Investors are encouraged to finance companies that can prove their social, environmental and governance performance (Environmental, Social and Governance / ESG) through the bond market: ESG Bonds.
Indeed, to have access to this privileged financing, companies will be forced to demonstrate the alignment of their activities with this taxonomy. In order to maintain their financing via the issuance of bonds on the primary market, companies will have to issue more and more bonds with ESG criteria. The bonds of those who do not, will not be subscribed or with much higher financing rates. To do this, companies will have to make strong commitments on ESG factors and therefore adopt models.
Extra-financial indicators (carbon, degree alignment, etc.) compete with financial indicators (sensitivity, agency ratings, etc.) to monitor the sustainability transition. Transition risk analysis has now become a considerable issue. The entire financial industry is therefore working on a tool box made of new rating models based on the ESG data of issuers on these themes. Fund managers, to meet their commitments, will be fond of ESG Bonds.
Financial regulation is also starting to get organized (Sustainability Finance Disclosure Regulation, Taxonomy) and market regulation bodies are getting involved in monitoring. For example, in 2022, one of the AMF’s supervisory themes aims to ensure compliance with the constraints and contractual commitments in terms of sustainable finance for asset managers.
New regulatory reports will be released on this subject, requiring the availability of ESG indicators for each line of assets present in the inventory of an investment fund, thus strengthening the regulatory reports that emerged with the Solvency II regulations.
First mentioned by Michael Grieves of the University of Michigan is according to the digital twin consortium.
“A digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. »
Digital twins accelerate the holistic understanding of the system in its environment and optimal decision making.
By using real-time and historical data, they also make it possible to represent the past, the present and to simulate future states.