Decarbonization and agro-industrial requalification: from digital plants to low-carbon industry

Abstract

This study aimed to evaluate the use of three-dimensional (3D) modeling and virtual reality (VR) as tools for spatial optimization and emission reduction in an agro-industrial requalification project in Ibema-PR, Brazil. The methodology comprised digital modeling in SketchUp, immersive validation in a Meta Quest 2 headset, and quantification of the carbon footprint associated with the main construction inputs (concrete, steel, masonry, and zinc), which included transportation, on-site diesel consumption, and end-of-life (EoL) emissions. The inventory indicated a baseline carbon footprint of 1,052.5 tCO₂, and the application of 3D and VR modeling demonstrated mitigation potential of 105.3 tCO₂ (10% – conservative scenario), 210.5 tCO₂ (20% – moderate scenario), and 315.7 tCO₂ (30% – optimistic scenario). These reductions correspond to up to 1.26 million km traveled by car, 31.6 thousand trees in annual sequestration, or 16.3 thousand months of residential electricity, respectively. In financial terms, the carbon credits avoided represent values ranging from R$ 5.26 thousand to R$ 94, 700, depending on the reference market. The results demonstrate the relevance of digital modeling as a sustainability strategy, while reconciling constructive efficiency, resource savings, and academic awareness for environmentally responsible practices.

Keywords
3D modeling; virtual reality (VR; CO₂ mitigation

Introduction

Agribusiness plays a strategic role in the socioeconomic development of Brazilian rural regions by connecting primary production to the consumer market through the transformation of agricultural raw materials into processed products (Santos, 2023). Despite their importance, many agro-industrial companies operate with obsolete infrastructure and inefficient workflows, which makes it difficult for them to integrate into more demanding value chains and compromises the sustainability of their operations (Sena & Sousa, 2022).

In this scenario, digital technologies such as three-dimensional (3D) modeling and virtual reality (VR) have emerged as promising tools for redesigning industrial spaces. These technologies allow creation of interactive simulations of manufacturing environments, thereby facilitating the analysis of layouts, workstation ergonomics, and internal logistics, in addition to reducing errors and waste in the process.

This article aims to demonstrate the applicability of these digital tools in the agro-industrial context, through the virtual restructuring of a deactivated agro-industry located in Ibema-PR. This project was developed by students from the Science and Technology course at Biopark using the SketchUp software.

Digital modeling and VR, even in their non-immersive forms, facilitate project sharing, improve spatial understanding, and provide a more accurate and interactive experience, as highlighted by Schreyer (2023). Tools such as SketchUp, which are widely used in architecture and engineering, allow users to create detailed 3D models, thereby making it easy to visualize and simulate different design scenarios (Schreyer, 2023). Integrating these technologies into the physical–functional planning of agribusiness improves the decision-making capacity, sustainability of operations, and connection with the principles of Industry 4.0.

Moreover, these digital tools gain greater relevance when considering the challenge of modernizing industrial spaces with obsolete infrastructure. According to Bonato (2015), 3D modeling associated with VR can be used as visual and technical support for restructuring proposals, in order to faithfully represent the constructive and operational elements of agribusiness. Additionally, as highlighted by Bonato (2015), even in their non-immersive form, these technologies offer accessible resources to share projects, thereby expanding understanding between technical teams, managers, and potential investors.

According to Feitosa (2019), the application of virtual environments—especially via virtual and augmented reality—allows users to better visualize and understand the project, thereby offering a clear perspective of its different stages. The author points out that these technologies promote greater immersion, favor spatial perception, and significantly contribute to the reduction of errors in the construction process, thus, allowing detailed preliminary simulations. Complementing this vision, Junqueira (2021) highlights that the adoption of VR has added value to the presentation of projects and properties, thereby creating a competitive advantage and a more immersive experience for end users.

In addition, recent advances in computer graphics and VR have democratized access to these technologies (Ramos & Borges Júnior, 2024). The combination of 3D modeling software possessing increasingly intuitive interfaces, such as SketchUp and Blender, coupled with the increasing availability of affordable devices, such as VR headsets and online platforms, has allowed professionals and students to develop, explore, and optimize their projects with greater precision, interactivity, and immersion. These tools offer resources ranging from basic object modeling to the creation of highly detailed environments, with applications in the areas of architecture, engineering, interior design, and education (Barreto Junior et al., 2021).

Therefore, by integrating these technological resources into the remodeling of a true agro-industry, the proposed activity not only developed technical modeling skills of the students but also brought academic practice closer to the real-world challenges faced in the rural productive sector. These experiences contribute to the development of professionals better equipped to propose innovative solutions aligned with contemporary demands for efficiency, sustainability, and digital connectivity.

The agro-industrial sector is responsible for a significant portion of global greenhouse gas emissions (Sesso et al., 2022), either through the intensive consumption of materials with high embodied carbon contents, such as steel and concrete, or through the generation of waste and energy consumption during the construction, operation, and demolition phases. According to the Global Alliance for Buildings and Construction (2023), buildings and infrastructure account for approximately 37% of energy emissions and related processes of carbon dioxide (CO₂), which underscores the urgency of strategies to reduce this impact.

In this context, the application of digital technologies has emerged as a tool to anticipate design decisions, optimize material usage, and reduce waste even before physical execution (Silva & Santos, 2025). By enabling realistic simulations and detailed life cycle analyses, these solutions offer both productivity gains and concrete opportunities to mitigate CO₂ emissions associated with construction (Silva et al., 2025).

This study presents a method for estimating the mitigation of carbon emissions by replacing traditional construction processes with digital flows based on 3D modeling and VR. The research quantitatively evaluates the potential for CO₂ reduction in different intervention scenarios, while considering factors such as maintenance of the existing structure, minimization of demolitions, and optimization of the layout. The results meaningfully contribute to the discourse on sustainability in civil construction by providing technical evidence that can guide public policies, regulations, and design practices aligned with decarbonization goals.

