Energy scarcity and material abundance characterize the current global economy. Demand for energy services in buildings, transport, and industry cannot be met, after Russia’s invasion of Ukraine triggered multiple sanctions and supply chain disruptions for fossil fuels, mainly in Western Europe but also beyond. But even before, energy costs were on the rise due to increasing carbon prices, increasing global demand for energy, and – in some countries – the phase-out or maintenance of nuclear reactors. The availability of materials, however, such as steel, cement, or plastics, does not seem to be much an issue. Though there are concerns about supply chain concentration, supply risks, and vulnerability to supply chain disruption for a number of smaller-scale so-called critical materials (Andrea et al., 2017), these have affected the economy much less than the lack of access to cheap energy.
My hypothesis is that this relation between energy and materials will reverse during the coming decades. With renewable energy, we are slowly tapping into the huge energy flows linked to solar irradiation, mainly photovoltaics and wind power. At the same time, the costs for these sources of electricity belong to the lowest of what we have seen so far. Cheap, abundant, and low-carbon energy is on the way!
But each form of energy supply has major downsides. The zero-cost and zero-impact energy flow, that enters many engineering fantasies at the lower left corner (see ref. [1] for an example), is a myth. For nuclear power, it is the multiple risks. For fossil fuels, it is the massive interference with the Earth’s carbon cycle and the dependency on autocratic regimes. For hydropower, it is the massive alteration of cultural landscapes and ecosystems. For bioenergy, it is land use and land use change. And for solar and wind-based energy, probably the two main forms of future energy supply globally, it is the materials. “Material requirements per unit generation for low-carbon technologies can be higher than for conventional fossil generation: 11–40 times more copper for photovoltaic systems and 6–14 times more iron for wind power plants.”, a seminal study on the topic finds (Hertwich et al., 2015). Next to these bulky materials, there is much concern about the supply of specialty materials for low-carbon technologies. Here, lithium is probably the best example.
What does that mean for sustainability?
First, because also in the future, energy will not be free and zero impact, all energy service assessments need to include energy supply impacts, such as land use or material use, into their system boundary, for example, in life cycle assessments and life cycle costing calculations. Such a comprehensive supply chain perspective rules out extremely inefficient energy use paths, such as the production of synthetic fuels compare to the direct use of electricity by battery-electric vehicles.
Second, The material implications of the energy transition must be monitored (Elshkaki and Shen, 2019; Kalt et al., 2022). Material production is difficult to decarbonize and may be a tough hurdle to very-low-impact energy supply in the future. Moreover, raw material extraction, like copper ore mining, has very large negative local impacts on environment and societies, and the acceptance for large-scale mining projects needed to build the future energy system may decline (see ref. [2] for a recent example).
Third, the current ease of access to materials in high-income countries is based on cheap extraction mostly in countries with low social and environmental standards. Raising these standards and rolling out service-intensive lifestyles across the globe may limit the amount of materials that is available to the individual in the long run. Hence, different resource efficiency measures, labelled under 3R (reduce-reuse-recycle) are needed to decouple first service provision and then human wellbeing from material consumption.
The need to quantify and assess the material implications of service provision and technology
Against this background, the material implications of service provision and technology are receiving more attention. They are increasingly included in environmental assessments. To assess the relation between sustainable development and materials, a quantitative systems analysis of different products and services is needed, typically via material and energy flow analysis (MEFA), which is then extended by a model of the supply chains of the different raw materials and energy carriers. The supply chain assessments follow the methodology of life cycle assessment (LCA). Figure 1 shows the main methods used to assess aggregate material implications in supply chains.
Figure 1: Measuring aggregate material implications in supply chains, starting from the material flow inventory of a product system, overview of methods. The three approaches are briefly introduced below. The material footprint method (middle) is highlighted, as it will be applied to quantify the material implications of low-carbon energy services further below.
There are three broad groups to quantify the material implications of products and services (Figure 1). In LCA, the different resource flows in the life cycle inventory of a product or service are converted to a common unit by applying scarcity weights such as the static depletion time (remaining reserves divided by current extraction rates), see Fig. 1, left side. Different scarcity-weighted methods have been developed (see Klinglmair et al. (2014) for a review of the different resource depletion methods) and are available as part of the standard life cycle impact assessment (LCIA) packages such as ReCiPe (Goedkoop et al., 2009). LCIA can also convert the material flows into so-called environmental midpoint indicators, such as the material production’s impact on global warming, on emission of toxic substances to the environment, or the total primary energy requirements (Fig. 1, right side). This conversion directly shows the environmental implications of material use.
Finally (Figure 1, middle), the different material flows can simply be added up to give an overview of the overall material requirements of a product system. More precisely, the material flows are first converted to their raw material equivalents (RME), which denotes the material flows that are extracted from nature and enter industrial processing (Mostert and Bringezu, 2019). Then, the raw material equivalents are aggregated into the four broad categories, biomass, fossil fuels, metal ores, and non-metallic minerals. The material footprint is the contemporary scientific representation of the material input per service (MIPS, (Ritthoff et al., 2003)) and commonly calculated as part of the four major environmental footprints (GHG, water, land, and materials) (Tukker et al., 2014; Wiedmann et al., 2015). The conversion of material flows into raw material equivalents has so far not been part of standard LCA calculations. This has changed with the work by Bringezu and Mostert (2019), who compiled a comprehensive list of characterization factors for RME and total material requirement (TMR, total extracted material, including overburden) for the process database ecoinvent (Wernet et al., 2015). Their dataset was later updated to ecoinvent 3.7 and 3.8 (Pauliuk, 2022), and this set of characterization factors for RME and TMR is available on Zenodo for use in openLCA together with an ecoinvent license [3].
