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Eco versus traditional denim: LCA analysis

and pesticide use for cotton farming) were based on range of environmental concerns: climate change
Ecoinvent datasets for cotton-producing countries (100-year global warming potential, expressed in
relevant to Bangladesh’s supply chain, such as India. kg CO -equivalents); 33,34 water consumption (blue
2
These were assumed to be representative of the cotton water use, measured in m ) terrestrial acidification
3 35
used in both scenarios. All assumptions were made to (emissions contributing to acid rain, reported in kg
approximate Bangladeshi supply chain conditions as SO₂-equivalents), freshwater eutrophication (nutrient
36
accurately as possible. Allocation procedures followed pollution in aquatic ecosystems, measured in kg
default principles embedded in the secondary data P-equivalents) land use (agricultural land occupation,
37
sources. For instance, the environmental impacts of reported in m year), FRS (depletion of fossil fuels,
38
2
cotton farming were economically allocated between expressed in kg oil-equivalents), and HTP (potential
39
cotton fiber and cottonseed. No further allocation was human health effects from chemical exposure,
necessary within the manufacturing stages, as these measured in kg 1,4- dichlorobenzene -equivalents).
25
processes primarily yield the denim product. Reusable All calculations were performed using openLCA, with
waste (e.g., cotton scrap) was treated under a cut-off background processes sourced from the Ecoinvent
approach, with no credit assigned for recycling. 3.9.1 database. ReCiPe’s default characterization
Table 1 presents the LCI details for major inventory factors were applied for each flow. No normalization or
flows of S1 and S2. In summary, the production of weighting was applied, in line with the ISO 14040/44
1,000 pairs of traditional denim pants (S1) required guidelines, and results are reported in absolute terms
approximately 800 kg of raw cotton fiber (610 kg for each category. This approach ensures that each
incorporated into the final products plus additional impact category can be examined on its own merit, and
material to cover processing losses), 2,500 m of water potential trade-offs between categories can be observed.
3
(primarily for cotton irrigation and fabric washing),
and significant energy inputs in the form of electricity 2.4. Sensitivity analysis
and heat. The S1 factories relied on grid electricity To test the robustness of our conclusions against
and natural gas-fired steam boilers. The S2 scenario key assumptions, targeted sensitivity analyses were
followed a similar process flow but with cleaner inputs. conducted on consumer use behavior and energy
Organic cotton – often rain-fed and grown without sourcing in manufacturing. First, we examined how
synthetic pesticides or fertilizers – was used. The varying the frequency of washing in the use phase
process incorporated water-saving dyeing technologies would affect the outcomes. We considered a moderate
(e.g., water recycling units and reduced wash cycles) laundering rate (each pair worn approximately 10 times
and partially substituted renewable energy sources before washing). In the case of high wash frequency, the
(e.g., solar photovoltaic systems) for manufacturing impact increases. The variability in washing frequency
electricity. Consequently, the S2 inventory showed reflects differing consumer habits. While the use phase
reduced freshwater use for agriculture and processing, was not the primary focus of differences (as it was
lower chemical usage (especially of hazardous initially assumed to be the same for S1 and S2), this
substances), and decreased fossil fuel consumption. check helps determine whether an aggressive change
Both scenarios assumed identical distribution in user behavior could overshadow manufacturing
logistics, use-phase behavior, and end-of-life treatment improvements.
pathways to ensure an unbiased comparison. LCI data Second, we explored the effects of renewable
of manufacturing phases – including yarn production, energy adoption in manufacturing. While S2 already
dyeing, fabric finishing, and garment assembly – were incorporated some renewable energy, we compared it
collected directly from Bangladeshi industries for S1 and with a case where both scenarios rely entirely on fossil-
S2. Data for cotton cultivation, transportation, and the based grid energy. This allowed for the assessment
use phase were adapted from the Ecoinvent database, the of the maximum potential benefit of cleaner energy.
Textile Exchange database, and published literature. 29-32 Energy-related emissions (CO , SO , etc.) and fossil
2
2
resource use were adjusted according to different
2.3. LCIA method energy mix assumptions, and the resulting impacts on
The ReCiPe 2016 Midpoint (H) characterization the climate change and FRS categories were evaluated.
method was used to convert the LCI flows into This sensitivity analysis directly addresses how much
environmental impact indicators for impact assessment. of the improvement in S2 is driven by energy choices
The seven selected midpoint categories capture a and what additional gains are possible.

Volume 22 Issue 3 (2025) 77 doi: 10.36922/ajwep.6241