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2021 | Buch

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Life Cycle Inventory Analysis

Methods and Data

herausgegeben von: Dr. Andreas Ciroth, Dr. Rickard Arvidsson

Verlag: Springer International Publishing

Buchreihe : LCA Compendium – The Complete World of Life Cycle Assessment

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Chapter 1 “Introduction to Life Cycle Inventory Analysis“ discusses the history of inventory analysis from the 1970s through SETAC and the ISO standard.

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.

Chapter 3 “Development of Unit Process Datasets” shows that developing unit processes of high quality and transparency is not a trivial task, but is crucial for high-quality LCA studies.

Chapter 4 “Multi-functionality in Life Cycle Inventory Analysis: Approaches and Solutions” describes how multi-functional processes can be identified.

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.

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. Since Heijungs and Suh (2002), this is the first time that this aspect of LCA has been fundamentally presented.

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.

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction to “Life Cycle Inventory Analysis”

Abstract

This chapter introduces the life cycle inventory (LCI) analysis – the topic of this volume. A brief history of the concept is provided, including its procedure according to different standards and guidance books. The LCI analysis phase of the life cycle assessment (LCA) framework has remained relatively constant over the years in terms of role and procedural steps. Currently, the LCI analysis is situated in between the goal and scope definition phase and the life cycle impact assessment phase in the LCA framework, although it is interconnected also with the interpretation phase. Central concepts in LCI analysis are defined, including product system, process, flow, functional unit, and system boundary. Four important steps of LCI analysis are outlined: constructing a flow chart, gathering data, conducting calculations, as well as interpreting results and drawing conclusions. The focus is on the process LCA approach, which is the most common in LCA practice. Environmentally-extended input-output analysis is also described briefly. Finally, an overview of the other chapters of this volume and their relevance to the topic of LCI analysis is provided.

Rickard Arvidsson, Andreas Ciroth

Chapter 2. Principles of Life Cycle Inventory Modeling: The Basic Model, Extensions, and Conventions

Abstract

The basic model of a life cycle inventory (LCI), with unit processes as smallest modeling entities, emerged already in the very early phases of life cycle assessment (LCA) method development. It is a rather simple, linear model, with a distinction between elementary flows, product flows, and waste flows. Since the early applications, this simple model proved to be very useful and allowed for various expansions. For certain issues related to LCI modeling, solutions and approaches have evolved as extensions of the basic model. Such issues and related modeling challenges include: the multifunctionality problem; the modeling of loops in product systems; the modeling of the use phase; the modeling of transport services; the consideration of time and long-term emissions in LCI; the definition of the boundary between the technosphere and biosphere; and how to address accidents, incidents, and risks. This chapter presents and explains the basic LCA model and its extensions, where some are commonly used in practice today, and some others not. Furthermore, conventions regarding the modeling of transport services, use phase and products, end of life, are presented.

Andreas Ciroth, Francesca Recanati, Rickard Arvidsson

Chapter 3. Development of Unit Process Datasets

Abstract

The development of unit process datasets is fundamental for any Life Cycle Assessment (LCA) study. Unit processes developed are not always of the quality desired, which impedes their usability and influences the overall credibility of the studied system. This chapter is based on the relevant LCA standards and guidelines and streamlines the detailed procedures of unit process development from a practical point of view. It aims to serve as a brief, structured, and practical guidance and suggests “basic requirements,” i.e., what is necessarily required to produce a unit process dataset with reasonable data quality as well as sufficient and transparent documentation. Detailed recommendations are provided for self-checking, sensitivity analysis for improving the overall data quality, data quality evaluation, documentation, reviews, and development of tools that facilitate the development and application of unit processes. The chapter is meant to inform and aid experienced LCA practitioners from industry, policy, regulatory organizations, consultancy, and academia in unit process development.

Xiaojin Zhang, Hongtao Wang, Karin Treyer

Chapter 4. Multifunctionality in Life Cycle Inventory Analysis: Approaches and Solutions

Abstract

This chapter gives an overview of the mainstream approaches and solutions to the problem of multifunctionality in the Life Cycle Inventory (LCI) phase. Many industrial processes are multifunctional. Their purpose generally comprises more than a single product or service. Practitioners in Life Cycle Assessment (LCA) are thus faced with the problem that the product system(s) under study provide more functions than the one investigated in the functional unit of interest. Among others, an appropriate decision must therefore consider which economic and environmental flows of the multifunctional process or system are to be allocated to which of its products and services. The discussion on multifunctionality goes back to energy analysis (a precursor of LCA), and several of today’s well-known solutions for the multifunctionality problem origin from this time. There is no generally accepted solution for the multifunctionality problem, and it is even hard to imagine that there will ever be a solution. On the other hand, it is generally recognized that different solutions may considerably influence LCA results depending on the exact position of the multifunctional process in the product’s flow chart. As a consequence, sensitivity analyses should be applied to test the influence of different solutions. An issue that deserves more attention is the fact that most LCA case studies so far apply one of the solutions without properly justifying where and what exactly the multifunctionality problem is and which criteria are used for determining that. In this chapter, these steps are therefore distinguished, explicitly aiming for more transparency in the discussion on multifunctionality approaches and solutions.

