Metrics, Volume 3, Issue 2 (June 2026) – 4 articles

Water scarcity is increasingly recognized as a slow-onset ecological crisis with major environmental, socio-economic and governance effects, yet systematic assessments of how research on this topic has evolved remain limited. This study addresses this gap through a bibliometric and thematic analysis of water-scarcity publications from 2000 to 2025, using VOSviewer (version 1.6.20), Biblioshiny™ (Bibliometrix version 4.3.1) and RStudio (version 2024.12.1 + 563) to map research trends, conceptual clusters and leading contributing countries, institutions and authors. The analysis shows that water scarcity research is organized around four dominant themes: adaptive water management and climate resilience, plant physiological responses to drought and water stress, ecosystem resilience and biodiversity under water scarcity, and water-limited agriculture and food security. Early scholarship focused heavily on biophysical processes such as drought tolerance and hydraulic conductivity, while recent studies increasingly incorporate socio-ecological, governance and policy dimensions, reflecting a shift toward holistic, solution-oriented approaches. Overall, the study provides a comprehensive overview of the evolution and global distribution of water scarcity research, highlighting the importance of integrating biophysical knowledge with human-centered strategies to support evidence-based decision-making, strengthen inclusive water governance, and enhance socio-ecological resilience in the face of a changing climate. Full article

The rapid growth of Artificial Intelligence (AI) is fundamentally transforming creative practices across all design disciplines. However, the commitment to addressing the ethical and environmental consequences of this transformation remains critically underexplored. This study aims to quantify the volume, track the evolution, and map the intellectual structure of academic literature at the intersection of AI, Design, and Sustainability. Using a comprehensive bibliometric approach, four distinct datasets were retrieved from the Scopus and Web of Science (WoS) databases on 3 October 2025. The study compares core “AI + Design + Sustainability” papers against an “AI + Design” baseline to assess the relative contribution of the sustainability dimension. The analysis identifies critical research gaps and offers strategic insights for scholars and institutions committed to fostering a more ethically and environmentally responsible design future. Full article

The rapid growth of artificial intelligence (AI) workloads and data center infrastructure is driving a surge in electricity demand, underscoring the need for robust metrics to evaluate energy generation and storage strategies. This study introduces the Bring Your Own Battery (BYOBattery) metric, a region-specific, temporally resolved indicator designed to quantify the ideal energy storage capacity required to mitigate generation-demand mismatches. The BYOBattery metric is computed as the minimum ideal battery storage required to eliminate generation-demand imbalances over a given time window, and is extended to incorporate curtailment via a convex optimization formulation to better manage peak generation and storage requirements. We applied the BYOBattery metric to wind, solar, and nuclear generation technologies across three major U.S. grid regions: the California Independent System Operator (CAISO), the Electric Reliability Council of Texas (ERCOT), and the Pennsylvania–New Jersey–Maryland Interconnection (PJM), using operational data from 2021 to 2024. Key findings are: (1) nuclear consistently requires the least storage in order to meet demand (i.e., one equivalent load hour compared with 10–25 h for wind and solar); (2) wind storage requirements decrease with increased capacity, whereas solar necessitates consistent levels of storage; and (3) the 30-year non-discounted cost per kWh for nuclear ($0.10/kWh) is substantially lower than that of wind or solar by a factor of 1–4 across all studied region. The BYOBattery metric enables comparative benchmarking of generation technologies under dynamic demand conditions and supports cost-informed planning for energy systems. This work contributes a reproducible, interpretable, and computationally efficient tool for energy system analyses and broader performance evaluations. Full article

The rapid expansion of bibliometric research has generated a large volume of descriptive “snapshot” studies that map publication trends but offer limited strategic or policy-relevant insight. Although improved database access and visualization tools have broadened participation in bibliometric analysis, methodological variability, limited reproducibility, and insufficient evaluative framing constrain its utility for research governance. We argue that bibliometric studies should not be conducted as ends in themselves, but as methods for addressing clearly defined, decision-relevant questions. We define evaluative bibliometrics as decision-oriented analysis grounded in explicit research questions, theoretically aligned indicator selection, temporal sensitivity, robustness assessment, and contextual interpretation. Key methodological considerations are examined, including database selection, search strategy design, attribution bias, normalization approaches, and science mapping parameters. We further synthesize emerging reporting frameworks and propose an evaluative extension framework that integrates decision-context specification with structured transparency requirements. By reframing bibliometrics as a decision-support discipline rather than a descriptive genre, this paper provides a methodological roadmap for researchers, editors, and institutions seeking to enhance the rigor, interpretability, and strategic relevance of bibliometric evidence. Full article