Material requirements for low-carbon energy technologies: A quantitative review

Deployment of clean energy technologies will require a considerable amount of materials. The surge in demand for metals related to emerging energy technologies may hinder the energy transition. In this study we provide a comprehensive overview and analysis of existing work in this field, a solid quantitative baseline for material requirements of different energy technologies and quantitative information that can be used to generate learning curves for the material requirements of different energy technologies. We conducted a quantitative review of the material requirements of low-carbon energy technologies in 132 scientific publications, and provided a comparative analysis of detailed data including material intensity and lifetime data. Besides providing a large amount of structured quantitative data, the results of our work indicate that: (1) research on the demand for low-carbon technology related metals has received much attention since the 2010s; (2) around 80% of the publications focus on the global level while national level studies are underrepresented; (3) science-based future scenarios are the main means of estimating total future material requirements; (4) most studies foresee material constraints of large-scale implementation of low-carbon technologies and the secure and responsible supply of these materials is still the subject of discussion; (5) changes in metal intensity caused by technological development and material requirements for non-critical components are important though often overlooked. •A comparative analysis of the research objects, scopes and methods of 132 publications.•Extracted and reviewed nearly 2000 material intensity data of these low-carbon technologies.•Most research focuses on the global level, while research on the national level is still limited.•Changes in material intensity caused by technological innovation are often ignored.•Material requirements for other parts other than critical components may be underestimated.