indicators and contributes to ongoing discussions on critical minerals governance and the energy transition. 2. DATA AND METHODOLOGY This research introduces a data-driven perspective by applying Social Network Analysis (SNA) to map the global copper concentrate trade network over the period 2010–2024, using international trade data from UN Comtrade and the BACI database. The analysis focuses on copper ores and concentrates classified under HS code 2603. Trade values are expressed in current U.S. dollars. For each year, only countries with at least one active trade relationship are included, ensuring that network indicators reflect effective participation in international trade. For each year in the period of analysis, a directed and weighted trade network is constructed. Countries are represented as nodes, and export flows of copper concentrates are represented as directed edges from exporter to importer, with edge weights corresponding to export values (Lee et al., 2026). This representation follows standard approaches in complex network analysis, where extensive systems are modeled as networks composed of vertices and edges representing relationships among elements (Federico et al., 2023). Separate annual networks are generated to preserve temporal dynamics and avoid aggregation bias. The network analysis applies standard SNA metrics to characterize both regional and country-level structural roles. Out-degree and weighted out-degree centrality capture export connectivity and intensity. Betweenness centrality identifies countries that act as intermediaries or bridges within the trade network, capturing influence beyond export volumes. In addition to weighted out-degree (export strength), total weighted degree (node strength, defined as the sum of incoming and outgoing trade flows) is used when assessing the structural weight of modular communities in the network. This approach aligns with the literature on international trade networks, which often exhibit core–periphery structures where a limited number of central nodes concentrate flows while peripheral actors remain weakly interconnected (Newman, 2003). Local cohesion is examined through clustering coefficients, while community detection is performed using modularity optimization based on the Louvain algorithm, which allows the identification of trade communities driven by relational intensity rather than geographic proximity (Blondel et al., 2008). All network construction, metric computation, and visualization are conducted using Gephi version 0.10.1 (Aguinaga, 2024). 3. RESULTS AND STRUCTURAL ANALYSIS 3.1 Regional Overview This section evaluates the structural position of Latin America and the Caribbean (LAC) within the global copper concentrate trade network between 2010 and 2024. Beyond volumetric 32
RkJQdWJsaXNoZXIy MTM0Mzk2