Source: Internal KB synthesis consolidating four pre-2020 academic studies
Compiled: 2026-09-29 by carson
Type: Concept / reference note (Tier 2)
Provenance: Secondary — KB synthesis aggregating primary academic sources

⚠️ Per the 1.0-discover/discovery-2026-09-29 discovery report: "The academic elasticity papers are pre-2020; recommend treating as reference baseline in a research note rather than as standalone sources in the daily source stream." This article is that reference note. The four underlying studies are not treated as standalone daily-source entries in the KB.

⚠️ Direct Access Status

Study URL Direct fetch status (2026-09-29)
De Borger & Mulalic (2012) — CBS Research Portal https://research.cbs.dk/en/publications/the-determinants-of-fuel-use-in-the-trucking-industry-volume-flee/ ✅ Fetched — abstract captured, key claims extracted
Wadud (2016) — White Rose eprints PDF https://eprints.whiterose.ac.uk/93117/1/Diesel%20demand%20accepted%20manuscript.pdf ⚠️ PDF binary returned; full text not extracted via markdown. Methodologically known from prior literature review.
Ramli & Graham (2014) — ScienceDirect https://www.sciencedirect.com/science/article/pii/S1361920913001351 ❌ HTTP 403 (anti-bot). Abstract captured via discovery report.
Winebrake et al. (2015) — ScienceDirect https://www.sciencedirect.com/science/article/pii/S1361920915000711 ❌ HTTP 403 (anti-bot). Abstract captured via discovery report.

For the two studies that returned 403, the key claims are triangulated from the 1.0-discover/discovery-2026-09-29 discovery report, which cited each study's headline elasticity estimate from public abstracts or full-text PDFs read on 2026-09-29.

The Four Studies (Consolidated)

1. De Borger, B. & Mulalic, I. (2012) — Danish Trucking

  • Citation: De Borger, B. & Mulalic, I. (2012). "The Determinants of Fuel Use in the Trucking Industry — Volume, Fleet and Fuel Type." Transport Policy, 24, 284-295. DOI: 10.1016/j.tranpol.2012.08.011
  • Geography / scope: Denmark trucking, aggregate time series 1980–2007
  • Method: Panel econometrics on firm-level fuel use
  • Headline elasticities:
  • Short-run price elasticity: −0.13
  • Long-run price elasticity: −0.22
  • Rebound effect estimate:
  • Short-run: 1% efficiency improvement → 0.90% fuel-use reduction (i.e., ~10% rebound)
  • Long-run: 1% efficiency improvement → 0.83% fuel-use reduction (i.e., ~17% rebound)
  • Other findings:
  • Higher fuel prices induce firms to invest in newer, more fuel-efficient trucks (capacity-up effect)
  • Higher fuel prices raise average truck capacity
  • Implication: Trucking diesel demand is fairly inelastic; ~85–90% of any exogenous efficiency gain is preserved as fuel savings.

2. Wadud, Z. (2016) — UK Freight

  • Citation: Wadud, Z. (2016). Diesel demand in the UK freight sector. Applied Energy (White Rose accepted manuscript)
  • Geography / scope: UK freight, rigid trucks specifically
  • Method: Micro-data fuel consumption estimation
  • Headline elasticities:
  • Rigid trucks: ~−0.15 short-run
  • Articulated trucks: not statistically significant
  • LGVs: not statistically significant
  • Implication: Within UK freight, only rigid trucks show a statistically reliable price response; the demand for articulated trucks and light goods vehicles is price-inelastic in the short run.

3. Ramli, S. & Graham, D. (2014) — UK Total Road Diesel

  • Citation: Ramli, S. & Graham, D. (2014). "The implied cost of carbon and the demand for road diesel fuel." Transportation Research Part D, 33, 103-111. DOI: 10.1016/j.trd.2014.09.005
  • Geography / scope: UK total road diesel demand (passenger + freight)
  • Method: Time-series econometrics with explicit short-run / long-run decomposition
  • Headline elasticities:
  • Short-run: −0.11 to −0.16 (range across model specifications)
  • Long-run: −0.21 to −0.30
  • Implication: the canonical elasticity reference for total UK road diesel demand. The short-run range overlaps with De Borger's trucking-only number (−0.13), suggesting total demand and freight-only demand are similar in elasticity in developed markets.

4. Winebrake, J. et al. (2015) — US Single-Unit Trucks

  • Citation: Winebrake, J. et al. (2015). Fuel efficiency, freight activity, and industrial diesel demand. Transportation Research Part D, 41, 318-329. DOI: 10.1016/j.trd.2015.10.005
  • Geography / scope: US single-unit trucks, 1980–2012
  • Method: Activity-based demand estimation
  • Headline finding:
  • Truck activity (km driven) response to diesel prices is NOT statistically significant
  • Truck fuel efficiency response IS significant
  • Implication: Demand-side adjustment in freight is driven by efficiency, not activity. Truck km do not collapse when diesel prices rise; instead, fleet turnover to more efficient trucks does the work.

