A Comparative Case Study: Cradle-to-Grave LCA for Asphalt Mixtures Containing RAP and WMA

Highlights

What are the main findings?

• WMA lowered Phase A (production + construction) GHG emissions by 5% in the evaluated field sections.
• Increasing RAP reduced Phase A GHG emissions by 0.9% per 1% RAP increase (15% → 30% RAP case).

What are the implications of the main findings?

• At high speeds and traffic volumes, use-phase emissions can dominate and offset material/construction savings.
• Combining WMA with high RAP achieved the lowest overall cradle-to-grave GHG.

Abstract

The U.S. transportation section contributed a third of the national Greenhouse Gas (GHG) emissions in 2022. As such, the Louisiana Department of Transportation and Development (DOTD) initiated federally funded efforts to create Life Cycle Assessment (LCA) models for pavement systems. The objective of this study was to quantify the holistic, cradle-to-grave environmental impacts of asphalt pavements containing Reclaimed Asphalt Pavement (RAP) and Warm Mix Asphalt (WMA) technologies using a closed-loop recycling assumption based on 100% RAP recovery at the end-of-life stage, consistent with current practice in Louisiana. Five field sections in service for up to 16 years were collected from DOTD’s LaPave database. The LCA framework followed ISO 14040 and included definition of cradle-to-grave system boundaries, a functional unit based on in-service pavement sections, inventory data derived from public databases and field performance records, and use-phase modeling based on pavement–vehicle interaction. Public datasets were used to quantify GHG emissions across all life cycle phases. Results indicated WMA additives reduced production and construction GHG emissions by 5%. An RAP increase by 1% decreased material/construction GHG emissions by approximately 0.9%; however, it potentially increased use-phase emissions due to roughness. Mixtures combining WMA and RAP emitted the lowest GHG among the studied mixtures, which promotes integrating sustainable pavement strategies.

1. Introduction

The transportation sector significantly contributes to U.S. greenhouse gas (GHG) emissions, which necessitates the need for more sustainable pavement innovations. While most prior studies have been limited to cradle-to-gate environmental Life Cycle Assessment (LCA), typically including only material production, transportation, and construction stages (A1, A2, and A3, respectively), this study conducted a comprehensive cradle-to-grave LCA with a closed-loop recycling approach for asphalt pavements, explicitly incorporating the use phase (B) and end-of-life phase (C). Unlike many previous studies that rely on assumed performance or design-life scenarios, this study integrates long-term field performance data from in-service pavements, enabling direct quantification of use-phase impacts associated with surface roughness and durability. Environmental impacts due to incorporating asphalt mixtures with RAP and WMA technologies were assessed.

In addition to quantifying environmental impacts across all life cycle phases, this research provided practical recommendations for GHG reduction based on sensitivity analyses that reflect observed long-term field performance. Specifically, optimizing the combination of WMA and high RAP content yields the lowest overall GHG emissions. In addition, improving transportation efficiency and asphalt plant operations was recommended. This study provides a vital roadmap for state Departments of Transportation (DOTs) to implement more sustainable pavement practices, directly supporting ambitious national GHG reduction targets in the transportation sector. The following section reviews prior LCA studies on RAP and WMA, highlighting their system boundary limitations and motivating the need for the present cradle-to-grave, field-based analysis.

The U.S. transportation system heavily relies on asphalt pavements. They cover over 94% of the nation’s 2.8 million miles of roads and represent an annual investment exceeding $150 billion [1]. This network supports the travel of over 85% of all passenger miles and carries a significant portion of the nation’s freight. Despite these benefits, the significant environmental footprint of asphalt pavements challenges their sustainability, often assessed using the triple bottom line concept (economic, social, environmental). In 2022, the transportation sector produced approximately 1810 million metric tons of Carbon dioxide equivalent emissions (CO₂eq), which represented 33% of greenhouse gases (GHG) in the U.S. [2], with an annual average growth rate of 1.7% since 1990 [3]. Increased atmospheric Greenhouse Gas (GHG) concentrations lead to greater heat retention, resulting in a progressive rise in Earth’s mean temperature, a phenomenon termed global warming (GW). In response to this urgent challenge, the National Asphalt Pavement Association (NAPA) aims to achieve a 50% reduction in its sector’s GHG emissions by the year 2030 [4]. This translates to a required average annual reduction rate of 3% in GHG emissions from the transportation sector until 2030 [3]. Carbon dioxide (CO₂eq) often represents GHG emissions since it contributes to 79.4% of GHG emissions [5].

Developing sustainable and eco-friendly technologies for asphalt pavement has become a growing area of interest for pavement agencies and researchers. For instance, the use of RAP and WMA in asphalt mixtures has demonstrated numerous sustainable social and economic benefits, often reflected in improved engineering performance. However, most existing studies have conducted only partial environmental LCAs for asphalt mixtures containing RAP and WMA, typically focusing on material production and construction stages (Phase A), while frequently omitting the use and maintenance phase (Phase B) and the end-of-life phase (Phase C). Consequently, very few studies have conducted or predicted complete cradle-to-grave LCA analyses. A complete LCA, often referred to as cradle-to-grave LCA, encompasses three phases: A (material and construction), B (use and maintenance), and C (end-of-life). Each phase contains subphases numbered sequentially (e.g., A1, A2, …, B1, B2, …, C1, C2, …).

Table 1. Literature summary for GHG reduction using RAP/WMA in asphalt.

