Displacement Factors for Aerosol Emissions From Alternative Forest Biomass Use

Abstract

ABSTRACT

Substituting alternative materials and energy sources with forest biomass can cause significant environmental consequences, such as alteration in the released emissions which can be described by displacement factors (DFs). Until now, DFs of wood‐based materials have included greenhouse gas (GHG) emissions and have been associated with lower fossil and process‐based emissions than non‐wood counterparts. In addition to GHGs, aerosols released in combustion processes, for example, alter radiative forcing in the atmosphere and consequently have an influence on climate. In this study, the objective was to quantify the changes in the most important aerosol emission components for cases when wood‐based materials and energy were used to replace the production of high‐density polyethylene (HDPE) plastic, common fossil‐based construction materials (concrete, steel and brick), non‐wood textile materials and energy produced by fossil fuels and peat. For this reason, we expanded the DF calculations to include aerosol emissions of total suspended particles (TSP), respirable particulate matter (PM10), fine particles (PM2.5), black carbon (BC), nitrogen oxides (NO_x), sulphur dioxide (SO₂) and non‐methane volatile organic compounds (NMVOCs) based on the embodied energies of materials and energy sources. The DFs for cardboard implied a decrease in BC, SO₂ and NMVOC emissions but an increase in the other emission components. DFs for sawn wood mainly indicated higher emissions of both particles and gaseous emissions compared to non‐wood counterparts. DFs for wood‐based textiles demonstrated increased particle emissions and reduced gaseous emissions. DFs for energy biomass mainly implied an increase in emissions, especially if biomass was combusted in small‐scale appliances. Our main conclusion highlights the critical need to thoroughly assess how using forest biomass affects aerosol emissions. This improved understanding of the aerosol emissions of the forestry sector is crucial for a comprehensive evaluation of the climate and health implications associated with forest biomass use.

Introduction

Bioeconomic aims are globally calling for wood-based solutions to replace fossil-based materials and fossil energy. Forests can contribute to climate change mitigation by sequestering carbon from the atmosphere and storing it in forests and wood-based products. In addition, the use of wood-based products and biomass energy substitutes the use of fossil-based materials and fossil energy. According to a wide range of studies, wood-based products typically have a lower carbon footprint than their fossil counterparts; therefore, using wood can decrease carbon emissions from the use of fossil raw materials in industries (e.g., Alam et al. 2017; Freer-Smith et al. 2023; Hudiburg et al. 2019; Leskinen et al. 2018; Nabuurs et al. 2017; Sathre and O'Connor 2010; Seppälä et al. 2019; Smyth et al. 2017, 2020).

The substitution impacts of wood-based products and energy can be described by displacement factors (DFs), which express the amount of reduced greenhouse gases (GHGs), including carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O), per mass unit of wood use when producing a functionally equivalent product or amount of energy from fossil counterparts (Sathre and O'Connor 2010). DFs are calculated by dividing the difference in the emissions of the production of non-wood material and wood-based material by the difference in the amount of wood in wood and non-wood alternatives (Sathre and O'Connor 2010). Until now, DF studies have particularly focused on GHG emissions, and to our knowledge, previous studies have rarely reported DFs for aerosol emission components from forest biomass use (Wolf et al. 2016). In Finland, aerosol emission data concerning industrial use of various fuels are collected and constitute the best available database for aerosol DF calculation.

Aerosols released from the use of forest biomass influence radiative forcing in the atmosphere and consequently have an influence on climate (IPCC 2021). Aerosol emissions also degrade ambient air quality, which is linked to potential public health effects (Arfin et al. 2023; Kaivosoja et al. 2013; Sippula et al. 2019). They contain several particulate and gaseous emission components that are relevant from both the climate and air quality perspectives. Particulate matter is often classified into fine particles (PM2.5), which is the mass of particles with aerodynamic diameters < 2.5 μm, and respirable particulate matter (PM10), which is the mass of particles with aerodynamic diameters < 10 μm. Furthermore, black carbon (BC) is an optically defined component in combustion particulate matter, which consists mainly of elemental carbon and exhibits strong light absorption across the visible light spectrum (Petzold et al. 2013). In addition, combustion and industrial processes also release gases that participate in the formation of secondary aerosols, such as nitrogen oxides (NO_x), sulphur dioxide (SO₂) and non-methane volatile organic compounds (NMVOCs). Aerosols released into the atmosphere can have either a cooling effect, such as SO₂ by forming light-scattering sulphate particles, or a warming effect, such as by strongly light-absorbing BC. Globally, the cooling effect that aerosols have contributed from 1850–1900 to 2010–2019 has been estimated as from 0.0°C to 0.8°C, whereas BC is considered a potent short-lived climate forcer with a radiative forcing of 0.11 (±0.31) Wm⁻² (Szopa et al. 2021). Simultaneously, well-mixed GHGs have contributed to a warming of 1.0°C–2.0°C (IPCC 2023). Forest fires, biomass combustion in small-scale appliances, traffic and flaring in oil fields have been the most important anthropogenic BC sources globally (Bond et al. 2013; Butt et al. 2016; Huang and Fu 2016; van der Gon et al. 2015).

Due to the predominant combustion of by-products associated with wood-based products, their usage can be connected to notable aerosol emissions throughout their entire life cycle (Vento et al. 2024). Nonetheless, wood-based materials can be used for replacing fossil resource-intensive materials, the production of which may also produce aerosol emissions (Alves et al. 2011; Ohlström et al. 2000; Sidhu, Graham, and Striebich 2001). For instance, steel and concrete production are well known for their high-energy requirements (Hasanbeigi et al. 2014), which often lead to high emissions. Similarly, high aerosol emissions in the oil industry (Huang and Fu 2016) increase emissions from plastic and synthetic textile production if oil is used as a raw material. The regulation of aerosol emissions in various industries has evolved in parallel with a growing understanding of air quality and health and environmental effects of aerosols (Suhr et al. 2015; Wilnhammer et al. 2017). There is significant variation in aerosol emissions between different biomass-to-energy technologies, depending mainly on the level of combustion and emission after-treatment technologies and fuel qualities (Ohlström et al. 2000; Savolahti et al. 2016). For instance, in small-scale biomass combustion, the aerosol emissions can be markedly reduced by clean technology innovations in combustion systems (Wilnhammer et al. 2017).

In this study, the objective was to quantify the substitution effects concerning aerosol emissions from the alternative use of forest biomass by expanding the DF calculations to include the most important aerosol emission components. They were calculated for wood-based materials replacing high-density polyethylene (HDPE) plastic, construction materials (concrete, steel and brick) and non-wood textile materials, and energy biomass replacing fossil fuels and peat.

