2. An IDA model with DMC

Here we present a model for index decomposition analysis (IDA), which describes drivers behind changes in primary abiotic resources use.

Changes over time in resource consumption are supposed to be driven by: a) substitution of renewables by non-renewables, b) increased use of secondary materials (recycling), c) improved resource efficiency and d) economic growth. We measure resource use with Domestic Material Consumption. This is an international standard indicator, and equals domestic extraction plus imports minus exports.

More formally, in our IDA, the variable (primary abiotic resource consumption) is written as the product of the drivers underlying changes in this resources use:

\(Prim_{abiotic} \:=\:\frac{Prim\:abiotic}{Prim}\:\times\:\frac{Prim}{Total}\:\times\:\frac{Total}{GDP}\:\times\:GDP\)

of which:

Prim_abiotic: Primary abiotic resource consumption, including abiotic but excluding biotic and secondary materials. A Dutch policy target is to reduce the use of primary abiotic resources with 50 percent in 2030.

• Prim: Primary resource consumption, including both biotic and abiotic materials.
• Total: Total material consumption including secondary, biotic and abiotic materials
• GDP: Gross Domestic Product of the Dutch economy.

Hence the drivers are:

• Prim_abiotic / Prim: Substitution of abiotic resources by biotic materials (reflected by lowering the share of primary abiotic materials in total primary material use)
• Prim / Total: Recycling of secondary materials (represented by lowering the share of primary materials in total material use)
• Total / GDP : Resource efficiency (decreasing total material use per unit of value added)
• GDP: Economic activity level.

This equation is converted into an additive (dynamic) equation for the change in resource consumption decomposed into the contributions of changes in the respective drivers:

\(\Delta\: Prim_{abiotic} \:=\Delta\:\frac{Prim\:abiotic}{Prim}\:+\:\Delta\frac{Prim}{Total}\:+\:\Delta\:\frac{Total}{GDP}\:+\:\Delta\:GDP\)

where ∆ indicates a change between two points in time. This may be a change between two subsequent years t-1 and t, but can also be a total change over a longer time period. In our application, we calculated annual changes of the resource use and drivers. In order to calculate the total change over a longer time period, we add up all annual changes in the period under consideration. The resulting figure is a change on balance, or net change.

We expect that more substitution, more recycling and higher resource efficiency exert a downward pressure on primary abiotic resource consumption, whereas more economic activity has an upward impact on the resource consumption. Note that if we say that substitution, recycling and efficiency increase, the ratios which implicitly reflect them in the equation are decreasing. Note also that we quantify the contribution of the drivers, which does not equal the volume changes in the drivers themselves. For instance, if substitution increases, the ratio Prim_abiotic / Prim decreases by some amount, a change in the ratio itself. This has some downward impact, that is, the quantified contribution, on primary abiotic resource consumption. If all other drivers would not exist, the resource consumption would decrease by the amount of the contribution of this driver.

One of the factors in the IDA, resource efficiency (Total / GDP), has a complex relationship with domestic primary abiotic resource use. This factor may comprise not only real improvements in efficiency but also other changes not specified explicitly. The indicator comprises a resource variable (numerator) and an economic variable (denominator). There might be changes in one or both variables independent of changes in the resource efficiency (i.e. the relationship between these two variables). For instance, a shift to a service economy or shift of goods production to abroad does not need to imply a improvement in resource efficiency in domestic production of goods. One should keep in mind that the label ‘resource efficiency’ might not cover the load.

Before turning to data and results of the IDA in the next sections, we emphasize that an IDA is not a causal model, but quantifies presumed interactions. Usually an IDA includes factors that are supposed to influence the dependent variable. Some factors have had a strong proven relationship with primary abiotic resource consumption, such as economic activity, while others exert a more complex influence, such as resource efficiency. Our aim is to look into correlations between the change in resource consumption and the change of the various factors. We do not state that these factors ‘cause’ or ‘drive’ the resource consumption exactly with their quantified contributions. Furthermore, there may be hidden mutual relationships between the factors themselves, creating indirect effects and loops. This may artificially resize the respective contributions of drivers in the decomposition.

Instead, we present the decomposition as an integrated macro-economic framework in which various factors play interacting roles in the change in resource use. It is not possible to trace back parts of changes in the resource use to exactly one factor. We pose that the IDA shows whether there are correlations, and that the quantified contributions cautiously indicate the extent of impact of the various factors. Our first aim is to show the relative contribution of the four factors. A second main contribution of our IDA is the longer time perspective, providing insight in structural changes over time. The results give an impression of developments in substitution, recycling, resource efficiency and economic growth, and their potential influence on materials use.