Driving Reductions in Emissions Unlocking the Potential of Fuel Economy Targets in Saudi Arabia

Ibrahem Shatnawi; Jeyhun I. Mikayilov

REVIEW 3 major objections 5 minor 3 references

Driving Reductions in Emissions Unlocking the Potential of Fuel Economy Targets in Saudi Arabia

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Saudi Arabia could have cut new car energy use and emissions by 20% from 2016 to 2020 by adopting CAFE fuel economy standards.

desk verdict Plausible policy arithmetic for Saudi fuel economy targets, but the headline 20% is not auditable as written because the BAU baseline and parameters are missing. read the letter →

arxiv 2412.02167 v1 pith:JUNOZDMI submitted 2024-11-22 physics.soc-ph econ.GNq-fin.EC

Ibrahem Shatnawi , Jeyhun I. Mikayilov This is my paper · ORCID

classification physics.soc-phecon.GNq-fin.EC

keywords fueleconomystandardsCAFESaudiArabialight-dutyvehiclesgreenhousegasemissionsenergyconsumptionAFLEETreboundeffect

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper estimates the energy and emissions savings Saudi Arabia could have achieved by adopting corporate average fuel economy (CAFE) standards for new light-duty vehicles. Using the AFLEET life-cycle tool and logistic-function projections of car sales, the authors compare business-as-usual performance against the CAFE targets proposed in 2016 with phase-in compliance. They find that following those targets from 2016 to 2020 would have reduced cumulative well-to-wheel energy consumption and greenhouse gas emissions by 20 percent. Extending fuel efficiency gains by 1 percent per year from 2021 to 2030 would yield an additional 10 percent cumulative reduction, while a 3 percent per year improvement adds only about 1.2 percent more. The paper argues that such standards are a practical first step toward net-zero transport in Saudi Arabia before electric vehicles scale up.

What carries the argument

The central machinery is the combination of the AFLEET life-cycle assessment tool, which computes well-to-wheel petroleum use and GHG emissions from vehicle fuel economy and annual mileage, with logistic-function projections of annual new car sales and a phase-in compliance schedule for the CAFE standard (80%, 90%, then 100% of sales must meet the target). The CAFE target values are taken from the ICCT 2014 proposal for Saudi Arabia; for 2021–2030, where no official targets exist, the paper imposes scenarios of 1%, 2%, or 3% annual fuel economy improvement. The BAU comparison case is fixed at 28.2 miles per gallon gasoline equivalent (MPGGE) for the new-vehicle fleet, with annual mileage of 16,000 miles and a 10% rebound effect on vehicle miles traveled taken from the literature. The identity that carries the calculation is that annual energy use (and, via an emissions factor, GHG emissions) equals car sales multiplied by annual mileage divided by fuel economy: $$E_j = \frac{\text{CarSales}_j \times \text{VMT}_j}{\text{FE}_j}$$.

What would settle it

Compare the actual sales-weighted fuel economy of new light-duty vehicles sold in Saudi Arabia during 2016–2020 against the 28.2 MPGGE business-as-usual baseline. If the observed average exceeded 28.2 MPGGE, the estimated 20% reduction would shrink proportionally. Alternatively, audit the share of new-vehicle sales that actually met the CAFE phase-in compliance schedule; if fewer than 80%, 90%, and 100% of sales hit the targets in the respective years, the realized savings would fall short of the paper's estimate.

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Extended reading notes

Core claim

The paper's central quantitative claim is that if Saudi Arabia had implemented the CAFE standards proposed in 2016 (with the phase-in compliance schedule of 80/90/100% of sales, per the ICCT 2014 update) for new light-duty vehicles, both cumulative energy consumption and well-to-wheel greenhouse gas emissions from the passenger car sales fleet would have been 20 percent lower over 2016–2020 than under business as usual. Under a 1% annual fuel economy improvement after 2020 (scenario 1), cumulative energy and emissions over 2021–2030 would fall an additional 10 percent; scenarios with 2% and 3% annual improvements yield only a marginal ~1.2% further reduction relative to scenario 1. Measured against total Saudi transportation-sector GHG emissions, adhering to the 2016–2020 CAFE standards would have cut emissions by about 1.6 percent, and scenario 3 would cut an additional 4.16 percent by 2030. The paper also assesses a 10% direct rebound effect in vehicle miles traveled, concluding that even under that worst-case behavioural response, scenario 3 still yields a net 4.5% energy and emissions increase in 2030 compared to the no-rebound case, which is small relative to the policy savings.

