Carbon Pricing and the Truckload Spot Market

Abstract

Background: Carbon pricing in the form of fuel taxes is an important tool for abating climate change. This study examines the impact and pass-through of fuel taxes in the truckload freight market. Methods: State-level truckload market data, integrated with retail diesel prices, are analyzed using fixed-effects regression modeling. Results: Taxes and fuel costs are not only passed on by diesel retailers to motor carriers; the results reveal the overshifting of diesel taxes from motor carriers to shippers. Conclusions: The findings are consistent with inelastic short-term demand for long-haul carriage, indicating that relatively large price increases will be necessary to reduce diesel consumption in the trucking industry.

1. Introduction

Reducing greenhouse gas emissions is an urgent global priority [1]; thus, identifying effective methods for reducing emissions can yield significant societal benefits [2,3]. The trucking industry has a critical role to play in achieving this objective [4]. Medium- and heavy-duty trucks account for approximately 23% of transportation-related emissions in the United States (US), or about 7% of total annual US carbon emissions [5]. While existing supply chain and logistics research has examined voluntary emissions reductions within this essential sector [6,7,8], relatively little attention has been given to the effects of broader policy interventions. This study investigates a previously unexplored yet potentially impactful policy mechanism: carbon pricing. Specifically, we explore the following research question: how does carbon pricing in the form of a diesel tax impact the truckload spot market?

Carbon pricing is widely regarded as an effective policy tool for reducing emissions [9]. In the trucking sector, it typically takes the form of fuel taxes or diesel price increases. Leveraging a six-year panel dataset of US state-level long-haul spot market rates, retail diesel prices, and diesel taxes, we estimate how the burden of diesel taxes is distributed across fuel retailers, motor carriers, and shippers. Consistent with prior research [10], our baseline findings reveal that fuel taxes are promptly and wholly passed through from retailers to motor carriers. However, we diverge from the prior literature in finding evidence of tax overshifting from motor carriers to shippers. Specifically, a 1-cent-per-mile increase in diesel costs results in a 1.2–1.7-cent-per-mile increase in the long-haul spot rate. This suggests that shippers bear the primary cost burden of diesel (i.e., carbon) taxes, and that shipping demand is largely inelastic regarding diesel price variation within the observed diesel price ranges (USD 1.00–USD 3.60 per gallon). Consequently, substantial diesel fuel price increases will be necessary to reduce carbon emissions in this sector.

This discovery of tax overshifting of diesel costs from motor carriers to shippers is novel and unexpected. Interestingly, motor carriers, far from being harmed by spatial variation in diesel prices, may actually benefit. We theorize that this benefit is likely explained by the market power granted to motor carriers, stemming from the byzantine complexity of state-level diesel tax assessments and rebates. Namely, the International Fuel Tax Association governs a taxation system in which, although diesel taxes vary across states, taxes are assessed based on where trucks are driven, as opposed to where fuel is purchased. This complex nuance necessitates extensive information gathering and reporting for compliance under this tax regime. The consequence is more limited competition between motor carriers on long-haul routes and thus higher prices for shippers.

This paper is organized as follows. In the Section 2, we discuss the previous literature, the trucking industry, and how fuel taxes are assessed. Section 3 details the data deployed to examine the research question. Section 4 estimates the pass-through of fuel taxes from diesel retailers to motor carriers, followed by an examination of the extent to which motor carriers are able pass on diesel costs to shippers. This study concludes with a discussion of the findings and opportunities for future research to extend exploration of this important policy mechanism in hopes of driving sustainable reductions in trucking emissions.

2. Background

2.1. Literature

Sustainability, particularly in the context of greenhouse gas emissions, has become an increasingly important consideration in the supply chain and logistics industry [11,12]. While some initiatives advocate for the benefits of technology integration to reduce emissions [13], many of the sustainability efforts to date have focused on voluntary programs [14], reflecting broader environmental policy trends toward elective, information-based approaches [15,16]. These programs often rely on information disclosure to consumers to foster “green” markets in which an environmental public good (e.g., reduced carbon emissions) is paired with a specific product or service.

While seminal research has established that information asymmetries can lead to market failures [17], the effect of greater information disclosure on market efficiency is often context-dependent. For example, the public disclosure of restaurant hygiene scores has been shown to increase hygiene standards and boost patronage [18]. In contrast, public reporting of cardiologist performance has had adverse consequences, such as case “cherry-picking” to avoid high-risk patients [19]. Environmental disclosure programs present similar tradeoffs.

