not already specified): "Where should I save this?"

Options:

• EPD Google Sheet — append rows to a dedicated EPD spreadsheet (separate from the FF&E product library). Ask for spreadsheet ID if not already known.
• Local CSV — save to a specified path (default: ./epd-data-YYYY-MM-DD.csv)
• Just the table — leave as markdown in the conversation

CSV Format

When saving to CSV, use the 42-column header:

EPD Link,Manufacturer,Product Name,Description,Declared Unit,Functional Unit,CSI Division,Material Category,EPD Registration No.,Program Operator,PCR Reference,PCR Expiry,Standard,System Boundary,Valid From,Valid To,GWP-total (A1-A3),GWP-fossil (A1-A3),GWP-biogenic (A1-A3),ODP (A1-A3),AP (A1-A3),EP (A1-A3),GWP (A4-A5),GWP (B1-B7),GWP (C1-C4),GWP (D),GWP-total (all stages),POCP (A1-A3),PERE (A1-A3),PENRE (A1-A3),Total Energy (A1-A3),FW (A1-A3),Recycled Content,Waste (A1-A3),LEED Eligible,EC3 ID,Plant/Facility,Country,Parsed At,Tags,Notes,Source

Edge Cases

• Scanned PDFs (image-only): PyMuPDF will return empty or garbage text. Detect this (very short text relative to page count) and tell the user: "This PDF appears to be scanned/image-based. Text extraction won't work — consider using an OCR tool first."
• Non-English EPDs: Common for European manufacturers (German, French, Spanish, Swedish). Impact indicator abbreviations (GWP, ODP, AP, EP, POCP) are the same internationally. Extract numeric data regardless; note the language in Notes.
• EN 15804+A1 vs +A2: Older EPDs use +A1 (single GWP row, different EP/AP units). Newer use +A2 (split GWP, different units for AP/EP/POCP). Always note which version in Notes. Map to schema as closely as possible.
• Multi-product EPDs: One row per product/variant. See Multi-product EPDs section above.
• Expired EPDs: Flag in Notes: EXPIRED — valid to YYYY-MM-DD. Still parse the data.
• Password-protected PDFs: PyMuPDF will fail to open. Catch the error and tell the user.
• Very large PDFs (50+ pages): Process in 15-page chunks. Give progress updates.
• EPDs with impact tables as images: Detect missing numeric data in what should be table sections. Flag: "Impact tables may be embedded as images — manual extraction needed."

Error Reporting

After processing, always report:

Parsed: X products from Y EPD PDF(s)
- filename.pdf: N products extracted
- filename2.pdf: M products extracted
Issues: [list any problems — expired, scanned, missing tables, etc.]