Load-bearing premise

The estimated 20 percent reduction rests on the assumptions that Saudi Arabia's business-as-usual new light-duty fleet averaged 28.2 miles per gallon gasoline equivalent over 2016–2020 and that the proposed CAFE targets were both achievable and fully enforced; the paper reports no year-by-year target values, no sales-weighted baseline data, and no compliance evidence with which to check these premises.

Editorial extensions

If this is right

If the 20% estimate holds, Saudi Arabia could achieve a substantial share of its transport decarbonization in the near term through fuel economy regulation alone, without waiting for fleet electrification.
The small difference between the 1% and 3% annual improvement scenarios (about 1.2% cumulative) implies that the bulk of the savings comes from establishing the CAFE baseline itself, not from how quickly standards tighten afterward.
The rebound-effect analysis shows that even a 10% increase in driving offsets only a small fraction of the fuel savings, so the policy remains effective under a worst-case behavioural response.
The paper's sector-level numbers (1.6% reduction for 2016–2020, additional 4.16% by 2030) provide concrete benchmarks against which actual Saudi policy outcomes can be measured.
Extending these findings suggests that combining CAFE standards with electric vehicle penetration targets, as the paper recommends, is a complementary rather than competing strategy for reaching net-zero transport.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The headline 20% figure is conditional on the assumed 28.2 MPGGE business-as-usual fleet average; if real-world new-vehicle efficiency in Saudi Arabia was already above this level, the achievable savings would be proportionally smaller.
The paper's exclusive focus on new car sales means the 20% reduction applies to the sales fleet, not the entire on-road fleet; cumulative on-road savings would grow over time as older, less efficient vehicles are retired.
The same AFLEET-based methodology could be applied to other Gulf Cooperation Council countries with similar vehicle markets and fuel subsidy structures to benchmark the potential of their own fuel economy proposals.
The 1.2% difference between the 1% and 3% annual improvement scenarios suggests that policy attention should focus more on compliance and enforcement than on the steepness of the target curve, since the marginal returns to faster tightening are small.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: the headline numbers are plausible but not auditable, and the BAU baseline is the main problem. The paper applies the ICCT 2014 proposed CAFE targets to Saudi new-vehicle sales with the AFLEET tool, and the central claim is that following those targets would have cut cumulative 2016-2020 new-car energy and GHG by 20%, with another 10% possible by 2030 under a 1%-per-year efficiency scenario. That specific quantification is new for Saudi Arabia and policy-relevant. The authors also deserve credit for using an established external tool, being explicit about the 5% sales growth assumption, and acknowledging the rebound effect with a reasonable literature-based value, even if they apply it only in scenario 3.

The soft spots are real and they are load-bearing for the specific percentages. Section 4 compares BAU at a flat 28.2 MPGGE against the CAFE targets, but Figure 3 in the same paper shows historical KSA fuel economy improving over 2014-2020. If the no-policy baseline is that improving trend, the 20% shrinks. The paper doesn't report the annual target fuel economies, the AFLEET input parameters, or the year-by-year BAU FE values, so the calculation cannot be independently checked. Equation (2) is incomplete in the text. The abstract mentions 'standard logistic functions' for projections, but the body simply assumes a 5% growth rate, which is an inconsistency. There is also a likely typo: the 2030 baseline is given as 2885 million metric tons of CO2, which would be enormous; presumably it is 285 or 288. No sensitivity or uncertainty analysis is provided, and rebound is only in scenario 3, not the headline 20% figure.

I agree with the reader's reject verdict for the current form. The qualitative direction is sound, but the paper is not reproducible. That said, a serious referee could extract the missing tables from the authors, so I would not desk-reject if the venue had appetite for policy-oriented work. As submitted, I would not cite the 20% or 10% numbers without contacting the authors for the parameter table.

Referee Report

3 major / 5 minor

Summary. The paper estimates the energy and well-to-wheel GHG emissions reductions that Saudi Arabia could have achieved from 2016-2020 had it implemented the ICCT 2014 proposed CAFE fuel economy targets for new light-duty vehicles, and the additional reductions possible under three post-2020 efficiency-improvement scenarios (1%, 2%, and 3% per year). The headline claims are a 20% cumulative reduction in energy consumption and GHG emissions over 2016-2020, a further 10% reduction under the 1% scenario over 2021-2030, and a 4.16% reduction in total transport-sector GHG emissions by 2030 under the 3% scenario. The analysis uses the AFLEET tool, a logistic car-sales projection, and an assumed BAU fuel economy of 28.2 MPGGE, with a rebound effect considered only as a sensitivity in Section 5.

