Reducing Carbon in Equity Portfolios
Investors are increasingly building portfolios with explicit carbon-reduction targets, temperature alignment goals, and climate objectives such as mitigation and adaption. To inform the thinking around such efforts, we recently constructed and back-tested five equity portfolios targeting carbon reduction, varying across risk and return dimensions.
Two portfolios used a screening approach, two used a tilting approach, and one used an optimization approach:
- Divest 10 (screened): The 10% highest carbon emitters in the MSCI World Index universe are removed (i.e., 10% of market cap is removed).
- Divest 20 (screened): The 20% highest carbon emitters in the MSCI World Index universe are removed (i.e., 20% of market cap is removed).
- Pure Tilting: A simple tilt is applied on carbon intensity with no constraints applied.
- Iterative Tilting: A cumulative normal distribution is applied to carbon emissions, and weights are assigned to securities based on their transformed carbon scores. Additional security, sector, and country constraints are applied.
- SSGA Low-Carbon Framework (optimized): A mean-variance optimization is employed to either minimize tracking error at a target carbon-reduction level or minimize carbon emissions at a target tracking error.
The optimized Low-Carbon Framework approach appears to produce the greatest amount of carbon reduction over time, and this same approach also appears to produce the most stable level of carbon reduction per unit of tracking error. The latter finding is critical, as this measure suggests the best potential outcome for investors who also care about risk.
The research reviewed the active country exposures of the different strategies and suggests that Iterative Tilting stood out with much more controlled active exposures. Sector exposures across the five portfolios were found to be similar to those of the country exposures, though we note also that a strategy of simply rotating out of carbon-intensive sectors and overweighting low-carbon sectors may result in significant tracking error. A more appealing approach for index investors might be to derive a majority of the carbon reduction from security selection within sectors.
In sum, our research suggests that the tradeoff between carbon reduction and tracking error could be best managed using optimization. In this preferred approach, investors can meet their carbon-reduction targets while also minimizing the amount of risk they have to assume in deviating from their policy benchmarks. In contrast to rules-based approaches, optimization is generally more risk-efficient. Please download the full article, containing detailed performance data and analysis, below.