Towards an equitable future of global photovoltaic waste recycling

Rapid deployment of PV systems accelerates decarbonization and expands energy access; however, the impending end-of-life (EOL) PV module waste crisis has become a global concern. By 2050, global PV waste is projected to reach 200 Mt (2 × 10⁸ t)². Unregulated disposal of these modules presents acute environmental hazards, including the leaching of toxic heavy metals (such as lead and cadmium) into soils and groundwater systems^3,4, a concern exacerbated by the untapped reservoir of valuable materials and strategic minerals (such as silicon, tellurium, silver and copper) contained within them^5,6. Effectively recycling EOL PV modules is therefore not only an environmental imperative but also a strategic necessity to secure material supply chains for future PV deployment and sustain global decarbonization goals.

Given the critical role of PV recycling in critical metal supply security and environmental safety, the issue has garnered worldwide attention^7,8. International bodies and major economies have implemented multifaceted policies and regulations to address this pressing issue. For example, the EU has mandated the collection and recycling rate of EOL PV modules through the Waste Electrical and Electronic Equipment (WEEE) Directive, and Victoria, Australia, has implemented a landfill ban for PV waste^9,10. China, the world’s largest PV installer, recently launched a comprehensive policy portfolio aiming to advance recycling technologies, reduce costs and improve resource efficiency¹¹. These policy incentives have spurred the development of mechanical, thermal and chemical recycling technologies, each with distinct trade-offs in cost, resource recovery and emissions^12,13. Compounding these dynamics is the practice of exporting EOL PV modules for recycling, driven by inadequate local capacity across regions^14,15. However, the effectiveness of current PV waste management strategies remains unclear, particularly amid substantial cross-national and regional heterogeneity in socioeconomic conditions, supply-chain structures, historical PV installations, policy frameworks and technological capabilities¹⁶. Furthermore, these contextual factors are expected to evolve substantially in the future. Understanding PV waste management performance across heterogeneous regions and under plausible future scenarios (for example, climate targets, material supply constraints, installation trajectories and policy shifts) is therefore essential for developing effective, economically viable and equitable global PV waste solutions.

A foundational challenge lies in forecasting PV waste generation. Existing studies have modelled waste trajectories at the global, regional and national scales^13,17, but these efforts rely predominantly on demand-side factors such as historical installations, electricity demand and population growth^18,19,20. Supply-side constraints, such as the price volatility of key materials such as silicon and silver, have been largely overlooked. Notably, sharp price increases for key materials (such as silicon, silver, copper) could delay global PV deployment by up to 13% (ref. ⁷)—a supply-side shock that shapes technology adoption, deployment scales, module lifetimes and, ultimately, waste generation dynamics. The omission of such supply-side drivers limits the accuracy and robustness of existing waste projections.

Even with improved projections of waste generation, evaluating the performance of recycling technology remains a persistent challenge. Earlier studies assessing national or regional PV recycling often adopt highly simplified approaches, assuming each region will deploy a single recycling technology^15,21, a ‘homogeneous scenario’ that implicitly assumes uniformity in recycling pathways across regions. In reality, technology selection is constrained by factors including existing patents, recycling infrastructure, environmental regulation stringency and waste volumes. Rather than optimal technology choices, decision-making in this space typically involves incremental adjustments within a set of feasible options, reflecting ‘bounded rationality’²², a dynamic ignored in PV waste management research. While some studies have incorporated temporal changes in recycling technologies²³, they rely on exogenous assumptions of rapid cross-regional technology substitution or diffusion, failing to capture the path dependence of technological development²⁴ driven by economic constraints, learning curves and institutional adaptability in practice.

Compounding these analytical gaps, existing research also predominantly assumes local treatment of decommissioned PV modules^21,25,26. Few studies have examined whether and to what extent PV waste trade drives inter-regional inequality in the distribution of economic and climate benefits from recycling. Yet the global landscape is far from uniform, characterized by an uneven distribution of recycling capabilities. Mature technologies are concentrated in high- and middle-income regions (such as the EU, the United States, China, Japan and South Korea), in contrast to low-income regions, which lack the technical expertise and industrial capacity for independent recycling and are therefore reliant on outsourced recycling. This imbalance underscores the need for cross-regional collaboration, yet the performance of outsourced recycling models remains poorly understood. Furthermore, while subsidies are widely used as a policy tool to improve the economic viability of PV recycling^5,27,28, it remains unclear to what extent different subsidy designs, such as continuous versus declining support, or linkage to carbon prices, affect the overall economic benefits and their distribution across regions.

To address these interconnected gaps, we developed an integrated model to evaluate multiple PV recycling scenarios (Methods). We apply this model to 32 regions and quantify disparities in economic benefits across 1,708 recycling practice scenarios (Extended Data Fig. 1). Our results show that sustained increases in material prices could delay global PV deployment and reduce module decommissioning by up to 8.4% and 2%, respectively. Under this constrained context, carbon-priority recycling technologies deliver the largest global economic and climate benefits. However, when international trade is introduced, a critical trade-off between efficiency and equality emerges. While economies of scale and technological learning lower global recycling costs, the associated benefits accrue increasingly to technologically advanced, upper-middle-income regions with large PV waste arisings, therefore widening regional disparities. Cost-based subsidies can narrow regional benefit gaps, whereas carbon-price-based subsidies may further widen global inequalities by disproportionately favouring regions with mature recycling technologies and established carbon markets. To reconcile efficiency and equality, we propose adaptive strategies—for example, phased subsidy frameworks that initially address critical deployment barriers while incentivising long-term innovation. Moreover, international cooperation mechanisms, such as Paris Agreement partnerships, should further prioritize technology transfer and targeted funding to build local recycling capacity in low-income regions. These insights offer actionable pathways toward equitable and scalable circularity for PV waste.

Driven by the explosive expansion of PV installed capacity, global PV waste generation is projected to increase substantially. Annual global PV installations have surpassed 100 GW (1 × 10¹¹ W) annually since 2020, lifting the total installed capacity from less than 800 GW in 2020 to 3,781 (under Shared Socioeconomic Pathway 1-6.0 (SSP1-6.0)), to 5,037 GW (under SSP1-2.6) by 2030 and further to 8,703 (under SSP3-2.6) and 24,544 GW (under SSP2-2.6) by 2060. Assuming an average PV module lifetime of 25–30 years, a global peak in module retirements is anticipated during 2030–2060. Our projections show that annual global PV module retirements rise from 0.02 Mt yr⁻¹ in 2020 to 0.5 Mt yr⁻¹ by 2030, then over 19 Mt yr⁻¹ by 2060, driving cumulative waste to surge by approximately 150–200-fold during 2030–2060, reaching 297 Mt (under SSP1-6.0) to 402 Mt (under SSP1-2.6) by 2060.

The geographical distribution of this waste burden is shifting over time (Fig. 1b,c). From 2020 to 2040, high-income regions dominate, generating over 50% of global PV waste (12.2–12.5 Mt). Among these, the EU-15 is the primary contributor, producing 5.8–5.9 Mt (25.2–26.0% of the global total). However, by 2060, upper-middle-income regions take the lead, with China emerging as the largest single source. China alone is projected to generate 112.8–160.5 Mt, accounting for 36.2–39.9% of global PV waste, substantially surpassing the EU-15 (30.9–39.7 Mt) and the United States (25.7–39.0 Mt). Concurrently, the share of lower-middle-income regions nearly doubles, increasing from 7.7–8.1% in 2040 to 15.9–16.9% in 2060. India dominates this group, contributing 8.6–9.2% (26.8–35.2 Mt) of global waste by 2060. By contrast, low-income regions remain minor contributors, accounting for less than 2% throughout 2020–2060.

Material prices have a pivotal role in shaping PV deployment and waste trajectories. Our modelling assesses the impacts of elevated copper, aluminium, silver and silicon prices on PV expansion and decommissioning dynamics (Extended Data Fig. 2). Compared with the low-price baseline, high-price scenarios reduce global cumulative installed PV capacity and waste volumes by 2060 by 5.7–8.4% (597–1,966 GW) and 1.0–2.0% (2.9–7.3 Mt), respectively, equivalent to a 5-year delay in meeting RCP2.6 installation targets. Even under less stringent RCP6.0 climate goals, higher material costs yield comparable reductions: 5.7–7.6% in capacity and 1.0–1.4% in waste. Regional PV waste volumes are differentially affected by material price increases (Extended Data Fig. 3). For regions with larger installed PV capacity, specifically China, the United States and India, their PV waste drops more substantially, by 0.93–2.47 Mt, 0.29–0.89 Mt and 0.31–0.68 Mt under high-price scenarios relative to the low-price scenarios, respectively. For regions with relatively small volumes of PV installations (such as Argentina), the material price has a limited impact, with PV waste dropping by 0.01–0.03 Mt.

We evaluated the global economic and climate performance of local PV waste recycling (excluding cross-border trade) across four technology pathways: business as usual (BAU), economic-priority, carbon-priority and technology diffusion. These pathways integrate chemical, thermal and mechanical recycling approaches, which present divergent economic returns and carbon mitigation potential (Supplementary Fig. 1).

