Abstract
Background, aim, and scope
Life cycle analyses (LCA) approaches require adaptation to reflect the increasing delocalization of production to emerging countries. This work addresses this challenge by establishing a country-level, spatially explicit life cycle inventory (LCI). This study comprises three separate dimensions. The first dimension is spatial: processes and emissions are allocated to the country in which they take place and modeled to take into account local factors. Emerging economies China and India are the location of production, the consumption occurs in Germany, an Organisation for Economic Cooperation and Development country. The second dimension is the product level: we consider two distinct textile garments, a cotton T-shirt and a polyester jacket, in order to highlight potential differences in the production and use phases. The third dimension is the inventory composition: we track CO2, SO2, NO x , and particulates, four major atmospheric pollutants, as well as energy use. This third dimension enriches the analysis of the spatial differentiation (first dimension) and distinct products (second dimension).
Materials and methods
We describe the textile production and use processes and define a functional unit for a garment. We then model important processes using a hierarchy of preferential data sources. We place special emphasis on the modeling of the principal local energy processes: electricity and transport in emerging countries.
Results
The spatially explicit inventory is disaggregated by country of location of the emissions and analyzed according to the dimensions of the study: location, product, and pollutant. The inventory shows striking differences between the two products considered as well as between the different pollutants considered. For the T-shirt, over 70% of the energy use and CO2 emissions occur in the consuming country, whereas for the jacket, more than 70% occur in the producing country. This reversal of proportions is due to differences in the use phase of the garments. For SO2, in contrast, over two thirds of the emissions occur in the country of production for both T-shirt and jacket. The difference in emission patterns between CO2 and SO2 is due to local electricity processes, justifying our emphasis on local energy infrastructure.
Discussion
The complexity of considering differences in location, product, and pollutant is rewarded by a much richer understanding of a global production–consumption chain. The inclusion of two different products in the LCI highlights the importance of the definition of a product's functional unit in the analysis and implications of results. Several use-phase scenarios demonstrate the importance of consumer behavior over equipment efficiency. The spatial emission patterns of the different pollutants allow us to understand the role of various energy infrastructure elements. The emission patterns furthermore inform the debate on the Environmental Kuznets Curve, which applies only to pollutants which can be easily filtered and does not take into account the effects of production displacement. We also discuss the appropriateness and limitations of applying the LCA methodology in a global context, especially in developing countries.
Conclusions
Our spatial LCI method yields important insights in the quantity and pattern of emissions due to different product life cycle stages, dependent on the local technology, emphasizing the importance of consumer behavior. From a life cycle perspective, consumer education promoting air-drying and cool washing is more important than efficient appliances.
Recommendations and perspectives
Spatial LCI with country-specific data is a promising method, necessary for the challenges of globalized production–consumption chains. We recommend inventory reporting of final energy forms, such as electricity, and modular LCA databases, which would allow the easy modification of underlying energy infrastructure.
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Acknowledgments
This work is part of the TREI-C project, partly funded by a grant from the Geneva International Academic Network (GIAN). The Swiss apparel company Switcher was a participant; we thank them for their collaboration. It benefited from student projects by César Lador and Priti Nigam and the masters thesis of Ana Rita Carvalho of the EPFL (Switzerland). We also thank Dr. Isabelle Blanc (Armines), Dr. Manuele Margni (École Polytechnique de Montréal), Shanna Shaked (University of Michigan), and Vincent Rossi (Ecointesys - Life Cycle Systems) for helpful discussions. The manuscript was significantly improved by the comments of anonymous reviewers. Any remaining errors are the responsibility of the authors. Ramesh Ramaswamy (Resource Optimization Initiative), who recently passed away, was always ready with cheerful advice and criticism. He is sorely missed.
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Steinberger, J.K., Friot, D., Jolliet, O. et al. A spatially explicit life cycle inventory of the global textile chain. Int J Life Cycle Assess 14, 443–455 (2009). https://doi.org/10.1007/s11367-009-0078-4
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DOI: https://doi.org/10.1007/s11367-009-0078-4