Unlocking the Circular Potential of Construction and Demolition Waste: A Pathway for Texas’ Construction Industry

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Jones, Ashrant Aryal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8166632/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract The construction industry is a global generator of waste, with Construction and Demolition Waste (CDW) representing the largest solid waste stream in the United States. Landfilling remains the dominant disposal method, carrying significant environmental consequences. A barrier to recycling is the lack of positive economic incentives, particularly for “vertical” CDW (VCDW), a mixed debris stream generated from building demolition activities. This study addresses this challenge by proposing a framework to unlock the circular potential of CDW currently landfilled in Texas. First, the technical feasibility of using minimally sorted VCDW as a fine aggregate replacement for commonly manufactured concrete products, such as concrete masonry units, was investigated. Second, a GIS-based logistical model was developed to site a statewide network of CDW reuse facilities using provenance and quantity data collected over seven years from landfill intake records maintained by the Texas Commission on Environmental Quality (TCEQ). Laboratory testing confirmed that concrete mixtures using 100 percent VCDW aggregate achieved compressive strengths suitable for masonry product applications, offering a pathway for further exploration and development. The GIS-based analysis also confirmed waste stream availability that is necessary to support sustained production. An optimized network of 23 facilities could capture approximately 6,527,768 short tons (5,921,891.6 metric tons), or 88.4 percent, of the average annual CDW landfilled leading to strong economic viability based on current market value of end-product. This study provides a foundational framework for advancing a scalable circular economy for CDW in Texas and other regions facing similar waste management challenges. Construction and Demolition Waste Circularity Recycling Concrete Aggregates Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Globally, the construction sector is a resource-intensive domain, consuming approximately 25% of all extracted natural resources and being responsible for up to 30% of global waste generation (Papargyropoulou, 2011). The extensive generation of waste from the built environment represents a critical global challenge in sustainability and resource management. Construction and demolition waste (CDW), a complex and highly heterogeneous solid waste stream, includes concrete, asphalt, metals, wood, gypsum, brick, glass, and plastic products (EPA, 2020).The scale of this waste stream places a substantial burden on national waste infrastructure. In 2018 alone, the United States generated approximately 600 million tons of CDW, more than double the amount of municipal solid waste (MSW) produced during the same year. This makes CDW the single largest contributor by volume to the national solid waste stream (EPA, 2020). Landfilling CDW also carries considerable environmental consequences, significantly contributing to greenhouse gas (GHG) emissions, including methane (Poon et al., 2004 ; Levis, 2008 ; Ding et al., 2016 ). Projections warn of a potential fourfold increase in natural resource consumption and a 75% rise in global waste generation by 2050 if current practices persist (EurActiv, 2011). Despite growing awareness, landfill remains the dominant disposal method for CDW. Globally, around 35% of CDW is still landfilled (Karbirifar, 2020; Menegaki, 2018 ), and in the UK, that figure reached 50% (Ghaffar, 2020). China is currently the leading producer of CDW, generating approximately 2.3 billion tons annually (Chen et al., 2021 ), while the United States landfilled 143.8 million tons of CDW in 2018 alone (EPA, 2020). In the European Union, more than one-third of the 820 million tons of CDW generated each year enters the MSW stream (Galvez-Martos et al., 2018; Chen et al., 2021 ). These statistics underscore the urgent need for enhanced reuse and recovery strategies to reduce environmental impacts and limit reliance on finite raw materials. The fundamental impediment to widespread CDW recycling is the lack of positive economic incentive and accessible market opportunity. Caldera et al. ( 2020 ) highlight that the absence of a formal and profitable marketplace for recycled CDW materials continues to hinder their adoption. A significant economic challenge lies in the conversion of mixed, contaminated demolition waste into a high-quality aggregate suitable for fabrication. This problem is especially acute for "Vertical" CDW (VCDW) from buildings, where the high cost of sorting and processing mixed debris often outweighs the value of the recovered material. This study explores this core economic and logistical barrier with an integrated, two-pronged strategy. First, it evaluates the technical feasibility of reusing minimally-sorted VCDW as a 100% fine aggregate substitute for concrete sand, specifically for high-volume, low-bearing or non-structural applications. Second, using this foundation, it develops a logistical and economic framework for potential implementation. The state of Texas is selected as a case study to model this implementation landscape, proposing a coordinated network of CDW reuse facilities designed to create a profitable, circular pathway for this challenging waste stream. 1.1 Literature Review Systemic Barriers to CDW Circularity A critical factor defining the CDW problem is its source, with a highly disproportionate 90% of the material originating from demolition activities, as opposed to new construction or renovation residuals (Arredondo Zepeda and Brunz LLC, 2021). The waste burden is intrinsically linked to the end-of-life phase of structures. This structural characteristic necessitates a targeted response from the civil construction industry to develop sustainable, scalable, and economically viable solutions to mitigate this escalating environmental liability. Despite widespread recognition of the environmental imperative to increase material recovery, industry practices continue to follow a linear economic model. A national survey by the EPA (2012) found that 63.6% of CDW-generating firms still dispose of mixed debris directly at landfills or transfer stations, and only 42.9% of project owners mandate recycling or diversion plans in their contracts. The infrastructure to support CDW processing is also limited; fewer than 1% of demolition contractors own the equipment necessary to independently recycle materials (Zhao, 2010). This leads to a reliance on third-party recyclers, often located far from the point of waste generation, thereby increasing logistical complexity and hauling costs. From an economic standpoint, CDW circularity faces multiple obstacles. The low bulk density of most CDW materials (Bolton, 1995 ) increases handling and sorting challenges, while also inflating transport costs (Tchobanoglous & Kreith, 2002 ). When landfills are geographically closer than recycling facilities, the default disposal option becomes obvious (Jin et al., 2017 ). Additional indirect costs associated with managing on-site recycling programs further deter participation from general contractors and project owners. At the same time, virgin materials are often cheaper and more consistent in quality than recycled alternatives (Yuan, 2017 ). Recycled aggregates are typically relegated to low-grade applications (Jin et al., 2017 ) such as road subbase or fill (Statista, 2023 ), due to perceived or actual inferior performance (Akhatar and Sarmah, 2018). This lack of quality assurance contributes to limited market acceptance, with many architects and engineers unwilling to specify recycled products (Mueller 2022 ). As a result, significant volumes of potentially reusable material have no established end-market, and manufacturers remain reluctant to invest in production unless supported by subsidies or regulatory mandates. Adding to these systemic barriers is a fundamental flaw in how CDW recycling performance is measured. Aggregating all CDW into a single reporting category masks key differences between Horizontal CDW (HCDW) (relatively clean and uniform materials like concrete and asphalt from transportation infrastructure demolition) and Vertical CDW (VCDW) (complex, mixed debris from buildings, including wood, drywall, brick, and contaminated concrete). HCDW is easier to process and is typically recycled at high rates, often fulfilling policy mandates such as the EU’s Waste Framework Directive (70% reuse by weight). However, this inflates national recycling statistics and creates a false sense of success, while the more challenging VCDW stream is largely neglected. A closer look at 2018 EPA data illustrates this imbalance. Of the 456.6 million tons of CDW reused, approximately 73.2% was concrete and 22.4% asphalt primarily from the HCDW stream. In contrast, when isolating VCDW, landfilling rates for non-metal building components ranged from 70% to nearly 88% (Table 1 ), underscoring a systemic failure to manage building-derived waste effectively. In Texas, the disparity is even more pronounced. The state landfilled an estimated 7.4 million short tons of CDW annually during the study period, yet diverted only 5.51% for reuse. This figure is based only on waste entering landfills, since source-separated materials are not tracked by the Texas Commission on Environmental Quality (TCEQ). Even after adjusting for concrete bias, Texas's diversion rate remains less than half the national average. This reflects a broader lack of infrastructure, market development, and regulatory consistency, especially in the vertical waste stream, where reuse pathways are nascent or nonexistent. Table 1 Vertical Construction and Demolition Waste (VCDW) landfilled in the United States, characterization derived from EPA Fact Sheet, 2018 (Displayed in Millions of Tons) Material Generated Reused Landfilled % Landfilled Concrete 102 30.8 71.2 69.8% Wood 39.5 9.9 29.6 74.9% Drywall 15.2 2 13.2 86.8% Metals 4.7 3.6 1.1 23.4% Brick/Tile 12.3 1.5 10.8 87.8% Asphalt Shingles 15.1 2.1 13 86.1% Total 188.8 49.9 138.9 Technical Innovations and Strategic Opportunities for Mixed CDW Reuse A study conducted by Arredondo Zepeda & Brunz LLC, 2021, concludes that landfilling will remain the dominant CDW management method until the private sector advances a scalable reuse solution. Although the study offers no specific vision, recent research shows promising developments. Mueller ( 2022 ) proposes a method to process unsorted CDW, potentially removing costly sorting steps. Similarly, Robayo-Salazar et al. ( 2022 ) are exploring ways to reuse finer, highly contaminated materials often excluded from recycling. These innovations signal a shift toward more flexible, integrated CDW management systems that could significantly reduce landfill dependency and support a broader move toward circular construction practices. Sadeghian ( 2025 ) supports this finding by proposing that geopolymers derived from CDW can substantially alleviate landfill pressures. Similarly, Yehya et al. ( 2025 ) advocate for the reuse of demolition waste in hollow block production, a practice aligned with circular economy principles. The conspicuous absence of a cost-effective, high-volume methodology for transforming highly mixed C&D debris into a secondary construction product defines a critical knowledge gap in the construction materials science and management domains. Previous research has largely concentrated on the technical properties of clean, homogeneous streams such as Recycled Concrete Aggregate (RCA) or Recycled Asphalt Pavement (RAP). Limited academic investigation has focused on the effective, economical, and quality-controlled processing of unsorted or minimally sorted, highly mixed C&D debris for integration into high-value applications, specifically structural materials like concrete. The concrete industry is specifically targeted due to its substantial annual material consumption volume, which represents the greatest potential for immediate, high-volume market absorption and, consequently, rapid economic turnover for both the demolition and recycling sectors. To directly address the core economic impediment, the central hypothesis guiding this research posits that extensive, manually intensive, and costly sorting techniques may not constitute a mandatory prerequisite for producing an aggregate blend suitable for specific high-volume, low-to-medium strength applications within vertical building construction. The validation of this hypothesis would offer a paradigm shift in the economic feasibility of C&D recycling. 