CPM LCA Database - Impact assessment method

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Characterisation Method Information

Characterisation Method Name:

Land-use impact on PDF

Version:

2000

Date Completed:

2000

Principal Method Name:

ECO-indicator: land occupation and land transformation

Method Description:

PDF: Potentially Disappeared Fraction

Description of the problem

The impact of land-cover changes on ecosystems is very significant. In most parts of Europe this influence is perhaps more significant than the effects of many other impact categories [MÜLLER-WENK 1998-2]. As we will see, land-cover changes do not only have effects on a specific local area, also the surrounding region can be affected. Furthermore we have to distinguish land occupation and land transformation. This means the damage model must be developed in four different versions.
Unlike other damage models, the data required for the land-use model is based on empirical data, such as observations of species numbers in different types of land-cover, in stead of extrapolations of laboratory data and computer models. This aspects has some important consequences for the model:
• The observed number of species is the result of many different influences, such as the concentration of toxic chemicals and the nutrient and acid level, or even the influence of increased UV patterns or climate changes. This means it is impossible to separate the effect of land-use changes from other impact categories. At the end of this chapter we will propose a method to avoid the most serious aspects of this double counting.
• There are many different land-cover types and the species number on these types can vary widely in different parts of Europe. We will use the CORINE inventory to classify land-use types [CORINE 1991]
• The data availability on field observations is a big problem in two ways:
• There is sufficient data for just a very few land-use types
• The land-use types for which data is available are not always suited for practical application in LCA
The methodology for the regional effect was originally developed by [MÜLLER-WENK 1998-2].
[KÖLLNER 1999] developed an approach and collected data to reflect the local effect of land-use. Approximately at the same time [LINDEIJER ET AL 1998] published his proposal, containing some conceptional elements as Köllner. For the Eco-indicator 99 the data and some of the theoretical concepts are taken from [KÖLLNER 1999] and [MÜLLER-WENK 1998-2].

The species area relationship

A complicating factor in land-use models is the species area relationship. The number of species increases with the area size.

The species area relationship implies that if a cornfield made in a natural area, there will be two effects:
• The number of species on the cornfield will decrease. This is referred to as the local effect; it is the effect that occurs on the area that is being used or converted.
• The number of species on the untouched natural area will be decreased, as the natural area becomes slightly smaller. This is referred to as the regional effect; it is the effect outside the area that is used or converted. Both the regional and the local effect will be modelled here.

Another consequence of the species-area relationship, is that when an existing area with cornfields is expanded at the expense of a natural area the species number on the cornfield area slightly increases, while the species number on the natural area decreases. In general the increase of species in the cornfields can be neglected, for two reasons:
• Most species that occur on artificial areas, can also occur in natural areas. The increase in artificial areas does not add anything to the species diversity in the region.
• At the present condition of the European environment the decrease of species in natural areas is much more important than the increase in non-natural areas 20
Land conversion and land occupation
There is a distinct difference between the following two cases:
1. Land that is being converted from one state to another.
2. Land that has been converted earlier and is occupied for a number of years.
It is useful to distinguish these two cases.
A typical example is the production of corn in an old agricultural area. In LCA, this activity cannot be held responsible for the fact that once the area was converted from a natural area long ago. However, each year a certain area remains occupied and can not return to its original natural stage. For this reason the damage due to land occupation is seen as the damage caused by preventing the occupied area from returning into its natural condition.
A typical example of land conversion is the mining activity in a pristine natural area. For each ton of extracted metal, a small additional area is converted from its natural conditions into a mining pit. After
the mineral has been extracted, it will take a considerable amount of restoration time before the area returns to a situation that has the same diversity as the original situation. If the mining operation occurs
in a agricultural area, the change in species numbers will be smaller, and it may take less time before the area returns to a situation that has the same diversity as the original condition.
The fact that we have impacts from two different types of processes (conversion and occupation) and impacts in two different types of areas (regional, local) to consider, results in four different versions of the damage model

The general principle for the damage model
The concept of PDF can be rather easily applied to model the regional and local damage caused by land occupation and conversion. The potentially disappeared fraction of vascular plant species is expressed as the relative difference between the number of species S on the reference conditions and the conditions created by the conversion, or maintained by the occupation.

The damage to Ecosystem Quality can be calculated when the PDF is multiplied with the appropriate area and time span. As we can see below the area size and time span is different for the four versions of the damage model.

The restoration time must be estimated depending on the type of land before and after the transformation. An important problem is that most land-use types are never restored into the exactly original condition. For instance a drained swamp will never become a swamp again. The criteria for
selecting the restoration time should not be the estimated time before the area is exactly the same as it was, but the time it takes to form a comparable quality as before. We propose the following defaults, if
no other information is available:
• For conversions from agricultural to urban areas and vice versa we choose a restoration time of 5 years
• For conversions between natural areas to urban or agricultural areas we choose a restoration time of 30 years, unless it is obvious that the restoration will take much more time.
These defaults are more or less compatible with the defaults used in the [ESU 1996] database. The main difference is that in this database the assumption is made that restoration of a natural area always
takes 100,000 years to return to it's original state. Since we do not take the original state with the original combination of species of an area as a reference, but a natural state with an equal state of biodiversity as the reference natural state, we do not think 100,000 years is appropriate in our case.
[MÜLLER WENK 1998-2] proposes to use a restoration time of 30 years as a general default.

