A life cycle and environmental impact analysis of sustainable concrete incorporating date palm ash and eggshell powder as supplementary cementitious materials

2.1 Materials

Type I cement was employed as the main binder, with a specific gravity (SG) of 3.15 and a specific surface area of 325 m²·kg⁻¹. DPA was collected from a nearby farm in unprocessed form. On the farm, date palm waste consisting of leaves, trunks, mesh, and branches was burnt in the open. The raw DPA was further processed in the laboratory to obtain the final product for use in concrete. Raw DPA was placed in the furnace at a temperature of 600°C for 2 h to eliminate any moisture and remove unburnt carbon from the ash [45,46]. DPA was ground using a high-powered grinder and subsequently sieved through a 150 μm mesh, and the particles that passed were collected as the processed DPA. DPA has a chemical composition, as shown in Table 1, which met the requirements of ASTM C618 [47] for pozzolanic materials for concrete application. The SG of DPA was found to be 2.14. The eggshell residues were acquired from different sources within Riyadh, Saudi Arabia, which included poultry farms, restaurants, and bakery. After collection, the eggshells were thoroughly washed to eliminate dirt and organic membranes. After washing, the eggshells were ground to smaller particles by hand and oven-dried for 24 h at a temperature of 100 ± 5°C to dry them completely and remove moisture. The eggshells were pulverized into a fine powder and sieved using a 63 μm mesh, and the particles that passed were collected and utilized as ESP, with their properties listed in Table 1. The manufacturing process of DPA and SP waste powder is shown in Figure 1. River sand was obtained from local suppliers and used as a fine aggregate. The aggregate’s SG is 2.63, and its water absorption is 1.26%. The coarse aggregate used was crushed gravel obtained from a nearby quarry. The coarse aggregate has an SG of 2.67, particle size of 19 mm, and water absorption of 0.54%. The particle size gradation of the fine and coarse aggregates is presented in Figure 2, and they fall within the limits specified by ASTM C33/C33M [48] for use in concrete.

Figure 1

Manufacturing process of DPA and ESP waste.

Figure 2

Particle size gradation of aggregates.

2.2 Mix design and proportioning

In this study, the procedure discussed in ACI 211.1R [49] was followed and implemented for designing the control (100% OPC) cement, which is used as the reference mix for comparison with all other mixes. DPA was used to substitute some fractions of the cement at different proportions (10, 20, and 30%). The replacement procedure used the volume method due to the large variation between the SGs of OPC (3.15) and the DPA (2.14). To enhance the pozzolanic reactivity in concrete, ESP was incorporated as an additive to cementitious materials in several dosages (0, 1, 2, and 3% by weight) of the binders. To optimize the study and lower the number of required mixes, the response surface methodology (RSM) technique was applied. The Design Expert software was utilized for the RSM process, and the central composite design method was selected. A total of ten mixes, including the control, were obtained using various combinations of DPA and ESP by the RSM, as shown in Table 2. This procedure saves time and cost for the experiment and the study [12].

2.3 Specimen preparation and experimental methodology

In this study, the methodology explained in ASTM C192/C192M [50] for the batching and mixing of the concrete was followed. After mixing, the workability of the fresh concrete was determined. The fresh concrete was cast into the specified molds and allowed to harden in the laboratory. The concrete was extracted from the molds 24 h later and immersed in water for curing prior to testing.

The slump test, conducted in accordance with ASTM C143/C143M [51], was used to evaluate the workability of the freshly prepared concrete. After 28 days of curing in water, the compressive strength of the concrete was determined using 100 mm cubes, as per BS EN 12390-3 [52]. As per the requirements of BS EN 12390-6 [53], split tensile strength testing was conducted on cylindrical concrete samples (200 mm height and 100 mm diameter) cured for 28 days. In line with the ASTM C78/C78M [54] standard, concrete prisms of size 100 mm × 100 mm × 500 mm were prepared and cured for 28 days to determine flexural strength. Finally, in line with ASTM C642 [55], the water absorption of the concrete was tested on 100 mm cubes cured for 28 days.

3.1 Goal and scope definition

The LCA of DPA–ESP-based concrete was carried out using IS0 14040, which involved four steps, as shown in Figure 3. The primary goal of this LCA was to assess the environmental impacts of producing 1 m³ of DPA–ESP-based concrete and to compare these impacts with conventional concrete mixes. The study aimed to identify the significant impact categories and primary contributors throughout the concrete’s life cycle. The functional unit was defined as 1 m³ of concrete, providing a standard basis for comparing the environmental performance of different materials and processes.

