Raw material production
Example: textile modelling
Modelling fibre production, spinning and weaving
Textiles used in furniture include both natural and synthetic fibres.
To ensure methodological robustness, raw material modelling is divided into:
- Fibre production
- Spinning, dyeing and plying
- Weaving and surface treatment
Each step is based on recognised EF (Environmental Footprint) and Ecoinvent datasets.
Fibre prodution
We model three main fibre categories:
Plant-based fibres
Example dataset (EF database):
“fibre production, flax, retting”
Animal-based fibres
Example dataset (Ecoinvent 3.11):
“wool; sheep; production mix, at farm; 1 kg wool”
Man-made filament and staple fibres
Example dataset (EF database):
“Polyethylene terephthalate (PET), petrochemical based; polymerisation of ethylene glycol and terephthalic acid; production mix, at plant”
For synthetic fibres, modelling includes:
- Polymer production
• Polymerisation
• Fibre spinning
• Production waste during spinning
• Electricity consumption
This ensures fibre formation impacts are not underestimated.
Mathematical scaling functions are derived from EF datasets to model:
Ring spinning
Staple fibres for woven fabrics
Staple fibres for knit fabrics
Energy intensity decreases as yarn becomes thicker (higher dTex).
Spinning datasets include:
Electricity consumption
Heat consumption
Production waste
Electricity mixes are geographically specific.
Heat is typically generated from fossil sources.
Spinning, dyeing and plying
Spinning impacts depend strongly on yarn thickness (dTex).
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Weaving and surface treatments
Weaving impacts depend on:
- Yarn weight (dTex)
• Fabric mass (GSM – grams per square metre)
• Weave type (plain, twill, satin)
Conversion between GSM and yarn characteristics is modelled using fabric geometry equations:
Modeling of Woven Fabrics Geometry and Properties,
IntechOpen, 2012
Weaving datasets include:
- Electricity consumption
• Heat consumption
• Process waste
Surface treatments (coating, finishing, etc.) are added when relevant.
The wool example
– why it is often misunderstood
Wool is frequently assumed to have low climate impact because it is ‘Natural’, renewable and biogenic
However, climate emissions from wool primarily originate from the sheep itself.
Emissions from one sheep
A global sheep emits approximately:
≈ 400 kg CO₂e per year
The main sources are:
- Methane (CH₄) from enteric fermentation (Methane is the dominant contributor.)
- Nitrous oxide (N₂O) from manure
- Feed production and stable management
Allocation challenge: wool vs meat
A sheep produces multiple products:
- Meat
- Wool
Climate emissions must therefore be allocated between co-products.
Different allocation methods give different results.
Economic allocation
(Ecoinvent approach)
Allocation is based on the relative market value of each co-product.
If wool represents 45% of the economic value of a sheep’s annual output, it receives 45% of total emissions.
Rationale:
Environmental burdens follow
economic demand.
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Protein-based allocation
(EU Product Environmental Footprint)
Allocation is based on the biophysical relationship between outputs — in this case protein content.
Under this method, wool may receive a larger share of emissions (e.g. 89%), as meat represents the primary protein function of the system.
Rationale:
Environmental burdens follow
physical causality within the biological production system.
Economic allocation
(Ecoinvent approach)
Emissions are distributed according to market value.
Typical economic split:
~55% allocated to meat
~45% allocated to wool
This gives wool a substantial share of total sheep emissions.
Protein-based allocation
(PEF approach)
Under EU Product Environmental Footprint rules (Annexes 1–2, 2021), biological products may be allocated based on protein content.
Typical PEF protein-based split:
~89% allocated to wool
~11% allocated to meat
This significantly increases the climate burden assigned to wool.
Why this matters
Depending on allocation method, wool can appear moderately intensive or very climate intensive. This explains why wool often surprises stakeholders in climate assessments. Målbar follows the PEF approach.
Textile raw material modelling in Målbar:
- Uses EF and Ecoinvent datasets
- Includes spinning waste
- Uses yarn-weight dependent scaling
- Converts fabric weight to yarn characteristics
- Applies geographically specific electricity mixes
- Documents allocation method for animal-based fibres
Modelling Dimension
Plant-based fibres
Animal-based fibres
Man-made filament & staple fibres
Primary dataset source
EU Environmental Footprint (EF)
Ecoinvent 3.11
EU Environmental Footprint (EF)
Dataset example
“fibre production, flax, retting”
wool; sheep; production mix, at farm; 1 kg wool”
“Polyethylene terephthalate (PET), petrochemical based; polymerisation of ethylene glycol and terephthalic acid; production mix, at plant”
Declared unit
1 kg fibre
1 kg fibre
1 kg polymer / fibre
Core emission drivers
Agricultural inputs, retting processes
Enteric methane (CH₄), manure (N₂O), feed production
Petrochemical feedstock production, polymerisation energy
Process coverage
Fibre production
Farm-level biological system
Polymer production + polymerisation
Additional processing included
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Spinning of fibres incl. production waste and electricity use
Scaling logic
Scaled by fibre weight and fabric weight per m²
Scaled by fibre weight and allocation logic
Scaled by fibre weight, fabric weight per m² and yarn characteristics (dTex where relevant)
Structural consistency adjustment
Fabric mass aligned with fibre mass
Allocation between wool and meat according to selected method
Fabric mass converted to yarn characteristics to ensure process-level consistency
Allocation methodology
Not applicable
Biophysical allocation aligned with EU PEF Annex (2021) or economic allocation (ISO 14044 compliant)
Not applicable
Standard alignment
EU Environmental Footprint method
EU PEF Annexes (2021) + ISO 14044 hierarchy
EU Environmental Footprint method
Energy modelling
Region-specific electricity mixes
Region-specific electricity mixes
Region-specific electricity mixes + fossil-based industrial heat where relevant
Data integrity controls
Documented dataset selection
Transparent allocation method
Explicit inclusion of spinning losses and upstream petrochemical processes