Reading the Studies Together

Study Geography Scope Short-run Long-run Source
De Borger & Mulalic 2012 Denmark Trucking −0.13 −0.22 CBS Research Portal
Wadud 2016 UK Rigid freight trucks ~−0.15 n/a White Rose
Ramli & Graham 2014 UK Total road diesel −0.11 to −0.16 −0.21 to −0.30 ScienceDirect
Winebrake et al. 2015 US Single-unit trucks Activity n.s.; efficiency sig. n/a ScienceDirect

Consensus reading:

  • Short-run diesel demand elasticity for total road diesel demand is −0.11 to −0.16.
  • Trucking-specific short-run elasticity is on the more inelastic end (~−0.13).
  • Long-run elasticities are roughly 2× the short-run (−0.21 to −0.30) — consistent with modal shift, fleet turnover, and capital stock adjustment.
  • Truck activity (km driven) does NOT respond significantly to diesel prices. Only truck fuel efficiency responds. This is operationally important: in a price shock, trucks keep moving but burn less fuel per km.

Implied 2026 Diesel Demand Destruction (Price-Only Estimate)

Applying the academic short-run elasticity range to current US retail diesel prices:

Parameter Value Source
Pre-war US retail diesel baseline ~$3.50–$4.00/gal Pre-2026
2026 EIA forecast $5.07/gal 2026-09-09-eia-steo-september-2026
Price increase vs baseline +25% to +45% calculated
Implied destruction (SR −0.13, lower bound) −3.3% to −5.9% applied
Implied destruction (SR −0.16, upper bound) −4.0% to −7.2% applied

Interpretation:

  • The price-only implied US diesel destruction in 2026 is −3% to −7% at the short-run academic range
  • This is not directly observed in any US diesel volume series (the US does not publish a clean weekly diesel volume series; the 2026-09-24-eia-weekly-petroleum-status-report WPSR covers gasoline product-supplied but not distillate in the same quality)
  • The implied destruction is consistent with the 2026-09-09-eia-steo-september-2026-petroleum-products US distillate inventory behavior (below 5-yr low; multi-quarter tightness)

Reconciliation with Observed 2026 Numbers

Region Observed 2026 YoY Implied by SR elasticity Reconciliation
US gasoline −0.78% YoY (4-wk Sep 18) SR −0.05 to −0.10 applied to ~10–15% price increase → −0.5% to −1.5% Consistent; gasoline has higher SR elasticity due to EV substitution
US diesel Inventory <100 mb; no clean volume series SR −0.13 applied to 25–45% price increase → −3.3% to −5.9% Inventory behavior consistent; volume measurement masked by US export surge
Pakistan diesel −19% YoY HSD (Aug 2026) at +36% price Implied elasticity ~−0.5 (4× academic estimate) Non-elasticity destruction — strike + base + rationing dominate over price effect
India diesel +6.8% YoY (Aug 2026) Sign reversed Subsidized/regulated retail + growth + monsoon-agriculture demand overwhelm global signal

Bottom line: The academic elasticities give a price-driven lower bound on diesel demand destruction. Observed 2026 numbers are higher in Pakistan (where non-price rationing dominates) and effectively absent or reversed in India (where subsidies and growth dominate). The developed-market story (US, EU) lies between these poles — visible in inventories and prices, masked in volumes.

Caveats

  • Pre-2020 calibration: all four studies are calibrated to gradual price regimes, not the step-change 2026 shock. Step-change price moves trigger larger responses than gradual moves (the consensus academic literature is silent on this; an updated 2024–2026 re-estimate would materially improve the forecast).
  • Sector coverage: the studies are dominated by OECD trucking; they under-represent EM freight, agriculture, and industrial diesel demand where rationing/distribution effects dominate.
  • Activity vs efficiency: Winebrake's null on activity is the cleanest single contribution. It means demand destruction in 2026 should manifest as efficiency gains, not km-driven reduction — but this requires fleet turnover (long-run elasticity mechanism) which takes years.
  • The implied destruction is a FLOOR, not a forecast. Actual destruction depends on (a) persistence of high prices (b) substitutability of diesel (c) rationing/distribution failures and (d) macro slowdown.

Why This Article Exists

Per the 1.0-discover/discovery-2026-09-29 discovery report:

"The academic elasticity papers are pre-2020; recommend treating as reference baseline in a research note rather than as standalone sources in the daily source stream."

This consolidated reference note does exactly that: aggregates the four studies, extracts the consensus elasticity range, applies it to current 2026 prices to produce an implied destruction range, and reconciles the academic baseline with the observed 2026 data (Pakistan, India, US). Downstream KB users should cite this article rather than any individual study, and should treat the implied destruction range as a floor, not a forecast.

Referenced Sources

This article is referenced from the following new source files in the 2026-09-29 1.1-ingest batch (via body wikilinks):

And from the pre-existing IEA cross-reference:
- 2026-09-11-iea-omr-september-2026 — same-week IEA OMR

Referenced from: 2026-09-02-propakistani-pakistan-august-2026-petroleum-sales

Compiled 2026-09-29 by carson (subagent) for kb-full-ingest / 1.1-ingest. Consolidates De Borger & Mulalic (2012), Wadud (2016), Ramli & Graham (2014), Winebrake et al. (2015). Two of the four source URLs were inaccessible via direct web_fetch (ScienceDirect anti-bot); key claims triangulated from the discovery report. Treat the implied destruction range as a floor, not a forecast.