Location	RAP, %	WMA Type	* GHG Reduced, %			Notes
			A1	A2	A3
Louisiana	30	-	38	29	0	A2: Materials are imported; however, RAP transportation distance was zero; A3: Assumed similar fuel consumption in plant [6].
	50	-	51	49	0
Pennsylvania	15	-	15	1	2	[7]
	30	-	26	2	4
	50	-	38	4	7
Washington	-	Maxam Foam	-	-	25	A3: Uninsulated parallel flow Drum [8].
Michigan	-	Evotherm 3G	-	-	19
Montana	-	Evotherm DAT	-	-	16	A3: Partially insulated parallel flow Drum [8].
Indiana	-	Gencor Foam	-	-	11	A3: Insulated counterflow Drum [8].
	-	Evotherm 3G	-	-	12
Ontario	-	Evotherm	-	-	17	[9]
Ohio	-	Evotherm, 5.3	-	-	20	[10]
Similar other studies						[11,12,13]

* Note: A1: Raw material extraction and production; A2: Row material transportation to asphalt plant facility; A3: Asphalt mixture production; DAT: Dispersed Asphalt Technology; (-): not included in the study.

presents a summary of the literature regarding GHG reductions achieved when RAP and/or WMA were incorporated into asphalt mixtures.

To summarize the table findings, using RAP in asphalt pavement usually reduces GHG emissions in subphase A1 due to the partial replacement of virgin materials. In contrast, WMA reduces GHG emissions in subphase A3 by reducing asphalt mixing temperature which consumes less energy and emits less GHG emissions. Considering average GHG reductions across literature findings, 30% RAP can reduce up to 30% of GHG emissions during A1, while WMA-Evotherm can reduce 15% of GHG emissions during A3. The partial replacement of asphalt binder and aggregates with RAP reduces GHG emissions during subphase A2 because contractors frequently utilize locally sourced RAP materials, whereas aggregates and binders are sometimes imported from greater distances or other states. When RAP and WMA are applied together, reported cradle-to-gate GHG reductions of up to approximately 45% reflect the combined but non-additive benefits across different life cycle subphases (A1–A3), rather than a direct summation of individual percentages. In addition, combining the use of RAP and WMA is well established in current asphalt practice and does not inherently require additional processing steps beyond conventional mixture design adjustments. Most reported studies have investigated cradle-to-gate LCA, and limited research has continued to investigate beyond A3.

2. Research Methodology

This study analyzed a complete cradle-to-grave LCA with a closed-loop recycled approach for asphalt pavement. To do that, the study research methodology contained four integrated steps as outlined by ISO 14040 [14]: defining goal and scope, collecting data inventory, conducting environmental impact assessment, and interpreting/harmonizing all steps together (Figure 1).

2.1. Step 1: Goal and Scope

The objectives of this study were to:

• Conduct a cradle-to-grave LCA for asphalt pavement field projects in Louisiana that incorporate RAP and/or WMA;
• Compare the GHG emissions of asphalt mixtures containing RAP/WMA to those of conventional mixtures without RAP or WMA;
• Recommend various scenarios and alternatives for reducing GHG emissions by performing a sensitivity analysis within the LCA.

To achieve these objectives, two asphalt pavement field projects (on routes LA 3121 and US 90), encompassing five sections of RAP and/or WMA asphalt mixtures, were included in this study (Figure 2). All projects consisted of 2-inch-thick wearing layers and were located in Louisiana. LCA Pave software was utilized to model the LCA based on impact assessment methodology [15]. This analysis utilized the “Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts (TRACI v.2.1)” as its framework for characterization factors [16]. To scale the LCA inputs and outputs, physical quantities and geometrical units were benchmarked as declared and functional units, respectively. The declared unit (physical quantity) scaled LCA subphases A1, A2, and A3 of a produced asphalt mixture and was valued as a short ton (907.18 Kg). The functional unit (geometrical unit) scaled LCA subphases A4 and A5, as well as phases B and C, and was defined as one lane-mile over the actual pavement service life.

Figure 3 presents the study system boundary used to investigate all LCA phases in pavements. A 1% cut-off criterion was applied, meaning any subphase emitting less than 1% of the total greenhouse gas (GHG) emissions (or CO₂eq) for phases A, B, or C was excluded from the LCA impact analysis.

2.2. Step 2: Data Inventory

To quantify potential environmental impacts, the life cycle inventory relied on publicly available background datasets recommended by “the American Center for Life Cycle Assessment (ACLCA) Open Standard” and TRACI. Complementing this, site-specific data were acquired from material suppliers, project contractors, and the pavement management system (PMS) maintained by the Louisiana Department of Transportation and Development (DOTD). The LCA data inventory for any phase and subphase was collected considering the validity of both data quality and quantity.

• Phase A: Production and Construction Phase
• Phase A includes 5 subphases:
• A1: raw material manufacture;
• A2: transport of raw material to the asphalt plant;
• A3: asphalt mixture production;
• A4: transport of asphalt mixture to the job site;
• A5: placing and constructing asphalt mixture and mobilizing construction equipment.

Table 2. Phase A: Data source and quality.