Material and Methods

Calculation of Displacement Factors for Wood-Based Materials and Energy

A DF of wood-based material substitution describes the amount of aerosol emission that is avoided by using wood-based materials instead of some other material (Leskinen et al. 2018; Sathre and O'Connor 2010; Schlamadinger and Marland 1996). Thus, DFs can be used to quantify the amount of change in aerosol emissions per unit of functionally equivalent product or energy. The DFs were calculated as follows:1$$$ \mathrm{DF}=\frac{{\mathrm{Aerosol}}_{\mathrm{non}-\mathrm{wood}}-{\mathrm{Aerosol}}_{\mathrm{wood}-\mathrm{based}}}{\mathrm{Unit}\ \mathrm{of}\ \mathrm{material}\ \mathrm{or}\ \mathrm{energy}} $$$where Aerosol_non-wood and Aerosol_wood-based are the aerosol emissions resulting from the use of the non-wood and wood-based alternatives expressed in mass units. In the case of a wood-based material, Unit of material is a mass unit (dry tonne, t), and in the case of energy, it is an energy unit (terajoule, TJ). Positive DF values indicate avoided emissions by the use of wood-based material or energy biomass, and negative DF values indicate an increase in the emissions.

In this study, DFs were calculated for wood-based materials replacing HDPE plastic, construction materials, non-wood textile materials and energy biomass replacing fossil fuels and peat, by utilising existing data on aerosol emissions. Since plastics have many harmful environmental effects (UN Environment Programme 2022), DFs for cardboard were calculated against the production of HDPE plastic, the production of which requires less energy than that of most other plastic qualities (Hammond and Jones 2011). DFs for sawn wood used in construction were calculated against the use of concrete, steel and brick. Wood-based viscose was assumed to replace flax, cotton, wool, polypropylene, polyester, acrylic or nylon.

DFs for energy were calculated for the energy biomass replacing coal, oil, natural gas, coke, diesel oil, gasoil and peat. The combustion of forest biomass for energy was considered separately in small-scale burning (SSB) and medium- and large-scale burning (LSB). In SSB, energy biomass was used for residential heating and combusted in small-scale appliances, such as wood-fired boilers and stoves, without any emission after-treatment technologies to decrease aerosol emissions. In LSB, energy biomass was combusted in medium- to large-scale boilers (i.e., plant power > 1 MWth). These boilers are typically based on grate combustion, fluidised bed combustion or pulverised fuel combustion technologies and are equipped with relatively efficient particle filtration systems.

System Boundaries and Data for the Calculation of Aerosols for Wood-Based Materials and Energy Biomass and Non-Wood Counterparts

System Boundaries

System boundaries for calculating aerosol emissions of wood-based materials and energy biomass were the same as for their non-wood counterparts (Figure 1). Emission calculations for wood-based and non-wood materials considered the manufacturing phase but excluded the end-of-life phase, which was not considered due to a lack of data and consequent large differences in aerosol emissions depending on the alternative end-use options of materials. Biogenic emissions, which are produced by metabolic processes in plants, were also excluded in the calculation of emissions for wood-based materials. In the case of energy, the emissions released in the combustion of forest biomass and alternative fossil fuels and peat were considered.

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Functional Units and Data for the Emission Calculation of Wood-Based and Non-Wood Materials

Aerosol emissions of all wood-based materials were calculated for the mass unit of timber needed to produce one tonne of product (g t⁻¹). In addition, emissions of sawn wood use were calculated for one square metre of wall (g m⁻²) and for one square metre slab with an average weight of one bearing column, which is a typical load-bearing structure in office buildings (g m⁻²) (Hildebrand 2014). Alternative functional units were used to demonstrate the use of DFs in alternative cases and the variability in displacement effects of wood-based materials. When alternative functional units were used, the nominator in the DF equation considered the difference in material masses needed to provide one functional unit using either wood-based or non-wood material (Equation 1). The amounts of wood-based materials were based on the yield of sawn wood or pulp in the forest industry (Hiltunen, Strandman, and Kilpeläinen 2021; Kilpeläinen et al. 2011) (Table A1). The side products of sawn wood and pulp productions were estimated to conform to the side streams from the Finnish forest industry in 2016 (Hassan et al. 2019).

Emissions for cardboard were calculated by summarising emissions from transport of pulp wood for 70 km and the combustion of the by-products of pulp production and considering the fossil fuel used in the pulping processes (Suhr et al. 2015) (Table 1). Aerosol emissions for sawn wood were calculated based on the emissions from the transport of sawn timber for 70 km, combustion of the sawmilling by-products (e.g., bark and saw dust) and the electricity needed (1314 MJ) for producing one dry tonne of sawn wood in the sawmills (Sahateollisuus 2020). Emissions for wood-based viscose were calculated based on emissions from the combustion of the by-products of chemical pulp production and the energy need of viscose fabric production (Barber and Pellow 2006). The yield of viscose pulp (36.4% from timber dry mass) was based on data from Canopy (2020). Aerosol emissions of electricity in the sawmilling and pulp industry were based on average fuel mixes in electricity production in Finland in 2022 (Energiateollisuus ry 2023) (Table 1) (i.e., nuclear 32.7%, hydropower 22.6%, biomass 18.3%, wind power 11.7%, natural gas 5.3%, coal 4.7%, peat 2.9%, recovered fuel 1.2%, solar power 0.4% and oil 0.2%).

TABLE 1 Embodied energies (EEs) and energy profiles used for the calculation of aerosol emissions for wood-based materials and non-wood counterparts.

