A cross-impact analysis of eight economic parameters in Iceland in the context of Arctic climate change

• Main Authors: Friðgeirsson, Þórður Víkingur, Steindórsdóttir, Freydís Dögg
• Format: Article in Journal/Newspaper
• Language: English
• Published: Research in applied business and economics 2018
• Subjects: Arctic region; economic parameters; decision analysis; delphi method; cross-impact analysis; E17; E66; F47; F62; L78; M21; M38; O11; O22; O33; P21; R10; Z30; Arctic; Climate change; Iceland
• Online Access: https://ojs.hi.is/index.php/efnahagsmal/article/view/a.2018.15.1.3

Previous studies have identified eight parameters as being the most influential on the Icelandic economy in the light of prevailing climate change. This study aims to investigate these economic parameters in relation to the Arctic Region and the Icelandic economy. The study is rooted in the theoretical framework of decision analysis and is intended to contribute to strategic and tactical decision making in the context of Iceland and developments resulting from climate changes in the Arctic Region. The study results could eventually lead do a “What if” decision model, where multiple scenarios can be analyzed by inserting/entering different probabilities of a particular outcome. The parameters under screening/review are interdependent, and to construct a decision model these interdependencies must be assessed. The Delphi method was employed, along with the cross-impact analysis, to shed light on the interconnections between eight of these parameters. Diverse views are revealed concerning the impacts between parameters, although a consensus was reached in the second round of the survey. In some instances, the level of agreement was high, while other estimates showed a wide range of responses. The diversity in answers suggests a higher level of uncertainty regarding some parameters and events compared to others. The survey also indicates how challenging the Delphi and cross-impact methods are in terms of the involvement of experts. However, when data is scarce, these methods provide significant insight.