Within the logistics industry, the most important voluntary environmental program is the Environmental Protection Agency’s (EPA) SmartWay Program (https://www.epa.gov/smartway, accessed on 21 August 2025), a public–private partnership between the EPA, motor carriers, and shippers. Participants measure and benchmark emissions data with the goal of improving freight transportation efficiency. SmartWay program participation has been linked to a reduction in average vehicle age among carriers [8]. While newer equipment can reduce pollutants such as particulate matter and nitrogen oxides (NOx), the impact on carbon emissions is limited. In fact, research has demonstrated that firms primarily join the SmartWay program to reduce costs, rather than for environmental reasons [20].

Given the limited carbon reduction effectiveness of voluntary programs, mandatory programs—particularly carbon pricing—warrant reconsideration. Previous research suggests that gasoline taxes are rapidly and wholly passed through from retailers to consumers [10,21,22,23,24], yet demand is relatively inelastic. Despite this, little is known about the effects of diesel taxes on motor carriers and shippers. In the absence of direct emissions monitoring, which is cost-prohibitive for implementation across the population of freight vehicles, carbon pricing will likely manifest as diesel fuel taxation.

2.2. International Fuel Tax Association

Fuel taxation in the trucking industry operates in a unique system. For qualified motor vehicles—those with three or more axles or exceeding 26,000 pounds—fuel taxes are assessed based on where miles are driven, not where fuel is purchased. For example, a carrier is liable for taxes on miles driven in Florida even if fuel was purchased in Georgia. This structure requires carriers to maintain intricate records of fuel purchases and miles driven by state, along with the fleet fuel economy.

Each quarter, carriers file reconciliations with the International Fuel Tax Association (IFTA), which processes payments or refunds and redistributes tax revenues to states as needed (https://www.iftach.org/Aboutus.php, accessed on 21 August 2025). IFTA compliance is enforced via roadside inspections, and each carrier must designate a base jurisdiction responsible for oversight. States are required to audit at least 3% of IFTA licenses annually to verify carriers’ reporting and internal accounting processes. Audits occur randomly and are typically announced a month in advance, but may occur without warning if there is “just cause” [25]. Due to the complexity of this system, many carriers rely on truck telematics and tax preparation software to manage compliance.

2.3. Interstate Fueling Decisions

Fueling decisions by interstate motor carriers are influenced by multiple factors. While the posted daily price of diesel is an obvious factor, it is often less critical than that for individual consumers. Large carriers routinely negotiate discounted, site-specific rates and participate in rewards programs with retailers such as Love’s or TA Travel Centers. These arrangements are tracked and paid for using industry fueling cards (e.g., EFS/Wex or COMDATA). Some carriers also utilize dedicated fueling terminals to purchase diesel in bulk at wholesale rates.

Additional considerations, including the driver’s expected route, equipment configuration, cargo weight, vehicle fuel economy, route elevation, and traffic, also factor into fuel consumption and purchasing decisions. The transportation routing problem—deciding the optimal order and routing to visit a list of pickup and destination locations—is a famously difficult problem within the field of logistics. Minimizing fuel costs along the route adds further complexity [26]. Advanced software solutions, such as ProMiles and Fleetio, are examples of tools utilized by motor carriers to optimize routing and fueling, while simultaneously maintaining the necessary IFTA documentation. Nonetheless, the human considerations of drivers play a role. Fuel stops offer drivers opportunities for rest, meals, hygiene, and entertainment. Because excessive control over fueling could alienate employed drivers and increase employee turnover, many carriers allow drivers to make fuel decisions independently, though they are often advised not to rely solely on advertised pump prices due to variability in taxes and available discounts.

2.4. Roadway Freight Markets and Fuel Surcharges

The truckload freight market consists of two main segments: (1) the contract market, where rates are negotiated in advance for repeated shipments over time, and (2) the spot market, which handles immediate, one-time shipments. These markets differ in terms of how they account for fuel costs. In contract markets, base rates exclude fuel, and a separate fuel surcharge is applied based on a benchmark (e.g., from the US Energy Information Administration (EIA) or Breakthrough Fuel indices). Contract agreements are often enacted a year in advance, before fuel prices are realized [27]. Thus, in contract markets, the shipper bears the risk from any fuel price variation as they must pay the fuel surcharge at the time the load is tendered.

In contrast, the fuel price is not separated in the spot market. Effectively, a bid in the spot market is a single “all-in” price, inclusive of real-time fuel rates. Thus, the motor carrier that accepts the load is exposed to the risk from fuel price variation, stemming from the spatial variation in fuel pricing. In summary, shippers bear the costs (and risk) of diesel fuel in the contract market, while motor carriers bear fuel costs in the spot market.

