Artificial Intelligence-Driven Optimization of Sustainable Cryptocurrency Ecosystems: A Literature Review
DOI:
https://doi.org/10.51137/wrp.ijsbe.752Keywords:
Cryptocurrency, Sustainability, Energy Optimization, ESG, Artificial IntelligenceAbstract
The rapid growth of cryptocurrency ecosystems has introduced concerns over their substantial energy consumption and environmental impact. The study adopts a systematic literature review (SLR) of high-quality research sourced from Scopus and Web of Science (WoS) to synthesize current empirical evidence on artificial intelligence (AI) applications for fostering sustainable cryptocurrency ecosystems. The review identified three main research themes: energy optimization in mining, predictive models for sustainable crypto adoption, and the interconnectedness of AI, Environmental, Social, and Governance (ESG), and fintech governance in emerging economies. The review reveals that AI models such as deep learning, predictive analytics, and reinforcement learning provide potential to mitigate energy consumption, enhance renewable integration, enable ensemble dynamic balancing, and improve the accuracy of sustainable adoption rates. Despite this growth, the body of knowledge remains fragmented, with limited focus on emerging economies characterized by unique challenges and opportunities, underscoring the need for context-specific research. Critical research gaps persist in data standardization, explainability of AI models, ethical governance, and the incorporation of technical solutions into regulatory and socio-economic realities. The review calls for the collaboration among researchers, industry participants, regulators, and policymakers to build resilient, low-carbon, and inclusive cryptocurrency ecosystems for the future, thereby bridging technological innovation with sustainability imperatives.
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