Material and Methods

The methodology adopted in this study was structured into four main stages: (i) data collection and characterization of the area, (ii) 3D modeling, (iii) validation in a VR environment, and (iv) quantification of carbon emissions and mitigation scenarios.

Characterization of the study site

The study site comprised a deactivated agroindustry located in the municipality of Ibema, Paraná. Characterization was conducted through on-site technical visits and an aerial photogrammetric survey using a drone, which allowed identification of the physical dimensions, arrangement of structures, and existing operational conditions.

Characterization of the site was carried out through technical visits to the site and analysis of aerial images captured by a drone, as illustrated in Figure 1. These data subsidized the modeling of the terrain and spatial organization of the existing sheds.

Figure 1
Isometric view of Ibema's agroindustry (Authors, 2025).

Tools and software used

The 3D modeling was developed exclusively in SketchUp, which was chosen for its intuitive interface and wide applicability in architectural and industrial projects. The generated model was subsequently exported to the SENTIO VR virtual reality environment and accessed via a Meta Quest 2 headset, which enabled immersive visualization and interactive validation of the project.

Modeling and Restructuring Methodology

Digital modeling was performed in SketchUp, in which the structural and functional elements of the agroindustry were reconstructed. This included sheds, production areas, and support infrastructure. The modeling considered real dimensions obtained in the field, which allowed for volumetric quantification of the main construction materials.

The first stage involved dividing the tasks among the participants. The class was organized into working groups, with each student modeling one or two specific sectors of the agroindustry analyzed. Such a strategy favored individual specialization and simultaneously stimulated collective cooperation, as each sector should be cohesively integrated into the set of the final models.

Subsequently, data collection was conducted as an essential step for the construction of an accurate and representative 3D model. Therefore, technical visits were conducted at the site, which allowed direct observation of the physical space and its operational dynamics. In addition, aerial images were captured using drones, which expanded the scope and accuracy of the research and provided fundamental subsidies for the subsequent modeling stage.

Model development began with the detailed definition of the building's boundaries and the consequent creation of the load-bearing walls, which served as the basis for the entire digital construction. Subsequently, the internal elements were incorporated, particularly equipment, furniture, and functional components indispensable for the operation of the agroindustry. This phase proved to be decisive for the fidelity of the spatial representation and the project’s suitability to real-world use.

Finally, the layout was restructured, with the main objective at this step being optimization of workflows and physical layout of the sectors. Accordingly, aspects of ergonomics, accessibility, and production efficiency were carefully considered to ensure that the final configuration reflected the existing structure and provided concrete improvements in terms of functionality and operational performance.

Application of the Model in Virtual Reality

Once the models were completed, they were united in a representation of the agro-industry terrain, and each shed was positioned in its relevant location with realistic spacing.

This final model, which includes all the warehouses created, was exported for viewing through a VR headset to evaluate and validate the layouts designed for agribusiness.

Figure 2 displays a portion of the process for developing the final model from the SketchUp editing screen.

Figure 2
View of the final model in the completion process (Authors, 2025).

The 3D dimensional model was exported to the SENTIO VR platform and viewed using a Meta Quest 2 headset. This step allowed for an immersive analysis of the layout, which enabled the identification of spatial inconsistencies, operational conflicts, and optimization opportunities prior to physical execution.

CO2 reduction estimates with modeling

The use of building information modeling (BIM) and integrated modeling technologies has demonstrated significant potential to reduce carbon emissions. Studies indicate that the adoption of BIM can lead to a 14% to 30% reduction in emissions during the project lifecycle, thereby reinforcing the concept of "CO₂ savings" through digital planning (Bortoli et al., 2023).

Research applying simulations with BIM also corroborates this capacity by recording measurable reductions, such as a decrease of approximately 21.7% in emissions during the construction phase, owing to the optimization of resource allocation (Cheffa, 2025). The integration of BIM with life cycle analysis (LCA) makes it possible to quantify and, consequently, reduce the carbon footprint, especially in the design phase and in the specification of materials, as pointed out by academic reviews (Liu et al., 2022).

To translate "material savings" into CO₂ emissions avoided, specific emission factors per material must be used. For example, cement has a high emission factor, which can range from hundreds to approximately 900 kg of CO₂ per metric ton of Portland cement, according to various inventories. To support these conversions, the use of technical references, such as the IPCC/GWP guidelines (Gibbs, 2000), is recommended. Similarly, for materials such as steel, global production averages indicate a factor of approximately 1.9 metric tons of CO₂ per metric ton of steel, which is an important reference value for calculations (Worldsteel, 2025).

Primary indicators

For example, consider a project with a certain material. Define:

• Qi = predicted amount of material i without digital modeling (in t or m³).

• EFi = material emission factor i (kgCO2_ per t or m³);

• RM = relative reduction of material consumption owing to modeling (for instance, 10–25% → Rm ∈ [0,10,0,25])

• RR = relative reduction of rework/errors (for example, 15–30% → RR_∈ [0,15,0,30]).

Basic formula of emissions avoided by the material

Emissions were estimated based on an LCA approach, while considering the main construction materials (concrete, steel, masonry, and zinc), as well as transportation, diesel consumption, and end-of-life (EoL).

Emissions were calculated according to the Equation below:

$\text{CO2,i=}\sum (Qi\times FEi)$

where:

• CO2,i = CO₂ emissions avoided for material i (in kgCO2);

• Qi = quantity predicted without the use of modeling (t or m³);

• Rm = relative reduction of the material (fraction, not %);

• EFi= material emission factor (kgCO_{2/t or kgCO2/m³).}

Adjustments and multipliers

The impact of reducing rework by converting saved working hours into transportation + energy emissions (estimated based on kilometers transported and machine consumption) was also included. Savings for all materials were added to achieve CO2_total_evitado.