Subphase	Materials/Equipment	Unit	Kg CO2eq/Unit	LCI Data Source–Year Published	Other Properties	Meta-Analysis
A1	Coarse aggregates	St	2.06	Martin Marietta—2017	EPD 4531 [17]	77.1%
	Fine aggregates	St	4.2	Martin Marietta—2017	EPD 952 [17]	77.1%
	Asphalt binder PG 76-22	St	694	Asphalt Institute LCA of asphalt binder—2019	3.5% SBS Polymer [18]	79.7%
	Asphalt binder PG 70-22 *	St	628		1.5% SBS Polymer [18]	79.7%
	RAP	St	1.26	Illinois Tollway LCI—2016	[19]	74.4%
	WMA	Kg	5.0	Estimated roughly in Table 4	N/A	N/A
A2	Barge (diesel)	St-mile	0.037	Iowa State University [20]	N/A	N/A
	Truck (diesel)	St-mile	0.2264	USLCI data by Federal LCA—2000	[21]	53.2%
A3	Asphalt plant	St	34.86	Prairie Contractors, LA—2023	[22]	N/A
A4	Hauling Truck (diesel)	St-mile	0.2264	USLCI data by Federal LCA commons in OpenLCA-2000	[21]	53.2%
A5	Material Transfer vehicle 175 hp	hr	29.64	MOVES2014b—2018	[23]	72.1%
	Paver 175 hp	hr	41.83
	Roller 25 hp	hr	6.78
	Mobilizing MTV	Gallon/mile	2	Construction Company—2023	N/A	N/A
	Mobilizing Paver	Gallon/mile	2	Construction Company—2023	N/A	N/A
	Mobilizing 3 rollers	Gallon/mile	3	Construction Company—2023	N/A	N/A

Note: LCI: Life Cycle Inventory; EPD: Environmental Product Declarations; PG: Performance Grade; SBS: styrene-butadiene-styrene; LCA: Life Cycle Assessment; CO₂eq: Carbon-Dioxide equivalent; RAP materials are available at the contractor site. St: Short ton = 907.18 Kg; * PG 70-22 data was interpreted according to the blended SBS percentage (1.5%) between unmodified asphalt binder PG 67-22, which has zero SBS, and PG 76-22 asphalt binder which contained 3.5%SBS by weight to asphalt binder; hr: hour; Kg: Kilograms; N/A: not available.

presents the data sources and quality for the components of the asphalt mixtures, while

Table 3. Data quantities for field projects (materials and transportation details for phase A).

Route	LA 3121			US 90
Section code	L7015	L7015W	L7030W	U7615	U7615W
Length of the section (miles) social	1.9	1.5	0.7	0.3	0.3
Gmm	2.467	2.460	2.460	2.501	2.501
Field density, %	93.3	93.2	93.9	94.0	94.0
Asphalt mix, Short-ton *	760	758	764	780	780
Virgin asphalt binder **	PG 70-22			PG 76-22
Virgin AC, %	4.1	4.1	3.3	3.2	3.2
AC—Transport distance, mile	82.7			188
SBS—Transport distance, mile	365			366
Coarse agg, %	38	38	31.3	45	45
Fine agg, %	42.9	42.9	35.4	36.8	36.8
Agg—barge distance, mile	658			700
Agg—truck distance, mile	81.1			51
RAP, %	15	15	30	15	15
WMA, % weight to asphalt binder	NA	WMA, 0.5	WMA, 0.5	NA	WMA, 0.5
WMA Transport Distance, mile	NA	173	173	NA	49
Mix temperature, °C	163	127	132	163	140
Distance to job site, miles	48.8			12.4
Roller passes number	26	17	17	26	17
Fumes, average CO2eq mg/m3	942	859	859	942	859
Mixture time to cool, minutes	54	34	38	54	42

Notes: Gmm: maximum specific density; PG: performance grade for asphalt pavement; **: PG 70-22 and PG 76-22 asphalt binders were modified by Styrene-Butadiene-Styrene, 1.5% and 3% by weight of unmodified PG 67-22 asphalt binder, respectively; AC: Asphalt binder content; Agg: Aggregates; NA: no additive; na: not applicable; *: the total quantity of loose asphalt mixture was calculated the field mixture density and volume. Field density was calculated by multiplying Gmm by the percent of field density. Asphalt mixture volume to pave one lane-mile with 2-inch thickness = 2-inch thickness (0.0508 m) × 12-feet width (3.6576 m) × 1-mile length (1609.34 m) = 300 m³. Therefore, the loose mass quantity of the asphalt mixture varied from one section to another depending on the associated field core density. Mass (short-ton) equals field core density (%) × maximum specific gravity (Gmm) × volume (m³) × conversion factor 1.10231 (metric-ton to short-ton).

presents data quantities. The LCA Pave software reports meta-analysis results using a qualitative scoring system ranging from 1 (highest quality) to 5 (lowest quality), accounting for criteria including reliability, TRACI compatibility, representativeness, data age, manufacturing technology, review status, and data completeness. This meta-analysis was averaged and weighted on a scale of 100%.

2.2.1. WMA Production Emissions

A sensitivity check indicated that reasonable variation in WMA production CO₂eq values has a minor influence on total cradle-to-grave GHG results compared to reductions driven by lower mixing temperatures and RAP content, and therefore does not alter the study’s main conclusions.

Table 4. CO₂eq emissions for producing 1 Kg of WMA.

WMA	Item	Item Quantity or %	Unit	CO2eq/Unit	CO2eq	Total CO2eq/Kg
Chemical WMA	Synthetic oil	0.75	Kg	0.1	0.075	5.0
	Wax	0.15	Kg	9.3	1.3971
	Zeolites	0.1	Kg	1.2	0.12
	Electricity for heating (85–115 °C) [24,25]	8.44	KWh	0.4	3.376

Notes: WMA: Warm mix asphalt; Kg: kilogram; KWh: kilowatt-hour; CO₂eq.: Carbon Dioxide.

presents the components of the Chemical WMA additive used in this study, the electricity consumed during manufacturing [24], average percentages, and associated GHG emissions. In addition, a sensitivity check indicated that reasonable variation in WMA production CO₂eq values has a minor influence on total cradle-to-grave GHG results compared to reductions driven by lower mixing temperatures and RAP content, and therefore does not alter the study’s main conclusions.