Relative to the BAU pathway, the carbon-priority and economic-priority pathways deliver substantially greater economic and climate benefits by shifting recycling capacity towards thermal technologies, which deliver the highest per-unit benefits among the three recycling technologies (Supplementary Fig. 1). Under all of the decommissioning scenarios, these two pathways yield comparable performance and significantly outperform the BAU pathway. By 2060, cumulative economic benefits reach US$670.8 billion and climate benefits reach 2.44 Gt carbon dioxide equivalent (CO₂ equiv.) under the carbon-priority pathway (Fig. 2k,o), compared with US$662.0 billion and 2.40 Gt CO₂ equiv. under the economic-priority pathway (Fig. 2j,n), representing gains of more than 60% relative to the BAU pathway. These outcomes are driven primarily by the larger role of thermal recycling, which accounts for 44.0–51.6% of total recycling capacity by 2060, versus 10.1% under BAU (Extended Data Fig. 4).

Minor discrepancies between the carbon-priority and economic-priority pathways mainly originate from variations in thermal recycling market penetration. Under the economic-priority scenario, investment constraints slow the early expansion of capital-intensive thermal recycling in middle-income regions where PV waste accumulates most rapidly. For example, in 2040, thermal recycling achieves a 22.7% market share in middle-income regions under the carbon-priority pathway versus 19.8% under the economic-priority pathway (Extended Data Fig. 4a,d). By contrast, the technology-diffusion pathway yields more-moderate gains, increasing global cumulative net economic and climate benefits by 37.0–44.3% and 34.3–36.1%, respectively, relative to the BAU pathway. While this pathway extends adoption across more regions, its impact is constrained by the dominance of mechanical recycling, which exceeds 50% and delivers lower economic and climate returns than thermal and chemical recycling.

PV waste recycling becomes economically viable across all scenarios between 2035 and 2040 (Fig. 2a–d,i–l), driven by rising material prices and technological progress. Higher market prices for recovered materials, especially silver, aluminium and silicon, increase unit recycling benefits from US$131.8–304.3 per tonne in 2020 to as much as US$457.1 per tonne by 2040, while the learning-by-doing effect reduces recycling costs from US$342–2,550 per tonne to US$55.9–402.7 per tonne over the same period. As a result, cumulative net economic benefits under high-price scenarios exceed those under low-price scenarios by 25.9–33.6% by 2060 across all pathways (Extended Data Fig. 5).

Distinct regional heterogeneities are observed across all pathways (Fig. 3a–h). Middle-income regions consistently produce larger economic and climate benefits than other income groups. For example, under the carbon-priority pathway, benefits in upper-middle-income regions cluster in the top-right quadrant: roughly two-thirds of economic benefits range from US$1,659.2 to 2,895.8 per tonne, and roughly two-thirds of climate benefits range from 6.5 to 8.5 t CO₂ equiv. per tonne. By 2060, these regions contribute over 50% of global cumulative net economic benefits and cumulative climate benefits, as they generate more than half of global PV waste and reach a thermal recycling share of up to 30%, establishing them as the primary contributors to global PV recycling benefits. Conversely, high-income regions contribute a smaller proportion. Despite accounting for 34.0–36.3% of global decommissioned PV waste, they deliver only 28.7% of cumulative net economic benefits and 37.6% of cumulative climate benefits by 2060 (Fig. 3k,o), due to higher recycling costs and lower waste throughput. Furthermore, low-income regions exhibit substantial underperformance. Even under the technology-diffusion pathway, their contribution is negligible—by 2060, they account for merely 1.3–2.1% of total economic gains (US$4.2–10.6 billion) and 1.2–1.8% of total avoided emissions (18.2–32.7 Mt CO₂ equiv.), mainly owing to limited recycling scale and lower technological maturity.

Regional heterogeneities are further intensified when material price effects are incorporated into recycling pathways. For example, under the carbon-priority pathway by 2060, the largest inter-regional gap in unit net economic benefits expands from US$1,945.7 per tonne under low-price scenarios to US$2,387.8 per tonne under high-price scenarios (Supplementary Fig. 2b). This growing divergence is driven by variations in recycling technology portfolios: regions with higher economic benefits (such as China and South Korea) depend more on thermal technologies, which enable superior metal recovery efficiencies. As material prices rise, these regions capture a disproportionate share of incremental value. By contrast, low-income regions dominated by mechanical recycling, a method associated with lower material recovery efficiency and output quality, accrue fewer incremental benefits.

To assess how cross-regional PV waste recycling influences economic and climate benefits, we extend the local recycling framework (which excludes trade) to incorporate three trade regimes: extended producer-responsibility-oriented trade (EPR), expanded global trade and regional trade (Methods). This generates scenarios combining technology pathways and trade regimes (such as BAU–EPR). For each scenario, we evaluate disparities in recycling benefits across 32 regions using economic and climate benefit variances (Fig. 4), ranging from 0 to 1. The scenarios fall into four distinct categories: high benefits with high inequality, high benefits with low inequality, low benefits with high inequality and low benefits with low inequality.

The least favourable outcomes occur under the BAU–EPR scenarios that entail low benefits and high inequality. By 2060, with mechanical recycling dominating (nearly 60%) in these scenarios, economic and climate benefits account for only 29.3–55.9% of the maximum observed potential. Meanwhile, the average normalized variance is approximately 0.12 (Fig. 4a,d), substantially higher than that observed in other trade scenarios (0.02–0.03). Similarly, local recycling scenarios show relatively high equality but lower overall benefits, as restrictions on cross-regional flows prevent PV waste from reaching middle- and high-income regions that have more advanced recycling technologies and faster learning. Consequently, the economic and climate benefits are 68.6–77.5% of those under trade-enabled scenarios (Extended Data Fig. 6).

Among all scenarios, the carbon-priority-oriented EPR scenarios achieve the maximum global recycling benefits; however, they generate greater regional disparity (Fig. 4). Under these scenarios, PV waste flows towards regions with advanced technologies and low recycling costs. This leads to global cumulative net economic benefits of US$529.1–935.5 billion and emission reductions of 2.2–3.32 Gt CO₂ equiv., both exceeding those of other scenarios (US$189.2–926.3 billion and 0.9–3.31 Gt CO₂ equiv.). However, this concentration of recycling activities in a few ‘low-cost sinks’ substantially exacerbates inequalities in the distribution of benefits. In particular, higher material prices further intensify this economic benefit disparity, with variance values (0.47–1.0) significantly surpassing those observed under low-price scenarios (0.29–0.64). This is because high material prices disproportionately improve unit net economic returns in technologically advanced regions (such as upper-middle-income regions). Consequently, upper-middle-income regions stand out as the primary recycling stakeholders (Extended Data Fig. 7): their share of cumulative total net economic benefits increases from roughly two-thirds in locally oriented recycling scenarios to around three-quarters under carbon-prioritizing EPR frameworks, alongside a corresponding 0.5–0.7 Gt CO₂ uplift in their cumulative carbon emission reductions.

By contrast, expanded global and regional trade scenarios lead to a more even distribution of recycling activities, reducing benefit variance to 0.003–0.448 and 0.008–0.489, respectively. However, these gains in equality come at the expense of global benefits. This is mainly because these trade scenarios disperse PV waste to regions with weaker learning effects (for example, lower-middle-income regions) and greater cost disadvantages (for example, high-income regions) (Extended Data Figs. 8 and 9), thereby diminishing the overall benefits. For example, the average share of recycling benefits accruing to lower-middle-income regions increases from 3.6–4.3% under EPR to 4.8–6.0% under expanded global trade and 6.7–8.2% under regional trade (Extended Data Fig. 7g–l). However, because unit net benefits in these regions remain lower than in upper-middle-income regions, both trade scenarios weaken overall performance relative to EPR. Compared with EPR, expanded trade reduces global cumulative net economic benefits by 5.8–8.6% and climate benefits by up to 4.9%, whereas regional trade lowers them by 2.1–5.4% and 2.8%, respectively. These results reveal a trade-off between maximizing total recycling benefits and distributing them more evenly.

To further evaluate the influence of policy interventions on economic benefits and regional distribution patterns, we developed five subsidy schemes: no subsidy, continuous subsidy, declining subsidy, low-carbon price and high-carbon price (Methods).

Among all subsidy schemes, the declining subsidy, continuous subsidy and low-carbon-price subsidy improve inter-regional equality by 5.8–8.1%, 5.2–6.9% and 0.5–3.8%, respectively, whereas the high-carbon-price subsidy scheme exacerbates inequality (Fig. 5). The declining-subsidy scheme is substantially more cost-effective: it requires only 2.9–11.9% (US$0.4–1.9 billion by 2060) of continuous subsidy expenditures yet achieves 0.6–1.2% greater equality improvements. For example, the inter-regional gap in unit net recycling benefits narrows from US$1,147 per tonne of recycled PV waste in the no-subsidy scheme to US$1,061 per tonne in the declining-subsidy scheme, demonstrating superior equality outcomes. This improvement stems from a time-differentiated phase-out mechanism that gradually removes subsidies as recycling becomes self-sustaining. The timeline varies by region’s economic capacity: middle-income regions reach profitability around 2032 and phase out subsidies by 2044, while high-income regions achieve viability by 2040 and phase out subsidies by 2058. By withdrawing fiscal support once recycling is profitable, this approach prevents some regions from accumulating excessive profits through continued subsidies, thereby reducing inter-regional inequality.