Emission mitigation scenarios

The estimation of emissions reduction was conducted through scenario analysis, while considering the impact of digital modeling on the reduction of waste, rework, and layout optimization.

Based on previous literature (Bortoli et al., 2023; Liu et al., 2022), which indicates reductions between 10% and 30% with the use of digital modeling and BIM, three scenarios were defined:

• Conservative (10%) – minimal reduction associated with decreased rework

• Moderate (20%) – optimization of layout and use of materials

• Optimistic (30%) – combination of waste reduction, rework and logistics improvement

The avoided emissions were calculated as follows:

where:

• R = reduction rate (0.10, 0.20, 0.30)

These scenarios represent sensitivity analysis, not direct measurements of the model, and are used to assess the mitigation potential associated with the adoption of digital technologies in construction planning.

Uncertainty considerations

Importantly, the results presented herein should be interpreted as order-of-magnitude estimates because they use average emission factors. For accuracy, it is recommended to use environmental product declarations and specific data from local suppliers.

Results and Discussion

Three-dimensional modeling and spatial organization

The 3D modeling allowed for complete reconstruction of the agroindustry, while considering the main productive sectors including dairy farming, pig farming, cattle confinement, fish farming, feed mills, administrative areas, and support infrastructure.

The digital representation enabled the integrated visualization of production flows, thereby allowing the identification of logistical bottlenecks, circulation conflicts, and opportunities for spatial reorganization. Reorganization of the layout promoted greater operational efficiency by reducing internal displacements and improving the functional distribution of the sectors.

Augmented reality (AR) and VR technologies share similar fundamentals, such as the use of electronic devices capable of generating interactive virtual environments. However, they differ in their purposes and forms of use. The main objective of VR is to provide complete user immersion in a digital environment, while fully replacing the perception of the real world. A typical example is the use of VR glasses, which allow users to be transported to a completely virtual scenario.

In contrast, AR enriches the real world by overlaying virtual elements onto it. This cybernetic adaptation not only complements the user's view but also "expands" it, thereby offering broader interactions with improved and more detailed viewing power of the proposal (Junqueira, 2021).

The central objective of both technologies is to optimize complex processes, especially in construction and planning. Through computer simulation, they allow the complete visualization of a project that does not yet exist, while facilitating the anticipation of possible problems, reduction of imminent risks, and ultimately the acceleration to achieve the final goal (Oliveira et al., 2019).

However, the goal of modeling is to reconstruct an old agribusiness by creating an environment in VR, which offers a vision of how the site would look without the need for renovations and demolitions. Importantly, this approach minimizes material, cost, and time expenditure, as well as associated pollution, by enabling the virtual creation of entire worlds encompassing ecological, environmental, territorial, social, cultural, economic, and political aspects of sustainability and environmental sciences.

Validation in a VR environment enabled the immersive analysis of the project, thereby allowing the identification of spatial inconsistencies that were not perceptible in two-dimensional visualizations.

Using the Meta Quest 2 headset allowed us to simulate the operation of agribusiness and evaluate aspects such as:

• Workstation ergonomics

• Movement of people and equipment

• Accessibility and operational safety

This step directly contributed to the reduction of rework and subsequent adjustments to the physical design.

The 3D dimensional modeling of the deactivated agroindustry resulted in the virtual reconstruction of all its sectors based on data collected in the field and aerial images. The project was developed by students of the discipline "Modeling and Virtual Reality", and totaled approximately 60 h of collaborative work in an academic environment.

Each student was responsible for modeling one or two specific structures of the agro-industrial complex. The buildings have been updated according to current animal production standards and adapted to contemporary functional requirements. Among the reconstructed spaces, the following sectors were significant:

• Dairy farming;

• Pig breeding (breeding and fattening);

• Cattle confinement;

• silage trench;

• Fish farming;

• Feed mill;

• Offices, warehouse and maintenance areas;

• Pastures and outdoor areas.

After modeling, the environments were integrated into a single 3D model and exported to the VR environment using the Meta Quest 2 headset, which supported the SENTIO VR platform. This immersive experience allowed the user to completely navigate the virtual space, thereby enabling the observation of internal flows, positioning of equipment, and functional organization of the sectors.

Virtual Reality (VR)

The application of VR proved effective in validating the proposed project, as it allowed anticipation of spatial adjustments and offered a realistic view of the future operation of the agroindustry. In addition, the simulation helped identify improvements related to ergonomics, internal logistics, and environmental sustainability, with an emphasis on the reuse of existing structures and optimization of land use.

Figure 3 shows one of the students responsible for the construction of the agroindustry using VR glasses to access the site. This allows interaction with the environment and visualization of the design prior to construction.

Figure 3
Student using virtual reality (Authors, 2025).

Figure 4 illustrates the fish farming shed, where there will be tanks with fish for reproduction and sale. The tanks, made of fiberglass concrete, store the water along with the animals and are inside the glass-walled shed to allow sunlight to enter.

Figure 4
Fish farming shed (Authors, 2025).

The dairy sector, represented in Figure 5, is where the milk-producing cows will be directed for milking. The barn has separate spaces for milking along with areas for storing feed, spare equipment, and maintenance tools.

Figure 5
Dairy farming (Authors, 2025).

The fattening shed, illustrated in Figure 6, will receive animals from the breeding site for growth. These animals are then sent to slaughter upon reaching the ideal time, size, and weight. This area is divided to support proper development of the pigs in accordance with standard space requirements. It also includes a feeding system in which the feed is delivered through a funnel to the animals, with entry points for the pigs on the sides of the shed.

Figure 6
Pig fattening shelter (Authors, 2025).

Cattle confinement, displayed in Figure 7, shows the area where cattle are help prior to slaughter. The facility includes side entrances for animal access, complies with current size standards, and features a nearby feed silo for storage and distribution.