Table 4 shows the approximate CO₂eq emissions for producing a kg of the chemical WMA additive. While the exact WMA production process is confidential, an approximate estimation of CO₂eq emissions was conducted based on a general production procedure and publicly available data on its key components [26]. This component-based life cycle inventory approach is consistent with established LCA practice when proprietary formulations are unavailable and has been supported by third-party verified EPDs for commercial WMA additives [27]. This approach ensures the necessary data transparency for a state DOT-level study. The WMA consists of a base oil (petroleum and/or synthetic oils) that is heated and blended with other additives, including a wax additive, which lowers asphalt viscosity, and zeolites, which trap and release heat more efficiently to allow for reduced mixing and paving temperatures [28]. The emissions from the production of the WMA additive were derived from a weighted sum of these publicly available data points, with the overall footprint corroborated by public documents, such as the EPD published by Ingevity (2020) [26], which provides a third-party verified environmental footprint without revealing confidential formulations. A sensitivity check indicated that reasonable variation in WMA production CO₂eq values has a minor influence on total cradle-to-grave GHG results compared to reductions driven by lower mixing temperatures and RAP content, and therefore does not alter the study’s main conclusions.

2.2.2. Transportation (Subphases A2 and A4)

Transporting materials (A2) and asphalt mixture (A4) were assumed to involve unloaded returns with reduced fuel consumption compared to loaded trips. Therefore, when assessing environmental impacts, the transportation distances in Table 3 were increased by 80% for trucks and 50% for trains. The barge return factor is sensitive to location. Although a default value of 40% is often assumed, a higher factor of 46% has been documented for operations along the Lower Mississippi River and is therefore considered more appropriate for this study. These factors were implemented as distance multipliers to approximate additional fuel use and emissions for empty backhauls, consistent with the cited fuel-efficiency ratios for loaded vs. unloaded operations,

Table 5. Average return haul factor for fuel consumption across transportation modes in southern states.

Mode		Reference	Summery	Percent Reduction (Unloaded/Loaded)	Return Haul Factor-Considered
Truck		[29]	6.2 MPG loaded 7.3 MPG unloaded	85%	80%
		[30]	5.5 MPG loaded 7.5 MPG unloaded	73%
Barge	General, USA	[31,32]	675 TMPG—loaded	40%	46%
		[33]	270 TMPG—unloaded
	Lower Mississippi River	[29]	1290 TMPG—loaded southbound 185 TMPG (31.5%)—unloaded northbound	185 × (100/31.5)/1290 = 46%
Train		[34]	528 TMPG loaded 260 TMPG unloaded	49%	50%
		[35]	500 TMPG loaded 250 TMPG unloaded	50%
		[36]	470 TMPG loaded 235 TMPG unloaded	50%

.

2.2.3. Asphalt Plant (Subphase A3)

NAPA reported that burner fuel typically accounts for 80% of the total energy used by asphalt plants. However, burner efficiency decreases significantly with time, such that, after 7 years of service, a 50% drop in efficiency of burning fuel would generally occur, meaning that 50% of fuel would be wasted [37]. As a result, the GHG emissions of asphalt plants could vary significantly from one plant to another at the same company. In this study, the published Environmental Product Declarations (EPD) report by the production company—Prairie Contractors, LLC [Opelousas, a stationary asphalt plant at 1334 Huntington Street, Opelousas, LA 70570] was used for conventional asphalt mixture without WMA [22]. It is worth mentioning that the asphalt plant type is stationary, counter-flow drum mix plant. However, by reducing the asphalt mixing temperature using WMA, the burner consumes less fuel. Lowering the asphalt mixing temperature by 100 °F results in a 55% decrease in burner fuel consumption and a 45% cut in CO₂eq emissions [28]. This proportional relationship between temperature and emissions holds for both hot mix asphalt and WMA production because both mixes were produced from the same plant under the same operational conditions. Accordingly, dropping the asphalt mixing temperature by 1 °C reduces CO₂eq emissions during the A3 subphase by 1.2%. This study applies a linear scaling assumption between mixing temperature reduction and burner fuel/CO₂eq for the same plant and fuel type, based on the FHWA guidance [26]. The 1.2% per °C factor is derived by distributing the reported CO₂eq reduction over the corresponding temperature change (100 °F = 55.6 °C). This consistent proportionality across both mix types ensures a valid and direct comparison of the environmental benefits from temperature reduction alone.

2.2.4. Asphalt Mixture Fumes

During asphalt mixture production and construction (subphases A3, A4, and A5), fumes containing CO₂eq gases are continuously released from the hot asphalt mixture, and CO₂ concentrations were obtained from in situ field measurements conducted during construction using a portable gas analyzer. The duration of fume evaporation was estimated for each subphase: per minute of mixing for A3, per minute of transportation for A4, and per minute of placing and construction for A5, continuing until the asphalt mixture cooled to 140 °F. The duration for the asphalt mixture to cool was calculated using the asphalt pavement cooling tool (PaveCool v 3.1), based on material type and mixing temperature [38]. Measured CO₂ concentrations (mg/m³) were averaged for each pavement section and combined with the estimated emission duration to quantify fume-related emissions. However, fumes emitted were neglected when the asphalt mixture temperature cooled down below 140 °F according to data interpolation analysis [39]. In this research, only the analysis was carried out on data collected in a previous study [39].

The concentrations of CO₂eq were measured using a portable Fluke 975V AirMeter™ (Fluke Corporation, Everett, WA, USA) with Velocity Probe (Fluke Corporation, Everett, WA, USA) [40]. The CO₂eq concentration was measured in parts per million per cubic meter or milligram CO₂eq per cubic meter (mg/m³). CO₂eq concentration readings were recorded during subphases A4 and A5 and averaged for each subsection, as shown in Table 3.

