Life cycle assessment of structural glued laminated timber production with different dimensions and exposure conditions

Glued laminated timber (glulam) is an essential material in modern wooden constructions that offers advantages in terms of strength and versatility. This study conducted a life cycle assessment (LCA) of glulam production in Japan and analyzed the environmental impacts based on different dimensions and exposure conditions. Primary data were collected from 12 factories representing 58.1% of Japan’s production. The assessment followed the ISO 14040/14044 standards and employed mass-based allocation as the primary approach, with economic allocation analyzed for comparison. The results revealed that the major contributors to greenhouse gas (GHG) emissions from glulam production were purchased lamina manufacturing (38%), transportation (27%), and electricity consumption (21%). Large-dimension glulam exhibited the highest environmental impact, largely due to their increased energy consumption. Despite the differences in adhesive types for various exposure conditions, their impact on overall emissions was relatively minor. A sensitivity analysis of the allocation methods revealed significant variations in the reported emissions, emphasizing the importance of methodological choices in LCA studies. This study provides geographically representative LCA data for glulam production in Japan, thereby contributing to improvements in sustainable manufacturing practices. These findings highlight the need to optimize raw material procurement, decarbonize energy, and improve transport efficiency to reduce environmental impacts. Future research should refine the LCA data quality, particularly for lamina production and international supply chains. These insights can support policy development and industrial efforts toward more environmentally sustainable wood utilization.

Wood, a commonly used building material, has recently gained attention as a renewable resource to mitigate climate change. Numerous studies have demonstrated that wooden buildings have lower environmental impacts throughout their life cycle, including reduced greenhouse gas (GHG) emissions [1, 2]. Structural glued laminated timber (glulam) is widely used in wooden buildings for key structural components such as columns, beams, and girders, rendering it an essential building material. In addition, with appropriate preservation treatments, glulam can be used in outdoor structures, including playground equipment.

Glulam is manufactured by applying synthetic resin adhesives to thin wood layers known as laminae, which are then arranged parallel to the grain and pressed to form larger wood products than the original sizes of elements. By adjusting the composition of the laminae, glulam can be fabricated with various shapes and dimensions. Compared to solid wood, glulam offers the advantage of reduced variability in strength owing to the removal of defects at the lamina stage. Glulam is produced in a wide range of sizes, from small to large dimensions, in compliance with Japanese Agricultural Standards (JAS) [3].

Life cycle assessment (LCA) is widely employed to evaluate the environmental impacts of glulam production. The principles and framework of LCA are outlined in ISO 14040 [4], whereas the requirements and guidelines are provided in ISO 14044 [5]. Several LCA studies on glulam have been conducted in various countries, including the United States [6], Canada [7], China [8, 9], Lithuania [10], and Japan [11,12,13,14,15].

In a case study from the United States [6], laminated timber production was evaluated in the Pacific Northwest (PNW) and Southeast (SE) regions. In the PNW, Douglas fir (Pseudotsuga menziesii) is primarily used as the raw material, whereas in the SE, southern pine species such as longleaf pine (Pinus palustris Mill.) were predominantly utilized. The GHG emissions associated with glulam production were reported as 119.12 kg-CO₂ eq/m³ for the PNW and 151.45 kg-CO₂ eq/m³ for the SE. Approximately half of total GHG emissions are attributed to the glulam manufacturing process. The environmental impact was allocated to other co-products, such as wood chips, based on the mass allocation. The data used in this study were obtained from 2000.

In the LCA case study of glulam produced in Quebec, Canada [7], the GHG emissions up to the glulam manufacturing stage were reported as 102 kg-CO₂ eq/m³. Because 97% of the electricity used in the region is derived from hydropower, GHG emissions were lower than those reported in the U.S. case study [6]. Diesel consumption accounted for 55% of total emissions, whereas adhesive production accounted for 38%. Black spruce was used as the primary raw material in this study. As in the U.S. case study, the environmental impact was allocated based on mass allocation. The assessment was conducted using data from a single plant collected between 2004 and 2009.

In the LCA case study of glulam using Douglas-fir imported from Canada to China [8], the GHG emissions were reported as 198 kg-CO₂ eq/m³. This study evaluated a case where logs were processed into laminae in Canada, transported to China, and then manufactured into glulam, resulting in transportation-related GHG emissions accounting for 80 kg-CO₂ eq/m³. However, the lamina manufacturing process in China has not yet been assessed. No description of the allocation method is provided. This assessment was based on data from a single plant in China. In another LCA case study on glulam using bamboo in China [9], GHG emissions were reported as 467 kg-CO₂ eq/m³, with significant contributions from electricity consumption, adhesive use, and transportation. No description of the allocation method is provided.

