This report aims to summarize the steps, arguments and market data that are needed to identify the long term marginal supplier affected by a long-term1 change in demand for Bleached Hardwood Kraft Pulp (BHKP) and Bleached Softwood Kraft Pulp (BSKP).
Put very briefly, if the general trend in the market is expanding or stable, a long-term change in demand is assumed to affect the supplier that has the best options for expanding or renewing the production capacity. This most competitive supplier is often the one with the lowest production costs.
If the market is sharply shrinking, so that production capacity is being reduced, a change in demand is assumed to affect the least competitive supplier.
In some situations, suppliers are constrained in their ability to react to a change in demand, e.g. because of lack of available raw materials or because of production quota. In these situations, an additional assumption is needed, namely that constrained suppliers are not affected by changes in demand. This implies that the demand has to be satisfied by another un-constrained supplier.
The above assumptions are based on simple market economics.
In order to identify the most or least competitive, un-constrained supplier, it is thus necessary to have information about:
Recent developments in Life Cycle Impact Assessment (LCIA) provide a basis for reducing the uncertainty in monetarisation of environmental impacts. The LCIA method “Ecoindicator99” provides impact pathways ending in a physical score for each of the three safeguard subjects humans, ecosystems, and resources. We redefine these damage categories so that they can be measured in terms of Quality Adjusted Life Years (QALYs) for impacts on human well-being, Biodiversity Adjusted Hectare Years (BAHYs) for impacts on ecosystems, and monetary units for impacts on resource productivity.
The monetary value of a QALY can be derived from the budget constraint, i.e. the fact that the average annual income is the maximum that an average person can pay for an additional life year. Since a QALY by definition is a life-year lived at full well-being, the budget constraint can be determined as the potential annual economic production per capita at full well-being. We determine this to be 74,000 EUR with an uncertainty estimate of 62,000 to 84,000 EUR. This corresponds well to the 74,627 EUR willingness-to-pay estimate of the ExternE project. Differences to other estimates can be explained by inherent biases in the valuation approaches used to derive these estimates.
The value of ecosystems can be expressed in monetary terms or in terms of QALYs, as the share of our well-being that we are willing to sacrifice to protect the ecosystems. While this trade-off should preferably be done by choice modelling, only one such study was found at the level of abstraction that allows us to relate BAHYs to QALYs or monetary units. Stressing the necessity for such studies, we resort to suggest a temporary proxy value of 1400 EUR/BAHY (or 52 BAHY/QALY), with an uncertainty range of 350 to 3500 EUR/BAHY.
The practical consequences of the above-described monetarisation values has been investigated by combining them with the midpoint impact categories of two recent LCIA methods, thus providing a new LCIA method with the option of expressing results in both midpoints and an optional choice between QALY and monetary units as endpoint. From our application of the new method to different case studies, it is noteworthy that resource impacts obtain less emphasis than in previous LCIA methods, while impacts on ecosystems obtain more importance. This shows the significance of being able to express impacts on resources and ecosystems in the same units as impacts on human well-being.
This presentation provides a definition and classification of rebound effects, and gives examples of the different kinds of rebound effects. In general, ignoring rebound effects leads to either under- or over-estimation of the effects of new technologies. This stresses the importance of including rebound effects in assessments of new technologies. In a recent study for the EU Commission, DG- JRC, IPTS in Sevilla, on the improvement potentials for meat and milk products in Europe, we estimated the rebound effects for 12 improvement options, showing that the rebound effects often emphasise the benefits of the improvement options; in one case the benefit with rebound effects was nearly five times the benefit without rebound effects. The recognition of rebound effects has important policy implications, stressing impact intensity as a central concept for in strategies for sustainable consumption. Although rebound effects may already now be quantified and applied in policy analysis, improvements in our modelling capacity is warranted. This could be achieved by better data on marginal consumption patterns, and time and space elasticities. More knowledge is also required of the best ways to influence consumer behaviour to convert the insights in the rebound effect into reductions in environmental impacts.
When dealing with system delimitation in environmental life cycle assessment (LCA), two methodologies are typically referred to: consequential LCA and attributional LCA. The consequential approach uses marginal data and avoids co-product allocation by system expansion. The attributional approach uses average or supplier-specific data and treats co-product allocation by applying allocation factors. Agricultural LCAs typically regard local production as affected and they only include the interventions related to the harvested area. However, as changes in demand and production may affect foreign production, yields and the displacement of other crops in regions where the agricultural area is constrained, there is a need for incorporating the actual affected processes in agricultural consequential LCA. This paper presents a framework for defining system boundaries in consequential agricultural LCA. The framework is applied to an illustrative case study; LCA of increased demand for wheat in Denmark. The aim of the LCA screening is to facilitate the application of the proposed methodology. A secondary aim of the LCA screening is to illustrate that there are different ways to meet increased demand for agricultural products and that the environmental impact from these different ways vary significantly.
