The global transition toward low-carbon energy systems and the rapid expansion of digital technologies, including artificial intelligence, are reshaping how modern societies produce, move, and process energy and information. Technologies such as renewable electricity networks, electric and hydrogen vehicles, green hydrogen production, and data centers are central to climate mitigation, digitalization, and AI-driven services. However, their large-scale deployment depends on substantial amounts of metals and minerals, raising concerns about resource availability and the environmental impacts associated with material extraction and production. Despite growing attention to energy efficiency and operational emissions, the material requirements and upstream environmental burdens of these emerging infrastructures remain insufficiently understood, particularly from a long-term and cross-sector perspective. This knowledge gap limits the ability to assess whether the low-carbon and digital transition can be realized sustainably. This PhD thesis addresses this gap by quantifying future material demand and associated environmental impacts for key energy and digital infrastructures using prospective material flow analysis, scenario modeling, and life-cycle assessment. By providing an integrated perspective across multiple infrastructure systems, the thesis contributes to a more comprehensive understanding of the material foundations of the low-carbon and digital transition.
Z. Chen (Thu,) studied this question.