A life cycle assessment study on glulam in Lithuania [10] is also available; however, unit process data have not been reported, and the details remain unclear. This assessment was based on data from a single plant in Lithuania.

Komata et al. [14] collected data on glulam production from five factories in Hokkaido, Japan, and quantified the GHG emissions up to the manufacturing stage as 336.1 kg-CO₂ eq/m³. Among these emissions, electricity accounted for 39%, heavy oil (A-type) for 37%, and kerosene for 16%. Most of the heavy oil and kerosene were used as heat sources for boilers. However, they also reported issues related to the data, as the transition from fossil fuel-based boilers to biomass boilers has been progressing in recent years [14]. Komata [15] reported that importing logs from the United States and Europe results in significant GHG emissions due to transportation. Additionally, they reported that switching the boiler heat source from fossil fuel to wood residues could reduce emissions to 217 kg-CO₂ eq/m³ [15]. However, this value is not based on actual factory data of biomass boilers but is instead calculated from the heat consumption of fossil fuels.

In the case study on large-section glulam (18,800 ×ばつ 200 ×ばつ 700 mm) in Japan [11], the GHG emissions were approximately 300 kg-CO₂/m³ when using domestically sourced timber. The glulam manufacturing process accounted for the majority of the emissions, followed by lamina production. However, the data for the glulam manufacturing stage were only collected from a single plant. In a case study of the Toshin larch from Nagano Prefecture, Japan [12], two glulam production scenarios were evaluated. In the case using a heavy oil boiler, GHG emissions were reported as 662.0 kg-CO₂/m³, while in the case using a biomass boiler, emissions were 568.2 kg-CO₂/m³. Additionally, another study [13] estimated emissions at 240 kg-CO₂/m³ based on data from a single company.

Several LCA case studies focusing on the GHG emissions of glulam have been conducted. However, these studies relied on data from only one or a limited number of manufacturing plants, resulting in significant variations in reported GHG emissions across cases. Moreover, although glulam is classified based on size and exposure conditions, these differences were not explicitly considered in the assessments. Large-section glulam are estimated to be approximately twice as expensive as small- or medium-section glulam [16], suggesting that their environmental impact may differ accordingly.

In Japan, geographically representative LCA data have been developed and analyzed for cross-laminated timber [17], particle boards, insulation boards, medium-density fiberboards, and hardboards [18]. However, no such studies have been conducted on glulam.

Therefore, this study aimed to develop geographically representative LCA data for glulam produced in Japan. By analyzing data from multiple manufacturing plants, we sought to clarify interplant variability and gain insights into more environmentally sustainable production methods. Additionally, we examined the effects of differences in cross-sectional dimensions and anticipated exposure conditions on the environmental impacts of glulam production.

Goal and scope definition

In this study, the environmental impact of glulam is quantified using the LCA [4, 5]. The target product for assessment is structural glulam, as defined by the Japanese Agricultural Standard (JAS) [3]. In 2021, the volume of structural glulam certified under JAS grading was 1,678,000 m³ [19], accounting for 89% of the total glulam production volume in Japan, which was 1,896,000 m³ [20]. The functional unit is set as 1 m³ of structural glulam production.

The system boundary was defined as roundwood to glulam production. The system boundaries are illustrated in Fig. 1. Glulam is produced from the laminae sawn from the roundwood. In Japan, in addition to imported laminae, domestic softwood species, such as Japanese cedar (Cryptomeria japonica), are primarily used as raw materials.

The roundwood is transported to glulam factories or lamina sawmills. The roundwood was then debarked, sawn into boards, and dried. Defects such as knots and cracks are then removed to produce laminae, which help minimize shrinkage and deformation during subsequent processing, while also enhancing the adhesive bonding strength. Steam is used for drying, and many factories operate biomass boilers fueled by bark, offcuts, and other residues generated within the facility to supply the required steam.

The laminae are joined using finger joints to produce laminae of predetermined lengths suitable for various glulam applications. The adhesive was applied and the laminated components were pressed together. The type of adhesive used varied depending on whether the glulam was intended for outdoor or indoor applications. After bonding, planing was performed to ensure uniform thickness and width. Depending on the intended application, glulam may also undergo curved lamination or taper processing. Additionally, treatments such as preservatives, insect-proofing, fire-resistance treatment, painting, and coating may be applied as needed.

