What the Scope 3 Footprint Planning Tool actually produces

CATEGORY 1 — PURCHASED GOODS & SERVICES: Materiality: VERY HIGH. Raw material and component spend dominates (~70% COGS estimated). Virgin/recycled paperboard, polyethylene resin, inks/adhesives all carbon-intensive upstream. Recommended Method: Hybrid (supplier-specific for top-5 suppliers by spend; average-data/EEIO for tail spend). Supplier engagement is critical. Data Quality Expectation: Tier 2 (hybrid) achievable with supplier questionnaire roll-out; Tier 1 (supplier-specific) only if major suppliers disclose. Spend data available (ERP); primary gaps are supplier-specific factors. Methodology Pitfalls: Spend-based (Tier 4) will underestimate if suppliers are carbon-intensive. EEIO factor vintage (NIST/USEEIO version) must be specified. CATEGORY 2 — CAPITAL GOODS: Materiality: MEDIUM. Plant equipment and maintenance machinery; less continuous than Cat 1. Recommended Method: Supplier-specific (for major equipment purchases) or hybrid (average factors for routine maintenance). Unlikely to exceed 10% of Cat 1. Data Quality: Tier 2–3 achievable with purchase order tracking. CATEGORY 3 — FUEL & ENERGY-RELATED: Materiality: LOW. Scope 1 & 2 emissions dominate; Cat 3 covers upstream energy extraction (refining, transmission losses). Typically 2–5% of total Scope 3 for manufacturing. Recommended Method: IPCC/GHG Protocol default factors (fuel upstream, electricity grid intensity). Data Quality: Tier 3 (average-data); minimal additional effort given Scope 1/2 baseline. CATEGORY 4 — UPSTREAM TRANSPORTATION: Materiality: MEDIUM. Inbound freight for raw materials from suppliers (some international). In-house freight (company trucking fleet) is Scope 1, not Scope 3. Recommended Method: Hybrid. Supplier delivery distances + outbound logistics data available (mileage logs); estimate modal split (truck/rail/ship). Use DEFRA/EPA distance-based factors. Data Quality: Tier 2 achievable if freight partner (3PL or customer) provides distance/weight data. Weak spot: supplier delivery last-mile (suppliers' logistics to Meridian plants). CATEGORY 5 — WASTE GENERATED: Materiality: MEDIUM-LOW. Manufacturing waste (scrap paperboard, resin trim, adhesive waste) and office waste. Recommended Method: Waste audit + landfill/incineration/recycling split per stream; IPCC/EPA default factors for CH₄/N₂O from landfill. Data Quality: Tier 2–3. Waste disposal records available from suppliers; methane generation factors standard but decomposition rates [verify local conditions]. CATEGORY 6 — BUSINESS TRAVEL: Materiality: LOW. Spend data from corporate card available; <50 full-time employees likely traveling frequently. Recommended Method: Spend-based (Tier 4) acceptable given low materiality. Convert spend to mileage using average cost/mile, then apply DEFRA distance-based factors. Distance-based (Tier 3) better if flight/hotel separation available. Data Quality: Tier 3 achievable with corporate travel platform data. CATEGORY 7 — EMPLOYEE COMMUTING: Materiality: MEDIUM. 1,200 employees, 65% survey response rate (780 respondents). Commuting data available. Recommended Method: Average distance method (survey median commute distance × employee count × working days × emission factor) or hybrid (EV adoption rates separated). Parking cash-out / transit subsidy policies [status unknown] affect emissions. Data Quality: Tier 2 (survey-based average). Weakness: 35% non-respondent bias; vehicle type / fuel splits [need to verify from survey or transportation survey literature]. CATEGORY 8 — UPSTREAM LEASED ASSETS: Materiality: LOW. Leased office space (2 cities). Landlord emissions [NOT the lessee's responsibility under equity-share boundary]. Lessee share only if Meridian is responsible for utility consumption. Recommended Method: If Meridian pays utilities: estimate energy consumption (leased square footage × energy intensity benchmark); apply grid-average emission factor. If landlord pays: zero (landlord reports). Data Quality: Tier 3–4 (benchmark-based), unless utility bills provided by landlord. CATEGORY 9 — DOWNSTREAM TRANSPORTATION: Materiality: MEDIUM-HIGH. Outbound freight to customers (food/beverage, CPG brands); company owns fleet (Scope 1) but also uses 3PL carriers. 3PL portion is Scope 3. Recommended Method: Hybrid. Mileage logs for company fleet (separate into Scope 1 vs Scope 3 based on whether fuel is company-paid or customer-reimbursed; clarify boundary). 