What’s on the Table
The portfolio optimizer draws from four sectors. Each intervention has a cost and an expected health benefit. The free interventions (T1, B1, retire DTE) form the baseline. Everything else is marginal.
| Intervention | Deaths Avoided | Cost ($B) | Sector |
|---|---|---|---|
| T1 Baseline (ACC II) | 961 | $0 | Transport |
| T2 Accelerated | 1,313 | $2.0B | Transport |
| T5 Heavy Duty | 996 | $1.5B | Transport |
| B1 Baseline | 50 | $0 | Building |
| B2 Accelerated | 120 | $2.0B | Building |
| Wildfire 5% | 724 | $0.8–2.5B | Wildfire |
| Wildfire 10% | 1,447 | $1.7–5.0B | Wildfire |
| Retire DTE Stockton | 3 | $0 | Biomass |
| CRF Resolution Study | — | $0.002B | Information |
Deaths avoided use the average of Di and Krewski CRFs (portfolio-level). Transport and building interventions are mutually exclusive within sector (choose one). Wildfire and biomass are additive.
Where Does the Marginal Dollar Go?
Given the free-lunch baseline, the question is: if you have $2B to spend, where does it do the most good? The answer is unambiguous.
Normalized to the same $2B budget, wildfire treatment avoids ~2.4× more deaths than accelerated transport electrification (~$2.3M/death vs $5.5M/death) and ~12× more than building retrofits. The $2B wildfire figure scales the 10% reduction (~$3.3B, ~1,447 deaths) linearly to the $2B share.
Six Policy Packages
T1 baseline + B1 baseline + retire DTE Stockton. No new spending required. This should happen regardless of any other decision.
Free lunch + T2 accelerated. The conventional policy choice: spend $2B on faster EV adoption. Adds 367 deaths avoided.
Free lunch + 5% wildfire reduction. Avoids 724 additional deaths above free-lunch baseline for $1.65B, versus 367 for $2B under Portfolio B. The portfolio optimizer at $2B converges to this exact allocation — wildfire dominates the efficient frontier.
T2 + B2 + retire DTE. Maximum electrification without wildfire. Spends twice as much as C for fewer deaths avoided.
Everything: T2 + B2 + 30% wildfire + retire DTE. The first portfolio where building B2 appears. B2 is never cost-effective until you’ve exhausted wildfire and transport.
29 Pareto-Optimal Portfolios
The efficient frontier traces the maximum deaths avoided at each budget level. Key patterns: T4 equity enters at $1B (concentrating reductions in LA/SJV is more cost-effective than statewide T2). Wildfire enters early and dominates the middle. T2 appears only after wildfire is saturated. Building B2 appears only at $13.9B.
| Transport | Wildfire | Deaths Avoided | Cost ($B) | $/Death |
|---|---|---|---|---|
| T1 | None | 1,015 | $0 | Free |
| T4 Equity | None | 1,300 | $1.0B | $769K |
| T1 | 5% | 1,738 | $1.65B | $949K |
| T4 Equity | 5% | 2,023 | $2.65B | $1.31M |
| T1 | 10% | 2,462 | $3.3B | $1.34M |
| T4 Equity | 10% | 2,747 | $4.3B | $1.57M |
| T2 | 10% | 2,829 | $5.3B | $1.87M |
| T1 | 20% | 3,562 | $6.6B | $1.85M |
| T2 | 20% | 3,929 | $8.6B | $2.19M |
| T2 | 30% | 4,533 | $11.9B | $2.63M |
| T2 + B2 | 30% | 4,602 | $13.9B | $3.02M |
All portfolios include B1 baseline and retire DTE (both free). Select frontier points shown — full frontier has 29 Pareto-optimal combinations. T4 entries highlighted: equity-weighted reductions are more cost-effective than uniform T2 below $5B. $/Death is average cost per death from $0 — monotonically rising up the frontier as each marginal dollar buys fewer lives.
The recommended sequence: (1) Implement the free-lunch portfolio (T1 + B1 + retire DTE) immediately — the CRF choice doesn’t affect the T2 vs T1 ranking. (2) Fund the $0.5M age-stratified meta-analysis to tighten the benefit-magnitude band before allocating the marginal $2B. (3) Allocate the remaining budget to wildfire treatment on the current efficient-frontier evidence; revisit the transport-vs-wildfire split once the post-meta-analysis burden estimate is in hand.
Portfolio optimization over 4 sectors · Averaged Di/Krewski CRF · 10,000 MC draws · Pareto frontier enumeration · VSL = $11.6M (EPA 2024) · Wildfire cost mid-range ($33–100/acre) · Transport and building costs from CEC scenario definitions