Foreground data

Input–output data for glulam manufacturing were obtained from manufacturing factories. The input data included energy consumption, roundwood usage, and auxiliary material consumption. The output data include the production volume of structural glulam, the sales volume of by-products such as wood chips, and the amount of waste generated.

The factory data used in this study were from 2021 when the surveyed factories were operating stably. In cases where specific circumstances, such as temporary shutdowns due to equipment upgrades and affected operations, occurred, data were collected from a 1-year period as close to 2021 as possible, excluding irregular periods.

To ensure that the production scale and product types of the selected factories reflect the representative data of Japan’s glulam industry, 12 factories from eight companies across the country were selected. The production scales of the factories surveyed are listed in Table 1. The total production volume of JAS-graded structural glulam at these selected factories was 975,000 m³, accounting for 58.1% of domestic production. We visited all the selected factories to explain the significance of the study and the required data. During these visits, we obtained the necessary production data from each factory for the assessment.

All factories utilized biomass boilers. The fuel for these biomass boilers consists of bark, chips, and other residues generated within the factories. As many factories do not precisely measure their biomass consumption, the amount of biomass fuel used is estimated based on steam consumption and the lower heating value (LHV) of the fuel. This approach ensured consistent mass balance in the calculations.

The primary product of the factories is glulam; however, some factories also sell by-products such as wood chips and sawdust. According to ISO 14044:2006 [5], when co-products are present, the system boundary should be expanded, or environmental impacts should be allocated to the co-products based on specific criteria. In LCA studies of wood products, allocation is commonly based on mass, volume, or economic value. The choice of allocation method significantly affects the results [21,22,23,24]. Allocation based on mass or volume reflects the physical properties of materials, and therefore, provides results that are not influenced by changes in socioeconomic conditions [25].

However, allocating the same environmental impact to co-products, such as wood chips, which have significantly different economic values, may not accurately reflect their relative impact. According to European standards [26], economic allocation is recommended when there are substantial differences in economic value among the co-products.

Considering these advantages, in this study, the primary analysis was conducted using mass-based allocation; the results based on economic allocation are also presented for reference. Additionally, in the LCA data for metal processing, environmental impacts are typically not allocated to scrap generated during the process. Based on this approach, an alternative case is also provided in which no environmental impacts are allocated to byproducts, such as wood chips, and the entire impact is allocated to glulam based on mass allocation. The apparent density, moisture content, and unit price used in this study are summarized in Table 2.

Background data

The environmental impacts associated with electricity, fuel, and auxiliary materials were collected from the Japanese process-based LCA database IDEA version 3.4 [32]. The primary domestically sourced wood species used in Japanese glulam production are Japanese cedar, Japanese cypress (Chamaecyparis obtusa), and Japanese larch (Larix kaempferi).

Structural glulam factories are divided into three types: factories that manufacture lamina from logs, factories that purchase lamina from outside, and factories that procure lamina through both routes. The unit process data for roundwood production were obtained from the literature [33]. Some factories procure roundwood from North America, for which LCA data from Oneil and Puettmann [34] were used. Regarding LCA data of procured laminae production, laminae were assumed to be equivalent to sawn timber, and publicly available LCA data for sawn timber were used. These data included sources from Japan [28], Sweden [35,36,37], Finland [38, 39], Norway [40, 41], and North America [42].

Allocation methods used in sawmills vary depending on the data source. To maintain consistency, data with the same allocation method were used; that is, when evaluating based on mass allocation, the sawmill data assessed using mass allocation were applied.

However, in most cases, LCA studies have reported results based on a single allocation criterion. Therefore, based on the sawmill survey data [28], it was assumed that 64% of the environmental impacts were allocated to laminae under mass-based allocation, whereas 95% were allocated under economic-value-based allocation. Using these assumptions, the results of the other allocation methods were estimated.

The characterization models differed significantly across the data for impact categories other than climate change. Domestic data [28] were used as approximations to maintain consistency. Table 3 summarizes the GHG emissions from lamina production in Japan, Europe, and North America as used in this study.

The International Convention for the Prevention of Pollution from Ships has tightened its regulations since 2020, requiring the sulfur content of marine fuels to be less than 0.5 wt%. To account for changes in sulfur oxide emissions from maritime transport, an inventory analysis was conducted assuming a sulfur content of 0.5 wt% in the heavy fuel oil used for international shipping. Furthermore, to incorporate fuel efficiency improvements in international shipping transport into the study, heavy fuel oil consumption associated with shipping transport was estimated using publicly available data from a major Japanese international shipping company [43], and 3.34 g-CO₂ eq/tkm for international ship transportation was used.