Figure 7
Fattening course for fattening (Authors, 2025).

Offices, warehouses, and maintenance areas will store hygiene items and tools for general maintenance and use in agribusiness. This also includes the office area, which will be responsible for logistics and site services, as shown in Figure 8.

Figure 8
Office, warehouse, and maintenance areas (Authors, 2025).

Figure 9 shows the feed mill, where all the grains and grasses planted and collected in the agroindustry will be converted to feed, which will then be fed to the on-site animals. The environment has the capacity to generate food—through qualified equipment—and ample space to move materials.

Figure 9
Feed mill (Authors, 2025).

Pig farming involves raising the animals since infancy followed by transferring them to the fattening shed. This structure has specific areas for farrowing and regions dedicated to piglet care, in addition to having entrances and exits on the sides, as shown in Figure 10.

Figure 10
Pig breeding pen (Authors, 2025).

Silage ditches, as shown in Figure 11, are silage storage sites for feed production or direct feeding to animals. They are located between the upper and lower parts of the agroindustry and feature a robust structure appropriate for material storage.

Figure 11
Silage trench (Authors, 2025).

Figure 12 displays the external environment of agribusiness, and allows a view of the entire site. The agro-industry is divided into sections, with each containing buildings, and the terrain is uneven.

Figure 12
Pastures and external areas (Authors, 2025).

The environmental impact of the building was calculated based on the volumetric and material quantification of the main structural and sealing elements, along with the application of emission factors consolidated in the technical literature and international reference bases.

Regarding the columns, the total volume was obtained considering a cross-section of 20 cm × 30 cm, a height of 5 m, and the presence of 22 pillars per shed. For the masonry walls, a perimeter of 90 m was adopted, with a height of 5 m and a thickness of 0.15 m, resulting in a volume of 67.5 m³ per shed, which corresponds to 607.5 m³ in all units. Regarding the floor, a thickness of 20 cm was calculated in an area of 450 m², obtaining a volume of 90 m³ per shed, equivalent to 810 m³ in total. The foundations were designed as approximately a continuous beam of 0.5 × 1.0 m along the entire perimeter, totaling 45 m³ per shed, or 405 m³ in the building as a whole.

For metallic elements, such as steel used in reinforcement, purlins, trusses, and tiles, quantification was performed based on explicit hypotheses recorded in the calculation spreadsheet, based on average masses per cubic meter, square meter, and linear meter of the truss. In the specific case of metal scissors, three different consumption scenarios were considered (30, 40, and 50 kg/m), adopting the intermediate value of 40 kg/m as a reference.

The emission factors applied to the different materials were based on consolidated publications and databases. For concrete, an emission factor of 0.35 tCO₂ per cubic meter was considered, in accordance with available technical studies (mecla.org; augreenconcrete.info). For steel, an average of 1.9 tCO₂ per tonne was used, based on data from Worldsteel and the International Energy Agency (IEA). For zinc, an emission factor of 3.89 kgCO₂ per kilogram (or 3.89 tCO₂ per ton) was applied, according to information from the International Zinc Association. For masonry blocks, an emission factor of approximately 0.29 tCO₂ per cubic meter was adopted, according to life cycle inventories available in the scientific databases ResearchGate and Climatiq.io.

The estimate of the total CO₂ equivalent emissions of the base scenario was obtained from the sum of all components, as detailed in Table 1. In addition, mitigation scenarios were simulated, considering progressive reductions of 10%, 20%, and 30% in the total impact, classified as conservative, moderate, and optimistic, respectively. For each of these scenarios, the amount of avoided emissions was calculated in comparison with the baseline, providing subsidies for the evaluation of constructive alternatives and environmental mitigation strategies, as shown in Table 1 and Figure 13, with an estimated total of 1,052.5 tCO₂.

Table 1
Estimate of total CO₂ equivalent emissions from the base scenario.

Component	Total Quantity	FAITH adopted	CO₂ emission (t)
Concrete - Pillars	59.4 m3	0.30 t/m3	17,82
Concrete – foundations	405 m3	0.30 t/m3	121,50
Concrete — Laje	810 m3	0,30 t/m3	243,00
Freemasonry	607.5 m3	0,29 t/m3	176,18
Steel - Rebar	127,44 t	1,80 t/t	229,39
Steel — purlins	20,25 t	1,80 t/t	36,45
Steel — scissors	59,4 t	1,80 t/t	106,92
Steel - Tiles	24,3 t	1,80 t/t	43,74
Zinc — galvanizing	0,81 t	0,38 t/t	0,31
TOTAL	-	-	1052,5

Figure 13
Graph of total CO₂ equivalent emissions of the base scenario.

To quantify the carbon emissions incorporated in civil construction, emission factors (FE) adapted to the Brazilian reality were considered to ensure greater adherence to local production, transportation, and material use conditions.

In the case of concrete, a factor of 0.30 tCO₂ per cubic meter was adopted, a value consistent with LCA studies conducted in the national territory and made available by reference entities, such as IBRACON and ABCIC. For steel, an average of 1.8 tCO₂ per metric ton was considered, in line with data from the national industry and values released by Worldsteel and Aço Brasil. For ceramic masonry blocks, a factor of 0.29 tCO₂ per cubic meter was applied, in accordance with inventories prepared by ANICER and LCA sectoral studies.

The value of 0.38 tCO₂ per ton of zinc used in galvanizing was used, originating from national corporate stocks and serving as a conservative reference for the regional context, which is below the global average values reported by the IPCC.

In addition to construction materials, transport emissions were incorporated into the model. For this, an average distance of 50 km (unilateral distance) was assumed for the displacement of concrete, blocks, and steel, applying a factor of 0.1 kgCO₂ per ton-kilometer, according to IPCC guidelines, the GHG Protocol, and international standards (EN16258, GLEC Table).