Wood-based	Non-wood	Reference
Embodied energies (EE)	Hammond and Jones (2011), Suhr et al. (2015)
EE of cardboard (9691 MJ t−1)	EE of HDPE plastic (22.4 MJ kg−1)
EE of sawn wood in construction (Wall 1382 MJ m−2, slab and column 150 MJ m−2, material tonne 10 MJ kg−1)	EE of steel (Wall 2298 MJ m−2, slab and column 961 MJ m−2, material tonne 20.1 MJ kg−1)	Hildebrand (2014)
EE of concrete (Wall 2959 MJ m−2, slab and column 437 MJ m−2, material tonne 0.75 MJ kg−1)	Hildebrand (2014)
EE of bricks (Wall 3038 MJ m−2, material tonne 3 MJ kg−1)	Hildebrand (2014)
EE of viscose cloth (192 MJ kg−1)	EE of flax cloth (102 MJ kg−1)	Barber and Pellow (2006)
EE of cotton cloth (147 MJ kg−1)	Barber and Pellow (2006)
EE of wool cloth (155 MJ kg−1)	Barber and Pellow (2006)
EE of polypropylene cloth (207 MJ kg−1)	Barber and Pellow (2006)
EE of polyester cloth (217 MJ kg−1)	Barber and Pellow (2006)
EE of acrylic cloth (267 MJ kg−1)	Barber and Pellow (2006)
EE of nylon cloth (342 MJ kg−1)	Barber and Pellow (2006)
Energy profiles
Energy profile of electricity for cardboard, sawn wood and viscose production in Finland (Nuclear 32.70%, hydropower 22.6%, biomass 18.30%, wind power 11.7%, natural gas 5.30%, coal 4.70%, peat 2.90%, recovered fuel 1.20%, solar power 0.4%, heavy fuel oil 0.20%	Energy profile of plastic industry (Natural gas 85.7%, electricity 9.8%, residual oil 3,8%, diesel fuel 0.4%, Gasoline 0,2%, liquefied petroleum gas 0.1%)	Benavides, Lee, and Zarè-Mehrjerdi (2020), Energiateollisuus 2022
Energy profile of concrete industry (Petroleum coke 41.1%, recovered fuel 27.9%, coal 18.0%, electricity 12.9%1)	Finnsementti. (2018)
Energy profile of steel industry (Electricity 50.6%1, natural gas 49.4%)	Metallinjalostajat. (2014)
Energy profile of brick industry (Natural gas 84.8%, Electricity 13.9%1, liquefied natural gas 0.7%, coal 0.5%, fuel oil 0.1%)	Wienerberger. (2019)
Energy profile of textile industry (Natural gas 47.9%, Electricity 41.7%, oil and petroleum products 5.1%, heat 3.1%, solid fossil fuels 1.3%, renewables and biofuels 0.5%)	European Union (2019)
Global energy profile of electricity (Coal 36.7%, natural gas 23.5%, hydropower 15.8%, nuclear 10.4%, wind 5.3%, oil 3.1%, solar 2.7%, other renewables 2.5%)	Ritchie, Roser, and Rosado (2022)
Global energy profile of heat production (Coal 41%, natural gas 23%, oil 11%, electricity 10%, renewables 9%, other [purchased steam] 6%)	World Business Council for Sustainable Development (2018)

Emissions for HDPE plastic, all non-wood construction materials and non-wood textile materials were calculated based on the embodied energies (EEs) of the materials (Barber and Pellow 2006; Hammond and Jones 2011; Hildebrand 2014) (Table 1). The transportation of raw materials was within the system boundaries. Particulate emissions of flaring in oil production were calculated based on Conrad and Johnson (2017) and Väätäinen (2019) and were considered in the emissions for HDPE plastic. Energy profiles for the production processes of HDPE plastic and construction materials (Benavides, Lee, and Zarè-Mehrjerdi 2020; European Union 2019; Finnsementti. 2018; Metallinjalostajat. 2014; Wienerberger. 2019) assumed the fuel mix used in electricity production in Finland (Energiateollisuus ry 2023). Emissions for electricity and heat in textile production were based on global average fuel mixes used in electricity and heat production (Ritchie, Roser, and Rosado 2022; World Business Council for Sustainable Development 2018) (Table 1).

Calculation of the Emissions of Energy Production

The emissions of energy production were calculated considering the amount of forest biomass, fossil fuel or peat needed to produce one unit of energy (kg TJ⁻¹) and based on the unit emissions of different energy sources in Finland (Finnish Environment Institute 2021) (Table 2). Emissions from the combustion of different parts of trees in LSB were based on Finland's informative inventory report (Finnish Environment Institute 2021), where the realistic use of tree parts in different applications is readily considered. The shares of different tree parts used in LSB were based on data from Natural Resources Institute Finland (2022a) and constituted small-sized trees (28%; 12,611 GWh), logging residues (13%; 5825 GWh), stumps (1%; 554 GWh), large-sized timber (3%; 1180 GWh), bark (25%; 10,964 GWh), sawdust (13%; 5646 GWh), chips (7%; 3117 GWh), other by-products (1%; 279 GWh), wood pellets and briquettes (5%; 2140 GWh) and recycled wood (5%; 2210 GWh). For SSB, we used estimations of the emissions and percentages of different small-scale applications in Finnish residential wood combustion in 2020 (Savolahti et al. 2019). The study by Tissari et al. (2019) was considered for the emissions from sauna stoves. Based on Savolahti et al. (2019), energy biomass was allocated to a wood chip boiler (18%), conventional masonry heater (15%), masonry oven (15%), conventional sauna stove (14%), manually fed boiler with accumulator (14%), kitchen range (9%), modern masonry heater (5%), manually fed boiler without accumulator (3%), open fireplace (3%), pellet boiler (2%), manually fed modern boiler (2%), conventional iron stove (2%) and modern iron stove (0.5%). The calorific values of the tree parts were based on data from Alakangas et al. (2016). The unit emission data are also available at Zenodo (Tikka et al. 2024).

TABLE 2 Unit emissions (t TJ⁻¹) used for calculation of aerosol emissions for biomass energy, fossil fuels and peat (Finnish Environment Institute 2021; Savolahti et al. 2019; Tissari et al. 2019).

Unit emissions of energy sources	TSP	PM10	PM2.5	BC	NOx	SO2	NMVOC
t TJ−1	t TJ−1	t TJ−1	t TJ−1	t TJ−1	t TJ−1	t TJ−1
Biomass (LSB)	0.0151	0.0067	0.0027	0.000089	0.0866	0.0081	0.0049
Biomass (SSB)	0.1353 a	0.1353 a	0.1301	0.0421	0.0804	0.0050	0.3429
Black liquor	0.0100	0.0092	0.0067	N/A	0.0602	0.0028	0.0009
Coal (S = 1.0%)	0.0033	0.0027	0.0005	0.000012	0.1015	0.0859	0.0023
Coke	0.1500	0.0390	0.0075	0.000150	0.2368	2.2689	0.0008
Diesel oil (S = 001%)	0.0188	0.0094	0.0023	0.000756	1.8325	0.0005	0.0037
Gasoil (for non-road use)	0.0122	0.0061	0.0015	0.000505	0.1308	0.0229	0.0039
Gasoline	0.0039	0.0039	0.0039	0.000196	0.2640	0.0003	0.9668
Heavy fuel oil (normal, S > 1%)	0.0018	0.0017	0.0012	0.000155	0.0575	0.0564	0.0032
Heavy fuel oil (S < 1%)	0.0146	0.0125	0.0081	0.002243	0.1565	0.2556	0.0014
Light fuel oil (S = 0.0915%)	0.0307	0.0153	0.0037	0.001239	0.6584	0.0384	0.0028
Liquid gas	0.0000	0.0000	0.0000	0.000000	0.0561	0.0002	0.0010
Milled peat	0.0048	0.0032	0.0011	0.000035	0.0891	0.0828	0.0032
Natural gas	0.0014	0.0011	0.0009	0.000064	0.0476	0.0000	0.0022
Petroleum coke	0.0254	0.0066	0.0013	0.000025	0.0353	0.0720	0.0040
Recovered fuel (REF 1)	0.0043	0.0011	0.0002	0.000012	0.0998	0.0306	0.0100
Recycled and waste oils	0.0009	0.0008	0.0006	0.000187	0.0820	0.1442	0.0030
Sod peat	0.1748	0.0456	0.0088	0.000290	0.1833	0.1357	0.0147

