This report has been prepared with the aim of demonstrating how a data
collection strategy can be based on understanding the causes of variation in
the technological, geographical and temporal aspects of the processes included
in a life cycle assessment.
The objective of a data collection strategy is to prioritise the data collection so
that the necessary data is obtained in an adequate quality with the least effort.
Therefore, a natural target for the data collection strategy is to reduce the
overall uncertainty of the life cycle inventory to the level necessary to obtain a
result upon which conclusions can be based. Uncertainty, its causes, and ways
to reduce it, are therefore natural objects of interest when designing a data
collection strategy.
To reduce the overall uncertainty level of the life cycle inventory with the least
effort, the largest uncertainties should be reduced first, since these
uncertainties will dominate the overall uncertainty. However, some
uncertainties may be easily reducible, while others are irreducible. If the result
of a life cycle inventory is expected to be inconclusive at the level of the
irreducible uncertainties, it does not make sense to seek a reduction of
uncertainty at all, i.e. data collection should not be initiated. Chapter 2 deals
with procedures to identify and estimate the largest uncertainties in a life cycle
inventory. Chapter 3 introduces the distinction between reducible and
irreducible uncertainties, and combines the procedures of chapter 2 with
procedures to reduce uncertainties, arriving at an overall uncertainty-based
data collection strategy, which is summarized in section 3.7. The extensive
annex A reports the findings of an investigation into the causes of
technological, geographical and temporal variation in life cycle inventory data
from a number of industrial sectors. Annex B reports on the statistical
terminology applied.
This technical report is based on research performed from 1998 and up to the
end of 1999. It therefore does not include sources of information that have
become available after this date.
Database flexibility is a crucial criterion for database applicability. If stored in a flexible format, the same LCI data may be useful in many different contexts. LCI results often depend on the assumptions made with respect to linking processes through a market. By modelling markets as processes, it is possible to combine the same unit processes in many different ways, depending on the scenario and market conditions appropriate for the individual LCI study. Market modelling is illustrated in two examples: 1) a database linking a comprehensive set of agricultural and food chain processes into product life cycles under actual and prospective market conditions, e.g. with and without production quotas, 2) a national input-output based database with both average and market-based modelling, illustrating the important differences and the possibilities for maintaining flexibility.
This report provides the background for the two guidelines “The product,
functional unit, and reference flows in LCA” (Weidema et al. 2003a) and
“Geographical, technological and temporal delimitation in LCA” (Weidema
2003). It provides further documentation of the examples provided in these
guidelines, as well as additional examples, further explanatory text, scientific
background and reference to earlier methodological guidelines. It also expands
on specific issues, which were not found to be of sufficient general interest to
merit inclusion in the guidelines.
This report and the two guidelines that it supports, carry two key messages:
1. The fundamental rule to apply in all methodological choices in life cycle
assessment is that the data used must reflect as far as possible the processes
actually affected as a consequence of the decision that the specific life cycle
assessment is intended to support. Thus, there is a close link between the goal
or application area of the life cycle assessment and the methodological
choices. This is elaborated in section 1.1.
2. Life cycle assessments, insofar as they deal with comparing potential
choices between alternative products, rely heavily on market information, i.e.
information on how the market affects the potential choices and how the
markets will react to these choices.
....
In a recent paper in this journal, embodied land appropriation in international trade activities was analysed using a physical input–output table (PIOT) (Ecological Economics 44 (2003) 137). The authors stated that there are significant differences between the physical and the monetary input–output tables in their results, which the authors argued to be due to the fact that the results from the monetary table are determined mainly by the monetary structure of final demand, while the structure of a physical table more closely resembles the ‘physical realities’ of an economy. In the present paper, it is argued that the methodological foundation that the authors based their analysis on is misleading and does not satisfy the overall material balance requirement. It is shown that the differences in the results between the monetary and physical tables presented by the authors have nothing to do with the resemblance to the physical realities. I also tried to further clarify a number of critical issues in applying physical input–output tables, related to double counting, treatment of wastes and the effect of closing the system toward direct material inputs. A number of consistent but different approaches to cope with these issues are presented, including their proofs. The embodied land appropriation of international trade activities is calculated and compared by applying those approaches. There are many advantages of using physical input–output tables, however, their superiority should not be exaggerated nor be regarded as absolute. Depending on how it is constructed and used, it is also possible that the results from a physical input–output table do not tell us more than that indeed some commodities are cheaper, or more costly, per unit of their mass.
Many research efforts aim at an extension of life-cycle assessment (LCA) in order to increase its spatial or temporal detail or to enlarge its scope. This is an important contribution to industrial ecology as a scientific discipline, but from the application viewpoint other options are available to obtain more detailed information, or to obtain information over a broader range of impacts in a life-cycle perspective. This article discusses three different strategies to reach these aims: (1) extension of LCA—one consistent model; (2) use of a toolbox—separate models used in combination; and (3) hybrid analysis—combination of models with data flows between them.
Extension of LCA offers the most consistent solution. Developments in LCA are moving toward greater spatial detail and temporal resolution and the inclusion of social issues. Creating a supertool with too many data and resource requirements is, however, a risk. Moreover, a number of social issues are not easily modeled in relation to a functional unit.
The development of a toolbox offers the most flexibility regarding spatial and temporal information and regarding the inclusion of other types of impacts. The rigid structure of LCA no longer sets limits; every aspect can be dealt with according to the logic of the relevant tool. The results lack consistency, however, preventing further formal integration.
The third strategy, hybrid analysis, takes up an intermediate position between the other two. This strategy is more flexible than extension of LCA and more consistent than a toolbox. Hybrid analysis thus has the potential to combine the strong points of the other two strategies. It offers an interesting path for further discovery, broader than the already well-known combination of process-LCA and input-output-LCA. We present a number of examples of hybrid analysis to illustrate the potentials of this strategy.
