Forest Biomass Fuels and Energy Price Stability: Policy Implications for U.S. Gasoline and Diesel Markets

Abstract

U.S. gasoline and diesel prices are often volatile, driven by geopolitical risks and disruptions in the fossil fuel market. Forest biomass fuels, particularly renewable diesel derived from logging residues, offer a low-carbon alternative with the potential to stabilize fuel prices. This study evaluates whether biomass can moderate fuel price volatility using ANOVA, Tukey post hoc tests, and quadratic regression based on monthly data for biomass production, inventories, and retail fuel prices. Findings reveal the existence of a significant nonlinear relationship between forest biomass inventory levels and fossil fuel prices. Average gasoline prices peaked in the medium-inventory group (M = 0.837) and dropped in the high-inventory group (M = 0.684). Diesel prices followed a similar pattern, with the highest values in the medium-inventory group (M = 0.963) and the lowest in the high-inventory group (M = 0.759). One-way ANOVA results were statistically significant for both gasoline (F(2, 99) = 7.39, p = 0.001) and diesel (F(2, 99) = 7.22, p = 0.0012). Tukey tests confirmed that diesel prices fell significantly from both medium to high and low to high-inventory levels. This result remains robust when using the biomass index level and the biomass production level. These results indicate a threshold effect: only at higher biomass inventories do fossil fuel prices decline, suggesting a potential for substitution. However, current policies inadequately support biomass integration, highlighting the need for targeted reforms.

1. Introduction

The United States still relies heavily on fossil fuels to power its transportation sector, a dependence that contributes substantially to greenhouse gas emissions and exposes fuel markets to global supply shocks and geopolitical instability [1]. Recent studies have emphasized that, beyond technological innovation, achieving emission reductions and fuel diversification requires coordinated governance frameworks that engage both state actors and private enterprises [2]. These collaborative models are crucial in advancing net-zero emission strategies and enhancing energy security while responding to climate imperatives. As energy price stability and climate change concerns grow, renewable alternatives like forest biomass fuels are gaining more attention in policy discussions. Forest biomass, particularly in the form of logging residues, mill waste, and thinnings, provides a renewable and locally sourced feedstock for creating advanced biofuels such as renewable diesel and cellulosic ethanol [3,4]. In this context, forest biomass can play a crucial dual role in supporting both the climate goals and economic growth. It can help lower carbon intensity by substituting fuels, while also providing a shield for regional markets against the price fluctuations that can accompany petroleum products. When used effectively, forest biomass can align with state-level low-carbon fuel standards, such as California’s Low-Carbon Fuel Standard (LCFS) and Oregon’s Clean Fuels Program, and contribute to achieving emissions targets. Additionally, it can promote rural economic development and improve the management of forest health [5]. Nevertheless, we still need to unravel how biomass supply interacts with fuel pricing, especially considering the differences in regional production and the effects of scale [6].

For instance, in 2023, biomass made up about 5% of the United States’ total energy use, adding up to roughly 4978 trillion British thermal units (TBtu) [7]. Most of this came from biofuels like ethanol and renewable diesel, which contributed 53% (2662 TBtu). Wood and wood waste, often from forestry operations and sawmills, made up 39% (1918 TBtu), while the remaining 8% (398 TBtu) came from other sources such as municipal solid waste, animal manure, and sewage. Using forest residues directly for transportation fuels accounts for less than 2% of the U.S. transportation energy needs [8]. At the same time, petroleum products continue to dominate, making up about 89% of energy use in the transportation sector, with biofuels contributing just under 6% [8]. These figures remind us of the untapped potential of forest residues and highlight the importance of understanding biomass supply conditions, as they can influence the retail prices of fossil fuels and shape clean energy policies.

While biomass is still a relatively small part of the national energy mix, it plays a crucial role, particularly in rural and forested areas. With proper support, biomass can help reduce the reliance on fossil fuels, lower carbon emissions, and make the energy systems more resilient. Including biomass more intentionally in energy planning can support clean fuel goals and provide economic opportunities in regions with strong natural resource sectors. Beyond energy production, biomass has made a significant contribution to regional economic development, particularly in rural areas. Recent data on biomass facility operations across the country, as reported by the U.S. Energy Information Administration (EIA) [9], show increases in job opportunities in the nearby facilities. In the southern U.S., 33 facilities are currently operating with the capacity to process over 10.4 million tons of biomass annually. These facilities employ approximately 1695 full-time workers, providing steady employment in areas where job opportunities can be limited. In the East, 28 facilities handle nearly 1.9 million tons of biomass per year and support 542 full-time jobs. Meanwhile, the West is home to 14 facilities with a combined annual capacity of 770,300 tons, employing 232 people. Altogether, there are now over 75 densified biomass facilities across the country with a combined capacity of more than 13 million tons per year, collectively, having an equivalent of 2798 full-time employees, but the contribution of forest biomass to reducing reliance on fossil fuels remains relatively small [9]. These figures demonstrate that biomass is more than just an energy source; it is a practical and locally grounded solution that supports both environmental and economic objectives. The sector not only helps reduce reliance on fossil fuels and lower greenhouse gas emissions, but also brings jobs and investment to communities that need them most. With policies like clean fuel standards, regional biomass inventories, and infrastructure grants, biomass has the potential to play an even greater role in creating a more resilient and low-carbon energy future for the United States.

This study examines the significant relationship between forest biomass inventory level, forest biomass production levels, and biomass index, and explores how these factors impact retail fuel prices of motor gasoline and automotive diesel across the United States. While forest biomass is well-known as a sustainable energy source, its potential to help manage fossil fuel prices is still largely unexamined. The analysis we present is especially relevant today, as we strive to reduce carbon emissions and ensure that energy remains affordable during the transition to a low-carbon economy. Our research aims to fill this gap by employing a quadratic regression model to investigate the nonlinear effects of biomass production on fuel pricing, a topic that has not received sufficient attention in earlier studies. Previous research has thoroughly evaluated the availability and economic impact of forest biomass for electricity and heat generation. For instance, Poudel et al. [10] estimate that the Lake States region could sustainably support an additional 9.72 million dry tons of biomass annually, resulting in significant job creation and economic benefits through expanded biopower capacity. Similarly, Abt et al. [11] note regional differences in forest biomass supply across the Southeastern United States and emphasize how excessive reliance on residue streams can lead to sharp price increases. While these studies primarily concentrate on supply economics and resource potential, they do not delve into the nonlinear effects on fuel markets, which is a significant innovation in our research.