Sensitivity Analysis of DFs for Wood-Based Materials and Energy

Sensitivity analysis was conducted to demonstrate the effect of the most influential factors on the DFs of wood-based materials. The sensitivity of DFs was tested by separately using the following: (1) 10%, 20%, 50% and 100% higher EE than that of HDPE in the baseline (22.4 MJ kg⁻¹); (2) ignorance of the flaring emissions in oil production; (3) alternative fossil fuel mixes in manufacturing processes of non-wood textiles and for electricity and heat production; (4) 15% lower and 15%, 30%, 45% and 60% higher EE of steel (material tonne) than the baseline (20.1 MJ kg⁻¹); 5) 25% and 50% lower and 25%, 50%, 75% and 100% higher amounts of cement in concrete mixture than the baseline (1/7 of the mass of concrete); and (6) 10% lower and 10% higher unit emissions of the energy use of forest biomass (Table 3).

TABLE 3 Changes in parameter values for high-density polyethylene (HDPE) plastic and alternative fuel mixes of the textile industry used in the sensitivity analysis of displacement factors (DFs) for wood-based materials.

Variable	Change in parameter value	Reference
Higher EE for HDPE plastic	EE of HDPE plastic 10%, 20%, 50% and 100% higher than the baseline, 22.4 MJ kg−1	Set by authors
Ignorance of the flaring emissions in oil production	Omitted particulate emissions (g m−3) from oil flaring, PM (1.1164) and BC (2.0815) in HDPE production	Conrad and Johnson (2017), Väätäinen (2019)
Alternative amounts of cement in concrete mixture	25% and 50% lower and 25%, 50%, 75% and 100% higher amount of cement in concrete mixture compared to baseline, 1/7 of the mass of concrete	Set by authors
Alternative EEs for steel	EE of steel 15% lower and 15%, 30%, 45% and 60% higher than the baseline, 20.1 MJ kg−1	Set by authors
Alternative fuel mixes in manufacturing processes of non-wood textiles	Energy profile of textile industry in Europe (Natural gas 47.9%, Electricity 41.7%, oil and petroleum products 5.1%, heat 3.1%, solid fossil fuels 1.3%, renewables and biofuels 0.5%)	European Union (2019)
	Energy profile of textile industry in China (Electricity 45%, coal/coke 30%, heat 20%, natural gas 2.50%, petroleum 2.50%)	Wang, Li, and He (2017)
	Energy profile of textile industry in US (Electricity 57.8%, natural gas 35.9%, liquified petroleum gas and natural gas liquids 15.6%, coal 15.6%, other 15.6%, residual fuel oil 7.8%, distillate fuel oil 7.8%)	US Energy Information Administration (2021)
	Energy profile of electricity in China (Coal 62.2%, Hydropower 17.3%, Wind 5.5%, Nuclear 4.8%, Natural gas 3.2%, Solar 3.1%, Other thermal 2.0%, Biomass 1.5%, Pumped storage hydropower 0.4%)	Guo, Stuckey, and Murphy (2013)
	Energy profile of electricity in US (Natural gas 39.8%, renewables 21.5%, coal 19.5%, nuclear 18.2%, petroleum liquids 0.4%, other gases 0.3%, petroleum coke 0.2%	US Energy Information Administration (2023)
	Energy profile of heat production in China (Hard coal 92.32%, oil 4.60%, natural gas 2.62%, biomass 0.46%)	International Energy Association (IEA) (2013)
Alternative unit emissions of the energy use of forest biomass	Unit emissions of energy biomass (SSB and LSB) 10% lower and 10% higher than the baseline	Set by authors

Higher EE values for HDPE represent values for alternative plastic qualities, which can vary remarkably depending on plastic type, being even twofold compared to HDPE plastics (Hammond and Jones 2011). In addition, aerosol emissions for HDPE plastic were also calculated without PM emissions from flaring in oil production, which constitute a high share of the total aerosol emissions of plastics (Conrad and Johnson 2017; Väätäinen 2019). In the case of non-wood textiles, alternative fuel mixes found in the literature were used, so that they represented conditions in Europe, China and the United States, which are the three large textile production countries or regions (Guo, Stuckey, and Murphy 2013; International Energy Association (IEA) 2013; Ritchie, Roser, and Rosado 2022; US Energy Information Administration 2021, 2023; Wang, Li, and He 2017). Alternative EE values for steel represent values for different steel qualities (Hammond and Jones 2011). The use of alternative amounts of cement in the concrete mixture was also tested because the amount of cement can be varied (Hammond and Jones 2011). Alternative unit emissions of the energy use of forest biomass were used to examine the influence of differences in emissions yearly or in different countries (Table 3).

Results

DFs for Wood-Based Materials in Replacing HDPE Plastic, Non-Wood Construction Materials and Non-Wood Textiles

DFs for cardboard in replacing HDPE plastic were negative for PM emission components (excluding BC) and for NO_x emissions (Table 4). In contrast, DFs for BC, NMVOCs and SO₂ were positive. The lowest DF was found for NO_x emissions (indicating an increase of 359.6 g per tonne of produced cardboard), while the highest DF, indicating a decrease of 61.8 g t⁻¹, was found for NMVOC emissions.

TABLE 4 Displacement factors (DFs) for (1) cardboard in replacing high-density polyethylene (HDPE) plastic, (2) sawn wood in replacing concrete, steel or bricks and (3) wood-based viscose in replacing non-wood textile materials. Positive values mean the reduction of aerosol emissions.

