An SFA is used to provide information on the flows and stocks of one specific substance or a limited group of substances. In order to do this, a quantified relationship between the economy and the environment of a geographically demarcated system is established by quantifying the pathways of a substance or a group of substances in, out and through that system (Voet, 2002). Note that not all SFAs consider flows and stocks within economy and environment; some focus on the economy only.
SFA describes the material or substance stocks within, and flows within and between, the economy and the environment in a certain time period and for a certain region. SFA is based on physical input-output analysis in which the mass-balance principle holds for each economic or environmental sector. Figure 1 illustrates the framework. There are three subsystems: the economy or technosphere, the environment or biosphere, and the lithosphere. Stocks in the lithosphere are assumed to be immobile, while all stocks in the economy and environment are mobile2.
The first thing to decide is the geographical boundaries of the system to other economic systems: a country, city, plant etc. When geographical boundaries are used, all processes that occur outside these boundaries are not analysed. As far as the borders between the defined system and the environment is concerned, it should be decided, for example, whether soil is part of the economy system or part of the environment system, or that some part (e.g., the topsoil) is part of the economy and the remaining soil part of the environment. This decisionhas implications on, for example, the treatment of flows of fertilizers: flows of nitrate fertilizerto (agricultural) soil may either be considered as internal flows in the economy or as flows(emissions) from economy to the environment. In most cases, net flows of fertilizer (that is theapplied amount of fertilizer minus the uptake of fertilizer by plants and crops) to agriculturalsoil are considered as flows (emissions) to the environment (soil). Moreover, in order to beable to get data on the actual flows, the base year or the beginning and end of the time seriesfor which stocks and flows are quantified needs to be quantified, or the time period for whichfuture flows will be assessed.
The task in the modelling step is to quantify the various stocks and flows in the selected time perspective and for the selected boundaries. In SFA, three types of modelling are used:
– Static modelling;
– Dynamic modelling.
Although accounting can hardly be considered modelling, it can provide very relevant information for environmental policy. The input for such a system consists of data regarding the size of the system’s flows and stocks of goods and materials for a single year or a series of years, and if necessary also data regarding the content of specific substances in those goods and materials. Most of these data can be obtained from trade and production statistics. Emissions and environmental flux or concentration monitoring can be used for environmental flows. Balancing all these data results into an overview of flows and stocks, which may then used for identifying existing or potential future problem flows and stocks. Most of all, accounting results may serve as an identification system for missing or inaccurate data.
For static modelling, the most important data are variables describing the relations between the flows and stocks of the system studied. Emission factors, but also distribution factors over the various outputs for the economic processes and partition coefficients for the environmental compartments can be used as such variables. A limited amount of “accounting” data is required as well for a solution of this set of equations. Static modelling can e.g. be used for analyzing the causes of environmental problems by tracing back identified problem flows or stocks to their economic origins, and for predicting the effectiveness of pollution abatement measures, or the (side) effects on environmental problems of other measures influencing economic material flows.
For a dynamic model, additional information is needed with regards to the time dimension of the variables, for example, the life span of applications in the economy, the half-time of compounds in the environment etc. With a dynamic model, calculations can be made on anticipated effects of certain (policy) measures in a specific year in the future, and on the time it takes before such measures become effective. A dynamic model is therefore the most suitable for scenario analysis. The data and modelling requirements, however, are by far the most extensive compared to static modelling and accounting.
The results of the modelling may be used to draw up conclusions by developing indicators and assessing the effects of policy measures. One example of coupling SFA results to indicators was developed by combining SFA with environmental fate and transport modelling by Guinée et al. (1999) using a Mackay level III multimedia model. Other examples include economic accumulation, environmental accumulation, pollution footprint, etc. (Voet et al., 1999).
SFA has also been applied in combination with LCA. One of the first studies combining LCA and SFA was the one by Tukker et al. (1998) on PVC and PVC additives. More recently, Azapagic et al. (2007) combined LCA, SFA, fate and transport modelling of pollutants and geographical information systems (GIS) for mapping the flows the flows of pollutants in the urban environment, following the pollutants from their sources through the environment to receptors. This combined method intends, while it focuses on the urban environment, to also help “understand the wider environmental implications of the activities that support urban living but occur elsewhere in the life cycle, often far away from the urban area of interest”, thus preventing shifting of problems to other areas.
SFA provides a complete overview of all flows of the substance(group) within the specified geographical area.
SFA is a relatively simple tool for finding the origins of environmental problems that are connected to specific groups of substances;
The cradle to grave approach used in SFA detects problem shifting from one flow to another and from one compartment to another;
The dynamic SFA modelling stocks of substances in both society and environment provides a better estimate of future waste and emissions;
The basic principle of SFA, the mass balance, provides a powerful check on the results level of detail is flexible which also makes the data needs flexible;
Next to complex dynamic models relative simple types and easy to grasp models are available: bookkeeping and steady state modelling. Although these models are relatively simple they already provide powerful insights.
SFA is limited to one substance(group) and when only one SFA is done problem shifting from one substance to another is not taken into account;
The scope of the SFA is limited to those environmental issues that are directly related to the chosen group of substances.
Dynamic modelling has high data needs and requires data and time consuming modelling.
SFA does not reveal many essential factors behind the sustainable use of natural resources, such as the energy flows or total material intensity of a system. The complicated links with monetary flows, which are often the main driving forces behind entire socioeconomic systems, are similarly beyond the scope of SFA (Atikainen et al., 2005).
SFA cannot be used to link physical flows to the wider cultural or social context, although they may be complemented with information on monetary flows within a system (Atikainen et al., 2005).
