Finnveden, 1996a) – method groups 1 and 4 the change in the environmental impact of extraction processes at some point in the future (e.g. as a result of having to extract lower-grade ores or recover materials from scrap) – method groups 4 and 6. Depending on the problem definition, this impact category is associated with various areas of protection: natural resources (option A and B), human health and the natural and man-made environment, now (option C), or natural resources, human health and the natural and man-made environment, in the future (option D). A. B. C. D. Part 3: Scientific background


In order to select a baseline method it is first necessary to opt for one of the available definitions. Option D is not consistent with the methods adopted for the other impact categories, where the concern is consistently not with the impact of future changes in processes and interventions, but with those of current interventions. Future changes in processes and interventions constitute changes in the product system and should be accounted for in the Goal and scope and Inventory phases, not during Impact assessment for a particular impact category. Under option D all impacts would be based on the changed product system, and not only abiotic depletion. Option C means that abiotic depletion is not deemed a relevant impact category. No separate assessment is then necessary, for if the Inventory analysis is correctly performed all environmental impacts should already be included within other impact categories. Thus, options C and D are rejected, leaving options A and B

Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002)


Thus, options C and D are rejected, leaving options A and B. If the decrease of the resource itself is taken as the key problem (option A), assessment based on reserves and/or current extraction rates appears to be the best available method (method group 3). This method relates resource extractions obtained from the Inventory analysis directly to existing reserves and/or (annual) extraction rates. There is still room for debate on several points, however: Which of the many types of reserves do we consider: economic reserves, ultimate reserves in primary media (ore, fossil fuels) or also reserves in the economy (in products, scrap)? Should we focus on reserves or on extraction rate, or should both be included? Should we take into account the economic value of the resource? According to Guinée & Heijungs (1995) a method based on ultimate reserves and rates of extraction is the best option, as these parameters best indicate the seriousness of resource depletion


As the notion of ‘economic reserves’ involves a variety of economic considerations not directly related to the environmental problem of resource depletion, ‘ultimate reserves’ appears to be a more appropriate yardstick. In their proposed method the indicator result is expressed in kg of a reference resource (antimony): and: Abiotic Depletion Potential of resource i (generally dimensionless); quantity of resource i extracted (kg); ultimate reserve of resource I (kg); extraction rate of resource i ultimate reserve of the reference resource, antimony (kg) extraction rate of The indicator result is expressed in kg of the reference resource, viz. antimony. This method is partly operational. Guinée ( 1995) has developed ADPs for many elements, using antimony as the reference element. Thus far, only ultimate reserves have been included. However, these ADPs for elements need to be updated and converted to ADPs for resources, as it is these that are documented in the Inventory analysis


However, these ADPs for elements need to be updated and converted to ADPs for resources, as it is these that are documented in the Inventory analysis. These resources are mainly compositions of several elements. The method is suitable for depletion, but not for competitive use, i.e. use of a resource that restricts the potential for others to use that same resource. If the scarcity of useful energy/exergy in the world is taken as the key problem (option B), it should be borne in mind that the resources will be ‘valued’ on the basis of exergy content only. However, it is debatable whether the value of the metals used in a particular process is dependent on the exergy content of the ore in question. Separate indicators for energy resources As discussed in Section 4.2, it may sometimes be relevant to add information about the total amount of (fossil) energy consumed by a system


Separate indicators for energy resources As discussed in Section 4.2, it may sometimes be relevant to add information about the total amount of (fossil) energy consumed by a system. One common approach is then simply to aggregate all forms of energy consumption, by multiplying the energy resources extracted from the environment by their respective (gross) heating value, this being the best indication of the extent to which the energy reserve is actually depleted (see Frischknecht et al., 1998). Frischknecht et al. (1998) propose including not only fossil fuels but also biofuels (wood, etc.), solar and wind energy, etc. as well as nuclear resources with: Part 3 : Scientific background


(uranium, etc.). These energy resources obviously cannot be aggregated on the basis of heating value alone, and this is therefore not recommended as an additional method in this Guide. If practitioners nonetheless opt to undertake additional assessment of energy resources in MJ, due care should be taken to avoid double-counting in the weighting step. PROSPECTS The subject of abiotic resource depletion is still being widely debated and much research is still in progress on developing indicators for this impact category, especially within method groups 5 and 6, above. CONCLUSIONS In this Guide we consider resource depletion to be an environmental problem in its own right, while recognising that views differ as to the precise definition of the resource problem. As argued, though, we consider that ultimate reserves and extraction rates together best reflect the seriousness of resource depletion (see Guinée & Heijungs, 1995)


As argued, though, we consider that ultimate reserves and extraction rates together best reflect the seriousness of resource depletion (see Guinée & Heijungs, 1995). We therefore recommend as a baseline the method employing these parameters developed by Guinée & Heijungs (1995; see also Guinée, 1995). As mentioned above, there is still a need to convert the ADPs for elements to ADPs for composite resources. It should also be borne in mind that this method covers depletion but not competitive use. Table 4.3.1.1 indicates how this method scores with regard to the (ISO-based) criteria of Table 4.3.1. Table 4.3.1.1: Evaluation of the baseline characterisation method for abiotic depletion, using the characterisation factor ADP, with respect to the (ISO-based) criteria of Table 4.3.1 . 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12


1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. criterion scientificallyandtechnicallyvalid environmentally relevant internationally accepted value-choices and assumptions focal point in environmental mechanism linearity time span fate, exposure/intake and effects less is better time- and location-independent operational uncertainty margins evaluation not relevant; no environmental mechanism involved (actual resource extraction is assessed) yes, if problem definition A is adopted no; method not authorised by an international body yes; problem definition implies a value-choice not relevant; no environmental mechanism involved yes not relevant not relevant yes, no threshold yes partly; factors still to be worked up from elements to resources considerable uncertainty about magnitude of current reserves Three alternative methods are included in this Guide as options for sensitivity analysis, viz


methods using: a characterisation factor based on extraction rates and economic (rather than ultimate) reserves, i.e. that part of the reserve base which can be economically extracted at the time of calculation; a characterisation factor based only on ultimate or economic reserves (R) and not on extraction rates: characterisation factor = 1/R; the factors based on exergy content developed by Finnveden (1996b) or Ayres et al. (1996), useful when focusing on declining global energy/exergy content. Recommendation for extended LCAs: recalculate ADPs for minerals (i.e. compositions) rather than chemical elements


(1996), useful when focusing on declining global energy/exergy content. Recommendation for extended LCAs: recalculate ADPs for minerals (i.e. compositions) rather than chemical elements. method status baseline alternative 1 alternative 2 alternative 3 additional variant characterisation method/factor based on ultimate reserves and extraction rates based on economic reserves and extraction rates basedonultimateoreconomicreservesonly based on exergy content – – reference Guinée & Heijungs, 1995 Guinée & Heijungs, 1995, adapted Guinée & Heijungs, 1995, adapted Finnveden, 1996b; Ayres et al., 1996 – – Part 3: Scientific background


Additional remark Because of the difference between flows on the one hand and deposits and funds on the other, it is not possible to aggregate all abiotic resources into one measure using any of the methods currently available. Until such time as a method for competitive use has been developed, it will not be possible to incorporate flows in the method. RESEARCH RECOMMENDATIONS Short-term research: To develop up-to-date ADPs research should be undertaken to establish contemporary data on reserves of abiotic resources and extraction rates. Using these data the ADPs for chemical elements of Guinée (1995) should be updated and extrapolated to yield ADPs for minerals and composite ores (see also: Recommendations for extended LCAs)


Using these data the ADPs for chemical elements of Guinée (1995) should be updated and extrapolated to yield ADPs for minerals and composite ores (see also: Recommendations for extended LCAs). It should be investigated whether abiotic depletion can be split into two subcategories: depletion of energy resources and of other resources, operationalising a dual methodology and subsequently reaggregating the results (among other requirements, the same units must then of course be used). Long-term research: Continued debate is required on defining the problem of abiotic depletion in LCA. Once a conclusion has been reached, the best available method based on the chosen definition should be further developed, for most methods are as yet only partly operational. It might be useful to develop a method covering competitive use of flows (and funds) and to investigate the scope for aggregating flows, deposits and funds


It might be useful to develop a method covering competitive use of flows (and funds) and to investigate the scope for aggregating flows, deposits and funds. The scope for distinguishing between competitive use and depletion in LCA should also be investigated. Some of the methods described above have potential for including competitive use, especially for minerals that accumulate in the economy (methods based on reserves, for instance). Methods based on total availability of reserves, including those in the economy, cover competitive use rather than depletion. Methods based on changes in future environmental impact might also be applied to competitive use if they were amended to incorporate extraction from scrap, etc., in addition to extraction from lower-grade ore. 4.3.2 Depletion of biotic resources TOPIC ‘Biotic resources’ are material resources (including energy resources) regarded as living, e.g. rainforests, elephants