Developments in the field of a toolbox or of hybrid analysis may become fully consistent with LCA, and then in fact become part of the first solution, extension of LCA.
One of the most important developments of the methodology of Life Cycle Assessment (LCA) in the last decade has been the improved the understanding of how market information can provide a transparent procedure for unambiguous delimitation of the described systems? the product life cycles - i.e. what processes to include and what processes to exclude from the systems. The developments have also resulted in a general solution to the problem of allocation of exchanges among co-products from joint production processes. It is the suggestion of this presentation that the system delimitation procedures now applied for consequential LCA are also applicable to Environmental Management Accounting (EMA), also solving many contentious cost allocation issues. Two industry examples of life cycle system delimitation and cost allocation are provided to illustrate these points.
A consequential life cycle assessment (LCA) is designed to generate information on the consequences of decisions. This paper includes a comprehensive presentation of the consequential approach to system boundaries, allocation and data selection. It is based on a text produced within the SETAC-Europe working group on scenarios in LCA. For most of the methodological problems, we describe ideal methodological solutions as well as simplifications intended to make the method feasible in practice.
We compile, summarize and refine descriptions of consequential methodology elements that have been presented in separate papers, in addition to methodological elements and general conclusions that have not previously been published.
A consequential LCA ideally includes activities within and outside the life cycle that are affected by a change within the life cycle of the product under investigation. In many cases this implies the use of marginal data and that allocation is typically avoided through system expansion. The model resulting from a consequential life cycle inventory (LCI) also includes the alternative use of constrained production factors as well as the marginal supply and demand on affected markets. As a result, the consequential LCI model does not resemble the traditional LCI model, where the main material flows are described from raw material extraction to waste management. Instead, it is a model of causal relationships originating at the decision at hand or the decision-maker that the LCI is intended to inform.
ShareIt link: http://rdcu.be/mXAR
Sustainable development requires methods and tools to measure and compare the environmental impacts of human activities for the provision of goods and services (both of which are summarized under the term “products”). Environmental impacts include those from emissions into the environment and through the consumption of resources, as well as other interventions (e.g., land use) associated with providing products that occur when extracting resources, producing materials, manufacturing the products, during consumption/use, and at the products' end-of-life (collection/sorting, reuse, recycling, waste disposal). These emissions and consumptions contribute to a wide range of impacts, such as climate change, stratospheric ozone depletion, tropospheric ozone (smog) creation, eutrophication, acidification, toxicological stress on human health and ecosystems, the depletion of resources, water use, land use, and noise—among others. A clear need, therefore, exists to be proactive and to provide complimentary insights, apart from current regulatory practices, to help reduce such impacts. Practitioners and researchers from many domains come together in life cycle assessment (LCA) to calculate indicators of the aforementioned potential environmental impacts that are linked to products—supporting the identification of opportunities for pollution prevention and reductions in resource consumption while taking the entire product life cycle into consideration. This paper, part 1 in a series of two, introduces the LCA framework and procedure, outlines how to define and model a product's life cycle, and provides an overview of available methods and tools for tabulating and compiling associated emissions and resource consumption data in a life cycle inventory (LCI). It also discusses the application of LCA in industry and policy making. The second paper, by Pennington et al. (Environ. Int. 2003, in press), highlights the key features, summarises available approaches, and outlines the key challenges of assessing the aforementioned inventory data in terms of contributions to environmental impacts (life cycle impact assessment, LCIA).
Life-cycle assessment (LCA) is a method for evaluating the environmental impacts of products holistically, including direct and supply chain impacts. The current LCA methodologies and the standards by the International Organization for Standardization (ISO) impose practical difficulties for drawing system boundaries; decisions on inclusion or exclusion of processes in an analysis (the cutoff criteria) are typically not made on a scientific basis. In particular, the requirement of deciding which processes could be excluded from the inventory can be rather difficult to meet because many excluded processes have often never been assessed by the practitioner, and therefore, their negligibility cannot be guaranteed. LCA studies utilizing economic input−output analysis have shown that, in practice, excluded processes can contribute as much to the product system under study as included processes; thus, the subjective determination of the system boundary may lead to invalid results. System boundaries in LCA are discussed herein with particular attention to outlining hybrid approaches as methods for resolving the boundary selection problem in LCA. An input−output model can be used to describe at least a part of a product system, and an ISO-compatible system boundary selection procedure can be designed by applying hybrid input−output-assisted approaches. There are several hybrid input−output analysis-based LCA methods that can be implemented in practice for broadening system boundary and also for ISO compliance.
In contrast to macroscopic tools, life cycle assessment (LCA) starts from the microstructure of an economic system: the production and consumption of functional flows. Due to the level of resolution required for function-level details, the model used for LCA has relied on process-specific data and has treated the product system as a stand-alone system instead of a system embedded within a broader economic system. This separation causes various problems, including incompleteness of the system and loss of applicability for a variety of analytical tools developed for LCA or economic models. This study aims to link the functional flow-based, micro-level LCA system to its embedding, commodity-based, meso- or macro-level economic system represented by input–output accounts, resulting in a comprehensive ecological–economic model within a consistent and flexible mathematical framework. For this purpose, the LCA computational structure is reformulated into a functional flow by process framework and reintroduced in the context of the input–output tradition. It is argued that the model presented here overcomes the problem of incompleteness of the system and enables various analytical tools developed for LCA or input–output analysis (IOA) to be utilised for further analysis. The applicability of the model for cleaner production and supply chain management is demonstrated using a simplified product system and structural path analysis as an example.
