INTERSECTION OF QUANTIZATION AND ENERGY EFFICIENCY IN GREEN CLOUD COMPUTING: A BIBLIOMETRIC ANALYSIS OF EMERGING TRENDS (2010–2025)
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This study aims to analyze, through a comprehensive bibliometric review, the evolution of scientific production that links sustainable computing and quantization techniques between 2010 and 2025, identifying trends, key actors, research gaps, and emerging directions in this field. This study adopts a deductive approach and is structured as a systematic bibliometric review. Data were collected from the Scopus database using a search string designed to capture the intersection between computational sustainability, energy efficiency and quantization. After applying predefined inclusion and exclusion criteria, 618 documents were selected, constituting both the universe and final analytical sample. Data analysis was conducted using quantitative scientific mapping techniques, including descriptive statistics, keyword co-occurrence analysis, co-authorship network analysis, and identification of the most influential journals and authors, supported by Python and VOSviewer software. The results reveal an accelerated growth in publications since 2018, with strong contributions from Asian countries, a predominance of applications in deep learning, and an increasing emphasis on energy-efficient hardware and architectures. The findings demonstrate that quantization has consolidated as a strategic technique in sustainable computing research, contributing to reductions in computational complexity and energy consumption. Simultaneously, the analysis highlights persistent gaps related to the integration of energy metrics, hardware–software co-design, and the extension of quantization strategies beyond AI-centric applications.
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