Industrial ecology: a new planning platform for developing countries

Ramesh Ramaswamy and Suren Erkman WHY DEVELOPING COUNTRIES? A great deal of manufacturing for the global market is increasingly moving to developing countries, and many countries such as China and India are experiencing rapid growth. Therefore, it is now a crucial time to in?uence their choice of an industrial development path. While it is an enormous opportunity to improve the living standards in these countries through increased employment and business opportunities, it is a serious load on the local resources (such as water, energy, land, and so on), whose availability to the populations of these countries is very poor. A development path that is based on resource availability could create industrial growth that uses the resources more e?ciently and judiciously, with minimal local and global impacts. A less careful industrial development plan that uses up scarce resources could spell danger to the very survival of over 80 per cent of the planet's population that lives in the developing world. GROUND REALITIES OF DEVELOPING COUNTRIES It is important to understand some aspects of life in the poor countries that are very much at variance to what is seen in the developed world. Among the many speci?c aspects that have to be borne in mind, is the fact that the pattern of resource ?ows in developing countries and hence the resultant environmental threat could be very di?erent from what we see in the industrialized West. Typically, the ?ows of materials through large, organized manufacturing facilities in developing countries..

Industrial Ecological Solutions

This chapter discusses how the industrial ecological systems can help in dealing with environmental issues in developing countries, and it presents three case studies from India that highlight some of the unique environmental issues of developing world. Industrial ecology explores the assumption that the industrial system can be seen as a certain kind of ecosystem. The scope of industrial ecology goes well beyond waste exchange to the optimization of resources flowing through the economic system. Among the various specific aspects of developing countries, which have to be born in mind, is the fact that the pattern of resource flows in developing countries, and hence, the resultant environmental threat could be very different than what it is in the industrialized west. Typically, the flow of materials through the large, organized manufacturing facilities in the developing countries could be very small in relation to the overall material flow as the small, informal ?industry? plays a key role and forms a very significant portion of the economic activity. The case studies of the Tirupur textile industries, and the leather industry in India, illustrate how redefining the problem from a perspective of resource conservation, and on the basis of resource flow data could point to totally new directions for strategy planning. The case study of the Damodar Valley region amplifies the importance of looking beyond formal industry to solve an environmental problem. It shows that even for globally critical programs, such as climate change program in developing countries, it is just not enough to estimate the emissions from the formal industrial sectors

A spatially explicit life cycle inventory of the global textile chain

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