Under the continuous-subsidy scheme, recycling benefits increase substantially, rising from US$189.6–935.5 billion in the no-subsidy scheme to US$198.9–953.7 billion by 2060. The inter-regional gap in unit net recycling benefits also narrows slightly, from US$1,150 per tonne of recycled PV waste under the no-subsidy scheme to US$1,068 per tonne under the continuous-subsidy scheme (Fig. 5). Despite these gains, the fiscal cost is prohibitive. In middle-income regions, for example, recycling benefits increase from US$110–128 per tonne in 2032 to US$1,131–1,782 per tonne in 2060, but this is only 1.7–2.9% higher than the performance under the declining-subsidy scheme. By contrast, cumulative expenditure under the continuous-subsidy scheme reaches US$16 billion by 2060, 9.5 times higher than that under the declining-subsidy scheme. Thus, continuous subsidies deliver only marginal additional benefits at a much higher cost, whereas the declining-subsidy scheme achieves nearly the same outcomes with a much lower expenditure.

Low-carbon price subsidies also improve inter-regional equality, although the effect is limited. Compared with the no-subsidy scheme, the low-carbon price scheme reduces the inter-regional gap in unit net recycling benefits by up to 3.8% over 2020–2060, owing to differentiated subsidy allocations. Regions with higher recycling costs, such as Canada, EU-12 and EU-15, receive carbon price subsidies of US$63.1–158 per tonne of CO₂ equiv., whereas lower-cost regions, such as China and South Korea, receive only US$7.5–89 per tonne of CO₂ equiv. (Extended Data Table 1). This differentiated allocation partially offsets cost disadvantages and narrows inter-regional gaps.

Most high-carbon price schemes (>85%) worsen inequality. The core issue is unequal subsidy distribution: high-income regions receive carbon price subsidies of up to US$200 per tonne of CO₂, nearly four times higher than those in developing economies. This disparity would be further amplified by higher material prices, which would disproportionately increase recycling profits in high-income regions. As a result, the inter-regional gap in unit net recycling benefits widens to US$1,198–1,434 per tonne of recycled waste, 1.4–4.3% above schemes without subsidies, entrenching rather than bridging the benefit gap between regions.

Our study integrates economic, climate, technological and policy dimensions to provide a methodology to assess PV waste management under regional heterogeneity and uncertainty. We show that even with technological learning, the break-even point remains more than a decade away, underscoring the need for new circularity strategies. Indeed, regions have distinct but complementary roles in the global recycling system, and coordination mechanisms (such as the Green Climate Fund²⁹ and the Global Environment Facility³⁰) are needed to mobilize cross-border capital, technology and expertise. Our results further reveal a central dilemma: while PV waste trade increases overall recycling benefits, it exacerbates regional revenue inequality. We further find that cost-based subsidies can effectively mitigate this imbalance, pointing to the need for a coordinated global policy mix that supports efficient, low-cost and equitable PV recycling.

Regulations and quantifiable targets are central to large-scale recycling, yet only a few regions (such as the EU and China) have introduced PV-specific recycling requirements³¹. The EU’s WEEE Directive mandated an 80% recycling rate by 2018, while China aims to recycle 250,000 t of retired PV modules by 2027 (ref. ³²). However, weight-based targets often prioritize recovering bulk materials such as glass and aluminium frames⁸. Regions with stronger institutional capacity therefore need more targeted goals for high-value but low-concentration materials, such as silver and silicon, to stimulate market development. By contrast, low-income regions require international support for institutional transfer and capacity building. Proven approaches, such as the EU’s EPR framework, mandatory recycling targets and landfill restrictions, can be adapted to local contexts. Meanwhile, tightening global waste-trade regulations (such as the Basel Convention, EU battery regulations and China’s plastic import ban)^33,34,35, together with rising geopolitical uncertainty, underscore the need for transparent and standardized cross-regional recycling cooperation. Such mechanisms can reduce the environmental and health risks of informal processing while also harmonizing standards, enabling data sharing and clarifying responsibilities. Together, these functions would form a more coordinated global governance network.

Technology-wise, rising prices for high-value recovered materials (such as silver, silicon and copper) have a particularly critical role in improving economic returns. However, existing recycling technologies struggle to extract high-purity materials. For example, photovoltaic-grade silicon requires a purity of over 6 N (and increasingly 8 N/9 N), yet it remains economically challenging to remove carbon and metallic impurities at scale, leading to downgraded applications such as battery anodes^36,37,38. Thus, investment in high-purity refining technologies such as directional solidification is urgently needed³⁸.

Regions such as Europe, the United States, Japan, South Korea and China have developed high-value recycling pathways based on thermal and chemical processes, supported by mature semiconductor industries and stable demand for high-purity materials³⁹. These regions are therefore well positioned for advanced refining and value recovery. By contrast, less-developed regions, particularly in Africa, rely mainly on low-cost mechanical dismantling or informal channels for basic preprocessing and sorting^40,41. For these regions, the priority is to build affordable, basic recycling capacity. With international cooperation, including under the Paris Agreement⁴², technology transfer and capacity building could help PV recycling in these regions progressively move from low-end processing to higher-value-added activities. Our results show that, under a technology diffusion scenario, low-income regions could still achieve net profits of US$4.2–10.6 billion by 2060, suggesting substantial scope to reduce global PV recycling inequalities and build a more resilient system.

From an economic perspective, technological innovation alone cannot ensure PV recycling viability. Our research indicates that global PV recycling may not yield a net profit in the near term arguably due to lower initial treatment capacity as well as high capital costs associated with technologies. A diversified set of economic incentives must be introduced, including fiscal subsidies, market-based mechanisms (such as carbon pricing) and investment support policies, to lower entry barriers and encourage industry participation⁴³. Existing policy tools, such as the US Qualifying Advanced Energy Project Credit (48C) Program, provide financial support⁴⁴. However, prolonged high subsidies may distort markets, impede innovation and even widen regional disparities (as observed in high-carbon-price subsidy scenarios)⁴⁵. A more sustainable approach is to provide early-stage support and phase it out as the market matures, for example, through declining subsidy schemes²⁷. Furthermore, most low-income and lower-middle-income regions lack the fiscal capacity to provide subsidies independently. Thus, cross-regional financial transfers, including those through climate finance mechanisms such as the United Nations Capital Development Fund⁴⁶, are essential to lower entry barriers by bridging fiscal gaps. Simultaneously, low-income regions should design locally adapted support schemes rather than simply replicating subsidy models for high-income regions, which may pose long-term distortion risks.

Note that refurbishment and reuse of PV modules can, in principle, deliver substantially greater environmental and resource-efficiency benefits⁴⁷. Such strategies effectively delay demand for resource-intensive recycling infrastructure in capacity-limited regions. Similarly, while surging prices of key metals (such as silver, copper and high-purity silicon) can enhance PV recycling viability, they often escalate deployment costs for price-sensitive developing economies⁷. By contrast, reuse-oriented strategies reduce reliance on producing new modules, allowing the benefits of the circular economy to decouple from rising material prices, therefore providing a strategic buffer against material cost pressures. This not only helps to mitigate revenue imbalances driven by regional cost disparities but also ensures the continuity of energy transitions in low-income regions⁷. However, large-scale reuse is constrained by performance heterogeneity, latent reliability risks and limited market confidence in the durability of second-hand products^48,49. Thus, at this stage, reuse is best positioned as a complementary transitional strategy alongside EOL recycling, to build a more resilient and inclusive global closed-loop PV system.

Although our study attempted to account for various real-world scenarios as comprehensively as possible, certain limitations remain, presenting opportunities for future research. To enhance comparability across scenarios, we primarily modelled subsidies in annualized form, with one-time grants simplified as equivalent continuous support. This approach may understate the role of upfront incentives in triggering early investment and accelerating technology diffusion⁵⁰. Future research could therefore consider more detailed subsidy timing, payment structures and dynamically adjustable policy designs.

Here 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 studies^7,51,52, 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:

where Q^t 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 Δp^t is the annual price change rate in period t, defined as

The initial price p₀ is exogenously specified use the global average price for each material in 2020.

Step 2: new capacity requirement and price determination. The required new mining capacity to meet the future demand is calculated as:

where O^t+1 denotes the newly required annual production capacity in period t + 1; L^t represents supply losses from mine closures due to resource depletion in period t and R^t is the secondary supply in period t.

The marginal cost of new mining capacity in period t (I^t) is determined using an incentive cost curve:

where a^t represents the minimum marginal cost of new mining capacity in period t and b^t captures the rate at which marginal costs increase in period t due to factors such as declining ore grade and more complex investment conditions. The equilibrium material price in year t is therefore determined by the most expensive marginal cost of the required new capacity:

Step 3: evolution of mining cost structure. The incentive cost curve evolves over time due to ore grade depletion, technological progress and changes in operating costs⁵¹. We introduce a cost-adjustment factor n to capture these dynamics:

where e is the annual growth rate of operating costs (for example, energy, labour, water, reagents, environmental compliance); t_g represents annual cost reduction rates from technological progress; and l is the annual ore-grade depletion rate.