Results of SFA studies are usually presented without uncertainty intervals, which may give a false impression of accuracy (Danius, 2002).
Antikainen et al. (2005) also mention as weaknesses that “SFA normally only considers the total mass of the studied substance flowing in the system, but certain forms of a substance can be very harmful, while others may be relatively inert. The interpretation of SFA results can consequently be difficult, and their usefulness in decision-making may be limited.” However, this may be a limitation of some SFAs but others do include an impact assessment of the substance(s) studied using indicators or using LCA Impact Assessment methods (e.g. Tukker et al. 1998; van der Voet et al., 2005).
Opportunities for broadening and deepening LCA
Dematerialisation, decoupling and re-use and recycling strategies as part of the Sustainable Consumptions and Production (SCP) strategy and as part of the Thematic Strategy on Sustainable Use of Natural Resources (TSURE), may stimulate the use of SFA, and offer opportunities for combined application of SFA/MFA with impact assessment methodologies from LCA for monitoring decoupling.
Another opportunity for SFA is to assess resources and materials that are important for future technologies on their potential constraints. For example, in the work by Elshkaki (2007), dynamic SFA scenarios for platinum in fuel cells are discussed, showing that platinum will become a constraint for this technology within one century. Increasing demand for platinum will also have profound consequences for co-produced metals, particularly nickel. The supply of Nickel from Pt ores exceeds its demand and this will likely affect both (other) mining and recycling of nickel. Similar developments may apply to other rare metals. Up scaling of contemporary technologies may also affect bulk metals such as currently seen for copper due to fast developments in China and other Asian countries.
Threats for broadening and deepening LCA
Main threat for SFA is the lack of reliable physical (not monetary) data on flows and stocks. Many physical data need to be derived from monetary data with all uncertainties attached. Another threat is somewhat paradoxical: the increase of development of LCA and EIOA. LCA answers questions on products, and companies are therefore interested in using, promoting the further development and providing data for LCA. EIOA answers questions on national economies, and governments therefore take the role of promoting EIOA. But who is interested in SFA? SFA is a shared but partial interest for companies and governments, but by having this position, much more research effort goes into LCA and EIOA.
1 This chapter is largely based on Voet (1996) and Guinée et al. (1999).
2 Mobile stocks are stocks that under the given conditions, on a time-scale of a few centuries (as opposed togeological time scales), can circulate within the system studied. Immobile stocks are just the opposite: they do not circulate within the (lithosphere) system, but there can be interactions with other system (e.g. extraction).
Azapagic, Adisa, Carol Pettit, Phil Sinclair (2007). A Life Cycle Approach to Mapping the Flows of Pollutants in the Urban Environment. Clean Technologies and Environmental Policy. 9(3) 199- 214.
Danius, L. 2002. Data uncertainties in material flow analysis. Local case study and literature survey. Licentiate thesis. Dept. of Chemical Engineering & Technology, Royal Institute of Technology.
Elshkaki, A. (2007). Systems analysis of stock buffering – development of a dynamic substance flow-stock model for the identification and estimation of future resources, waste streams and emissions. PhD Thesis, Leiden University.
Guinée, J.B., J.C.J.M. van den Bergh, J. Boelens, P.J. Fraanje, G. Huppes, P.P.A.A.H. Kandelaars, Th.M. Lexmond, S.W. Moolenaar, A.A. Olsthoorn, H.A. Udo de Haes, E. Verkuijlen, E. van der Voet (1999). Evaluation of risks of metal flows and accumulation in economy and environment. Ecological Economics 30, 47-65.
Lindqvist, A. (2002). Substance flow analysis for environmental management in local authorities – method development and context. Lindköping Studies in Science and Technology, Dissertation No 741. Lindköping University.
Palm, V. (2002). Material flow analyses in technosphere and biosphere – metals, natural resources and chemical products. Doctoral thesis. Royal Institute of Technology, Department of Civil and Environmental Engineering.
Riina Antikainen, Helena Dahlbo, Matti Melanen, Markku Ollikainen (2005). Decision support approaches: life cycle assessment (LCA) and substance flow analysis (SFA). http://www.mm.helsinki.fi/mmeko/tutkimus/SUNARE/pdf/63_Antikainen_etal.pdf
Tukker, A., R. Kleijn, L. van Oers, E.R.W. Smeets (1998). Combining SFA and LCA: The Swedish PVC Analysis. Journal of Industrial Ecology, Volume 1, Issue 4, p. 93-116.
Voet, E. van der (1996). Substances from cradle to grave – Development of a methodology for the analysis of substance flows through the economy and the environment of a region – with case studies on cadmium and nitrogen compounds. PhD Thesis, Leiden University.
Voet, E. van der (2002). SFA Methodology Chapter 2.8 in: Ayres, R.U. & L. Ayres (eds.): Handbook of Industrial Ecology. Edward Elgar Publishers.
Voet, E. van der, L. van Oers, J.B. Guinée (1999). Using SFA Indicators to Support Environmental Policy. Environmental Science & Pollution Research International, Vol. 6, No. 1, 49-58.
Voet, Ester van der, van Oers, Lauran, Moll, Stephan, Schuetz, Helmut, Bringezu, Stefan, de Bruyn, Sander, Sevenster, Maartje, and Warringa, Geert (2005). Policy Review on Decoupling: Development of indicators to assess decoupling of economic development and environmental pressure in the EU-25 and AC-3 countries. CML report 166, Department Industrial Ecology. 2005. Leiden, NL, Institute of Environmental Sciences (CML), Leiden University.