The weight of evaporated CO₂eq was quantified by multiplying its concentration (weight per volume) by the volume of the affected air. This volume was derived from the area of the lane-miles multiplied by a height. The height was calculated by multiplying the estimated evaporation duration by an assumed average evaporation speed of 0.3 m/s. Equation (1) represents the combination of these parameters to determine the weight of the evaporated CO₂eq. It is worth mentioning that GHG emissions from asphalt mixture fumes were attributed to and added to the total emissions of subphases A3, A4, and A5, corresponding to their respective contributions in this study.

$${\mathrm{A}\mathrm{m}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{o}\mathrm{f}\mathrm{m}\mathrm{g}\mathrm{C}\mathrm{O}}_{2\mathrm{e}\mathrm{q}}=\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\left({\displaystyle \raisebox{1ex}{${\mathrm{m}\mathrm{g}\mathrm{C}\mathrm{O}}_2$}\!\left/ \!\raisebox{-1ex}{${\mathrm{m}}^3$}\right.}\right)\times \mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a}\left({\mathrm{m}}^2\right)\times \mathrm{e}\mathrm{v}\mathrm{a}\mathrm{p}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{p}\mathrm{e}\mathrm{e}\mathrm{d}\left({\displaystyle \raisebox{1ex}{$\mathrm{m}$}\!\left/ \!\raisebox{-1ex}{$\mathrm{s}$}\right.}\right)\times \mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e}(\mathrm{s})$$

(1)

• Phase B: Use Phase

Phase B encompasses all GHG emissions associated with a pavement from the time a route is opened for use until its closure for milling. Phase B includes emissions from four contributors:

• B1: pavement–vehicle interaction,
• B2: albedo effect,
• B3: maintenance,
• B4: repair works.

The studied field projects have not undergone all subphases of phase B; records indicate no maintenance, e.g., surface treatments like a chip seal (B3) or repairs, e.g., adding an asphalt overlay (B4) since construction. Further, according to the DOTD pavement designers, the studied field projects (2 inches of pavement surface layer) were not expected to be maintained. Instead, they would be milled and repaved once the international roughness index (IRI) reaches 200 inches/mile, equivalent to a 70% reduction in the Roughness (RUFF) index as specified [41]. As a result, only subphases B1 and B2 were considered in phase B in this case study.

• B1: Pavement–Vehicle Interaction (PVI)

Three primary interactions between pavement and vehicles are expected to increase fuel consumption and GHG emissions; see Figure 4 [42]:

• Surface texture: the unevenness or irregularities present on the pavement surface [43].
• Microtexture (aggregate surface imperfections with wavelength = 0–0.5 mm), responsible for friction.
• Macrotexture (small pavement surface imperfections with wavelength = 0.5–50 mm), responsible for skid resistance.
• Megatexture (large pavement surface imperfections with wavelength = 50–500 mm), causes vibration in the vehicle’s suspension system.

Roughness: the measure of surface irregularities whose dimensional characteristics impact vehicle behavior, passenger comfort, and induced dynamic forces, and increase the resistance to movement, which consumes more fuel and emits more GHG emissions [43]. Roughness values are commonly reported in units of meters per kilometer (m/km) or inches per mile (in/mi).

Deflection: the vertical deformation of the pavement surface under the application of a load, such as the weight of a vehicle. This deflection is an indicator of the pavement’s structural response and its ability to support loads without excessive deformation [44]. Deflection is measured using devices like a falling wheel deflectometer and it is measured in millimeters or inches.

Several factors influence how pavement–vehicle interaction (PVI) arising from surface texture, roughness, and deflection contributes to a pavement’s overall environmental footprint. These include the pavement’s age, its surface characteristics, structural properties (stiffness and viscoelasticity), local climate, vehicle type, speed, and volume [45]. Excess fuel consumption refers to the fuel consumed above what is necessary for travel on a perfectly smooth and rigid pavement surface. Pavement surface texture is determined by the surface’s inherent smoothness and the frequency of its maintenance. It is crucial for ensuring sufficient surface friction, which is vital for safe braking and steering [46]. Despite its importance for safety, studies have indicated that surface texture has an insignificant impact on fuel consumption [47]. Deflection is eliminated from this study as it would theoretically exhibit no change across the studied sections, and also due to the uncertainty of modeling deflection’s impact on fuel consumption. The impact of the PVI mechanisms is shown in the elastic spring and dashpot for Figure 4 [42].

Roughness, influenced by the initial roughness at construction, causes tire vibrations that dissipate energy through friction. Increased roughness causes higher rolling resistance, requiring the engine to expend more power and consume more fuel to maintain speed. In addition, increased roughness necessitates increased suspension activity to absorb bumps and jolts, consuming energy through shock absorber damping and spring oscillations. Furthermore, increased roughness can disrupt smooth airflow around the vehicle, increasing aerodynamic drag and requiring additional engine power to overcome it. Consequently, vehicles consume excess fuel and generate more GHG emissions due to increased roughness. The Massachusetts Institute of Technology (MIT) model was applied to quantify the additional fuel consumption and GHG emissions linked to increased pavement roughness [48]. This model quantifies the excess GHG emissions resulting from a change in roughness (pre- and post-grind IRI) and traffic properties (speed, average daily traffic, growth rate, and heavy truck percentage). In this study, the model inputs included the change in IRI over time (measured and DOTD-predicted), operating speed, Average Daily Traffic (ADT), truck percentage, and traffic growth rate (

Table 6. Construction, traffic, and roughness details obtained from the PMS database.