The intercept of the operating mine cost curve (a^t+1) is updated as the minimum of two factors: (1) the cheapest operating mine cost in the last period t adjusted by n_t and (2) the operating cost of the cheapest mine that newly opened in the current period (t + 1) (after subtracting annualized capital cost c_a):

The upper bound of the operating curve C^t+1(1), which represents the operating cost of the most expensive active mine in the period t + 1, is updated as

This formulation follows two mechanisms: (1) mines with operating costs exceeding the previous period’s market price (p^t) are assumed to exit the market, and (2) new mines enter the market only if their operating costs are covered by the current material price net of annualized capital cost (p^t+1 − c_a). Taking the maximum of these two values ensures that the operating cost frontier is consistent with both the exit of unprofitable existing mines and the entry condition for new capacity.

The slope of the operating cost curve is then updated as

Further details of the model structure and assumptions can be found in our previous paper⁷.

Material price scenarios

To characterize long-term uncertainty in critical material prices and its impacts on global PV deployment and decommissioning, we construct alternative price scenarios within the material price modelling framework. Scenario variation is introduced by varying parameters that reflect (1) technological progress in mining productivity and (2) material substitution potential. Improvements in mining productivity are represented as t_g − e, which is the difference between the reduction in mining costs due to technological progress (t_g) and the increases in operating cost (e) (see Eq. 6). Material substitution is captured by the price elasticity parameter ε in equation (1), which reflects the responsiveness of demand to price changes through substitution toward alternative materials.

These parameters take values within the ranges reported in the literature, and details are provided in Supplementary Table 4. For both mining productivity and substitution elasticity, we classify parameter values into low and high categories using the midpoint of each reported range as the threshold. Combining these assumptions yields two contrasting price scenarios that span plausible bounds of long-run material price uncertainty:

1. (1)

   Low-price scenario: assumes strong technological progress in both mining productivity (resulting in fast mining cost reductions and declining operating costs) and material substitution capabilities, thereby moderating future price increase.

2. (2)

   High-price scenario: assumes limited technological advancement in mining productivity and weak material substitution potential, resulting in tighter supply conditions and higher long-run prices.

Under these scenario settings, the price trajectories for four solar-relevant critical materials (copper, aluminium, silver and silicon) are simulated. Extended Data Fig. 2 illustrates the projected price pathways under the two scenario settings.

GCAM model

The GCAM is an integrated assessment model that links five interconnected systems: water, energy, land use, socioeconomics and climate. It is a common model that has been used in international and national scenario assessments^52,53. GCAM represents a broad portfolio of electricity generation technologies, including solar photovoltaics and wind power. Renewable resources are assumed to be region-specific and non-tradable across regions. Technology deployment is determined through an internally consistent, multistage process that links technology costs, resource supply curves and market-based technology choice.

From capital cost to electricity generation in GCAM

Incorporating material price shocks into capital costs

To quantify the effect of critical material price increases on the deployment of solar PV, simulated material price trajectories are translated into technology-specific capital cost changes under price scenarios. The capital cost of solar PV in the year t is defined as

where \({C}_{\text{BLS},t}\) is the baseline capital cost of PV following GCAM’s default setting⁵⁴ and ΔC_t represents the change of cost increment induced by material price changes:

where MI_m is the material intensity of material m in solar PV, P_mt is the simulated material price under alternative price scenarios in year t, and \({P}_{mt,\mathrm{BLS}}\) is the corresponding baseline material price.

Total technology cost

The total cost of electricity generation technology in GCAM is calculated as⁷

where t(C_t) represents capital and fixed operating costs; p_j is the marginal cost of input resource j; gk is the cost associated with emissions of greenhouse gas k; and v_l is the value of secondary outputs. For solar technologies, p_j is determined through the renewable resource supply curve Q (equation (15)).

Technology choice and market shares

Technology shares in electricity generation are determined using a relative-cost logit formulation in GCAM⁷. The share of generation technology j is calculated as

where α_j is a technology-specific share weight, C_total,j is the total cost of technology j, and γ is the logit exponent controlling the sensitivity of technology shares to cost differences. Technologies with lower relative costs gain larger market shares.

The electricity generation Q^e from technology j is then given by:

where D is the total electricity demand under a given SSP.

Renewable resource supply curves

To capture spatial and technological heterogeneity in renewable resource availability, GCAM uses region- and technology-specific resource supply curves. The cumulative generation potential available at or below a given marginal cost p is defined as⁷

where MaxSubResource represents the maximum exploitable resource potential, MidPrice is the cost at which half of this potential becomes available, and curveExponent is the steepness of the supply curve.

For PV, GCAM assumes an effectively flat and abundant resource base, reflecting high solar potential in many regions and the modular scalability of PV systems. Under this assumption, marginal resource costs do not increase with deployment, allowing material price shocks to transmit primarily through capital costs.

To ensure the accuracy of historical data and the model’s backcasting capability, we cross-validated the historical electricity generation outputs from GCAM against IRENA’s official statistics; detailed comparisons are provided in Supplementary Note 1 and Supplementary Fig. 3.

From electricity generation to installed capacity

To estimate solar PV waste, electricity generation outputs from GCAM are converted into installed capacity. As GCAM reports generation in exajoules (EJ), values are first converted to gigawatt-hours (GW h) using a constant conversion factor (ω = 277,778.8). Installed capacity is then derived by dividing annual electricity generation by the product of annual operating hours (T = 8,760) and the capacity factor^7,55:

where \({\mathrm{IC}}_{i,t}\) is the installed capacity (GW) in region i and year t; EJ_i,t represents the annual electricity generation (EJ) for region i in year t; and CF_i denotes the region-specific capacity factor.

Integrated socioeconomic–climate scenarios

The GCAM generates internally consistent energy–economy–land–climate pathways under alternative socioeconomic and climate policy assumptions. In this study, we couple five SSPs (SSP1–SSP5) with three Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP6.0) to construct a comprehensive set of socioeconomic–climate scenarios for evaluating future PV deployment⁵³. For presentation purposes only, these combinations are classified into frequently used benchmark pathways and supplementary stress-test pathways according to their prevalence in the literature⁵⁶. This classification does not alter model simulations or parameterization. The full classification and detailed descriptions of scenarios are provided in Supplementary Table 3, with further discussion on specific scenarios in Supplementary Note 2. All simulations are conducted using GCAM v.8.2. For each scenario, we extract regional solar electricity generation trajectories and subsequently aggregate GCAM regions into four income groups (Supplementary Table 1). Detailed documentation of the model structure and assumptions is available in the GCAM v.8.2 Documentation: GCAM Model Overview, accessible at https://jgcri.github.io/gcam-doc/overview.html.

Projection of future PV waste generation

Future PV waste generation is estimated using a dynamic MFA model. MFA is extensively used to quantify the evolution of material stocks and flows over time, including the estimation of waste streams from solar energy systems^57,58. A key input to the MFA model is annual PV in-use capacity, comprising historical installed capacity and projected additions. Historical installed capacity for 2000–2024 is updated using the 2025 statistics from the IRENA⁵⁹ (Supplementary Tables 10–13). Extending the time series to 2000 enables calibration of early decommissioning volumes. The primary analytical period reported in the main results is 2020–2060. Future PV capacity trajectories are derived from GCAM projections at five-year intervals and interpolated to annual values using cubic spline interpolation to estimate newly added capacity. The estimation is then performed in two steps: (1) calculating annual inflows and outflows of PV capacity; and (2) converting decommissioned capacity into waste mass.

Step 1: PV capacity inflow and outflow are determined using a Weibull lifetime distribution⁵⁸:

where t denotes year (2000–2060), t′ is the installation year of the PV panel, and t − t′ represents the service time of the panel. F(t − t′) is the cumulative Weibull distribution function. T is the average lifetime of PV panels (30 years), and β is the shape parameter. A regular-loss scheme is adopted, assuming no premature loss over the module lifetime. Under this assumption, the Weibull shape parameter is set to β = 5.3759 (ref. ⁶⁰). The quantities outflow_i(t), stock_i(t) and inflow_i(t) denote decommission capacity, in-use capacity and newly added capacity for region i in year t, respectively. The initial stock is assumed equal to the first-year inflow.

Step 2: decommissioned capacity is converted to waste mass PVWaste_i(t) using timing-varying weight-to-power ratios perPVton(t) (refs. ^26,57):

where perPVton(t) denotes the PV module weight-to-power ratio for period t, obtained from IRENA reports⁶⁰ (Supplementary Table 14). As projections in IRENA reports are available only until 2050 (ref. ⁶⁰), the 2050 ratio is assumed constant thereafter. Annual inflow, annual outflow and in-use stock trajectories for 2020–2060 are presented in Supplementary Figs. 4–6. The analysis focuses exclusively on crystalline-silicon (c-Si) PV modules, which have accounted for more than 90% of global PV installations since 2012 (ref. ⁶¹); other PV technologies are therefore excluded. Detailed assumptions are described in Supplementary Note 3.1.