Material Footprint Implications of Low-Carbon Technologies
Let us now look at results! We start with electricity supply. Fig. 2 shows the four major environmental footprints (GHG, materials, land, water) for a reference flow 1 kWh of electricity from lignite (Fig. 2, left) and solar PV (Fig 2, right).
Figure 2: Four major environmental footprints (GHG, materials, land, water) for a reference flow 1 kWh of electricity from lignite (left bars) and solar PV (right bars). Results from ecoinvent 3.8 with openLCA 1.10.2. The LCIA methods are ReCiPe 2016 for global warming, land occupation, and waster depletion, and the above-described material footprint method. The numbers on top of each box show the absolute value of each axis (100%), and thus the impact value of the respective technology/product system with the higher impact in that category.
Clearly, this particular comparison (and possible supply shift in reality) does the job on the climate side. Per kWh, GHG go down by more than 90%, so does the fossil part of the material footprint. Land use declines as well, and water is largely unchanged. But there is quite some rise in the metal ore fraction of the material footprint! This reflects the rise in material intensity of many low-carbon energy technologies as described above (Hertwich et al., 2015).
The next product system we look at is the production of a passenger vehicle (Figure 3). Not the driving and end-of life phases, only the production.
Figure 3: Four major environmental footprints (GHG, materials, land, water) for a reference flow of a gasoline car (left) and a battery electric car (right), both with a weight of 1500 kg. Results from ecoinvent 3.8 with openLCA 1.10.2. The LCIA methods are ReCiPe 2016 for global warming, land occupation, and waster depletion, and the above-described material footprint method. The numbers on top of each box show the absolute value of each axis (100%), and thus the impact value of the respective technology/product system with the higher impact in that category.
Here, ecoinvent results support what is now almost common knowledge: The production impacts of electric vehicles are higher, partly substantially higher, than for a vehicle with an internal combustion engine. Major drivers include the heavy battery but also the copper for the electric motors and controllers (Bekel and Pauliuk, 2019). The GWP of producing the car is about 50% higher than the conventional case, and the material footprint is roughly three times higher, mostly driven by a surge in metal ores when shifting drive technologies. A gasoline gar contains 20-25 kg of copper, a battery electric vehicle more than 100 (Wolfram et al., 2021).
After looking at the product (Fig. 3) and the energy supply (Fig. 2), let’s combine the two, include the road infrastructure and the end-of-life stage, and look at the function of the product system: the kilometer driven (Fig. 4).
Figure 4: Four major environmental footprints (GHG, materials, land, water) for a reference flow of 1 km driven with a gasoline car (left) and a battery car + solar PV electricity (right). Results from ecoinvent 3.8 with openLCA 1.10.2. The LCIA methods are ReCiPe 2016 for global warming, land occupation, and waster depletion, and the above-described material footprint method. The numbers on top of each box show the absolute value of each axis (100%), and thus the impact value of the respective technology/product system with the higher impact in that category.
The function of the product system, expressed in km driven, leads to a carbon footprint reduction of about 50%, and the remaining GWP results mostly from the current fossil-intensive vehicle and battery production. The material footprint of the low-carbon alternative more than doubles, however, driven by a quadrupling of the material footprint.
Figure 5 shows the main component of this material footprint. One can see that metals, above all copper, dominate the footprint, but that also a number of fossil fuels are listed, they enter the product system via the energy supply for the manufacturing and material processing. Finally, also gravel contributes, due to the road construction included.
Figure 5: Process breakdown of the material footprint of the functional unit of 1 km driven by a battery electric vehicle, 1500 kg total weight, 150000 km lifetime, 2.2 MJ/km direct electricity demand, solar PV as electricity source.
Finally, we look at another service – the supply of thermal comfort (Fig. 6).
Figure 6: Four major environmental footprints (GHG, materials, land, water) for a reference flow of 1 MJ of useful heat supply to a building, first with natural gas (left) and via heat pump with the Swiss electricity mix of ca. 40 g CO2-eq/kWh (right). Results from ecoinvent 3.8 with openLCA 1.10.2. The LCIA methods are ReCiPe 2016 for global warming, land occupation, and waster depletion, and the above-described material footprint method. The numbers on top of each box show the absolute value of each axis (100%), and thus the impact value of the respective technology/product system with the higher impact in that category.
Here, the switch from natural gas to low-carbon electricity again leads to a drop of the climate impact of more than 80%, as expected, and here also to a drop in overall material need of ca. 50%, due to the massive reduction in fossil fuel demand. The metal fraction of the material footprint, however, is about five times larger than the metal needed in the gas-based product system.
These findings corroborate the above statements, that low-carbon lifestyles are more material-intensive than their fossil counterparts. It is therefore imperative to include materials into the environmental assessment of sustainable development strategies and consider them in the monitoring of large-scale transformations such as the energy transition. Finally, to anticipate and mitigate possible supply shortages, resource efficiency and decoupling of wellbeing and service provision from resource use are needed. A circular economy with a combination of new lifestyles, new products and services (Tukker, 2015), resource efficiency at all stages of the material cycle, economic incentives, new business models (Bocken et al., 2016), and standards (Barkhausen et al., 2022; Lotz et al., 2022) can lead to a deep decoupling of material extraction and human wellbeing along the energy and material service cascade (Kalt et al., 2019).
The limitations and current uncertainties of the material footprint method for ecoinvent is documented and discussed in Pauliuk (Pauliuk, 2022).
Supplement
This file (MF_Data_PlotScript) contains the data file with ecoinvent results (.xlsx) and a Python script to create the figures.
References, all URLs accessed on October 29, 2022.
[1] https://static.dw.com/image/40139624_906.png
[3] https://doi.org/10.5281/zenodo.6470355
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