Impact assessment method

LIME2 [44] was employed for impact assessment. This study primarily assessed the impact on climate change. Other impact categories were also assessed for reference, including ozone layer destruction, acidification, urban area air pollution, photochemical ozone, human toxicity, cancer effects, human toxicity, non-cancer effects, aquatic toxicity, biological toxicity, eutrophication, land-use—occupation, land-use—transformation, and abiotic resource depletion. For climate change, characterization was conducted using the 100-year global warming potential (GWP) from the IPCC Sixth Assessment Report [45]. Given that wood products are gaining attention as a measure of climate change mitigation, a detailed analysis of the climate change impact category was conducted. In IDEA version 3.4, the GHG emissions associated with land use and transformation were calculated for a 50-year period. This study also included the impacts of land use and transformation.

Cross-sectional dimensions and exposure conditions

Data collection was conducted based on the cross-sectional dimension and exposure condition. In Japan, the cross-section dimension is classified into three categories: large, medium, and small in accordance with JAS for glued laminated timber [3]. For example, large dimension glulam was defined as having a short side of at least 15 cm and a cross-sectional area of at least 300 cm. It is used in large-scale building structures (e.g., gymnasiums and commercial facilities), bridges, and large-span roof structures.

A medium dimension glulam is defined as having a short side of at least 7.5 cm and a long side of at least 15 cm, except the large dimension glulam. It is commonly used in detached houses (e.g., beams, floor structures), as well as in medium-sized commercial facilities and warehouses. It can be used in both load-bearing and non-load-bearing applications.

A small-dimension glulam refers to timber with a short side of less than 7.5 cm or a long side of less than 15 cm. It is often used in indoor structural elements, such as columns, joists, rafters, and partition walls.

There are three types of exposure conditions: A, B, and C [3]. Exposure condition A refers to glulam, which can be used in environments requiring high durability. It is used not only in ordinary wooden buildings, but also in outdoor structures, such as bridges, outdoor decks, exterior columns, and beams, where it is exposed to sunlight and high humidity. Moreover, buildings and structures need to withstand fire damage for some duration of time. Therefore, adhesives with excellent weather, water resistance, and heat resistance were used.

Exposure condition B is glulam intended for indoor environments and can also be used in locations where it needs to withstand fire damage for a certain period of time. The adhesives used in this category must have a certain level of water, weather, or heat resistance. Exposure condition C is primarily intended for dry indoor environments. It is commonly used as an interior structural material in residential and commercial buildings such as columns, beams, and flooring. Because the type and quantity of adhesives and auxiliary materials used vary depending on the exposure condition, data were collected accordingly for each type.

Table 4 lists the number of factories surveyed. Although the data were collected from 12 factories, some manufactured multiple types of products, resulting in a total count exceeding 12. For large-dimension glulam, there was no production in exposure conditions B and C. As mentioned previously, the overall data coverage exceeded 50%; however, some categories did not reach this threshold. Sufficient representativeness was achieved for a medium dimension glulam in exposure condition B; however, data were collected from only two companies. As a result, in accordance with the confidentiality agreements made during the survey, the results for the medium dimension glulam in exposure condition B were not disclosed to protect corporate data.

Glued laminated timber production process (gate to gate)

The key process data for the gate-to-gate operations in the glulam factories are summarized in Table 5. Detailed process data are presented in Table S1 in the Electronic Supplementary Material. Table S1 presents the process data based on the three allocation methods: mass-based, economic value-based, and allocation exclusively to glulam. Additionally, the weighted average values for both medium and small dimension glulam are provided.

In Japanese glulam manufacturing factories as a whole, the roundwood procurement volume was 0.16 m³-roundwood/m³-glulam, while the lamina procurement volume was 1.01 m³-lamina/m³-glulam under mass allocation. This indicates that a greater proportion of glulam production relies on purchased lamina rather than on mechanical processing of roundwood. However, this figure also includes cases in which companies produce laminae at separate facilities within the same company and use them for glulam production.

Of the total lamina procurement, 0.30 m³-lamina/m³-glulam was sourced domestically, while the remainder was imported from North America and Europe. Consequently, container ship transport accounted for the highest transportation amount at 5603 tkm, whereas truck transport remained relatively low, even when combined.