Figure 3

LCA as per ISO 14040.

Figure 4 illustrates the system boundaries, which followed a cradle-to-grave framework, encompassing all life cycle stages: raw material extraction, processing, transportation, mixing, usage, and end-of-life management [56,57,58]. This included acquiring raw materials such as cement, river sand, aggregates, DPA, and ESP; processing DPA and ESP (drying, grinding, and sieving); mixing concrete; and handling end-of-life processes like demolition, waste management, and potential recycling or disposal.

Figure 4

Graphical representation and boundary condition adopted for LCA.

3.2 Life cycle inventory (LCI)

The LCI phase detailed quantifying all inputs and outputs of producing 1 m³ of DPA–ESP-based concrete. The assessment considered various mix designs (M1 to M14) with different percentages of DPA and ESP, replacing portions of conventional cement.

The inventories used in this analysis (Table 3) provide a comprehensive overview of the materials, processing, electricity consumption, and transportation considered in the LCA. The environmental burdens associated with processing DPA and ESP were calculated based on their specific energy consumption: DPA required 150 kW·h for drying and 149.7 kW·h for grinding and sieving. In comparison, ESP required 25 kW·h for drying and 72.83 kW·h for grinding and sieving. These processes were modeled using the “Electricity, high voltage (CN-SA) | Electricity production, hard coal, system S” dataset to account for electricity consumption. Materials were transported using light commercial vehicles over specified distances: DPA was transported 150 km, ESP 50 km, and other materials such as cement, river sand, and aggregates were transported 50 km to the production site. All transportation activities were included in the LCI using the “Light Commercial Vehicle (RoW) | Cut-off, S” dataset.

3.3 Life cycle impact assessment (LCIA)

The SimaPro v9.6 software and ReCiPe Midpoint (H) methodology were used for the LCIA phase. The ReCIPe midpoint (H) method assesses 18 environmental impacts and offers a detailed evaluation of products or processes. The methodology was selected based on its capacity to conduct an in-depth examination of environmental impacts at midpoints and help point out areas that need improvements in emissions, resource use, and other types of environmental burdens. Some limitations were recognized, such as using Ecoinvent v3.9 data that may not cover regional differences and assumptions that DPA and ESP are considered waste materials with no previous environmental burdens. Additionally, transportation distances and processing of materials could change the results if these parameters vary. The research did not include any potential advantages of recycling concrete at the end-of-life, reusing or reusing it again, which may improve the entire ecological footprint.

3.4 Interpretation

The LCA for DPA–ESP-based concrete, performed by ISO 14040 guidelines, included a complete analysis of the environmental effects to discern relevant factors and ensure data integrity and methodological coherence. By using the ReCiPe Midpoint (H) method analysis, it was found that global warming potential (GWP) was mainly influenced by cement manufacturing. Due to their energy specifications, heavy contributions also came from DPA and ESP processing. Completeness and consistency checks confirmed the proper inclusion of all relevant life cycle stages and inputs. In subsequent sections, LCIA results interpretation and ancillary improvement opportunities are discussed.

3.5 Analytical hierarchy process (AHP) and one-way sensitivity analysis

The selection of an optimal mix design for DPA–ESP-based concrete requires a methodical approach to evaluating multiple criteria, including technical performance, environmental impact, and economic feasibility, as shown in Figure 5. The AHP is a widely used multi-criteria decision-making (MCDM) technique that aids in assigning priority weights to these criteria and ranking the available alternatives accordingly. The AHP methodology involves structuring the decision problem into a hierarchy, constructing a pairwise comparison matrix, normalizing the judgments, computing priority vectors, and ensuring consistency in decision-making.

Figure 5

Experimental methodology followed in the present study.

The pairwise comparison matrix is a square matrix A, where each element a _ij represents the relative importance of criterion i over criterion j, satisfying the following condition:

Normalization of this matrix is performed to ensure comparability of the elements by dividing each entry by the sum of its respective column:

The priority vector, representing the relative importance weights of each criterion, is obtained by averaging the values across the rows of the normalized matrix:

To ensure the reliability of the judgments, a consistency check is performed by first computing the weighted sum vector:

The principal eigenvalue λ _max is estimated using

Using λ _max, the consistency index (CI) is calculated as

The consistency ratio (CR) is then computed as

where RI is the random index, a predefined value based on the number of criteria. A CR < 0.1 is considered acceptable; otherwise, the pairwise comparisons need to be revised.

