California Freight Cleanup → Cross-Cutting Methods

What holds the eight focus areas together?

Five threads of analysis don’t fit cleanly under any one focus area: how the candidate portfolios get built, whether adaptive sequencing pays, what the full Pareto frontier looks like beyond the named portfolios, where the next research dollar should go, and how all the uncertainty propagates from one investigation to the next. Without them, the individual focus areas don’t hold up to scrutiny.

→ Decision Dashboard — compare portfolios across CRF anchors and budget scales.

The five threads

Investigation M-1 — Building the candidate portfolios

The starting point. We assemble roughly 120 combinations of transport, building, and wildfire interventions and name six representative portfolios from $0 B (the “free lunch”) to $13.9 B (max impact). Every downstream investigation operates on these. Headline finding: at the $2 B marginal, transport electrification avoids 66.8 deaths per year — 5.8× the return of wildfire fuel management at the same dollar.

Investigation M-2 — Does adaptive sequencing pay?

If you can re-plan year by year as health data comes in, instead of committing to a 10-year budget upfront, do you save more lives? Yes, by about +27.1% (1,692 vs 1,332 deaths over a $4 B, ten-year horizon) — but with an honest caveat. The full belief-conditioned adaptive policy collapsed to a single dominant action in our setup. The gain we’re seeing is from rolling-horizon re-optimization, not true belief adaptivity. We say so in the writeup.

Investigation M-3 — The full Pareto frontier

The named portfolios in Investigation M-1 collapse health, equity, and cost into a single net-benefit score. That hides an implicit exchange rate between disadvantaged-community deaths and non-DAC deaths. NSGA-II refuses that collapse: we let it search a 5-dimensional design space and find a 100-point Pareto frontier across all three objectives. Result: 48 of those points strictly beat E_smart (the equity-targeted hand-crafted portfolio) on every dimension. The best one saves 580 more lives at $0.23 B less. The lever pulling the equity corner of the frontier is indoor air quality — a control absent from the original menu.

Investigation M-4 — Where the next research dollar goes

The cascade-wide sensitivity analysis (Investigation 3-10) tells us which inputs actually move the answer. Combine that with the cost of resolving each input, and you get a research-priority ranking. Top of the list: nailing down how effective the interventions actually are at scale — an EVSI of $7.7 B for $3.0 M of research. Tightening the dose-response number (the CRF) is second at $3.8 B for $2.5 M. Both are positive ROI; the CRF just isn’t the top priority anymore.

Investigation M-5 — Tracing uncertainty through the chain

The standard sensitivity analyses (Investigation 3-6, Investigation 3-10) decompose variance at the inputs. This one asks the complementary question: if we doubled the uncertainty on any one upstream investigation, how much would the final portfolio recommendation budge? Doubling the dose-response uncertainty — the input we expected to matter most — shifts P(optimal) by less than 1.1%. Nothing we tested crossed the threshold that would change the answer. The framework covers 6 of 57 investigations (10.5%); the deliverable is the framework itself, not the coverage.

Methodological assumptions threaded through the cascade

CRF shape: log-linear (no-threshold) form is assumed throughout the cascade per EPA 2023 PM_2.5 TSD. Supralinear (Burnett GEMM 2018) and threshold (Goodkind 2019) shapes are not evaluated; only β-parameter uncertainty is propagated, not shape uncertainty.
CRF national-to-California transfer: Di and Krewski cohorts are US-wide; their hazard ratios are applied to California under the assumption that the dose-response is identical at the state level. California-specific re-estimation lives in Investigation 6-3 (which produces a higher β, attributed to region-mean exposure attenuation; see Investigation 6-2 for the reconciliation).
Cross-investigation independence: the joint sampler treats per-investigation uncertainty channels as independent. Positive correlation across channels (e.g., shared confounders biasing CRF and surrogate the same way) would widen the cascade-level left tail; this approximation lives outside the per-investigation Sobol decompositions.
EVSI factorizations: Investigation 6-2 / Investigation 6-5 use the product form p_success × shrinkage × residual_EVSI, which is an exact identity only under Normal-conjugate updates with linear payoffs. The log-linear CRF introduces a mild concavity; treat ROI multipliers as upper bounds on what a properly decision-theoretic computation would yield.