Electricity consumption was highest for large dimension glulam, at 236 kWh/m³. For medium dimension glulam, it ranged from 77 to 85 kWh/m³, while for small dimension glulam, it ranged from 46 to 70 kWh/m³. The results indicate that electricity consumption increases with larger cross-sectional dimensions, and this trend is particularly pronounced for glulam with large dimension.

In large dimension glulam for the exposure condition A, 14.5 kg/m³ of phenol-resorcinol resin system adhesive was used, while the usage of aqueous polymer isocyanate adhesive was less than 0.1 kg/m³. In contrast, for medium and small dimension glulam in exposure condition C, approximately 7 kg/m³ of aqueous polymer isocyanate adhesive was used. Because exposure condition C is primarily used for indoor applications, a higher amount of aqueous polymer isocyanate adhesive was used to minimize formaldehyde emissions. In Japan, if volatile organic compounds such as formaldehyde are emitted from manufacturing facilities, companies are required to report them to the government under the Pollutant Release and Transfer Register (PRTR) Law. However, for the factories surveyed, emissions were below the reporting threshold of the PRTR Law.

Glued laminated timber production (cradle to gate)

The results of the environmental impact assessment up to the glulam manufacturing stage are summarized in Fig. 2. The detailed numerical values are provided in Table S2 of the Electronic Supplementary Material. Across all environmental impact categories, large dimension glulam exhibited the highest environmental impact. For medium- and small dimension glulam, exposure condition C showed slightly higher environmental impacts than the other exposure conditions across most environmental impact categories. This was likely due to the slightly higher wood input required for usage in exposure condition C.

In the climate change impact category, 38% of the total weighted average GHG emissions were attributed to the manufacture of purchased laminae, whereas transportation and electricity consumption accounted for 27% and 21%, respectively. The total weighted average GHG emissions were 140 kg-CO₂ eq/m³, whereas large dimension glulam exhibited significantly higher emissions at 285 kg-CO₂ eq/m³. For a large dimension glulam, 34% of the GHG emissions were associated with the manufacturing of purchased lamina, whereas electricity consumption contributed 44% of the total emissions. Although different types of adhesives were applied in exposure condition A and C, no significant differences were observed in their GHG emissions. For small dimension glulam in the exposure condition C, a high proportion of domestically sourced lamina resulted in a relatively large GHG emission of 65 kg-CO₂ eq/m³ from domestic lamina production. However, the GHG emissions from transportation were relatively low at 14 kg-CO₂ eq/m³. The emissions for medium and small dimension glulam ranged between 108 and 161 kg-CO₂ eq/m³.

Lamina manufacturing accounted for the majority of environmental impacts in the impact categories of ozone layer depletion, acidification, urban air pollution, photochemical oxidants, toxic chemicals, and land use. For a large dimension glulam, electricity consumption contributed to a significant portion (several tens of percent) of the impacts in the categories of photochemical oxidation, aquatic toxicity, biological toxicity, and land transformation. In contrast, in the impact categories of eutrophication and resource consumption, the influence of electricity was minimal, whereas adhesives accounted for a significant proportion, reaching several tens of the percent. The impact of transportation contributed approximately 20% to the impact category of climate change and approximately 10% to acidification; however, its share in the other impact categories was relatively small. For land-use occupation, the impacts were almost entirely attributed to both lamina and roundwood procurement.

A breakdown of GHG emissions from transportation is shown in Fig. 3. In all categories, truck transport and international container shipping accounted for approximately half of the total impact of transportation. The weighted average GHG emissions from transportation across all glulam types were 38 kg-CO₂ eq/m³. Among these, transportation by 15-ton trucks contributed 10 kg-CO₂ eq/m³, while 4-ton trucks accounted for 7 kg-CO₂ eq/m³. International shipping resulted in GHG emissions of 19 kg-CO₂ eq/m³. For small dimension glulam in exposure condition C, GHG emissions from transportation were significantly lower at 14 kg-CO₂ eq/m³, representing a 38% reduction compared to the weighted average for all glulam types.

Across weighted average, the GHG emissions from lamina manufacturing accounted for 38% of the total emissions, making the reduction in GHG emissions during lamina production the most critical factor. An analysis of the breakdown of GHG emissions from lamina manufacturing indicates that the majority originate from electricity consumption at sawmills and the impacts of roundwood production [28]. Furthermore, during roundwood production, a significant portion of GHG emissions comes from diesel fuel consumption during logging and transportation [33]. Therefore, to reduce GHG emissions from lamina manufacturing, the decarbonization of electricity used in sawmills and fuel used in forest operations is essential.