Economic cost–benefit analysis

The economic feasibility of recycling EOL PV panels is evaluated using an LCC framework, which is consistent with the LCA system boundary and quantifies the economic costs and benefits associated with recycling PV modules over their entire life cycle^23,62. Recycling costs are first estimated for the base year (2020) at the unit level (that is, US$ per tonne of PV waste), then adjusted dynamically to reflect technological learning and scale effects, and finally multiplied by projected regional PV waste volumes to estimate total economic costs. Economic benefits are calculated based on the market value of recovered secondary materials. The detailed calculation steps are presented as follows.

The unit total costs, uTC, of PV waste recycling comprises stage-specific costs and whole-process costs. Stage-specific costs are variable expenditures incurred at each processing stage, whereas whole-process costs refer to overhead expenditures spanning the entire recycling chain.

The base year (2020) unit total recycling cost (uTC₀) is calculated as follows⁶²:

where uC_ss represents the stage-specific unit costs for collection (ss = 1), transportation (ss = 2), dismantling (ss = 3), technical treatment (ss = 4) and disposal (ss = 5). uC_w denotes the whole-process unit costs, including labour (uC_l), management (uC_m), depreciation of fixed assets (uC_f), operation and maintenance (uC_ope), and opportunity costs (uC_opp).

Owing to the limited global data on differentiated recycling costs, we adopted a comprehensive, internally consistent cost-benefit inventory for China from our previous study⁵⁶ as the reference baseline. Except for region-specific labour, collection, and transportation costs, other unit cost components (such as technical treatment, dismantling and operational expenses) are extrapolated to region iusing purchasing power parity (PPP) adjustments (detailed descriptions of the assumptions are provided in Supplementary Note 3.2):

where uC_i,0 is the estimated unit cost for region i in the base year 2020, uC_CN,0 is the 2020 baseline cost in China, PPP_CN,0 and PPP_i,0, respectively, represent the PPP conversion factors in China and region i in 2020, and ER_i,0 denotes the market exchange rate of region i in 2020 (Supplementary Table 15).

Region-specific collection costs are determined following previous study⁶³. Labour costs are estimated based on national per capita gross national income (GNI) data from the United Nations Trade and Development Data Hub⁶⁴, assuming a per capita PV recycling capacity of 100 tonnes per year⁶². Transportation costs include domestic and international maritime transport. Domestic maritime costs are calculated following equation (23) due to limited national level data. International maritime transport costs are estimated based on bilateral maritime distances and a unit sea freight cost of US$0.1 per nautical mile per twenty-foot equivalent unit⁶⁵, assuming a maximum capacity of 30 t per container following ISO standards⁶⁶. Bilateral maritime distances are derived from a global port network approach^67,68. Shortest port-to-port navigable maritime routes are computed for all possible pairs between trading regions, rather than relying on spherical distances between national centroids. To avoid bias from exceptionally short routes, the 10th percentile of the resulting route-distance distribution is adopted as the representative bilateral shipping distance for each region pair. Given that the shortest port-to-port distances often underestimate actual maritime routes, a correction factor of 1.3 is applied to approximate real-world sailing conditions⁶⁷. Region-level costs are then aggregated to the 32 GCAM regions using arithmetic means. Regional base-year (2020) unit costs are illustrated in Extended Data Fig. 9.

It is expected that unit recycling costs of PV waste will decrease over time as a result of technological learning and scale expansion, adhering to the learning curve principle^23,43. To account for irreducible production inputs that limit cost reductions, we use a modified learning curve formulation with an irreducible cost floor⁵⁴:

where uTC_t denotes the unit recycling cost in year t and uTC₀ denotes the initial cost in 2020. t₀ is the base year (2020) and LR is the learning rate. The learning rate values are derived from previous studies^23,69,70. Detailed descriptions of the assumptions regarding learning rate can be found in Supplementary Note 3.3. The parameter C_{min_ratio} represents the minimum cost coefficient reflecting irreducible production inputs, such as energy consumption, chemical reagents and base labour, that cannot be entirely eliminated through learning. Based on the evidence that global PV costs declined by up to 87% between 2010 and 2024 (ref. ⁷¹), we set C_{min_ratio} to 0.13, implying an asymptotic lower bound of C₀ × C_{min_ratio}, with an assumption that recycling cost will follow a similar trajectory.

Total recycling cost (TC_t) in year t is calculated as the product of the dynamically adjusted unit recycling cost (uTC_t) and the total PV waste volume (PVWaste_t):

where π denotes the assumed annual inflation rate calibrated based on the global average inflation rate over 2000–2024 (ref. ⁷²).

Total recycling benefits in year t (B_t) are derived from the market value of recovered secondary materials, including aluminium, glass, silver, copper and silicon^73,74. It depends on material prices, recovery efficiencies and decommissioned PV volumes. Material prices are set to correspond with different material price escalation scenarios to reflect potential resource scarcity and market volatility (Extended Data Fig. 2 and Supplementary Fig. 1). Total benefits are calculated as

where P_mt is the simulated price of material m under alternative price scenarios in year t and uR_m denotes the unit recovery quantity (Supplementary Tables 16–18). All unit costs and material prices are first projected in constant 2020 US$. Nominal values are subsequently derived by applying an inflation factor.

The nominal net economic benefit (NB_t) and the unit nominal net benefit (UNB_t) are calculated as:

Detailed data and accounting procedures for unit costs and benefits are provided in our previous paper⁶².

Climate benefits from PV recycling

We quantify the climate benefit of PV module recycling as the net avoided greenhouse gas (GHG) emissions (in CO₂ equiv.) generated by recycling 1 t of EOL c-Si PV panels:

where CB_s,i,c,t denotes the net climate benefit of recycling technology s in region i under climate pathway c in year t; RB_s,i,c,t represents the recycling benefit (avoided GHG) generated during recycling; and RG_s,i,c,t denotes GHG emissions released during the recycling process. The recycling benefit RB_s,i,c,t comprises two components: one is that recovered materials substitute for virgin materials of equivalent quality and quantity, thereby avoiding emissions from primary extraction, refining and manufacturing of virgin PV materials^21,75, the other is that, where applicable, energy recovery from polymeric fractions (for example, backsheets and encapsulants) offsets emissions that would otherwise arise from conventional energy supply⁶². The recycling burdens RG_s,i,c,t includes all emissions generated throughout the EOL treatment chain, covering electricity use, auxiliary inputs and transportation of waste modules, intermediate fractions and residual wastes^76,77.

Following the life-cycle assessment framework^62,78, the functional unit is defined as 1 t of EOL c-Si PV panels. The system boundary from collecting PV module wastes to recovering secondary materials and energy is shown in Supplementary Fig. 7.

Note that the GHG emission intensity of electricity generation evolves over time under different climate policy pathways and varies substantially across regions. To account for this heterogeneity, we adjust the electricity-related emission intensity in the LCA framework using region-, pathway- and year-specific electricity-sector emission factors. The actual electricity emission factor is defined as

where EF_i,c,t is the electricity-sector emission factor in region i under climate pathway c in year t; EF_i,c,base is the base year (2020) emission factor; and α_i,c,t is the adjustment coefficient that captures deviations from the baseline electricity emission intensity. Therefore, the final climate benefit of PV recycling is

where \({\mathrm{RB}}_{s}^{\mathrm{elec}}\) and \({\mathrm{RB}}_{s}^{\text{non-elec}}\) represent the electricity-related and non-electricity-related recycling benefits, respectively, under base-year electricity emission factors for recycling technology s. \({\mathrm{RC}}_{s}^{\mathrm{elec}}\) and \({\mathrm{RC}}_{s}^{\text{non-elec}}\) denote the electricity-related and non-electricity-related recycling burdens associated with the recycling process.

Given that carbon dioxide accounts for more than 70% of total GHG emissions⁷⁹ and constitutes the primary contributor to the global warming potential (GWP) of PV recycling, mainly due to transport-related fuel consumption, polymer incineration and coal-based electricity generation^38,77,78, this study assumes that the temporal evolution of regional GHG emission factors is consistent with the carbon emission trajectories projected by GCAM (Supplementary Fig. 8). Detailed process inventories are reported in Supplementary Tables 16–18 and described in ref. ⁶².

Recycling technology scenarios

To capture the dynamic evolution of PV recycling technologies across regions and over time, we developed an integrated framework combining scenario design, a Logit-based technology choice model and region-specific heterogeneity constraints⁸⁰. The framework explicitly accounts for cross-regional differences in low-carbon technologies investment, technological capacity, environmental regulation stringency and projected PV waste generation, all of which collectively determine feasible recycling technology mixes. The mechanical, thermal and chemical recycling technologies coexist and compete, with market shares evolving endogenously under heterogeneous regional conditions.

Based on this framework, four recycling technology scenarios are constructed: BAU, economic-priority, carbon-priority and technology diffusion.

Under the BAU scenario, the recycling technology structure in each region is assumed to remain constant throughout the study period. Technology market shares are fixed at base-year (2020) levels and do not respond to changes in economic performance or climate benefits. Base-year shares are calibrated using information on recycling patent distributions and facility locations reported by IEA³¹ (Supplementary Tables 19 and 20), reflecting existing technological endowments and path dependency across regions.