Another contribution of this study is the introduction of a biomass index, a composite variable created to reflect net availability and price pressure from forest biomass. This innovative approach builds on the valuable insights of Sozinho et al. [12], who explored price trends in native and plantation forests in Brazil and noted how supply driven price shifts evolved in response to changing policy and conservation factors. However, unlike their descriptive trends, our index is designed to investigate formal statistical relationships with downstream fuel prices, establishing an empirical link that has not been explored before. Additionally, this study aims to improve how biomass and energy market models are integrated. Jåstad et al. [13] illustrate that coupled forest–energy sector models can significantly enhance biomass demand forecasts and more accurately capture policy trade-offs in low-carbon scenarios. Similarly, Lundmark [14] highlights the price sensitivity of biomass feedstocks in energy markets, particularly when resources are in competition. While these studies emphasize the importance of price effects, they do not directly connect biomass availability to retail fuel costs. Our research addresses this gap by using a national dataset to examine how variations in biomass availability relate to trends in gasoline and diesel pricing.

The nonlinear (inverted-U) pattern discovered in this study is quite novel and brings fresh insights. Unlike many studies that model biomass supply and utilization in a linear approach, our findings reveal that fuel prices respond in a more complex manner to biomass inventory levels. They tend to peak at moderate inventory levels and drop when inventories are high. This hints at transitional hurdles, such as infrastructure inertia or policy delays, which may hinder price changes during the early stages. This observation is backed by adjustment cost theory and supply competition models [15,16]. Moreover, our focus on the U.S. market is both deliberate and well-justified. The United States has a unique biomass policy landscape, influenced by federal and state-level renewable fuel standards, tax incentives, and wildfire risk mitigation programs. Policies such as California’s Low-Carbon Fuel Standard (LCFS) and the national Renewable Fuel Standard (RFS) significantly affect both the availability and valuation of forest biomass for energy use [16,17,18]. This unique environment makes the U.S. particularly well-suited for exploring how policies impact fuel price responses in relation to biomass dynamics. Therefore, forest biomass is recognized as a sustainable source of energy, and its potential role in stabilizing fuel prices has not been explored [19]. In the following sections, we discuss the policy context, present some empirical results, and provide a collection of evidence-based recommendations that can strengthen the role of forest biomass in fostering stable, low-carbon fuel markets.

1.1. Policy Problem and Context

The United States has adopted a range of policies aimed at promoting renewable energy and reducing carbon emissions. However, it is essential to note that much of the federal and state efforts have traditionally focused on electricity generation or on biofuels such as corn ethanol and soy biodiesel. In recent years, federal initiatives such as the Sustainable Aviation Fuel (SAF) Grand Challenge launched by the U.S. Department of Energy, the Department of Transportation, and the Department of Agriculture have focused on accelerating the development, commercialization, and integration of SAFs into the national aviation system, with the goal of producing 3 billion gallons annually by 2030 and achieving net-zero emissions in aviation by 2050 [20]. While these fuels have gained significant market presence, they do raise ongoing concerns regarding the competition between food and fuel, land-use changes, and lifecycle emissions [21,22]. On the other hand, forest biomass gathered from logging residues, thinning operations, and low-value timber may offer a promising, low-carbon alternative that not only sidesteps these trade-offs but also may enhance our options for transportation fuel [23]. Programs like the Renewable Portfolio Standards (RPSs) and the Biomass Crop Assistance Program (BCAP) have been significant supporters of forest biomass development, particularly in generating electricity [24,25]. Similarly, initiatives such as the Clean Energy Standard (CES) strive to encourage low-emission energy sources. However, it is essential to note that transportation fuels produced from forest biomass often still fail to receive the direct incentives they deserve.

This policy gap restricts the opportunity for forest biomass to play a significant role in stabilizing gasoline and diesel prices, even though it has great potential to enhance the variety of our domestic fuel supply and lower overall emissions throughout its lifecycle. Additionally, there are some technical and logistical challenges that make it tough to achieve broader market integration. The high moisture and ash content in feedstocks can lead to reduced fuel efficiency, and when transport distances are long and supply chains are spread out, costs can increase [26,27]. These challenges vary by region, particularly in the western U.S., where the risk of wildfires intersects with the availability of biomass. This situation calls for targeted infrastructure investments, technology upgrades, and policies tailored to each region of the nation to enhance the quality of biomass fuel and improve delivery systems [28,29]. To maximize the remarkable potential of forest biomass in promoting fuel price stability and mitigating climate change, U.S. biofuel policy should broaden its focus beyond traditional crops. It is important to establish clearer pathways for forest-derived fuels to enter the liquid fuel market. This policy brief explores these pathways and offers recommendations for enhancing both the economic benefits and environmental advantages of utilizing forest biomass in U.S. energy markets.

1.2. Legislative Framework

The Renewable Fuel Standard (RFS), introduced through the Energy Policy Act of 2005 and later expanded by the Energy Independence and Security Act of 2007, encourages the blending of renewable fuels into the U.S. transportation fuel supply and permits forest biomass as a valuable feedstock for advanced biofuels [30]. Even though forest biomass has a favorable carbon profile and minimal food-versus-fuel tradeoffs, it is still not utilized to its full potential due to high production costs, logistical challenges, and a lack of targeted support [31,32]. However, state-level programs like California’s Low-Carbon Fuel Standard (LCFS) and Oregon’s Clean Fuels Program (CFP) provide strong market incentives by rewarding fuels with lower lifecycle carbon intensity, and forest-derived fuels often excel in this area [33,34]. The Inflation Reduction Act of 2022 broadens federal clean energy tax credits to include low-carbon fuels but adopts a feedstock-neutral approach, meaning that it does not prioritize or favor any specific type of biomass feedstock (e.g., forest residues, agricultural waste, or energy crops). While this promotes flexibility and inclusiveness across different renewable fuel sources, it may also dilute the targeted benefits for specific sectors, such as forest biomass, that face higher production costs or infrastructure barriers [35]. Consequently, the sector continues to encounter structural barriers, despite its remarkable potential to contribute to climate benefits and support rural economies.

2. Materials and Methods

To investigate how fluctuations in forest biomass supply influence retail fuel prices in the United States, we employed two complementary modeling approaches. First, we applied a categorical linear regression model using analysis of variance (ANOVA) based on inventory, production, and biomass index group classifications to explore whether fuel prices vary across distinct levels of biomass availability. This method is suitable for detecting mean differences between categorical groups, such as inventory, production, and biomass index levels, grouped into low, medium, and high quantiles. ANOVA provides a straightforward way to determine if the price differences across these categories are significant, and it does so without requiring the assumption that the data follows a linear or continuous pattern. Second, this complements our quadratic regression approach by identifying potential threshold effects that may not be evident in continuous models, helping to detect potential nonlinear relationships between biomass availability and fuel prices. The analysis is based on a cross-sectional dataset comprising 102 monthly observations spanning from 2016 to 2024. The definitions, measurement units, and sources for all variables are presented in

Table A1. Data Variable Definitions and Measurement Units.

