Example chain: Non-renewable resource use

Non-renewable resources are broadly defined as natural resources, which do not regenerate in due time with regard to their consumption rates. Typical examples of non-renewable resources are minerals and fossil fuels such as crude oil and natural gas. Another example is land, which, however, is not considered in detail here.

Instead of 'non-renewable resources', the term 'abiotic resources' is also used. Abiotic resources can be flows, funds or stocks. Depending on the definition, abiotic resources include both minerals and fossil fuels or only minerals (see e.g. Guinée et al., 2001; Goedkoop et al., 2009).

The issue of non-renewable resource depletion has been prominently raised by Meadows et al. in their book 'The Limits to Growth' (Meadows et al., 1972).

Since then, the focus has shifted away from this issue. However, with the increasing discussions on peak oil and decreasing uranium resources as well as the emergence of technologies based on scarce metals such as gallium, indium, platinum or ruthenium in, e.g., automotive catalysts, consumer and office electronics or photovoltaics, the issue has recently come to the fore again.

A necessary prerequisite for measuring resource depletion is the application of a life cycle approach which considers all relevant processes from cradle to gate, from cradle to grave, or from well to wheel. The target resource indicator in transport applications, however, must be the dissipation of critical resources, which stands for the irreversible loss of these resources. This, in turn, requires a model of the system, which adequately represents resource dissipation and the associated processes, including recycling.

5.3.1. Indicators of non-renewable resource use

In the Life Cycle Impact Assessment (LCIA) community, possible indicators for abiotic (stock) resource depletion have been discussed for many years, e.g.

in the context of the life cycle initiative of the Society of Environmental Toxicology and Chemistry (SETAC). Whereas there is a broad consensus that impact category indicators should represent significant environmental issues, there seems to be less consensus on how relevant the issue of abiotic resource depletion is and by which type of indicator it should be represented.

For Udo de Haes et al. (1999), possible abiotic resource depletion indicators are:

− rareness of resources

− exergy content of resources

− mineral concentrations

− degree of use of flow resources in relation to the size of the flow

− total material requirement

− indicators related to other categories, such as energy requirement or land use

According to Steen (2006), who mainly focused on mineral deposits, mainly four types of abiotic resource depletion indicators are discussed in the Life Cycle Assessment (LCA) community:

1. Indicators based on energy and mass

2. Indicators based on the relationship between use and deposits 3. Indicators based on the future consequences of resource extractions 4. Indicators based on exergy consumption or entropy production

Goedkoop et al. (2009) propose an alternative method based on the geological distribution of mineral and fossil resources and assess how the use of these resources causes marginal changes in the efforts to extract future resources.

5.3.1.1. Indicators based on energy and mass

Simply adding all abiotic resources on the basis of mass or energy suggests that they are exchangeable and equally important with respect to their mass or energy content. An indicator summing up the quantities of resources used for a product or a service has e.g. been proposed by Schmidt-Bleek (1994) in his Material Intensity per Service Unit (MIPS) concept (see section 6.2.4). In the

LCIA community, there is little support for these types for indicators (Steen, 2006).

5.3.1.2. Indicators based on the relationship between use and deposits

The characterisation factors typically used in LCIA are 1/R, U/R and 1/R * U/R, where R corresponds to the mass of a specific resource (e.g. mineral ore reserves) and U corresponds to the present use of the resource. The last of these formulas is applied in the CML 2000 method (Guinée et al., 2001).

5.3.1.3. Indicators based on the future consequences of resource extractions

The basic idea of these indicators is that extracting high-concentration resources today will force future generations to extract lower-concentration resources, which will lead to an increased impact on environment and economy.

For example, the increase of land use and energy requirements for future provision of the currently used quantities per type of abiotic resource could be taken as a damage indicator. An approach based on the surplus energy use for future mining of low-grade resources has e.g. been applied in the Eco-indicator 99 model (Goedkoop and Spriensma, 2001; Steen, 2006).

5.3.1.4. Indicators based on exergy consumption and entropy production

Exergy has been suggested as an indicator for abiotic resource depletion.

This is based on two assertions. First, exergy is the ultimate limiting resource because it has an associated energy cost that will be limiting to some extent when it becomes too high. Second, matter, in contrast to exergy, will not be depleted.

5.3.1.5. Indicators based on the marginal increase in costs due to the extraction of a resource

Goedkoop et al. (2009) base their model (ReCiPe) on the geological distribution of mineral and fossil resources and assess how the use of these resources causes marginal changes in the efforts to extract future resources.

