Intelligent Digital Framework for Art Investment Amid Geopolitical Conflicts: A Sustainable Economic Approach

Abstract

This study addresses the challenges of art investment in geopolitical conflict zones by proposing a framework for a predictive model. Utilizing meta-analysis, Q-sort methodology, and Hierarchical Cluster Analysis (HCP), the research identifies key factors influencing art valuation in unstable regions. The framework integrates political stability indicators, economic factors, cultural significance metrics, and market trends, employing machine learning algorithms to forecast price movements and assess investment risks. Results demonstrate the significant impact of geopolitical factors on art valuation and provide insights for model development. The study concludes that this framework offers a foundation for a robust tool to navigate art investment in conflict zones, fostering a more sustainable approach. Future trends impacting effectiveness include advancements in AI, the growing importance of ESG factors, and increasing art market digitization. Potential barriers to adoption include data availability in conflict zones, resistance from traditional market players, and ethical concerns regarding cultural heritage commodification. These can be mitigated through collaborative data-sharing initiatives, stakeholder education, and ethical guideline development. The framework's successful implementation could revolutionize art investment strategies in volatile regions, contributing to cultural heritage preservation and sustainable economic development.