Following the derivation of weights, each mix design (M1 to M13) is evaluated against the technical, environmental, and economic criteria using a decision matrix. The overall score for each mix design is determined using the weighted sum method:

where s _ki represents the performance score of the mix. The mix design with the highest score is considered the optimal choice.

To assess the robustness of the rankings, one-way sensitivity analysis is conducted by varying the weight of a selected criterion while proportionally adjusting the others. If the weight of a particular criterion w _i is modified, the adjusted weight is given by

The remaining weights are proportionally redistributed as

The recalculated score for each mix design under the new weight distribution is

By analyzing the variation in ranking as a function of ∆w, the sensitivity of the decision model is assessed. If small changes in the criterion weights result in significant rank reversals, the model’s stability is questioned, and adjustments to the decision criteria may be necessary.

4.1 Experimental results

The experimental results discussed in this study were already documented in Adamu et al. [12,13], and a summary is presented in Table 4 for reference and understanding of the LCA. A reduction in concrete slump was observed with higher DPA replacement levels, likely due to the coarse, irregular particle sizes of DPA, leading to increased internal friction and a lower slump. Similarly, adding ESP led to a reduction in the concrete slump caused by the high surface area of ESP that absorbed excess water during mixing and lowered workability. Replacing a portion of the cement with DPA reduced the compressive, flexural, and split tensile strengths, as well as an increase in water absorption. For example, in comparison to mix 1 (0% DPA and 0% ESP), mix M12 (20% DPA and 0% ESP) has lower compressive, split tensile, and flexural strengths by 19.48, 12.3 and 9.05%, respectively, and has higher water absorption by 14.88%. The cause of this effect was related to the slower pozzolanic reaction of DPA during hydration, which hinders C–S–H formation and strength gain and increases porosity in the cement matrix. From Table 4, the addition of ESP to the concrete mixes with or without DPA led to enhancements in its mechanical strengths. For instance, comparing mix M1 (0% DPA and 0% ESP) with M4 (0% DPA and 2% ESP), the latter exhibits higher compressive, split tensile, and flexural strengths by 5.73, 3.6, and 6.98%, respectively, and lower water absorption by 6.08%. The enhancements in the mechanical strengths and reduction in water absorption were linked to the finer sizes and the pore-filling effectiveness of ESP, and the high CaO in the ESP promotes the pozzolanic and hydration reactions and increases C–S–H formation and consequently densified microstructure and higher strengths. From Table 4, blending up to 20% DPA with up to 4% ESP in the concrete led to improvements in mechanical strengths and water absorption due to the combined effects of the DPA in terms of contributing more SiO₂ and ESP contributing to the production of more Ca(OH)₂, where these two compounds react via pozzolanic reaction to form surplus C–S–H for strength development. The findings in this study were similar to those of Hamada et al. [59], who reported improvement in strengths and reduction in water when POFA was blended with ESP. Rasid et al. [60] also reported significant improvements in the mechanical and durability performance of concrete when blends of POFA and ESP were used to partially substitute cement, where they found that the combination of 15% POFA with 5% ESP yielded the highest strengths.

4.2 Environmental impact results

The production and transportation of materials such as cement, Portland, aggregates, and sand significantly drive the environmental impacts of the concrete mixes analyzed in this study. Figure 6(a–r) shows the characterized values of environmental impacts of materials due to the production of various concrete mixes. Across all mixes, cement production is a dominant contributor to the GWP, with contributions ranging from 45.8 to 64.4% of total emissions. For instance, in the M2 mix, cement production accounts for approximately 51.2% (325.79 kg CO₂ eq) of the total 636.6 kg CO₂ eq emissions, while in the M4 mix, this increases to around 64.4% (465.42 kg CO₂ eq) of the total 722.84 kg CO₂ eq emissions. The notable impact of cement arises from its high-energy production process, involving limestone calcination and clinker production, which are key contributors to CO₂ emissions.

Figure 6

Characterized values of all environmental impacts of DPA–ESP-based concrete.

