Author: Andreas Ciroth
Publisher: Springer
ISBN: 9783030622695
Size: 76.82 MB
Format: PDF, Mobi
Category : Science
Languages : en
Pages : 235
View: 6613
Get Book
by Andreas Ciroth, Life Cycle Inventory Analysis Books available in PDF, EPUB, Mobi Format. Download
Life Cycle Inventory Analysis books, Life Cycle Inventory (LCI) Analysis is the second phase in the Life Cycle Assessment (LCA) framework. Since the first attempts to formalize life cycle assessment in the early 1970, life cycle inventory analysis has been a central part. In fact, it might be older than the LCA framework itself. LCI analysis is situated in between the goal and scope definition phase and the life cycle impact assessment (LCIA) phase, and it is interconnected with the interpretation phase. To each of the other phases, there are feedback loops, enabling an agile workflow. Chapter 1 “Introduction to Life Cycle Inventory Analysis“ discusses the history of inventory analysis from the 1970s through SETAC and the ISO standard. How LCI situates itself as the data collection phase between the goal scoping and assessment steps is presented. In Chapter 2 “Principles of Life Cycle Inventory Modeling”, the general principles of setting up an LCI model and LCI analysis are described by introducing the core LCI model and extensions that allow addressing reality better. It is shown how the method, essentially now three decades old, was valid in its conception and has evolved. Chapter 3 “Development of Unit Process Datasets” regards the development of unit processes, which can be seen as the very cells or atoms of LCI analysis. It is shown that developing unit processes of high quality and transparency is not a trivial task, but is crucial for high-quality LCA studies. The critical review process is highlighted. Chapter 4 is titled “Multi-functionality in Life Cycle Inventory Analysis: Approaches and Solutions”. Products and services can have more than one function, though LCA generally analyzes only one of these. How the different inputs are allocated is, therefore, part of the calculation. This chapter describes how multi-functional processes can be identified categorized. Furthermore, different solutions to the multi-functionality problem are discussed and analyzed. In Chapter 5 “Data Quality in Life Cycle Inventories”, the quality of data gathered and used in LCI analysis is discussed. State-of-the-art indicators to assess data quality in LCA are described and the fitness for purpose concept is introduced: data quality is not an absolute property of a dataset, but instead depends on the application. Chapter 6 “Life Cycle Inventory Data and Databases“ follows up on the topic of LCI data and provides a state-of-the-art description of LCI databases. It describes differences between foreground and background data, recommendations for starting a database, data exchange and quality assurance concepts for databases, as well as the scientific basis of LCI databases. Chapter 7 “Algorithms of Life Cycle Inventory Analysis“ provides the mathematical models underpinning the LCI. It is a theoretical contribution where the algorithms of LCI analysis are described. Since Heijungs and Suh (2002), this is the first time that this aspect of LCA has been fundamentally presented, with distinct extensions. In Chapter 8 “Inventory Indicators in Life Cycle Assessment”, the use of LCI data to create aggregated environmental and resource indicators is described. Such indicators include the cumulative energy demand and various water use indicators. These have the advantage of being simple and robust, but at the same time have the disadvantage of being less connected to actual impacts than midpoint and endpoint indicators used in LCA. Chapter 9 “The Link Between Life Cycle Inventory Analysis and Life Cycle Impact Assessment” uses four examples to discuss the link between LCI analysis and LCIA. A clear and relevant link between these phases is crucial, since LCI data that does not fit into existing LCIA models will remain unused, and similarly, if no LCIA models exist which fit the LCI data gathered, it too will remain unused. Only with a clear link between them, an LCA study can make use of all the gathered data.