Under the economic-priority scenario, recycling technology choices are driven primarily by economic rationality. The attractiveness of each technology is determined by its net economic benefit, the difference between revenues from recovered secondary materials and associated recycling costs. Technology market shares evolve endogenously in response to relative net profits, capturing competitive dynamics among alternative recycling options under profit-oriented decision-making. However, the evolution of clean and low-carbon technologies is also shaped by regional disparities in investment capacity. As noted in previous studies^81,82,83, investment in low-carbon technologies and related R&D remains concentrated in developed regions. Most emerging markets and developing regions have limited access to these technologies owing to constrained investment capabilities, and therefore continue to rely on existing technologies, experiencing slower rates of technological upgrading. Consequently, under this scenario, mechanical recycling, which is less capital-intensive but exhibits lower recovery efficiency for high-value metals and therefore generates smaller net profits per unit of waste recycled than the other two technologies, captures a larger market share in many middle- and low-income regions. Moreover, its market share declines more slowly in these regions than in high-income regions.

The carbon-priority scenario represents a climate-driven decision framework in which technology attractiveness is determined by carbon mitigation performance. Specifically, the choice variable is defined as the carbon reduction benefit of PV waste recycling, measured as avoided GHG (for example, kg CO₂ equiv.). To capture cross-regional differences in regulatory ambition and policy enforcement, region-specific parameters (climate policy stringency index) are introduced based on the OECD Environmental Policy Stringency Index⁸⁴. Higher policy stringency drives technology evolution towards technologies with the greatest carbon mitigation benefits.

The technology diffusion scenario is designed by analogy with historical PV patent diffusion patterns observed between 1970 and 2022. Given the projected sharp increase in PV waste generation after 2040 (ref. ⁵⁸), recycling technology diffusion is modelled as a staged process comprising four phases: before 2030, technology shares remain at baseline levels; 2030–2040 represents an early adoption phase, analogous to the 1970–2000 PV patent diffusion period; 2040–2050 corresponds to an accelerated expansion phase, reflecting the 2001–2011 period; and 2050–2060 represents a maturation phase, similar to 2012–2022. Recipient regions are assumed to gradually converge toward the technology structures of leading regions as diffusion progresses. Stage-specific diffusion parameters are calibrated based on historical PV patent data reported in a previous study⁸⁵. Detailed descriptions of the assumptions are provided in Supplementary Note 3.4.

Logit-based technology choice model

In the economic-priority and carbon-priority scenarios, recycling technology market shares are determined using a logit-based choice model that allows multiple technologies to coexist and compete within each region⁸⁰.

For each region i and time t, technology market shares satisfy the adding-up constraint:

where s index recycling technologies (mechanical, thermal and chemical recycling technologies).

The market share of technology s in region i at time t, denoted as \({\mathrm{Share}}_{s,i,t}\), is given by

where P_s,i represents the base-year (2020) weight of technology s in region i, capturing existing infrastructure and path dependency; V_s,i,t denotes the scenario-specific performance indicator, defined as the net economic benefit in the economic-priority scenario and the carbon reduction benefit in the carbon-priority scenario; and γ_i is a region-specific parameter representing the investment capacity index for low-carbon technologies under the economic-priority scenario, or the climate policy stringency index under the carbon-priority scenario, where larger values indicate a stronger concentration of market shares toward the best-performing technology (Supplementary Table 21).

Recycling trade scenarios

Current strategies for managing EOL PV modules generally fall into two categories: domestic recycling and outsourced recycling. In some regions, such as the EU and South Korea, regulatory frameworks based on the principle of EPR¹⁰ require manufacturers to take responsibility for the collection, transport, and recycling of decommissioned PV modules. Meanwhile, the globalization of PV supply chains, where 76.89% of newly installed capacity in 2017 involved imported modules⁸⁶, suggests that future EOL management may increasingly transcend national boundaries. Consequently, cross-border waste trade is likely to become a structural feature of global EOL PV management, particularly for regions that have relied heavily on imported modules without developing corresponding domestic recycling capacity.

PV recycling capabilities are unevenly distributed worldwide. Only a limited number of regions, including China, the United States, the EU, Japan and South Korea, possess mature infrastructure and patented technologies necessary for formal recycling processes⁸⁷. By contrast, many regions in the Global South face considerable constraints related to limited industrial capacity, lack of technical expertise and weak regulatory enforcement^41,88. In such contexts, EOL PV modules may either be exported to technologically advanced regions or processed through informal recycling sectors⁴⁰, which often operate outside regulatory oversight and may entail environmental and health risks⁸⁸.

To examine how alternative international cooperation structures influence climate and economic outcomes, four recycling trade scenarios are constructed: local recycling, extended producer-responsibility-oriented trade, expanded global trade and regional trade. These scenarios reflect varying degrees of domestic versus outsourced recycling, informed by trade data, policy frameworks and access to technology. In this study, the term transboundary flow refers specifically to the international shipment of intact EOL PV modules. The underlying assumptions and rationale are described in Supplementary Note 3.5.

Under the local recycling scenario, regions with established PV recycling technologies are assumed to process 100% of their domestically generated EOL modules within national borders, and international trade in PV waste is not permitted. Regions without recycling technologies are assumed unable to recycle, resulting in zero recycling benefits. This scenario serves as a benchmark for isolating the role of domestic recycling capacity and for evaluating the economic, climate and equality implications of international trade.

The extended producer responsibility–oriented trade scenario reflects an international recycling system governed by EPR principles. EOL PV modules are exported back to the regions or regions responsible for their original production (a return-to-producer rule), following historical bilateral PV module trade routes, consistent with scenario assumptions adopted in previous studies⁸⁶. Recycling activities are conducted in PV producer (exporting) regions, which bear the associated treatment costs while retaining revenues from recovered secondary materials. Historical trade structures are assumed to remain fixed through 2060. This scenario represents a regulatory-driven recycling framework emphasizing producer accountability and path dependence. The PV module trade data are adopted from a previously published study⁸⁶. Extended Data Fig. 8 illustrates the geographical distribution and allocation patterns of waste flows under various trade scenarios.

The expanded global trade scenario represents a highly integrated international recycling system in which all regions with PV recycling technologies are eligible to act as waste importers. Regions without recycling technologies must export their EOL PV modules to recycling-capable regions. The allocation of exported waste across eligible importing regions is determined by a composite weighting scheme based on economic globalization and trade openness indices derived from established datasets⁸⁹ (Supplementary Table 22). Regions with higher index values receive larger shares of imported PV waste, reflecting lower trade barriers, stronger logistics capacity and greater institutional readiness for cross-border material circulation. Trade structures are assumed to be constant through 2060. The underlying assumptions and rationale are detailed in Supplementary Note 3.6.

The regional trade scenario constrains international PV waste trade within geographically defined continental regions, disallowing intercontinental trade. Following the United Nations’ geographical classification, all regions are grouped into five continental regions: Africa, Asia, Europe, the Americas and Oceania. Within each region, regions without recycling technologies export EOL PV modules to recycling-capable regions in the same region. As in the expanded global trade scenario, the distribution of waste among eligible importers is determined by relative economic globalization and trade openness indices, calculated at the regional level. This scenario represents a geographically bounded form of international cooperation, reflecting potential logistical, regulatory and political constraints on intercontinental waste transport. Trade structures are likewise assumed to remain constant through 2060 (ref. ⁸⁶).

Variance analysis of recycling benefits

To evaluate how recycling trade scenarios impact PV recycling equality, we use the cross-regional dispersion of realized recycling benefits as a transparent and comparable proxy for distributional equality. Specifically, for each scenario j, we calculate the variance of regional benefits across the 32 modelled regions. The variance \({\sigma }_{\mathrm{sc}}^{2}\) is computed as

where EB_i,sc represents the recycling-related benefit (economic or climate) of region i under scenario sc and \(\overline{{\mathrm{EB}}_{\mathrm{sc}}}\) denotes the corresponding mean benefit across all n = 32 regions. A larger variance indicates greater dispersion in realized benefits and thus a more unequal distribution across regions.

To facilitate comparison across scenarios with potentially different absolute dispersion levels, variances are normalized to the range [0, 1] using min–max normalization:

where \({\sigma }_{\min }^{2}\) and \({\sigma }_{\max }^{2}\), respectively, represent the minimum and maximum variance values across all scenarios. The normalized variance \({\sigma }_{\mathrm{sc}}^{2* }\) reflects the relative inequality level of each scenario: 0 corresponds to the most equitable distribution (lowest variance) and 1 corresponds to the most unequal distribution (maximal variance).

Note that this variance-based indicator captures disparities in realized economic and climate benefits across regions under each scenario without accounting for procedural equity or environmental justice. In this context, the metric serves as a descriptive and comparable measure of distributional dispersion.

Subsidy scenarios

To encourage participation in PV waste recycling and mitigate the risk of excessive spatial concentration of recycling activities, we developed a set of alternative recycling subsidy scenarios. These scenarios represent policy instruments widely discussed in PV and electronic waste recycling literature, and capture both direct fiscal interventions and market-based environmental incentives^73,90,91,92.