Route		LA 3121			US 90
Section Code		L7015	L7015W	L7030W	U7615	U7615W
Construction Year		2009			2012
Traffic Speed, mph		45			65
Traffic Volume, ESALs		80,000			2,800,000
Section Log Miles (from/to)		0.3/2.2 & 5.5/5.9	2.5/4.0	4.4/5.1	5.0/5.3	3.1/3.4
IRI (in/mile)	2010	81.2	77.0	76.0	--	--
	2012	81.5	84.7	80.0	--	--
	2014	92.3	91.0	114.0	46.3	56
	2017	96.1	91.9	123.0	50.0	63.0
	2019	97.4	91.9	133.0	57.8	66.3
	2021	98.3	93.7	135.1	71.4	66
	2023	96.8	94.1	139.1	66.4	79
Pavement Service Life (PSL), years		20.8	20.9	17.9	19.7	19.8

Notes: ESAL: Equivalent Single Axle Load; IRI = International Roughness Index.

), which were used to estimate excess fuel consumption and convert it to CO2eq following the model procedure [47]. Characterization of the Virginia Department of Transportation’s pavement network has been conducted using the MIT model; despite relying on simplified inputs for structure, climate, and load, it was considered applicable for this study [49].

The PMS database at DOTD stores IRI values for each 0.1 lane-mile measured biannually using Fugro’s Automatic Road Analyzer (ARAN) vehicle. These IRI values are converted into roughness (RUFF) index values, ranging from 0 to 100, which indicate the severity of the measured roughness. Notably, a RUFF index of 100 represents newly constructed asphalt pavements with an average IRI ranging from 0 to 50 inches/mile. Pavements with an IRI value of 200 inches/mile are assigned a RUFF index of 70. Table 6 presents the average IRI recorded in the PMS for each subsection of the studied field projects. Similarly, traffic speed, ADT, traffic growth rate, and truck traffic percentage were also collected and used to compute the equivalent standard axle loads (ESALs), Table 6. It is worth mentioning that IRI increases throughout pavement service life, resulting in a decrease in the RUFF index percentage, as depicted in Figure 5. The pavement service life (PSL) in years was calculated for each field section separately using the RUFF index curve in Figure 5. Since the two studied projects are still in service, the future roughness index (RUFF index) was predicted using the model adopted by Louisiana DOTD [41]. Therefore, the total pavement service life and excess fuel consumption were estimated for each section using the MIT model [48].

• B2: Albedo Effect

The Albedo effect describes the fraction of incoming solar radiation that is reradiated, measured as the energy alteration per unit surface area of the Earth, and is closely linked with CO₂eq concentration [50]. Albedo ratio ranges from 5 to 20% in asphalt pavement [51]. The radiative forcing attributed to albedo changes can be expressed in terms of CO₂eq equivalence through a meteorological model [52], this factor was excluded from the study. This exclusion is justified by the theoretical expectation that there would be negligible albedo differences between the studied pavement sections. Because all evaluated sections are asphalt wearing courses with similar surface appearance and no reflective treatment, albedo differences were assumed negligible; therefore, the albedo pathway was excluded following the approach described in [51].

• Phase C: End of Life Phase

Phase C represents the end-of-life phase for asphalt pavement. It comprises three subphases:

• C1: Demolishing/milling

Demolishing/milling the upper layer of existing asphalt pavement, including the fuel consumption of milling equipment and the emissions resulting from the disturbance and processing of aged asphalt materials, as well as the mobilization of site equipment.

• C2: Transporting milled materials.

Milled materials would be transported to the asphalt plant for processing and then recycling.

• C3: Processing milled materials

Processing milled materials for reuse as reclaimed asphalt pavement (RAP) and/or disposing of milled materials that are not recycled.

For the purposes of this study, it is assumed that 100% of the milled asphalt (RAP) is recycled. This assumption represents a best-case scenario based on current practice in Louisiana, where established RAP supply and reuse practices result in little to no material being sent to landfills. Consequently, the end-of-life (Phase C) analysis does not include environmental burdens associated with material disposal. It should be noted that lower RAP recovery rates would increase Phase C emissions and reduce the net environmental benefits observed in this study. This critical assumption defines the system boundary of this LCA and is essential for the study’s cradle-to-grave with a recycling loop designation.

Table 7. Phase C—Data source and quality.

Subphase	Equipment	Unit	Kg CO2eq/Unit	LCI Data Source—Publish Year	Meta-Analysis
C1	Milling machine (Diesel)—300 hp	13.28 gal/hr	131	Federal LCA Commons—2018 [21]
	Mobilizing the milling machine	3 gallon/mile	30.54	Contractor company—2023	N/A
C2	Hauling truck (diesel)	St-mile	0.2264	Federal LCA Commons—2000	53.2% [21]
C3	Processing milling materials (RAP)	Ton	1.225	[53]	N/A

Notes: LCI = life cycle inventory; LCA = life cycle assessment; hp = horse power; CO₂eq = carbon dioxide equivalent; N/A = not available.

presents the equipment, energy consumption, equivalent CO₂eq, data sources, and data quality analysis for the subphases of phase C.

2.3. Step 3: Environmental Impact Analysis

The life cycle inventory data collected for each LCA subphase underwent environmental impact analysis. This study leveraged publicly available background datasets and TRACI to assess potential environmental burdens. The evaluation of these impacts employed an enhanced pedigree matrix from the EPA, specifically adapted for pavement LCA [45]. Since a comprehensive industry-wide aggregate lifecycle inventory was unavailable, this research utilized publicly accessible environmental impacts within LCA Pave. Logistical distances were calculated from the spatial positions of material providers and asphalt production facilities. For assessing the environmental effects attributed to pavement materials, the LCA Pave tool incorporates TRACI 2.1. an EPA-developed and ISO-recognized method for calculating mid-point indicators [16].