4.3.2 Depletion of biotic resources TOPIC ‘Biotic resources’ are material resources (including energy resources) regarded as living, e.g. rainforests, elephants. Depending on the precise definition adopted, this impact category has only natural resources, or natural resources, human health and the natural and the man-made environment as areas of protection (see Figure 4.2.2). DEVELOPMENTS IN THE LAST DECADE Heijungs et al. (1992) The method used by Heijungs et al. (1992) to characterise biotic resources considers reserves and deaccumulation rates (see below): The indicator result is expressed in BDFi or is the Biotic Depletion Factor of resource i, is the quantity of resource i extracted, (kg, number or is the reserve of resource i and is the deaccumulation rate of resource i, which is defined as the extraction rate, expressed in kg/yr, number/yr or minus the regeneration rate, expressed in the same units. Part 3 : Scientific background


Most developments since 1992 in the area of resource depletion have already been discussed in Section 4.3.1. Most of these tend to focus on abiotic resources. Heijungs et al. (1997) provide an overview focused particularly on biotic resources. Based on these reviews, supplemented with several methods developed since, three groups of methods can be distinguished for characterising biotic resources: no aggregation (Lindfors, 1996) aggregation based on reserves and deaccumulation rates (see Heijungs et al. (1992) as described in the textbox) and aggregation based on (regional) production rates divided by (regional) regeneration rates. methods that also cover impacts on biodiversity and life support functions. This approach is explored by Sas et al. (1997), who propose three types of indicator results: 1. 2. 3


methods that also cover impacts on biodiversity and life support functions. This approach is explored by Sas et al. (1997), who propose three types of indicator results: 1. 2. 3. to indicate the risk of species extinction by harvesting an individual; to indicate the risk of species extinction by disrupting an ecosystem; and to indicate the decrease in life support functions, i.e. ecological structures and processes that sustain the productivity, adaptability and capacity for renewal of lands, water and/or the biosphere as a whole (after IUCN/WWF/UNEP, 1991). In these equations is the quantity of biomass of species i extracted (in kg fresh weight or units), the reproduction (regeneration) time of species i (yr), the recovery time for species density or primary production in ecosystem i (yr), the species density within ecosystem i (number of species per standard unit of area), the area of ecosystem i disrupted and the net primary production of that ecosystem


According to Heijungs et al. (1997) the method of Heijungs et al. (1992), based on reserves and deaccumulation rates (= method group 2) still appears to the most practicable, while the method of Sas et al. (1997; method group 3) is the most promising, although not yet operational. However, the last two formulae proposed by Sas et al. (1997) belong more to the impact category ‘Impacts of land use’, subcategory ‘Loss of biodiversity and life support functions’ employed in this Guide (Section 4.3.3.2). They are very similar to the formulae developed for land use by Lindeijer et al. (1998). The first formula of Sas et al. (1997) in fact represents a method based on reserves, boiling down to assessment based on the inverse of maximum regeneration, viz


(1998). The first formula of Sas et al. (1997) in fact represents a method based on reserves, boiling down to assessment based on the inverse of maximum regeneration, viz. divided by regeneration,, the latter in An entirely different approach would be to collect four types of inventory data: timber with a Forest Stewardship Council (FSC) certificate (in kg); timber without an FSC certificate (in kg); fish with a certificate for marine stewardship (in kg); and fish without a certificate for marine stewardship (in kg). These four groups could than be assessed and aggregated into a single category indicator providing an overall indication of the sustainability of harvest. Concluding, once again a choice of ‘best available procedure’ depends on the definitions adopted. In the case of biotic depletion there are, broadly speaking, two groups of definitions1: A. definitions focusing on the decrease of the resource itself as the main problem – method group 2 B


In the case of biotic depletion there are, broadly speaking, two groups of definitions1: A. definitions focusing on the decrease of the resource itself as the main problem – method group 2 B. definitions focusing on the environmental impacts of resource extraction processes –method groups 1 and 3. Depending on the definition adopted, this impact category has natural resources (option A) or natural resources, human health and the natural and man-made environment (option B) as areas of protection. Option B would mean that all relevant impacts are accounted for under other impact categories (land use, for example), leaving no separate impact category ‘biotic depletion’. We therefore focus on the decrease 1 The other two types of definitions discussed above for abiotic depletion are not considered for biotic depletion. Part 3: Scientific background


of the resource itself as the main problem (option A). Assessment based on reserves and/or current deaccumulation rates appears to be the best available method for this problem definition (method group 2). This method relates resource extraction directly to current reserves and/or depletion rate. There still remain several debatable issues, however: Which reserves are to be considered: only those ‘in the wild’ or also reserves in zoos? Should the focus be solely on reserves or also on deaccumulation rate, i.e. including regeneration? Because biotic resources generally behave more like funds than deposits, the regeneration part of the deaccumulation rate is here a particularly important topic to consider. Should we take into account the economic value or intrinsic value of the resource? The first formula of Sas et al. (1997) does not include deaccumulation rate but only maximum annual regeneration


Should we take into account the economic value or intrinsic value of the resource? The first formula of Sas et al. (1997) does not include deaccumulation rate but only maximum annual regeneration. In the view of Guinée & Heijungs (1995) the following method is the best within this group, because it considers both reserves and rates of deaccumulation: Biotic Depletion Potential of resource i, (generally dimensionless); quantity of resource i extracted (kg or number) reserve of resource i (kg or number) deaccumulation rate of resource i or number. reserve of African elephants, the reference resource (kg or number) deaccumulation rate of or number The indicator result is expressed in numbers of the reference resource, e.g. the African elephant. PROSPECTS The subject of biotic resource depletion is still being widely debated and there is sure to be additional research on developing indicators for this impact category


the African elephant. PROSPECTS The subject of biotic resource depletion is still being widely debated and there is sure to be additional research on developing indicators for this impact category. CONCLUSIONS Neither of the aforementioned methods has yet been operationalised for more than a handful of species. Guinée (1995) has developed BDPs for a few biotic resources, using the African Elephant as a reference resource. Sas et al. (1997) have developed factors for timber and fish only (selection criterion 12, Table 4.3.1). No baseline method can therefore be recommended for this impact category. For extended LCAs in which biotic depletion is anticipated to play a significant role, it may be useful to calculate characterisation factors according to the method of Guinée (1995). This Guide recommends no particular methods for sensitivity analysis


This Guide recommends no particular methods for sensitivity analysis. Recommendations for extended LCAs: In extended LCAs in which biotic depletion is likely to be relatively prominent, characterisation factors may be calculated according to Guinée (1995). A category indicator based on the certification of timber and fish might be developed. with: Part 3 : Scientific background


method status baseline alternative additional variant characterisation method/actor based on reserves and deaccumulation rate reference Guinée, 1995 RESEARCH RECOMMENDATIONS Short-term research: BDPs based on reserves and/or deaccumulation rates should be developed. To develop these factors, research should be undertaken to establish the most recent values for reserves, extraction rates and regeneration rates. 4.3.3 Impacts of land use The category ‘Impacts of land use’ covers a range of consequences of human land use. It is a relatively new topic in LCIA and still being debated and developed. In the cited SETAC-Europe WIA–1 report (Udo de Haes,1996) a distinction was made between use of land with impacts on the resource aspect and use of land with impacts on biodiversity, life support functions, etc. On the intervention side a distinction is often made between land occupation1 (i.e. occupancy and use) and land transformation (i.e. changing its quality )


On the intervention side a distinction is often made between land occupation1 (i.e. occupancy and use) and land transformation (i.e. changing its quality ). On the impact side Guinée & Heijungs (1997) distinguish between competition (reducing the total stock of available land regardless of its quality) and depletion (exhausting the total reserve of a specific class of land quality). Occupation is then associated with competition, and transformation with depletion. These different aspects of intervention and impact have not yet been integrated into a single conceptual scheme for land use for use in Impact Assessment. This topic is presently being addressed by SETAC WIA–2, in the subgroup on land use impacts (Lindeijer, 2000). We first describe the conceptual framework now under discussion in SETAC WIA–2. Given its developmental status, there are still several key areas of debate, some of which will be addressed towards the end of this section


Given its developmental status, there are still several key areas of debate, some of which will be addressed towards the end of this section. Despite its open-ended nature and potential shortcomings, the conceptual scheme of SETAC WIA–2 has here been adopted for the principal reason that the majority of experts working in the field of land use impacts participate in this forum. The operational methods for assessing land use impacts described in Sections 4.3.3.1 and 4.3.3.2 will be evaluated within this SETAC WIA–2 framework. SETAC WIA–2 framework for assessment of land use impacts On the intervention side the SETAC WIA–2 framework distinguishes two aspects of land use: occupation and transformation. Occupation versus transformation Land use is a direct physical intervention in the environment, attributable to a unit process


Occupation versus transformation Land use is a direct physical intervention in the environment, attributable to a unit process. Two aspects can be distinguished: the associated changes in the quality of the land: transformation, typically expressed in terms of biodiversity and/or life support functions; and the length of time for which the land is used: occupation. 1 The term ‘occupation’ is intended solely in the neutral sense of ‘occupancy’, with no political implications, to mean human use of a particular area of land for a given duration. Part 3: Scientific background


Figure 4.3.3.1 illustrates these two aspects of land use. Transformation Land transformation is the process of changing aspects of biodiversity and life support functions, e.g. the flora, fauna, soil or soil surface from its initial state to an altered state. The altered state (level B in Figure 4.3.3.1, which may represent a state of lower or higher quality than the initial level A) may be temporary. After the termination of human activity (at the flora, fauna, soil or soil may undergo a certain degree of recovery (with or without human intervention), eventually attaining a new steady state: level C in Figure 4.3.3.1, which may represent a state of lower, higher or identical quality than or to the original level A1. The difference between level A and level C is the net impact of transformation. The net transformation impact represents (the effects of) the permanent or irreversible changes in the quality of an area of land