1.3 Study Aims and Objectives This study seeks to address the significant underutilization of vertical construction and demolition waste (VCDW) by proposing a combined materials science and construction management strategy. By integrating logistical planning with technical validation, the research aims to develop a market-based framework that not only demonstrates the feasibility of recycling VCDW into fine aggregates for concrete applications, but also identifies a regionally coordinated system for waste stream availability and sustained processing in Texas. The primary aim is to design a coordinated siting strategy for a network of CDW reuse facilities across Texas, with the goal of maximizing material diversion from landfills and directing it toward the production of recycled fine aggregate for use in low- to moderate-strength concrete products. In doing so, the study offers a preliminary blueprint for municipal planners, policymakers, and industry stakeholders to operationalize circular economy principles in one of the most challenging segments of the construction waste stream. This study makes a two-pronged original contribution: 1. Technical Feasibility of VCDW as Fine Aggregate in Concrete : Conduct preliminary experimentation to assess the performance of blended VCDW aggregates as fine aggregate replacements in concrete mix designs to determine their suitability non-structural or low-load-bearing applications, thereby unlocking a viable reuse pathway for the processed waste stream. 2. Logistical Modeling for CDW Reuse in Texas : Develop a data-driven siting strategy for CDW reuse facilities based on the actual volumes of CDW currently landfilled in the state of Texas. This includes compiling facility intake data from the Texas Commission on Environmental Quality (TCEQ) and applying Geographic Information System (GIS) modeling to identify optimal facility locations, ensure adequate feedstock supply for local concrete plants, and estimate potential operational revenue for economic viability. By partially diverting CDW from landfill and repurposing it into secondary construction materials, the proposed strategy contributes to reducing landfill volumes, conserving virgin resources, and stimulating new economic activity within the construction sector. Ultimately, the findings aim to equip stakeholders with a regionally adaptable framework for accelerating the transition toward circularity in construction and demolition waste management. 2. Methodology 2.1 Technical Feasibility of Mixed CDW as Fine Aggregate in Concrete The following section provides a description of the materials used to make a series of concrete cylinders subsequently mechanically evaluated for potential compressive strength development. Construction and demolition waste (CDW) was processed and combined to produce a fine aggregate blend. The constituent materials were obtained directly from a landfill located in Conroe, Texas. The age, provenance, and prior function of the materials were undetermined. No cleaning procedures were performed. The materials were pre-sorted for crushing to a specified gradation in accordance with ASTM C33-23 (Table 2 ) subsequently re-blended according to the determined volumetric proportions as seen in Fig. 1 (Mueller 2022 ). Several blends were formulated to replicate representative building types with varying material compositions as seen in Table 3 (Dempewolf 2020 ). The percentage composition of each blend was established based on the average volumetric fractions of constituent materials typically observed in industrial, commercial, and mid-rise building structures. These averages were derived from a quantitative assessment conducted as part of a case study involving actual buildings. Although variations in material content and quantity exist among different typologies, the selected volumetric ranges were considered representative of typical construction practices. The CDW aggregate blend comprised commonly used construction materials, including concrete, brick, drywall, wood, glass, granite, marble, composite countertop material, ceramic tile, and ceiling tile. The volumetric proportions of these constituents are presented in Table 3 . The aggregate compacted unit weight or bulk density of the blended material was determined following ASTM C29-23. Roofing materials (e.g., asphalt shingles), insulation products, and metallic materials, although prevalent in construction, were excluded from the blends due to the absence of appropriate laboratory equipment for their adequate processing. This exclusion is acknowledged as a limitation of the present investigation. A concrete mixture was designed incorporating 100% CDW as the aggregate source. The mixture employed a water-to-cement ratio (w/c) of 0.45 and an aggregate-to-cement volume ratio of 6:1. The CDW aggregates consisted entirely of the processed materials derived from the CDW fine aggregate blend described previously. No natural aggregates were included. The mixture proportion was selected to evaluate the feasibility of use in the manufacturing of concrete hollow block or masonry units. Mixing was performed in a laboratory bowl mixer to ensure uniform distribution of materials. The design aimed to evaluate the feasibility of utilizing 100% recycled aggregates in concrete production while maintaining acceptable compressive strength performance characteristics. Three cylindrical specimen replicates measuring 3 × 6 in. (75 × 150 mm) were prepared for each mixture by applying a combination of vibration and manual compaction to the dry mix. The specimens were cured in molds for 24 hours, demolded, and subsequently stored in a 100% relative humidity (RH) environment at 70°F (21°C). Compressive strength testing was conducted after 28 days of curing in accordance with ASTM C39-23. Table 2 Aggregate gradation Gradation Sieve Size Cumulative Percent Passing 3/8” 100% No.4 75% No.8 60% No.16 45% No.30 30% No.50 15% No.100 5% Pan 0% Table 3 CDW aggregate composition and unit weight CDW Blend Composition (Percent) Materials A B C Concrete 50 25 20 Brick 8 45 20 Drywall 10 8 10 Wood 20 10 15 Glass 8 8 15 Tile / Ceramic 2 2 10 Ceiling Tiles 2 2 10 Unit Weight – tons/yd 3 (kg/m 3 ) 1.084 (1226) 1.033 (1286) 0.925 (1098) 2.2 GIS Modeling for CDW Reuse in Texas Having established the preliminary technical feasibility of the VCDW aggregate, the second aim of this study was to evaluate the logistical and economic viability of a statewide reuse network. To evaluate this pathway, an analysis of waste stream availability, geographic distribution, and processing economics was conducted. The State of Texas was selected as a case study to assess the feasibility and potential economic return of implementing construction and demolition waste (CDW) reuse facilities for fine aggregate production. A coordinated siting strategy for a statewide network of CDW reuse facilities, designed to maximize material diversion from landfills, is developed in this study. A GIS-based location–allocation methodology, a well-established approach in facility siting (Kao et al., 2013 ; Ross et al., 1994 ; Sambiani et al., 2023 ), is employed. Building on the Thiessen polygon method introduced by Richter et al. ( 2019 ), limitations identified in prior studies are addressed through the incorporation of local constraints into the siting process. To account for these factors, multi-criteria decision-making (MCDM) techniques, including the Analytic Hierarchy Process (AHP) and broader Multi-Criteria Decision Analysis (MCDA), are applied (Karimi et al., 2020 ; Zionts, 1979 ; Khan et al., 2018). Material availability data from the Texas Commission on Environmental Quality (TCEQ) are used to support the GIS-based modeling framework, through which optimal facility locations capable of capturing a substantial portion of CDW otherwise destined for disposal are identified. Data Collection Publicly available annual reports submitted to the Texas Commission on Environmental Quality (TCEQ) by Type I, Type IV, and combined Type I/IV landfills were compiled for the period 2015–2022. Although complete FY2022 data was unavailable at the time of collection, the selected seven-year span provided a robust dataset, effectively smoothing anomalies associated with pandemic-related disruptions in the construction industry (Ataei et al., 2021 ; Duan et al., 2023 ). Only landfills reporting non-zero CDW intake were included, resulting in a final dataset of 166 facilities—slightly above the 159–161 range typically cited by TCEQ, likely due to the inclusion of sites nearing closure that continued to report activity. All data were cross-verified with TCEQ’s GI-611 permit list and validated against the agency’s annual Municipal Solid Waste in Texas: A Year in Review reports. For each landfill, total annual CDW tonnage was recorded and converted to average daily intake, assuming a 252-working-day year in alignment with standard business and financial modeling conventions. Thiessen Polygon Creation Thiessen polygons were constructed around each landfill to delineate catchment basins, defined as the presumptive geographic source areas for CDW intake. Although travel-time-based service areas would offer greater precision, the Thiessen approach provides a practical and spatially normalized approximation at the statewide scale (Fig. 2 ). Each polygon was then attributed with the corresponding landfill’s average annual CDW tonnage through a spatial one-to-one join, allowing for accurate assignment of waste volumes to their respective catchment areas within the GIS environment. Fishnet Generation A uniform point-based fishnet was generated using a 5.5-mile (8.85 km) grid spacing to ensure compatibility with the ArcGIS Online ‘Best Facilities’ tool, which imposes a maximum limit of 1,000 input variables per run. This spacing was strategically selected to maintain full spatial coverage while guaranteeing that each Thiessen polygon contained at least one fishnet point, thereby preserving spatial granularity and the integrity of volume assignments. Alternative grid sizes (5-mile and 6-mile) were evaluated; however, only the 5.5-mile spacing successfully balanced tool constraints with the requirement for comprehensive statewide representation. Attribute Table Calculation The ‘Summarize Within’ tool was used to proportionally allocate CDW quantities to each fishnet point based on the Thiessen polygon in which it was located. New attribute fields were added to represent both annual and daily CDW quantities per point. As a result, each fishnet point came to represent a geographically distributed unit of daily CDW demand, establishing a high-resolution demand surface suitable for subsequent routing, siting, and facility optimization analysis. Fishnet Splitting by Region To comply with ArcGIS Online’s processing constraints, the statewide fishnet was subdivided into 16 regional layers, corresponding to the established boundaries of the Texas Commission on Environmental Quality (TCEQ) regions (Fig. 3 ). These regions were selected based on their administrative relevance, demographic representation, and practical scale for waste infrastructure planning. Each point within the regional layers retained its assigned CDW volume, preserving the integrity of the spatial demand distribution for subsequent analysis and facility siting. Optimization with the ‘Best Facilities’ Tool Facility siting optimization was conducted using ArcGIS Online’s ‘Best Facilities’ tool, incorporating a tiered capacity model to reflect varying operational scales. A conservative upper limit of 1,500 short tons per day (tpd) was selected, based on the highest observed seven-year average intake from a single landfill (1,494 tpd), and rounded for clarity. However, a CDW reuse facility processing 1,500 short tons per day would generate substantial transportation demand. While a theoretical truckload carrying 14 short tons would require approximately 107 trucks per day (one every 4–5 minutes), actual conditions are often more demanding due to the lower bulk density of mixed CDW materials. This results in significantly more trips, increasing traffic congestion and placing considerable strain on local road infrastructure (Bolton, 1995 ). At this scale, and assuming 15-ton payload vehicle and a 10-hour workday, a 1,500 tpd facility would necessitate more than 100 truckloads per day, or approximately one truck arrival every six minutes underscoring the potential burden on local transportation infrastructure. To assess scalability and regional feasibility, additional scenarios using 1,000 tpd and 500 tpd thresholds, representing lower yet still economically viable facility sizes, were also modeled. These scenarios enabled a comparative analysis of facility distribution and site viability across different intake capacities. For each TCEQ region, the theoretical maximum number of reuse facilities was calculated by dividing the region’s total landfilled CDW volume by the 500 tpd threshold, considered the minimum economically viable scale. The ArcGIS Online ‘Best Facilities’ tool was then configured with three key constraints: (1) Material haul times were limited to within one hour to maintain cost competitiveness by minimizing transportation distance and associated fuel and labor expenses. (2) Facility intake was capped at 1,500 tons per day (tpd), based on the maximum daily CDW intake observed across the seven-year dataset, ensuring modeled facilities reflect realistic upper operational limits. (3) The number of candidate facilities was constrained to not exceed the calculated regional maximum, ensuring alignment with projected CDW volumes and maintaining economic feasibility within each TCEQ region (Fig. 4 ). See Supplementary Information (SI) for results figures for other regions. 