Cross-Impact Analysis with Crowdsourcing for Constructing Consistent Scenarios

Cross-impact analysis is frequently used in scenario-analogous studies to identify critical factors influencing ecological change, strategic planning, technology foresight, resource allocation, risk mitigation, cost optimization, and decision support. Scenarios enable different organizations to comprehend prevailing situations, prepare for probable futures, and mitigate conceivable risks. Unfortunately, cross-impact analysis methods are often criticized for their difficulty in handling complex interactions, cognitive bias, time-intensiveness, heavy reliance on a limited pool of experts, and inconsistency in assigning judgment, which can affect the expected outcomes. This paper introduces a novel method for constructing consistent scenarios that addresses these criticisms and those associated with scenario methods. The method is based on cross-impact analysis and crowdsourcing for constructing consistent scenarios. The cross-impact analysis component of the method is based on advanced impact analysis and cross-impact balance analysis to, respectively, provide a time-efficient reduction in complex interdependent factors and construct consistent scenarios from a set of reduced factors. The crowdsourcing element leverages the cumulative intelligence of a group of experts to help mitigate cognitive bias and transparently give a more inclusive analysis. The method was implemented and validated with a practical case of renewable energy adoption, a vital challenge for socioeconomic progress and climate change resilience. While the method provides a sturdy foundation for writing scenario narratives, the result confirms its robustness for constructing consistent scenarios and suggests that the future of renewable energy adoption can be enhanced through careful cogitation of best-case, base-case, and worst-case scenarios, which include varying states of perceived value, awareness, and perceived support. These findings contribute to a more nuanced understanding of how socio-cognitive and institutional factors interact to influence the pace and direction of sustainable energy transitions.