DF	TSP	PM10	PM2.5	BC	NOx	SO2	NMVOC
Cardboard	g t−1	g t−1	g t−1	g t−1	g t−1	g t−1	g t−1
Cardboard–HDPE plastic	−20.3	−84.6	−54.1	56.7	−359.6	31.4	61.8
Construction materials
Walls	g m−2	g m−2	g m−2	g m−2	g m−2	g m−2	g m−2
Wood–Concrete	−10.9	−7.5	−4.0	−0.2	−161.5	15.2	−12.8
Wood–Steel	−19.5	−9.8	−4.4	−0.2	−199.0	−23.0	−16.0
Wood–Brick	−15.1	−6.6	−2.0	0.0	−70.6	−18.8	−10.0
Slab and column	g m−2	g m−2	g m−2	g m−2	g m−2	g m−2	g m−2
Wood–Concrete	−1.1	−1.1	−0.7	−0.03	−24.9	6.5	−2.0
Wood–Steel	−3.4	−1.7	−0.8	−0.03	−34.1	−3.9	−2.8
Wood–Brick	−3.6	−1.8	−0.8	−0.03	−37.7	−4.4	−3.0
Material tonnes	g t−1	g t−1	g t−1	g t−1	g t−1	g t−1	g t−1
Wood–Concrete	−19.3	−10.7	−5.2	−0.2	−168.3	−10.4	−18.0
Wood–Steel	−19.0	−9.3	−4.0	−0.1	−115.5	−20.5	−16.1
Wood–Brick	−18.8	−8.5	−2.9	−0.01	−55.0	−23.9	−13.4
Textiles	g t−1	g t−1	g t−1	g t−1	g t−1	g t−1	g t−1
Viscose–Flax	−240.2	−219.1	−160.3	−4.5	−1299.9	97.9	−16.9
Viscose–Cotton	−161.2	−158.6	−126.1	−2.2	1020.2	980.6	73.2
Viscose–Wool	−147.2	−147.8	−120.0	−1.8	1432.7	1137.5	89.2
Viscose–Polypropylene	−55.9	−77.9	−80.4	0.9	4113.7	2157.5	193.3
Viscose–Polyester	−38.3	−64.5	−72.8	1.4	4629.3	2353.7	213.3
Viscose–Acrylic	49.4	2.8	−34.8	4.0	7207.3	3334.5	313.4
Viscose–Nylon	181.1	103.6	22.2	7.9	11074.2	4805.6	463.6

Similarly as for cardboard, most of the DFs for sawn wood against concrete, steel or bricks were negative, implying an increase in the emissions (Table 4). Positive DFs were found only for SO₂ emissions in replacing concrete. DFs for NO_x emissions were remarkably low, whereas DFs for BC had close to zero values, describing notable differences in the magnitude of an increase in the emissions. Generally, DFs for wall or material tonne were many times lower than those for the production of slab or column due to differences in the material needs for the final products.

DFs of wood-based viscose, in turn, for TSP, PM10 and PM2.5 emissions were negative for each textile quality, except for the viscose-acrylic DFs for TSP and PM10 emissions and the viscose–nylon DFs (Table 4). DFs of wood-based viscose for the SO₂ emissions were, however, positive for every textile material, as well as those for NO_x and NMVOC emissions excluding the case in which wood-based viscose replaced flax. This often indicates an increase in TSP, PM10 and PM2.5 emissions and a decrease in the gaseous emissions due to the use of forest biomass as a substitute for alternative textile materials. For BC emissions, both positive and negative DFs were found.

DFs for the Energy Use of Forest Biomass in Replacing Fossil Fuels and Peat

For biomass combustion in small-scale appliances (SSB), DFs were mainly negative, implying higher aerosol emissions compared to fossil counterparts (Table 5). There were a few exceptions: positive DFs indicate that NO_x and SO₂ emissions could decrease in some cases, as well as TSP emissions if forest-based energy replaced the energy use of sod peat. DFs for energy biomass combusted in medium- to large-scale appliances (LSB) differed notably from those for SSB, mainly having remarkably higher values implying lower emissions. However, DFs for NO_x and SO₂ were higher for SSB than for LSB. In the case of LSB–sod peat, always positive DFs showed a clear substitution benefit. DFs for the replacement of natural gas, coke and gasoil were negative for any emission component, both in the case of SSB and LSB (Table 5).

TABLE 5 Displacement factors (DFs) (kg TJ⁻¹) for energy use of forest biomass in small (SSB) or medium- and large-scale boilers (LSB) when replacing energy from common fossil fuels and peat produced in medium- and large-scale boilers. Positive values mean the reduction of aerosol emissions when wood energy is used instead of fossil fuels.

DF	TSP	PM10	PM2.5	BC	NOx	SO2	NMVOC
kg TJ−1	kg TJ−1	kg TJ−1	kg TJ−1	kg TJ−1	kg TJ−1	kg TJ−1
SSB–LSB	−120.2	−128.6	−127.4	−42.0	6.2	3.1	−337.9
SSB–Coal	−132.0	−132.7	−129.6	−42.1	21.1	80.9	−340.6
SSB–Heavy fuel oil (normal, S > 1%)	−133.5	−133.6	−128.9	−41.9	−22.9	51.4	−339.6
SSB–Heavy fuel oil (S < 1%)	−120.7	−122.8	−122.0	−39.8	76.1	250.6	−341.4
SSB–Light fuel oil (S = 0.0915%)	−104.7	−120.0	−126.4	−40.8	578.0	33.3	−340.0
SSB–Milled peat	−130.5	−132.1	−129.1	−42.0	8.7	77.8	−339.7
SSB–Sod peat	39.4	−89.7	−121.3	−41.8	102.9	130.7	−328.1
SSB–Diesel oil (s = 0.001%)	−116.5	−125.9	−127.9	−41.3	1752.2	−4.6	−339.1
SSB–Natural gas	−134.0	−134.2	−129.2	−42.0	−32.8	−5.0	−340.7
SSB–Coke	−135.2	−135.3	−130.1	−42.1	−80.2	−2.8	−342.9
SSB–Gasoil (for non-road use)	−135.3	−135.3	−130.1	−42.1	−80.3	−5.0	−342.9
LSB–SSB	120.2	128.6	127.4	42.0	−6.2	−3.1	337.9
LSB–Coal	−11.8	−4.1	−2.1	−0.1	14.9	77.8	−2.7
LSB–Heavy fuel oil (normal, S > 1%)	−13.3	−5.0	−1.4	0.1	−29.1	48.3	−1.7
LSB–Heavy fuel oil (S < 1%)	−0.5	5.8	5.5	2.2	69.9	247.5	−3.5
LSB–Light fuel oil (S = 0.0915%)	15.5	8.6	1.0	1.2	571.8	30.2	−2.1
LSB–Milled peat	−10.3	−3.5	−1.6	−0.1	2.5	74.7	−1.8
LSB–Sod peat	159.6	38.9	6.1	0.2	96.6	127.6	9.8
LSB–Diesel oil (s = 0.001%)	3.7	2.7	−0.4	0.7	1745.9	−7.7	−1.2
LSB–Natural gas	−13.8	−5.6	−1.8	0.0	−39.1	−8.1	−2.7
LSB–Coke	−15.0	−6.7	−2.7	−0.1	−86.4	−5.9	−4.9
LSB–Gasoil (for non-road use)	−15.1	−6.7	−2.7	−0.1	−86.5	−8.1	−4.9

Sensitivity Analysis

Sensitivity analysis indicated that the EE value had a great influence on DFs (Table 6). The DFs of cardboard, especially those for gaseous emission, improved by increasing EE assumptions of HDPE. When EE was increased by 100%, TSP emissions changed from negative to positive. DFs were also positive for NO_x when EE increased by over 50%.

TABLE 6 Sensitivity analysis for cardboard displacement factors (DFs) to demonstrate the influence of different embodied energy (EE) values of high-density polyethylene (HDPE) plastic or ignoring emissions from flaring in oil production with the baseline EE.

