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    NGFS Publishes Progress Report on Bridging Gaps in Climate Data

    May 26, 2021

    NGFS published a progress report that forms part of the first phase of the work program of the NGFS workstream on bridging the data gaps in context of climate-related risks. The report sets out issues that need to be considered going forward, thus laying the groundwork for a comprehensive assessment of climate-related data needs and gaps. The progress report also constitutes a valuable input to nourish the work on climate-related data undertaken at the international level by FSB and G20. Additionally, certain documents related to the NGFS scenarios are being planned for release shortly. NGFS also announced that the Reserve Bank of India (as a member) and the FSB (as an observer) have recently joined the Network.

    The report highlights that reliable and comparable climate-related data are crucial for financial sector stakeholders to assess financial stability risks, properly price and manage climate-related risks, and take advantage of the opportunities arising from the transition to a low-carbon economy. However, persistent gaps in climate-related data hinder the achievement of these objectives. Stakeholders report the need for more forward-looking data (for example targets or emissions pathways) and granular data
    (for example, geographical data at entity and asset-levels). Stakeholders are also calling for assurance about the quality of climate-related data through verification and audit mechanisms as well as improvements in data accessibility. The report notes that a mix of policy interventions is needed to catalyze progress toward better data, based on the following three building blocks:

    • Rapid convergence toward a common and consistent set of global disclosure standards
    • Efforts toward a minimally accepted global taxonomy
    • Development and transparent use of well-defined and decision-useful metrics, certification labels, and methodological standards

    Going forward, NGFS will continue its evidence-based identification of the most prevalent data gaps—including by further engaging with other stakeholders such as non-financial corporates, data providers, and ratings agencies—and issue recommendations on how to bridge them. NGFS will examine possible recommendations for increasing data availability, including initiatives that make data available free of charge or at nominal cost to cover data processing. NGFS will also consider the types of verification scheme that could enhance the quality of raw data items and issue recommendations for achieving greater transparency and comparability on methodologies. To develop policy recommendations to help bridge data gaps, NGFS will, in liaison with relevant stakeholders:

    • identify how the progressive harmonization of metrics and methodological standards, certification labels, and taxonomies can contribute to the reliability and comparability of data, together with a wider implementation of mandatory disclosures in financial statements. In doing so, NGFS will engage with relevant stakeholders, including non-financial corporates and methodology providers.
    • examine how publicly accessible databases can improve data availability and comparability. In doing so, NGFS will reach out to initiatives that pool climate-related raw data in a single point and to relevant stakeholders in the field of geospatial data, focusing on the use of new technologies (such as artificial intelligence).

    This report was informed by a systematic literature review, outreach to a variety of international organizations and other relevant stakeholders, and various closed-door workshops. NGFS will continue to leverage the best practices identified within its membership to help central banks and supervisors, as well as the relevant stakeholders, to better assess and mitigate climate-related risks.

     

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    Keywords: International, Banking, Insurance, Securities, Climate Change Risk, Sustainable Finance, ESG, Data Gaps, Scenarios, Disclosures, Taxonomy, Certification Labels, Regtech, Artificial Intelligence, NGFS

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