This distinction is central to our study. While contract language clearly separates base rates from fuel costs, in practice, the rates and surcharges are jointly determined. In the spot market, where these costs are inseparable, it remains an open empirical question whether carriers pass on changes in diesel fuel costs to shippers, and if so, to what extent.

3. Data

To investigate our research question, we constructed a panel dataset with monthly state-level lane-rate data from January 2016 to September 2021, derived from three sources. The truckload dry-van spot-rates per mile were obtained from DAT Freight and Analytics, a leading provider of freight pricing data. We limited our analysis to long-haul routes (>550 miles), where drivers are likely to refuel en route. Diesel price data (in USD per gallon) was sourced from the Oil Price Information Service (OPIS) (https://www.opis.com/, accessed on 21 August 2025) and converted to USD per mile using a standard conversion factor of 6.0 miles per gallon (mpg) from the Department of Energy. (The average value of publicly available fuel efficiencies is represented by 6.0 mpg [28]. Prior research reports a median fleet efficiency of 6.5 mpg (min 4.4, max 8.8) [29]. The North American Council for Freight Efficiency reports average fuel efficiencies ranging from 5.83 to 6.02.) Tax-rate data was drawn from the Urban Institute and Brookings Institution Tax Policy Center (https://taxpolicycenter.org/, accessed on 21 August 2025). Tax-rate data was similarly converted to per-mile units. Other key variables are reported by the Bureau of Economic Analysis (BEA) and the Federal Highway Administration (FHA). We report summary statistics for the key study variables in

Table 1. Summary statistics.

Variable Name	Description	Source	Obs.	Mean	Std. Dev.
Lane Variables
Spot Price	Spot Market Lane Rate (USD/mi)	DAT	98,256	1.73	0.58
Q	Number of Truckloads	DAT	98,256	402.12	705.01
Distance	Lane Distance (mi)	DAT	98,256	1184.92	614.98
WTI	West Texas Intermediate (USD/gal)	EIA	98,256	1.26	0.28
Origin and Destination Variables (Origin Summary Statistics Reported)
Diesel	Retail Diesel Price (USD/mi)	OPIS	98,256	0.40	0.07
Tax	Diesel Tax (State + Federal) (USD/mi)	TPC	98,256	0.09	0.02
GDP	State GDP	BEA	98,256	479,248	537,647
GDP_Manufacturing	State Manufacturing GDP	BEA	98,160	59,281	66,990
GDP_Ag	State Agricultural GDP	BEA	94,716	6216	8833
GDP_Retail	State Retail GDP	BEA	98,256	28,397	29,462
GDP_Transport	State Transportation GDP	BEA	98,256	13,905	14,309
Population	State Population	BEA	98,256	8,599,023	8,255,289
Balance	Outbound–Inbound Truckloads	DAT	98,256	346	6855
Tractor	State HD vehicle registrations	FHA	98,256	73,572	77,543

.

Figure 1 maps summary statistics for spot rates and state-level diesel taxes. Spot rates are heavily influenced by existing transportation routing networks. Thus, motor carriers consider not only the prospective price for a particular delivery destination but also the likelihood and expected price of the next route from that prospective destination. We calculated a headhaul index for each state as the volume-weighted outbound price from the state minus the volume-weighted inbound price to condense this information. Under the assumption that diesel taxes are more salient for motor carriers and shippers at the origin than at the destination, these taxes should tip the headhaul index positively. (This is not the only possible mechanism. Taxes may also affect route selection decisions and destination prices. This requires more rigorous regression-based analysis, presented in the Section 4). Average diesel taxes in USD per gallon are given in the lower panel of Figure 1.

4. Results and Discussion

4.1. Diesel Taxes and Retail Diesel Prices

The first step in understanding how diesel taxes affect the trucking spot market is to quantify the extent to which diesel taxes are passed through by fuel retailers to motor carriers. Previous research has shown that energy taxes are often quickly and wholly passed through to consumers [10,24]. We replicated this analysis using state-level tax rates from the Tax Policy Center, OPIS state–month average retail diesel prices, and the price of West Texas Intermediate crude oil (from EIA). All prices are measured in nominal USD per gallon. To begin, we examine model-free evidence [30] of diesel fuel price and tax trends for our 69-month sample period (January 2016–September 2021). The trends in Figure 2 reveal initial insights: (1) retail diesel prices closely follow fluctuations in crude oil prices, and (2) the average diesel tax rate increases over time in discrete increments, with most increases occurring in January and July.