DF	TSP	PM10	PM2.5	BC	NOx	SO2	NMVOC
g t−1	g t−1	g t−1	g t−1	g t−1	g t−1	g t−1
EE Baseline	−20.3	−84.6	−54.1	56.7	−359.6	31.4	61.8
Plastic EE +10%	−16.7	−82.0	−52.1	56.8	−238.0	45.6	71.7
Plastic EE +20%	−13.2	−79.4	−50.1	57.0	−116.5	59.7	81.5
Plastic EE +50%	−2.5	−71.6	−44.2	57.4	248.0	102.1	111.0
Plastic EE +100%	15.3	−58.6	−34.3	58.2	855.6	172.8	160.2
Without flaring	−109.1	−101.4	−68.2	−1.2	−359.6	31.4	61.8

The substitution effects of cardboard deteriorated notably when PM emissions from flaring in HDPE plastic production were ignored. For example, DF for BC turned from positive to negative values (Table 6).

Sensitivity analysis also showed that the assumed fossil fuel mix in energy production for non-wood textiles had a notable influence on DFs. Using the energy mix of China decreased wood-based viscose's DFs for PM2.5 and BC emissions, whereas DFs for TSP, PM10, NO_x, SO₂ and NMVOC emissions increased compared to the European energy profile. The energy mix of the United States led to smaller differences compared to Europe than the energy sources of China and deteriorated DFs of wood-based viscose. Shortly, DFs for TSP, PM10, NO_x, SO₂ and NMVOC emissions were highest when non-wood textiles were produced with the energy mix of China, suggesting the greatest substitution benefit. DFs for PM2.5 and BC were highest when the European energy profile was used instead (Table 7).

TABLE 7 Sensitivity analysis showing the wood-based viscose's displacement factors (DFs) with different energy mixes to demonstrate the importance of used energy mix for DFs.

DF	TSP	PM10	PM2.5	BC	NOx	SO2	NMVOC
g t−1	g t−1	g t−1	g t−1	g t−1	g t−1	g t−1
Viscose–Flax
Europe	−240.2	−219.1	−160.3	−4.5	−1299.9	97.9	−16.9
China	−134.3	−130.2	−184.1	−7.8	1897.0	5054.4	−16.7
United States	−279.8	−243.2	−168.8	−4.8	−1596.0	−144.2	−32.3
Viscose–Cotton
Europe	−161.2	−158.6	−126.1	−2.2	1020.2	980.6	73.2
China	−8.7	−30.5	−160.3	−7.0	5627.6	8123.8	73.5
United States	−218.3	−193.3	−138.3	−2.6	593.6	631.6	51.0
Viscose–Wool
Europe	−147.2	−147.8	−120.0	−1.8	1432.7	1137.5	89.2
China	13.7	−12.8	−156.1	−6.8	6290.9	8669.5	89.5
United States	−207.3	−184.4	−132.9	−2.2	982.8	769.6	65.8
Viscose–Polypropylene
Europe	−55.9	−77.9	−80.4	0.9	4113.7	2157.5	193.3
China	158.9	102.4	−128.7	−5.8	10,601.8	12,216.3	193.6
United States	−136.3	−126.8	−97.7	0.3	3512.9	1666.1	162.0
Viscose–Polyester
Europe	−38.3	−64.5	−72.8	1.4	4629.3	2353.7	213.3
China	186.9	124.6	−123.4	−5.6	11,430.8	12,898.4	213.7
United States	−122.6	−115.7	−90.9	0.8	3999.5	1838.5	180.5
Viscose–Acrylic
Europe	49.4	2.8	−34.8	4.0	7207.3	3334.5	313.4
China	326.5	235.4	−97.0	−4.6	15,575.9	16,308.9	313.8
United States	−54.2	−60.3	−57.0	3.3	6432.3	2700.6	273.1
Viscose–Nylon
Europe	181.1	103.6	22.2	7.9	11,074.2	4805.6	463.6
China	536.0	401.6	−57.5	−3.2	21,793.5	21,424.5	464.1
United States	48.3	22.8	−6.2	6.9	10,081.6	3993.7	411.9

Sensitivity analysis on the EE of steel indicated that Wood–Steel DFs were not as sensitive to EE values as those calculated for cardboard. The largest absolute increases appeared in DFs for NO_x and SO₂ emissions; however, those were still negative even if EE was 60% higher than in the baseline. Using this EE, the DF for BC turned from −0.1 to 0 g t⁻¹. Sensitivity analysis on the amount of cement in concrete mixture made DF for SO₂ emissions turn positive when the amount of cement increased 75% compared to baseline. Other DFs were still negative, with the largest increase appearing in DFs for NO_x and TSP emissions in addition to that for SO₂ emissions (Table 8).

TABLE 8 Sensitivity analysis for displacement factors (DFs) of sawn wood calculated for material tonnes using alternative amounts of cement in concrete and alternative embodied energy (EE) values for steel.

	TSP	PM10	PM2.5	BC	NOx	SO2	NMVOC
g t−1	g t−1	g t−1	g t−1	g t−1	g t−1	g t−1
DF, Wood–Concrete
Baseline	−19.3	−10.7	−5.2	−0.2	−168.3	−10.4	−18.0
Cement −50%	−21.5	−11.3	−5.3	−0.2	−179.3	−19.7	−18.8
Cement −25%	−20.4	−11.0	−5.3	−0.2	−173.8	−15.1	−18.4
Cement +25%	−18.2	−10.4	−5.2	−0.2	−162.9	−5.7	−17.5
Cement +50%	−17.2	−10.1	−5.1	−0.2	−157.4	−1.1	−17.1
Cement +75%	−16.1	−9.8	−5.0	−0.2	−152.0	3.6	−16.7
Cement +100%	−15.0	−9.4	−5.0	−0.2	−146.5	8.3	−16.3
DF, Wood–Steel
Baseline	−19.0	−9.3	−4.0	−0.1	−115.5	−20.5	−16.1
Steel EE −15%	−19.7	−9.7	−4.2	−0.1	−126.7	−21.7	−16.7
Steel EE +15%	−18.4	−8.9	−3.7	−0.1	−104.2	−19.2	−15.6
Steel EE +30%	−17.7	−8.5	−3.5	−0.1	−93.0	−17.9	−15.1
Steel EE +45%	−17.0	−8.1	−3.3	−0.1	−81.8	−16.6	−14.5
Steel EE +60%	−16.3	−7.8	−3.0	0.0	−70.6	−15.3	−14.0

Sensitivity analysis on the unit emissions of energy biomass showed that a decrease or increase of 10% in the unit emissions could alter DFs, but they were not very sensitive to small variation in the unit emissions. We found one case in which the sign of the DF changed: LSB–milled peat DF for NO_x emissions if the unit emissions of forest biomass were increased. In general, DFs for other emission components were not as sensitive to variation in the unit emissions of forest biomass as DFs for NO_x emissions (Table 9).