Significance. If fully substantiated, the results would provide useful, policy-relevant evidence for Saudi Arabia's fuel-economy standard design and for evaluating the emissions abatement potential of CAFE-style regulations. The paper also usefully connects the proposed CAFE targets to the AFLEET well-to-wheels accounting framework and to KSA-specific sales and mileage assumptions. However, the contribution is currently weakened by a lack of transparency: the annual CAFE target values, the BAU fuel-economy path, the AFLEET parameterization, and the complete calculation formula are not reported, so the headline percentage reductions cannot be independently reproduced or checked.

major comments (3)

[Section 3, Table 3, Eq. (2)] The annual CAFE target fuel-economy values for 2016-2020 are not reported anywhere in the manuscript. Table 3 gives only the phase-in compliance percentages (80, 90, 100, 100, 100), and Figure 3 is graphical rather than numerical. In addition, Equation (2), which is the basis for the GHG calculations, is printed incompletely, and the AFLEET parameter set (VMT, emission factors, fuel properties, scaling factors) is described only qualitatively. As a result, the headline 20% cumulative reduction in Section 4 cannot be independently computed from the text. I request a complete data appendix listing, for each year 2016-2030, the CAFE target fuel economy, the BAU fuel economy, car sales, VMT, fuel consumption, and resulting energy and GHG values, together with a correctly typeset version of Equation (2).
[Section 4] The BAU scenario is held constant at 28.2 MPGGE for the entire evaluation period, while Figure 3 in the same paper plots KSA historical new-vehicle fuel economy as an increasing series over 2014-2020. This is not an apples-to-apples policy comparison: a no-policy baseline should extrapolate the historical efficiency trend (or provide evidence for a flat baseline), otherwise part of the estimated "policy" reduction is actually background technical progress. The manuscript should present year-by-year BAU FE_j values and justify the flat assumption, or re-estimate the baseline from the historical trend, and show how the 20% headline changes under that alternative.
[Section 5] The rebound effect is introduced only in the Section 5 sensitivity analysis for scenario 3, not in the headline calculations in Section 4. Therefore the 20% (2016-2020) and 10% (2021-2030) reduction figures implicitly assume a zero rebound effect. Given the authors themselves cite a 10% rebound estimate from the literature, the central estimates should be presented both with and without rebound, or the headline numbers should be explicitly labeled as upper-bound engineering potentials. In addition, the analysis contains no uncertainty or sensitivity analysis for other key point assumptions (5% annual sales growth, 16,000 miles VMT, emission factors); a simple one-way sensitivity table would substantially improve confidence.

minor comments (5)

[Introduction] In the paragraph on gasoline prices, the text says "The corresponding increase in the 91-octane gasoline price was 288%" but the context and the values (0.60 to 2.33 SAR/L) indicate this should be 95-octane gasoline.
[Endnote 4] The phrase "to end up with a well-to-well number" appears to be a typo; it should read "well-to-wheels."
[Section 5] In the text describing scenario 3, the sentence "which considers a 3% annual improvement in fuel efficiency from 2021 to 2023" should be "from 2021 to 2030" to match the scenario definition in Section 3.
[Table 3] The header "EC no." is not defined; please spell out the term or replace it with a clearer label such as "Compliance phase" or "Year."
[Abstract] The phrase "we demonstrated the impact" should be "we demonstrate the impact" to match the present-tense convention of an abstract.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the 20% reduction is a transparent scenario calculation from external ICCT targets and AFLEET inputs, not a fitted prediction.

full rationale

The central claim (20% lower cumulative energy and GHG for 2016-2020) is computed by comparing a BAU fuel economy of 28.2 MPGGE with the ICCT (2014) CAFE target trajectory under phase-in compliance (Table 3). The targets are externally published values, and the AFLEET tool is an independent DOE/Argonne model; no parameter is fitted to the reported 20% outcome. Equation (2) defines cumulative GHG as a sum over Car Sales_j, VMT_j, FE_j, and EF_j, so the result is an arithmetic consequence of the assumed FE paths and sales projections. The 1% and 3% annual improvement scenarios for 2021-2030 are stated assumptions, not derived from the outcomes. The only self-citation is Shannak et al. (2024), which includes coauthor Mikayilov, used to scale sector-wide percentage reductions (1.6% and 4.16%); this normalization is not the load-bearing central result, which stands on external ICCT and AFLEET inputs. Reproducibility concerns (missing annual target values, no AFLEET parameter table, flat BAU vs improving historical trend) are correctness and auditability issues, not circularity. Therefore no circular step is present.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The analysis rests on a small number of externally sourced parameters (fuel-economy targets, VMT, rebound) plus three chosen scenario rates and a chosen sales growth rate. No new entities are introduced, but the lack of sensitivity analysis means the parameter choices directly determine the headline percentages.