With regard to construction emissions, the consumption of 2 L of diesel per cubic meter of concrete placed was adopted, which is a conservative reference value for the handling and operation activities of light machinery. As a fuel emission factor, 2.68 kgCO₂ per liter was considered, according to data from the Energy Information Administration.

Finally, emissions associated with the EoL of the building were accounted for. For this stage, a 5% increase in total embodied emissions was adopted in order to represent the impacts resulting from the demolition, transport and final disposal of waste processes, in accordance with practical estimates reported in international LCA guides, such as those of the IEA's EBC program.

Emission mitigation scenarios

Emission reduction scenarios associated with waste reduction, rework, and layout optimization were estimated based on the application of digital modeling and validation in VR (Table 2 and Figure 14).

Table 2
Estimated emission reduction scenarios associated with waste reduction, rework, and layout optimization.

Scenario	Reduction (%)	CO₂ avoided (t)
Conservative	10%	105,3
Moderate	20%	210,5
Optimistic	30%	315,7

Figure 14
Graph of Emission Reduction Associated with Waste Reduction, Rework and Layout Optimization.

The results indicate that the application of 3D modeling and VR can significantly contribute to the reduction of emissions associated with agro-industrial construction.

Mitigation occurs primarily by:

• Reduction of rework

• Optimization of material usage

• Improvement in spatial organization

• Reduction of unnecessary physical interventions

The values obtained represent estimates of an order of magnitude that is compatible with studies indicating reductions between 10% and 30% in the use of resources in projects with digital support.

The application of digital tools, such as 3D modeling and VR, in the context of agro-industrial restructuring has proven to be a technically feasible, low-cost alternative with high potential for replication. The simulation of deactivated rural spaces, conducted without physical interventions, allowed the visualization and proposition of relevant structural and operational changes, thereby contributing to the efficient redesign of production flows and functional modernization of the plant.

Additionally, 3D modeling used the principles of production engineering and industrial ergonomics to identify logistical bottlenecks, spatial inefficiencies, and opportunities for reorganization. The use of SketchUp combined with VR validation expanded the analytical potential by providing an immersive experience, which favors spatial perception and the anticipation of construction problems. These results corroborate those of previous studies, such as Oliveira et al. (2019), which highlight the role of VR as a strategic tool in the physical planning of industrial environments.

From an environmental perspective, the construction of the virtual model represents a relevant solution because it enables decision anticipation and avoids unnecessary physical interventions. This results in a reduction in the consumption of materials, waste generation, and the need for rework—factors that are directly associated with greenhouse gas emissions in civil construction. The requalification of existing structures, instead of the construction of new facilities, also reinforces the principles of a circular economy and efficient use of resources.

Moreover, the carbon footprint estimate considered the main construction components, including the structural and sealing elements, as well as emissions associated with transportation, diesel consumption on the construction site, and EoL. The emission factors adopted were based on Brazilian and international references to ensure representativeness for the national context. Briefly, for concrete, 0.30 tCO₂/m³ was used; for steel, 1.8 tCO₂/t; and for masonry, 0.29 tCO₂/m³; these values are compatible with life cycle inventories available in the literature.

A total footprint of approximately 1,052.5 tCO₂ was estimated based on these assumptions. Mitigation scenarios were defined based on this value and were associated with waste reduction, rework, and layout optimization that was enabled by digital modeling and VR validation. The conservative, moderate, and optimistic scenarios indicated reductions of 105.3 tCO₂ (10%), 210.5 tCO₂ (20%), and 315.7 tCO₂ (30%), respectively.

These reduction intervals align with recent literature which points to efficiency gains of between 10% and 30% with the use of digital technologies, such as BIM and integrated modeling, in construction planning (Bortoli et al., 2023; Liu et al., 2022). Thus, the results obtained in this study reinforce the potential of these tools as emission mitigation strategies in the agro-industrial sector.

Despite these promising results, some limitations should be considered. The emission factors used represent average values and may vary according to the origin of the materials, production processes, and regional conditions. Additionally, the mitigation scenarios adopted were not directly derived from empirical measurements of the model, but were based on intervals reported in the literature and should be interpreted as order-of-magnitude estimates. The absence of specific EPDs also limits the accuracy of the results.

In addition, future studies should incorporate quantitative data directly extracted from digital models, such as optimized volumes and effective material reductions, as well as updated national databases for emission factors. Such advances will allow for greater precision and robustness in the analyses, thereby extending the applicability of the methodology to real projects.

Conclusions

The proposal to reactivate the deactivated agroindustry in Ibema-PR provided a students with a valuable opportunity to apply knowledge acquired in the Modeling and Virtual Reality course, and integrated

technological innovation, strategic planning, and sustainability. The on-site analysis, added to the 3D modeling, allowed us to understand the potential of a rural property with a solid and well-planned structure, such as the organization in terraced levels, efficient silo arrangement, sheds, feed mills, and biodigesters, and the use of gravity for waste management. This configuration, even in an pre-existing installation, demonstrates a clever original design that can be adapted to current standards with a low environmental impact.

The application of 3D modeling and VR has enabled the accurate representation of the current state and proposal of functional improvements, while respecting logistical, health, and environmental aspects. The biodigester, integrated into the operation, reinforces the potential for sustainable management by transforming waste into clean energy. The simulation of three intervention scenarios demonstrated the positive impact of structural preservation: in the conventional scenario, the estimated emissions were 320 tCO₂; in the moderate scenario, there was a mitigation of 210 tCO₂; and mitigation of at least 285 tCO₂ in the optimistic scenario—corresponding to reductions of 65% to 89% in emissions—along with lower waste generation and energy consumption.

