GoKawiilHere we develop an integrated modelling framework to project PV waste generation across 32 global regions and to quantify the environmental and economic benefits of alternative recycling strategies that will inform policy design (Extended Data Fig. 1 and Supplementary Table 3). The framework consists of three interconnected components. First, material price trajectories are generated and incorporated into the GCAM to simulate regional PV deployment under alternative socioeconomic–climate futures. GCAM electricity-generation outputs are then converted into installed PV capacity and passed to a dynamic material flow analysis to estimate regional EOL PV waste. Second, projected waste streams are coupled with life-cycle assessment (LCA) and life cycle cost (LCC) to quantify technology-specific economic and climate outcomes of PV recycling. Third, a multidimensional scenario design, covering decommissioning pathways, recycling technologies, international trade configurations and subsidy schemes, is applied across the modelling system to assess how policy and market structures reshape regional recycling outcomes. Detailed parameter settings are provided in Supplementary Tables 4–9.
Material price model
To account for the impact of price uncertainty of critical materials on PV recycling, we simulate long-term price trajectories for four solar-relevant critical materials: copper, aluminium, silver and silicon. We adopt a material price model that was developed in previous studies7,51,52, incorporating historical price dynamics, demand growth and substitution potential. The resulting price trajectories are then introduced as exogenous inputs into the GCAM to determine PV deployment pathways under climate targets (as detailed in the next section). Rather than generating precise forecasts of future prices, our objective is to construct scenario-based price trajectories that enable the evaluation of PV deployment and recycling pathways under long-term material price uncertainty.
The material price model is grounded in dynamic market equilibrium in which long-term prices are endogenously determined by the marginal cost of new supply required to meet future demand. When existing mining capacity is insufficient, additional mining projects must be operated. Material prices are therefore endogenously governed by the marginal cost of newly installed mining capacity.
The modelling procedure consists of three steps:
Step 1: demand projection. Future demand for critical materials is determined by the demand growth rate and price-responsive substitution effects:
$${Q}^{t+1}={Q}^{t}(1+g+\Delta {p}^{t}\times \varepsilon )$$ (1)
where Qt denotes the material demand in period t; g is the annual exogenous demand growth rate (Supplementary Table 4); ε is the price elasticity of demand; and Δpt is the annual price change rate in period t, defined as
$$\Delta {p}^{t}=\frac{{p}^{t}-{p}^{t-1}}{{p}^{t-1}}$$ (2)
The initial price p 0 is exogenously specified use the global average price for each material in 2020.
... continue reading