Unlike the model used in Eco-indicator 99, they do not assess the increased energy requirement in a distant future but rather base their model on the marginal increase in costs due to the extraction of a resource.

To this end, Goedkoop et al. (2009) develop a function that reflects the marginal increase of the extraction cost due to the effects that result from continuing extraction. In terms of minerals, the effect of extraction is that the average grade of the ore declines, while for fossil resources, the effect is that not only conventional fossil fuels but also less conventional fuels need to be

exploited, as the conventional fossil fuels cannot cope with the increasing demand.

The marginal cost increase (MCI) is the factor that represents the increase of the cost of a commodity r (US $/kg) due to an extraction or yield (kg) of the resource r. The unit of the marginal cost increase is dollars per kilogramme squared (US $/kg²).

!

MCI

= "Cost

"Yield

5.3.2. Evaluation of non-renewable resource use indicators

The following advantages and disadvantages of the indicators presented have been reported (Steen, 2006):

• Non-renewable resource indicators based on summing up energy or mass are easy to calculate and apply; on the other hand they are oversimplifying as they suggest that abiotic resources are exchangeable and equally important.

• Problems associated with indicators based on the relation between use and deposits are:

(i) There is no common idea of what a resource is and what substances to include: R could be ores that are identified reserves of concentrates, which can be economically extracted, or anticipated amounts of such concentrates, or even the total amount of a substance in the earth's crust.

(ii) If indicator results for different elements or substances are added up (as in an LCA), the total value will depend on how many substances are included and on how the resources are grouped. For example, if all fossil fuels are added together, an extraction of a certain amount of oil will have a lower result than if only oil reserves are considered.

(iii) Typically there is an underlying assumption about exchangeability of resources; depletion of specific resources is considered to be a second order problem. Compared to indicators based on only energy and mass there seems to be a little more support for type of indicators related to use and deposits. However, these indicators are sometimes considered to be indicators of economic rather than environmental sustainability (Steen, 2006).

• For indicators based on the possible future consequences of resource extraction, there seems to be some consensus in the LCIA community on further developing the future consequences option with consideration of the resource functionality (Steen, 2006). Here, different methods have been proposed, each with its own specific limitations, e.g. with regard to the time perspective (decades, centuries, no temporal limitations). In particular, however, these indicators are rather uncertain because their

building methods need to assume future scenarios (Goedkoop et al., 2009).

• Indicators based on exergy consumption and entropy production typically do not truly express ore depletion, because exergy reflects the effort to produce the resource irrespective of its scarcity. Therefore, even if a resource becomes depleted rapidly, the exergy value will not change (Goedkoop et al., 2009). Furthermore, as Steen (2009) points out, exergy only will be limiting at galactic scales, as long as the sun will shine on earth.

Table 29. Evaluation of non-renewable resource indicators Category

Representation Operation Application

Indicator

Validity Reliability Sensitivity Measurability Data Availability Ethical concerns Transparency Interpretability Target relevance Actionability

Indicators based on energy

and mass x xx x xxx xxx xxxx n.a. xx x x

Indicators based on the relationship between use and deposits

xx xx xx xx xxx xxxx n.a. xx x x Indicators based on the future

consequences of resource extractions

xxx xx xxx xx xxx xxxx n.a. xx x x Indicators based on exergy

consumption and entropy production

xx xx xx xx xxx xxxx n.a. x x x Indicators based on the

marginal increase in costs due to the extraction of a resource

xx xx xx xx xxx xxxx n.a. xx x x x=poor; xx=limited; xxx=good; xxxx=excellent

n.a.: not applicable; evaluation depends on specific application context

• Indicators based on the marginal increase in costs due to the extraction of a resource indirectly determine the damage of abiotic resource extraction via their exploitation costs (Goedkoop et al., 2009).

In Table 25 criteria to be considered when building or selecting an indicator have been defined. In Table 29, the non-renewable resource indicator groups presented above are evaluated with regard to these criteria.

For reliability and transparency, the critical issue appears to be the modelling of the life cycle. Because transparency depends on how this is done in the specific application context, an evaluation is not possible for this criterion.

5.3.3. Location in the DPSIR chain

As regards the DPSIR chain, all indicators addressed here belong to the I (impact) category.

5.4. Example chain: Loss of cultural heritage