To empirically estimate the tax pass-through (from diesel fuel retailers) to motor carriers, we estimate permutations of the following regression model:

$${\mathrm{D}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_{\mathrm{i}}+{\mathsf{\gamma }}_{\mathrm{t}}+\mathsf{\beta }{\mathrm{t}\mathrm{a}\mathrm{x}}_{\mathrm{i}\mathrm{t}}+\mathsf{\delta }{\mathrm{W}\mathrm{T}\mathrm{I}}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(1)

where ${\mathrm{D}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}_{\mathrm{i}\mathrm{t}}$ is the tax-inclusive retail price of diesel fuel in state i in month–year t, ${\mathsf{\alpha }}_{\mathrm{i}}$ reflects state fixed effects, ${\mathsf{\gamma }}_{\mathrm{t}}$ includes time fixed effects (month–year), ${\mathrm{t}\mathrm{a}\mathrm{x}}_{\mathrm{i}\mathrm{t}}$ is the tax rate (state + federal), ${\mathrm{W}\mathrm{T}\mathrm{I}}_{\mathrm{t}}$ is the price of crude oil, and ${\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$ is the error term. The fixed-effects estimation is leveraged to account for time-invariant state heterogeneity, enabling the identification of price responses based on state-level variation in taxes. Results from this regression are reported in

Table 2. Retail diesel prices and taxes.

Dependent Variable:	(1)	(2)	(3)	(4)	(5)	(6)
Diesel Price	Pooled	GLS	Within	Between
WTI	0.990 ***	0.990 ***	0.990 ***		1.180 ***	1.210 ***
	(0.0169)	(0.0119)	(0.0120)		(0.0513)	(0.0294)
Tax (State + Federal)	1.182 ***	1.156 ***	1.154 ***	1.188 ***	1.094 ***	0.518 ***
	(0.0432)	(0.0730)	(0.0756)	(0.285)	(0.0385)	(0.0586)
State FE		Random	YES			YES
Year–Month FE					YES	YES
Observations	3312	3312	3312	3312	3312	3312
R-squared	0.563		0.784	0.274	0.669	0.894
Number of States	48	48	48	48	48	48

Standard errors are in parentheses; *** p < 0.01; ** p < 0.05; * p < 0.1.

.

The pooled OLS results in column 1, which exclude time and state fixed effects, indicate that changes in crude oil prices are completely passed through to retail diesel prices. Specifically, a USD 1.00 per-gallon increase in crude oil corresponds to a USD 0.99 increase in retail diesel prices—an effect statistically indistinguishable from full pass-through. Extending evidence from prior research [10], we also observe overshifting in the case of diesel taxes: a USD 1.00 tax increase results in a USD 1.18 increase in the price of retail diesel. This suggests that diesel taxes are not only fully passed through to motor carriers but may also carry an additional markup.

The estimated tax coefficients are stable across alternative model specifications. The random-effects panel specification in column (2), the state fixed effects specification in column (3), and the between specification in column (4) all show similar results. When time fixed effects are included in column (5), the crude oil coefficient increases while the tax coefficient is slightly reduced. Finally, the two-way fixed-effects model in column (6) demonstrates a notably reduced tax coefficient (albeit still statistically significant at the p < 0.01 level), likely due to attenuation bias from the inclusion of both time and unit fixed effects [31]. (Column 6 is a more complicated continuous-treatment analog of the more common staggered difference-in-difference specification with dichotomous treatment. These staggered difference-in-difference (two-way fixed-effects) models are easily biased. See [32,33,34] for in-depth explanation.) Conceptually, this approach identifies tax effects using variation across both states and time, which can dilute the estimate. Overall, Table 2 provides strong empirical support for our belief that diesel taxes are at least sometimes fully passed through to retail diesel prices.

4.2. Retail Diesel Prices and Truckload Spot Rates

Figure 3 displays the average truckload spot rate per mile, as well as the average per-mile tax rate and per-mile retail price of diesel. As outlined previously, per-mile averages are calculated under the assumption that trucks obtain 6.0 miles per gallon of diesel fuel. On average, trucks incur fuel costs of USD 0.35–USD 0.50 per mile, representing roughly one third of total mile spot rates, with higher shares for fuel-inefficient trucks (this is consistent with figures in prior research [28], where fuel accounts for roughly 24% of the median fleet’s costs).

The model-free evidence in Figure 3 details the truckload spot rate, diesel prices, and tax rates, highlighting two key observations. First, spot rates exhibit greater volatility than overall diesel prices; second, temporal variation in diesel taxes appears to have a limited impact on spot rates. Diesel taxes are small in magnitude, display limited temporal variation, and do not track closely with fluctuations in spot pricing. These observations suggest that factors other than fuel taxation play a more dominant role in shaping spot market dynamics.

