Climate change-related statistical indicators (European Central Bank (ECB), 2024)
Climate change entails risks to the global economy and impacts financial stability. Beyond managing related risks, the financial sector can also contribute to the transition toward a net-zero economy. Guided by the ECB's climate and nature plan1 , this paper discusses the methodology and key findings of statistical indicators developed in three areas: sustainable finance, carbon emissions, and physical risk. Our work aims to enhance data transparency in climate change analysis, while informing monetary policy, financial stability and banking supervision. The indicators we have developed focus on the euro area financial sector and are built from harmonised granular datasets. They also utilise climate information from public sources to the extent possible. The sustainable finance metrics are built on well-established securities statistics and are at a more mature stage of development when compared with the other two climate risk indicators. While there are several data gaps that need to be addressed, the proposed statistical methodology offers a valuable framework for assessing climate risks in the European context, ensuring comparability across countries, time frames and under various climate scenarios. Meanwhile, the sustainable finance indicators track issuances and holdings of sustainable debt securities in the euro area, thus providing insights on funds for sustainable projects and reflecting progress in the transition towards a net-zero economy. The carbon emission indicators study the financial sector's exposure to counterparties with carbon-intensive business models and the carbon intensity of the securities and loans portfolio. They are useful to assist in evaluating the sector's contribution to financing the transition to a net-zero economy and the associated risks. Several methodological improvements are detailed in this paper to make it easier to interpret the indicators over time and understand the trends: imputation strategies for emission and financial data, a novel balancing algorithm that accounts for changes in the composition of the underlying non-financial corporations over time, adjustments for inflation and exchange rates, and a time series decomposition. Meanwhile, the physical risk indicators evaluate the impact of climate changeinduced natural hazards on the performance of financial institutions' loan and securities portfolio. The metrics cover a range of acute and chronic hazards, presenting risk scores and expected losses, enabling historical baselines to be benchmarked with climate scenarios where data permit. From the financial side, the framework we present accounts for maturities of the loan portfolios and the collateral pledged, as well as national insurance practices, thus providing a comprehensive risk assessment. This paper discusses the methodology, underlying data, and findings for each set of indicators, while also flagging possible constraints and opportunities for future development.
Keywords: Q51; Q54; Q59; 330; Statistical methodology; sustainable finance; climate change; carbon footprint; emissions; physical risk; data gaps