Accelerating climate transition through finance: Towards an improved methodology for carbon accounting in the financial sector

Abstract

Carbon accounting in the financial sector has experienced a rapid growth over the last years. Both private and public institutions have focused their attention on the role the financial sector can play in accelerating the transition to a low carbon economy. More than 80% of the financial institutions worldwide acknowledge the importance of GHG accounting for their loans and investment. However, less than 20% of these institutions actually measure and report on their climate impact. An often heard argument for this lacking carbon disclosure is data quality and the need for estimation models to improve this data. Less attention in academic research has been focused on the basics of carbon accounting methodologies. The larger part of capital in the world finds itself in the financial sector in public and private institutions. Accelerating the transition to a low carbon economy will require a shift of this capital to a more sustainable focus. In order to identify carbon intensive investments and to track the decarbonisation of loans and investments as a financial institution, carbon accounting plays an important role. This research aims to improve the approach that forms the basis for carbon accounting used in financial institutions. More attention in this field can possibly trigger a line of events with more attention to the climate impact of the investing world. Ideally this can lead to more sustainable investments and carbon reduction on a global scale. The question aimed to answer in this research is the following: What is the most adequate approach for financial institutions to measure financed emissions?

This study goes into the existing carbon accounting methodologies and metrics. The current approaches are analysed and the four most common metrics selected. The major accounting issue following from this overview and related interviews is that there is no approach combining investments in equity and debt when allocating emissions. Based on this observation three alternative accounting approaches are proposed and assessed on their contribution and sensitivity in order to come to an improved practice for carbon accounting. The three discussed approaches are the use of Enterprise Value Including Cash, Balance Sheet Total and separate allocation for equity and debt. The alternatives of using Enterprise Value Including Cash and Balance Sheet Total together with four of the existing carbon accounting metrics are evaluated on a set of 7 qualitative criteria. This evaluation brings the use of Enterprise Value Including Cash forward as the preferred metric. The second evaluation of the metrics is done through a quantitative approach using a sample investment portfolio to test the metrics in a practical situation. Here, the four remaining metrics are compared in their performance over a time-span of 5 years. With the use of experiments this performance is evaluated, where the use of Enterprise Value Including Cash and Balance Sheet Total show the most potential.

The research concludes that the use of Enterprise Value Including Cash is the preferred approach. This approach enables the financial institution to assess the carbon footprint of their investment portfolio for both equity and bonds investments in listed companies. The advantage of this method is that it avoids double counting and the emissions are allocated to the actual invested values. A downside of the Enterprise Value Including Cash is the dependency on market development and therefore market volatility. This can cause issues when analyzing the trend of decarbonisation of an investment portfolio. This research recognizes this problem and proposes research into corrections to mitigate the issue. This improved practice should enable the financial sector to harmonize their approach in carbon accounting to ensure higher comparability, transparency and consistency in the landscape of carbon accounting. The next step would be piloting the improved methodology in a real investment environment on real-time data.