The net transformation impact represents (the effects of) the permanent or irreversible changes in the quality of an area of land. The transformation impact is expressed in units of quality*area The unit of this aspect of the intervention is thus square metres only. Occupation Occupation refers to the time period during which the land is unavailable for other uses, i.e. the duration of the change of quality, how long the altered state is maintained. It thus includes both the duration of human land use and also the time taken for a new steady state to be reached, i.e. the recovery time2. In Figure 4.3.3.1 the occupation impact is shown as a shaded area between the curve and the final level C against which the change is measured. The occupation impact represents (the effects of) the temporary changes in the quality of an area of land. The occupation impact can be expressed in units of quality*area*time


The occupation impact represents (the effects of) the temporary changes in the quality of an area of land. The occupation impact can be expressed in units of quality*area*time. This aspect of the intervention can be expressed in terms of quality*area*time and the unit is therefore square (kilo)metres*years. Points of discussion Figure 4.3.3.1, above, does not distinguish between the interventions and impacts associated with human land use, while such a distinction is necessary for the purposes both of discussion and of actual Inventory analysis and Impact assessment. From the perspective of interventions, four aspects should be distinguished instead of two: - a change of state due to the activity (transformation) a state during the activity (occupation); the area required by the activity; the time required by the activity. 1 If there is no such recovery, B is the final state in Figure 4.3.3.1. 2 A human activity of no duration, i.e


1 If there is no such recovery, B is the final state in Figure 4.3.3.1. 2 A human activity of no duration, i.e. involving only a transformation at time t1, thus still has both transformation and occupation impacts. A human activity with a certain duration, and which changes the current state but does not affect the level of the final steady state, has no transformation impact but only an occupation impact. 1. 2. Part 3 : Scientific background


Part 3: Scientific background Observe that the time and area aspects can be expressed as a cardinal variable (100 days, whereas the (change of ) state requires nominal variables (from state A to state B). As is the case with noise, time and area aspects can be combined into a single parameter, areaxtime (e.g. The change of state can also be combined with the area from state A to state B). Thus, it is possible to describe a unit process with reference to two intervention items: change in state×area (transformation); statexarea×time (occupation). In a discussion of the inventory-related issues, ’state’ is perhaps a better concept than ’quality’. Note that the term ‘state’ is not a state in terms of state indicators (e.g. concentrations, plant density, etc.)


Note that the term ‘state’ is not a state in terms of state indicators (e.g. concentrations, plant density, etc.). In this context, the term ‘state’ stands merely for the type of land use, a nominal term with no quality aspects, deliberately omitted from the present inventory-based exposition because assessment of quality is an aspect of impact assessment. Types of land use include ‘natural forest’, ‘silvicultural plantation’, ‘pasture’, ‘arable land’, ‘roadway’ and ‘built-up area’. Choice of the final state for measuring transformation and occupation impacts Above, the transformation impact was defined as the difference between the initial and the final steady state (level C in Figure 4.3.3.1). Lindeijer (2000.) state that the (temporary) occupation impact should therefore be defined in such a way as to avoid any overlap with the irreversible transformation impact, while covering all impacts not captured in the transformation impact


According to Lindeijer (2000) this can only be done by relating the current state to the final steady state: level C in Figure 4.3.3.1, representing the final steady state to which the land would recover (with or without human aid) if occupation were to end immediately: the current recovery potential, Thus, the occupation impact is measured as the product of the duration of the change (incl. recovery time) and the current recovery potential, times the area. As the SETAC WIA–2 workgroup states, data availability may prove to be a limiting factor for using a final steady state as a reference, however. Points of discussion In Figure 4.3.3.1 the assumption that human activity is of finite duration is an essential prerequisite for modeling the impacts of land use. Both the transformation and the occupation impact are defined with reference to a final steady state, the recovery potential or relaxation potential


Both the transformation and the occupation impact are defined with reference to a final steady state, the recovery potential or relaxation potential. As stated by the SETAC WIA–2 workgroup, data availability and data uncertainty may prove to be a limiting factor for using a final steady state as a reference, however. The method assumes that, for any type of land use in any kind of initial state, a final state can be estimated to which the particular area of land will recover after termination of the activity in question. If not in principle impossible, the choice of a certain final state will at least lead to speculations and/or major uncertainties


If not in principle impossible, the choice of a certain final state will at least lead to speculations and/or major uncertainties. Allocating land transformation Attributing land transformation to functional units is problematical, as the relationship between the two is Attribution of land transformation and occupation to a functional unit: an example from SETAC WIA–2 Assume that a plot of land measuring is utilised for a period of 10 years, thereby delivering 5 functional units. For 1 functional unit the intervention associated with transformation is then and that associated with occupation The occupation can also be calculated using the annual figures. Use of for 1 year (i.e. an occupation of delivers 0.5 f.u. So for 1 functional unit the occupation is generally anything but transparent


Use of for 1 year (i.e. an occupation of delivers 0.5 f.u. So for 1 functional unit the occupation is generally anything but transparent. What will be the agricultural output from an area of cleared forest, and what proportion of the clearing is to be attributed to one kilogram of any one of these crops? How many cars and trucks will use a new rural road and what share of the associated land transformation should be allocated to a functional unit of traffic or transport? One approach to this problem is as follows In many cases an activity performed in the service of a given functional unit is not be accompanied by any significant degree of land transformation, merely occupying land, as in the case of road traffic, sustainable silviculture and agriculture


At the macro level, however, the area of land made available for that activity in a particular country may change over time, according to policy or private initiative, and there will then be a quantitative increase or decrease in the man-made resource ‘land type X’. The transformation associated with this change in land use can then be attributed to a trend (rise or decline) in the economic output of ‘land type X’ over a particular number of years. In the Netherlands, for example, over a particular 10-year period of agricultural land was


Part 3 : Scientific background converted into (9500 km of) roadway, while in the same period domestic road transport grew by car-kilometres and tonne-kilometres. In such cases where reliable statistics are available, land transformation can thus be allocated to a functional unit of traffic/transport in the same way as land occupation. For activities of finite duration such as mining, the activities associated with both transformation and occupation can be more readily attributed to a functional unit, as the aggregate output over this period is generally known or at least estimable. An increase in a certain land type area can also be seen as a core process to be analysed (building a road on agricultural land, converting tropical forest into a silvicultural plantation, or either of the latter into agricultural land). This process includes land transformation as one of the main impacts and transportation capacity as the main economic outflow


This process includes land transformation as one of the main impacts and transportation capacity as the main economic outflow. Whether this local transformation is part of a generic trend may not be immediately obvious, but regarding it as such may be the only way to solve the allocation problem within the framework of LCA, especially when the future economic output of the activity in question cannot be established with any certainty. The other approach to the problem of local land transformation is to abandon the life cycle perspective altogether and mark land transformation as a major environmental aspect that is simply not compatible with the principles of LCA. This approach, which implies ‘forgetting’ that the service-delivering activity (involving transformation, e.g. the forest or the road) is not only produced but also used for an indefinite amount of time, may be adequate for an environmental impact assessment, EIA, of the activity (rather than an LCA)


the forest or the road) is not only produced but also used for an indefinite amount of time, may be adequate for an environmental impact assessment, EIA, of the activity (rather than an LCA). From a life cycle perspective, however, attributing all local transformation to that particular activity is an overestimate as it ignores the temporal aspect of the ‘use phase’. Points of discussion As mentioned earlier, an essential assumption of the model used for assessing the impact of land use (Figure 4.3.3.1) is that the human activity in question is of finite duration, implying that the duration of each activity can be estimated beforehand. If not in principle impossible, this will at least lead to speculations and/or major uncertainties. In the example of allocating land transformation to vehiclekilometres: on relatively quiet roads, too, traffic volume has risen over the past 10 years, so national trends are not a good measure


In the example of allocating land transformation to vehiclekilometres: on relatively quiet roads, too, traffic volume has risen over the past 10 years, so national trends are not a good measure. Particular roads may even have been closed, although total vehiclekilometres have risen. Allocating land occupation Land occupation must also be allocated to the functional unit. This is probably less problematical then in the case of land transformation, however, as annual occupation and annual economic output are generally both known quantities. Thus, occupation can readily be attributed to economic output. Reversibility of land transformation A related problem with respect to land transformation is its reversibility. In the above approach to allocating transformation to land-occupying activities, the past trend is extrapolated, yielding either a net positive or net negative transformation


In the above approach to allocating transformation to land-occupying activities, the past trend is extrapolated, yielding either a net positive or net negative transformation. Future natural recovery and/or human rehabilitation efforts are thereby incorporated only in as far as proven in the past and then converted to an average trend. If, on the other hand, the planned level of rehabilitation is also known in addition to the future output (mainly the case for modern mining activities), both can be taken into account in determining the net transformation due to the activity. This net transformation may again be either positive or negative. Thus, following the principle of distinguishing net transformation from occupation, we use the term ‘net transformations’ to refer to changes in land use perceived as ‘not reversed’ or, more loosely, ‘not compensated’. This is not the same as ‘irreversible’ or ‘impossible to compensate’


This is not the same as ‘irreversible’ or ‘impossible to compensate’. Points of discussion Just as metals extraction may be ‘compensated’ by recovery from waste, it is to be queried whether theoretical recovery processes should be taken into account, such as returning land to a near-to-natural state (as is done in the ETH database developed by Frischknecht et al., 1993/1995/19961). After all, if one does so in the inventory, why not also for other types of interventions (resource extractions, emissions)? If this course is not adopted, it is better to treat ‘irreversible’ transformations merely as 1 At present the ETH database is virtually the only LCA data source for land use data. It is based on estimates of the time involved for all transformations and occupations, and includes both occupation and (often theoretical) recovery times. In 1994, however, no distinction was made between the concept of net transformation and that of occupation


In 1994, however, no distinction was made between the concept of net transformation and that of occupation. In practice, only the occupation related to a certain type of land use or occurring during a transformation process is given (in and a full and instantaneous recovery to the original state is implied. Reliable net transformation data for LCA purposes is thus still lacking.