From an educational perspective, the project provided multidisciplinary learning, strengthened technical skills, and integrated theory and practice. The use of low-cost technologies, such as SketchUp and Meta Quest 2, proved feasible and replicable in other contexts, thereby expanding access to innovation in this area. Therefore, the reactivation of the agroindustry is technically feasible, environmentally responsible, and economically strategic, and serves as a rural modernization model based on efficiency, innovation, and sustainability.

Parameter Value (tCO₂) Total impact area (materials A1–A3 + conveyance + construction diesel + EoL) 1.052,49 CO₂ avoided — Conservative (10%) 105,25 CO₂ avoided — Moderate (20%) 210,50 CO₂ avoided — Optimistic (30%) 315,75

Parameter	Value (tCO₂)
Total impact area (materials A1–A3 + conveyance + construction diesel + EoL)	1.052,49
CO₂ avoided — Conservative (10%)	105,25
CO₂ avoided — Moderate (20%)	210,50
CO₂ avoided — Optimistic (30%)	315,75

The core assumptions used to estimate the carbon footprint encompassed the main construction inputs and associated processes. For concrete, the factor of 0.30 tCO₂ per cubic meter was adopted; for steel, 1.8 tCO₂ per tonne; and, in the case of masonry, 0.29 tCO₂ per cubic meter. Regarding the transportation of materials, an average distance of 50 km and an emission factor of 0.1 kgCO₂ per tonne-kilometer were considered. A consumption of 2 L of diesel per cubic meter of concrete was assumed for fossil fuel usage, with an emission factor of 2.68 kgCO₂ per liter. Finally, for the EoL, an additional fraction of 5% of the total body emissions was applied. Notably, these values constitute approximations whose purpose is to indicate the order of magnitude of the mitigation potential when applying 3D modeling and VR prior to the physical execution of the work.

Several mitigation strategies could be identified in the scope of this project. The first of these is the reduction of waste and reformulation on site. Notably, 3D modeling, associated with VR visualization, enabled the validation of the layout in advance, thereby avoiding later reformulations and repetition of services. Consequently, the consumption of materials, emission of dust, generation of construction waste, as well as sound and visual impacts, were minimized.

Another relevant measure was the preservation of the existing structure and soil. The proposal to requalify a deactivated agro-industry avoided the need for extensive demolitions, new landfills, or excessive earth movements. Accordingly, the generation of debris was reduced and soil pollution was avoided.

Space optimization and internal logistics were also highlighted. Digital simulation allowed the redesign of workflows, while considering aspects of ergonomics and circulation. This reduced the need for additional construction or physical expansion, which translated into lower consumption of materials, energy, and internal transportation in the project.

The savings in construction materials were quantified from the calculations and scenarios that were carried out previously. The application of digital modeling showed a potential reduction between 105 and 315 tCO₂, which was directly associated with reduced material consumption and on-site redesign. This mitigation entails less extraction of raw materials, as well as a smaller carbon footprint built into the project.

Further, the project had a formative and educational character. The participation of students in the activity promoted environmental awareness and highlighted the importance of sustainable planning, thereby reinforcing the role of digital technologies as sustainability tools. This impact extends beyond the project itself and contributes to the training of future professionals with awareness and readiness to apply environmentally responsible practices in rural and urban contexts.

Regarding the assessment of CO₂ avoided, we seek to translate the environmental benefit into economic terms and equivalences perceptible by the general public. According to the World Bank's Carbon Pricing Panel (2024), the average carbon price in regulated markets ranges between US$40 and US$60 per tonne of CO₂, while in the voluntary market, it ranges between US$10 and US$15 per ton. Thus, the estimated mitigation for the project may correspond to economic values between approximately US$ 1.05 million and US$ 18,900, depending on the reference market considered.

From the perspective of environmental equivalences, according to data from the EPA and the IEA, 1 metric ton of CO₂ corresponds to approximately 4000 km traveled by an average gasoline car, the carbon absorption of 16 trees over 10 years, or the average electricity consumption of a Brazilian household for approximately 1.5 months. Therefore, the mitigation potential of 105 to 315 metric tons of CO₂ obtained in the project can be expressed as the elimination of emissions equivalent to up to 1.26 million kilometers traveled by automobiles, the planting of up to 5040 trees, or the saving of sufficient electricity to supply approximately 470 Brazilian households for a year.

Scenario CO₂ avoided (t) Economic value (US$ 10/t) Economic value (US$ 50/t) Equivalent in km of car avoided Equivalent in trees planted Residential electricity avoided (months) Conservative 105,25 1.052,50 5.262,50 421.000 1.684 158 Moderate 210,50 2.105,00 10.525,00 842.000 3.368 316 Optimistic 315,75 3.157,50 15.787,50 1.263.000 5.052 474

Scenario	CO₂ avoided (t)	Economic value (US$ 10/t)	Economic value (US$ 50/t)	Equivalent in km of car avoided	Equivalent in trees planted	Residential electricity avoided (months)
Conservative	105,25	1.052,50	5.262,50	421.000	1.684	158
Moderate	210,50	2.105,00	10.525,00	842.000	3.368	316
Optimistic	315,75	3.157,50	15.787,50	1.263.000	5.052	474

The estimate of emissions avoided in this project was converted into monetary terms and environmental equivalences to make the environmental benefit of using 3D modeling and VR before physical execution more tangible. The three mitigation scenarios considered were conservative (105.25 tCO₂), moderate (210.50 tCO₂), and optimistic (315.75 tCO₂).

From an economic perspective, the carbon price was used as a reference in different market contexts. In the voluntary market, credits have an average value of approximately US$10 per ton of CO₂, while in regulated markets the variation varies between US$40 and US$60 per ton, according to data from the World Bank's Carbon Pricing Panel (2024). Based on the exchange rate of R$ 5.00 per dollar, the potential benefits were expressed in reais. Thus, in the conservative scenario, the amount associated with mitigation varies between R$ 5.26 thousand and R$ 31.57 thousand; in the moderate scenario, between R$ 10.52 thousand and R$ 63.15 thousand; and in the optimistic scenario, between R$ 15.79 thousand and R$ 94.73 thousand.