To statistically examine the extent to which fuel costs are passed on (from motor carriers) to shippers, we estimate permutations of the following unweighted regression:

$$P_{ijt}={\alpha }_{ij}+\beta { Diesel}_{it}+\theta {Diesel}_{jt}+\delta X_{it}+\mu Y_{jt}+{\epsilon }_{ijt}$$

(2)

where $P_{ijt}$ is the spot rate from origin state i to destination state j in month t; ${\alpha }_{ij}$ are lane fixed effects; ${ Diesel}_{it}$ is the tax-inclusive average price of diesel fuel from origin state i in month t, while ${ Diesel}_{jt}$ is the average price of diesel fuel from destination state j in month t; $X_{it}$ and $Y_{jt}$ are vectors of origin and destination state control variables including population and state-level gross domestic product, as well as agricultural, manufacturing, retail, and transportation output (all from the Bureau of Economic Analysis); and finally ${\epsilon }_{ijt}$ is the regression error term. Results from this regression are reported in

Table 3. Lane spot rate and diesel price.

	OLS			2SLS
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
				First Stage		Second Stage
Dependent Variables:	Spot Price	Spot Price	Spot Price	Diesel	Diesel	Spot Price	Spot Price
Diesel (Origin)	1.232 ***	1.165 ***	1.559 ***			−0.310 ***	2.288 ***
	(0.0385)	(0.0390)	(0.0421)			(0.0989)	(0.157)
Diesel (Destination)	1.354 ***	1.817 ***	1.721 ***			5.980 ***	3.158 ***
	(0.0382)	(0.0388)	(0.0419)			(0.100)	(0.157)
Tax (Origin)				1.009 ***
				(0.0106)
Tax (Destination)					1.014 ***
					(0.0109)
Control Variables:	No	Yes	Yes	Yes	Yes	Yes	Yes
Lane FEs	No	No	Yes	No	No	No	Yes
Observations	98,256	91,107	91,107	94,716	94,458	91,107	91,107
R-squared	0.061	0.241	0.449	0.178	0.174	0.197	0.321
Number of Lanes	1424	1399	1399	1416	1411	1399	1399

Standard errors are in parentheses; *** p < 0.01; ** p < 0.05; * p < 0.1.

.

In column (1) of Table 3, we report results from a pooled regression excluding lane and time fixed effects and without the inclusion of control variables. The coefficients for diesel price—for both the origin and destination—are significantly greater than one, indicating that changes in diesel prices are more than fully passed through from carriers to shippers. Specifically, a 1-cent increase in the retail diesel price (per mile) in the origin state results in a 1.2-cent increase in the spot rate, demonstrating tax overshifting. Fundamentally, for shippers, this finding means that the costs of freight are increasing beyond the cost of the tax. In essence, the tax motivates a motor carrier to increase prices in the spot market to compensate the carrier for the higher fuel cost. Yet in doing this, the carrier also sees other firms in the spot market increase their prices, signaling to the carrier that the spot market can bear higher prices. Thus, the carrier increases their bid further.

In column (2), we added a vector of origin and destination control variables, while lane fixed effects were added in column (3). Notably, the estimates in column (3) suggest somewhat inflated pass-through estimates relative to those reported in the pooled OLS in column (1).

As depicted in Figure 4, this finding is sensitive to the assumed fuel efficiency of 6.0 miles per gallon. (First-stage models (columns (4) and (5) in Table 3) are not represented in Figure 4.) However, we emphasize that the selected 6.0 mpg conversion factor does not affect the statistical significance of the results, but merely their interpretation. If actual fuel efficiency exceeds this threshold, the implied pass-through rate will be even greater. Conversely, fuel efficiency of ${\displaystyle \frac{6.0}{\widehat{\beta }}}$ mpg would result in exact pass-through.

Endogeneity is a potential concern in these models. Because heavy trucks represent a significant fraction of diesel fuel consumption, it is possible that spot-rates and retail diesel prices are simultaneously determined. For example, an increase in shipper demand might result in an increase in freight rates. The increase in shipper demand, in turn, increases diesel demand and diesel prices. Moreover, other factors such as traffic congestion, regional emissions policies, and market seasonality could further confound the relationship between diesel prices and freight costs. Although many of these variables are controlled for through the use of lane-specific fixed effects, we further address this potential endogeneity by estimating two-stage least-squares (2SLS) models in columns (4) through (7) of Table 3.