Part 3: Scientific background factually unreversed transformations, also to avoid complex discussions as to when a transformation is truly irreversible. Estimating recovery times to arrive at fully reversed transformations, as in the ETH database (Frischknecht et al., 1993/1995/1996) is therefore not recommended. It is better to estimate and account for net transformations as interventions separate from land occupation. Impacts of occupation One direct impact of land occupation for a particular use is to limit or even curtail the scope for other people to use that land for the period of use. Land thus becomes scarcer, leading to greater human competition for land resources. Impacts of land occupation will be discussed as a separate subcategory in Section 4.3.3.1. Points of discussion There is, hence, an analogy with abiotic resources (see Section 4.3.1). This analogy can be made more vivid by recalling the distinction between flows and deposits


Points of discussion There is, hence, an analogy with abiotic resources (see Section 4.3.1). This analogy can be made more vivid by recalling the distinction between flows and deposits. Occupation may be seen as affecting land as a flow; transformation as affecting land as a deposit. For most abiotic resources, in addition to competition there will also be depletion. However, in the case of flows such as land (rather than deposits and funds) competition does not concern the future availability of a natural resource (which is depletion), but to the present scarcity of the stock as experienced by man1 (see also Section 4.3.1). Another direct impact of land occupation may be the imposition of certain characteristics on the area of land in question, at least for the duration of occupation. Depending on the reference situation to which they are compared, implicitly or explicitly, these characteristics may be perceived as positive or negative


Depending on the reference situation to which they are compared, implicitly or explicitly, these characteristics may be perceived as positive or negative. Set off against ‘average contemporary European land quality’, organic agriculture is to be deemed positive, for example. From a perspective of more afforestation being better for the ‘naturalness’ of the European land mass or permitting greater biodiversity, though, it may be judged rather less positively. But if ‘naturalness’ is taken as a reference point, virtually all the land used for human activity must be deemed of ‘inferior’ quality. What is to be understood by the term ‘occupation impact’ is thus highly dependent on the defined reference situation. This issue is discussed in more detail below, in Section 4.3.3.2


What is to be understood by the term ‘occupation impact’ is thus highly dependent on the defined reference situation. This issue is discussed in more detail below, in Section 4.3.3.2. Certain forms of land occupation may, furthermore, have an indirect impact by perpetuating the stress on the natural environment associated with previous habitat destruction and fragmentation in the area or region around the land in question. In stressed areas (with high population densities) this may lead to continued loss of biodiversity (see Müller-Wenk, 1998, p.18). In this sense, occupation of land in one part of an ecosystem may render another part more vulnerable. The indirect impacts of occupation on life support functions are less evident. Ensuing changes in the ‘albedo effect’ (the capacity of the earth’s surface to reflect solar radiation) or in evaporation rates may significantly affect climate regulation, but as yet no assessment methods have been developed for such impacts


Impacts of transformation The primary impact of net transformation is a reduction of land area with a high nature quality (for instance in terms of biodiversity and life support functions2; see the discussion under these subcategories in Section 4.3.3.2 for a closer examination of these issues), with replacement by land of lower quality (or the reverse). Here, too, this quality depends on the desired reference quality, although one might also assess differences before and after the transformation, ignoring the reference situation. This replacement implies direct habitat destruction and attendant reduction of life support functions and biodiversity in the surrounding area, too. In terms of areas of protection, then, we are concerned explicitly with the natural environment. Large biodiversity losses in Europe are said to be caused by transformations (see e.g. RIVM, 1992)


In terms of areas of protection, then, we are concerned explicitly with the natural environment. Large biodiversity losses in Europe are said to be caused by transformations (see e.g. RIVM, 1992). Summary Concluding, land occupation leads to an increase in land competition and to institution of a certain (generally low) quality for a particular period of time due to an economic process/activity and depending on the reference situation of the interpreter and selected indirect impacts. Land transformation changes the quality of the land itself as well as that of the surrounding area or region. Data relating to land transformation are of very poor quality and due attention should be paid to whether and how reversibility is accounted for. 1 As this scarcity is relevant solely to man, it might even be said that land competition impacts only on human welfare, not on the natural environment


1 As this scarcity is relevant solely to man, it might even be said that land competition impacts only on human welfare, not on the natural environment. 2 Besides increase of land competition Lindeijer (2000) lists the following subcategories: degradation of biodiversity, degradation of life support functions and degradation of cultural values. The last of these is not discussed in any detail for lack of methods.


Part 3 : Scientific background The difference between land transformation and occupation shows certain parallells with that between depletion and competetive use, as discussed under abiotic depletion (Section 4.3.1). The impact of competition for land is an impact on natural resources and, perhaps, man-made environment. The impact of decreasing quality of land can have meaning for the natural environment (ecosystems) and man-made environment. Biodiversity and life support functions are well known indicators for these endpoints. Therefore three subcategories are discerned: increase of land competition; loss of biodiversity; loss of life support functions Impact assessment approaches for these three subcategories are discussed in the following two subsections: Section 4.3.3.1 for land competition and Section 4.3.3.2 for loss of biodiversity and loss of life support function


General discussion of the SET AC WIA–2 framework In the above description some main discussion points on the SETAC framework for land use impacts were spotted. In these discussion points some suggestions were made, which are summarised below. In the example given below much attention is given to a stricter separation of the intervention aspects and impact aspects of land use. Description of a unit process in relation to land use Imagine an area of land, described as being in a state A (e.g. ‘forest’). At time t1 a human activity (e.g. crop cultivation) is initiated on the land, leading to a new state B. At time t2 the activity ends and a new state C emerges (e.g. grassland). We assume, provisionally, that the human activity starts and ends abruptly, and that no preparatory or clearing (up) activities are required before the human activity starts or after it ends. See Figure 4.3.3.2


See Figure 4.3.3.2. How might one describe human activity as a unit process in the Inventory analysis? Let us consider some concrete hypothetical data: a plot of land with an area of 100 square metres producing 1000 kg of crops a year, on which 1 kg/y of pesticides is used. Dividing out the year dimension, we could then attribute the following data items to this unit process: input of 1 kg pesticides; output of 1000 kg crops; occupation of land in state B; transformation of land from state A to state C.


Part 3: Scientific background Note that we consistently speak of state A rather than quality A, because the quality aspect is to be assessed in Impact assessment only. Lindeijer (2000) provides a similar picture, except that it is geared towards Impact assessment. Discussion in terms of state rather than quality implies that the cardinally defined quality scale in Lindeijer et al. has been replaced here by a nominally defined state scale, in which the order of and the difference between states makes no sense, and in which an integral is also undefined. Another difference is that the relaxation period has been added, while it has been postponed here


Another difference is that the relaxation period has been added, while it has been postponed here. One final difference is that the finite duration of the human activity is essential in the discussion by Lindeijer et al., while, as is shown above, an accounting of the yearly production and the land use aspects leads to sensible expressions for both the occupation of land (of state B) and the transformation of land (from state A to state C) regardless of whether the activity is of finite or infinite duration. Attribution of land use to a functional unit In LCA the interventions associated with the unit processes need to be attributed to a functional unit. Suppose that the functional unit here is related to 1 kg crop. The environmental interventions per kg crop are then: occupation of land in state B; transformation of land from state A to state C


Suppose that the functional unit here is related to 1 kg crop. The environmental interventions per kg crop are then: occupation of land in state B; transformation of land from state A to state C. This procedure can be repeated for all unit processes in the product life cycle and will lead to many types of land use, of the occupation type (state A, state B, ..., state Z, in and of the transformation type (state A to B, state B to A, ..., state Z to Y, in Interventions of identical types can then be added over the life cycle, e.g. all occupations of land of state A can be added and all transformations of land from state A to state B can be added. Definition of impact categories In the literature three different impact categories recur, relating to competition for land as a resource and to impacts on life support functions and biodiversity. Here, these will be referred to by the following names: competition; loss of life support functions; loss of biodiversity


Here, these will be referred to by the following names: competition; loss of life support functions; loss of biodiversity. ‘Competition’ is concerned with land as a scarce resource. In contrast to most types of resource, this is a resource of the flow type: every year a certain amount of of land is available, and every activity that occupies land means there is less left for other activities. Therefore, competition is related to occupation. The other impact categories are related to transformation, as it is changes in land quality that may have implications for life support functions and for biodiversity. The link between interventions and impact categories for land use Note that in the SET AC framework this exclusive link between the type of intervention and the type of impact category is not made