Environmental equivalences were calculated based on internationally accepted average parameters. For vehicle emissions, a factor of 0.25 kgCO₂ per kilometer traveled by an average car was considered. Thus, the scenarios correspond, to 421 thousand, 842 thousand and 1.263 million kilometers avoided, respectively. Regarding forest capture, a rate of 10 kgCO₂ absorbed per adult tree per year was used. The values obtained indicate that the mitigation achieved is equivalent to the planting of approximately 10, 500, 21, 000, and 31, 600 trees, respectively.

In terms of energy consumption, the Brazilian average factor of 0.1295 kgCO₂ per kWh and the typical residential consumption of 150 kWh per month were adopted, which result in approximately 19.4 kgCO₂ emissions per month of electricity in a household. Therefore, the estimated mitigation is equivalent to 5,418 residential months in the conservative scenario, 10,837 residential months in the moderate scenario, and 16,255 residential months in the optimistic scenario, which represents the average supply of a Brazilian residence for up to 1,354 years.

These results quantify the gains in technical terms while also translating them into financial and environmental metrics that are more understandable and communicable to different audiences, from managers to local communities. However, it is noteworthy that these are equivalences of an order of magnitude, subject to the assumptions made. For practical applications in environmental offset projects or certifications, it is recommended to use more accurate regional data, such as EPDs and local carbon sequestration fees.

In summary, this study demonstrated that the application of 3D modeling and VR in the requalification of agroindustries is an effective tool for spatial optimization and reduction of carbon emissions. The quantification of the carbon footprint indicated a base value of approximately 1,052.5 tCO₂, evidencing the significant impact of construction materials. Meanwhile, the application of mitigation scenarios based on the literature revealed a reduction potential of between 10% and 30%, which corresponds to savings of up to 315.7 tCO₂. These results reinforce the use of digital technologies for anticipating decisions, reducing waste, and increasing the efficiency of projects, thereby contributing to more sustainable practices in the agro-industrial sector. From a practical perspective, the proposed methodology can be used as a tool to support decision-making in rural engineering projects and assist in the evaluation of alternatives with lower environmental impact. From a future perspective, it is recommended to integrate primary data extracted directly from digital models, along with using product-specific emission factors through EPDs to increase the accuracy of the analyses.

In future work, it is recommended to integrate quantitative data extracted directly from digital models, allowing for a more accurate quantification of material and emission reductions. Moreover, researchers should explore the use of EPDs specific to the Brazilian context and application of the methodology in real case studies with empirical validation. Future investigations can also explore integration with BIM platforms and more detailed lifecycle analyses, thereby increasing the robustness and applicability of the results.

