Climate change is projected to adversely affect agriculture worldwide. This requires farmers to adapt incrementally already early in the twenty-first century, and to pursue transformational adaptation to endure future climate-induced damages. Many articles discuss the underlying mechanisms of farmers’ adaptation to climate change using quantitative, qualitative, and mixed methods. However, only the former is typically included in quantitative metanalysis of empirical evidence on adaptation. This omits the vast body of knowledge from qualitative research. We address this gap by performing a comparative analysis of factors associated with farmers’ climate change adaptation in both quantitative and qualitative literature using Natural Language Processing and generalized linear models. By retrieving publications from Scopus, we derive a database with metadata and associations from both quantitative and qualitative findings, focusing on climate change adaptation of farmers. We use the derived data as input for generalized linear models to analyze whether reported factors behind farmers’ decisions differ by type of adaptation (incremental vs. transformational) and across different global regions. Our results show that factors related to adaptive capacity and access to information and technology are more likely to be associated with transformational adaptation than with incremental adaptation. Regarding world regions, access to finance/income and infrastructure are uneven, with farmers in high-income countries having an advantage, whereas farmers in low- and middle-income countries require these the most for effective adaptation to climate change.

Understanding climate change adaptation constraints for different actors — governments, communities, individuals, and households — is essential, as adaptation turns into a matter of survival. Though rich qualitative research reveals constraints for diverse cases, methods to consolidate knowledge and elicit patterns in adaptation constraints for various actors are scarce. Therefore, this work analyzes associations between different adaptations and actors’ constraints to climate-induced floods and sea-level rise. Our novel approach derives textual data from peer-reviewed articles (published before February 2024) by using natural language processing, thematic coding books, and network analysis. The results show that social capital, economic factors, and government support are constraints shared among all actors.

Previous research on the relationship between geographical distance and the frequency of contact between family members has shown that the strength of family ties differs between Northern and Southern Europe. However, little is known about how family ties are reflected in peoples’ conversations on social media, despite research showing the relevance of social media data for understanding users’ daily expressions of emotions and thoughts based on their immediate experiences. This work investigates the question of whether Twitter use patterns in Europe mirror the North–South divide in the strength of family ties by analyzing potential differences in family-related tweets between users in Northern and Southern European countries. This study relies on a longitudinal database derived from Twitter collected between January 2012 and December 2016. We perform a comparative analysis of Southern and Northern European users’ tweets using Bayesian generalized multilevel models together with the Linguistic Inquiry and Word Count software. We analyze the association between regional differences in the strength of family ties and patterns of tweeting about family. Results show that the North–South divide is reflected in the frequency of tweets that are about family, that refer to family in the past versus in the present tense, and that are about close versus extended family.

The escalation of conflict in Ukraine has triggered the largest refugee crisis in Europe since WWII. As of early April 2024, over 5.9 million people have fled Ukraine. Large-scale efforts have been made to identify the major receiving countries. However, less is known about the subnational areas within host countries where refugees have migrated. Identifying these areas is key for the appropriate allocation of humanitarian aid. By combining digital Facebook API data and traditional data from Eurostat, this paper aims to identify and characterise potential settlement areas of Ukrainians across the main destination countries in Europe. We identify high concentrations of Ukrainians in urban areas with a preexisting diaspora and tight labour market conditions across southern, northern-west and central Poland and the city of Prague in the Czech Republic. We also find potential settlements in key urban agglomerations with a moderate diaspora and high levels of unemployment in Spain. Only in Romania, refugees seem to have settled in rural areas which show a moderate diaspora but low levels of unemployment. Potential settlement areas in Germany, Italy and the United Kingdom are spread across the country. Surprisingly, we do not identify potential settlement areas in bordering regions with Ukraine within neighbouring countries, suggesting that refugees may have used them as transit points. Our findings point out that different packages of humanitarian assistance may be needed according to the number of refugees and the characteristics of settlement areas.