TABLE 9 Sensitivity analysis for displacement factors (DFs) of the energy use of forest biomass by using 10% lower and 10% higher unit emissions for energy biomass.

DF	TSP	PM10	PM2.5	BC	NOx	SO2	NMVOC
kg TJ−1	kg TJ−1	kg TJ−1	kg TJ−1	kg TJ−1	kg TJ−1	kg TJ−1
SSB–Coal
Baseline	−132.0	−132.7	−129.6	−42.1	21.1	80.9	−340.6
SSB −10%	−118.5	−119.1	−116.6	−37.9	29.2	81.4	−306.3
SSB +10%	−145.5	−146.2	−142.6	−46.3	13.1	80.4	−374.9
SSB–Milled peat
Baseline	−130.5	−132.1	−129.1	−42.0	8.7	77.8	−339.7
SSB −10%	−117.0	−118.6	−116.1	−37.8	16.8	78.3	−305.4
SSB +10%	−144.0	−145.7	−142.1	−46.3	0.7	77.3	−374.0
SSB–Natural gas
Baseline	−134.0	−134.2	−129.2	−42.0	−32.8	−5.0	−340.7
SSB −10%	−120.4	−120.7	−116.2	−37.8	−24.8	−4.5	−306.4
SSB +10%	−147.5	−147.8	−142.2	−46.2	−40.9	−5.5	−374.9
LSB–Coal
Baseline	−11.8	−4.1	−2.1	−0.1	14.9	77.8	−2.7
LSB −10%	−10.3	−3.4	−1.9	−0.1	23.6	78.6	−2.2
LSB +10%	−13.3	−4.7	−2.4	−0.1	6.2	77.0	−3.2
LSB–Milled peat
Baseline	−10.3	−3.5	−1.6	−0.1	2.5	74.7	−1.8
LSB −10%	−8.8	−2.8	−1.4	0.0	11.1	75.5	−1.3
LSB +10%	−11.8	−4.2	−1.9	−0.1	−6.2	73.9	−2.3
LSB–Natural gas
Baseline	−13.8	−5.6	−1.8	0.0	−39.1	−8.1	−2.7
LSB −10%	−12.3	−5.0	−1.5	0.0	−30.4	−7.3	−2.2
LSB +10%	−15.3	−6.3	−2.0	0.0	−47.7	−8.9	−3.2

Discussion

We calculated DFs for the most important aerosol emission components when wood-based materials replaced HDPE plastic, construction materials and non-wood textile materials, and when energy biomass replaced fossil fuels and peat. We applied the same methodology used earlier to calculate DFs for GHGs (Sathre and O'Connor 2010) and assessed the uncertainties arising from EEs and energy profiles in the calculation of DFs. Since alternative construction elements require different amounts of materials, and there is variability in the EEs of materials, we determined the DFs per material tonne and per alternative functional unit.

The DFs of cardboard replacing HDPE plastic had negative values for TSP, PM10, PM2.5 and NO_x, indicating that substitution benefits were not reached for those emission components. DFs for BC, SO₂ and NMVOC were positive. In earlier studies, recyclable corrugated cardboard (CCB) has been found to have environmental benefits from the viewpoint of mitigating climate change and forming photochemical oxidants and particulate matter when it is used to replace HDPE plastic in product transportation (Koskela et al. 2014). Our DFs could be in line with the other benefits, but not with those concerning the mitigation of particulate matter formation. A reason for the difference might be that our DFs were calculated for one tonne of material, not for the end product (packing box, for example), which may need less cardboard than plastic in mass units. As the density of HDPE plastic can be even more than 15 times that of CCB used for packing boxes (Plastics Europe 2023; Rivers 2023), DFs for a packing box made of cardboard instead of plastic could be positive for each emission component.

The increased EE of HDPE plastic indicated that the DFs of cardboard were also sensitive to changes in EE. Doubling EE for HDPE increased the DF for TSP (175%), NO_x (338%), SO₂ (450%) and NMVOC (159%), whereas DF for BC increased only 3%. Thus, cardboard–HDPE plastic DFs are not suitable for use with other plastic qualities if their EEs are different from that of HDPE. As many of them have a higher EE, wood often has more positive substitution effects for other plastic qualities. DF for BC, in turn, was very sensitive to emissions from oil flaring, ignorance of which decreased DF for BC by 102% and DFs for TSP, PM10 and PM2.5 by 437%, 20% and 26%, respectively. Thus, forest biomass has significant substitution benefits when displacing oil production.

DFs of sawn wood calculated per material tonne and walls had notably lower values compared to those for slab and column, which showed how alternative functional units may affect the comparative performance of alternative materials. Massive slabs typically have the highest environmental impact among the load-bearing structures of office buildings (Hildebrand 2014). In general, the DFs for PM, NO_x and NMVOC were negative. Positive DFs of sawn wood were found only for SO₂ emissions in the case of wood replacing concrete in the production of walls or slabs and columns. Mainly negative DFs of sawn wood could be explained by emissions from the use of side products for energy generation, which were allocated to the main product. If the use of side products were calculated to displace the use of some fossil-based energy, DFs could have been significantly higher and even positive. Generally, the lowest DFs of sawn wood were found for NO_x emissions, and DFs for BC had close to zero values.

Sensitivity analysis of DFs of sawn wood showed that, for example, 60% higher EE of steel increased DFs (for material tonne) for TSP, PM10, PM2.5 and BC by 14%, 16%, 25% and 100%, respectively, and DFs for NO_x, SO₂ and NMVOC by 39%, 25% and 13%, respectively. This indicates that steel quality can substantially affect the substitution effects of wood. Alternative steel qualities often have higher EEs compared to our baseline value. In the case of wood–concrete DF (for material tonne), an increase in the amount of cement in the concrete mixture by 100% increased DFs for TSP, PM10 and PM2.5 by 22, 12 and 4, respectively, and those for NO_x, SO₂ and NMVOC emissions by 13%, 180% and 9%, respectively. Since concrete can be produced with different mixtures, this should be considered when assessing the substitution effects of wood and using DFs in other conditions.

DFs for wood-based textiles depended on the replaced non-wood counterpart. DFs for TSP, PM10 and PM2.5 were negative, except for the viscose-acrylic DFs for TSP and PM10 emissions and the viscose–nylon DFs. DFs for BC were negative for flax, cotton and wool and positive for synthetic textile materials. However, DFs for NO_x, SO₂ and NMVOCs were positive apart from DFs for NO_x and NMVOCs in the case of flax, which was the least energy-intensive textile material in the study. Our DFs did not include emissions from material transportation, which could notably impact the displacement effects. For example, if viscose were produced in Europe and transported to China to produce textiles, the DFs for CO₂ compared to locally produced non-wood textiles would deteriorate (Hurmekoski et al. 2022, 2023). The influence on DFs for aerosol emissions would be parallel. Nevertheless, modern innovations have been suggested to reduce both GHG and aerosol emissions of wood-based textiles, so that more positive displacement effects seem to be achievable (Metsä Spring 2021; Spinnova 2023). Our analysis did not consider, however, emissions during the life cycle of products, for example, from washing, which can alter the actual substitution effects of wood-based textiles.