Headline findings

Transport dominates the $2B sector choice by 5.8×: At the $2B marginal, transport electrification (T2 over T1) avoids 66.8 deaths/yr versus 45.4 for building and 11.5 for wildfire treatment (Investigation M-1).
48 NSGA-II portfolios strictly dominate E_smart on all three objectives simultaneously; best dominator gains 580 deaths at $0.23B less cost. The indoor AQ lever, absent from Investigation 6-4’s hand-crafted menu, drives the high-equity corner of the frontier (Investigation M-3).
Sequential adaptivity is worth +27.1% in cumulative deaths avoided over a static one-shot allocation, but PBVI policy collapses to a single dominant action (alloc_kr) at every belief state—the gain is best-static-allocation-under-value-iteration, not belief-conditioned adaptivity (Investigation M-2).
Effectiveness_scale is the top research priority (adjusted EVSI $7.7B, ROI 2,529×), ahead of β_PM2.5 CRF research ($3.8B, 1,519×). Investigation 6-5’s CRF recommendation remains positive-ROI but is no longer the Sobol-weighted top priority (Investigation M-4).
Doubling Investigation 6-3’s β sigma shifts D’s P(opt) by −1.09%—the headline recommendation is robust to per-investigation sigma inflation at all six covered upstream nodes. Investigation M-5 corroborates Investigation 3-10’s Sobol attribution via an independent methodology.

Investigations

Inv	Slug	Tier	One-line	Deep-dive
15	portfolio-frontier	Tier 2	Efficient frontier + 6 named portfolios; $2B head-to-head winner = transport (5.8× wildfire)	Deep-dive
22	portfolio-pomdp	Tier 2	5-level sequential policy ladder; PBVI L4 +23.9% over one-shot; half-normal cost-overrun baked in	Deep-dive
29	nsga-pareto-equity	Tier 2	NSGA-II 3-objective Pareto (health × equity × cost); 48 frontier points dominate E_smart	Deep-dive
59	sobol-research-priority	Tier 2	Sobol-weighted EVSI synthesis; effectiveness_scale #1, β_PM2.5 #2; Investigation 6-5 still positive-ROI	Deep-dive
61	cross-investigation-mc-propagation	Tier 2	6-adapter sigma-propagation framework; Investigation 6-3 σ×2 shifts D P(opt) by −1.09%	Deep-dive

Cross-element notes

Investigation M-1 feeds Element 6 (Ratepayer Burden). The six named portfolios defined here are the candidate set consumed by Investigation 6-4 (robust optimization) and Investigation 6-6 (CRF-conditional decision), both of which are the primary analytical backbone of the Element 6 ratepayer-burden page. If the Investigation M-1 frontier composition shifts, Investigation 6-4 and Investigation 6-6 must re-run.

Investigation M-2 cost-overrun model feeds Element 6. The half-normal cost-overrun distribution (mean 1.16×, P95 1.392×) calibrated in Investigation M-2 is also consumed by Investigation 6-6 (Phase 3 regret-surface computation) and Investigation 3-10 (cost uncertainty as an input axis in the 6-D Sobol). It is the investigation that quantifies program-execution risk — the number any ratepayer-burden calculation needs before it is defensible.

Investigation M-3 NSGA candidates feed Element 6 via Investigation 6-4 and Investigation 6-9. The Q_nsga_1 and Q_nsga_2 portfolios identified by NSGA-II are imported into Investigation M-2 as external comparators, and the Q_nsga_2 portfolio ($1.78B indoor-heavy) was evaluated by Investigation 6-9 (ISRM validation), which returned VERDICT AMBIGUOUS under the Di CRF. That caveat is carried forward in the Element 6 narrative.

Investigation M-4 refines the Element 6 CRF research roadmap (Investigation 6-5). Investigation 6-5’s CRF research roadmap is the research-priority annex for Element 6. Investigation M-4’s Sobol-weighted synthesis demotes CRF to rank 4 by raw Sobol share but preserves its positive-ROI status. The honest framing on the Element 6 page is: CRF research is the right second priority, not the exclusive priority.

Investigation M-5 corroborates Element 3 cascade Sobol (Investigation 3-6 and Investigation 3-10). Element 3 (Atmospheric Models & Validation) surfaces Investigation 3-6 and Investigation 3-10 as the cascade uncertainty-attribution layer. Investigation M-5’s independent methodology (per-investigation sigma propagation rather than input-prior Saltelli) corroborates Investigation 3-10’s finding that β_PM2.5 contributes ~15% of NB variance: a 2× inflation of the CRF sigma moves the headline P(opt) by only 1.09%, consistent with a 15% variance share.