Table 8. Calculations for GHG emissions Phase A of section L7015.

Subphase A1	Quantity (st)	CO2eq conversion Factor			Kg.CO2eq/st
Coarse aggregates	0.380	2.06			0.78
Fine aggregates	0.429	4.20			1.80
RAP	0.150	1.26			0.19
Asphalt binder and SBS	0.041	628			25.75
					28.52
Subphase A2	Quantity (st)	Distance (mi)	Transportation means	Equivalent CO2eq Factor/st. mile	Kg.CO2eq/st
Coarse aggregates	0.380	658	Barge	0.0795	26.86
		81.1	Truck	0.2264
Fine aggregates	0.429	658	Barge	0.0795	30.32
		81.1	Truck	0.2264
Asphalt binder	0.041	82.7	Truck	0.2264	0.82
SBS	0.041 × 0.015	365	Truck	0.2264
					57.99
Subphase A3	Quantity/st	GWP Equivalent Factor			Kg.CO2eq/st
Burner (MFC)	276.7	0.05			13.84
Electricity (kWh)	3.62	0.36			1.30
Fuel (Gallon)	0.02	10.21			0.20
					15.34
Subphase A4	Distance (mi)	Quantity (st)	Truk Capacity (st)	Trips	Kg.CO2eq/st
Haul trucks (6.5 gph)	48.8	760	18	43	1.06
					1.06
Subphase A5		Quantity (st)			Kg.CO2eq/st
MTV (6 hrs, 3.7 gph)		760			0.30
Paver (6 hrs, 3.3 mpg)		760			0.27
Rollers (4 mpg)	26 passes	760			0.09
Fumes (0.3 m3/s)	60 min	760	Area (m2) = 3.6 × 1609	0.00096 Kg.CO2eq/m3	7.90
Mobilization (5 mpg)	48.8 mi	760	6 equipment		1.57
					10.13

Notes: RAP = Reclaimed Asphalt Pavement; SBS = Styrene-Butadiene-Styrene; MFC = Thousand Cubic Feet; MTV = Material Transfer Vehicles.

presents an example of GHG emissions calculations for route LA 3121 section L7015. Subphase A2 showed the highest contribution to GHG to the long-distance import of virgin aggregates from another state, followed by subphase A1 due to the contribution of asphalt binder refining and manufacturing. Subphase A4 was the lowest contributor to the GHG of phase A, attributed due to the limited travel distance separating the asphalt manufacturing facility from the project location.

Table 9. Calculations for GHG emissions Phase B and C of route LA 3121 section L7015.

Phase B1	Years	Kg.CO2eq/L.M	Expected PSL	Quantity (st)	Kg.CO2eq/st
Roughness	1	21.00 [48]	20.8	760	0.57
					0.57
Phase B
Subphase C1				Quantity (st)	Kg.CO2eq/st
Milling	13.28 GPH	2.93 mph	4.53 gpm	760	0.06
Mobilization	48.8 miles		3 gpm	760	0.44
					0.50
Subphase C2
Haul trucks (6.5 GPH)	48.8	760	18	43	1.06
					1.06
Subphase C3
Processing RAP					1.23

Notes: L.M = lane.mile; PSL = Pavement Service Life; RAP = Reclaimed Asphalt Pavement.

presents the calculated GHG emissions for phases B and C of section L7015. It is worth noting that only pavement roughness was included in the use phase (Phase B) over the pavement’s service life, and it was subsequently normalized to a single short ton. This normalization was deliberately chosen to ensure a consistent functional unit across all life cycle phases, from material production (Phases A1–A3) through the use stage (Phase B), and thus enable a unified, material-centric analysis. By scaling all emissions to a single unit of pavement material, the study provides a direct and coherent basis for comparing the relative environmental contributions of each phase. This approach aligns with the core objective of the research: to assess the life cycle environmental impact of the pavement material itself, rather than focusing on traffic performance, which is a related but distinct topic. This normalization was used to enable a consistent comparison across phases within this case study; however, Phase B and C results are fundamentally driven by the lane-mile and service-life functional unit.

The study addresses the environmental benefits of recycling through a detailed allocation method. Emissions from milling, hauling, and initial processing of reclaimed asphalt pavement (RAP) are allocated to the end-of-life (EOL) phase (C1, C2, and C3), as detailed in Table 8. However, the burden of any further processing required to prepare the RAP for its integration into a new mix is allocated to the material production phase (A1), shown in Table 7. This approach aligns with the “cradle-to-grave” recycling model, which attributes the environmental impact of preparing a reusable material to the production of the new product, ensuring a consistent and reproducible framework for the full LCA.

2.4. Step 4: Interpretation

Figure 6 presents the GHG emissions for phase A in the studied field projects. WMA reduced GHG emissions by 5% of the production (A3) and construction (A5) when comparing sections L7015 (without WMA) and L7015W (with WMA). The increased usage of RAP from 15% to 30% content reduced the GHG emissions by 13% when comparing sections L7015W and L7030W. This translates to approximately a 0.9% reduction in Phase A GHG emissions for every 1% increase in RAP content. The observed reduction in Phase A GHG emissions associated with WMA is primarily attributed to lower mixing and compaction temperatures, which reduce burner fuel consumption and associated CO₂ emissions during asphalt production, consistent with previous findings [8,26]. Similarly, RAP reduces material-phase emissions by decreasing the demand for virgin asphalt binder and aggregates, which are the most energy- and carbon-intensive components of asphalt mixtures [7,11]. An overall reduction in GHG emissions is achievable when WMA and high RAP content are combined, as demonstrated in section L7030W compared to the other studied sections. This further indicates that the environmental benefit of WMA in this study is primarily governed by thermal energy savings during production and construction rather than uncertainty in additive manufacturing emissions.