3PL carriers: request supplier-specific freight factors; fallback to average-data if unavailable. Data Quality: Tier 2 if 3PL provides distance/weight/mode; Tier 3 if only mode assumptions available. Company fleet mileage available → Tier 1 feasible after boundary clarification. CATEGORY 10 — PROCESSING OF SOLD PRODUCTS: Materiality: LOW. Packaging is end-product; minimal further processing. Not applicable unless customers add material modifications. Recommended Method: Skip unless customer processing is significant (e.g., customer fills/converts packaging = processing). [Clarify with customer base if applicable]. CATEGORY 11 — USE OF SOLD PRODUCTS: Materiality: MEDIUM. Packaging in use: typically minimal (packaging is inert). BUT if product contains carbon-emitting material (foam, thermal insulation) or if packaging is used to ship perishables (refrigeration energy), materiality rises. [Clarify product mix]. Recommended Method: Life cycle assessment (LCA) data from product category (e.g., lifecycle emissions of corrugated box carrying frozen food; refrigeration burden if any). Or customer feedback on in-use energy/emissions. Data Quality: Tier 2–3 if LCA studies available; Tier 4 (spend-based proxy) if not. Major uncertainty: product-specific use assumptions. CATEGORY 12 — END-OF-LIFE OF SOLD PRODUCTS: Materiality: MEDIUM. Packaging disposal fate: landfill, incineration, recycling. Recycling rates available from one major customer (reference point). Company claims "67% recyclability rate" (design feature). Actual recycling rate [may differ from design recyclability]. Recommended Method: Hybrid. Recyclability design data (Tier 2); end-of-life split from customer (one major customer 65% response); apply IPCC factors for landfill CH₄, incineration energy recovery. Data Quality: Tier 2–3. Gaps: (1) actual end-of-life fate for customer base [not fully known — extrapolate from one customer]; (2) regional landfill practices vary (CH₄ capture %). CATEGORY 13 — DOWNSTREAM LEASED ASSETS: Materiality: LOW. No material leases mentioned. CATEGORY 14 — FRANCHISES: Materiality: LOW. No franchise model. CATEGORY 15 — INVESTMENTS: Materiality: LOW. No VC/investment arm; not a financial institution.

TIER 1 (Supplier-specific data): • Category 4 Upstream Transportation: IF 3PL provides detailed distance/weight/mode, Tier 1 feasible. Currently: likely Tier 2. TIER 2 (Hybrid — supplier-specific + average-data): • Category 1 Purchased Goods: TOP 5 suppliers → request Scope 1/2 or product-specific carbon data via CDP/questionnaire. Tail 95% spend → EEIO average factors. Expected uncertainty: ±30% (supplier responses incomplete; tail factors may differ). RATIONALE: raw material dominates; hybrid improves over spend-based but supplier engagement effort is moderate. • Category 9 Downstream Transportation: Company fleet (Scope 1 boundary clarification); 3PL partners → request supplier-specific factors. RATIONALE: mileage data available; supplier engagement feasible. Expected uncertainty: ±20% if 3PL responsive, ±50% if using mode-average factors. • Category 12 End-of-Life: Design recyclability (Tier 1) + regional end-of-life split from customer data + IPCC landfill factors (Tier 3). RATIONALE: recyclability design available; actual fate partially known. Expected uncertainty: ±40% (regional variation, assumed recycling efficiency). TIER 3 (Average-data): • Category 3 Fuel & Energy: GHG Protocol default factors. Uncertainty: ±10%. • Category 5 Waste: Landfill/incineration split available; IPCC factors. Uncertainty: ±20% (CH₄ generation assumptions vary by region/time). • Category 6 Business Travel: Spend-based (if distance/mode unavailable); DEFRA factors. Uncertainty: ±25%. • Category 7 Employee Commuting: Survey-based average distance + fuel-mix benchmarks. Survey response 65% — non-response bias risk. Uncertainty: ±30% (assumes similar commute pattern in non-respondent 35%). • Category 8 Upstream Leased Assets: Benchmark energy intensity (kkWh/m²) if utility bills not provided. Uncertainty: ±35% (buildings vary widely). • Category 11 Use of Sold Products: LCA literature (if available) or assume minimal use-phase emissions (packaging is inert). Uncertainty: ±50% if no product-specific LCA (wide range of potential use scenarios). TIER 4 (Spend-based): • None currently assigned; Category 1 tail spend could use spend-based fallback. Uncertainty: ±50–100% (sector-average carbon intensity may not match this company's supply chain). OVERALL DATA QUALITY: • HIGH-materiality categories (1, 4, 9, 12): Tier 2, expected uncertainty ±20–40%. • MEDIUM-materiality categories (5, 7, 11): Tier 2–3, expected uncertainty ±20–50%. • LOW-materiality categories: Tier 3–4, acceptable for immaterial items.

