Our portfolio analysis is predicated on metal scores based on GHG emissions during extractive processes – i.e. mining/new production. Assessing the linkage between spot rates (physical demand) and inventory could potentially allow for more accurate estimation of GHG emissions per metal. We examine the constituents of precious metals portfolios as an example.
Newly mined gold comprises 75% of annual gold supply1 ; the residual 25% comes from recycling (of which 90% is attributed to jewelry and 10% to technology hardware). The processes involved in recycled gold purification are dependent on the degree of purity, the scale of the production/refining process and which particular impurities need removing. This makes it difficult to estimate GHG emissions for recycling processes. Similarly, over the 2011-2020 period, 82% of annual demand for silver was sourced through mining—with 18% recycled2. Once again, there are various techniques to refine silver.
If a reliable source of GHG estimates for recycled metals were available, a more comprehensive measure could be constructed via the weighted average GHG of primary and recycled estimates. In the case of gold and silver, mining-only data captures the bulk of physical demand and the mining-to-recycling ratios for these metals are approximately the same. This suggests that unless recycling emissions differ significantly, the portfolio weights using the weighted average measure should not differ meaningfully. An analogous study for industrial metals is a more intensive task. While the covariance-driven substitutability would be unchanged, it may lead to results that produce different GHGD estimates.