Resource scarcity is one the main challenges facing humanity. Improving living standards, together with a world population that is expected to reach 9 billion in the year 2050 will push the demand for resources into unchartered waters.
One of these resources is copper. Copper demand has been growing rapidly all through the 20th century. It is used in a broad range of applications, is difficult to substitute and will become even more crucial in the future, given the expected increase of copper-intensive low carbon energy and the electrification of transport technologies.
Rapidly rising demand may cause future supply problems and contribute to environmental issues. For example, declining ore grades result in higher energy requirements for the same amount of copper extraction (Memary et al., 2012; UNEP, 2013a), thus increasing greenhouse gas emissions.
A circular economy has been proposed as an answer to tackle this challenge (European Commission, 2015). Closing the material loop would help avoid resource supply problems, and reduce environmental impact through cutting the need for mining: secondary copper production requires only 20% of the energy used for primary copper production (International Copper Study Group, 2013).
However, even if all the copper were to be recovered, it would not be enough, given that the copper demand is still growing. It is only possible to reach a circular economy of a resource when the demand stabilizes. Understanding how the copper demand will develop in the future is one of the central factors that will determine how and when a circular economy of copper can be achieved.
This paper explores how copper demand might develop until the year 2100 under different socio-economic and technology scenarios. Two methods are adopted to study this question, a top-down method, based on a regression model, and a bottom-up method, based on a stock dynamics model.
- The top-down method based on regression modelling establishes the relationship between copper demand and general development variables such as GDP and population. The future trend can be extrapolated from the estimated relationship on the basis of empirical data from the past.
- The bottom-up method based on stock dynamic modelling is usually applied in small-scale case studies, to estimate the stock dynamics of metals and assess the environmental impact of the flows (Bergbäck et al., 2001). Here the authors have used such this approach to estimate future demands.
The top-down approach is simpler and requires less data, making it quicker and easier to apply, but it does not provide mechanistic insight or help identify the key applications contributing to copper demand. Similarly, it does not allow the inclusion of technological developments, in particular the decoupling of GDP and copper demand, which could have a very significant effect. As a result, top-down methods seem more suitable for short-term predictions in which technology is relatively constant, while long-term predictions require a bottom-up approach to allow for changes in the relationships between the variables. Furthermore, the bottom-up method, which is more data and time intensive, gives insight into the applications and sectors that are expected to contribute the most to copper demand, and can therefore help in the development of strategies and policies aimed at urban mining and circular economy.
The copper demand estimations for the year 2100 depend strongly on the method used and the scenarios’ differences in terms of population, welfare and uptake of renewable energy technologies, resulting in a range of between 3 and 21 times the current copper demand. None the less, this highlights the urgency for a transition towards the circular economy of copper.
The top-down estimations suggest that copper demand will continues to grow until the year 2100, with a 3 – 21 fold increase in demand compared to 2012 production.
For the bottom-up stock dynamics method, a strong growth of copper demand is found in the scenarios with a high share of renewable energy, in which a much higher copper intensity for the electricity system and the transport sector is seen. The bottom-up method predicts a 3.5 – 5.5 fold increase in demand compared to 2012 production.
Under all considered scenarios, the projected increase in demand for copper results in the exhaustion of the identified copper resources, unless high end-of-life recovery rates are achieved. It is highly unlikely the highest estimate could be supplied.
One of the limitations of the research is the lack of geographical detail in both methods. While in some places copper demand is still increasing due to build-up of infrastructure, for example in growing economies such as China, in other places where the infrastructure is already at a built up level, copper demand could be trending towards a stable level.
A second limitation involves uncertainties due to unknown future technologies. Innovations may have consequences for future copper demand, which has not been accounted for.
Finally, decreasing ore grades, uncertainty of supply, and overproduction result in price fluctuations. The impact that supply or prices might have on copper demand has not been considered.
The authors emphasise the role of such scenario analyses. The generated timelines until 2100 must in no way be interpreted as predictions or even forecasts, but instead interpreted as stories exploring the impacts of potential futures on copper demand. The value of this is that we see what the relevant variables for copper use are, and where potential measures to improve society’s copper metabolism might be taken.