3. Results and Discussion 3.1 Technical Feasibility of VCDW Aggregates The preliminary technical investigation confirmed the feasibility of using 100% minimally-sorted VCDW as a fine aggregate replacement. Figure 5 presents the compressive strength results for all three mixture designs. All mixtures achieved compressive strengths exceeding the minimum requirement for non-load-bearing masonry units (500 psi [3.45 MPa]). Notably, Mixtures A and B demonstrated strengths meeting the specifications for structural masonry applications (2000 psi [13.79 MPa]) (ASTM C90-24). These results indicate the feasibility of utilizing 100% CDW aggregates in masonry unit production based on mechanical performance alone. However, further investigation is necessary to evaluate long-term durability and dimensional stability characteristics. Still, this preliminary investigation offers insight on the feasibility of utilizing a CDW blend, including building demolition mixed waste, into the production of construction product. This demonstrate a potential high-volume application and pathway for achieving circularity for this waste stream. As previously described, supply and price point of the recycled material also needs to be addressed to ensure market viability. 3.2 Logistical Model for CDW Reuse in Texas Having established the technical feasibility of the VCDW aggregate product, this section details the results of the statewide logistical and economic analysis. Three siting scenarios, 500, 1,000, and 1,500 short tons per day (tpd), were developed to evaluate statewide configurations for construction and demolition waste (CDW) reuse facilities. Each scenario models a different facility scale to assess trade-offs in material capture, system utilization, and infrastructure footprint. Table 4 summarizes the number of facilities supportable within each TCEQ region, with a minimum of one facility per region enforced. Statewide, 66 facilities would be required under the 500 tpd scenario, compared to 44 under the 1,000 tpd scenario, and just 29 facilities under the 1,500 tpd model. Table 4 Number of CDW Reuse Facility Capacity by TCEQ Region Region 500 tpd 1,000 tpd 1,500 tpd 01 - Amarillo 1 1 1 02 - Lubbock 2 1 1 03 - Abilene 2 1 1 04 - DFW Metroplex 16 11 6 05 - Tyler 1 1 1 06 – El Paso 1 1 1 07 - Midland 3 2 1 08 – San Angelo 1 1 1 09 - Waco 2 1 1 10 - Beaumont 2 1 1 11 - Austin 5 3 2 12 - Houston 20 13 7 13 – San Antonio 6 4 2 14 – Corpus Christi 2 1 1 15 - Harlingen 1 1 1 16 - Laredo 1 1 1 Sum 66 44 29 It is important to note that the 1,500 tpd facility counts were rounded up. As a result, some regions showing only one facility may not produce enough CDW to sustain operations at that scale. Regions 1, 5, 6, 8, 15, and 16 should therefore be approached cautiously in early implementation, pending site-specific feasibility analysis. Regions 2, 3, 9, 10, and 14 may also warrant closer review. Conversely, regions 4 (DFW), 11 (Austin), 12 (Houston), 13 (San Antonio), and potentially 7 (Midland) emerged as the most viable for initial deployment, with regions 4 and 12 offering the strongest potential based on volume and efficiency. In the 500 tpd scenario (Fig. 6 ), the model proposes a distributed network of 66 facilities across Texas. This configuration captures approximately 7.14 million of the 7.38 million short tons (6.48 of 6.70 million metric tons) of CDW generated annually between 2017 and 2021—equating to 96.78% coverage . With a total annual processing capacity of 8.316 million short tons (7.544 million metric tons), the scenario achieves an impressive utilization rate of 85.91%, demonstrating both high efficiency and moderate room for expansion. The 1,000 tpd scenario (Fig. 7 ) reduces the number of sites to 44 while capturing approximately 6.94 million short tons (6.30 million metric tons), or 94.05% of available CDW. The fleet’s capacity increases to 11.1 million short tons (10.07 million metric tons), but the initial utilization rate decreases to 62.6%. This scenario sacrifices some coverage in favor of scalability and regional operational flexibility, particularly in fast-growing or high-variance areas. In the most consolidated configuration, the 1,500 tpd scenario (Fig. 8 ) requires just 29 facilities to provide statewide coverage. Despite the lower site count, this model still captures 6.88 million short tons (6.24 million metric tons), or 93.21% of CDW material. With a total fleet capacity of 10.962 million short tons (9.945 million metric tons), the system achieves a utilization rate of 62.77%. This scenario minimizes infrastructure requirements and may be best suited to areas where land availability, permitting constraints, or community impact may be primary concerns. While all three models are viable, the 500 tpd network delivers the highest material capture and utilization, whereas the 1,000 and 1,500 tpd scenarios offer surplus capacity and operational resilience. However, at the state level, the marginal benefits of siting additional facilities diminish quickly. The gains in CDW capture, only 3.57% from the 1,500 to 500 tpd scenario, do not justify the significantly larger infrastructure footprint, particularly when new facilities must be located in less optimal, lower-yield areas. These findings indicate that a network of fewer, high-throughput facilities may represent the most efficient and cost-effective strategy for statewide CDW reuse implementation. Regions 4 (DFW) and 12 (Houston) emerge as top candidates for early deployment, each capable of independently capturing over 98–99% of their regional CDW material. Regions 11 (Austin) and 2 (Lubbock) also demonstrate strong potential, with capture rates of approximately 97% and 94%, respectively. Notably, these regions align with high population densities and major urban centers characterized by elevated construction activity and limited access to raw material sources. This reinforces the strategic importance of CDW reuse in these areas, not only to support circular economy goals, but also to meet local demand for construction materials. The regional analysis of construction and demolition waste (CDW) facility siting reveals stark contrasts across the 16 TCEQ regions. • Region 1 (Amarillo) : Lacks sufficient population density and material volume. A single facility capturing 100% of the region’s CDW would leave nearly three-quarters of the area uncovered and is not viable for development. • Region 2 (Lubbock) : Shows a promising concentration of population and CDW. A single 500 tpd facility could be fully supported, while a second facility yields minimal material gain. • Region 3 (Abilene) : Offers marginal feasibility. One 500 tpd facility near Abilene reaches only 81% utilization, and two facilities would be underutilized. • Region 4 (DFW) : Presents one of the most favorable opportunities. Its population and CDW are well distributed, allowing a 1,500 tpd facility to capture over 99% of material with 88% utilization. • Region 5 (Tyler) : Cannot support even a 500 tpd facility efficiently (59% utilization) due to sparse northern populations. • Region 6 (El Paso) : Performs even worse (just 39% utilization) since the population is concentrated in a small urban area. • Region 7 (Midland/Odessa) : Is viable for one facility, fully supporting 500 tpd with potential to scale to 1,000 tpd, but adding more provides diminishing returns. • Region 8 (San Angelo) : Is uneconomical, with only 30% utilization for a single facility. • Region 9 (Waco) : Could support one well-placed facility near Waco/Killeen with room to scale. • Region 10 (Beaumont) : Supports two 500 tpd facilities, with a possible third as demand grows. • Region 11 (Austin) : Has strong central population density that supports two facilities—one at 500–1,000 tpd (north) and another at 1,000–1,500 tpd (south). • Region 12 (Houston) : Is ideal, much like DFW. Seven 1,500 tpd facilities can capture nearly all CDW; expanding yields only a 0.4% gain and introduces competition. • Region 13 (San Antonio) : Is best served with two 1,000 tpd facilities, as population and CDW are tightly clustered in the eastern portion. • Region 14 (Corpus Christi) : Can support one 500 tpd facility with some expansion potential, but a second facility is underutilized. • Region 15 (Harlingen) : Struggles to support even one facility (70% utilization at best). • Region 16 (Laredo) : Is the least viable. A 500 tpd facility would be only 23% utilized due to widely dispersed communities. In summary, the least effective regions for siting CDW reuse facilities, based on simulated post-placement utilization, are Regions 16, 8, 6, 5, 1, and 15. These findings underscore the importance of population concentration and material availability in determining economic viability. Regions 4 (DFW), 12 (Houston), 11 (Austin), and 2 (Lubbock) emerge as the most promising areas for initial facility development due to their strong CDW generation and centralized population centers. Geographical and supplemental information can be found at corresponding manuscript SI. To minimize potential facility overlap and inefficiencies, an optimized configuration is proposed (Fig. 9 ). This scenario sites 23 facilities strategically across Texas and captures approximately 6,527,768 short tons (5,921,891.6 metric tons), or 88.4% of the average annual CDW landfilled during the study period. Of these, 15 facilities are sized at 1,500 tpd, five at 1,000 tpd, and three at 500 tpd, yielding a total fleet capacity of 7.285 million short tons (6.609 million metric tons) annually. This scenario achieves an initial fleet utilization rate of 89.6%, outperforming all comparable capacity-based scenarios, particularly the 1,000 tpd and 1,500 tpd options, which averaged just 62–63% utilization. Only TCEQ regions deemed viable based on previous regional analysis are included in this proposal; regions that do not meet minimum CDW availability thresholds were excluded. Revenue Potential and Urban Implementation If construction and demolition waste (CDW) intake were processed into a fine aggregate material suitable for use as a concrete sand replacement as previously demonstrated, the resulting product could generate substantial revenue. Assuming a competitive market sale price of $ 40 per ton for processed fine aggregates (Aggregate Markets, 2025 ) and 252 operating days per year, a fully operational 1,500 tpd facility could generate approximately $ 15 million in operational revenue annually. Similarly, a 1,000 tpd facility could produce around $ 10 million, while a 500 tpd facility might yield approximately $ 5 million per year. When scaled across the entire facility network modeled in the CDW reuse study, total operational revenue from aggregate sales could approach $ 261 million annually for the State of Texas. This based on the assumption of 6.4 million tons processed by the fleet of recycling plants. This revenue stream is strengthened when combined with processing fees (or tipping fee). The average landfill tipping fee in Texas is approximately $ 45 per ton (TCEQ 2025), though this figure can vary significantly as each landfill operator sets their own fee. If a recycling plant were to charge a competitive processing fee, this component alone could represent up to $ 294 million annually. This brings the total potential operational revenue for the statewide facility network to over $ 555 million per year, highlighting strong economic viability for CDW reuse infrastructure in Texas. To illustrate the potential output of a single CDW reuse facility in an urban context, an example scenario was developed for a plant sited in the Greater Houston area (Region 12). The region currently contains thirteen active landfills, comprising four Type I (municipal solid waste) and nine Type IV (construction and demolition waste) facilities. The estimated remaining service life of these landfills varies, with four projected to close within ten years and five within twenty years. One of the urban landfills accepting an average of approximately 1.1 million tons of combined MSW and CDW annually, while two Type IV landfills each accepting roughly 340,000 tons of CDW per year. To effectively service the area, three 1,500 tpd CDW reuse facilities were sited, diverting approximately 1.1 million tons per year. Based on an average material density of 1.014 ton per cubic yard (Table 3 ), the annual fine aggregate production output from a single facility could support the manufacturing of approximately 35 million concrete masonry units (CMUs). This quantity would be sufficient to supply three to five high-output CMU manufacturing plants; in turn providing enough material for the construction of approximately 15,000 single-family residential units averaging 2,000 ft² (186 m 2 ) each. Under the proposed scenario for the greater Houston area, siting CDW reuse plants could also achieve a 99% capture rate of CDW, which would substantially extend the service life of nearby urban landfills. Given that approximately 60 million cubic yards (46 million cubic meters) of concrete are produced annually in Texas (Texas Aggregates & Concrete Association, 2023), this model demonstrates a viable pathway for manufacturing concrete products incorporating recycled fine aggregates. Strategically locating CDW recycling plants in major urban centers such as Houston could substantially reduce natural sand demand while extending the operational lifespan of urban landfills and advancing statewide recycling and circularity goals. 