Diesel taxes, which are mostly pre-determined based on fuel prices, vary independently of these potential confounders and thus serve as valid instruments for diesel price. (The notable exception is that large increases in diesel prices can trigger diesel tax holidays or tax cuts. This does not threaten our study validity, given the monotonic increase in tax rates in our sample (see Figure 2).) The first-stage estimates in columns (4) and (5) mirror results from Table 2: we find that an increase in diesel tax results in statistically exact pass-through to retail diesel price. Using fitted diesel prices from the first stage, we estimate model (6) with control variables and model (7) with control variables and lane fixed effects. The identification of the coefficients in model (7) comes from the subset of lanes with changes in tax rates, so it is no surprise that controlling for lane fixed effects has a substantial effect on our estimates. Thus, our preferred 2SLS estimates are those in column (7), providing further support (relative to OLS estimates in columns (1) through (3) of Table 3) for evidence of large overshifting of diesel prices to shippers.

To further explore the relative impact of diesel tax increases as a potential mechanism to reduce carbon emissions, we analyzed the relationship between tax increases and truckload volume. The results reported in

Table 4. Tax effects on truckload volume.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Variables	ln(Q)	ln(Q)	ln(Q)	ln(Q)	ln(Q)	ln(Q)	ln(Q)
ln(Tax_Origin)	−0.309 ***	−0.313 ***	−0.365 ***	−0.318 ***	−0.324 ***	−0.460 ***	−0.288 ***
	(0.0162)	(0.0161)	(0.0160)	(0.0189)	(0.0189)	(0.0154)	(0.0152)
ln(Tax_Dest.)		−0.248 ***	−0.345 ***		−0.438 ***	−0.508 ***	−0.424 ***
		(0.0166)	(0.0163)		(0.0199)	(0.0169)	(0.0174)
Demand Controls
ln(GDP_Origin)	−0.547 ***	−0.548 ***	−0.469 ***				−0.814 ***
	(0.0178)	(0.0177)	(0.0199)				(0.0205)
ln(Pop_Origin)	1.438 ***	1.440 ***	1.364 ***				0.993 ***
	(0.0191)	(0.0191)	(0.0266)				(0.0260)
ln(Manfctg_Origin)			0.581 ***				0.544 ***
			(0.00782)				(0.00808)
ln(Retail_Origin)			−0.625 ***				−0.366 ***
			(0.0263)				(0.0260)
ln(Ag_Origin)			0.0542 ***				−0.165 ***
			(0.00345)				(0.00566)
ln(GDP_Dest)	−0.296 ***	−0.229 ***	−0.263 ***				−0.756 ***
	(0.0177)	(0.0184)	(0.0199)				(0.0200)
ln(Pop_Dest)	1.123 ***	1.064 ***	0.989 ***				0.766 ***
	(0.0191)	(0.0195)	(0.0258)				(0.0251)
ln(Manfctg_Origin)			0.281 ***				0.265 ***
			(0.00767)				(0.00798)
ln(Retail_Origin)			−0.216 ***				0.0481 **
			(0.0250)				(0.0243)
ln(Ag_Origin)			0.106 ***				0.0546 ***
			(0.00319)				(0.00346)
Supply Controls
ln(Trnsp_Origin)				0.489 ***	0.486 ***	0.579 ***	0.436 ***
				(0.00507)	(0.00506)	(0.00420)	(0.00997)
ln(Tractors_Origin)				0.220 ***	0.225 ***	0.273 ***	0.565 ***
				(0.00467)	(0.00468)	(0.00384)	(0.00999)
ln(Price_Dest)				2.015 ***	2.194 ***	0.0421 **	0.269 ***
				(0.0163)	(0.0183)	(0.0180)	(0.00751)
ln(Trnsp_Dest)						0.778 ***	−0.216 ***
						(0.00348)	(0.0193)
Dest Balance						1.36 × 10−05 ***	1.35 × 10−05 ***
						(4.64 × 10−07)	(4.69 × 10−07)
Constant	−25.20 ***	−25.32 ***	−25.74 ***	−4.413 ***	−4.901 ***	−11.65 ***	−20.06 ***
	(0.139)	(0.138)	(0.228)	(0.0504)	(0.0542)	(0.0534)	(0.231)
Observations	98,256	98,256	91,107	98,256	98,256	98,256	91,107
R-squared	0.518	0.519	0.573	0.330	0.334	0.546	0.611

Robust standard errors are in parentheses; *** p < 0.01; ** p < 0.05; * p < 0.1.

reveal that a 1% increase in diesel taxes reduces truckload shipment quantity by between 0.248% and 0.508% (rows 1 and 2 of Table 4). This finding reveals that diesel tax increases have a small effect on reducing truckload volume, demonstrating that large diesel tax increases are necessary to reduce diesel consumption (and carbon emissions) through the pathway of reduced truckload volume.