The link between interventions and impact categories for land use Note that in the SET AC framework this exclusive link between the type of intervention and the type of impact category is not made. In the suggestion presented above the intervention type ‘occupation’ is linked to the impact category ‘competition’ and the intervention type ‘transformation’ is linked to the impact category ‘depletion in terms of loss of biodiversity and/or loss of life support functions’. In the SETAC framework and in the operational methods, which will be described in Section 4.3.3.2, both the ‘transformation’ and ‘occupation’ type of intervention are linked to ‘depletion’ (loss of biodiversity and loss of life support functions) (see Table 4.3.3.1). In the SETAC framework the ‘occupation type of intervention’ can be characterised as ‘loss of biodiversity’ because the framework provides a model in which the quality (e.g


In the SETAC framework the ‘occupation type of intervention’ can be characterised as ‘loss of biodiversity’ because the framework provides a model in which the quality (e.g. in terms of biodiversity) during occupation is related to a recovery potential, i.e. the quality (biodiversity) at the final steady state if the occupation were to end immediately and the land fully recover (with or without human aid), i.e. a change from to In the suggestion made above the state during occupation is not compared to a reference state in terms of biodiversity or life support functions but is compared to the total area available in the model in order to derive characterisation factors for competition. Table 4.3.3.1 provides an overview of the methods that have been proposed for linking interventions to impact categories in the context of land use. The details of these methods are discussed in Sections 4.3.3.1 and 4.3.3.2.


Part 3 : Scientific background Table 4.3.3.1: Overview of the relationship between land use related intervention types and impact categories, with the panoply of available methods. Observe that, given the proposed exclusive link between occupation and competition on the one hand and between transformation and depletion on the other, the cell at the juncture of occupation and depletion appears to be spurious. Intervention type Impact category Depletion (loss of biodiversity, loss of life support functions) competition occupation SETACWIA–2 Lindeijer, 1998 Köllner, 2000 Proposal in this guide (SETAC WIA–2) transformation Proposal in this guide SETAC WIA–2 Lindeijer, 1998 Köllner, 2000 Conversion of interventions into impact category results For almost all impact categories, the concept of characterisation factors makes sense


In an abstract sense, the structure of the formulae for calculating impact category results is as follows: Let us then apply this formula to the impact categories: competition; loss of life support functions; and loss of biodiversity. There are two categories of intervention, with several types within each category: occupation, of state A, state B, etc.; transformation, from state A to state B, from state B to state A, from state A to state C, etc. Since occupation contributes only to competition and transformation only to decrease of life support functions and decrease of biodiversity, characterisation factors must be defined for linking several types of occupation to competition and for linking several types of transformation to decrease of life support functions and decrease of biodiversity. In the following sections, some specific proposals for deriving these factors are discussed


In the following sections, some specific proposals for deriving these factors are discussed. Consistency with other impact categories The ISO standards stress the need for consistency among the various elements of LCA. One of the issues of concern is therefore the consistency between the classification methods adopted for the various impact categories. Including fate modeling in toxicity models but not in models of acidification is inconsistent, for example, and might bias results. This does not imply that fate should be omitted from the toxicity model but that it should, in principle at least, also be developed for acidification and that due care should be taken when employing the models as they currently stand. The category ‘impacts of land use’ differs in many fundamental respects from other impact categories. For instance, while the pathway of chemical emissions can be described in fate models, there is no such thing as a ‘pathway’ of land occupation or transformation


For instance, while the pathway of chemical emissions can be described in fate models, there is no such thing as a ‘pathway’ of land occupation or transformation. In certain respects, there are analogies, however. In change-oriented LCA, the starting point is a permanent marginal change of demand, with marginal effects on production and emission characteristics. In modeling the resultant impacts steady-state assumptions are then made. In discussing land use, one might now proceed from a permanent marginal change in land use required for extra production. Then, there is no t2 at which the land use stops, and there is no final state. This means that the concept of transformation is perhaps less suitable in the context of permanent marginal changes. It is clear that a consistent point-wise comparison of the models for land use and other impact categories might lead to new insights and recommendations for both groups of models. This requires a major research project, however.


Part 3: Scientific background 4.3.3.1 Land competition TOPIC This subcategory of land use impacts is concerned with the loss of land as a resource, in the sense of being temporarily unavailable. The areas of protection are natural resources and the man-made environment. ‘Loss of biodiversity’ and ‘loss of life support functions’, the other two subcategories, are treated in the next section (Section 4.3.3.2). DEVELOPMENTS IN THE LAST DECADE It was not yet recognised by Heijungs et al. (1992) that all changes land use are in fact transformations from one quality to another followed by an occupation. Their formula (see textbox) can now be seen as consisting of two parts, one describing transformation (in transformed to built or degraded systems), the other occupation (in of land occupied as built or degraded systems). Only the latter would now be regarded as leading to an increase of land competition, as well as possibly to impacts on biodiversity in already stressed areas


Only the latter would now be regarded as leading to an increase of land competition, as well as possibly to impacts on biodiversity in already stressed areas. Transformation has an impact on both biodiversity and life support functions. Since 1992 there has been a lot of work on the topic of land use. Most has been concerned with Heijungs et al. (1992) In Heijungs etal. (1992) no distinction was made between occupation and transformation impacts, norwas the concept of competition then recognised. Land use impacts were therefore expressed in terms of of land transformed from one land quality category to another. They recommended a provisional solution for characterising land use. Five types of ecosystems were defined: I) natural systems, II) modified systems, III) cultivated systems, IV) built systems and V) degraded systems. This was based on the Hemerobie-stufen concept proposed by IUCN in 1991. The land use qualities were defined according to an approximate notion of ‘naturalness’


This was based on the Hemerobie-stufen concept proposed by IUCN in 1991. The land use qualities were defined according to an approximate notion of ‘naturalness’. The five types of ecosystems were then aggregated into two main categories: "natural" (type I, II and III) and "non-natural" (type IV and V). This fairly arbitrary solution was considered no more than temporary. The provisional assessment was then expressed as: Thus all changes from I, II and III to IV and V were weighted with a factor 1, all other changes with 0. The total indicator result was expressed in R follows from the inventory and represents the quantity in each category. developing transformation and occupation indicators for various types of land use (in terms of ‘naturalness’), including indicators for loss of biodiversity and life support functions (see, for example, Knoepfel, 1995; Blonk & Lindeijer, 1995; Heijungs et al., 1997 and Lindeijer et al., 1998)


However, since the latter are treated here as separate subcategories, they will not be discussed here. With regard to the topic of land competition, developments have been sparse. Simple aggregation of land occupation, in or is obviously the least sophisticated approach. When the focus is solely on competition for land as a ‘resource’, it would be preferable to use the same method as for abiotic and biotic depletion. This would imply use of the deaccumulation and reserves formulae or quality reduction relationships proposed for depletion of mineral resources. This approach would also enable a distinction to be made between different land qualities from the perspective of competition (thus quality is here meant ‘for human use’, in contrast to ecosystem quality); we would have to assess how large the resource of each land quality type is, and how fast this is decreasing or increasing1


There is one problem with this approach, however: there is as yet no agreement on how the ‘total land resource’ is to be defined. Among the options under discussion are: the total area of land potentially available to man, in the total area of land currently used by man, in the total area of land potentially available to man, in (first option multiplied by the time the land will be available to man). Of these proposals the last is most in line with the definition for abiotic resources but it is hard to operationalise, given the likely difficulty of reaching agreement on the period deemed relevant (how long will humans populate the earth?). PROSPECTS No specific developments are foreseen in this area. 1 A priority indicator for this quality may be land fertility.


Part 3 : Scientific background CONCLUSIONS Given the lack of agreement on a more sophisticated indicator for land competition, we recommend simple aggregation of the area of land used (in is the land use of state (or quality) s attributable to the functional unit, expressed in The total indicator result is expressed in This method is evaluated against the (ISO-based) criteria in Table 4.3.3.1.1. Table 4.3.3.1.1: Evaluation of the baseline characterisation method for land competition, characterisation factor = 1 , with respect to the (ISO-based) criteria of Table 4.3.1 . criteria 1. scientifically and technically valid 2. environmentally relevant 3. internationally accepted 4. value-choices and assumptions 5. focal point in environmental mechanism 6. linearity 7. time span 8. fate, exposure/intake and effects 9. less is better 10. time- and location-independent 11. operational 12


focal point in environmental mechanism 6. linearity 7. time span 8. fate, exposure/intake and effects 9. less is better 10. time- and location-independent 11. operational 12. uncertainty margins evaluation not relevant category indicator is close to intervention no no close to intervention yes not relevant not relevant yes yes yes none No methods are included in the Guide as options for sensitivity analyses. No additional methods are recommended for extended LCAs. characterisation method baseline alternative additional variant characterisation model unweighted aggregation – – – reference Heijungs et al., 1992 – – – RESEARCH RECOMMENDATIONS Long-term research: It might be useful to develop a method to account for competitive use of flows (and funds) and investigate the potential for aggregating flows, deposits and funds, including the impacts of land occupation on land competition


Research is recommend to examine how the difference between competitive use and depletion might be accounted for in LCA and how land competition might be included in the category ‘abiotic depletion’ (see also Section 4.3.1). It might be interesting to try to incorporate the differences in the quality of the land that is occupied. However, this would require a definition of the ‘reserves’ of each land use type, at least if formulae similar to those discussed under abiotic depletion are used. Besides the problem of defining the reserves in or other problems are to be resolved, for instance, relating to the effects of national land use policies in determining the degree of competition. 4.3.3.2 Loss of biodiversity and loss of life support functions TOPIC These two subcategories are discussed here together in one section because this is generally the case in the literature and it is difficult to separate the strands of the associated discussions


The topic 1 Note that the composite notation A×t is sometimes used in the literature instead of U. This suggests, however, that the area A and time t attributable to a functional unit are known separately, while in fact only the composite quantity is known.