Variable	Definition	Measurement Unit	Data Sources
Inventory (Biomass)	The total stock of wood biomass fuel available in storage at a given point in time. This reflects biomass that is accessible for immediate use or distribution.	Metric tons	U.S. EIA [9]
Production (Biomass)	The amount of wood biomass fuel generated or processed during a specific time period, typically monthly or annually.	Metric tons	U.S. EIA [9]
Average Price (Biomass)	The market price per ton of wood pellets, serving as a proxy for the economic cost of biomass fuel. This variable reflects regional market dynamics and demand for biomass.	USD/ton	U.S. EIA [9]
Quantity (Biomass)	The total volume of wood pellets available for consumption or sale, including both newly produced and stored volumes.	Metric tons	U.S. EIA [9]
Biomass Index	A composite indicator constructed to reflect net regional biomass availability and economic pressure. Calculated as:
Biomass Index = (Quantity – Inventory) × Average Price.	This index is used to assess the influence of biomass market conditions on fuel prices. Composite Index (unitless)
Motor Gasoline	The average retail price of motor gasoline in a given region or time period, used as a dependent variable to examine market response to biomass availability.	USD/liter	U.S. IEA [80]
Automotive Diesel	The average retail price of automotive diesel fuel, also used as a dependent variable to assess the impact of forest biomass availability and cost.	USD/liter	U.S. IEA [80]

, while

Table A2. Summary Statistics.

Variable	Observations	Mean	Std. Dev.	Min	Max
Biomass Inventory	105	272,512.4	147,888.6	46,642	629,110
Biomass Production	105	145,861.6	22,737.42	85,170	218,864
Biomass Quantity	105	157,188.7	52,739.13	59,354	289,032
Biomass Average Price	105	182.259	27.51307	141.72	238.51
Motor Gasoline	102	0.758824	0.173692	0.5	1.3
Automotive Diesel	102	0.879412	0.238119	0.5	1.5

summarizes the dataset’s structure and descriptive statistics [36]. All variables were carefully defined and sourced from credible databases to ensure transparency and accuracy. Biomass inventory refers to the total stock of wood biomass fuel available in storage at a given time, measured in metric tons, while biomass production captures the quantity of biomass generated or processed during a specific period. The quantity includes the total volume of wood pellets available for consumption or sale, encompassing both production and existing stock. The average biomass price is recorded as the market price per ton of wood pellets (USD/ton), serving as a proxy for regional biomass demand. This study aims to explore whether retail fuel prices for motor gasoline and automotive diesel respond in a linear or nonlinear manner to changes in forest biomass supply. We utilize a regionally sensitive “biomass index” that blends physical and economic aspects of supply. This index was constructed using the following formula (Equation (1)):

$$Biomass Index=\left(Quantity-Inventory\right)\times Average Price$$

(1)

This index is based on inventory-adjusted supply pressure models from commodity economics, where net available supply (after accounting for inventory) and prevailing prices together reflect market tightness and the potential for substitution effects [37,38]. By subtracting inventory from total quantity, the index focuses on the portion of biomass that is actively used for energy, rather than being stored. This understanding is crucial for assessing real market pressure and aligns with methodologies in bioenergy and commodity market modeling [37,38]. Multiplying by average price adds the economic value of available biomass, showing how marginal costs impact downstream market behavior, as highlighted by recent bioenergy market studies [38,39]. This integrated approach enables a detailed evaluation of how both the availability and valuation of biomass influence fuel prices, particularly under the assumption that price responses may become more pronounced in a nonlinear way as adequate supply surpasses certain thresholds, driven by market substitution, infrastructure growth, or policy actions [39]. This framework is particularly significant given findings showing that inventory behavior can either stabilize or exacerbate price fluctuations depending on market circumstances, and that supply certainty and scale economies are essential for facilitating fuel substitution [37,38]. By capturing these dynamics, the biomass index serves as a theoretically solid and practically useful tool for analyzing the effects of forest biomass supply on retail fuel markets. Therefore, the purpose of the biomass index is to capture how much forest biomass is actively available in the U.S. market and how it might influence fuel prices. Although the idea behind the index could be applied to regional markets, our analysis uses national-level data. As such, the index reflects broad market conditions across the United States over time, not differences between states or regions. For example, a higher index value could indicate that less biomass is in storage, more is being used, and prices are higher, signaling a tight supply or strong demand. This combined measure of physical availability and price gives us a useful snapshot of biomass market pressure at the national level. It plays a central role in our study by helping us explore whether changes in biomass supply and value are linked to changes in fossil fuel prices, such as motor gasoline and diesel. This is especially important as the U.S. moves toward cleaner, low-carbon fuel options.

2.1. Categorical Inventory-Level Model (ANOVA Framework)

First, the study estimates a one-way analysis of variance (ANOVA) model, reformulated as a linear regression where the independent variable is a categorical factor representing biomass inventory levels, biomass index level, and production levels (low, medium, and high), a method supported by research using ANOVA to quantify the influence of such factors on biomass model predictions [40]. The low-inventory, low-biomass, and low-production groups serve as the reference categories. Separate models are estimated for each fuel type (Equations (2) and (3)).

Motor Gasoline:

$${MotorGasoline}_i= \alpha +{\beta }_1\times D_{{Medium}_i}+ {\beta }_2\times D_{{High}_i}+ {\epsilon }_i$$

(2)

Automotive Diesel:

$${AutomotiveDiesel}_i= \alpha +{\gamma }_1\times D_{{Medium}_i}+ {\gamma }_2\times D_{{High}_i}+ {\mu }_i$$

(3)

where $D_{{Medium}_i} \mathrm{a}\mathrm{n}\mathrm{d} D_{{High}_i}$ are binary indicator variables equal to 1 if observation I falls into the medium- or high-inventory group, respectively, and 0 otherwise. The low-inventory group is the base/reference category. The coefficients β1, β2, γ1, and γ2 capture the average price differences relative to the low-inventory group, and εi and μi are assumed to be independent and identically distributed error terms.

2.2. Continuous Nonlinear Model (Quadratic Specification)

To capture the potential nonlinear effects of biomass supply on fuel prices, we estimate a quadratic regression model, again separately for motor gasoline and diesel, a method shown to improve the accuracy of fuel price forecasting by modeling nonlinear relationships in energy markets [41]. This approach treats inventory, biomass, and production as continuous variables and includes both linear and squared terms (Equation (4)).

$${FuelPrice}_i= {\beta }_0+{\beta }_1\times {Inventory}_i+ {\beta }_2\times {Inventory}_i^2+ {ϵ}_i$$

(4)

Alternatively, if using biomass production or a constructed biomass index as the independent variable, the model is specified similarly (Equations (5) and (6)).

$${FuelPrice}_i= {\beta }_0+{\beta }_1\times {Production}_i+ {\beta }_2\times {Production}_i^2+ {ϵ}_i$$

(5)

$${FuelPrice}_i= {\beta }_0+{\beta }_1\times {Biomass Index}_i+ {\beta }_2\times {Biomass Index}_i^2+ {ϵ}_i$$

(6)

In these models, β₂ indicates whether the marginal effect of biomass availability on fuel prices increases or decreases at higher levels. A negative β₂ suggests a concave (inverted U-shaped) relationship, implying diminishing or reversing price impacts at high-inventory levels. This econometric framework allows for a robust examination of whether and how biomass supply conditions exert asymmetric or nonlinear effects on fuel markets.

