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We estimate the country‐level risk of extreme wildfires defined by burned area (BA) for Mediterranean Europe and carry out a cross‐country comparison. To this end, we avail of the European Forest Fire Information System (EFFIS) geospatial data from 2006 to 2019 to perform an extreme value analysis. More specifically, we apply a point process characterization of wildfire extremes using maximum likelihood estimation. By modeling covariates, we also evaluate potential trends and correlations with commonly known factors that drive or affect wildfire occurrence, such as the Fire Weather Index as a proxy for meteorological conditions, population density, land cover type, and seasonality. We find that the highest risk of extreme wildfires is in Portugal (PT), followed by Greece (GR), Spain (ES), and Italy (IT) with a 10‐year BA return level of 50'338 ha, 33'242 ha, 25'165 ha, and 8'966 ha, respectively. Coupling our results with existing estimates of the monetary impact of large wildfires suggests expected losses of 162–439 million € (PT), 81–219 million € (ES), 41–290 million € (GR), and 18–78 million € (IT) for such 10‐year return period events. SUMMARY: We model the risk of extreme wildfires for Italy, Greece, Portugal, and Spain in form of burned area return levels, compare them, and estimate expected losses. [ABSTRACT FROM AUTHOR]

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The emergence of a mass market for electric vehicles (EVs) offers development opportunities for countries that have abundant resources of cobalt, nickel, lithium, copper, aluminium and manganese. Not surprisingly, developing countries have proposed ambitious plans to expand production of these raw materials. However, an observation from the resource curse literature is that strong institutions are required if they are to mitigate the risk of poorly directed, often excessively procyclical, investment, not least because of the complexity, opacity and price volatility of many raw materials utilised by global EV value chains. This paper examines the outlook for EV demand and associated raw material usage paying attention to the drivers and sensitivities required to assess and track future market transformations. These end use shifts are then placed in the context of the broader supply chain adjustments and trends shaping the demand. For resource exporters, adapting to structural change will require fiscal, regulatory, environmental and institution reforms designed to capture shifting patterns of resource wealth in a way which takes appropriate account of comparative advantages in specific value chains and mitigates adverse environmental and social consequences from their extraction and processing. [ABSTRACT FROM AUTHOR]

We estimate the country-level risk of extreme wildfires defined by burned area (BA) for Mediterranean Europe and carry out a cross-country comparison. To this end, we avail of the European Forest Fire Information System (EFFIS) geospatial data from 2006 to 2019 to perform an extreme value analysis. More specifically, we apply a point process characterization of wildfire extremes using maximum likelihood estimation. By modeling covariates, we also evaluate potential trends and correlations with commonly known factors that drive or affect wildfire occurrence, such as the Fire Weather Index as a proxy for meteorological conditions, population density, land cover type, and seasonality. We find that the highest risk of extreme wildfires is in Portugal (PT), followed by Greece (GR), Spain (ES), and Italy (IT) with a 10-year BA return level of 50'338 ha, 33'242 ha, 25'165 ha, and 8'966 ha, respectively. Coupling our results with existing estimates of the monetary impact of large wildfires suggests expected losses of 162-439 million € (PT), 81-219 million € (ES), 41-290 million € (GR), and 18-78 million € (IT) for such 10-year return period events. SUMMARY: We model the risk of extreme wildfires for Italy, Greece, Portugal, and Spain in form of burned area return levels, compare them, and estimate expected losses., (© 2022 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis.)