5. Conclusion The findings of this study demonstrate that substantial opportunities exist to advance circularity within the construction and demolition waste (CDW) stream, particularly within the understudied vertical CDW (VCDW) sector. Through combined materials characterization and geospatial modeling, the research shows that mixed CDW, common to the vertical construction sector can be technically and logistically repositioned as a viable secondary resource for producing fine aggregates used in low- to moderate-strength concrete applications. Laboratory testing indicated that concrete mixtures incorporating 100% recycled VCDW-derived fine aggregates achieved compressive strengths suitable for non-load-bearing and, in some cases, structural masonry uses. These results provide preliminary evidence that highly mixed, minimally sorted CDW materials can be repurposed without extensive preprocessing, helping to address one of the major economic barriers associated with CDW recycling. The statewide logistical analysis further showed that establishing a coordinated network of CDW reuse facilities in Texas is both feasible and economically promising. Using a GIS-based location–allocation framework, optimal facility siting was identified, allowing up to 88.4% of currently landfilled CDW to be captured. The modeling also indicated that higher-capacity, regionally distributed facilities offer superior utilization compared to larger numbers of smaller sites, especially in dense metropolitan regions such as Dallas–Fort Worth, Houston, Austin, and San Antonio. Projected revenue potential from recycled aggregate production and tipping fees underscores the strong economic drivers that could support a circular CDW management system. Overall, the study provides a foundational framework for advancing CDW circularity by integrating technical feasibility with spatially informed infrastructure planning, demonstrating a scalable, market-aligned pathway for VCDW reuse that offers substantial environmental and economic benefits. Declarations Funding This study was supported by Texas A&M University, and, partially, under the sponsorship of the National Science Foundation, I-Corps program at Texas A&M University. Competing Interests The authors declare that they have no financial interests. Author Contributions Dr. Julie Ann Hartell is the principal research investigator responsible for conceptualization, funding acquisition and project management; along with devising research methodology and investigation, writing of original draft, review and editing. Research assistant, Mr. Billy L. Jones, performed research tasks, data collection and analysis, and writing of original draft. Dr. Ashrant Aryal, co-principal investigator, was responsible for conceptualization along with devising research methodology and investigation, review and editing of manuscript. 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Supplementary Files SupplementalInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Dec, 2025 Reviews received at journal 12 Dec, 2025 Reviews received at journal 07 Dec, 2025 Reviewers agreed at journal 07 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers agreed at journal 30 Nov, 2025 Reviewers invited by journal 30 Nov, 2025 Editor assigned by journal 27 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 20 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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09:38:08","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122329,"visible":true,"origin":"","legend":"","description":"","filename":"02a8609b195a4d45a1c9c2251304e38b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/f9ad477db9b34a95ea3d0c4f.xml"},{"id":97554050,"identity":"2d8461ff-fc96-4e81-839d-8baaa8d66c61","added_by":"auto","created_at":"2025-12-05 18:04:25","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130500,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/fda7cf77fca0d96bac1834db.html"},{"id":97554027,"identity":"ba74aa8a-a66c-4339-8b9c-d76cc7c2fb08","added_by":"auto","created_at":"2025-12-05 18:04:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1560039,"visible":true,"origin":"","legend":"\u003cp\u003eExample of (a) processed CDW material by type and (b) blended at specified particle gradation (Mueller 2022)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/2ce4f4b76ed4b03257c57c52.png"},{"id":97554030,"identity":"ff36dedc-a1c0-4361-bef8-8b094c97a990","added_by":"auto","created_at":"2025-12-05 18:04:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":394918,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Thiessen polygons delineating landfill catchment basins in Texas\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/0f7faf42edb8d5cd14eedfdb.png"},{"id":97554028,"identity":"3c871d80-8f35-4f11-891c-be582d4da80a","added_by":"auto","created_at":"2025-12-05 18:04:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":195279,"visible":true,"origin":"","legend":"\u003cp\u003eThe fishnet split by TCEQ Region\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/2ff7271d6fe9b5ec29f753f7.png"},{"id":97554038,"identity":"b2b90492-6a13-4469-bd57-481f8bac9d6c","added_by":"auto","created_at":"2025-12-05 18:04:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":656624,"visible":true,"origin":"","legend":"\u003cp\u003eExample of facility optimization analysis in the Houston area (TCEQ Region 12).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/96cef7a0cf708c4aed7cfbb8.png"},{"id":97554045,"identity":"854df457-5944-483f-8518-39028e86b820","added_by":"auto","created_at":"2025-12-05 18:04:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25474,"visible":true,"origin":"","legend":"\u003cp\u003eAverage 28-day compressive strength (psi) for concrete mixes using 100% CDW aggregate (1000 psi = 6.895 MPa)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/167a00759b788fa515bbc8c4.png"},{"id":97554036,"identity":"ff75a13a-d676-4cfb-a95b-841cd98a919d","added_by":"auto","created_at":"2025-12-05 18:04:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":292582,"visible":true,"origin":"","legend":"\u003cp\u003eThe 500 short ton per day siting scenario\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/d54407df1b392b8f4555b3d7.png"},{"id":97554039,"identity":"d1784f93-fd73-4804-9921-eeeddfe6627a","added_by":"auto","created_at":"2025-12-05 18:04:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":287013,"visible":true,"origin":"","legend":"\u003cp\u003eThe 1000 short tons per day siting scenario\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/9f805f13dcd0aaf1a4ce5236.png"},{"id":97672947,"identity":"d546bbdd-f59a-4e01-b6c0-e5c52f94c308","added_by":"auto","created_at":"2025-12-08 09:39:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":281263,"visible":true,"origin":"","legend":"\u003cp\u003eThe 1500 short ton per day siting scenario\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/869b0c264427ad917a4dcfbd.png"},{"id":97554044,"identity":"f439d178-a8c7-42de-96c3-155296b48d8f","added_by":"auto","created_at":"2025-12-05 18:04:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":591601,"visible":true,"origin":"","legend":"\u003cp\u003eThe optimized 23-facility siting scenario, showing facility placement, size, and service areas\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/bf8a06c64076d6ac3e7a2afb.png"},{"id":97678760,"identity":"a8cf8aff-e40a-4277-b585-9349327062ee","added_by":"auto","created_at":"2025-12-08 09:56:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5292322,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/9d726d0e-d44d-4e6f-9b9d-65d034ac4d04.pdf"},{"id":97554032,"identity":"c13d8cd9-a478-4b97-8756-1809507dc7f1","added_by":"auto","created_at":"2025-12-05 18:04:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6497320,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8166632/v1/c17f8e10698f129e1e5ddcc2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unlocking the Circular Potential of Construction and Demolition Waste: A Pathway for Texas’ Construction Industry","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, the construction sector is a resource-intensive domain, consuming approximately 25% of all extracted natural resources and being responsible for up to 30% of global waste generation (Papargyropoulou, 2011). The extensive generation of waste from the built environment represents a critical global challenge in sustainability and resource management. Construction and demolition waste (CDW), a complex and highly heterogeneous solid waste stream, includes concrete, asphalt, metals, wood, gypsum, brick, glass, and plastic products (EPA, 2020).The scale of this waste stream places a substantial burden on national waste infrastructure.\u003c/p\u003e\u003cp\u003eIn 2018 alone, the United States generated approximately 600\u0026nbsp;million tons of CDW, more than double the amount of municipal solid waste (MSW) produced during the same year. This makes CDW the single largest contributor by volume to the national solid waste stream (EPA, 2020). Landfilling CDW also carries considerable environmental consequences, significantly contributing to greenhouse gas (GHG) emissions, including methane (Poon et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Levis, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ding et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Projections warn of a potential fourfold increase in natural resource consumption and a 75% rise in global waste generation by 2050 if current practices persist (EurActiv, 2011).\u003c/p\u003e\u003cp\u003eDespite growing awareness, landfill remains the dominant disposal method for CDW. Globally, around 35% of CDW is still landfilled (Karbirifar, 2020; Menegaki, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and in the UK, that figure reached 50% (Ghaffar, 2020). China is currently the leading producer of CDW, generating approximately 2.3\u0026nbsp;billion tons annually (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while the United States landfilled 143.8\u0026nbsp;million tons of CDW in 2018 alone (EPA, 2020). In the European Union, more than one-third of the 820\u0026nbsp;million tons of CDW generated each year enters the MSW stream (Galvez-Martos et al., 2018; Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These statistics underscore the urgent need for enhanced reuse and recovery strategies to reduce environmental impacts and limit reliance on finite raw materials.\u003c/p\u003e\u003cp\u003eThe fundamental impediment to widespread CDW recycling is the lack of positive economic incentive and accessible market opportunity. Caldera et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlight that the absence of a formal and profitable marketplace for recycled CDW materials continues to hinder their adoption. A significant economic challenge lies in the conversion of mixed, contaminated demolition waste into a high-quality aggregate suitable for fabrication. This problem is especially acute for \"Vertical\" CDW (VCDW) from buildings, where the high cost of sorting and processing mixed debris often outweighs the value of the recovered material.