Figure 24. Comparison of medium-term primary and total indium supply in 2016
The significant range of total supply between the various scenarios leads to some preliminary
conclusions about the medium term:
The biggest medium-term supply opportunity is recovery efficiency. A focus on
concentrates are shipped to indium-capable smelters) could increase total supply by a
factor of 2.8X in 2016 and 3.4X in 2031.
Higher recovery efficiency increases supply and lowers the average production cost.
Although short-term market conditions might allow prices to drop to a $150–$200/kg
level, prices are unlikely to be lower than $400/kg for any prolonged period. At prices
lower than $400/kg, primary and secondary suppliers are unlikely to continue investing to
maintain productive capacity.
Furthermore, although not explicitly modeled as part of this exercise, secondary supply from
consumer waste (old scrap) could become a source of additional supply in the medium term. It is
difficult to estimate the cost of such supply, but given that current price levels have not justified
the recovery of indium from laptops, cellular phones, and other electronic devices (mostly
because these items are so widely dispersed), prices would likely need to exceed $700/kg to
make recovery from these sources profitable. Furthermore, although a less disperse and possibly
more profitable source of old scrap could be found in EOL solar panels, we do not expect this to
contribute to supply until the 2030s, because the average expected life of solar panels is
approximately 20 years and only at this point would this potential resource would become
(tonnes of indium metal per annum)
Medium-term indium supply curves (2031)
total supply (primary + secondary)
total supply (base case + recovery efficiencies)
When thinking about the long term, it is helpful to keep in mind that all factors are flexible
(capital, labor, technology, and our level of geological knowledge). That is, new discoveries are
possible, and there are no constraints to production beyond the amount of resource in the Earth’s
crust and human ingenuity. Economists and others thus find it difficult to make accurate long-
A complete analysis of supply requires estimates of both price (on the vertical axis of a supply
curve) and quantity (on the horizontal axis). Our analysis here is limited to price and builds on
Green (2009), who argues there is a fundamental relationship between price and concentration
(or weight percent) of an element in a mineral deposit. A lower mineral concentration implies
more difficult extraction and higher production costs, and thus a higher price to justify
investment in a mine and associated facilities. To be sure, factors other than concentration
influence production costs, but the lower the concentration, the larger the quantity of unwanted
material that must be separated from the desired element.
Largely derived from Green (2009), Figure 26 plots average mineral concentration in typical
against average price over the period of 2005 to 2009
for different minerals. By
which they are mined, estimates of the market price of indium (if mined from available ores) can
be obtained. When plotted in a log-log scale, a strong linear relationship and good fit (R
can be seen. If one believes that minerals will generally follow this relationship in the
By using the terminology of “ores” in line with Green (2009), we explicitly rule out deposits of no economic interest.
For 4N quality indium we use a more recent price of $600/kg.
For a long-term analysis, it would be ideal to use longer term prices (i.e., average over the past ~20 years).
A simple linear regression of the data in Figure 26 where log(price) = α
log(concentration) + ε yields highly significant
estimates for the regression coefficients as summarized below where values in brackets below the regression equation represents
A plot of the residuals indicates the presence of heteroskedasticity, which may indicate that the standard errors for our
normally distributed, thus support our use of the t-statistics in determining the significance of the estimators for our coefficients.