3. Results and Discussion

3.1. Biomass Inventory Levels and Fuel Prices

The study investigated whether fossil fuel prices vary systematically with biomass inventory levels by categorizing observations into low-, medium-, and high-inventory quantiles. Descriptive statistics (

Table 1. Mean and standard deviation of fuel prices (USD/L) by biomass inventory group.

Inventory Group	Motor Gasoline (Mean ± SD, USD/L)	Automotive Diesel (Mean ± SD, USD/L)
Low	0.749 ± 0.140	0.906 ± 0.221
Medium	0.837 ± 0.185	0.963 ± 0.243
High	0.684 ± 0.163	0.759 ± 0.208
Total	0.759 ± 0.174	0.879 ± 0.238

) suggest the existence of a nonlinear pattern in average fuel prices across biomass inventory levels. For motor gasoline, prices peaked in the medium-inventory group (M = 0.837, SD = 0.185), followed by the low (M = 0.749, SD = 0.140)- and high (M = 0.684, SD = 0.163)-inventory groups. Automotive diesel exhibited a similar pattern, with the highest average price in the medium group (M = 0.963, SD = 0.243) and the lowest in the high-inventory group (M = 0.759, SD = 0.208). To test the statistical significance of these patterns, a one-way analysis of variance (ANOVA) was conducted. The results confirm that mean fuel prices differ significantly across inventory categories for both motor gasoline (F(2, 99) = 7.39, p = 0.001; R² = 0.13) and automotive diesel (F(2, 99) = 7.22, p = 0.0012; R² = 0.127) (

Table 2. ANOVA results: effect of biomass inventory group on motor gasoline prices.

Source	df	SS	MS	F	p-Value
Model (Between)	2	0.396	0.198	7.39	0.001
Residual (Within)	99	2.651	0.0268
Total	101	3.047
R-squared = 0.13

and

Table 3. ANOVA results: effect of biomass inventory group on automotive diesel prices.

Source	df	SS	MS	F	p-Value
Model (Between)	2	0.729	0.365	7.22	0.0012
Residual (Within)	99	4.998	0.0505
Total	101	5.727
R-squared = 0.127

). These R-squared values indicate that inventory classification explains approximately 13% of the variation in fuel prices. To further verify the presence of a nonlinear relationship, we estimated a quadratic regression model using continuous inventory data. The results show that both the linear and squared inventory terms are statistically significant for motor gasoline and diesel (p < 0.001 in all cases). Specifically, the negative coefficients for the squared terms confirm the existence of an inverted U-shaped relationship, indicating that fuel prices initially rise with inventory levels, then decline beyond a certain threshold. This provides strong empirical support for the nonlinear pattern observed in the descriptive analysis.

Post hoc comparisons using Tukey’s HSD test (

Table 4. Tukey post hoc test: motor gasoline prices (USD/L) by inventory group.

Comparison	Mean Difference (USD/L)	Std. Error	95% Confidence Interval (USD/L)
Medium vs. Low	0.089	0.039	−0.005 to 0.182
High vs. Low	−0.064	0.040	−0.159 to 0.031
High vs. Medium	−0.153	0.040	−0.248 to −0.058

and

Table 5. Tukey post hoc test: automotive diesel prices (USD/L) by inventory group.

Comparison	Mean Difference (USD/L)	Std. Error	95% Confidence Interval (USD/L)
Medium vs. Low	0.057	0.054	−0.071 to 0.185
High vs. Low	−0.146	0.055	−0.277 to −0.016
High vs. Medium	−0.203	0.055	−0.334 to −0.073

) highlight this interesting inverted-U pattern. When examining motor gasoline, the comparison between high- and medium-inventory levels revealed a statistically significant mean difference of −0.153 (SE = 0.040, 95% CI: −0.248 to −0.058). This finding confirms that gasoline prices tend to drop quite noticeably once biomass inventory exceeds the medium level. On the other hand, the differences seen between medium and low, as well as between high and low inventory levels, did not reach the level of statistical significance according to conventional thresholds. In the case of automotive diesel, both the comparisons of high versus low (mean difference = −0.146, SE = 0.055, 95% CI: −0.277 to −0.016) and high versus medium (mean difference = −0.203, SE = 0.055, 95% CI: −0.334 to −0.073) were found to be statistically significant. This finding highlights the existence of a strong inverse relationship between inventory levels and diesel prices, particularly when examining the data beyond the medium quantile.

These findings suggest the existence of a nonlinear or threshold effect (see

Table 6. Nonlinear effect of biomass inventory on fuel prices (USD/L).

Dependent Variable	Variable	Coefficient	Std. Error	t-Value	p-Value	95% Confidence Interval
Motor Gasoline	Inventory	1.77 × 10−6	4.32 × 10−7	4.09	0.000	9.09 × 10−7 to 2.62 × 10−6
	Inventory2	−3.11 × 10−12	6.46 × 10−13	−4.81	0.000	−4.39 × 10−12 to −1.82 × 10−12
	Constant	0.577	0.061	9.50	0.000	0.457 to 0.698
	R-squared	0.227
	N	102
Automotive Diesel	Inventory	1.85 × 10−6	5.93 × 10−7	3.12	0.002	6.76 × 10−7 to 3.03 × 10−6
	Inventory2	−3.61 × 10−12	8.88 × 10−13	−4.07	0.000	−5.37 × 10−12 to −1.85 × 10−12
	Constant	0.722	0.083	8.65	0.000	0.557 to 0.888
	R-squared	0.224
	N	102

), where biomass inventory starts to influence fossil fuel prices only after reaching a certain level (see Figure 1 and Figure 2). This aligns with economic theories of adjustment costs and real options, which propose that firms may delay switching to alternative fuels like biofuels when inventories are uncertain or only moderately high [42,43]. Such hesitation may be driven by practical barriers, such as limited infrastructure, uncertain long-term supply, or the high cost of reconfiguring existing systems. Recent empirical studies reinforce this logic. For example, Liu and Huang [44] demonstrate that energy substitution patterns are time-dependent and affected by market maturity. Adnan et al. [45] highlight how supply chain logistics and economic viability influence the adoption of biomass-based fuels. Similarly, Júnior and Diniz [46] demonstrate that integration into international biomass markets is heavily dependent on policy support and regional readiness. However, once inventories become more stable and abundant and biofuel markets mature, these obstacles become smaller. Substituting biofuels becomes more practical and less risky, which helps reduce reliance on fossil fuels and contributes to more stable overall fuel prices. Other studies have shown that as biofuels become better integrated into the market, they can help lower gasoline prices and reduce price volatility in energy markets [47]. Another factor that can support the quantity, production, and inventory level of biomass in terms of cost-effectiveness may depend on factors such as feedstock quality, transportation, and integration with existing infrastructure. These factors can create localized cost advantages, further supporting price stability [48,49,50].