PHASE 1 (Months 1–6) — SUPPLIER ENGAGEMENT & QUICK WINS: 1. Category 1 (Purchased Goods) — PRIORITY 1 (Highest materiality): • Send CDP supplier questionnaire or GHG Protocol Scope 1/2 data request to top-20 suppliers by spend (likely 70–80% of volume). • Target: Collect supplier data from top 5 by Q3 2024. Build spreadsheet of responses; calculate weighted-average carbon intensity. • Fallback: EEIO average factors (USEEIO 2.0.1 or NIST equivalent). Document baseline by Q2 2025. • Outcome: Move from Tier 3/4 to Tier 2 hybrid model. 2. Category 9 (Downstream Transportation) — PRIORITY 1: • Clarify boundary: which portion of 3PL freight is customer-reimbursed vs. Meridian-paid? Classify accordingly. • Request freight data from 3PL carrier (distance, weight, mode, fuel type). Frame as supplier data request, not cost audit. • Company fleet: reconcile mileage logs (Scope 1 vs. Scope 3 split). • Outcome: Tier 2 data by Q3 2025. 3. Category 7 (Employee Commuting) — PRIORITY 2: • Analyze 2023 survey data for vehicle-type breakdown and electric-vehicle adoption. If unavailable, send follow-up survey (target: 80%+ response). • Model sensitivity: EV vs ICE (15–30% emissions difference). Separately track if transit/carpool options are used. • Outcome: Tier 2 (improve from Tier 2/3 with better fuel-mix data). PHASE 2 (Months 6–12) — ENHANCE DATA INFRASTRUCTURE: 4. Category 12 (End-of-Life) — PRIORITY 2: • Expand end-of-life data collection: interview top 10 customers on packaging disposal fate. Document regional recycling rates, landfill practices. • Pilot: implement packaging take-back or recycling tracking with 1–2 willing customers. • Outcome: Move from single-customer extrapolation to representative end-of-life split; Tier 2. 5. Category 11 (Use of Sold Products) — PRIORITY 3: • Conduct product-use survey: does customer use packaging involve energy (e.g., refrigerated shipping) or extended storage? • Research LCA literature for comparable packaging products. Determine if Category 11 is truly immaterial or if assumption requires revision. • Outcome: Clarify scope, reduce uncertainty from ±50% to ±20–30%. PHASE 3 (Year 2+) — ADVANCED METHODOLOGIES: 6. Category 1 — Advanced: • For top 3 suppliers, commission or conduct product LCA (e.g., full lifecycle for virgin/recycled paperboard). Invest in supplier partnerships for continuous improvement. • Outcome: Tier 1 data for >50% of material; Tier 2 for tail. 7. Integrate into Operations: • Establish quarterly Scope 3 tracking dashboard (by category, by supplier, by product). Embed in ERP/sustainability reporting software. • Link supplier carbon performance to procurement decisions (e.g., supplier scorecards). • Outcome: Continuous improvement and transparency. RESOURCES & TIMELINE: • Phase 1 (6 months): ~100 hours (procurement + sustainability coordinator). • Phase 2 (6 months): ~150 hours + potential external LCA consultant (£10k–20k if commissioned). • Phase 3 (ongoing): 10–15 hours/quarter + external supplier engagement. • Estimated cost: £15k–40k over 18 months (mostly external LCA/consultant time).