Figure 7 presents the GHG emissions for phase B in the studied field projects. The PVI produced less GHG emissions when WMA was incorporated into asphalt mixtures. Specifically, incorporating WMA in section U7615W reduced GHG emissions by 50% when compared to section U7615 due to significant differences in roughness. However, increasing the RAP content from 15% to 30% increased roughness and slightly increased the GHG emissions when comparing sections L7015W and L7030W. This observed increase in roughness is specific to the evaluated field sections and may be influenced by a combination of mixture design, construction practices, ageing, and inherent variability in field roughness measurements. It is not intended to represent a generalized effect of increased RAP content. From a mechanical perspective, increased surface roughness amplifies pavement–vehicle interaction by increasing rolling resistance and vehicle suspension activity, which leads to higher fuel consumption and GHG emissions, as reported in prior studies [41,47]. These effects become more pronounced under higher operating speeds and traffic volumes, explaining the dominance of use-phase impacts on US-90.

Overall, route US-90 exhibited substantially higher Phase B GHG emissions than route LA-3121. This outcome is attributed to the combined effects of higher operating speeds, greater traffic volumes, and higher surface roughness, which increase vehicle fuel consumption sensitivity to pavement condition. Under such operating conditions, use-phase emissions dominate the life cycle impacts, and the material-related GHG reductions achieved through RAP and WMA in earlier phases are insufficient to offset the increased emissions associated with pavement–vehicle interaction.

Figure 8 presents the GHG emissions for Phase C in the studied field projects. Route US 90 sections exhibited lower emissions than Route LA 3121 sections, primarily due to the shorter distance required to mobilize the milling machine. Similarly, transporting the milled materials produced less GHG emissions on route US 90 as compared to Route LA 3121. An overall reduction in GHG emissions was observed when WMA and RAP were used. Specifically, using WMA in sections L7015W and U7615W reduced the GHG emissions by 10% and 12%, respectively, compared to sections L7015 and U7615, which contained no WMA. Furthermore, using 30% RAP content in section L7030W reduced the GHG emissions by 12.4% compared to the 15% RAP content in section L7015W. These reductions are consistent with prior LCA studies showing that recycling asphalt materials at end-of-life offsets emissions associated with virgin material production, particularly when high recovery rates are achieved [11,52].

Figure 9 presents the GHG emissions for phases A, B, and C in the studied field sections. The GHG emissions for Phases B and C were estimated on a functional unit (lane-mile-year) and then normalized to a declared unit (short ton) to enable a clear overall comparison. The functional unit was normalized to the declared unit for easier comparison with field measurements, due to its smaller size. The section L7030W exhibited the lowest GHG emissions among all studied sections, attributed to its incorporation of WMA and high RAP content (30%).

3. Conclusions

This study applied a cradle-to-grave LCA, following ISO 14040, to five in-service asphalt pavement sections in Louisiana to quantify greenhouse gas (GHG) emissions across material production, construction, use, and end-of-life phases. The analysis integrated long-term field performance data, pavement–vehicle interaction modeling, and a closed-loop recycling assumption to evaluate the environmental impacts of RAP and WMA technologies. By employing a comprehensive cradle-to-grave LCA on five sections across two Louisiana DOTD field projects, the results showed that WMA effectively lowers emissions during the production, construction, and use phases. Increased RAP content provides substantial reductions in material and construction emissions, although potential increases in roughness-related use-phase emissions should be considered. The combination of WMA and high RAP content yields the most significant overall emission reductions, highlighting the efficacy of integrated sustainable pavement practices. Following the interpretation of the study’s findings and an in-depth analysis, the following conclusions were made:

• Incorporating WMA reduced GHG emissions during production and construction (Phases A3 and A5) by approximately 5%, primarily due to lower asphalt mixing and compaction temperatures that reduced burner fuel consumption.
• WMA also reduced use-phase (Phase B) GHG emissions by up to 50% in certain sections by improving surface smoothness, which lowered pavement–vehicle interaction and excess fuel consumption.
• Increasing RAP content provided substantial material-phase benefits; each 1% increase in RAP content reduced material and construction GHG emissions by approximately 0.9%, mainly by offsetting virgin asphalt binder and aggregate production.
• Higher RAP content was associated with increased surface roughness in specific field sections, which led to increased use-phase GHG emissions due to higher rolling resistance and vehicle energy demand.
• Asphalt mixtures incorporating both WMA and high RAP content achieved the lowest overall cradle-to-grave GHG emissions, demonstrating that combining material and temperature-reduction strategies is more effective than using either approach alone.

It is important to note that the findings of this study are influenced by region-specific assumptions, including the availability of RAP markets, a closed-loop recycling scenario assuming 100% RAP reuse at end of life, and Louisiana-specific traffic loading, climatic conditions, and pavement management practices. Consequently, extrapolation of these findings to regions with different conditions should be undertaken with caution. This study focused on asphalt wearing courses, as surface roughness and texture primarily govern pavement–vehicle interaction and use-phase GHG emissions. Extending the analysis to the full pavement structure would increase material-phase emissions due to additional layers, while use-phase trends would remain largely controlled by surface-layer properties.

Future investigations should incorporate a broader range of materials, such as asphalt binder modified with rubber, plastic, recycling agents, or other additives. In addition, different models should be used for quantifying GHG emissions during the use phase, including those considering deflection and albedo effects. Innovative techniques and devices could also be employed to validate those models by comparing field GHG emissions measurements.