References

• Barreto Junior, C. d. L., Cardoso, A., Lamounier Júnior, E. A., Silva, P. C., & Silva, A. C. (2021). Designing virtual reality environments through an authoring system based on CAD floor plans: a methodology and case study applied to electric power substations for supervision. Energies, 14 (21), 7435. https://doi.org/10.3390/en14217435
  » https://doi.org/10.3390/en14217435
• Bonato, R. K. (2015). Revisão bibliográfica em realidade virtual e modelagem 3D aplicada à engenharia civil e à exploração e produção de petróleo e gás natural [Undergraduate thesis, Universidade Estadual Paulista]. https://biblioteca.unesp.br/exlibris/bd/capelo/2017-03-15/000880111.pdf
  » https://biblioteca.unesp.br/exlibris/bd/capelo/2017-03-15/000880111.pdf
• Bortoli, A. de, Baouch, Y., & Masdan, M. (2023). Can BIM help decarbonize the construction industry? Primary evidence from pavement management systems life cycle. Journal of Cleaner Production, 391, 136056. https://doi.org/10.1016/j.jclepro.2023.136056
  » https://doi.org/10.1016/j.jclepro.2023.136056
• Carbon Credits. (n.d.). Carbon credits in 2024: What to expect in 2025 and beyond. https://carboncredits.com/carbon-credits-in-2024-what-to-expect-in-2025-and-beyond-250b-by-2050/
  » https://carboncredits.com/carbon-credits-in-2024-what-to-expect-in-2025-and-beyond-250b-by-2050/
• Cheffa, P. (2025). Integrated BIM simulation framework for carbon emissions during construction (Master's thesis, Louisiana State University). https://repository.lsu.edu/gradschool_theses/6086
  » https://repository.lsu.edu/gradschool_theses/6086
• Feitosa, L. C. (2019). Application of virtual and augmented reality for project simulation in civil construction (Final paper, Federal Rural University of the Semi-Arid Region). https://repositorio.ufersa.edu.br/server/api/core/bitstreams/6602ead8-70df-49b6-af9d-77ff4cb1bfef/content
  » https://repositorio.ufersa.edu.br/server/api/core/bitstreams/6602ead8-70df-49b6-af9d-77ff4cb1bfef/content
• Global Alliance for Buildings and Construction. (2023). 2023 Global Status Report for Buildings and Construction: Beyond foundations - Mainstreaming sustainable solutions to cut emissions from the buildings sector. United Nations Environment Programme. https://globalabc.org/sites/default/files/2024-11/global_status_report_buildings_construction_2023.pdf
  » https://globalabc.org/sites/default/files/2024-11/global_status_report_buildings_construction_2023.pdf
• Gibbs, M. J., Soyka, P., Conneely, D., & Kruger, M. (2000). CO₂ emissions from cement production. In Good practice guidance and uncertainty management in national greenhouse gas inventories https://www.scirp.org/reference/referencespapers?referenceid=1763524
  » https://www.scirp.org/reference/referencespapers?referenceid=1763524
• IBRACON. (n.d.). Technical Committee for Environmental Management of Concrete and Concrete Structures. https://site.ibracon.org.br/comites-tecnicos/ct-101-comite-tecnico-de-gestao-ambiental-de-concreto-e-estruturas-de-concreto/
  » https://site.ibracon.org.br/comites-tecnicos/ct-101-comite-tecnico-de-gestao-ambiental-de-concreto-e-estruturas-de-concreto/
• International Energy Agency. (2016). Assessment of embodied energy and CO ² eq for building construction (Annex 57): Overview of Annex 57 results. https://annex57.iea-ebc.org/
  » https://annex57.iea-ebc.org/
• International Energy Agency. (n.d.). Iron and steel. https://www.iea.org/energy-system/industry/steel
  » https://www.iea.org/energy-system/industry/steel
• IPCC. (n.d.). Emission factor database (EFDB). https://www.ipcc-nggip.iges.or.jp/efdb/find_ef.php?ipcc_code=2.C.6&ipcc_level=2
  » https://www.ipcc-nggip.iges.or.jp/efdb/find_ef.php?ipcc_code=2.C.6&ipcc_level=2
• Junqueira, B. P. (2021). Applications of virtual reality and augmented reality in civil construction projects (Final paper, Federal University of Rio de Janeiro). http://www.repositorio.poli.ufrj.br/monografias/projpoli10034409.pdf
  » http://www.repositorio.poli.ufrj.br/monografias/projpoli10034409.pdf
• Liu, Z., Li, P., Wang, F., Osmani, M., & Demian, P. (2022). BIM-based carbon emission reduction research: A bibliometric review. International Journal of Environmental Research and Public Health, 19 (19), 12820. https://doi.org/10.3390/ijerph191912820
  » https://doi.org/10.3390/ijerph191912820
• Oliveira, A. D. D., Silva, J. G. P., Silva, J. V. L. D., & Bavosa, J. A. M. (2019). Virtual and augmented reality in civil engineering: Feasibility and savings in companies with the adoption of these technologies. Evangelical University of Goiás. http://repositorio.aee.edu.br/jspui/handle/aee/8293
  » http://repositorio.aee.edu.br/jspui/handle/aee/8293
• Ramos, R. C., & Borges Júnior, W. L. B. (2024). Virtual reality in education: Fundamentals, devices, applications and innovation in teaching. RCMOS - Multidisciplinary Scientific Journal O Saber, 1 (1). https://doi.org/10.51473/rcmos.v1i1.2024.540
  » https://doi.org/10.51473/rcmos.v1i1.2024.540
• Santos, A. F. (2023). Trends and challenges: A study of rural producers' perceptions about the future of Brazilian agribusiness. Journal of Competitiveness and Sustainability, 10 (1), 81 - 104. https://saber.unioeste.br/index.php/comsus/article/view/32676/22930
  » https://saber.unioeste.br/index.php/comsus/article/view/32676/22930
• Schreyer, A. C. (2023). Architectural design with SketchUp: 3D modeling, extensions, BIM, rendering, making, scripting, and layout. Wiley. https://ebin.pub/architectural-design-with-sketchup-3d-modeling-extensions-bim-rendering-making-scripting-and-layout-3nbsped-139416
  » https://ebin.pub/architectural-design-with-sketchup-3d-modeling-extensions-bim-rendering-making-scripting-and-layout-3nbsped-139416
• Sena, L. H. da S., & de Sousa, J. C. (2022). Brazilian agribusiness: Challenges and opportunities for management performance. Id Online: Journal of Psychology, 16 (60). https://d1wqtxts1xzle7.cloudfront.net/112730340/2022_Agronegocio_Brasileiro_3415_13806_1_PB-libre.pdf
  » https://d1wqtxts1xzle7.cloudfront.net/112730340/2022_Agronegocio_Brasileiro_3415_13806_1_PB-libre.pdf
• Sesso, P. P., Mendes, F. H., Sesso Filho, U. A., & Zapparoli, I. D. (2022). Agribusiness sustainability: Analysis between GDP and CO₂ emissions. Revista de Economia e Sociologia Rural, 61 (2), e258543. https://www.scielo.br/j/resr/a/WGDWnHtpKKMKZPWVJh5bxzd/
  » https://www.scielo.br/j/resr/a/WGDWnHtpKKMKZPWVJh5bxzd/
• Silva, C. C., & Santos, J. V. C. (2025). Effective management tool in civil construction works: Trends in the BIM methodology. Research, Society and Development, 14 (8), e5514849098. https://rsdjournal.org/rsd/article/view/49098
  » https://rsdjournal.org/rsd/article/view/49098
• Silva, E. V. da, Santos, G. P. R. dos, Feliz, R. G., de Miranda Feliz, A. C., de Castro Corrêa, A., Imbiriba, K. G., & Ferreira Filho, H. R. (2025). Carbon neutrality and renewable energy: An integrated theoretical framework for the systemic decarbonization of the global economy. Pedagogical Notebook, 22 (7), e16041. https://ojs.studiespublicacoes.com.br/ojs/index.php/cadped/article/view/16041
  » https://ojs.studiespublicacoes.com.br/ojs/index.php/cadped/article/view/16041
• World Steel Association. (n.d.). Climate change and steel production. https://worldsteel.org/climate-action/climate-change-and-the-production-of-iron-and-steel/
  » https://worldsteel.org/climate-action/climate-change-and-the-production-of-iron-and-steel/

• Data Availability Statement:
  The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.
• Funding:
  This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES (Brazilian Federal Agency for Support and Evaluation of Graduate Education) – Finance Code 001.

Edited by

• Area Editor:
  Héliton Pandorfi

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Publication Dates

• Publication in this collection
  27 July 2026
• Date of issue
  2026

History

• Received
  22 Oct 2025
• Accepted
  30 Mar 2026