4.3. Robustness Check: COVID-19 Exclusion

The COVID-19 pandemic produced significant disruptions to logistics and supply chain activity globally, impacting freight traffic volumes, market dynamics, and diesel demand [35,36]. Exogenous shifts in consumer demand attributable to the COVID-19 shock may have affected both the price and consumption of diesel fuel [37]. Although our core empirical models include time fixed effects to account for such shocks, we conducted a robustness check by excluding the pandemic-affected years (2020 and 2021) in our study period. We replicated the empirical analysis reported in Table 2 and Table 3 using this restricted dataset and display the results in

Table 5. Retail diesel prices and taxes (COVID-19 exclusion).

Dependent Variable:	(1)	(2)	(3)	(4)	(5)	(6)
Diesel Price	Pooled	GLS	Within	Between
WTI	1.104 ***	1.101 ***	1.101 ***		0.999 ***	1.016 ***
	(0.0243)	(0.0163)	(0.0163)		(0.0721)	(0.0383)
Tax (State + Federal)	1.158 ***	1.233 ***	1.243 ***	1.146 ***	1.101 ***	0.741 ***
	(0.0531)	(0.0963)	(0.102)	(0.303)	(0.0480)	(0.0765)
State FE		Random	YES			YES
Year–Month FE					YES	YES
Observations	2304	2304	2304	2304	2304	2304
R-squared	0.54		0.805	0.237	0.633	0.899
Number of States	48	48	48	48	48	48

Standard errors are in parentheses; *** p < 0.01; ** p < 0.05; * p < 0.1.

and

Table 6. Lane spot rate and diesel price (COVID-19 exclusion).

	OLS			2SLS
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
				First Stage		Second Stage
Dependent Variables:	Spot Price	Spot Price	Spot Price	Diesel	Diesel	Spot Price	Spot Price
Diesel (Origin)	0.635 ***	0.711 ***	1.703 ***			−2.024 ***	2.288 ***
	(0.0354)	(0.0341)	(0.0383)			(0.0796)	−0.157
Diesel (Destination)	1.132 ***	1.611 ***	1.523 ***			3.837 ***	3.158 ***
	(0.0351)	(0.0339)	(0.0383)			(0.0800)	−0.157
Tax (Origin)				1.111 ***
				(0.0121)
Tax (Destination)					1.128 ***
					(0.0123)
Control Variables:	No	Yes	Yes	Yes	Yes	Yes	Yes
Lane FEs	No	No	Yes	No	No	No	Yes
Observations	68,352	66,012	66,012	67,248	67,116	66,012	66,012
R-squared	0.046	0.285	0.366	0.220	0.216	0.248	0.116
Number of Lanes	1424	1378	1378	1403	1399	1378	1378

Standard errors are in parentheses; *** p < 0.01; ** p < 0.05; * p < 0.1.

, respectively. As expected, sample sizes are lower due to the exclusion of two years of data.

The results in Table 5 and Table 6 confirm the robustness of our main findings. Specifically, the evidence of overshifting of diesel prices to shippers persists even when pandemic-affected years are excluded, suggesting that our base results are not driven by COVID-19-era distortions in the data.

5. Conclusions

Leveraging state-level truckload data from the spot market, combined with retail diesel prices, the study findings reveal that taxes and fuel costs are not merely passed on by diesel retailers to motor carriers. Surprisingly, our analysis provides evidence of tax overshifting from retailers to motor carriers. These empirical results provide support for the prevalence of inelastic short-term demand for long-haul freight carriage in the spot market. In essence, because the truckload spot market consists of primarily “one-off”, time-sensitive truckload shipments, shippers are willing to pay higher prices. Our analysis reveals that the percentage change in tax is larger than the percentage change in shipping quantity. Thus, it is likely that relatively large tax increases will be necessary to reduce diesel fuel consumption in the trucking industry. The average tax rate in our dataset is 48 cents per gallon. A 10-cent increase in fuel taxes by a state would translate into a 20.8% increase in taxes. Assuming no change in fuel efficiency on the part of motor carriers, this would translate to between a 5.1% and 10.6% reduction in truckload shipments in the state.

Notably, diesel taxes are assessed based on the use principle of taxation: motor carriers pay state diesel taxes according to where they drive, not where they purchase fuel. Because state tax rates vary widely, this approach requires a complex infrastructure of reporting, tax rebates, and assessments, with substantial administrative and compliance costs. Given this complexity—and the fact that long-haul trucking is inherently interstate—a question arises: why are diesel taxes not federally administered? A uniform federal tax rate, redistributed through block grants, would simplify administration and reduce compliance burdens for both motor carriers and states.