\u003c/p\u003e\u003cp\u003eThis study explores this core economic and logistical barrier with an integrated, two-pronged strategy. First, it evaluates the technical feasibility of reusing minimally-sorted VCDW as a 100% fine aggregate substitute for concrete sand, specifically for high-volume, low-bearing or non-structural applications. Second, using this foundation, it develops a logistical and economic framework for potential implementation. The state of Texas is selected as a case study to model this implementation landscape, proposing a coordinated network of CDW reuse facilities designed to create a profitable, circular pathway for this challenging waste stream.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Literature Review\u003c/h2\u003e\u003cp\u003e\u003cb\u003eSystemic Barriers to CDW Circularity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA critical factor defining the CDW problem is its source, with a highly disproportionate 90% of the material originating from demolition activities, as opposed to new construction or renovation residuals (Arredondo Zepeda and Brunz LLC, 2021). The waste burden is intrinsically linked to the end-of-life phase of structures. This structural characteristic necessitates a targeted response from the civil construction industry to develop sustainable, scalable, and economically viable solutions to mitigate this escalating environmental liability.\u003c/p\u003e\u003cp\u003eDespite widespread recognition of the environmental imperative to increase material recovery, industry practices continue to follow a linear economic model. A national survey by the EPA (2012) found that 63.6% of CDW-generating firms still dispose of mixed debris directly at landfills or transfer stations, and only 42.9% of project owners mandate recycling or diversion plans in their contracts. The infrastructure to support CDW processing is also limited; fewer than 1% of demolition contractors own the equipment necessary to independently recycle materials (Zhao, 2010). This leads to a reliance on third-party recyclers, often located far from the point of waste generation, thereby increasing logistical complexity and hauling costs.\u003c/p\u003e\u003cp\u003eFrom an economic standpoint, CDW circularity faces multiple obstacles. The low bulk density of most CDW materials (Bolton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) increases handling and sorting challenges, while also inflating transport costs (Tchobanoglous \u0026amp; Kreith, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). When landfills are geographically closer than recycling facilities, the default disposal option becomes obvious (Jin et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additional indirect costs associated with managing on-site recycling programs further deter participation from general contractors and project owners.\u003c/p\u003e\u003cp\u003eAt the same time, virgin materials are often cheaper and more consistent in quality than recycled alternatives (Yuan, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Recycled aggregates are typically relegated to low-grade applications (Jin et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) such as road subbase or fill (Statista, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), due to perceived or actual inferior performance (Akhatar and Sarmah, 2018). This lack of quality assurance contributes to limited market acceptance, with many architects and engineers unwilling to specify recycled products (Mueller \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, significant volumes of potentially reusable material have no established end-market, and manufacturers remain reluctant to invest in production unless supported by subsidies or regulatory mandates.\u003c/p\u003e\u003cp\u003eAdding to these systemic barriers is a fundamental flaw in how CDW recycling performance is measured. Aggregating all CDW into a single reporting category masks key differences between Horizontal CDW (HCDW) (relatively clean and uniform materials like concrete and asphalt from transportation infrastructure demolition) and Vertical CDW (VCDW) (complex, mixed debris from buildings, including wood, drywall, brick, and contaminated concrete). HCDW is easier to process and is typically recycled at high rates, often fulfilling policy mandates such as the EU\u0026rsquo;s Waste Framework Directive (70% reuse by weight). However, this inflates national recycling statistics and creates a false sense of success, while the more challenging VCDW stream is largely neglected.\u003c/p\u003e\u003cp\u003eA closer look at 2018 EPA data illustrates this imbalance. Of the 456.6\u0026nbsp;million tons of CDW reused, approximately 73.2% was concrete and 22.4% asphalt primarily from the HCDW stream. In contrast, when isolating VCDW, landfilling rates for non-metal building components ranged from 70% to nearly 88% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), underscoring a systemic failure to manage building-derived waste effectively.\u003c/p\u003e\u003cp\u003eIn Texas, the disparity is even more pronounced. The state landfilled an estimated 7.4\u0026nbsp;million short tons of CDW annually during the study period, yet diverted only 5.51% for reuse. This figure is based only on waste entering landfills, since source-separated materials are not tracked by the Texas Commission on Environmental Quality (TCEQ). Even after adjusting for concrete bias, Texas's diversion rate remains less than half the national average. This reflects a broader lack of infrastructure, market development, and regulatory consistency, especially in the vertical waste stream, where reuse pathways are nascent or nonexistent.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVertical Construction and Demolition Waste (VCDW) landfilled in the United States, characterization derived from EPA Fact Sheet, 2018 (Displayed in Millions of Tons)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaterial\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenerated\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReused\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLandfilled\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% Landfilled\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcrete\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e69.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrywall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrick/Tile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e87.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsphalt Shingles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e86.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e188.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTechnical Innovations and Strategic Opportunities for Mixed CDW Reuse\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA study conducted by Arredondo Zepeda \u0026amp; Brunz LLC, 2021, concludes that landfilling will remain the dominant CDW management method until the private sector advances a scalable reuse solution. Although the study offers no specific vision, recent research shows promising developments. Mueller (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) proposes a method to process unsorted CDW, potentially removing costly sorting steps. Similarly, Robayo-Salazar et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) are exploring ways to reuse finer, highly contaminated materials often excluded from recycling. These innovations signal a shift toward more flexible, integrated CDW management systems that could significantly reduce landfill dependency and support a broader move toward circular construction practices. Sadeghian (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) supports this finding by proposing that geopolymers derived from CDW can substantially alleviate landfill pressures. Similarly, Yehya et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) advocate for the reuse of demolition waste in hollow block production, a practice aligned with circular economy principles.\u003c/p\u003e\u003cp\u003eThe conspicuous absence of a cost-effective, high-volume methodology for transforming highly mixed C\u0026amp;D debris into a secondary construction product defines a critical knowledge gap in the construction materials science and management domains. Previous research has largely concentrated on the technical properties of clean, homogeneous streams such as Recycled Concrete Aggregate (RCA) or Recycled Asphalt Pavement (RAP). Limited academic investigation has focused on the effective, economical, and quality-controlled processing of unsorted or minimally sorted, highly mixed C\u0026amp;D debris for integration into high-value applications, specifically structural materials like concrete.\u003c/p\u003e\u003cp\u003eThe concrete industry is specifically targeted due to its substantial annual material consumption volume, which represents the greatest potential for immediate, high-volume market absorption and, consequently, rapid economic turnover for both the demolition and recycling sectors.\u003c/p\u003e\u003cp\u003eTo directly address the core economic impediment, the central hypothesis guiding this research posits that extensive, manually intensive, and costly sorting techniques may not constitute a mandatory prerequisite for producing an aggregate blend suitable for specific high-volume, low-to-medium strength applications within vertical building construction. The validation of this hypothesis would offer a paradigm shift in the economic feasibility of C\u0026amp;D recycling.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Study Aims and Objectives\u003c/h2\u003e\u003cp\u003eThis study seeks to address the significant underutilization of vertical construction and demolition waste (VCDW) by proposing a combined materials science and construction management strategy. By integrating logistical planning with technical validation, the research aims to develop a market-based framework that not only demonstrates the feasibility of recycling VCDW into fine aggregates for concrete applications, but also identifies a regionally coordinated system for waste stream availability and sustained processing in Texas.\u003c/p\u003e\u003cp\u003eThe primary aim is to design a coordinated siting strategy for a network of CDW reuse facilities across Texas, with the goal of maximizing material diversion from landfills and directing it toward the production of recycled fine aggregate for use in low- to moderate-strength concrete products. In doing so, the study offers a preliminary blueprint for municipal planners, policymakers, and industry stakeholders to operationalize circular economy principles in one of the most challenging segments of the construction waste stream. This study makes a two-pronged original contribution:\u003c/p\u003e\u003cp\u003e1. \u003cb\u003eTechnical Feasibility of VCDW as Fine Aggregate in Concrete\u003c/b\u003e: Conduct preliminary experimentation to assess the performance of blended VCDW aggregates as fine aggregate replacements in concrete mix designs to determine their suitability non-structural or low-load-bearing applications, thereby unlocking a viable reuse pathway for the processed waste stream.\u003c/p\u003e\u003cp\u003e2. \u003cb\u003eLogistical Modeling for CDW Reuse in Texas\u003c/b\u003e: Develop a data-driven siting strategy for CDW reuse facilities based on the actual volumes of CDW currently landfilled in the state of Texas. This includes compiling facility intake data from the Texas Commission on Environmental Quality (TCEQ) and applying Geographic Information System (GIS) modeling to identify optimal facility locations, ensure adequate feedstock supply for local concrete plants, and estimate potential operational revenue for economic viability.\u003c/p\u003e\u003cp\u003eBy partially diverting CDW from landfill and repurposing it into secondary construction materials, the proposed strategy contributes to reducing landfill volumes, conserving virgin resources, and stimulating new economic activity within the construction sector. Ultimately, the findings aim to equip stakeholders with a regionally adaptable framework for accelerating the transition toward circularity in construction and demolition waste management.