Part 3: Scientific background encompasses the entire range of impacts on biodiversity and life support functions of physical interventions due to a particular form of land use, involving destruction or alteration of land for economic purposes, for example, or the harvesting of biotic resources, probably in areas already under biodiversity stress. These two subcategories have the natural environment and, indirectly, natural resources as areas of protection. DEVELOPMENTS IN THE LAST DECADE Methods for dealing with loss of biodiversity and life support functions as a result of land use or use of Heijungs et al. (1992) These impact categories were not included in Heijungs et al. (1992). biotic resources in LCA have been put forward in many recent publications, including Knoepfel (1995), Lindfors et al. (1995c), Blonk & Lindeijer (1995), Sas et al. (1996), Heijungs et al. (1997), Lindeijer et al. (1998) and Köllner (2000)


(1995c), Blonk & Lindeijer (1995), Sas et al. (1996), Heijungs et al. (1997), Lindeijer et al. (1998) and Köllner (2000). The methods proposed by the various authors fall broadly into three groups, as described below (mainly from Heijungs et al., 1997 and Lindeijer et al., 1998). 1. 2. 3. Ecosystem classes Classes of ecosystem are distinguished and transformations from one class to another and the occupation of each class are collected in the inventory. The classes are then weighted. Knoepfel et al. (1995) and Lindeijer et al. (1998) present overviews of such classifications, including weighting methods (based on panel preference, biodiversity or other properties of the land). Beetstra (1998) has added a monetary weighting method to the classes proposed by Knoepfel et al. (1995). Biodiversity and life support function indicators Examples are the last two formulae of Sas et al. (1996), described in Section 4.3.2 on biotic resources. Lindeijer et al


(1995). Biodiversity and life support function indicators Examples are the last two formulae of Sas et al. (1996), described in Section 4.3.2 on biotic resources. Lindeijer et al. (1998) propose a similar approach in which the (change in) free Net Primary Production (fNPP; i.e. the Net Primary Production minus the Net Primary Production for human use, where Net Primary Production is defined as the net increase in the dry weight weight of plants) or species density is compared with a reference situation. Köllner (2000) has elaborated the basic biodiversity indicator to include impacts on the region surrounding the assessed area and a marginal assessment of indicator results. Functional aspects Baitz (1998) has developed a method in which several characterisation factors are calculated, for the quality of an area before, during and after an intervention. These are based on the capacity of the land to fulfil ecosystem and human life support functions and are expressed in physical terms


These are based on the capacity of the land to fulfil ecosystem and human life support functions and are expressed in physical terms. The method has been elaborated for a single caseat a specific location. In this method a set of eight characterisation factors are proposed for land use. These can be calculated for concrete local situations and will in the future be able to be estimated for generic processes using GIS systems and other readily available data. Such generic information is not presently available, however, nor is a set of local data suitable for extrapolating to the generic level. Schweinle (1998) has developed a similar approach for the forestry sector, but omits the area parameter in his Impact assessment formulae. It is not clear what the relationship is between these three groups and the aforementioned three types of indicators for biodiversity


It is not clear what the relationship is between these three groups and the aforementioned three types of indicators for biodiversity. It can be expected that the methods designed to describe loss of biodiversity and life support functions (group 2) are presently the best available practical methods for these impact categories. Some of the indicators using classes (group 1) are also based on biodiversity or life support function parameters (e.g. species density and NPP). However, a disadvantage of any method based on classes is that it can only describe rough categories of land use. Such methods do not permit detailed description of changes in biodiversity or life support functions, nor do they allow for regionalisation. The methods based on functional aspects (group 3) are not aimed specifically at biodiversity or ecosystem life support functions, but at a large number of mid-point indicators


The methods based on functional aspects (group 3) are not aimed specifically at biodiversity or ecosystem life support functions, but at a large number of mid-point indicators. Moreover, there is currently insufficient data available for this method to be operationalised. Concluding, we shall focus on the methods considering loss of biodiversity and life support functions (group 2), these being the most appropriate for present characterisation of loss of biodiversity and life support functions. Of quite a different nature is the approach to loss of biodiversity adopted by Anonymous (1997b; 2000a; 2000b). Two pressure indicators are defined: one for the environmental pressure on the area of interest due to the present activity and one for general pressure on the region. It is a nominal approach having a


Part 3 : Scientific background large overlap with other impact categories such as ecotoxicity and eutrophication. Moreover, it assumes a fairly detailed knowledge of local aspects and requires data on the previous history of land conversion. Last, the concept of characterisation factors does not appear to be applicable. All these features lead us to conclude that it may be an interesting approach for certification but is not presently suitable for application in LCA. Finally, the connection with other types of impact such as biotic depletion, desiccation and ecotoxicity should be mentioned. These impact categories are discussed in separate sections (see Sections 4.3.2, 4.3.4 and 4.3.8), but it is clear that biodiversity and life support functions are closely related to these other impact categories. To the best of our knowledge, three methods are currently available for biodiversity and life support functions


To the best of our knowledge, three methods are currently available for biodiversity and life support functions. All three are based on the category indicators plant species density and Net Primary Production (NPP) of biomass for specific regions or ecosystems. These methods have been developed by Sas et al.(1996), Lindeijer et al. (1998) and Köllner (2000). For the Eco-lndicator 99 Goedkoop & Spriensma (1999) use a slightly adapted verison of Köllner (2000). Lindeijer et al. (1998) have reviewed all land use methods developed to date and included Köllner (2000) and others in a later review (Lindeijer, 2000). Some general aspects of biodiversity and life support functions and their indicators Before examining the methods available for life cycle impact assessment of loss of biodiversity and loss of life support functions, it is appropriate to look at these two types of loss in further detail in a more general sense, i.e


not in relation to LCA Indicators for biodiversity In the literature three aspects of biodiversity are generally distinguished: type of species (the intrinsic value of a unique genetic code); type of ecosystem (a unique combination of types of species and their interrelationships); species density (the number of different species per unit area, independent of the type of species or ecosystem). On this basis, the loss of biodiversity in a given area might, in principle, be expressed in terms of three, related category indicators: loss of species, loss of types of ecosystem and reduction of species density. These indicators require information on overall species and ecosystem diversity, present occurrence (at least biomass) and current rate of disappearance. There is a paucity of knowledge on all these elements, however, and for this reason ‘loss of biodiversity’ is frequently expressed solely in terms of reduction of species density


There is a paucity of knowledge on all these elements, however, and for this reason ‘loss of biodiversity’ is frequently expressed solely in terms of reduction of species density. However, this is a highly abstract indicator that says nothing about the types of species and types of ecosystems that are vanishing. Indicators for life support functions For life support functions, too, a variety of indicators are conceivable. That most commonly used is Net Primary Production (NPP), i.e. the net increase in the dry weight of plant matter. The assumption then made is that if current biomass production on an area A is high, then soil quality will be stable, freshwater cycling intact and rainfall adequately absorbed by the soil. However, high biomass production under the influence of man (agriculture and silviculture) may be associated with high rates of erosion and low quality freshwater run-off


However, high biomass production under the influence of man (agriculture and silviculture) may be associated with high rates of erosion and low quality freshwater run-off. Due consideration should therefore be given to the fraction of biomass remaining to support non-human life forms. This may also give an indication of the long-term capacity for life support. An alternative parameter is free Net Primary Production fNPP (also referred to as biomass appropriateness), i.e. total NPP minus the portion of NPP removed from area A as an agricultural or silvicultural product. Table 4.3.3.2.1 reviews the characteristics of the three methods, which are briefly described below.


