Level of analysis: Micro at the level of substances and products, meso at the level of businesses and sectors.
Assessed aspects of sustainability: Environmental
Main purpose of the assessment: Hybrid analysis is a term that can in principle refer to any combination of two or more specific forms of analysis. However, in many cases, it is used for the combination of a process-based LCA and an environmentally extended input-output analysis. The idea is that a foreground system is specified in terms of detailed processes, while the data for more general upstream processes are taken from environmental IO tables. Thus the strength of process-based LCA and the strength of environmental IOA are used together.
Hybrid analysis is a term that can in principle refer to any combination of two or more specific forms of analysis (Udo de Haes et al., 2004). However, in many cases, it is used for the combination of a process-based LCA and an environmentally extended input-output analysis (EIOA). The idea is that a foreground system is specified in terms of detailed processes (e.g., for the production of a can of beer), while the data for more general upstream processes (e.g., relating to equipment and transport) are taken from environmental IO tables. Thus the strength of process-based LCA and the strength of EIOA are used together.
For Lave et al (1995), a main motivation to use EIOA for product assessment (i.e. EIO-LCA), thus instead of traditional ISO-LCA, is the more complete system and the easier availability of data. After all, IO-tables (and their environmental satellites) are collected by statistical offices in many countries for a large number of years, and they cover a substantial part of the supply chain: agriculture, chemicals, metals, etc. However, the use of EIOA for product assessment has clear problems. We mention two:
1) EIOA cover the production part of the economy and the waste treatment part, but not the consumption part. In an assessment of personal cars, the use stage is the stage with the largest share in emissions of pollutants. It is also the stage in which the combination of car and gasoline is made, so that it adds important information on how much gasoline is needed and how many cars to drive a specified distance. This information is not part of a traditional (E)IO-table, but it is also cumbersome to add it, as the use of a car does not produce a marketable output.
2) (E)IO-tables provide a fairly complete coverage of the production system, but it does so on the expense of sectoral detail. Most of Eurostat’s IO-tables divide the economy into 30 or 60 sectors, and the most detailed tables from the US-BEA offer a resolution of 480 sectors. But even then, different types of plastic are lumped into one homogeneous material. Thus, EIOA cannot see the difference between a product from PVC or PE. Likewise, it cannot see the difference between a small car and a SUV, or between an incandescent bulb and a fluorescent bulb. Thus, many traditional comparative LCAs cannot be performed with EIOA due to a lack of resolution.
Process-based LCA is able to include the consumption stage in a natural way, and it has been able for many years to differentiate between several plastics, several car types, or several bulb types. LCA-practitioners have been distinguishing a foreground system and a background system for quite a while (Clift et al, 1998).
The foreground system then covers all processes, products and materials that form the core of the system, and that need to be defined and specified in a precise way. For instance, in an LCA of cars, the composition of the car itself, its fuel and emission characteristics, the way it is produced and disposed, etc. are clearly part of the foreground system.
The background system refers to those parts of the life cycle that are more remote to the central function, and for which less detail suffices. For instance, the transport of steel by bulk carrier from the steel factory to the car factory must be specified in terms of approximate distance, but the exact specifications of the bulk carrier may not be needed and emission data for an average bulk carrier may be good enough. Extending this idea, to produce cars, one needs factory buildings, and if we just know the total cost for buildings and the total car production, we can calculate the cost for buildings per car, and connect it to average data for the building sector. These data for the building sector in turn connect to the steel sector, to the concrete sector, etc. All these relationships are available in IO-tables.
Thus there emerges a way of working in which specific data are put in the foreground system with a process-based format, and more generic data in the background system with an IObased format. This concept has been described, elaborated, illustrated and applied by a large number of authors (see the references underneath for a survey). In this, we see two main lines occurring:
1) Process-based LCA can be made more complete by adding EIO-data for more remote partsof the system. In this way, simple EIO-results can be fed into the otherwise process-based LCA. The data traffic is one way: EIO-data into the process-based framework. No real EIO-calculations are made: the database contains Leontief-multipliers that refer to the environmental repercussions per monetary unit of a material, product, or service. This line is relatively straightforward, and is readily available. Descriptions and elaborations can be found in (Suh, 2001).