Jeroen Guinée, Reinout Heijungs, Rolf Frischknecht

Chapter 5. Data Quality in Life Cycle Inventories

Abstract

This chapter explores data quality in life cycle inventory (LCI) datasets and calculation results, introduces the history, explains the relevance of data quality for life cycle assessment (LCA), and the difficulty to deal with the application-dependency of data quality. Recent data quality systems, introduced by the United States Environmental Protection Agency (US EPA) and in the course of the European “Product Environmental Footprint” (PEF) project, are elaborated in more detail. The application-dependency of data quality has led to a more refined view on data quality in a recent United Nations GLAD (Global Life Cycle Access to Data) project. GLAD distinguishes between data quality when a dataset is created and when it is used. In addition, data quality is broadened by including modeling details that are typically set differently in different application contexts. Outcomes of the GLAD project are therefore introduced in this chapter as well and it is expected that these might lead to a more comprehensive, better management of data quality for differing application contexts, as well as for creating inventory datasets.

Andreas Ciroth

Chapter 6. Life Cycle Inventory Data and Databases

Abstract

Life cycle inventory (LCI) databases are commonly used in life cycle assessment (LCA) studies. They enable modern, larger case studies, make data collection more efficient, and help to establish comparability across different case studies. A database typically tries to provide one coherent and consistent modeling space, thereby allowing users to take different datasets in the appropriate database, which implies that the goal and scope of datasets in the database match the goal and scope of case studies done with the database.

This chapter explains the principal elements of LCI data, different types of databases in LCA, and explores common issues in modern LCA databases: starting a database, maintaining it, providing quality assurance, and not the least, making the database available to users. The second part of the chapter deals with data exchange and data exchange formats, as well as with interoperability concepts to allow the use of datasets from different databases in one study.

Andreas Ciroth, Salwa Burhan

Chapter 7. Algorithms of Life Cycle Inventory Analysis

Abstract

Algorithms are an essential part of a life cycle assessment (LCA) study. In this chapter, algorithms for calculating and analyzing life cycle inventory (LCI) results are described. These algorithms transform the inventory data of a product system into the information on which the impact assessment is based. It is shown how product systems can be translated into computable structures and how the latter are used to algorithmically compute the inventory results. It is also demonstrated how this formalism allows linking the product system to background databases containing thousands of unit process datasets. In this way, sources of impacts can be tracked down deeply in the supply chain paths.

Michael Srocka, Flavio Montiel

Chapter 8. Inventory Indicators in Life Cycle Assessment

Abstract

This chapter presents the concept of inventory indicators, which are indicators assessed at the inventory level by aggregating inventory flows at the start of the impact pathway. Although the ISO 14040 standard prescribes that a life cycle assessment (LCA) should contain an assessment of environmental impacts, inventory indicators are frequently applied for assessing energy and water use, but sometimes also for assessing waste generation, land use, material use, and emissions. For energy use, the cumulative energy demand is probably the most common indicator, which considers all renewable and non-renewable primary energy. Other energy use inventory indicators consider only non-renewable, or fossil, energy, and some consider secondary rather than primary energy. For water use, common inventory indicators include water extraction (or withdrawal), water consumption, the blue water footprint, and the green water footprint. Contrary to midpoint and endpoint indicators, inventory indicators do not consider which potential impacts the aggregated elementary flows might have. Therefore, inventory indicators have the drawback of being simplified in terms of impact modeling compared to midpoint and endpoint indicators. However, inventory indicators also have benefits: they are easy to apply, easy to interpret, and can serve as proxy indicators for damage at the endpoint level. In particular, they can be used also in cases when midpoint and endpoint characterization factors are lacking. Because of these advantages, inventory indicators are foreseen to play a role in LCA also in the future.

Rickard Arvidsson

Chapter 9. The Link Between Life Cycle Inventory Analysis and Life Cycle Impact Assessment

Abstract

In this chapter, the link between life cycle inventory analysis (LCI) and life cycle impact assessment (LCIA) is discussed. For the feasibility of conducting a life cycle assessment (LCA) and for making its results more robust, it is necessary that data collected in the LCI stage are suitable for the LCIA methods, and in particular for comparative studies, it is relevant to provide matching levels of detail for all compared options. Four illustrative examples are provided: (i) the differences in receiving compartment resolution for toxic emissions, (ii) differences in stressor resolution for particulate matter formation, (iii) lacking characterization factors for metal use, and (iv) lacking characterization factors for sum parameters and not fully specified emissions (such as BOD, TOC and “alkanes, unspecified”). Two important lessons to consider for maintaining a strong link between LCI and LCIA are highlighted based on these examples. First, it is suggested that it is important to have the same resolution between LCI data and LCIA methods. Scenario analysis, where different resolutions are assumed and tested, can be a strategy in cases where differences in resolutions are unavoidable. Second, ways to handle the absence of characterization factors are discussed, including the development of additional characterization factors that match the available LCI data and derivation of characterization factors from process information.

Jutta Hildenbrand, Rickard Arvidsson

Backmatter

Titel: Life Cycle Inventory Analysis
herausgegeben von: Dr. Andreas Ciroth
Dr. Rickard Arvidsson
Verlag: Springer International Publishing
Electronic ISBN: 978-3-030-62270-1
Print ISBN: 978-3-030-62269-5
DOI: https://doi.org/10.1007/978-3-030-62270-1