free parameters (5)

BAU annual car sales growth rate (2023-2030) = 5%
Assumed as the midpoint of the reported 4.60% (excluding 2020) and 6.28% (including 2020) historical growth rates, with no sensitivity analysis.
Post-2020 annual fuel economy improvement scenarios = 1%, 2%, 3% per year
Chosen arbitrarily for 2021-2030 because SASO did not publish targets; the central 'additional 10% reduction' result is a direct function of the 1% scenario.
Average annual vehicle miles traveled = 16,000 miles
Constant from Sheldon (2019); no sensitivity analysis or variation over time.
Rebound effect on VMT = 10%
Taken from Gillingham (2018); applied only to Scenario 3 in Section 5, increasing 2030 energy and emissions by 4.5%.
Emission scaling factors (CO2 to GHG, tank-to-wheel to well-to-wheel) = 1.2 and 1.2
Used in Footnote 4 to derive the 170 Mt baseline and sector-level percentages; taken from Climate Watch and Zuccari/Fueleconomy.

assumptions (4)

domain assumption Equation (2), GHG_j = sum over j of (Car Sales_j * VMT_j / FE_j * EF_j), correctly represents annual fleet GHG emissions.
This standard emissions identity is the core of the calculation, but the paper does not justify the formula or the choice of emission factors.
domain assumption The AFLEET tool, developed for U.S. conditions, provides valid emission factors and vehicle categories for Saudi Arabia.
The paper adjusts inputs but gives no evidence that MOVES-derived factors match Saudi fleet composition, fuel quality, or driving cycles.
domain assumption The ICCT 2014 CAFE targets and the 80/90/100% phase-in compliance schedule are feasible and fully enforceable.
The analysis treats the proposed policy as achievable and fully complied with; no evidence is provided for compliance rates or manufacturer response.
domain assumption The business-as-usual average fuel economy of the Saudi new LDV fleet is 28.2 MPGGE.
Stated in Section 4 as the BAU baseline without showing the supporting data or how it was derived.

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Cite this review

Pith. "Pith review of Driving Reductions in Emissions Unlocking the Potential of Fuel Economy Targets in Saudi Arabia." pith.science (2026). https://pith.science/paper/JUNOZDMI

@misc{pith2026241202167,
  author       = {Pith},
  title        = {Pith review of: Driving Reductions in Emissions Unlocking the Potential of Fuel Economy Targets in Saudi Arabia},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JUNOZDMI}},
  note         = {Machine review of arXiv:2412.02167}
}

read the original abstract

The adoption of more stringent fuel economy standards represents a pivotal pathway toward achieving net zero emissions in the transportation sector. By steadily increasing the fuel efficiency of vehicles, this approach drives a gradual but consistent decline in emissions. When coupled with the simultaneous integration of electric and alternative fuel vehicles into the market, the goal of net zero emissions becomes increasingly feasible.

Figures

Figures reproduced from arXiv: 2412.02167 by the authors.

**Figure 1.** Passenger car emissions and consumption, normalized to the new European driving cycle (NEDC). KSA (Proposed target) KSA (Enacted target) KSA (Historical) 6.0 6.5 7.0 Fuel Consumption I/100 KM gasoline equivalent 7.5 8.0 0 Brazil (Historical) 2010 2015 2020 Year 2025 2030 2035 25 50 75 100 CO2 emissions (g/km), normalized to NEDC 125 150 175 Brazil (Enacted target) Brazil (Proposed target) Canada (Historical) Canada … view at source ↗

**Figure 2.** Car sales (historical and future projections): historical in light green, projected in dark green. Source: Statista (2023); Saleh (2023); Authors’ Analysis. Car Sales (Thousands) 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 0 100 200 300 400 500 600 700 800 900 1000 [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** Fuel economy values for KSA: historical, targeted, and projected. Source: ICCT (2014); Updated Policy (2014); Authors’ Interpretation. KSA (Historical) KSA (Fuel Economy Target as Per Published Policy in 2014) Scenario 1 (1% Improvement per year in Fuel Economy) Scenario 2 (2% Improvement per year in Fuel Economy) Scenario 3 (3% Improvement per year in Fuel Economy) 26 31 36 41 46 Fuel Economy (MPGGE) 2014 2016 2018… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Petroleum use of passenger car sales fleets (the left-axis represents annual values, and the right-axis represents cumulative values). Source: Authors’ Analysis. 0 Annual Well-to-Wheels Petroleum Use (Barrels) - Passenger Car Fleet Accumulative Well-to-Wheels Petroleum…