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Technical Feasibility of Mixed CDW as Fine Aggregate in Concrete\u003c/h2\u003e\u003cp\u003eThe following section provides a description of the materials used to make a series of concrete cylinders subsequently mechanically evaluated for potential compressive strength development. Construction and demolition waste (CDW) was processed and combined to produce a fine aggregate blend. The constituent materials were obtained directly from a landfill located in Conroe, Texas. The age, provenance, and prior function of the materials were undetermined. No cleaning procedures were performed.\u003c/p\u003e\u003cp\u003eThe materials were pre-sorted for crushing to a specified gradation in accordance with ASTM C33-23 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) subsequently re-blended according to the determined volumetric proportions as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Mueller \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Several blends were formulated to replicate representative building types with varying material compositions as seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Dempewolf \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The percentage composition of each blend was established based on the average volumetric fractions of constituent materials typically observed in industrial, commercial, and mid-rise building structures. These averages were derived from a quantitative assessment conducted as part of a case study involving actual buildings. Although variations in material content and quantity exist among different typologies, the selected volumetric ranges were considered representative of typical construction practices.\u003c/p\u003e\u003cp\u003eThe CDW aggregate blend comprised commonly used construction materials, including concrete, brick, drywall, wood, glass, granite, marble, composite countertop material, ceramic tile, and ceiling tile. The volumetric proportions of these constituents are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The aggregate compacted unit weight or bulk density of the blended material was determined following ASTM C29-23. Roofing materials (e.g., asphalt shingles), insulation products, and metallic materials, although prevalent in construction, were excluded from the blends due to the absence of appropriate laboratory equipment for their adequate processing. This exclusion is acknowledged as a limitation of the present investigation.\u003c/p\u003e\u003cp\u003eA concrete mixture was designed incorporating 100% CDW as the aggregate source. The mixture employed a water-to-cement ratio (w/c) of 0.45 and an aggregate-to-cement volume ratio of 6:1. The CDW aggregates consisted entirely of the processed materials derived from the CDW fine aggregate blend described previously. No natural aggregates were included. The mixture proportion was selected to evaluate the feasibility of use in the manufacturing of concrete hollow block or masonry units.\u003c/p\u003e\u003cp\u003eMixing was performed in a laboratory bowl mixer to ensure uniform distribution of materials. The design aimed to evaluate the feasibility of utilizing 100% recycled aggregates in concrete production while maintaining acceptable compressive strength performance characteristics. Three cylindrical specimen replicates measuring 3 \u0026times; 6 in. (75 \u0026times; 150 mm) were prepared for each mixture by applying a combination of vibration and manual compaction to the dry mix. The specimens were cured in molds for 24 hours, demolded, and subsequently stored in a 100% relative humidity (RH) environment at 70\u0026deg;F (21\u0026deg;C). Compressive strength testing was conducted after 28 days of curing in accordance with ASTM C39-23.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAggregate gradation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGradation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSieve Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCumulative Percent Passing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3/8\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCDW aggregate composition and unit weight\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eCDW Blend Composition (Percent)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaterials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcrete\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrick\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrywall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTile / Ceramic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCeiling Tiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnit Weight \u0026ndash; tons/yd\u003csup\u003e3\u003c/sup\u003e(kg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.084 (1226)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.033 (1286)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.925 (1098)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.2 GIS Modeling for CDW Reuse in Texas\u003c/h2\u003e\u003cp\u003eHaving established the preliminary technical feasibility of the VCDW aggregate, the second aim of this study was to evaluate the logistical and economic viability of a statewide reuse network. To evaluate this pathway, an analysis of waste stream availability, geographic distribution, and processing economics was conducted. The State of Texas was selected as a case study to assess the feasibility and potential economic return of implementing construction and demolition waste (CDW) reuse facilities for fine aggregate production.\u003c/p\u003e\u003cp\u003eA coordinated siting strategy for a statewide network of CDW reuse facilities, designed to maximize material diversion from landfills, is developed in this study. A GIS-based location\u0026ndash;allocation methodology, a well-established approach in facility siting (Kao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ross et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Sambiani et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), is employed. Building on the Thiessen polygon method introduced by Richter et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), limitations identified in prior studies are addressed through the incorporation of local constraints into the siting process. To account for these factors, multi-criteria decision-making (MCDM) techniques, including the Analytic Hierarchy Process (AHP) and broader Multi-Criteria Decision Analysis (MCDA), are applied (Karimi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zionts, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Khan et al., 2018). Material availability data from the Texas Commission on Environmental Quality (TCEQ) are used to support the GIS-based modeling framework, through which optimal facility locations capable of capturing a substantial portion of CDW otherwise destined for disposal are identified.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePublicly available annual reports submitted to the Texas Commission on Environmental Quality (TCEQ) by Type I, Type IV, and combined Type I/IV landfills were compiled for the period 2015\u0026ndash;2022. Although complete FY2022 data was unavailable at the time of collection, the selected seven-year span provided a robust dataset, effectively smoothing anomalies associated with pandemic-related disruptions in the construction industry (Ataei et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Duan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Only landfills reporting non-zero CDW intake were included, resulting in a final dataset of 166 facilities\u0026mdash;slightly above the 159\u0026ndash;161 range typically cited by TCEQ, likely due to the inclusion of sites nearing closure that continued to report activity. All data were cross-verified with TCEQ\u0026rsquo;s GI-611 permit list and validated against the agency\u0026rsquo;s annual \u003cem\u003eMunicipal Solid Waste in Texas: A Year in Review\u003c/em\u003e reports. For each landfill, total annual CDW tonnage was recorded and converted to average daily intake, assuming a 252-working-day year in alignment with standard business and financial modeling conventions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThiessen Polygon Creation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThiessen polygons were constructed around each landfill to delineate catchment basins, defined as the presumptive geographic source areas for CDW intake. Although travel-time-based service areas would offer greater precision, the Thiessen approach provides a practical and spatially normalized approximation at the statewide scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Each polygon was then attributed with the corresponding landfill\u0026rsquo;s average annual CDW tonnage through a spatial one-to-one join, allowing for accurate assignment of waste volumes to their respective catchment areas within the GIS environment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFishnet Generation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA uniform point-based fishnet was generated using a 5.5-mile (8.85 km) grid spacing to ensure compatibility with the ArcGIS Online \u0026lsquo;Best Facilities\u0026rsquo; tool, which imposes a maximum limit of 1,000 input variables per run. This spacing was strategically selected to maintain full spatial coverage while guaranteeing that each Thiessen polygon contained at least one fishnet point, thereby preserving spatial granularity and the integrity of volume assignments. Alternative grid sizes (5-mile and 6-mile) were evaluated; however, only the 5.5-mile spacing successfully balanced tool constraints with the requirement for comprehensive statewide representation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAttribute Table Calculation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe \u0026lsquo;Summarize Within\u0026rsquo; tool was used to proportionally allocate CDW quantities to each fishnet point based on the Thiessen polygon in which it was located. New attribute fields were added to represent both annual and daily CDW quantities per point. As a result, each fishnet point came to represent a geographically distributed unit of daily CDW demand, establishing a high-resolution demand surface suitable for subsequent routing, siting, and facility optimization analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFishnet Splitting by Region\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo comply with ArcGIS Online\u0026rsquo;s processing constraints, the statewide fishnet was subdivided into 16 regional layers, corresponding to the established boundaries of the Texas Commission on Environmental Quality (TCEQ) regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These regions were selected based on their administrative relevance, demographic representation, and practical scale for waste infrastructure planning. Each point within the regional layers retained its assigned CDW volume, preserving the integrity of the spatial demand distribution for subsequent analysis and facility siting.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eOptimization with the \u0026lsquo;Best Facilities\u0026rsquo; Tool\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFacility siting optimization was conducted using ArcGIS Online\u0026rsquo;s \u0026lsquo;Best Facilities\u0026rsquo; tool, incorporating a tiered capacity model to reflect varying operational scales. A conservative upper limit of 1,500 short tons per day (tpd) was selected, based on the highest observed seven-year average intake from a single landfill (1,494 tpd), and rounded for clarity. However, a CDW reuse facility processing 1,500 short tons per day would generate substantial transportation demand. While a theoretical truckload carrying 14 short tons would require approximately 107 trucks per day (one every 4\u0026ndash;5 minutes), actual conditions are often more demanding due to the lower bulk density of mixed CDW materials. This results in significantly more trips, increasing traffic congestion and placing considerable strain on local road infrastructure (Bolton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). At this scale, and assuming 15-ton payload vehicle and a 10-hour workday, a 1,500 tpd facility would necessitate more than 100 truckloads per day, or approximately one truck arrival every six minutes underscoring the potential burden on local transportation infrastructure.\u003c/p\u003e\u003cp\u003eTo assess scalability and regional feasibility, additional scenarios using 1,000 tpd and 500 tpd thresholds, representing lower yet still economically viable facility sizes, were also modeled. These scenarios enabled a comparative analysis of facility distribution and site viability across different intake capacities.