Analysis of Potential for Critical Metal Resource Constraints in the International Energy Agency’s Long-Term Low-Carbon Energy Scenarios

Abstract

As environmental problems associated with energy systems become more serious, it is necessary to address them with consideration of their interconnections-for example, the energy-mineral nexus. Specifically, it is unclear whether long-term energy scenarios assuming the expansion of low carbon energy technology are sustainable in terms of resource constraints. However, there are few studies that comprehensively analyze the possibility of resource constraints in the process of introducing low carbon energy technology from a long-term perspective. Hence, to provide guidelines for technological development and policy-making toward realizing the low carbon society, this paper undertakes the following: (1) Estimation of the impact of the expansion of low carbon energy technology on future metal demand based, on the International Energy Agency (IEA)'s scenarios; (2) estimation of the potential effects of low carbon energy technology recycling on the future supply-demand balance; (3) identification of critical metals that require priority measures. Results indicated that the introduction of solar power and next-generation vehicles may be hindered by resource depletion. Among the metals examined, indium, tellurium, silver, lithium, nickel and platinum were identified as critical metals that require specific measures. As recycling can reduce primary demand by 20%~70% for low carbon energy technology, countermeasures including recycling need to be considered.

Requirements for Minerals and Metals for 100% Renewable Scenarios

“…A range of variables affect the results, including the installed capacity and technology type in the energy scenario; the assumed market demand of technology types (e.g., type of battery or solar panel); and the material intensity assumptions. The projected future demand for lithium is higher than in previous studies, and that for cobalt is similar or higher (Månberger and Stenqvist 2018;Watari et al 2018;Valero et al 2018;Watari et al 2018), as shown in Table 11.5. This is primarily attributable to the ambitious renewable energy scenario used in this study, which includes achieving a 100% renewable transport system by 2050, whereas the other studies have still included a large share of gasoline-powered cars in 2050.…”

“…A number of studies have specifically and directly addressed the Paris Agreement targets (ensuring that the temperature rise does not exceed 2.0 °C), although the modelling frameworks have been slightly different. Some of the authors of the present chapter have been involved in these studies (Tokimatsu et al 2017;Watari et al 2018).…”

“…For example, Mohr et al (2012) developed commodityfocused supply projections for lithium, with an emphasis on the origins and locations of available lithium deposits over time, which are contrasted with a simple demand function. Other studies have placed more emphasis on demand scenarios, either for a specific geographic location, such as Germany (Viebahn et al 2015) or globally (Valero et al 2018;Watari et al 2018), and some have explored the role of technology mixes and material substitution in detail (Månberger and Stenqvist 2018).…”

PV Waste Management at the Crossroads of Circular Economy and Energy Transition: The Case of South Korea

“…Two parameters are necessary to estimate the lifespan distribution of PV in South Korea using the Weibull function: shape parameter (α) and mean lifespan (1/λ). The lifespan distribution or the loss/failure distribution varies according to the shape parameter [29]. In other words, it illustrates how the failure rate of a device changes according to time.…”

“…Among these parameters, this study uses 5.3759 from Kuitche [31] and 3.5 from EC [31] as shape parameters for estimating PV module waste generation in South Korea. The shape parameter estimated by Kuitche [31] is used for many studies [22,29,32] because it was found using field data. The shape parameter being used in EC [33] is also used to estimate PV waste generation.…”