Five subsidy scenarios are considered: (1) no subsidy; (2) continuous subsidy; (3) declining subsidy; (4) low-carbon price; and (5) high-carbon price. No subsidy represents a reference scenario where recycling operations receive no direct fiscal support or carbon price-based financial incentives. The continuous subsidy and declining subsidy scenarios represent cost-based fiscal interventions that reduce the effective cost of recycling, while the latter two represent market-based environmental incentives linked to avoided carbon emissions. Scenario design and parameter ranges are informed by existing policy documents and previous studies on recycling and circular economy policies^{43,90,91,92,93,94}.

Cost-based subsidy scenarios

The continuous subsidy scenario reflects sustained fiscal support throughout the project lifetime, consistent with recent circular economy policy frameworks⁹². Under this scenario, governments provide an annual subsidy proportional to total recycling costs, including both capital expenditure (CapEx) and operating expenditure (OpEx). The annual subsidy for region i in year t is expressed as

where \({\mathrm{TC}}_{i,t}^{\mathrm{total}}\) denotes the total recycling cost in region i and year t and \({w}_{i,t}\) represents the subsidy rate. Owing to the limited region-specific policies and substantial uncertainty regarding appropriate subsidy magnitudes, subsidy rates were sampled from a range of 0–15%, consistent with the upper bound observed in existing circular economy investment programs⁹². Both cost-based subsidy scenarios are evaluated using 1,000 Monte Carlo simulations to reflect this uncertainty.

The declining subsidy scenario represents a transitional support mechanism in which subsidies are gradually withdrawn as recycling activities become economically viable⁹⁰. Subsidies are initially applied at the same rate as in the continuous subsidy scenario, but decline annually by a fixed proportion δ once recycling becomes economically variable until it fully phases out. Break-even is defined as the first year when annual net recycling revenue in region i becomes non-negative. This declining subsidy is formulated as

where δ = 0.10 represents the annual reduction rate of the subsidy after break-even, \({t}_{i}^{* }\) denotes the break-even year, and \({w}_{i,0}\) denotes the subsidy rate in the base year (2020) in region i.

Carbon-price-based subsidy scenarios

The low-carbon-price and high-carbon-price scenarios are derived from two policy pathways proposed by the International Energy Agency⁹⁵: the Stated Policies Scenario (STEPS) and the Announced Pledges Scenario (APS), respectively. In the high-carbon price scenario, regions that have committed to net-zero emission targets adopt carbon prices consistent with the APS pathway over time. By contrast, the low-carbon-price scenario applies carbon prices equivalent to 30–80% of the APS levels and only to regions that have implemented, or formally announced plans to implement carbon pricing mechanisms (for example, Canada, South Korea, China and the EU).

Under these scenarios, recycling subsidies are proportional to avoided carbon emissions from PV module recycling. The annual carbon-related subsidy received by region i in year t is calculated as

where \({P}_{i,t}^{\text{C}{{\rm{O}}}_{2}}\) denotes the region-specific carbon price in region i and year t and \({\mathrm{TEI}}_{i,t}\) represents the avoided carbon emissions resulting from PV recycling activities in region i and year t. Carbon price data for 2030 and previous years were obtained from the World Bank report⁹⁶. Carbon price data after 2030 were sourced from the International Energy Agency⁹⁵.

Incorporation of subsidies into net benefits

Across all scenarios, subsidies are incorporated into the annual net benefit (NB) calculation either as a reduction in effective recycling costs (cost-based subsidy scenarios) or as an additional revenue (carbon-price based subsidy scenarios). The annual net benefit for region i in year t is calculated as

where \({B}_{i,t,\mathrm{sc}}^{\mathrm{total}}\) denotes the revenue from recovered materials in region i and year t under a given scenario sc, S_i,t,sc represents the subsidy received in region i and year t under a given scenario sc and \({{\rm{TC}}}_{i,t,\mathrm{sc}}^{\mathrm{total}}\) is the total recycling cost in region i and year t under a given scenario sc, including both capital and operating expenditures.

Equality evaluation of subsidy effects

To evaluate how subsidy policies affect regional disparities in recycling benefits, we quantify inequality in unit net recycling benefits across the 32 regions using two complementary indicators: (1) variance (dispersion) and (2) maximum-minimum benefit gaps (extreme disparity), and compare pre- and post-subsidy indicators. Both indicators are calculated for each scenario sc and year t, before and after subsidy implementation.

The pre-subsidy variance of unit net benefits is calculated as

where UNB_i,sc,t denotes the unit net recycling benefit in region i under scenario sc in year t before subsidies, \({\bar{\mathrm{UNB}}}_{\mathrm{sc},t}\) is the corresponding regional mean and N is the number of regions (N = 32).

After subsidy implementation, unit net benefits are recalculated as

and the post-subsidy variance becomes

where \({\overline{\mathrm{SUB}\_\mathrm{UNB}}}_{\mathrm{sc},t}\) represents the mean unit net benefit across regions under scenario sc in year t after subsidies.

To capture extreme disparities, we computed the net unit recycling benefit gaps for scenario sc in year t as

where \({\mathrm{Gap}}_{\mathrm{sc},t}^{\mathrm{pre}}\) and \({\mathrm{Gap}}_{\mathrm{sc},t}^{\mathrm{post}}\) denote the net unit recycling benefit gaps before and after the implementation of subsidies, respectively, in scenario sc and year t.

Uncertainty analysis

To assess the robustness of the modelling results and core conclusions, we conducted a series of uncertainty and sensitivity analyses (Supplementary Note 4, Supplementary Table 23 and Supplementary Fig. 9), focusing on PV module lifetime, material intensity, inflation rates and carbon price parameters. First, alternative lifetime extensions and reductions in material intensity were implemented to reflect potential technological progress. The results indicate that each of these measures can substantially reduce future PV waste generation. Second, we evaluated the impact of macroeconomic uncertainty by testing alternative high- and low-inflation trajectories and recalculating net economic benefits of PV recycling. Across these scenarios, variations in inflation rates exert only a limited influence on estimated net benefits. Third, we evaluated the sensitivity of distributional outcomes to alternative carbon price growth trajectories during 2050–2060, particularly under high carbon-price subsidy assumptions. The results show that even under alternative carbon price growth rates, high carbon prices consistently exacerbate inequalities in recycling benefits across regions, confirming the robustness of our findings.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

The primary data sources include (1) PV installed capacity data from the International Renewable Energy Agency (https://www.irena.org/Data); (2) PV recycling technology patent data and recycling facility information from the International Energy Agency Photovoltaic Power Systems Programme (https://iea-pvps.org/key-topics/advances-in-module-recycling-literature-review-and-update-to-empirical-lci-data-and-patent-review/); (3) inflation rates, PPP indices and exchange rate data from the World Bank (https://data.worldbank.org/); (4) GNI data from the United Nations Trade and Development Data Hub (https://unctadstat.unctad.org/datacentre/dataviewer/US.GNI); (5) the Environmental Policy Stringency Index from the Organisation for Economic Co-operation and Development (https://www.oecd.org/en/publications/measuring-environmental-policy-stringency-in-oecd-countries_90ab82e8-en.html); and (6) carbon price data from the World Bank (https://www.worldbank.org/en/publication/state-and-trends-of-carbon-pricing) and the International Energy Agency (https://www.iea.org/reports/world-energy-outlook-2024). The numerical results supporting the main findings of this research are available at Zenodo (https://doi.org/10.5281/zenodo.21507969)⁹⁷. Source data are provided with this paper.

1. Xia, S., Yang, Y., Liu, Y. & Poon, J. Green decommissioning for renewables in China. Science 381, 954–955 (2023).

   Article  ADS  CAS  PubMed  Google Scholar 

2. World Energy Transitions Outlook 2022: 1.5 °C Pathway (IRENA, 2002); https://www.irena.org/publications/2022/Mar/World-Energy-Transitions-Outlook-2022.

3. Jain, S., Sharma, T. & Gupta, A. K. End-of-life management of solar PV waste in India: situation analysis and proposed policy framework. Renew. Sust. Energ. Rev. 153, 111774 (2022).

   Article  Google Scholar 

4. Prince, K. J. et al. Sustainability pathways for perovskite photovoltaics. Nat. Mater. 24, 22–33 (2025).

   Article  CAS  PubMed  Google Scholar 

5. Wagner, L. et al. Actions for sustainably scalable multi-terawatt photovoltaics. Nat. Rev. Clean Technol. 2, 107–122 (2026).

   Article  Google Scholar 

6. Li, B. et al. Closed-loop manufacturing for sustainable perovskite photovoltaics. Nat. Rev. Mater. 11, 10–25 (2026).

   Article  Google Scholar 

7. Yuan, P. et al. Material price surges threaten an equitable and timely renewable energy transition. One Earth 8, 101466 (2025).

   Article  Google Scholar 

8. Sim, Y., Ankit, Tay, Y. B., Ravikumar, D. & Mathews, N. Open challenges and opportunities in photovoltaic recycling. Nat. Rev. Electr. Eng. 2, 96–109 (2025).