Transportation accounted for 27% of the total GHG emissions. In this study, the transportation distance for container shipping from Europe to Japan via the Suez Canal was set at approximately 22,000 km. However, in the case of conflict in the Middle East, container ships may be rerouted via the Cape of Good Hope, increasing the transportation distance to approximately 28,000 km. This results in higher GHG emissions and increased emissions of sulfur oxides and other environmental pollutants.

Although the GHG emission intensity of international freight transport has decreased in recent years, efforts to minimize transportation remain essential. For truck transport, no decisive solution exists to reduce environmental impact; continuous efforts, such as optimizing transportation efficiency, are necessary. In addition, the proactive adoption of low-carbon technologies, such as fuel cell trucks, should be considered. These measures would not only contribute to reducing GHG emissions but also help mitigate other environmental impacts, such as acidification and urban air pollution.

The GHG emissions from electricity consumption in glulam factories accounted for 21% of the total emissions. Many glulam factories and sawmills operate biomass boilers using the wood residue generated during processing. Some of the surveyed factories also generate electricity, contributing to a reduction in fossil fuel-derived electricity consumption. Several factories have introduced solar power systems. To achieve net-zero GHG emissions, further expansion of electricity generation from renewable energy sources such as biomass and solar power is essential.

Regarding land-use—occupation, most of the impact is attributed to roundwood and lamina production. This category was assessed simply by summing the utilized area (m²·year). Although plantation forests may have lower biodiversity than natural forests, they are still classified as forests; thus, their environmental impact is considered relatively low. Thus, no significant issues were identified.

The environmental impact of adhesives contributed significantly to eutrophication and resource consumption but had a minimal impact in other categories. Historically, adhesives have been a major source of formaldehyde emissions that contribute to indoor air pollution. However, with advancements in volatile organic compounds reduction measures, the toxic impact of adhesives is not significant across different exposure conditions.

In the various toxicity impact categories, most of the environmental impacts were attributed to lamina production, which was primarily driven by electricity consumption. While reducing the environmental impact of adhesives is desirable, the key priority is to lower electricity-related environmental impacts in both lamina and glulam manufacturing.

Electricity-mix changes in the future

Electricity consumption is a major source of GHG emissions. In 2020, Japan set the goal of achieving net-zero GHG emissions by 2050, and the power sector will advance decarbonization efforts. To assess the potential impact of these changes, we estimated the variation in GHG emissions from glulam manufacturing using the projected energy mix for 2030 and 2040 (Fig. 4). The assumed energy mix for these years is based on data from the Agency for Natural Resources and Energy of Japan. Details of the assumed energy composition are provided in Table S3 of the Electronic Supplementary Material.

Although Japanese power companies are considering the use of carbon capture and storage, their high uncertainty led us to exclude them from this estimation. Instead, only changes in the power generation mix were considered. The GHG emission factor of electricity was estimated to be 0.333 kg-CO₂ eq/kWh in 2030 and 0.283 kg-CO₂ eq/kWh in 2040. Electricity is used in various processes. However, only power mix changes for glulam factories and Japanese sawmills were considered in this estimation. As a result, GHG emissions from glulam production, which were calculated at 213 kg-CO₂ eq/m³ in 2021, are projected to decrease to 192 kg-CO₂ eq/m³ in 2030 and 187 kg-CO₂ eq/m³ in 2040.

As the decarbonization of electricity supply progresses, GHG emissions associated with glulam production are expected to decrease. However, to achieve net-zero GHG emissions by 2050, the contribution of glulam remains limited.

Efforts to reduce the environmental impact, including GHG emissions, are expected for laminas purchased overseas. However, the continuous monitoring of environmental impacts and proactive engagement in reduction initiatives are crucial. Additionally, fossil fuels, such as diesel, for truck transport and forest operations, and heavy fuel oil for international shipping are widely used. Therefore, decarbonization efforts should not be limited to glulam factories but should also extend to the entire supply chain.

Allocation method

In this study, environmental impact allocation to co-products was conducted using a mass-based allocation. As a sensitivity analysis, Fig. 5 illustrates the amount of roundwood and lamina input under three different allocation methods: mass-based allocation, economic value-based allocation, and allocation of the entire input to glulam (glulam case).