3.2. Biomass Index and Fuel Price

This section presents the empirical findings using the biomass index to examine how regional differences in forest biomass availability are related to motor gasoline and automotive diesel prices in the United States. You can find a summary of all the results in

Table 7. Mean and standard deviation of fuel prices (USD/L) by biomass index group.

Biomass Group	Motor Gasoline (Mean ± SD, USD/L)	Automotive Diesel (Mean ± SD, USD/L)
Low	0.727 ± 0.215	0.812 ± 0.278
Medium	0.815 ± 0.150	0.947 ± 0.181
High	0.734 ± 0.139	0.877 ± 0.234
Total	0.759 ± 0.174	0.879 ± 0.238

,

Table 8. ANOVA results: testing price differences across biomass groups.

Dependent Variable	F-Statistic	p-Value	R-Squared	Adj. R-Squared
Motor Gasoline	2.75	0.0691	0.0525	0.0334
Automotive Diesel	2.79	0.0665	0.0533	0.0342

,

Table 9. Pairwise comparisons of group means (Tukey HSD test) by motor gasoline prices (USD/L).

Comparison	Mean Difference (USD/L)	95% CI (USD/L)
Medium vs. Low	+0.087	[−0.012, 0.187]
High vs. Low	+0.007	[−0.092, 0.106]
High vs. Medium	−0.080	[−0.178, 0.017]

,

Table 10. Pairwise comparisons of group means (Tukey HSD test) by automotive diesel prices (USD/L).

Comparison	Mean Difference	95% CI (USD/L)	Significant? (p < 0.05)
Medium vs. Low	+0.135	[−0.001, 0.271]	Borderline (p~0.05)
High vs. Low	+0.065	[−0.070, 0.200]
High vs. Medium	−0.070	[−0.204, 0.064]

and

Table 11. Quadratic regression results for motor gasoline and automotive diesel (USD/L).

Dependent Variable	Coefficient	Estimate	Std. Error	t-Value	p-Value	Interpretation
Motor Gasoline	Intercept (cons)	0.7900	0.0190	41.54	<0.001	Baseline gasoline price
	Biomass index	−5.37 × 10−7	1.81 × 10−7	−2.97	0.004	Negative linear effect on price
	Biomass index2	−2.22 × 10−12	4.94 × 10−13	−4.49	<0.001	Negative quadratic effect
Automotive Diesel	Intercept (cons)	0.9338	0.0262	35.66	<0.001	Baseline diesel price
	Biomass index	−4.76 × 10−7	2.49 × 10−7	−1.92	0.058	Marginally significant negative effect
	Biomass index2	−2.62 × 10−12	6.80 × 10−13	−3.85	<0.001	Negative quadratic effect

. Our data were grouped into three categories based on their biomass levels: low, medium, and high. You can find a summary of fuel prices for each of these groups in Table 7. Interestingly, the average prices for motor gasoline and automotive diesel were the highest in the medium-biomass category, with gasoline averaging 0.815 ± 0.150 and diesel at 0.947 ± 0.181. On the other hand, the low-biomass group displayed the most significant variability in fuel prices, indicating that areas with less biomass availability tend to experience more unpredictable market conditions. A one-way ANOVA was conducted to explore the differences in prices among biomass index groups (see Table 8). The F-statistics for both fuel types showed only slight statistical significance in the group differences (motor gasoline: F(2, 101) = 2.75, p = 0.0691; diesel: F(2, 101) = 2.79, p = 0.0665). These findings indicate that, while there re some differences, they do not quite reach the conventional levels of statistical significance at the 5% threshold.

Follow-up pairwise comparisons using Tukey’s HSD test further explored these group-level differences. As shown in Table 9, for motor gasoline prices, the comparison between the medium- and low-biomass groups revealed a positive mean difference (+0.087), but the confidence interval (−0.012, 0.187) included zero, indicating that the result was not statistically significant. Similarly, differences among other pairs (high vs. low, high vs. medium) were not statistically significant. A similar pattern was observed for diesel prices (Table 10). The comparison between the medium- and low-biomass groups approached significance (mean difference = +0.135, 95% CI: [−0.001, 0.271]), with a p-value near 0.05. Although this result may not meet the usual benchmark for strong statistical significance, the narrow margin suggests a trend that warrants further investigation. The factors behind this pattern likely include both market substitution effects and incentives driven by policy. When biomass inventory levels are low to moderate, fuel suppliers might hesitate to change their sourcing strategies due to uncertainties, transition costs, or the insufficient scale of biofuel infrastructure. This aligns with the idea that financial and policy incentives are often essential to overcome initial hurdles to adopting biofuels and reconfiguring supply chains [51,52,53].

As biomass availability becomes more substantial and predictable, it can help to lessen input price volatility and promote the greater integration of biomass-derived fuels like renewable diesel, especially when bolstered by targeted incentives or carbon pricing policies [51,53,54]. This situation resonates with economic theories of fuel substitution, which suggest that certainty in supply and economies of scale enhance the technical and economic viability of alternatives, making market shifts more likely as availability increases [55,56]. Research also shows that combining subsidies and taxes can significantly boost the replacement of fossil fuels with bio-based options, and that stable, predictable policy environments are key to sustained market transformation [54,56]. Thus, even without strong statistical significance, the observed trend is theoretically supported and backed by evidence regarding the interaction of market forces and policy interventions in the adoption of biomass-derived fuels. Additionally, state-level low-carbon fuel standards and renewable fuel blending mandates may only become fully activated or economically effective at higher volumes of biomass. In this sense, policy-driven demand interacts with physical supply to generate observable effects on fuel pricing. Evidence from both U.S. and international contexts shows that policy incentives, such as tax subsidies, carbon pricing, and blending mandates, can significantly lower the price of biomass-derived fuels and increase supply, but these effects are most pronounced when supply is ample and infrastructure is in place to support large-scale integration [57,58,59]. Future work could explore these dynamics more deeply by incorporating disaggregated regional policy data.

However, it is important to note that none of the group comparisons provided conclusive evidence of significant mean differences in fuel prices across biomass inventory groups alone. To better capture the continuous variation in biomass availability, fuel prices were subsequently modeled as a function of the biomass index using quadratic regression (Table 11). These models produced more robust and statistically significant results. For motor gasoline, both the linear (p = 0.004) and quadratic (p < 0.001) terms were negative and statistically significant, indicating a concave relationship: as the biomass index increases, gasoline prices tend to decline, with a stronger decrease observed at higher levels of biomass availability (see Figure 3 and Figure 4). A similar nonlinear pattern was found for automotive diesel. The quadratic term was highly significant (p < 0.001), while the linear term approached significance (p = 0.058), indicating that diesel prices also decline as biomass availability increases, particularly once inventory levels exceed a certain threshold.

