Cross-country risk quantification of extreme wildfires in Mediterranean Europe

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.     

The European Union's migration management aid: Developing democracies or supporting authoritarianism?

The European Union and its member states have invested billions of euros in migration management programs that purport to promote “good migration governance” around the world. But what is the impact of migration management aid on governance outside of the EU? In this article, we theorise the impact of migration management aid on governance in recipient countries by analysing the key policy areas of intervention. To do so, we focus on the effects of the European Union Emergency Trust Fund for Africa and draw on data collected from policy and government documents, in addition to secondary literature, to understand how migration management aid negatively impacts institutions of governance in recipient countries. While migration management aid may build the capacity of states to monitor borders and prevent irregular migration, we argue that this fails to account for negative externalities including human rights abuses and increased surveillance for migrants and citizens alike.     

Remitting Behaviour of “Temporary” and “Permanent” Migrants: The Case of Greeks in Germany and Australia

This paper makes the point that migrants have different motives for sending remittances and remittance receivers have different claims on migrants' income, depending on whether people move to accumulate capital to improve their living at home after they return — temporary migration — or to start a new life in a foreign country — permanent migration. This hypothesis is empirically tested with data from Greek–German and Greek–Australian migration. The findings attest to the fact that German remittances constitute obligatory income streams to close family at home, while Australian remittances are gifts. Some quantitative estimates of the relative impact of individual factors on remittances are also obtained. Some hints are also given for a changing remitting behaviour of Greek migrants in Germany, along with the changing character of migration in that country.     

Neural Networks: A New Tool for Predicting Thrift Failures*

A neural network model that processes input data consisting of financial ratios is developed to predict the financial health of thrift institutions. The network's ability to discriminate between healthy and failed institutions is compared to a traditional statistical model. The differences and similarities in the two modelling approaches are discussed. The neural network, which uses the same financial data, requires fewer assumptions, achieves a higher degree of prediction accuracy, and is more robust.     

The COVID-19 pandemic,mask-wearing,and emotion recognition during late-childhood

Face masks are an effective and important tool to prevent the spread of COVID-19, including among children. However, occluding parts of the face can impact emotion recognition, which is fundamental to effective social interactions. Social distancing, stress, and changes to routines because of the pandemic have also altered the social landscape of children, with implications for social development. To better understand how social input and context impact emotion recognition, the current study investigated emotion recognition in children (7–12 years old, N = 131) using images of both masked and unmasked emotional faces. We also assessed a subsample of participants (“pre-pandemic subsample,” n = 35) who had completed the same emotion recognition task with unmasked faces before and during the pandemic. Masking of faces was related to worse emotion recognition, with more pronounced effects for happy, sad, and fearful faces than angry and neutral faces. Masking was more strongly related to emotion recognition among children whose families reported greater social disruption in response to the pandemic. Finally, in the pre-pandemic subsample, emotion recognition of sad faces was lower during versus before the pandemic relative to other emotions. Together, findings show that occluding face parts and the broader social context (i.e., global pandemic) both impact emotion-relevant judgments in school-aged children.     

Robustly assigning unstable items

Journal of Combinatorial Optimization - We study the robust assignment problem where the goal is to assign items of various types to containers without exceeding container capacity. We seek an...