The proposed framework mainly builds on the work of Ekvall T, Weidema BP (Int J Life Cycle Assess 9(3):pp. 161–171, 2004), agricultural statistics (FAOSTAT, FAOSTAT Agriculture Data, Food and Agriculture Organisation of the United Nations (2006), http://faostat.fao.org/ (accessed June)), and agricultural outlook (FAPRI, US and world agricultural outlook, Food and Agriculture Research Institute, Iowa, 2006a). The framework and accompanying guidelines concern the suppliers affected, the achievement of increased production (area or yield), and the substitutions between crops. The framework, which is presented as a decision tree, proposes four possible systems that may be affected as a result of the increased demand of a certain crop in a certain area.
The core of the proposed methodology is a decision tree, which guides the identification of affected processes in consequential agricultural LCA. The application of the methodology is illustrated with a case study presenting an LCA screening of wheat in Denmark. Different scenarios of how increased demand for wheat can be met show significant differences in emission levels as well as land use.
The great differences in potential environmental impacts of the analysed results underpin the importance of system delimitation. The consequential approach is appointed as providing a more complete and accurate but also less precise result, while the attributional approach provides a more precise result but with inherent blind spots, i.e. a less accurate result.
The main features of the proposed framework and case study are: (1) an identification of significant sensitivity on results of system delimitation, and (2) a formalised way of identifying blind spots in attributional agricultural LCAs.
It is recommended to include considerations on the basis of the framework presented in agricultural LCAs if relevant. This may be done either by full quantification or as qualitative identification of the most likely ways the agricultural product system will respond on changed demand. Hereby, it will be possible to make reservations to the conclusions drawn on the basis of an attributional LCA.
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Traditionally, comparative life cycle assessments (LCA) have not considered rebound effects, for instance in case of significant price differences among the compared products. No justifications have been made for this delimitation in scope. This article shows that price differences and the consequent effects of marginal consumer expenditure may influence the conclusions of comparative LCA significantly. We also show that considerations about rebound effects of price differences can be included in LCAs.
The direct rebound effect of a price difference is marginal consumption. Based on statistical data on private consumption in different income groups (Statistics Denmark 2005a, 2005b), the present article provides an estimate of how an average Danish household will spend an additional 1 DKK for further consumer goods, when the household has gained money from choosing a cheaper product alternative. The approach is to use marginal income changes and the following changes in consumption patterns as an expression for marginal consumption. Secondly, the environmental impact potentials related to this marginal consumption are estimated by the use of environmental impact intensity data from an IO-LCA database (Weidema et al. 2005). Finally, it is discussed whether, and in which ways the conclusions of comparative LCAs can be affected by including the price difference between product alternatives. This is elucidated in a case study of a comparative LCA screening of two different kinds of Danish cheese products (Fricke et al. 2004).
Car purchase and driving, use and maintenance of dwelling, clothing purchase and insurance constitutes the largest percentages of the marginal consumption. In a case study of two cheeses, the including the impact potentials related to the price difference results in significant changes in the total impact potentials. Considering the relatively small price difference of the two products, it is likely also to have a significant influence on the results of comparative LCAs more generally.
The influence of marginal consumption in comparative LCAs is relevant to consider in situations with large differences in the price of the product alternatives being compared, and in situations with minor differences in the impact potentials related to the alternatives. However, different uncertainties are linked to determining the pattern for marginal consumption and the environmental impact potential related to this. These are first of all related to the method used, but also include inaccurate data of consumption in households, aggregation and weighting of income groups, aggregation of product groups, estimation and size of the price difference, and the general applicability of the results.
Incorporating marginal consumption in consequential LCAs is possible in practice. In the case study used, including the rebound effects of the price difference has a significant influence on the result of the comparative LCA, as the result for the impact categories acidification and nutrient enrichment changes in favour of the expensive product.
It is recommended that the rebound effects of price differences should be included more frequently in LCAs. In order to ensure this, further research in marginal consumption and investment patterns and IO data for different countries or regions is required. Furthermore, this study does not consider the economic distributional consequences of buying an expensive product instead of a cheaper product (e.g. related to how the profit is spent by those who provided the product). It should also be noted, that more expensive products not necessarily result in less consumption, as those who provided the product also will spend the money they have earned from the sale. Ideally, these consequences should also be further investigated. Likewise, the development of databases to include marginal consumption in PC-tools is needed. In general, considerations of marginal consumption would favour expensive product alternatives, depending, however, on the type of consumer.
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This series on consequential modelling is part of an on-going effort to provide the LCA community with freely available education materials on very specific details of Life Cycle Assessment. We regularly teach on-site university courses at Aalborg University (Denmark), at the International Life Cycle Academy (Barcelona), and at in-house courses for companies, where this series of online lectures provides the foundation for QA sessions and targeted exercises.
