Multi-State EPR Cost Modeling: From $85K to $13M in Annual Exposure
Annual EPR fee exposure for a multi-state US brand can range from roughly $85,000 to over $13 million. Here is how to model that range and what drives the spread.
By Kevin Kai Wong, Managing Partner at gCurv Technologies
May 2, 202612 min read
Finance leaders at multi-state CPG brands are being asked a question that did not exist three years ago: what is our annual EPR fee exposure, and how confident is the number? In 2026, with seven active US state programs, the honest answer for most brands is a range, not a point estimate. That range can stretch from roughly $85,000 a year for a small regional brand to well over $13 million a year for a national brand with heavy flexible packaging exposure.
This post explains how to build a defensible cost model, what drives the spread, and how to move from a wide range to a tight forecast.
For program-level context, see Seven US States with Active Packaging EPR in 2026 and US State EPR Programs Compared.
Why a range, not a number
EPR fees in the US are computed as covered weight by material category, multiplied by a base rate, modulated by recyclability and recycled content factors. Every input in that formula carries uncertainty in the early cycles of a program:
- Covered weight. Many brands do not yet have clean SKU-level packaging weights for every state.
- Material category. PRO categories do not always match how brands describe their own packaging.
- Base rates. Rates are set per cycle and are subject to revision.
- Modulation. Recyclability and recycled content adjustments depend on classifications that themselves evolve.
- Volume by state. Sales volume by state shifts year to year.
Stacking those uncertainties produces a range. The job of a cost model is to make the range explicit, defensible, and narrowable.
The drivers that matter most
Across multi-state portfolios, three drivers explain most of the spread:
1. Material mix
Flexible plastics, multilayer films, and laminates carry the highest base rates in most state programs because MRFs cannot recover them economically. A brand whose mix is heavy in flexibles will sit at the top of the range. A brand built on rigid PET, HDPE, aluminum, steel, and clear glass will sit much closer to the bottom.
2. Covered volume by state
Total covered weight scales linearly with sales volume into each state. A brand with most of its volume concentrated in California, Washington, and the Northeast will see materially higher fees than a brand of similar size that sells more heavily in non-EPR states.
3. Recyclability and recycled content position
Modulation factors can move the bill significantly in either direction. Brands that have already invested in recyclable formats and verified recycled content will see lower fees than brands whose packaging falls into penalty categories.
What an $85K brand looks like
A small regional brand with national distribution but modest volume, primarily rigid containers in widely recyclable materials, with minimal flexible exposure, can land at the low end of the range. Annual fees in the tens of thousands across all active states are realistic when covered weight is small and the mix is favorable.
What a $13M+ brand looks like
A large national brand with heavy flexible packaging exposure, significant volume into California and the other large states, and limited recycled content claims can see annual fees in the eight figures across the seven active programs combined. The largest contributors are typically California by volume and Maine on a per-ton basis where applicable.
How to build the model
A defensible model has four layers:
1. SKU-level packaging dataset. Material, weight, format, recyclability classification, recycled content percentage, and the states each SKU is sold into. 2. Per-state rate sheets. Current base rates and modulation factors for each PRO. 3. Per-state fee logic. The formulas applied to your dataset to produce per-state fee outputs. 4. Scenario engine. The ability to flex inputs (rate increases, format changes, volume shifts) and see fee outcomes.
Spreadsheets can produce a first pass, but they break down quickly when rate sheets change cycle to cycle and material category mappings drift between states.
For the spreadsheets vs. software argument, see Spreadsheets vs. Software for EPR Compliance.
Narrowing the range
The fastest way to move from a wide range to a tight forecast is to invest in three things:
1. Clean SKU-level packaging weights and material classifications. 2. A documented mapping from internal categories to each PRO's schema. 3. A scenario engine that finance can rerun whenever rate sheets update.
Most of the residual uncertainty in early-cycle estimates comes from data quality, not from rate ambiguity.
What to do in 2026
1. Pull your SKU-level packaging dataset and audit it for completeness. 2. Tag each SKU for state-specific coverage and exemptions. 3. Build per-state fee models with current rate sheets. 4. Run sensitivity analyses on the top three drivers (material mix, state volume, modulation position). 5. Publish a quarterly fee forecast to finance with explicit confidence bands.
How Packgine helps
Packgine builds the SKU-level dataset once, applies per-state rate sheets and modulation curves automatically, and produces per-state fee forecasts with the scenario controls finance teams need. Producers can move from "we don't know" to a defensible range in weeks, not quarters.
Related reading
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