\u003c/p\u003e\u003cp\u003eFor each TCEQ region, the theoretical maximum number of reuse facilities was calculated by dividing the region\u0026rsquo;s total landfilled CDW volume by the 500 tpd threshold, considered the minimum economically viable scale. The ArcGIS Online \u0026lsquo;Best Facilities\u0026rsquo; tool was then configured with three key constraints: (1) Material haul times were limited to within one hour to maintain cost competitiveness by minimizing transportation distance and associated fuel and labor expenses. (2) Facility intake was capped at 1,500 tons per day (tpd), based on the maximum daily CDW intake observed across the seven-year dataset, ensuring modeled facilities reflect realistic upper operational limits. (3) The number of candidate facilities was constrained to not exceed the calculated regional maximum, ensuring alignment with projected CDW volumes and maintaining economic feasibility within each TCEQ region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). See Supplementary Information (SI) for results figures for other regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Technical Feasibility of VCDW Aggregates\u003c/h2\u003e\u003cp\u003eThe preliminary technical investigation confirmed the feasibility of using 100% minimally-sorted VCDW as a fine aggregate replacement. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the compressive strength results for all three mixture designs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll mixtures achieved compressive strengths exceeding the minimum requirement for non-load-bearing masonry units (500 psi [3.45 MPa]). Notably, Mixtures A and B demonstrated strengths meeting the specifications for structural masonry applications (2000 psi [13.79 MPa]) (ASTM C90-24). These results indicate the feasibility of utilizing 100% CDW aggregates in masonry unit production based on mechanical performance alone. However, further investigation is necessary to evaluate long-term durability and dimensional stability characteristics. Still, this preliminary investigation offers insight on the feasibility of utilizing a CDW blend, including building demolition mixed waste, into the production of construction product. This demonstrate a potential high-volume application and pathway for achieving circularity for this waste stream. As previously described, supply and price point of the recycled material also needs to be addressed to ensure market viability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Logistical Model for CDW Reuse in Texas\u003c/h2\u003e\u003cp\u003eHaving established the technical feasibility of the VCDW aggregate product, this section details the results of the statewide logistical and economic analysis.\u003c/p\u003e\u003cp\u003eThree siting scenarios, 500, 1,000, and 1,500 short tons per day (tpd), were developed to evaluate statewide configurations for construction and demolition waste (CDW) reuse facilities. Each scenario models a different facility scale to assess trade-offs in material capture, system utilization, and infrastructure footprint. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the number of facilities supportable within each TCEQ region, with a minimum of one facility per region enforced. Statewide, 66 facilities would be required under the 500 tpd scenario, compared to 44 under the 1,000 tpd scenario, and just 29 facilities under the 1,500 tpd model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of CDW Reuse Facility Capacity by TCEQ Region\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500 tpd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,000 tpd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,500 tpd\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e01 - Amarillo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e02 - Lubbock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e03 - Abilene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e04 - DFW Metroplex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e05 - Tyler\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e06 \u0026ndash; El Paso\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e07 - Midland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e08 \u0026ndash; San Angelo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e09 - Waco\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10 - Beaumont\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11 - Austin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12 - Houston\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13 \u0026ndash; San Antonio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14 \u0026ndash; Corpus Christi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15 - Harlingen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16 - Laredo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIt is important to note that the 1,500 tpd facility counts were rounded up. As a result, some regions showing only one facility may not produce enough CDW to sustain operations at that scale. Regions 1, 5, 6, 8, 15, and 16 should therefore be approached cautiously in early implementation, pending site-specific feasibility analysis. Regions 2, 3, 9, 10, and 14 may also warrant closer review. Conversely, regions 4 (DFW), 11 (Austin), 12 (Houston), 13 (San Antonio), and potentially 7 (Midland) emerged as the most viable for initial deployment, with regions 4 and 12 offering the strongest potential based on volume and efficiency.\u003c/p\u003e\u003cp\u003eIn the 500 tpd scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the model proposes a distributed network of 66 facilities across Texas. This configuration captures approximately 7.14\u0026nbsp;million of the 7.38\u0026nbsp;million short tons (6.48 of 6.70\u0026nbsp;million metric tons) of CDW generated annually between 2017 and 2021\u0026mdash;equating to \u003cb\u003e96.78% coverage\u003c/b\u003e. With a total annual processing capacity of 8.316\u0026nbsp;million short tons (7.544\u0026nbsp;million metric tons), the scenario achieves an impressive utilization rate of 85.91%, demonstrating both high efficiency and moderate room for expansion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe 1,000 tpd scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) reduces the number of sites to 44 while capturing approximately 6.94\u0026nbsp;million short tons (6.30\u0026nbsp;million metric tons), or 94.05% of available CDW. The fleet\u0026rsquo;s capacity increases to 11.1\u0026nbsp;million short tons (10.07\u0026nbsp;million metric tons), but the initial utilization rate decreases to 62.6%. This scenario sacrifices some coverage in favor of scalability and regional operational flexibility, particularly in fast-growing or high-variance areas.\u003c/p\u003e\u003cp\u003eIn the most consolidated configuration, the 1,500 tpd scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) requires just 29 facilities to provide statewide coverage. Despite the lower site count, this model still captures 6.88\u0026nbsp;million short tons (6.24\u0026nbsp;million metric tons), or 93.21% of CDW material. With a total fleet capacity of 10.962\u0026nbsp;million short tons (9.945\u0026nbsp;million metric tons), the system achieves a utilization rate of 62.77%. This scenario minimizes infrastructure requirements and may be best suited to areas where land availability, permitting constraints, or community impact may be primary concerns.\u003c/p\u003e\u003cp\u003eWhile all three models are viable, the 500 tpd network delivers the highest material capture and utilization, whereas the 1,000 and 1,500 tpd scenarios offer surplus capacity and operational resilience. However, at the state level, the marginal benefits of siting additional facilities diminish quickly. The gains in CDW capture, only 3.57% from the 1,500 to 500 tpd scenario, do not justify the significantly larger infrastructure footprint, particularly when new facilities must be located in less optimal, lower-yield areas. These findings indicate that a network of fewer, high-throughput facilities may represent the most efficient and cost-effective strategy for statewide CDW reuse implementation.\u003c/p\u003e\u003cp\u003eRegions 4 (DFW) and 12 (Houston) emerge as top candidates for early deployment, each capable of independently capturing over 98\u0026ndash;99% of their regional CDW material. Regions 11 (Austin) and 2 (Lubbock) also demonstrate strong potential, with capture rates of approximately 97% and 94%, respectively. Notably, these regions align with high population densities and major urban centers characterized by elevated construction activity and limited access to raw material sources. This reinforces the strategic importance of CDW reuse in these areas, not only to support circular economy goals, but also to meet local demand for construction materials. The regional analysis of construction and demolition waste (CDW) facility siting reveals stark contrasts across the 16 TCEQ regions.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 1 (Amarillo)\u003c/b\u003e: Lacks sufficient population density and material volume. A single facility capturing 100% of the region\u0026rsquo;s CDW would leave nearly three-quarters of the area uncovered and is not viable for development.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 2 (Lubbock)\u003c/b\u003e: Shows a promising concentration of population and CDW. A single 500 tpd facility could be fully supported, while a second facility yields minimal material gain.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 3 (Abilene)\u003c/b\u003e: Offers marginal feasibility. One 500 tpd facility near Abilene reaches only 81% utilization, and two facilities would be underutilized.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 4 (DFW)\u003c/b\u003e: Presents one of the most favorable opportunities. Its population and CDW are well distributed, allowing a 1,500 tpd facility to capture over 99% of material with 88% utilization.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 5 (Tyler)\u003c/b\u003e: Cannot support even a 500 tpd facility efficiently (59% utilization) due to sparse northern populations.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 6 (El Paso)\u003c/b\u003e: Performs even worse (just 39% utilization) since the population is concentrated in a small urban area.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 7 (Midland/Odessa)\u003c/b\u003e: Is viable for one facility, fully supporting 500 tpd with potential to scale to 1,000 tpd, but adding more provides diminishing returns.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 8 (San Angelo)\u003c/b\u003e: Is uneconomical, with only 30% utilization for a single facility.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 9 (Waco)\u003c/b\u003e: Could support one well-placed facility near Waco/Killeen with room to scale.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 10 (Beaumont)\u003c/b\u003e: Supports two 500 tpd facilities, with a possible third as demand grows.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 11 (Austin)\u003c/b\u003e: Has strong central population density that supports two facilities\u0026mdash;one at 500\u0026ndash;1,000 tpd (north) and another at 1,000\u0026ndash;1,500 tpd (south).\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 12 (Houston)\u003c/b\u003e: Is ideal, much like DFW. Seven 1,500 tpd facilities can capture nearly all CDW; expanding yields only a 0.4% gain and introduces competition.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 13 (San Antonio)\u003c/b\u003e: Is best served with two 1,000 tpd facilities, as population and CDW are tightly clustered in the eastern portion.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 14 (Corpus Christi)\u003c/b\u003e: Can support one 500 tpd facility with some expansion potential, but a second facility is underutilized.\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 15 (Harlingen)\u003c/b\u003e: Struggles to support even one facility (70% utilization at best).\u003c/p\u003e\u003cp\u003e\u0026bull; \u003cb\u003eRegion 16 (Laredo)\u003c/b\u003e: Is the least viable. A 500 tpd facility would be only 23% utilized due to widely dispersed communities.\u003c/p\u003e\u003cp\u003eIn summary, the least effective regions for siting CDW reuse facilities, based on simulated post-placement utilization, are Regions 16, 8, 6, 5, 1, and 15. These findings underscore the importance of population concentration and material availability in determining economic viability. Regions 4 (DFW), 12 (Houston), 11 (Austin), and 2 (Lubbock) emerge as the most promising areas for initial facility development due to their strong CDW generation and centralized population centers. Geographical and supplemental information can be found at corresponding manuscript SI.