payment defaulted for more than 90 days. In other countries, such as Mexico and Peru, the overdue days for recording NPLs depend on the type of credit portfolio, usually varying from 15 to 90 days. In addition, while some countries classify the sectoral exposures of their banks’ credit portfolios based on the International Standard Industrial Classification of All Economic Activities (ISIC), not all of them use the same revision and most countries employ a self-defined classification of economic activities. This affects sector comparability since the high level of aggregation of the publicly available information does not allow leveraging on standard correspondence tables. Therefore, to ensure comparability of banking exposure to economic sectors across countries, we associate them manually. See Annex C for more details.

Information on hazard mapping at the level of country-province is sourced from Thinkhazard!, which is a web-based tool enabling users to consider the impacts of disasters on development projects. It was developed by the Global Facility for Disaster Reduction and Recovery in partnership with various partners, including the World Bank Group. The hazard levels are derived from modeling information that combines the spatial distribution (e.g., flood depth, ground shaking, etc.) at a given frequency or return period.<sup>17</sup> We gather disasters at the level of countryprovince from the Emergency Events Database (EM-DAT) maintained by the Center for Research on the Epidemiology of Disasters. The data set includes the worldwide natural disasters of all types and records the dates, human death toll, material damages, and geographic locations of the disasters.

Our transition risk assessment, in addition to credit outstanding data, relies on firm-level information and CO2 emissions. Firm-level data is gathered from Orbis, a worldwide firm-level database with detailed information of balance sheets and income statements of firms compiled by Bureau Van Dijk from various national sources. After data cleaning, our sample covers 38,227 firms with total outstanding debt of US$ 2,112 billion. Data is as of the most recent available for each firm between 2018 and 2019. Table 4 reports the number of firms in each country and sector. Data on CO2 emissions are sourced from Climate Watch.

# **Methodology**

# **_Physical risks_**

To obtain a first indication of the value-at-risk of banks’ loan portfolios related to physical risks, we first compare the provincial distribution of banks' nonfinancial corporate credit portfolios with country-province-specific hazards. Our physical risk assessment is grounded in first-level administrative units, which carries unavoidable caveats. Banks’ credit portfolios are commonly available at the first level administrative units in our country sample (denoted as provinces throughout the paper for simplicity), so we use them as our unit of analysis across countries. This approach entails gains in terms of getting a homogeneous identification approach across countries in our sample, yet we lose the richness of exploiting more granular data. Besides, we follow a conservative approach for the province hazards’ classification by adopting the higher

> 17 For instance, the hazard level of an earthquake is assessed as high if there is more than 20 percent chance of potentially-damaging earthquake shaking in the next 50 years; the hazard level of water scarcity (drought) is classified as medium if there is up to a 20 percent chance droughts will occur in the coming 10 years. For additional reference, see https://gfdrr.github.io/thinkhazardmethods/#about-thinkhazard.

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