2) Process-based LCA can be integrated with EIO-based LCA. For this purpose, the system is formulated in terms of processes that exchange products (the process-based part), sectors that connect to sectors (the EIO-based part), and two connecting blocks, from the process-based part to the EIO-part and the other way around. The mathematics of this line is more complicated (Suh, 2004; Heijungs et al, 2006), especially when more than the plain results are desired (e.g., when a contribution analysis is to be made). The practical implementation is, as far as we know, not yet publicly available (although the new version of CMLCA will contain it).
Although both approaches (adding or integrating) can be referred to as hybrid analysis, the second one is also referred to as integrated hybrid analysis. As a matter of fact, the first line, the non-integrated hybrid analysis, might be considered not to be a truly hybrid analysis, but rather a convenient way to estimate missing data in a process-based LCA-framework on the basis of EIO-data. The absence of algorithms to compute a Leontief inverse in software or databases that offer the non-integrated hybrid analysis is a clear sign of this data estimation focus. One of the first packages that was made available for this purpose is called MIET, which is an abbreviation for Missing Inventory Estimation Tool.
Hybrid analysis employs a strength of EIOA (economy-wide coverage) to cure a weakness of process-based LCA (truncation error). As such it has a purposeful strength as its raison d’être.
At the same time, it offers a remedy against the weakness of pure EIO-based LCA (lack of detail) by using a strength of process-based LCA (process- and product-specific data).
Hybrid analysis is flexible with respect to the balance of adding more or adding more completeness. This is a goal and scope dependent issue, and in principle hybrid LCA allows the user to select the boundary between foreground-process-based and background-EIO based in a case-dependent way.
EIOA offers data that apply to monetary information on products and services. As such, required inputs in terms of, say, equipment and buildings, must be specified in or translated into monetary terms.
With integrated hybrid analysis, the computational structure of the model becomes more complicated. Moreover, the data matrices grow larger, so that more memory and computation time is needed.
Opportunities for broadening and deepening LCA
Opportunities and threats coming from external (policy) developments.
Traditional ISO-LCA has been criticized for lack of completeness. The alternative EIO-LCA (employing EIO-based LCA rather than process-based analysis) cures this problem, but introduces other problems, such as lack of sectoral detail and absence of data on the use phase. Hybrid LCA provides an opportunity to meet the demand for detail at the foreground while offering a more complete view at the background.
Another opportunity is that combining physical process-based data and monetary IO-based data opens the possibility of combining environmental and economic aspects. The degree to which this really yields a broader analysis is not clear, however, as the economic information is available only for some parts of the system.
Threats for broadening and deepening LCA
Opportunities and threats coming from external (policy) developments.
EIO-tables are very much industry-oriented and the EIOA-model is simple: in its basic form it lacks dynamics, spatial resolution, non-linearities, etc. As such, there can be a tendency to mistake hybrid analysis as a way to solve the shortcomings of process-based LCA, whereas it in fact only solves the problem of truncation error for industrial upstream parts. As discussed by Udo de Haes et al. (2004) the term hybrid pertains potentially to more than just the present elaboration, but can also refer to a distinction between a foreground system with dynamics and a background system without, or to a foreground system with non-linear relationships and a background system without, etc. Reserving the term hybrid analysis for the present discussion on process-based and EIO-based has thus the risk of disappointing those that expect too much.
The aspect of “complicated computational structure” reported under weaknesses, could also be considered a threat. However, software implementations of hybrid analysis are already available, and once implemented, this potential weakness shouldn’t be a barrier for wide use of the hybrid analysis.
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