The regression results show that while the availability of biomass does not create significant price differences across the low, medium, and high groups, it does have a notable nonlinear effect on fuel prices when examining the broader sample. Typically, more biomass is associated with lower motor fuel prices. This could be due to various factors, such as local biofuel production, the competitiveness of the energy market, or cost benefits in areas rich in biomass. These findings suggest that forest biomass resources help mitigate fluctuations in regional fuel prices, making it crucial for strategies aimed at reducing energy costs and informing bioenergy policies. Studies show that while switching to biomass (e.g., wood pellets or residue boilers) can be economical, the price impact is more pronounced when a larger share of energy demand is met by biomass, rather than just moving between low, medium, and high supply categories [60]. However, the economic feasibility and minimum fuel price may be sensitive to biomass properties and supply levels. For example, higher-quality or more abundant biomass can lower the minimum fuel selling price, but the relationship may not be strictly linear, as certain thresholds or quality factors can amplify the price effect [48,61].

3.3. Biomass Production and Fuel Price

Table 12. Mean and standard deviation of fuel prices (USD/L) by biomass production quantiles.

Biomass Production Group	Motor Gasoline (Mean ± SD, USD/L)	Automotive Diesel (Mean ± SD, USD/L)
Low	0.706 ± 0.156	0.806 ± 0.200
Medium	0.818 ± 0.187	0.962 ± 0.241
High	0.751 ± 0.163	0.869 ± 0.249
Total	0.759 ± 0.174	0.879 ± 0.238

shows a distinct pattern: average gasoline prices were lowest in the low-production group (0.706 ± 0.156 USD/liter), peaked in the medium-production group (0.818 ± 0.187 USD/liter), and then declined modestly in the high-production group (0.751 ± 0.163 USD/liter). A similar trend was evident for automotive diesel, where prices rose from 0.806 ± 0.200 USD/liter in the low-production group to 0.962 ± 0.241 USD/liter in the medium-production group before falling to 0.869 ± 0.249 USD/liter in the high-production group. This suggests the existence of a potential nonlinear association between biomass production and fuel prices. ANOVA was conducted to assess these differences statistically. Results indicate that mean gasoline prices vary significantly across biomass production levels (F(2, 99) = 3.69, p = 0.028), while diesel price differences were marginally significant (F(2, 99) = 3.05, p = 0.052). Pairwise comparisons using Tukey’s HSD test (

Table 13. Tukey pairwise comparisons of fuel prices (USD/L) across biomass production levels.

Fuel Type	Comparison	Mean Difference (USD/L)	95% CI (USD/L)
Motor Gasoline	Medium vs. Low	0.112	[0.013, 0.210]
	High vs. Low	0.045	[−0.052, 0.143]
	High vs. Medium	−0.066	[−0.163, 0.031]
Automotive Diesel	Medium vs. Low	0.156	[0.021, 0.290]
	High vs. Low	0.063	[−0.071, 0.196]
	High vs. Medium	−0.093	[−0.226, 0.040]

) reveal that fuel prices in the medium-biomass production group were significantly higher than in the low-production group for both gasoline (mean difference = 0.112, 95% CI [0.013, 0.210]) and diesel (mean difference = 0.156, 95% CI [0.021, 0.290]). However, differences between the high and medium groups, as well as between the high and low groups, were not statistically significant. These findings suggest that fuel prices increase with biomass production to a point, then plateau or decrease, indicating the existence of a nonlinear relationship.

Results are presented in

Table 14. Quadratic regression models predicting fuel prices (USD/L) from biomass production.

Dependent Variable	Motor Gasoline (USD/L)	Automotive Diesel (USD/L)
Production	0.0000163 (p = 0.014)	0.0000220 (p = 0.016)
Production2	−5.31 × 10−11 (p = 0.017)	−7.19 × 10−11 (p = 0.019)
Constant	−0.465 (p = 0.341)	−0.766 (p = 0.254)
R2	0.060	0.058
N	102	102

for nonlinearity suggested by group-level comparisons. For both motor gasoline and automotive diesel, the coefficient of the linear term was positive and statistically significant (p < 0.05), while the squared term was negative and significant (p < 0.05). This pattern confirms a concave (inverted U-shaped) relationship, where fuel prices initially increase with biomass production but begin to decline at higher levels, suggesting diminishing returns (see Figure 5 and Figure 6). The quadratic regression explains approximately 6% of the variation in fuel prices (R² = 0.060 for gasoline, 0.058 for diesel), a modest but significant improvement given the complexity of regional fuel price determinants.

3.4. ANOVA Assumption Checks for Biomass Level

To validate the application of ANOVA across biomass-related groupings, we assessed the core assumptions of normality of residuals, the independence of observations, and the homogeneity of variances. Visual inspections using Q–Q plots and residual histograms (Appendix Figure A1, Figure A2 and Figure A3) showed that the residuals from the motor gasoline and automotive diesel models were approximately normally distributed, with no severe skewness or outliers. Given the moderate sample size (n = 102) and the robustness of ANOVA to minor deviations from normality, this assumption is deemed satisfied across biomass inventory, index, and production groupings. The independence of observations was ensured by the study design, which assigned time periods to mutually exclusive inventory, biomass index, and production quantile groups without overlap. Regarding the homogeneity of variances (see

Table 15. Fuel price summary and robust Levene’s test results by biomass inventory group.

Fuel Type	Inventory Group	Mean (USD/L)	Standard Deviation	N	Levene’s W0 (p)	Brown–Forsythe W50 (p)
Motor Gasoline	Low	0.749	0.140	35	0.97 (p = 0.383)	0.81 (p = 0.446)
	Medium	0.837	0.185	35
	High	0.684	0.163	32
	Total	0.759	0.174	102
Automotive Diesel	Low	0.906	0.221	35	0.65 (p = 0.526)	0.66 (p = 0.519)
	Medium	0.963	0.243	35
	High	0.759	0.208	32
	Total	0.879	0.238	102

,

Table 16. Fuel price summary and robust Levene’s test results by biomass index group.

Fuel Type	Biomass Index Group	Mean (USD/L)	Standard Deviation	N	Levene’s W0 (p)	Brown–Forsythe W50 (p)
Motor Gasoline	Low	0.727	0.215	33	4.32 (p = 0.016)	0.88 (p = 0.416)
	Medium	0.815	0.150	34
	High	0.734	0.139	35
	Total	0.759	0.174	102
Automotive Diesel	Low	0.812	0.278	33	2.92 (p = 0.058)	0.59 (p = 0.554)
	Medium	0.947	0.181	34
	High	0.877	0.234	35
	Total	0.879	0.238	102

and

Table 17. Fuel price summary and robust variance test results by biomass production level.