Part 3: Scientific background Table 4.3.3.2.1: Characteristics of three operational methods to assess impacts on biodiversity and life support functions. method Lindeijer et al. Sas et al. Köllner NPP fNPP type of intervention Transf. Occ. + + + + + category indicator biodiversity plant species density species density plant species density correction factor life support functions fNPP NPP regional impacts operational for regions? world climate zones ecosystems; sea and forest Swiss lowland Net Primary Production free Net Primary Production = NPP minus NPP for human consumption recovery time to the original biodiversity or biomass, a weighting factor expressing the vulnerability of the ecosystem. Method of Lindeijer et al. The method of Lindeijer et al. (1998) operationalises one indicator for biodiversity and one for life support, and makes a distinction between occupation and transformation


The method of Lindeijer et al. (1998) operationalises one indicator for biodiversity and one for life support, and makes a distinction between occupation and transformation. The following formulae are used:1 For net ecosystem transformation, the change in the quality of an area of land following recovery from a land use intervention is compared with that prior to that intervention: For ecosystem occupation the quality during occupation (including recovery) is compared with the quality in the reference, i.e. unoccupied state: In which: A t fNPP ini fin act is the area of land used, is the occupation time2, is the free Net Primary Production3, is the number of plant4 species per calibrated to a reference measurement area5


stands for initial, the situation before the intervention, for final, the situation after the recovery period, when the intervention has stopped for actual, the situations during the intervention and 1 Note that most of the formulae that follow, from Lindeijer et al., Sas et al. and Köllner, have no summation over different types of land, ecosystems, species, etc. although such summation will in most cases be intended. Another feature is that Lindeijer et al. and Sas et al. do not explicitly introduce characterisation factors, while Köllner does so in the form of the SPEP. 2 Note that in the impact assessment of an LCA A is the attributed area of the land use activity to the functional unit and t is the attributed time period. These two quantities are often not known separately, but only in composite form


These two quantities are often not known separately, but only in composite form. 3 There is however still some uncertainty as to how fNPP is determined (especially as this measure is relatively high for tropical rainforests, which is unexpected for the Net Primary Production in an ecosystem in equilibrium). 4 The general indicator on the basis of species density is thus approximated by an indicator on the basis of plant species density. 5 This calibration is necessary because the area-species relationship is not linear. When biodiversity mapping data are used to express the reference, initial, actual or final situation all these data should be calibrated to the same standard (reference cell) on this curve. For this procedure the following formula is used: For the small changes considered in LCA linearity is assumed. In other words: the land change assessment method is only valid for non-critical changes (such as the last area available for rhino’s)


In other words: the land change assessment method is only valid for non-critical changes (such as the last area available for rhino’s). Also, only local biodiversity changes are assessed; the area around the land use change is in first instance assumed to allow for reversibility of the local impact in principle.


Part 3 : Scientific background ref for reference, the present most ‘natural’ situation in the region where the land use takes place (see also Figure 4.3.3.2.1). In Figure 4.3.3.2.1 the model for the calculation of the transformation and occupation impact according to the method on Lindeijer et al. (1998) is illustrated. In this figure also the proposed method by SETAC WIA–2 (Lindeijer, 2000) is given. Illustration for the impact category “loss of biodiversity” based on the intervention “transformation”. The category indicator for biodiversity is plant species density. Assume two interventions with the same magnitude of area (e.g. Starting from a natural situation, changing from 100 to 20 species per in a tropical rainforest is considered equal to changing from 10 to 2 species per in boreal rain forests. A change of 8 species per in tropical forest will cause less impact than a change of 8 species per in boreal forest


A change of 8 species per in tropical forest will cause less impact than a change of 8 species per in boreal forest. A same relative change as above due to industry in former intensive agricultural land (e.g. from 5 to 1 species per yields a lower impact than coming from a natural situation (i.e. 10 species per This is because for an impact due to transformation the initial situation is expressed relative to the most natural situation. Meaning that a relative change in a non natural situation, e.g. intense agriculture, is considered less problematic than a same relative change in a natural ecosystem. The method for ecosystem occupation accounts for occupation time, the method for ecosystem transformation only for net changes. The method for occupation relates the actual situation to the reference situation 1


The method for occupation relates the actual situation to the reference situation 1. The method for transformation relates the changed situation to the situation before change For biodiversity, expressed in terms of plant species density, the absolute terms are expressed as relative to the reference state to correct for spatial differentiation of plant species density between regions, due to differences in ecosystems and climate zones etc., all over the world. So changes in plant species density are expressed relative to a reference to ensure the intrinsic value of each ecosystem type The reference is defined as the highest species density currently found in the 1 Note that this definition is not in line with the proposal of the SETAC WIA–2, which has proposed to use instead of to derive the occupation impact. Illustration


Part 3: Scientific background region (generally). The region is on the level of physiotopes, large regions related to climate zones on a world level. In the approach of Lindeijer et al. aggregation of occupation and transformation is discouraged, as this would require the assumption that all land transformations are reversed within a certain (to be estimated) recovery time. The method for occupation thus focuses on the maintenance of biodiversity and biomass characteristics due to land occupation relative to a near-natural reference. Indirect impacts on biodiversity via stress on the surrounding environment is not assessed. Remarks on the reference situation Lindeijer et al. (in prep.) provide no guidelines on a choice of reference situation


Remarks on the reference situation Lindeijer et al. (in prep.) provide no guidelines on a choice of reference situation. Should this be at the level of physiotopes (tropical, boreal, etc.), continents (Europe, Asia, etc.), countries (Netherlands, Switzerland etc.) or ecosystems (heathland, meadow, forest)? Should it be the maximum biodiversity of the region, the near-natural situation in the absence of human activity or the maximum biodiversity in recent history (for Europe around 1900)? As yet, the choice of the reference situation thus seems rather arbitrary. For biodiversity, measured in terms of species density, Lindeijer (1998) has chosen to take the “maximum biodiversity of the region”, with the region defined in terms of its physiotope (tropical, desert, boreal, etc.). This is contrast to Köllner (2000), who opts for the maximum biodiversity in recent history at the national level (for Switzerland around 1900)


This is contrast to Köllner (2000), who opts for the maximum biodiversity in recent history at the national level (for Switzerland around 1900). Note that the indicator “species density” says nothing about loss of unique species or ecosystems. For example, consider an activity on heathland (an ecosystem with few species, say 5 a level artificially maintained below the level to be expected in a near-natural state in this geographical region, say 15 After discontinuation of the activity the area will return to a new steady state. In terms of species density this final state is likely to be closer to the near-natural state then the initial state of the heathland (say 10 The transformation impact for the impact category “loss of biodiversity” based on species density is therefore –1/3 (i.e. 5 minus 10, divided by 15), which is a negative value, signifying an increase in biodiversity (read species density)


5 minus 10, divided by 15), which is a negative value, signifying an increase in biodiversity (read species density). Although the activity is thus beneficial for the number of plant species, then, an ecosystem is lost (viz. heathland) and the intrinsic value of that ecosystem is therefore not respected. For the same reason, conversion of an area of desert (low species density) into a meadow (higher species density) would be considered beneficial for biodiversity (represented by plant density), although the overall impact is in fact destruction of a desert ecosystem. Lindeijer et al. (1998) have calculated characterisation factors for seven combinations of land use and region. However, the authors stress that the data on fNPP and after the intervention (=act or fin) are rough estimates based on rather scanty data. For other combinations of land use and region, characterisation factors must be developed case by case


For other combinations of land use and region, characterisation factors must be developed case by case. The and fNNP in the reference situations for different regions worldwide required for calculation of these factors are already available in the form of two maps and two tables. These maps are based on empirical data and expert judgement. Data on and fNNP resulting from land use other than the seven types mentioned (Lindeijer et al. state that these data on and fNNP after intervention are generally applicable) should be collected on a case by case basis. This may be very time consuming. On the other hand, Lindeijer et al. state that these seven types of land use are the most important and that most LCAs can be satisfactorily performed using the data (see also under data availability).


Part 3 : Scientific background Method of Sas et al. The method of Sas et al. was originally developed for biotic depletion and is described in Section 4.3.2. The formulae used there for biodiversity and life support impacts on ecosystems were (rewritten for this section): to indicate the risk on the extinction of species by destroying an ecosystem and to indicate the loss of life support functions, with: S recovery time to original biodiversity or biomass, initial species diversity for a fixed area and NPP (not subtracting the amount of biomass used by humans, as in fNPP). Remarks on Net Primary Production Not excluding human biomass consumption makes a big difference, for instance for commercial forestry. The majority of biomass in silviculture is wood which will not contribute to the carbon cycling via natural degradation; other contributions to substance cycles are either taken into account in good LCAs (for instance for or are disturbed by the forestry activities


Therefore, we consider NPP not as good an indicator for life support as fNPP. This method implicitly only assesses transformation and not occupation. The recovery time is here a weighting factor, expressing the vulnerability of the ecosystem. It is different for S and for NPP. The method of Sas et al. (1996) is only partly operational. They give some rough data on species density, NNP and regeneration time for forest ecosystems and a marine ecosystem. Method of Köllner The method of Köllner (2000) assesses only biodiversity and not life support functions. The category indicator used for biodiversity is plant species density. Besides transformation and occupation the method also incorporates a correction factor for regional impacts of the land use in the region surrounding the local area where the actual intervention takes place. The characterisation factor for the impact on biodiversity is SPEP, which stands for Species-Pool Effect Potential


The characterisation factor for the impact on biodiversity is SPEP, which stands for Species-Pool Effect Potential. Each type of land use has its own which is based on the ratio between the number of plant species found in the area occupied for a specific type of land use and the average number of plant species found in the region as a reference. The relationship between SPEP and the relative number of species for a land use type is described as a negative log-curve. The method is thus a marginal approach, determining the slope of a species-area curve for each land use type. Characterisation factor for occupation The characterisation factor for local occupation through an activity associated with a specific land use type is defined as follows: with: Species-Pool Effect Potential on the local scale for occupying a specific land use type species number on specific land use type (e.g. industrial area, intensive meadow) average species number in the region (reference)


industrial area, intensive meadow) average species number in the region (reference). The parameters a and b are set to a = 0.8 and b = 0.9 to get a SPEP curve which has the following characteristics: SPEP is 0 when about 30% of the regional species richness is present on the specific land use type. This type of land use is assumed to be a moderate condition. SPEP is 1, when about 10% of regional species richness is observed for the specific land use type. This type of land use is assumed to be a bad condition.


