Energy & Commodities mixed · 1–3 years
A what‑if from the future

What if EV adoption tips into permanent gasoline demand destruction?

Faster EV adoption in China and Europe structurally destroys gasoline demand, slowly bleeding crude and refining margins while easing the pump-price tax on consumers. This is a multi-year structural drift, not an event — the nearest read is post-2014 European diesel/gasoline demand erosion and China's 2023-2024 EV-driven gasoline plateau. Forward angle: the first-order loser is the gasoline crack and refiners, not flat crude, since petrochemical and jet demand backfill barrels — fade the idea this collapses Brent.

26%
our model probability
over 1–3 years
prediction markets — the market's odds
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Anchored to measured history 26% · 90% range 9–42% · 16 dated precedents behind it — a wider range means thinner evidence

Every number ships with its receipt — the odds, the range, the precedents, and a public grade at Reality Check. The statistical machinery that produces it is proprietary.

The butterfly cascade

How this trigger trickles across markets, left → right — the root shock, its first‑order moves, then the ripple effects. Drag any node; tap a market for its real price history.

Resolution timeline — how this probability is moving

Our model's odds (electric blue) over time vs the market's (Polymarket, amber), from the past toward the 1–3 years horizon. Each dot is a real macro event that nudged the probability — green pushed it up, red pushed it down. Tap a dot for the source. Loading the probability audit trail…

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What it would mean

If this plays out, it is a mixed shock. Faster-than-expected EV adoption in China and Europe triggers structural gasoline demand destruction. The trigger decomposes into signed root‑shocks — Oil demand ▼ — which propagate through our causal graph to the markets below.

Methodology. Probability and impact are anchored to history and scored against what actually happens — wins and losses, in public, at Reality Check. Market odds live from Polymarket & Kalshi. By Vikas Singh, Quantitative Strategist. Updated 2026-08-13.