For the weighted average across all glulam types, the roundwood and lamina input was 1.2 m³ under mass allocation and 1.5 m³ under economic allocation. When the entire input was assigned to glulam (the glulam case), the input amount was 1.6 m³. This result was expected because allocating all inputs to glulam naturally results in the highest input values.

However, for large dimension glulam, the input amount in the glulam case was 2.0 m³, whereas in the economic allocation, it increased to 2.3 m³. A similar trend was observed for the medium dimension glulam. For example, in the exposure condition A of medium dimension glulam, the input amount under economic allocation was 1.5 m³, whereas in the glulam case, it was 1.4 m³. This discrepancy is due to the higher unit price of the large and medium-dimension glulam compared to that of the small-dimension glulam. Consequently, economic allocation led to a reduction in the allocated input for small-dimension glulam.

Under mass allocation, the input amount for large-dimension glulam was 1.6 m³, whereas for medium and small-dimension glulam, the input ranged from 1.1 to 1.2 m³. This lower value is observed in cases where by-products such as wood residues are sold as a portion of the input allocated to these by-products.

Figure 6 presents the results of GHG emissions for a small dimension glulam using different allocation methods. In all cases, the mass allocation resulted in the lowest GHG emissions. For the exposure condition C, the case where all impacts were allocated to glulam resulted in emissions of 237 kg-CO₂ eq/m³, whereas the mass allocation yielded significantly lower emissions at 136 kg-CO₂ eq/m³. This substantial difference highlights the importance of carefully considering the allocation methods when utilizing published LCA data for wood products.

Comparison with other studies

In this study, the GHG emissions were calculated as 140 kg-CO₂ eq/m³, which is approximately one-fourth of the value reported in a case study on Toshin larch from Nagano Prefecture [12]. In their study [12], the environmental impacts were allocated based on volume; even when using a biomass boiler, the reported emissions were as high as 568.2 kg-CO₂/m³. However, biomass boilers are used only in glulam factories. Since the majority of CO₂ emissions originated from lamina production, the high emission values in that case can likely be attributed to the extensive use of fossil fuels during the lamina manufacturing process.

A previous case study on large-dimension glulam [11] reported CO₂ emissions of approximately 300 kg-CO₂/m³. Since that study [11] appears to have allocated all environmental impacts to glulam, its results are similar to the 285 kg-CO₂ eq/m³ calculated in this study under the case where all impacts are allocated to glulam. However, the background data used in that study had lower CO₂ emission factors than those used in this study. For example, the CO₂ emission factor for roundwood in the previous study was 6.33 kg-CO₂/m³, whereas this study used approximately 20 kg-CO₂ eq/m³, showing a significant difference. Similarly, the CO₂ emission factor for sawn timber in the previous study was 29.4 kg-CO₂/m³, considerably lower than the 78 kg-CO₂ eq/m³ used for domestically sourced lamina in this study. Therefore, it is likely that the impact of fossil fuel-based boilers was offset by lower CO₂ emission factors, resulting in values similar to those obtained in this study.

Another study evaluating CO2 emissions from glulam manufacturing [13] reported emissions of 240 kg-CO₂/m³. That study [13] also appears to have allocated all environmental impacts to glulam because there is no description of allocation. When compared to the weighted average GHG emissions of 214 kg-CO₂ eq/m³ in this study, the values are relatively close.

Similarly, this value is close to the estimated 217 kg-CO₂ eq/m³ reported by Komata et al. [15], which was derived using data from glulam production in Hokkaido after transitioning to biomass boilers.

Some LCA studies of glulam in Japan [11, 12, 14] have shown that a significant portion of GHG emissions was attributed to boilers using fossil fuels. However, with the transition to biomass boilers, GHG emissions from boilers have been reduced.

Therefore, comparisons with previous studies should be made with caution, especially in light of differences in geographic context, system boundaries, and whether the study was conducted using processes and assumptions consistent with those of the present study.

Data quality check and limitations

In this study, primary data with high geographical and technological representativeness for Japan were collected from glulam factories accounting for 58.1% of the national production share. In 2021, there were 72 JAS-certified factories in Japan according to production records for JAS-certified glulam [19]. The factories surveyed accounted for 17% of this total. It should be noted that unit process data may differ in relatively small-scale factories.

Among the surveyed factories, some procured roundwood as raw material and carried out the entire manufacturing process up to glulam production, while others externally sourced lamina for glulam manufacturing. Because the procurement volume of lamina is large, the LCA data for lamina production had a significant impact on the results. Although IDEA ver.3.4 database, a representative process-based database in Japan, includes data for sawn timber and roundwood, this study utilized existing literature, which is considered more appropriate.