Part 3: Scientific background If less than 30% of the regional species are found for the land use type the SPEP is positive, indicating damage. If more than 30% of the regional species are found for the land use type SPEP turns negative, indicating benefit. The SPEP curve steeply decreases for types of land use with less than 30% of the regional species and smoothes out for types of land use with more than 30% of the regional species. The log-function arises from references stating that the relationship between species richness and ecosystem functions have this form. Characterisation factor for transformation For transformation of a specific land use type I into a land use type II a can be defined by relating of land use type I to of land use type II. Land use types I and II are nominal classes, e.g. intensive meadow, organic meadow


Land use types I and II are nominal classes, e.g. intensive meadow, organic meadow. The local effect factor for transformation is defined as: with: Species-Pool Effect Potential on the local scale for occupying a specific land use type Species-Pool Effect Potential on the local scale for transforming land use type I into land use type II species number on specific land use type (e.g. type I or II) average species number in the region (reference) Weighting factor for regional impact The local effect factor is weighted with a regional effect factor, which is the same for occupation and transformation. The reasoning behind the weighting factor proceeds from the assumption that, for example, transforming natural forests is more damaging to the region if there are only few in the region left and less damaging to the region if large areas in the region are covered with natural forests


So a relative high intensity land use in the surrounding region makes the region more vulnerable for the local interventions. As a result, greater weight should be attached to the local intervention. The regional effect factor again considers marginal changes, given as the derivative of a species-area relationship1 in an area with low land use intensity (LI), calibrated with the species number: 1 Instead of the log-function used for the species-area function in Lindeijer et al. (1998), Koellner (1999) uses the Arrhenius formula: In this formula a is a parameter for species richness and b is a parameter for species accumulation rates. These are different parameters from the a and b used in the formulae for local land use impacts. These a and b can be in the range of 10 to 270 and 0.1 to 0.5 (0.9 for continuous urban land use) respectively.


Part 3 : Scientific background with: Species-pool Effect Potential on the regional scale average species number in the region first derivative of the equation to derive LI is the proportion of low-intensity land use in the region dLI Thus, as the proportion of high-intensity land use in the region increases, LI will decrease and as the correction factor therefore increase. Relatively high-intensity land use in the surrounding region therefore renders the regional area more vulnerable


Relatively high-intensity land use in the surrounding region therefore renders the regional area more vulnerable. The occupation impact of a land use activity is now defined as: and the transformation impact of a land use activity as: with: A T potential impact of land occupation (hectare–year) potential impact of land transformation ([hectare) area occupied or transformed (hectare) duration of occupation (year) Species Pool Effect Potential for occupying a specific land-use type on the local scale Species Pool Effect Potential for transformation of land-use type I into type II on the local scale Species Pool Effect Potential for the regional scale Data were gathered on and for 16 land use types, taking the average species richness of the present Swiss Lowlands as a reference. Some land use types score positively, to be interpreted as a positive effect on (present) species richness


Some land use types score positively, to be interpreted as a positive effect on (present) species richness. The main advantage of Köllner’s method is that it appreciates the marginal nature of impacts on biodiversity. Comparison of the transformation and occupation impacts of SETAC-WIA and Köllner In Figure 4.3.3.2.3 the occupation and transformation impact according to Köllner and SETAC-WIA (Lindeijer, 2000) are compared. The quality Q represents the biodiversity indicator, viz. plant species density. In Köllner's method the SPEP of a specific land use type is 0 when about 30% of the regional species richness is present on the specific land use type. This type of land use is assumed to be a moderate condition. So implicitly for the occupation impact an actual quality due to a certain land use type (in Figure 4.3.3.2.3, land use type II) is compared to a moderate quality, defined as 30% of the average quality in the region


In this respect Köllner's method differs from that of SETAC WIA–2, which states that the impact should be based on the difference between the actual level (level B) and the level of the current recovery potential, i.e. the final steady state if the occupation were to end immediately and the land recover (with or without human aid) (level C). In other words, Köllner assumes one final steady state independent of the type of actual land use from which the area should recover and independent of the initial type of land use before the intervention took place. As shown in Figure 4.3.3.2.3 the definition of the transformation impact also differs from the proposal of Lindeijer (2000).


Part 3: Scientific background Regionalisation Potential for applying the method on a global scale is a general starting point for the selection of category indicators (see Section 1.2.2.3 and Section 1.2.3.3). However, for the assessment of the impact of land use on biodiversity and life support functions regionalisation might well be inevitable. In a global (rather than regionalised) LCA it would be necessary to employ an and fNNP for the 'average' global ecosystem. Using the maps produced by Lindeijer et al. (1998) this would already introduce a major variation: and most land use transformations would fall within this variation. In fact, the data used by Lindeijer et al. (1998) to arrive at this average already contain an uncertainty of 50–100% due to natural variability. This suggests that it may be of no value to try to assess land use impacts in the absence of regionalisation


This suggests that it may be of no value to try to assess land use impacts in the absence of regionalisation. In Köllners method regionalisation is not yet operational; at present the Swiss Lowlands constitute the sole reference, i.e. no reference values are yet available for other regions of the world. Data availability The methods of Lindeijer et al. (1998) and Sas (1996) are based on a regional LCA approach and need a regionalised inventory. The latter method only assesses transformations and requires very specific data. It is necessary to know in which ecosystem the land is used. The method of Lindeijer et al. (1998) allows for a rough but worldwide regionalisation for occupation, operationalised with maps on reference data. For the inventory, this means that it should be known in which of the four regions of Europe European production processes, say, take place; generally the mid-European region can be taken as baseline


In general, by taking into account which type of activity (process) is responsible for the land use, the actual state for occupation can be taken into account. The reference state can only be taken into account in a general way, by assessing where on earth which percentage of the activity takes place. In Lindeijer et al. (1998) this was done roughly for most mining, agriculture and silviculture activities. Köllner (2000) has published at least two articles that include characterisation factors for over ten land use types. These data have been used in the Eco-lndicator 99. However, at present the EI 99 database does not yet fully include land occupation (status March 2000). The IVAM ER database has incorporated land occupation data for all processes based on the method of Lindeijer et al. (1998)


The IVAM ER database has incorporated land occupation data for all processes based on the method of Lindeijer et al. (1998). The land occupation data has also been translated into land use types used in the Eco-lndicator 99 method, thereby allowing the IVAM ER database also to be assessed with the method of Köllner (2000).


Part 3 : Scientific background For transformation data on the initial and final states are also required. This has only been attempted in the ETH database (Frischknecht et al., 1993/1995/1996), and then in a very generic and non-transparent fashion. In fact, no meaningful transformation data can be extracted from this database. This means that no transformation data are available, other than the data for several specific cases mentioned in Lindeijer et al. (1998). Transformation data will therefore have to be gathered on a case-by-case basis. Issues to be addressed The above formulae have been derived from the concept presented by SETAC WIA–2. Note, however, that this concept is still under development and that many issues are still the subject of discussion. Some of these issues are presented below. The absolute change in plant density is expressed as relative to the reference state


Some of these issues are presented below. The absolute change in plant density is expressed as relative to the reference state. The motive is to correct for spatial differentiation of plant species density between regions, due to differences in ecosystems and climate zones, etc., all over the world. However, the question is: how detailed should this spatial differentiation be: ecosystems, nations, continents, climate zones? Another issue is what quality of the region is to be taken as a reference: the situation 100 years ago, the natural climax vegetation, ...? In practice, derivation of the initial and final quality of the area may also be rather problematical. Only the actual quality of the area will generally be known and assumptions will therefore have to be made on the situation prior to land use. Furthermore, little information is available on the quality level to which an area will recover after a certain type of land use in a certain type of region has stopped


Furthermore, little information is available on the quality level to which an area will recover after a certain type of land use in a certain type of region has stopped. In practice a comparison of the actual state with the reference rather than the final situation might well be the only feasible option. This would mean that the presently operational method of Lindeijer et al. (1998) is practically the best feasible option. Is the present indicator ‘plant species density’ a representative indicator for biodiversity? After all, by focusing solely on plant species it ignores other important elements of biodiversity, such as other (types of) species and types of ecosystems. Furthermore, it would be theoretically preferable to use, instead of only plants, a set of species picked from the whole taxonomic system with a view to better representation of species


Furthermore, it would be theoretically preferable to use, instead of only plants, a set of species picked from the whole taxonomic system with a view to better representation of species. If more indicators are chosen, how should the different indicators be weighted to one score for biodiversity? Is the present indicator ‘free Net Primary Production’ a representative indicator for life support functions? Alternative indicators have been derived based on soil properties, e.g. organic matter content and physical properties (Mattson et al., 2000; Baitz, 1998; Milài Canals et al., 2000). Again, the question is: should more indicators be used to represent the impact category ‘life support functions’? Or is there one indicator that can be used to represent the whole? There is an interaction between the biodiversity on a particular plot of land and that in the region surrounding the plot. For life support functions, too, this kind of interaction is to be anticipated