Fuel Type	Production Group	Mean (USD/L)	Standard Deviation	N	Levene’s W0 (p)	Brown–Forsythe W50 (p)
Motor Gasoline	Low	0.706	0.156	33	0.29 (p = 0.747)	0.13 (p = 0.874)
	Medium	0.818	0.187	34
	High	0.751	0.163	35
	Total	0.759	0.174	102
Automotive Diesel	Low	0.806	0.200	33	0.98 (p = 0.377)	0.38 (p = 0.685)
	Medium	0.962	0.241	34
	High	0.869	0.249	35
	Total	0.879	0.238	102

), Levene’s robust test (via the robvar command) was employed. For the inventory and production groups, the test results were non-significant (e.g., W₀ = 0.97, p = 0.38 for gasoline; W₀ = 0.65, p = 0.53 for diesel; W₅₀ = 0.87, p = 0.874 for production-level gasoline), supporting the assumption of equal variances across groups. However, for biomass index groupings, Levene’s W₀ test was significant for gasoline (p = 0.016) and marginal for diesel (p = 0.058), suggesting potential heteroscedasticity. In response, we applied the Brown–Forsythe version of Welch’s ANOVA (W₅₀), which adjusts for unequal variances. The W₅₀ results revealed no statistically significant group mean differences for either fuel type (gasoline: p = 0.416; diesel: p = 0.554), thus preserving the validity of the ANOVA results under relaxed assumptions. Therefore, the assumption checks confirm that ANOVA results are reliable and robust across all biomass-related grouping structures.

We also implemented the Newey–West estimator, which corrects for both autocorrelation and heteroskedasticity in time-series data. Using Newey–West standard errors with a lag of one, we examined nonlinear relationships between key biomass variables and fuel prices (see

Table A3. Newey–West Regression Estimates for the Effect of Inventory on Fuel Prices.

Variable	Motor Gasoline		Automotive Diesel
	Coef.	Std. Err.	Coef.	Std. Err.
Inventory	1.77 × 10−6	5.26 × 10−7	1.85 × 10−6	7.36 × 10−7
Inventory2	−3.11 × 10−12	7.74 × 10−13	−3.61 × 10−12	1.04 × 10−12
Constant	0.577	0.065	0.722	0.103
F-statistic	11.26		6.33
Prob > F	0.0011		0.0134

,

Table A4. Newey–West Regression Estimates for the Effect of Biomass Index on Fuel Prices.

Variable	Motor Gasoline		Automotive Diesel
	Coef.	Std. Err.	Coef.	Std. Err.
Biomass Index	−5.37 × 10−7	2.14 × 10−7	−4.76 × 10−7	3.29 × 10−7
Biomass Index2	−2.22 × 10−12	5.15 × 10−13	−2.62 × 10−12	7.33 × 10−13
Constant	0.790	0.025	0.934	0.037
F-statistic	6.30		2.10
Prob > F	0.0137		0.1506

and

Table A5. Newey–West Regression Estimates for the Effect of Biomass Production on Fuel Prices.

Variable	Motor Gasoline		Automotive Diesel
	Coef.	Std. Err.	Coef.	Std. Err.
Production	0.0000163	5.26 × 10−6	0.000022	7.23 × 10−6
Production2	−5.31 × 10−11	1.77 × 10−11	−7.19 × 10−11	2.46 × 10−11
Constant	−0.465	0.380	−0.766	0.513
F-statistic	9.65		9.29
Prob > F	0.0025		0.0030

). The robustness checks using inventory and biomass index variables confirmed the existence of similar concave relationships. For gasoline, the inventory term was positive and significant (1.77 × 10⁻⁶; p = 0.001), with a negative squared term (−3.11 × 10⁻¹²; p < 0.001). For diesel, both terms were also statistically significant (p < 0.05). The biomass index, on the other hand, showed a negative and significant effect for gasoline (−5.37 × 10⁻⁷; p = 0.014) and a strongly negative squared term (−2.22 × 10⁻¹²; p < 0.001). Although the linear effect for diesel was not significant (p = 0.151), the squared term remained robust (−2.62 × 10⁻¹²; p = 0.001). These results provide consistent support for diminishing returns in the fuel price response to biomass-based indicators, even after correcting for time-dependent structure, thus enhancing the validity and policy relevance of the findings. Additionally, biomass production had a statistically significant concave effect on both gasoline and diesel prices. Specifically, for gasoline, the coefficient on production was 0.0000163 (p < 0.01), and the squared term was negative and significant (−5.31 × 10⁻¹¹; p < 0.01). Similarly, for diesel, the linear term was 0.000022 (p < 0.01) and the squared term −7.19 × 10⁻¹¹ (p < 0.01), indicating diminishing marginal effects.

4. Policy Implications and Implementation

The analysis reveals the existence of a clear inverse relationship between national forest biomass availability and average retail fuel prices: when biomass inventories are higher, gasoline, and diesel prices tend to be lower. While the data does not allow us to draw regional-level conclusions, the national trend suggests that forest biomass, particularly when converted into renewable diesel or other advanced biofuels, can play a significant role in stabilizing fossil fuel markets and supporting the transition toward cleaner energy systems. The observed pattern of forest biomass availability and its relationship to fuel price stability shows the long-term potential of using forest-derived resources to support cleaner energy transitions. This is important in diesel markets where renewable alternatives are becoming more prioritized. In the United States, most renewable diesel is currently sourced from lipid-based feedstocks, like waste cooking oil, animal fats, and vegetable oils, through hydrotreating technologies within existing petroleum refinery infrastructure [62]. However, the landscape is quickly changing as new technological pathways emerge.

Advancements in thermochemical conversion technologies, such as fast pyrolysis, hydrothermal liquefaction (HTL), and gasification followed by Fischer–Tropsch synthesis, are creating promising opportunities for producing drop-in fuels from lignocellulosic feedstocks, including forest residues and woody biomass [63,64]. These methods have several benefits: they offer flexibility in feedstock selection, allow for the integrated conversion of whole biomass, and can be adapted to existing refinery processes, which reduces operational costs and enhances scalability [65,66,67,68]. While these technologies are still primarily in pre-commercial or pilot phases, they are progressing rapidly and are considered vital for achieving long-term decarbonization goals, particularly in sectors like heavy transport and aviation, where energy-dense liquid fuels remain essential [68,69].

One key challenge in utilizing lignocellulosic biomass for fuel production is the properties of the resulting bio-oil, which can often exhibit chemical instability, low heating value, and high viscosity. However, recent advancements in upgrading methods, such as catalytic cracking, hydrogenation, and plasma-assisted catalysis, have significantly improved the quality of bio-oil, making it much more suitable as a drop-in fuel [70]. Moreover, the catalytic conversion of lignin fractions, inspired by crude oil refining techniques, enables the production of high-value fuel components and chemicals, enhancing the economic viability of these processes [65,67].

It is essential to integrate biomass inventory indicators into national energy modeling, fuel price forecasting tools, and clean fuel planning strategies as we navigate this evolving technological field. This integration can enhance how energy policy responds to supply dynamics, help reduce market volatility, and ensure a smoother transition toward low-carbon fuel systems as forest biomass technologies approach commercial viability [69,71]. As these thermochemical conversion pathways continue to mature and scale up, they are poised to play a crucial role in stabilizing fuel prices and supporting the broader shift toward sustainable, low-carbon energy systems.