\u003c/p\u003e\u003cp\u003eTo minimize potential facility overlap and inefficiencies, an optimized configuration is proposed (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This scenario sites 23 facilities strategically across Texas and captures approximately 6,527,768 short tons (5,921,891.6 metric tons), or 88.4% of the average annual CDW landfilled during the study period. Of these, 15 facilities are sized at 1,500 tpd, five at 1,000 tpd, and three at 500 tpd, yielding a total fleet capacity of 7.285\u0026nbsp;million short tons (6.609\u0026nbsp;million metric tons) annually. This scenario achieves an initial fleet utilization rate of 89.6%, outperforming all comparable capacity-based scenarios, particularly the 1,000 tpd and 1,500 tpd options, which averaged just 62\u0026ndash;63% utilization. Only TCEQ regions deemed viable based on previous regional analysis are included in this proposal; regions that do not meet minimum CDW availability thresholds were excluded.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRevenue Potential and Urban Implementation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIf construction and demolition waste (CDW) intake were processed into a fine aggregate material suitable for use as a concrete sand replacement as previously demonstrated, the resulting product could generate substantial revenue. Assuming a competitive market sale price of \u003cspan\u003e$\u003c/span\u003e40 per ton for processed fine aggregates (Aggregate Markets, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and 252 operating days per year, a fully operational 1,500 tpd facility could generate approximately \u003cspan\u003e$\u003c/span\u003e15\u0026nbsp;million in operational revenue annually. Similarly, a 1,000 tpd facility could produce around \u003cspan\u003e$\u003c/span\u003e10\u0026nbsp;million, while a 500 tpd facility might yield approximately \u003cspan\u003e$\u003c/span\u003e5\u0026nbsp;million per year. When scaled across the entire facility network modeled in the CDW reuse study, total operational revenue from aggregate sales could approach \u003cspan\u003e$\u003c/span\u003e261\u0026nbsp;million annually for the State of Texas. This based on the assumption of 6.4\u0026nbsp;million tons processed by the fleet of recycling plants.\u003c/p\u003e\u003cp\u003eThis revenue stream is strengthened when combined with processing fees (or tipping fee). The average landfill tipping fee in Texas is approximately \u003cspan\u003e$\u003c/span\u003e45 per ton (TCEQ 2025), though this figure can vary significantly as each landfill operator sets their own fee. If a recycling plant were to charge a competitive processing fee, this component alone could represent up to \u003cspan\u003e$\u003c/span\u003e294\u0026nbsp;million annually. This brings the total potential operational revenue for the statewide facility network to over \u003cspan\u003e$\u003c/span\u003e555\u0026nbsp;million per year, highlighting strong economic viability for CDW reuse infrastructure in Texas.\u003c/p\u003e\u003cp\u003eTo illustrate the potential output of a single CDW reuse facility in an urban context, an example scenario was developed for a plant sited in the Greater Houston area (Region 12). The region currently contains thirteen active landfills, comprising four Type I (municipal solid waste) and nine Type IV (construction and demolition waste) facilities. The estimated remaining service life of these landfills varies, with four projected to close within ten years and five within twenty years. One of the urban landfills accepting an average of approximately 1.1\u0026nbsp;million tons of combined MSW and CDW annually, while two Type IV landfills each accepting roughly 340,000 tons of CDW per year. To effectively service the area, three 1,500 tpd CDW reuse facilities were sited, diverting approximately 1.1\u0026nbsp;million tons per year.\u003c/p\u003e\u003cp\u003eBased on an average material density of 1.014 ton per cubic yard (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the annual fine aggregate production output from a single facility could support the manufacturing of approximately 35\u0026nbsp;million concrete masonry units (CMUs). This quantity would be sufficient to supply three to five high-output CMU manufacturing plants; in turn providing enough material for the construction of approximately 15,000 single-family residential units averaging 2,000 ft\u0026sup2; (186 m\u003csup\u003e2\u003c/sup\u003e) each. Under the proposed scenario for the greater Houston area, siting CDW reuse plants could also achieve a 99% capture rate of CDW, which would substantially extend the service life of nearby urban landfills.\u003c/p\u003e\u003cp\u003eGiven that approximately 60\u0026nbsp;million cubic yards (46\u0026nbsp;million cubic meters) of concrete are produced annually in Texas (Texas Aggregates \u0026amp; Concrete Association, 2023), this model demonstrates a viable pathway for manufacturing concrete products incorporating recycled fine aggregates. Strategically locating CDW recycling plants in major urban centers such as Houston could substantially reduce natural sand demand while extending the operational lifespan of urban landfills and advancing statewide recycling and circularity goals.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe findings of this study demonstrate that substantial opportunities exist to advance circularity within the construction and demolition waste (CDW) stream, particularly within the understudied vertical CDW (VCDW) sector. Through combined materials characterization and geospatial modeling, the research shows that mixed CDW, common to the vertical construction sector can be technically and logistically repositioned as a viable secondary resource for producing fine aggregates used in low- to moderate-strength concrete applications.\u003c/p\u003e\u003cp\u003eLaboratory testing indicated that concrete mixtures incorporating 100% recycled VCDW-derived fine aggregates achieved compressive strengths suitable for non-load-bearing and, in some cases, structural masonry uses. These results provide preliminary evidence that highly mixed, minimally sorted CDW materials can be repurposed without extensive preprocessing, helping to address one of the major economic barriers associated with CDW recycling.\u003c/p\u003e\u003cp\u003eThe statewide logistical analysis further showed that establishing a coordinated network of CDW reuse facilities in Texas is both feasible and economically promising. Using a GIS-based location\u0026ndash;allocation framework, optimal facility siting was identified, allowing up to 88.4% of currently landfilled CDW to be captured. The modeling also indicated that higher-capacity, regionally distributed facilities offer superior utilization compared to larger numbers of smaller sites, especially in dense metropolitan regions such as Dallas\u0026ndash;Fort Worth, Houston, Austin, and San Antonio.\u003c/p\u003e\u003cp\u003eProjected revenue potential from recycled aggregate production and tipping fees underscores the strong economic drivers that could support a circular CDW management system. Overall, the study provides a foundational framework for advancing CDW circularity by integrating technical feasibility with spatially informed infrastructure planning, demonstrating a scalable, market-aligned pathway for VCDW reuse that offers substantial environmental and economic benefits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Texas A\u0026amp;M University, and, partially, under the sponsorship of the National Science Foundation, I-Corps program at Texas A\u0026amp;M University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Julie Ann Hartell is the principal research investigator responsible for conceptualization, funding acquisition and project management; along with devising research methodology and investigation, writing of original draft, review and editing. Research assistant, Mr. Billy L. Jones, performed research tasks, data collection and analysis, and writing of original draft. Dr. Ashrant Aryal, co-principal investigator, was responsible for conceptualization along with devising research methodology and investigation, review and editing of manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are not publicly available as they are still under analysis by the corresponding author but are available on reasonable requests. \u0026nbsp;Supplemental information is also provided with this manuscript in the form of additional figures and geographical location results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAggregate Markets (2025) \u003cem\u003eTexas aggregate markets\u003c/em\u003e [Website]. 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Statements \u0026amp; Declarations\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Construction and Demolition Waste, Circularity, Recycling, Concrete, Aggregates","lastPublishedDoi":"10.21203/rs.3.rs-8166632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8166632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe construction industry is a global generator of waste, with Construction and Demolition Waste (CDW) representing the largest solid waste stream in the United States. Landfilling remains the dominant disposal method, carrying significant environmental consequences. A barrier to recycling is the lack of positive economic incentives, particularly for \u0026ldquo;vertical\u0026rdquo; CDW (VCDW), a mixed debris stream generated from building demolition activities. This study addresses this challenge by proposing a framework to unlock the circular potential of CDW currently landfilled in Texas. First, the technical feasibility of using minimally sorted VCDW as a fine aggregate replacement for commonly manufactured concrete products, such as concrete masonry units, was investigated. Second, a GIS-based logistical model was developed to site a statewide network of CDW reuse facilities using provenance and quantity data collected over seven years from landfill intake records maintained by the Texas Commission on Environmental Quality (TCEQ). Laboratory testing confirmed that concrete mixtures using 100 percent VCDW aggregate achieved compressive strengths suitable for masonry product applications, offering a pathway for further exploration and development. The GIS-based analysis also confirmed waste stream availability that is necessary to support sustained production. An optimized network of 23 facilities could capture approximately 6,527,768 short tons (5,921,891.6 metric tons), or 88.4 percent, of the average annual CDW landfilled leading to strong economic viability based on current market value of end-product. This study provides a foundational framework for advancing a scalable circular economy for CDW in Texas and other regions facing similar waste management challenges.\u003c/p\u003e","manuscriptTitle":"Unlocking the Circular Potential of Construction and Demolition Waste: A Pathway for Texas’ Construction Industry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 18:04:20","doi":"10.21203/rs.3.rs-8166632/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-14T23:51:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-13T01:29:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T00:42:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155547502828691144399132437907016436741","date":"2025-12-08T00:38:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93604207935668583370606907280311365990","date":"2025-12-04T21:29:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164871159330812586962607869511202827503","date":"2025-12-03T07:39:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170278702855762043394214396757139132922","date":"2025-12-01T08:43:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250508505166974885487875430806481519970","date":"2025-11-30T22:35:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-30T21:41:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-27T16:52:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-21T02:11:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clean Technologies and Environmental Policy","date":"2025-11-20T16:31:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3f0cc4e7-6173-4e4e-8ee0-8a1f1afab83c","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T15:55:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 18:04:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8166632","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8166632","identity":"rs-8166632","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}