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).
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.
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.
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.
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.
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.