Data for the species number per land-use type
There are many different types of land-cover, which makes it rather difficult to develop a coherent set of characterisation values. [KÖLLNER 1999] uses the [CORINE 1991] land-cover nomenclature and definitions. The Corine system describes land-cover types on different levels of detail. The data found by [KÖLLNER 1999] is not always very suited for LCA practitioners. For instance he finds data on different types of fallow land. Fallow land is not a result of economic activities modelled in LCA, therefore they are not included here.
[KÖLLNER 1999] has carefully analysed available data, mainly from [REIDL 1989] for artificial surfaces and [STREIBER 1995] for agricultural surfaces.

The data on agricultural land-use is based on observations on the fields only. The diversity of the edges, waterways and hedges in between the plots are not included. It is clear that these areas usually contain a relatively high species diversity. We assume that the real species diversity lies between a factor of 1 and 4 higher. Therefore we propose to assume that the actual species diversity (S) in agricultural areas is twice as high as is indicated in the data presented by Köllner. Of course this is a somewhat arbitrary choice, but we feel this correction factor gives a result that is more realistic than applying no correction factor at all.
Although the species area relationship is a well-established phenomenon, it is not very easy to use in LCA. In fact it is not only a very complicating factor, it also leads to fundamental allocation problems.

It is usually not possible to establish the absolute size of the area influenced by a product life cycle. For instance, if we are analysing an electric razor, we know that the razor factory occupies a certain amount of space, let us assume 1 hectare. If the factory produces 1 million razors per year, we can say the hectare is needed during one millionth of a year for a single razor. However, we could also say that a single razor needs one 1 square meter during 3.65 days. Both types of reasoning are correct.
The example shows that the species area curves have no direct meaning in LCA application, as we do not know the area without specifying a time period. There are several ways to use the species area relationship to derive values for the S (species-number) parameter:
1. Use the species accumulation factor
2. Use the species richness factor
The species richness and species accumulation factors are presented for a number of area types in the report. It is clear that the factors result in very different (and practical inverse) ranking. [LINDEIJER ET AL 1998] proposes to use the species accumulation factor. [KÖLLNER 1999] argues that the accumulation factor is not a very good indicator for the quality of ecosystems. In fact most natural
ecosystems have a relatively low accumulation factor, compared to artificial or agricultural systems. This means the species number increases rapidly when small areas are increased, but when areas become bigger the species diversity is not increasing very much anymore.
The species richness factor is an inherent property of the land cover type. Therefore we use the species richness factor to characterise the differences between the land-use types. This means the species
number S can now be replaced by the species richness factor.

For occupation the reference should be the species richness factor of the natural systems. As the term “natural system” is not very well defined, and as natural systems have very different characteristics in
different parts of Europe, there is a serious problem here.

As a temporary solution, we propose to use the species richness factor calculated by [KÖLLNER 1999] for the Swiss lowlands. Due to simple lack of data, we do not know if this is a proper choice.

The values for the species richness factors found by [KÖLLNER 1999] presents upper and lower limits of the 95% confidence interval.

The PDF values are calculated with the species richness of the Swiss lowlands as the reference values. These PDF values can thus be directly applied for occupation.
In the calculation of the uncertainty we have taken into account the uncertainty in PDF and we have assumed the correction factor could be between 1 and 4 .

Modelling regional effects

When a natural area is transformed into an industrial complex, the species area relationship dictates that the species number in the remaining natural area will decrease. At the same time there will
be a slight increase in the industrial area, as its size increases. In the introduction of the land-use model we have already stated that this increase is insignificant, and thus can be neglected. The decrease of
species in the natural area is the regional effect of land conversion.
The regional effect for land occupation can be described in a similar way. As long as the area is prevented from returning to the natural state, the species number in the natural area is kept low in comparison to the natural state. The only difference between the regional effect of occupation and
conversion is the use of different types of time periods. In this paragraph the term
conversion is used, to avoid having to write “conversion or occupation” many times.
In order to explain the regional effect we assume there are only two types of land-use, called Hi (High intensity) and Li (Low intensity) use of land, as proposed by [MÜLLER-WENK 1998-2]. Low intensity
(Li) used land can be interpreted as areas that are not under pressure from human activities. Li land does not necessarily need to be a forest. Some forests are under high pressure, while on the other hand
abandoned railroads and industrial areas can have many species. In this simplified approach the species diversity on the Hi used land is not taken into account.

An important, but unanswered question so far, is the value of the species accumulation factor b for Li areas. From the data collected by [KÖLLNER 1999] it is clear that more or less natural systems tend to
have a relatively low species accumulation factor and a high species richness factor. [KÖLLNER 1999]

To verify this choice, we have studied the work of [MÜLLER-WENK 1998-2]. In a completely different approach [MÜLLER-WENK 1998-2] also presents a calculation for the regional effect for the Swiss lowlands and for the former West Germany.