Sensitivity analysis indicated that DFs of wood-based viscose varied remarkably if the energy profile of the production of non-wood textiles was changed. For instance, the baseline (based on European energy profile) DF for TSP increased by 588% with China's energy profile or decreased 220% with the US's energy profile for polyester, which is a generally used synthetic fibre. However, DF for BC emissions decreased 500% when China's energy profile was used and 43% if US's energy profile was used, indicating a reduction in both cases. The higher emissions from the production of non-wood textiles because of the changed energy profile led to higher DFs of wood-based viscose (and more probable substitution benefits). Thus, variations in DF can be remarkable between countries and even for different emission components.

The DFs for the energy use of forest biomass were often negative, implying an increase in the emissions, especially if biomass was assumed to be combusted in SSB. Wolf et al. (2016) also estimated the DFs for PM2.5 for forest biomass replacing other energy sources and similarly found that they mainly imply an increase in emissions. In Finland, 23% of the total solid wood fuel consumption consists of residential combustion, whereas 77% is combusted in medium- to large-scale heat and power plants (Natural Resources Institute Finland 2022b). Less NO_x and SO₂ were released from forest biomass use in SSB than from the use of peat and several fossil fuels in LSB, and in replacing sod peat, the DF of SSB was also positive (39 kg TJ⁻¹) for TSP emissions. In all other cases, the respective DF values for particulate pollutants ranged from −135 to −105 kg TJ⁻¹, depending on the replaced energy source. The greatest increase was estimated to occur for NMVOCs, for which the DFs ranged from −343 to −328 kg TJ⁻¹. For LSB, DFs mainly had notably higher values, although both positive and negative impacts appeared for all energy sources, depending on the emission component in question. However, DFs for energy biomass in replacing natural gas, coke and gasoil had negative values, even for LSB.

Sensitivity analysis showed that DFs for the energy use of forest biomass were not particularly sensitive to minor variation in unit emissions. Possible year-by-year variation in the unit emissions may slightly influence the DFs, and they can also vary between different countries if the unit emissions are significantly different. In addition, variations in the DFs for energy use of forest biomass can change the DFs for forest-based products if the side products are combusted and the emissions are allocated for the main product.

Our findings emphasise the need to address life cycle impacts and benefits in forest biomass use to minimise drawbacks in climate aims and air quality. Additionally, Suter, Steubing, and Hellweg (2017) found a similar conclusion. DFs for PM emissions implied an increase with wood-based materials, while SO₂ emission showed partially positive values, as also highlighted by Petersen and Solberg (2005). When considering the replacement of non-wood construction materials with wood-based alternatives, it is crucial to enhance the efficiency of the use of side products of sawn wood. The complexity of aerosol impacts can be characterised by the mainly negative DFs for particulate pollutants, and it demands consideration of aerosol emissions alongside other climate-relevant factors. The climate impact of replacing fossil-based construction materials with wood-based ones may be further improved if wood-based materials are considered to act as stores for biogenic carbon capacity. From an aerosol emission mitigation viewpoint, promoting the forest bioeconomy through an increase in SSB does not appear rational. LSB of energy biomass might be preferable against some energy sources, such as sod peat or light fuel oil, although it appears to increase all aerosol emission components that we considered when compared to the use of coke or gasoil, for instance. Our findings also suggest that the aerosol emissions of forestry sector can be notably influenced by rational allocation of forest biomass use. Finally, our approach supports sustainable development by offering new opportunities for both mitigating climate change and promoting public health.

The interplay of various emission components in the atmosphere adds intricacy to the consideration of displacement effects. For example, an increase in NO_x and NMVOC emissions contributes to higher ozone formation, while combustion-based NMVOCs may also participate in the formation of secondary aerosols in the atmosphere (Wu et al. 2020). Positive DFs for BC also indicate a more effective absorption of sun radiation, which would have a warming effect in the atmosphere. However, an increase in SO₂ emissions induces cooling. Additionally, the emissions of SO₂, NO_x and particles influence cloud formation with complicated, most likely cooling, effects on climate (Rosenfeld et al. 2014; Spracklen, Bonn, and Carslaw 2008). Even if aerosol emissions cool the climate, it is crucial to acknowledge that they can deteriorate air quality in many ways and induce adverse public health effects (Lepeule et al. 2012). Furthermore, alterations in aerosol emissions have a more immediate impact on the climate than changes in CO₂ or CH₄ emissions. This is because aerosols have a shorter residence time in the atmosphere than long-lived GHGs (Szopa et al. 2021).

Conclusions

Our study expanded the DF calculations to include the most important aerosol emission components resulting from the use of forest biomass as a substitute which has received limited attention in previous research. The findings reveal that DFs for aerosol emissions of wood-based materials and energy often resulted in emission increases compared to non-wood counterparts. The results highlight the importance of considering DFs of aerosol emissions for wood-based materials and energy alongside long-lived GHG emissions. The complex atmospheric interactions of aerosol emissions complicate the assessment of their total substitution effects when considering the overall effects of air pollution. While DFs for aerosol emissions facilitate future assessments of climate and health impacts related to forest biomass use, there are still uncertainties in their calculations. The global shift towards using more renewable energy sources has been previously noted to potentially change the displacement effects associated with wood use in the future (Brunet-Navarro et al. 2021). This observation appears to also be applicable to aerosol emissions. Consequently, the displacement effects related to aerosol emissions and GHG emissions vary not only in terms of emission quantities but also in the perspective of which substitution benefits are more likely to be realised. Transitions in energy use and technological developments influencing aerosol emissions are likely to shift substitution effects in the near future, underlining the need for established methods of DF estimations.

Author Contributions

Aapo Tikka: data curation, formal analysis, investigation, methodology, visualization, writing – original draft. Anni Hartikainen: conceptualization, formal analysis, resources, validation, writing – review and editing. Olli Sippula: conceptualization, funding acquisition, supervision, validation, writing – review and editing. Antti Kilpeläinen: conceptualization, funding acquisition, methodology, supervision, validation, writing – review and editing.

Acknowledgements

This study was supported by the Finnish Cultural Foundation, North Karelia and North Savo Funds (AEROLCA project, Grant numbers 55201441 and 65202068), and by the Research Council of Finland (UNITE flagship, Decision numbers 337127 and 357906).

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are openly available in Zenodo at .

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