4.1. Practical Policy Implications

Building on the empirical findings, the following policy implications outline specific strategies for integrating forest biomass into the U.S. fuel market while balancing climate, economic, and ecological priorities.

Forest biomass integration into low-carbon fuel programs can be more effective when tailored to the region. In wildfire-prone western states like California and Oregon, policies should prioritize residues from forest thinning and hazard-fuel treatments, coupled with incentives for forest regrowth to ensure long-term GHG benefits [72,73,74]. In contrast, eastern states such as Georgia and North Carolina, with higher forest productivity, may focus on small-diameter hardwoods and sawmill residues. Federal guidance should harmonize biomass definitions, simplify credit systems, and promote coordination across agencies to support consistent implementation [73,75]. Carbon accounting frameworks must reflect regional harvest cycles and carbon dynamics, using science-based thresholds such as minimum payback periods, and align with IPCC reporting standards [76,77,78]. Such targeted policies will enhance environmental integrity, attract investment, and balance climate goals with forest sustainability.

To enhance practicality and reflect regional biomass heterogeneity, policies should prioritize investment in forest-rich rural regions like Oregon’s Douglas County or Georgia’s Appling County. These areas can serve as feedstock hubs due to their abundant residues and proximity to active forestry operations. Targeted infrastructure upgrades, such as rural road improvements and rail access, can reduce biomass transport costs. Advanced logistics tools (e.g., GIS mapping, AI-driven routing) should be integrated to ensure supply chain efficiency and lower emissions. Policies must also include sustainability safeguards, requiring that biomass sourcing prioritize wood waste and low-value timber. This approach supports both fuel price stability and wildfire risk reduction, especially in fire-prone western states.

Policymakers should treat biomass availability as a dynamic variable, not a fixed quota. For example, in Southeastern states like Alabama, where logging residues are seasonally abundant, flexible subsidy thresholds tied to real-time biomass inventory data can better match supply conditions. In contrast, Western states with wildfire-prone forests may require separate incentive models focused on hazard-fuel removal. Implementing regionally adaptive pricing floors or supply triggered tax credits can help stabilize fuel markets where biomass volumes surge. To ensure reliability, this strategy must be supported by ongoing inventory monitoring and localized supply chain assessments.

Aligning biomass policy with broader climate and energy goals requires the tailoring of strategies to regional contexts. In the wildfire-prone Western U.S., policies should prioritize biomass removal through fuel treatment programs that simultaneously reduce fire risk and supply bioenergy feedstock. In contrast, the Southeast, with its commercial pine plantations, can focus on converting logging residues into renewable diesel under state-level low-carbon fuel standards. Incorporating biomass into regional clean energy plans can reduce reliance on volatile fossil fuel markets while advancing decarbonization. To avoid unintended environmental trade-offs, each region should adopt localized land-use guidelines and community benefit frameworks that ensure equitable economic returns and ecological integrity.

Implementing state-level biomass inventory tracking systems tailored to regional characteristics can significantly enhance the execution of renewable fuel policies. In the Pacific Northwest, the real-time monitoring of forest residues from thinning operations can help optimize supply chains for renewable diesel facilities. Southeastern states, with abundant mill waste and logging byproducts, can integrate GIS-based inventory systems into state energy dashboards to inform subsidy allocation and site selection for biorefineries. Such systems should incorporate local transportation constraints, weather patterns, and seasonal harvesting cycles to optimize efficiency. These spatially explicit data tools enable policymakers to forecast supply fluctuations, coordinate investments, and deploy region-specific infrastructure, making the biomass-to-fuel transition more reliable and cost-effective.

4.2. Implementation Strategies

To effectively incorporate forest biomass into clean fuel initiatives, a few friendly and collaborative strategies can make a big difference. First, states with rich forest resources such as Oregon, Washington, and Maine are encouraged to expand their clean fuel programs to specifically include forest biomass as a valuable feedstock, along with offering targeted production credits and climate impact scores tailored for fuels derived from forests [73]. Next, it is crucial for federal agencies, such as the USDA, DOE, and EPA, to collaborate more closely. By coordinating efforts, they can support the process, from collecting and preparing forest residues to expanding biorefineries and integrating markets. At the same time, grants or loan guarantees from various agencies can help tackle financial and logistical hurdles [4,73]. Third, establishing strong partnerships between the public and private sectors can connect landowners, biomass processors, and fuel producers, thereby fostering innovative and scalable renewable diesel solutions and bolstering reliable supply chains [4,73]. Additionally, involving rural and tribal communities is vital to ensure everyone benefits fairly, especially since these communities manage large forest areas and can contribute to sustainable biomass practices [73,79]. To ensure smooth operation, it is beneficial to have clear definitions, supportive regulations, investments in infrastructure, and a comprehensive approach to addressing social and environmental impacts. Thus, both climate and economic benefits can be maximized for all stakeholders involved.

5. Conclusions

Forest biomass is more than just a byproduct of sustainable forest management; it is seen as a valued energy resource that can help stabilize fuel prices, strengthen energy resilience, and support our climate goals. This study offers new insights into how the availability of forest biomass influences the stability of gasoline and diesel prices in the United States, which is crucial for energy policy and low-carbon fuel strategies. From the connection between biomass inventory levels, production levels and retail fuel prices, the findings show that forest biomass, mainly when used as renewable diesel feedstock, can play a key role in stabilizing fuel markets, particularly in rural areas rich in forests. While the impacts of prices can vary depending on the type of fuel and regional circumstances, there is a wealth of evidence supporting the smart integration of forest biomass into clean energy initiatives. This research emphasizes the importance and often-overlooked role of biomass, highlighting the need for policies that nurture supply chain growth, invest in essential infrastructure, and integrate biomass inventory metrics into our long-term fuel strategies. Therefore, these findings suggest that policy interventions such as subsidies or infrastructure support may be most effective when targeted at states or regions with moderate to high biomass availability, where biofuel integration yields the most significant fuel price impact before diminishing returns set in. As the U.S. strives to achieve ambitious carbon reduction targets while maintaining affordable energy, forest biomass emerges as a practical and environmentally friendly option that can enhance both market resilience and climate outcomes.

There are a few important limitations to consider. First, the analysis is based on national-level data, which might hide some interesting regional differences in how biomass is used and priced, or how the fuel market behaves. Second, while the cross-sectional design provides a snapshot of current trends, it cannot fully capture temporal dynamics or causality. For future studies, it would be beneficial to examine panel datasets, incorporate data from specific firms or regions, and investigate how biomass availability interacts with policies such as low-carbon fuel standards or tax incentives. This type of research could help better understand how biomass influences fuel markets and inform more informed regional policies. As the U.S. moves toward decarbonization and aims to keep energy affordable, forest biomass stands out as a promising yet often underutilized resource to support both energy needs and climate goals.