Transportation also plays a crucial role in the environmental footprint, particularly in GWP, ozone formation, and ecotoxicity categories. For example, in the M6 mix, the transportation of aggregates and sand contributes about 20.2% to the total GWP (139.95 kg CO₂ eq out of 693.39 kg CO₂ eq). In the M3 mix, aggregate transportation alone accounts for approximately 14.3% of the total GWP (94.05 kg CO₂ eq out of 666 kg CO₂ eq). Transporting these heavy materials results in substantial fuel consumption and emissions of greenhouse gases (CO₂) and air pollutants (NO _x , SO₂, PM2.5).

Alternative materials such as DPA and ESP show promising potential for reducing environmental impacts. In the M2 mix, the processing of DPA contributes 11.1% (70.54 kg CO₂ eq) to the total GWP, whereas the M3 mix has a 7.1% (47.03 kg CO₂ eq) contribution from DPA. For the M13 mix, incorporating DPA contributes only 7.1% of the total GWP (47.03 kg CO₂ eq out of 663.99 kg CO₂ eq). In terms of terrestrial ecotoxicity, cement and aggregate production are significant contributors, accounting for up to 27.3 and 22.5%, respectively, of the total impact in the M6 mix (610 kg 1,4-DCB eq out of 2231.15 kg 1,4-DCB eq).

While less impactful than cement or aggregate transport, plasticizers still contribute to multiple categories. For example, in the M13 mix, plasticizers contribute about 1.0% to the total GWP (6.6 kg CO₂ eq out of 663.99 kg CO₂ eq) but have a more noticeable impact in toxicity categories, such as contributing 9.5% to the freshwater ecotoxicity impact (0.476 kg 1,4-DCB eq out of 21.71 kg 1,4-DCB eq).

The analysis reveals that conventional materials like cement and aggregates primarily contribute to the environmental impact. Alternative materials like DPA and ESP can reduce these impacts across several categories. For instance, in the M8 mix, including DPA and ESP helps reduce the impact of freshwater eutrophication to 3.1 and 1.2%, respectively, showing a potential for mitigating environmental harm. Optimizing the use of these materials and improving transportation logistics could reduce the environmental footprint.

4.3 Comparison of various mixes with a conventional mix

The LCA results of the DPA–ESP-based concrete mixes highlight variations in environmental impacts across different mix proportions. This discussion compares these mixes (M2, M3, M4, M6, M7, M8, M10, M12, and M13) to identify the optimal mix design regarding environmental performance. Figure 6 shows the comparison of mixes with conventional concrete

4.4 Summary of LCA results

Mix M8, with the highest DPA content (40%), consistently shows the lowest environmental impacts across most categories, including global warming, ozone formation, acidification, and resource use, demonstrating its potential as the most sustainable mix. Other mixes, such as M2 and M13, also offer significant environmental benefits but to a lesser extent. Mix M4, with no DPA, often shows increases or minimal reductions in impacts, underscoring the importance of DPA and ESP in enhancing the environmental performance of concrete. Although there are slight increases in human toxicity categories, these are relatively minor and may be manageable with proper processing and handling of materials. Overall, DPA–ESP-based mixes, particularly M8, present a promising alternative for reducing the environmental footprint of concrete production.

The characterized environmental impacts presented in Figure 7 were normalized by the world population using the ReCiPe 2016 Midpoint (H) V1.08 method (Figure 8). This normalization allows for a more standardized comparison across different processes by adjusting the impacts relative to the global population. The data reveal that the most significant environmental impacts are seen in categories like “Human carcinogenic toxicity,” “Freshwater ecotoxicity,” and “Marine ecotoxicity.” These high values suggest that the processes analyzed have notable contributions to human health risks and ecosystem toxicity. Lower impact values are noted for categories such as “Global warming” and “Stratospheric ozone depletion,” indicating relatively lesser contributions to these specific impacts.

Figure 7

Characterized values of environmental impacts for all relevant mixes. Note: GWP, terrestrial ecotoxicity, and HNCT are plotted against the primary axis; the rest of the impacts are plotted against the secondary axis.

Figure 8

Normalized values of environmental impacts for relevant mixes.

4.5 Discussion on the selection of the best mix

4.5.1 AHP methodology

AHP methodology was used to rank the mix designs (M1–M13) against technical, environmental, and economic criteria. The corresponding weights assigned are shown in Figure 9. AHP framework was used to break down the comparison of mixes into constituent parts and then logically combine them by weighting and aggregating multiple criteria.

Figure 9

Pairwise matrix weights for the selection of best mix.