The study results suggest that a “Baptists and Bootleggers” story [38] may help explain the persistence of state-level diesel taxation. Our findings reveal that although motor carriers appear to be the party most burdened by diesel taxes, they may in fact benefit from the spatial variation in tax rates. In this analogy, the administrative complexity of reporting (the “Baptists”) restricts competition in long-haul markets, allowing carriers (the “Bootleggers”) to pass on not only the full costs of fuel but an additional markup as well, effectively profiting from the opacity of the situation. While the magnitude of this effect is modest, it is statistically significant and economically meaningful. Our empirical analysis demonstrates that motor carriers engage in tax overshifting, such that a one-standard-deviation (USD 0.06 per-mile) increase in diesel prices results in USD 0.07–USD 0.19 per mile increases in the spot price (4–11% relative to the average spot rate). Larger pass-through estimates are identified from changes in taxes, but these taxes are a miniscule fraction of the spot rate per mile. These observations lead to a key policy insight: even large increases in fuel taxes are unlikely to generate major changes in freight rates or shipper/carrier behaviors unless demand elasticity increases or complementary policies are introduced.

Viewed through the lens of climate mitigation, our results are somewhat sobering. Inelastic shipping demand implies that significant reductions in diesel consumption (and, therefore, meaningful reductions in trucking-related carbon emissions) would require much steeper price increases than current political or economic conditions can sustain. This raises concerns about the efficacy of carbon pricing alone as a policy tool in the US trucking industry.

However, several other theoretical and managerial implications follow from our results. First, for policymakers, our findings suggest that while carbon pricing via diesel taxation indeed shifts costs within the freight system, it does little to change shipper or carrier behavior at the current demand elasticity. Modest fuel taxes, it turns out, are insufficient to generate substantial emissions reductions. As such, it is possible that a more effective approach would include the integration of carbon policy into a more comprehensive legislative framework. Such a framework might include direct investment in electric vehicle charging infrastructures, expanded incentives for alternative fuel adoption, and/or regulatory mandates requiring emission reductions across entire carrier fleets. Moreover, harmonizing diesel taxation at the federal level could additionally reduce administrative costs while limiting the ability of carriers to exploit tax-induced opacity. Without such complementary measures, carbon pricing in isolation may serve merely as a financial rather than environmental policy lever.

For motor carriers, our results highlight that participation in complex tax compliance regimes, though operationally burdensome, may yield strategic tax advantages. The extremely complex nature of the IFTA system, combined with the state-level variation in diesel taxes, creates information asymmetries that carriers can leverage to their benefits. Large and small firms within the industry have different strategic and administrative abilities but also different incentives with respect to regulatory compliance [39]. Although small firms can more easily evade IFTA reporting requirements, they cannot evade retail diesel taxes. In contrast, larger carriers with advanced telematics systems and tax optimization tools can capitalize on pricing latitude and potentially negotiate favorable surcharge structures. Moreover, since compliance with IFTA rules represents a fixed cost to motor carriers, the average cost of compliance will diminish with fleet size. On balance, we expect that IFTA rules will favor larger carriers. In effect, the complexity of compliance could serve as a means of competitive insulation from more stringent rate competition. Rather than simply enduring fuel tax burdens, carriers could use them to differentiate their operations from those of competitors and leverage greater control over spot market pricing.

For shippers, the practical takeaway is that exposure to fuel tax volatility is compounded by the structural nature of the freight system, extending beyond input costs. Since fuel prices are passed through with a margin, and because these margins contextually vary, shippers must adopt more sophisticated procurement strategies to manage transportation costs and ultimately reduce carbon emissions [27]. This includes favoring long-term contracts with transparent surcharge formulas, working with carriers that offer predictability in pass-throughs as a key benefit, or exploring brokers and third-party logistics providers that can mitigate risk exposure through consolidated buying power. In markets where price visibility is limited, shippers who treat fuel costs as a manageable financial variable, rather than a passively accepted or absorbed cost, will be better equipped to maintain cost efficiencies.

In conclusion, this study reveals untapped opportunities for innovation at the intersection of logistics, energy policy, and data-driven pricing. As carbon pricing mechanisms expand, firms that can anticipate and strategically manage fuel cost pass-through will gain competitive advantages. For researchers, future work could explore how emerging technologies, such as real-time emissions tracking, electric vehicle deployment, route optimization, and digital freight marketplaces, reconfigure cost structures and carbon accountability in freight markets.