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This paper contributes to the recent literature on sustainability in the Arctic as a political concept. Parliamentary proceedings have increasingly been recognized as an important source of information for eliciting political issues. In this paper, we use unsupervised text mining techniques to analyze parliamentary speeches for Norway from the period from 2009 to 2016 to answer whether political coalitions talk differently about sustainability in the Arctic depending on being in opposition or government. We find that the difference between being in government and opposition, controlling for political label (left-right), is far more important than the difference between left and right, controlling for role (opposition-government). The results suggest that in the trade-off between political preferences and election success, the balance is tilted in favour of the latter. Our interpretation is that opportunistic behavior seems to dominate partisan behavior in the politics related to sustainability in the Arctic.

AbstractThe concept of resilience has been applied in several fields of academic research and has also grown in popularity among economists. The main contribution of this article is the systematic analysis and interpretation of the existing large body of resilience literature in economics by using topic modeling, a modern machine‐learning research method. The advantage of this method is that it offers a more in‐depth understanding of the themes in the resilience literature as opposed to the terminological classifications typically used in bibliometric studies. The results show that the identified topics are spread widely across different subareas of economics and deal with diverse themes, such as adaptation to climate change, stability of the financial system, and various types of shocks in regional economies. The findings reveal that the literature can be divided into two domains: one that deal with incremental changes occurring over a long period of time and the other dealing with unexpected, transient, and sudden changes. Furthermore, according to the results, well‐known, highly cited research papers combine knowledge from different fields. Policymakers seeking to support cutting‐edge research projects may benefit from this finding, as it emphasizes the need for policy measures to enhance cross‐fertilized research.

The European Union (EU) has recognized that universities and research institutes play a critical role in regional Smart Specialisation processes. Our research aims to identify thematic cross-border research domains across space and disciplines in Arctic Scandinavia. We identify potential domains using an unsupervised machine-learning technique (topic modelling). We uncover latent topics based on similarities in the vocabulary of research papers. The proposed methodology can be utilized to identify common research domains across regions and disciplines in almost real time, thereby acting as a decision support system to facilitate cooperation among knowledge producers.