Using the Saaty scale, which forms the core of the AHP, the technical and environmental properties of several mixes of concrete were then evaluated and compared. Pairs of each mix were compared using the ratios of values for the technical properties of slump, compressive strength, flexural strength, and split tensile strength, where higher values are judged better. The ratio of the higher value to lower value was specifically calculated, and these were subjected to the assignment of preferences using the Saaty scale so that the mixes with the best technical performance had the highest priorities. Conversely, when lower values are desirable (in the case of environmental properties related to GWP, fine particulate matter formation, ozone formation [human health], terrestrial acidification, mineral resource scarcity, and fossil resource scarcity), inverse ratios were chosen. In this case, we determined the ratio of the larger value to the smaller value for a series of mixes with minimal environmental impact. The resulting pairwise comparison matrices were developed using the Saaty scale: Values between 1 and 1.1 were mapped back to 1 (equal preference), between 1.1 and 1.2–2 (slightly preferred), 1.2 and 1.3–3 (moderately preferred), 1.3 and 1.5–4 (strongly preferred), and values higher than 1.5 were mapped back to 5 (very strongly preferred). Figure 10 shows the weighted criteria for the technical, environmental, and economic properties. This structured methodology provided a robust basis for MCDM in concrete mix design since this transparent and consistent evaluation of mixes was based on both technical performance and environmental sustainability.

Figure 10

Weighted attributes assigned based on the pairwise matrix: (a) technical property and (b) environment and economic properties.

The properties that were given higher importance were the technical properties (0.493), such as compressive strength, flexural strength, split tensile strength, and water absorption, since they are directly related to the performance of buildings. Out of the five mixes, M3 was found to have the highest compressive strength, therefore making it the most appropriate mix for the structural member that requires high mechanical and physical properties. Flexural strength and split tensile strength, which are the ability of the concrete to resist bending and cracking, also showed great differences between the mixes. Of all the mixes, M10 presented a very good performance in these attributes, thus showing its overall good technical properties. These include M1 and M4, which, although not very strong in compressive strength, were very good in workability and very crucial in handling and placing concrete during construction. The environmental attributes (0.311) were carefully considered to capture the sustainability aspects of the mixes. Criteria such as GWP, fine particulate matter formation, terrestrial acidification, and resource scarcity were evaluated. Here, M8 emerged as a standout mix with the lowest environmental impact. Its reduced reliance on energy-intensive materials contributed significantly to its performance in this category. However, the adoption of M8 in critical structural applications would require careful deliberation, as its technical scores were not competitive with high-performing mixes like M3 and M10. Mixes such as M12 and M13, which showed moderate environmental benefits, represent viable alternatives for projects prioritizing sustainability without compromising entirely on performance.

From the economic point (0.196) of view, the efficiency of mixes from the perspective of performance versus cost was evaluated using cost–benefit analysis. Mix M8, owing to its composition consisting of economic materials, was the most cost-efficient and is an excellent candidate for cost-sensitive projects. M2 also achieved a high level of economic viability yet appropriate technological attributes. Conversely, mixes such as M3 and M10, though they are more costly, displayed better technical performance and hence could be justified for use in projects where long-term durability and structural integrity are more important. Therefore, identifying the particular priorities of a project makes clear the necessity to align the mix selection to minimize the cost/performance trade‐off.

It was also observed (Figure 11) from the integration of the attributes within the AHP framework that M3 had the highest cumulative weighted attributes (0.143), especially in compressive strength, flexural strength, and split tensile strength. With moderate environmental and economic attributes and possessing technical dominance, it is the most robust choice for structural applications. M10 stood second with a strong performance in compressive strength and flexural strength. Although lagging in technical attributes, M2 was very competitive, with a balanced environmental and economic score. M4 was characterized as having a good balance across workability and moderate compressive strength technical attributes useful for applications demanding moderate structural behavior coupled with ease of handling. Technical attributes like flexural strength and split tensile strength exhibited the competitive performance of M6. It had a moderate environmental and economic score, suggesting that it was a viable option for mid-range applications. M2 exhibited good cost–benefit attributes and tolerable environmental performance. Low on the technical scores than the top performer mixes, its economic viability provided a good value proposition to undertake such a project from the cost perspective. However, due to the lower technical scores of M8, the option was limited in application to only non-structural or environmentally driven projects. Good workability but lower scores in other technical, environmental, and economic attributes were shown by M1. This is a better mix for operations such as handling and placement, where structural strength is not. M12 scored moderately for all attributes and is appropriate for projects that have balanced priorities but do not excel at any one criterion. Compressive and flexural properties were shown for M13, but their cumulative weighted attributes were relatively lower than other mixes.

