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California Freight Cleanup → Investigation 6-5

Which CRF studies, in what order, are worth funding next?

666× ROI — meta-analysis ($0.5M); $4.9B net — L3 staged ($7.5M total)

The earlier CRF investigation settled which concentration-response function to use. This one answers what comes next: which research studies, in what order, are worth funding to sharpen future decisions on sub-state allocations and post-2030 portfolio refinements.

The CRF analysis (Investigation 21) quantified $0.25B in residual information value still available from further concentration-response research — but also showed the Phase 1 $4B portfolio choice is already clear: the all-in option wins every scenario. So why fund more CRF research? Because the program doesn’t stop at Phase 1. Sub-state allocations, AB 617 community-level work, and post-2030 refinements all face decisions where a tighter CRF estimate will matter. This investigation computes the ROI on each candidate study against those future decisions and recommends the cheapest sequence that captures most of the value.

All five rungs read the Investigation 6-3 posterior parameters live via upstream_value("21", ...) (drift-detected). The $0.25B residual EVSI from Investigation 6-3 is the upper bound on what a single one-shot study could deliver against the current portfolio decision. The L3 staged pathway ($4.87B) exceeds this ceiling because each sequential stage tightens the posterior, generating compounding EVSI against the broader future decision landscape (Q3 sub-programs, AB 617 sub-state work); the $0.25B ceiling applies only to the current Phase 1 decision.

L1 — Point-estimate EVSI ceiling. The $0.25B Investigation 6-3 residual EVSI is the theoretical perfect-information maximum (cost = $0). Reported for reference; excluded from best-level ranking (comparing a costless ceiling against cost-bearing L2–L5 designs is a tautology).

L2 — Per-design pre-posterior EVSI. Five candidate designs evaluated independently (meta-analysis, retrospective cohort, Medicare extension, prospective CA, pooled consortium). For each: EVSI = p_success × shrinkage × residual_EVSI. Net value subtracts cost. ROI = net/cost.

L3 — Staged sequential pathway. Studies run in cost-ascending order; each stage consumes the posterior σ left by the prior stage. Tests whether cheap-first ordering captures most value at a fraction of the full-portfolio cost.

L4 — Multi-arm bandit (Gittins-index greedy, $25M / 5yr). Arms selected greedily by expected information yield per dollar. Four arms selected; total cost $22.5M.

L5 — POMDP adaptive portfolio (500 MC rollouts, greedy policy). Sequential learning with early-stop option. Greedy heuristic lower-bounds true adaptive value. Mean trajectory: 1.94 arms, $6.3M cost.

Rungs fused via precision-weighted Bayesian pooling to produce a summary posterior (mean $2.92B, σ $1.53B).

Design / PathwayCostEVSI ($B)Net ($B)ROI
Meta-analysis (L2)$0.5M 0.3330.332666×
Retrospective cohort (L2)$2.0M 0.6780.676339×
Medicare extension (L2)$5.0M 0.1520.14730.5×
Prospective CA cohort (L2)$15.0M 0.000−0.015
Pooled consortium (L2)$25.0M 0.6250.60025×
L3 staged (meta→retrospective→Medicare ext.) $7.5M4.8704.862
L4 bandit (4 arms, $22.5M)$22.5M3.6803.658
L5 POMDP (mean 1.94 arms, $6.3M)$6.3M2.2312.225

Run the meta-analysis first, gate the retrospective cohort on the results — $4.86B net

L3 staged beats L4 bandit by 33% and L5 POMDP by 118% in expected net value. The information surface is smooth enough that a 2-rung staged pathway captures it. Run the meta-analysis first ($0.5M), gate the retrospective cohort ($2.0M) on its results—that is the dominant strategy. L4 and L5 add procurement complexity without proportional value.

This research is for future decisions — the Phase 1 choice is already settled

Investigation 6-6 confirms D_all_in_4B as uniquely optimal at P(optimal) = 1.000: this staged research does not change that recommendation. We recommend it because the Q3 $300M scale—Investigation 6-8, AB 617 air-basin sub-state work, and post-2030 portfolio refinements—will face CRF precision requirements where the Di/Krewski gap is decision-controlling. A $7.5M staged investment delivers $4.9B in expected future-decision value.