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How central bank monetary policies are now being shaped by climate change

Climate-centric financial risks are gradually becoming a principal aspect of risk analysis for central banks in the formulation of their monetary policy. This is, however, hardly a simple exercise. It requires a thorough assessment of data relating to climate risks faced by various industries, the development of a novel methodology, as well as time. Given the lack of historical evidence or a possible recourse to backward testing, validating new methodologies automatically becomes a major challenge.

For this, central banks partly rely on risk assessments carried out by multiple credit-rating agencies. These agencies have already started incorporating climate-related risk indicators such as green scores, and other environmental commentary in their assessment of firms and industries. Most rating agencies, however, signal risk probabilities over a period of between one and three years – whereas most climate risks are not expected to happen for at least another decade.

Policymakers today are trying to eclipse this ‘horizon problem’ by pressuring companies to internalize costs relating to climate change. They are doing so by acting as catalysts, linking availability of assets to climate-based reporting. For example, by reducing purchase of securities from firms that do not properly disclose their climate risks or by reducing credit scores for those with a lack of transparency, central banks are gradually aiming to implement a more consolidated monetary policy that includes several qualitative environmental, governance or social metrics.

In addition, by also incorporating scenario analyses to their current approach for classification (i.e. analysing essential business ratios such as earnings before interest, depreciation, taxes, etc.), central banks (such as the German Bundesbank) are also calculating potential costs for firms engaging in policy-driven changes – like considering increased costs owing to the carbon tax –  thereby allowing them to better estimate default probabilities.

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