Figure 11

Ranking of the mixes based on the analytical hierarchical process methodology.

4.5.2 Sensitivity analysis

A sensitivity analysis was performed to assess the robustness of the AHP results between five distinct cases by varying the weightage of the criteria or changing the mix pair comparison (uses of study). The priority rankings of the mixes shift as weights and comparisons change, revealing the stability of the decision-making process through this analysis. The sensitivity scenarios considered were based on the compressive strength, flexural strength, cost–benefit analysis (M3 vs M10), technical vs economic trade-offs, and cost–benefit analysis (M8 vs M10).

4.5.2.1 Case I: Sensitivity analysis of compressive strength

The most important observation from the compressive sensitivity analysis is the stability of M3 dominance for comparison pairs M3 and M10 (Figure 12). In comparison, by changing the ratio of M3/M10, we always found M3 higher and concluded the higher compressive strength of M3, which is its critical technical attribute. It is also observed that the design change sensitivity of the decision was 55%, i.e., a small change in the weightage made M3 stand inferior to M10 in terms of ranking. The overall hierarchy, however, remained consistent, with its high compressive strength keeping M3 in a top spot.

Figure 12

One-way sensitivity analysis of M3 vs M10 for compressive strength.

4.5.2.2 Case II: Sensitivity analysis for flexural strength

In this case, M3 vs M10 was varied with a focus on the flexural strength attribute (Figure 13). Its superior technical attributes allowed M3 to once again retain its top position and prove its resilience. Furthermore, the sensitivity analysis indicated that the decision change sensitivity was 53%, meaning that weights to flexural strength greatly affected the overall final rankings. However, M3’s overall dominance was threatened when the weight shifted; however, owing to its technical superiority, it continued to hold its place in the rankings.

Figure 13

One-way sensitivity analysis of M3 vs M10 for flexural strength.

4.5.2.3 Case III: Second analysis: cost–benefit analysis (M3 vs M10)

The cost–benefit of M3 and M10 was in a balanced competitive situation regarding technical performance and economic viability (Figure 14). The analysis showed that with 50% sensitivity, the economic weightage could change M10 toward M3’s rank. Nevertheless, M3’s technical advantages still provided it an edge in cases where attributes of performance were put first. Trade-offs between cost and performance highlighted the importance of matching mix selection to project-specific requirements.

Figure 14

One-way sensitivity analysis of M3 vs M10 for cost–benefit analysis.

4.5.2.4 Case IV: technical vs economic attributes

The fourth sensitivity evaluated the trade‐offs between technical and economic (Figure 15) attributes by varying the weights between these two criteria. The analysis showed a 36.25% decision change sensitivity, with M3 maintaining superiority in terms of technical consciousness. However, if economic attributes received larger weightage, M10 and M8 were competitive alternatives. A green vertical line was shown in the sensitivity plot for this case, indicating a point when weightage assigned to technical and economic criteria was equal. This inflection point delineated the specific level of concentration at which technical and economic considerations started to revolve around different priorities. Beyond this point, the prominence of cost-efficient mixes such as M8 increased, and M3’s dominance slightly decreased as the technical attributes decreased in importance. The presence of this vertical line acted as a useful reference in making a clear sense of the balance between these technical and economic attributes and, hence, as a guideline for projects that place different emphasis on both.

Figure 15

One-way sensitivity analysis of technical vs economic trade-offs.

4.5.2.5 Case V: Cost–benefit analysis (M10 vs M8)

Sensitivity analysis for cost–benefit comparison between M8 and M10 (Figure 16) showed that each of these mixes inherently excels in certain contexts and where each of these mixes should be used as they are designed. Due to M10’s well-known superior cost efficiency and sustainability attributes, M8 challenged M10 in scenarios where economic and environmental concerns were of greater importance. The relative rankings of M8 and M10 were found to be sensitive to moderate changes in weightage for this case (sensitivity of 33.75%). However, despite its economic benefits, M8 was limited by being a lower-performing technology than M10, which remained strong in scenarios that emphasized structural attributes.

Figure 16

One-way sensitivity analysis of cost–benefit analysis.