This Randomized trial analyzes governance challenges in copper and lithium economies, indicating the need for adaptive strategies for resilience.
Critical mineral supply chains face multiple vulnerabilities arising from high production concentration in a few countries, under multiple risks of geopolitical, technological, and socio-environmental pressures, making the quality of sustainability governance in these countries a decisive factor in global supply chain resilience. Current frameworks that use symmetric analysis across indicators (e.g., SDG dashboards, ESG indices) cannot detect directional interrelationships among sustainability governance domains. We analyse 43 sustainability indicators across 12 copper- and lithium-producing countries (2005–2021), covering 80% of global copper production and over 90% of lithium production, using Structural Paradox Diagnosis (SPD). SPD combines the Inner Composition Alignment (IOTA), a range-based method that detects monotonic directional relationships without linearity assumptions, with a complex network analysis. Our analysis identifies four structural patterns (paradoxes) that characterize governance challenges threatening supply chain resilience: (a) social indicators exhibit strong internal synergies yet systematically drive environmental trade-offs particularly on biodiversity indicators; (b) economic indicators operate in structural isolation from socio-environmental indicators, preventing feedback mechanisms that would signal emerging supply disruptions; (c) indicators lack cross-dimensional relationships, creating fragmentation where sectoral efficiency undermines systemic resilience; and (d) indicators exhibit context-dependent systemic roles across countries, requiring adaptive rather than standardized interventions. SPD is not merely addressing the symptoms, enabling differentiated governance strategies: global coordination frameworks for universal structural patterns, regional cooperation for systematic variations, and adaptive national implementation for context-dependent indicators.
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Fuentes et al. (2026) studied this question.
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