Multiple environmental product declarations (EPDs) were used for the LCA data of European and North American lamina in this study. As different EPD programs have varying Product Category Rules (PCRs), they cannot be directly applied to other LCA studies without adjustments. In this study, only the allocation method, which had the greatest impact, was adjusted. However, significant differences existed in the characterization models for impact categories other than climate change; thus, Japanese values were used as substitutes. To further improve the appropriateness of the analysis, it is important to carefully consider the methodological differences in LCA and develop primary data tailored for the study.

Structural glulam, a building material derived from renewable resources, has gained attention as a measure of climate change mitigation. In this study, an LCA was conducted on structural glulam in Japan, analyzing the environmental impact variations based on differences in cross-sectional dimension and exposure condition. The results revealed significant differences in environmental impact depending on the size of the glulam.

The primary source of GHG emissions was purchased lamina production, which accounted for 38% of the total emissions. This suggests that improving efficiency and promoting decarbonization in sawmills and forest operations is crucial. Additionally, 27% of the GHG emissions were attributed to transportation, with long-distance transport having a significant impact. Optimizing raw-material procurement and reducing transportation distances are essential strategies for minimizing transport-related environmental impacts. Electricity consumption in the glulam factories contributed to 21% of the total GHG emissions. Therefore, further decarbonization of electricity in both sawmill and glulam factories is necessary. The use of fossil fuels as a heat source for boilers results in substantial GHG emissions. However, the shift toward biomass boilers has led to a reduction in boiler-related GHG emissions.

The environmental impact of glulam production was found to depend on the cross-sectional dimension, with large-dimension glulam exhibiting the highest environmental impact. In contrast, while the type of adhesive varied depending on the exposure condition, no clear differences in the environmental impact were observed based on the exposure condition alone. This suggests that efforts to reduce the environmental impacts of adhesives have yielded positive results.

In this study, the environmental impacts were allocated to glulam and its co-products based on mass criteria. However, the sensitivity analysis showed that alternative allocation methods, such as economic allocation, resulted in significant variations in environmental impacts. Therefore, when interpreting LCA results, careful consideration of the allocation methods is essential.

This study provides geographically representative LCA data on structural glulam production in Japan. These findings are expected to contribute to the development of low-impact manufacturing methods and the formulation of policies for sustainable wood utilization. Future research should focus on improving data quality, particularly in lamina manufacturing and international supply chains.

The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request, except for companies’ proprietary data and data licensed by third parties.

BDt:

Bone dry tonne

EPD:

Environmental product declaration

GHG:

Greenhouse gas

GWP:

Global warming potential

HHV:

Higher heating value

JAS:

Japanese agricultural standard

LCA:

Life cycle assessment

LHV:

Lower heating value

PCR:

Product category rule

PNW:

Pacific Northwest

PRTR:

Pollutant Release and Transfer Register

SE:

Southeast

The authors thank the eight companies that provided glued-laminated-timber production data for this study. The authors express their gratitude to the Japan Laminated Wood Products Association for coordinating the survey.

This research was supported by grants from the Forestry Agency, Japan.

Authors and Affiliations

1. College of Policy Science, Ritsumeikan University, Iwakura-Cho 2-150, Ibaraki, Osaka, 567-8570, Japan

   Katsuyuki Nakano

2. Prof. Emeritus, Tokyo University of Agriculture and Technology, Toyogaoka 2-11-2-103, Tama, Tokyo, 206-0031, Japan

   Nobuaki Hattori

3. Sustainable Management Promotion Organization (SuMPO), Uchikanda 1-14-8, Chiyoda, Tokyo, 101-0047, Japan

   Masahiro Koide & Mai Imago

4. S-Pool Blue Dot Green, Sotokanda 3-12-8, Chiyoda, Tokyo, 101-0021, Japan

   Yuta Yamada

5. Usuki Energy, Sashiu 5154-1, Usuki, Oita, 875-0001, Japan

   Takuya Ogawa

Contributions

Conceptualization: Nobuaki Hattori; Methodology and formal analysis: Katsuyuki Nakano; investigation: Masahiro Koide, Mai Imago, Yuta Yamada, and Takuya Ogawa; writing—original draft preparation: Katsuyuki Nakano; writing—review and editing: Nobuaki Hattori; funding acquisition and supervision: Nobuaki Hattori.

Corresponding author

Correspondence to Nobuaki Hattori.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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