**Figure 5.** Figure 5: GHG emissions1 in short tons2 for the passenger car sales fleet (the left-axis represents the annual values, and the right-axis represents the cumulative values). Source: Authors’ Analysis. BAU GHGs for FE Target and Scenario 1 (1% Improvement per year in Fuel Economy)…

**Figure 6.** Figure 6: Petroleum use of passenger car sales fleet with the rebound effect. Source: Authors’ Analysis. by Enerdata (2021), the transportation sector in Saudi Arabia had approximately 170 million metric tons of CO2 emissions4 in 2020. This figure is projected to increase to 288…

**Figure 7.** Figure 7: GHG emissions6 of the passenger car sales fleet with a rebound effect. Source: Authors’ Analysis. Accumulative GHGs for FE Target and Scenario 3 (3% Improvement per year in Fuel Economy with Rebound Effect in VMT) Accumulative GHGs for FE Target and Scenario 3 (3% Impr…

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Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages

[2]

Energy Price Reform to Mitigate Transportation Carbon Emissions in Oil-Rich Economies

“Energy Price Reform to Mitigate Transportation Carbon Emissions in Oil-Rich Economies.” Environmental Economics and Policy Studies 26 (2): 263–283. https://doi. org/10.1007/s10018-024-00400-9 . Sheldon, Tamara. 2019. “Drivers of New Light-Duty Vehicle Fleet Fuel Economy in Saudi Arabia.” Riyadh: King Abdullah Petroleum Studies and Research Center (KAPSAR...

work page arXiv 2019
[2014]

Passenger Vehicle Greenhouse Gas Emissions and Fuel Consumption

https://theicct.org/sites/default/files/publications/ ICCTupdate_KSA-CAFE-proposal_20141218.pdf . The International Council on Clean Transportation (ICCT). 2023. “Passenger Vehicle Greenhouse Gas Emissions and Fuel Consumption.” https://theicct.org/ pv-fuel-economy/ . UNDP. 2022. “Online Services Reduce CO 2 Emissions from Saudi Cars With Some 0.77 Millio...

work page arXiv 2023
[2024]

Transport Technologies

https://www.aramco.com/en/what-we-do/ energy-products/retail-fuels . ARAMCO. 2023. “Transport Technologies.” https://www. aramco.com/en/creating-value/technology-development/ transport-technologies . Argonne. 2023. “Welcome To AFLEET.” https://afleet. es.anl.gov/home/. Climate Watch. 2023. “Historical GHG Emissions.” Accessed May 15, 2024. https://www.cli...

work page 2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [2]

Energy Price Reform to Mitigate Transportation Carbon Emissions in Oil-Rich Economies

“Energy Price Reform to Mitigate Transportation Carbon Emissions in Oil-Rich Economies.” Environmental Economics and Policy Studies 26 (2): 263–283. https://doi. org/10.1007/s10018-024-00400-9 . Sheldon, Tamara. 2019. “Drivers of New Light-Duty Vehicle Fleet Fuel Economy in Saudi Arabia.” Riyadh: King Abdullah Petroleum Studies and Research Center (KAPSAR...

work page arXiv 2019

[2] [2014]

Passenger Vehicle Greenhouse Gas Emissions and Fuel Consumption

https://theicct.org/sites/default/files/publications/ ICCTupdate_KSA-CAFE-proposal_20141218.pdf . The International Council on Clean Transportation (ICCT). 2023. “Passenger Vehicle Greenhouse Gas Emissions and Fuel Consumption.” https://theicct.org/ pv-fuel-economy/ . UNDP. 2022. “Online Services Reduce CO 2 Emissions from Saudi Cars With Some 0.77 Millio...

work page arXiv 2023

[3] [2024]

Transport Technologies

https://www.aramco.com/en/what-we-do/ energy-products/retail-fuels . ARAMCO. 2023. “Transport Technologies.” https://www. aramco.com/en/creating-value/technology-development/ transport-technologies . Argonne. 2023. “Welcome To AFLEET.” https://afleet. es.anl.gov/home/. Climate Watch. 2023. “Historical GHG Emissions.” Accessed May 15, 2024. https://www.cli...

work page 2023