The distinction between the two investment approaches is directly related to the role of commodities within investors’ portfolios. Asset owners tend to view commodities as having three main uses: to provide diversification to equity and fixed income holdings, hedge inflation risk and deliver exposure to the economic cycle. Given differences in sector return profiles, production levels and economic roles, meeting the three objectives requires exposures to a broad-basket of commodities—for example the Bloomberg Commodity index which contains 24 US dollar denominated commodities. At the forefront of investors’ minds is whether introducing a sustainable objective maintains the asset allocation properties of their existing commodity portfolios. Significant differences would likely necessitate a re-evaluation of the role of commodities within the policy portfolio. In contrast to equity and fixed income markets, commodity portfolios contain far fewer securities and correlations between those securities tend to be relatively low. This can make the task of re-weighting commodity exposures tricky given the need to balance sustainability objectives and maintaining the existing returns profile.
Why investors should care about sustainable development
Sustainable development can broadly be defined as a plan for economic development that is mindful of the depletion and/or degradation of natural resources. Measures such as greenhouse gas (GhG) emissions, water usage and biodiversity are among the more popular measures cited by practitioners as indicative of natural resource depletion. This, together with GhG emissions being one of the key inputs into the assessment of the consequences of climate change, makes GhG emissions a natural starting point from which to evaluate portfolios.
Unlike for equity and fixed income portfolios, commodity investors tend to get exposure via exchange traded derivatives. Given futures holdings do not directly affect physical demand and supply and that the derivatives volume, for a given commodity, is multiples of the physical volume, why do financial investors need to care about the underlying GhG emissions?
There are two important reasons. First, the derivatives’ returns reflect movements in the price of the physical underlying. As it stands, without the commodity, there is no commodity futures return. To gain exposure to the return profile offered by a broad benchmark, the physical commodity must be produced. This, in turn, creates GhG emissions from the production processes. Second, the price sensitivity of commodity producers and consumers differ, with producers generally more price sensitive (this is referred to as the ‘elasticity of supply and demand’ respectively). The result is that output tends to decline more during periods of falling prices than demand falling during periods of equivalent price rises.
Academic research on corporate investment under uncertainty (e.g., Investment under Uncertainty, Dixit and Pindyck), which can be interpreted as either increasing variability in profitability or declining profitability, highlights a negative correlation between capital expenditure and increasing uncertainty. Since capital expenditure typically entails purchasing new equipment, this suggests increasing expenditure results in more efficient production processes—often both in terms of output per unit and GhG emissions. Long-only investors provide price support to producers seeking to hedge future output (funding support), which can be viewed as facilitating productivity improvements.
The framing of long-only investors providing funding support together with the use of derivatives-based instruments has important implications. In the case of equity portfolios, a commonly used measure is portfolio emissions—meant to be representative of the GhG emissions ‘owned’ by the investor. Investors in commodity futures cannot lay claim directly to the underlying physical asset yet support prices resulting in a benefit to producers hedging output. A metric reflecting commodity investors’ role is an adaptation of the portfolio emissions measure. For each US dollar invested in a commodity portfolio, funding support can be defined as a weighted sum of the GhG emissions associated with each of the commodities. This measure is a macroeconomic analogy to portfolio emissions; representing the weighted amount of emissions supported by the futures investment. Translated to the notion of a sustainable portfolio, the measure of interest is the difference in funding support between the traditional benchmark and the sustainable portfolio—with the difference driven solely by the re-allocation of commodity weights.
How is the data collected?
Typically, GhG emissions data is collected via companies’ annual and sustainability reports. However, in this case, as each commodity is produced by multiple companies, many of which are private (and hence non-reporting), this is not an option. Furthermore, whereas the data collected by companies tend to be defined by scope 1,2 & 3 boundaries, it is more meaningful to define the emissions cycle of commodities according to the production processes that are required to produce those commodities. In the case of commodity futures, this can reference the futures specifications of the underlying commodity, which can be viewed as the equivalent of scope 1 and 2 emissions as reported in companies’ sustainability reports.
The emissions data is estimated using an approach called Life Cycle Assessment (LCA). This relies on a specified model of the production processes along with data sets which determine input values and parameter settings. Typically, datasets collected by industry and academic researchers are fed into modelling software to construct emissions estimates per commodity. Naturally, the results are dependent on both the dataset and modelling parameters. Accordingly, selecting representative datasets (process and geography wise) are important, as is selecting parameters according to some generally accepted standard (for example the IPCC 2021 Report). The estimates can be refreshed on a regular frequency to account for new datasets, model updates and shifts in reporting standards. Given the potential complexity of timely management of model updates and collecting and assessing the quality of new data sets, it can be operationally intensive to manage the estimation process in-house. An alternative is to use consulting firms that specialize in the field.
For illustration purposes, Figure 1 displays the result of the LCA-based estimates calculated in the first half of 2023. Grouped by sector and whether they are primary or derived in nature (of which we discuss more a little later), the heatmap represents a rescaling of the raw estimates.