Our interpretation of the findings of [MÜLLER-WENK 1998-2] is that there is a good match between the values he finds and the value of 0.2 we have proposed.

Separating damage from land-use and emissions

The species richness indicator is based on empirical data. It is clear that changes in species richness are partially caused by emissions to soil. Especially on agricultural land the application of pesticides and fertiliser will have very important impacts on the species richness.
To avoid double counting the following rules should be observed in the inventory by applying this concept in an LCA-study:
• Fertilisers that are directly applied on agricultural soil should not be counted as an emission. The damages caused by these substances are adequately represented in the species diversity of agriculturally managed land. This means the Eco-indicator does not allow for modelling subtle changes in the application of such substances. Only larger changes, like moving from conventional
farming practices to biological practices can be modelled.
• Fertilisers that evaporate or leach out to the water should be taken into account. For evaporation an air emission of N should be included. For the emission to water no damage model is included in the Eco-indicator 99. Additional damage assessment for this emission is necessary.
• Pesticides that are directly applied onto the agricultural soil should be included as an emission to agricultural soil in an inventory. The fate and effect of this emission has been calculated in the damage model for ecotoxicity. In the ecotoxicity damage model the damage that occurs in water and areas surrounding the agricultural land is included, but the damage on the agricultural soil itself is excluded (set to 0) in the calculation to avoid double counting with land-use.
• Pesticides in the form of application losses, which are emitted directly to air or water during application should be included in the inventory respectively as emissions to air or water. These rules are important for the consistency of the application of the method. The benefit of the rules
is the simplicity of application, combined with the rather sophisticated modelling. The disadvantage is that agricultural practices can only be differentiated if sufficient data on area-species relationships are
available.

Literature Reference:

1. [Corine 1991-1] Commission of the European Communities, CORINE biotopes, The design, compilation and use of an inventory of sites of major importance for nature conservation in the European Community, ISBN 92-826-2431-5, Luxembourg, 1991. 2. [Corine 1991-2] Commission of the European Communities, CORINE biotopes manual, methodology and data specifications (3 volumes), ISBN 92-826-3228-8 (3 volumes), Luxembourg, 1991. 3. [Müller-Wenk 1998-2] Müller-Wenk R. (1998-2): Land-use - The Main Threat to Species. IWOE Discussion Paper no. 64, IWOE University of St.Gallen 4. [Köllner 1999] Köllner, T.; Species-pool Effect Potentials (SPEP) as a yardstick to evaluate land-use impacts on biodiversity. Submitted to and accepted by the Journal of Cleaner Production. August 1999 5. [ESU 1996] Frischknecht R. (final editor), U. Bollens, S. Bosshart, M. Ciot, L. Ciseri, G. Doka, R. Hischier, A. Martin (ETH Zürich), R. Dones, U. Gantner (PSI Villigen), 1996. Ökoin-ventare von Energiesystemen, Grundlagen für den ökologischen Vergleich von Energiesystemen und den Einbezug von Energiesystemen in Ökobilanzen für die Schweiz, 3 rd Edition, Gruppe Energie - Stoffe - Umwelt, ETH Zürich, Sektion Ganz-heitliche Systemanalysen, PSI Villigen 6. [Reidl 1989] Reidl, K. . Floristische und vegetationskundliche Untersuchungen als Grundlage für den Arten- und Biotopschutz in der Stadt - dargestellt am Beispiel Essen. GHS Essen, Essen, Dissertation.

Methodological Range:

Geographical range is Europe Data are based on hierarchist perspective

Notes:

The ESU database, produced at the ETH Zurich, is one of the few large databases that has consistently included land-use data. Unfortunately no distinction is made between conversion and occupation, the two are always combined. This means a restoration time is always included, and this restoration time cannot be separated in a elegant way. In order to be able to use this large database damage factors: land-use II-III, land use II-IV, land-use III-IV and land-use IV-IV have been estimated using the following (rather crude) assumptions: • ESU land-use type II can be interpreted as near to natural area • ESU land-use type III can be interpreted as green urban or rail areas. These are the not very intensively used areas • ESU land-use type IV can be interpreted as continuos urban land • ESU assumes a 5 year restoration time between type IV and III. In many cases an occupation time for industrial activities of 25 or 30 years is used. As a result the restoration time results in an overestimation of 20% for land-use II-IV. In the figure presented here the damage factor is thus lowered by 20%. • After the conversion from Land-use II-IV the ESU database uses the factors II-III for the restoration time between type II and III. As we do not want to include these, in general they should be omitted. Unfortunately for processes like the production of hydropower this class is used in a different way and should thus be included Using the ESU database is thus not very straightforward, but with the factors presented here a reasonable first order approximation can be obtained, except for instance for processes that involve agricultural production and hydropower.

Existing Characterisation Factors of Land-use impact on PDF

Characterisation Parameter

Category Indicator

Impact Indication Principle

Aspect

Substance

Quantity

Unit

Notes

CFactor

PDF