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**41**

**Chapter 4**

*Tomas Ekvall*

**1. Introduction**

**Abstract**

Attributional and Consequential

An attributional life cycle assessment (ALCA) estimates what share of the global environmental burdens belongs to a product. A consequential LCA (CLCA) gives an estimate of how the global environmental burdens are affected by the production and use of the product. The distinction arose to resolve debates on what input data to use in an LCA and how to deal with allocation problems. An ALCA is based on average data, and allocation is performed by partitioning environmental burdens of a process between the life cycles served by this process. A CLCA ideally uses marginal data in many parts of the life cycle and avoids allocation through system expansion. This chapter aims to discuss and clarify the key concepts. It also discusses pros and cons of different methodological options, based on criteria derived from the starting point that environmental systems analysis should contribute to reducing the negative environmental impacts of humankind or at least reduce the impacts per functional unit: the method should be feasible and generate results that are accurate, comprehensible, inspiring, and robust. The CLCA is more accurate, but ALCA has other advantages. The decision to make an ALCA or a CLCA should ideally be taken by the LCA practitioner after discussions with the client and pos-

Life Cycle Assessment

sibly with other stakeholders and colleagues.

consequential LCA, allocation, marginal data, electricity

**Keywords:** life cycle inventory analysis, methodology, attributional LCA,

Life cycle assessment (LCA) is the quantification of potential environmental impacts and the resource use throughout a product's life cycle: from raw material acquisition, via production and use phases, to waste management [1]. It has been frequently applied by consultants, researchers, industry, and authorities for the past 30 years. It has proven useful for gaining knowledge on the life cycle, for communication of environmental information, and for various kinds of decision-making. Meanwhile, it was clear almost from the start that results from different LCAs can contradict each other. This is still true, despite many attempts to harmonize, standardize, and regulate LCA. From history, we learn that it is not realistic to expect LCA to deliver a unique and objective result. It should not be regarded as a single unique method; it is more fruitful to consider it a family of methods. Attributional LCA (ALCA) and consequential LCA (CLCA) are important groups within this family of methods. The choice between ALCA and CLCA guides other methodological decisions in the LCA, such as the choice of input data and the modeling of processes with multiple products. However, within ALCA and CLCA,
