A spatially explicit life cycle inventory of the global textile chain
A spatially explicit life cycle inventory of the global textile chain
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全球纺织链的空间明确生命周期清单
DOI：
10.1007/s11367-009-0078-4
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
S. Erkman
中科院分区：
文献类型：
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作者：
J. Steinberger;D. Friot;O. Jolliet;S. Erkman
背景、目标和范围生命周期分析方法需要加以调整，以反映生产日益向新兴国家转移的情况。这项工作通过建立一个国家级的、空间上明确的生命周期清单来应对这一挑战。这项研究包括三个独立的方面。第一个维度是空间维度：过程和排放被分配到它们发生的国家，并在建模时考虑到当地因素。新兴经济体中国和印度是生产地，消费发生在经济合作与发展组织国家德国。第二个维度是产品层面：我们考虑两种不同的纺织服装，一件棉T恤和一件聚酯夹克，以突出生产和使用阶段的潜在差异。第三个维度是库存构成：我们跟踪二氧化碳、二氧化硫、氮氧化物和颗粒物这四种主要大气污染物以及能源使用。这个第三维丰富了空间差异（第一维）和不同的产品（第二维）的分析。材料和方法我们描述了纺织品的生产和使用过程，并定义了一个服装的功能单元。然后，我们使用优先数据源的层次结构的重要过程建模。我们特别强调建模的主要地方能源过程：电力和运输在新兴countries.ResultsThe空间明确的库存分解的排放量的位置，并根据研究的维度：位置，产品和污染物进行分析。该清单显示了所考虑的两种产品之间以及所考虑的不同污染物之间的显著差异。对于T恤，超过70%的能源使用和二氧化碳排放发生在消费国，而对于夹克，超过70%发生在生产国。这种比例的逆转是由于服装使用阶段的差异。相比之下，三分之二以上的二氧化硫排放发生在T恤和夹克的生产国。CO2和SO2之间的排放模式的差异是由于当地的电力过程，证明我们重视当地的能源infrastructure.DiscussionThe的复杂性，考虑在位置，产品和污染物的差异是奖励一个更丰富的全球生产消费链的理解。在LCI中列入两种不同的产品突出了产品功能单位的定义在分析和影响结果方面的重要性。几个使用阶段的情况表明，消费者行为的重要性超过设备的效率。不同污染物的空间排放模式使我们能够了解各种能源基础设施要素的作用。排放模式还为关于环境库兹涅茨曲线的辩论提供了信息，该曲线仅适用于易于过滤的污染物，而不考虑生产转移的影响。我们还讨论了在全球范围内，特别是在发展中国家的LCA方法的适用性和局限性，ConclusionsOur空间LCI方法产生重要的见解，由于不同的产品生命周期阶段的排放量和模式，依赖于当地的技术，强调消费者行为的重要性。从生命周期的角度来看，消费者教育促进空气干燥和冷却洗涤是更重要的比efficientappliance.Recommendations和perspectivesSpatial LCI与国家特定的数据是一个有前途的方法，必要的全球化的生产-消费链的挑战。我们建议对最终能源形式（如电力）和模块化LCA数据库进行库存报告，这将使基础能源基础设施的修改变得容易。
Background, aim, and scopeLife 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, NOx, 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 methodsWe 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.ResultsThe 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.DiscussionThe 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.ConclusionsOur 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 perspectivesSpatial 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.