Green data centers have become a central concern in computing infrastructure management as organizations pursue energy efficiency alongside environmental sustainability. In this study, an attempt is made to conduct a bibliometric analysis to investigate the intellectual structure, research trends, key contributors, and emerging themes in green data center and computing energy efficiency scholarship. The data were collected from the Scopus database using keywords “green data center”, “green computing”, and “energy efficiency” and analyzed using VOSviewer to conduct co-occurrence analysis, citation analysis, co-authorship analysis, institutional collaboration analysis, and country collaboration mapping. The results show that green computing, energy efficiency, and data centers form the core themes linked with cloud computing, virtual machine consolidation, cooling systems, and renewable energy integration. Based on citation analysis, key contributions such as energy-aware resource allocation heuristics, dynamic virtual machine consolidation algorithms, and thermal-aware cooling strategies significantly influence the field. The collaboration analysis demonstrates that the United States, China, India, and the United Kingdom serve as major contributors to the global research network, supported by a small group of highly prolific scholars, while institutional affiliation reporting across the field remains fragmented and generically labeled. Furthermore, thematic evolution indicates a transition from infrastructure-level cooling and consolidation concerns toward machine-learning-driven scheduling and carbon-aware resource management. This study contributes by providing comprehensive mapping of green data center research development and identifying critical research gaps for future scholarship, particularly in integrating renewable energy with intelligent workload orchestration.
Loso Judijanto, Tina Isnaeni, Rani Eka Arini· West Science Social and Huma...· 0 citations
The rapid advancement of Artificial Intelligence (AI) has created increasing dependence on large-scale datasets, while simultaneously generating significant legal challenges related to privacy protection, data governance, and individual rights. This study examines whether synthetic data and privacy protection mechanisms can become the future foundation of responsible AI development through a normative legal analysis approach. The research analyzes relevant legal frameworks, regulatory principles, and conceptual developments concerning personal data protection, AI governance, and the utilization of synthetic data as a privacy-preserving alternative. The findings indicate that synthetic data provides substantial potential to reduce privacy risks by minimizing direct exposure to identifiable personal information while improving data accessibility for AI training and innovation. However, synthetic data does not automatically eliminate legal concerns, particularly regarding re-identification risks, accountability allocation, transparency, and regulatory uncertainty. The analysis demonstrates that effective AI governance requires a shift from traditional data protection approaches toward adaptive frameworks based on risk assessment, privacy-by-design principles, and responsible technology development. The study argues that synthetic data should not be viewed as a complete replacement for real-world data but as a complementary mechanism within a broader privacy-preserving AI ecosystem. Therefore, the future of artificial intelligence development depends on the integration of technological innovation and legally enforceable privacy protection frameworks that ensure transparency, accountability, and respect for fundamental rights.
Istiarsyah Istiarsyah, Tina Isnaeni, Rival Pahrijal· West Science Information Sys...· 0 citations
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