   Article  Google Scholar 

9. How to Manage End-of-Life Solar PV (Solar Victoria, 2025); https://www.solar.vic.gov.au/manage-end-life-solar-pv.

10. Majewski, P. et al. Recycling of solar PV panels—product stewardship and regulatory approaches. Energy Policy 149, 112062 (2021).

    Article  Google Scholar 

11. China Issues Guidelines on Recycling Wind-Power, Photovoltaic Equipment (National Development and Reform Commission, 2023); https://www.gov.cn/zhengce/zhengceku/202308/content_6898747.htm.

12. Chowdhury, P., Chowdhury, T., Chowdhury, H., Corkish, R. & Chang, N. L. Necessity for recycling photovoltaic glass: managing resource constraints and environmental impacts of antimony in terawatt scale photovoltaics. Sol. Energy Mater. Sol. Cells 295, 114012 (2026).

    Article  CAS  Google Scholar 

13. Heath, G. A. et al. Research and development priorities for silicon photovoltaic module recycling to support a circular economy. Nat. Energy 5, 502–510 (2020).

    Article  ADS  CAS  Google Scholar 

14. Kastanaki, E. Dynamic assessment of photovoltaic waste streams in the EU-27 countries under the circular economy principles of ‘reduce, reuse and recycle’. Resour. Conserv. Recycl. 214, 108033 (2025).

    Article  Google Scholar 

15. Gautam, A., Shankar, R. & Vrat, P. Managing end-of-life solar photovoltaic e-waste in India: a circular economy approach. J. Bus. Res. 142, 287–300 (2022).

    Article  Google Scholar 

16. Huang, B. & Long, Y. Toward traceable global systems for end-of-life photovoltaic waste. Nat. Commun. 17, 1928 (2026).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

17. Tian, X., Stranks, S. D. & You, F. Life cycle assessment of recycling strategies for perovskite photovoltaic modules. Nat. Sustain. 4, 821–829 (2021).

    Article  Google Scholar 

18. Lennon, A., Lunardi, M., Hallam, B. & Dias, P. R. The aluminium demand risk of terawatt photovoltaics for net zero emissions by 2050. Nat. Sustain. 5, 357–363 (2022).

    Article  Google Scholar 

19. Tan, V., Deng, R. & Egan, R. Solar photovoltaic waste and resource potential projections in Australia, 2022–2050. Resour. Conserv. Recycl. 202, 107316 (2024).

    Article  Google Scholar 

20. Al Zaabi, B. & Ghosh, A. Managing photovoltaic waste: sustainable solutions and global challenges. Sol. Energy 283, 112985 (2024).

    Article  Google Scholar 

21. Faircloth, C. C., Wagner, K. H., Woodward, K. E., Rakkwamsuk, P. & Gheewala, S. H. The environmental and economic impacts of photovoltaic waste management in Thailand. Resour. Conserv. Recycl. 143, 260–272 (2019).

    Article  Google Scholar 

22. Xia, Y. et al. Bounded rational decision-making modeling and analysis in local energy markets: a state-of-the-art review. Renew. Sust. Energ. Rev. 226, 116310 (2026).

    Article  Google Scholar 

23. Shao, J., Li, J. & Yao, X. Net benefits change of waste photovoltaic recycling in China: projection of waste based on multiple factors. J. Clean. Prod. 417, 137984 (2023).

    Article  CAS  Google Scholar 

24. Eitan, A. & Hekkert, M. P. Locked in transition? Towards a conceptualization of path-dependence lock-ins in the renewable energy landscape. Energy Res. Soc. Sci. 106, 103316 (2023).

    Article  Google Scholar 

25. Ren, K., Tang, X. & Höök, M. Evaluating metal constraints for photovoltaics: perspectives from China’s PV development. Appl. Energy 282, 116148 (2021).

    Article  CAS  Google Scholar 

26. Wang, C. et al. Facilitating circularity of end-of-life photovoltaic in China with environmental benefits and costs informed by a high-resolution waste map. iScience 28, 112332 (2025).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

27. Walzberg, J., Carpenter, A. & Heath, G. A. Role of the social factors in success of solar photovoltaic reuse and recycle programmes. Nat. Energy 6, 913–924 (2021).

    Article  ADS  Google Scholar 

28. Li, Y., Zhang, Q., Wang, G. & Lu, X. Recycling schemes and supporting policies modeling for photovoltaic modules considering heterogeneous risks. Resour. Conserv. Recycl. 180, 106165 (2022).

    Article  Google Scholar 

29. About the Green Climate Fund (Green Climate Fund, 2025); https://www.unep.org/about-un-environment/funding-and-partnerships/green-climate-fund.

30. About the Global Environment Facility (Global Environment Facility); https://www.thegef.org/who-we-are.

31. Wambach, K., Libby, C. & Shaw, S. Advances in Photovoltaic Module Recycling—Literature Review and Update to Empirical Life Cycle Inventory Data and Patent Review (IEA PVPS, 2024); https://iea-pvps.org/key-topics/advances-in-module-recycling-literature-review-and-update-to-empirical-lci-data-and-patent-review/.

32. Guiding Opinions of Six Departments Including the Ministry of Industry and Information Technology on Promoting the Comprehensive Utilization of Photovoltaic Modules (Ministry of Industry and Information Technology of the People’s Republic of China, 2026); https://www.miit.gov.cn/jgsj/jns/wjfb/art/2026/art_eb4997a4cf1c4c27ba9b51524af16a3d.html.

33. Lin, C.-H., Chen, Y., Chang, K.-I. & Chi, P.-Y. The evaluation of economic impact of the Basel Convention on the trade of waste electrical and electronic equipment. J. Asian Econ. 100, 101996 (2025).

    Article  Google Scholar 

34. Wen, Z., Xie, Y., Chen, M. & Dinga, C. D. China’s plastic import ban increases prospects of environmental impact mitigation of plastic waste trade flow worldwide. Nat. Commun. 12, 425 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

35. Melin, H. E. et al. Global implications of the EU battery regulation. Science 373, 384–387 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

36. Lee, J.-K. et al. Crystalline silicon solar cell with an efficiency of 20.05% remanufactured using 30% silicon scraps recycled from a waste photovoltaic module. Sol. Energy Mater. Sol. Cells 277, 113102 (2024).

    Article  CAS  Google Scholar 

37. Komoto, K. et al. Status of PV Module Recycling in IEA PVPS Task 12 Countries (IEA PVPS, 2025); https://iea-pvps.org/key-topics/t12-status-recycling-2025/.

38. Wei, G. et al. Review of c-Si PV module recycling and industrial feasibility. EES Solar 1, 9–29 (2025).

    Article  CAS  Google Scholar 

39. Ou, S., Yang, Q. & Liu, J. The global production pattern of the semiconductor industry: an empirical research based on trade network. Humanit. Soc. Sci. Commun. 11, 750 (2024).

    Article  Google Scholar 

40. Dong, D. et al. Solar photovoltaic development in West Africa will face million-ton waste challenges, and off-grid systems will dominate. Environ. Sci. Technol. 59, 21102–21116 (2025).

    Article  ADS  CAS  PubMed  Google Scholar 

41. Parikh, P., Wang, R. & Meng, J. The potential and challenges of off-grid solar photovoltaics in resource-challenged settings: the case of sub-Saharan Africa. Nat. Rev. Mater. 9, 151–153 (2024).

    Article  Google Scholar 

42. Paris Agreement in a new era. Nat. Clim. Change 15, 1123 (2025).

43. Zhang, L., Chang, S., Wang, Q. & Zhou, D. Is subsidy needed for waste PV modules recycling in China? A system dynamics simulation. Sustain. Prod. Consum. 31, 152–164 (2022).

    Google Scholar 

44. Qualifying Advanced Energy Project Credit (48C) Program (US Department of Energy, 2025); https://www.energy.gov/infrastructure/qualifying-advanced-energy-project-credit-48c-program.

45. Liu, D., Chen, T., Liu, X. & Yu, Y. Do more subsidies promote greater innovation? Evidence from the Chinese electronic manufacturing industry. Econ. Model. 80, 441–452 (2019).

    Article  Google Scholar 

46. About UNCDF (UNCDF, accessed 20 April 2026); https://www.uncdf.org.

47. Pivatto, M., Vidotto, L., Pereira, F. O. R. & Rüther, R. Circular solar economy: PV modules decision-making framework for reuse. J. Clean. Prod. 493, 144941 (2025).

    Article  Google Scholar 

48. Tsanakas, J. A. et al. Towards a circular supply chain for PV modules: review of today’s challenges in PV recycling, refurbishment and re-certification. Prog. Photovolt. 28, 454–464 (2020).

    Article  Google Scholar 

49. Salim, H. K., Stewart, R. A., Sahin, O. & Dudley, M. Drivers, barriers and enablers to end-of-life management of solar photovoltaic and battery energy storage systems: a systematic literature review. J. Clean. Prod. 211, 537–554 (2019).

    Article  Google Scholar 

50. Zhao, Y. et al. Exploring recycling strategies for retired wind turbine blades: the impact of policy subsidies and technological investments using a game-theoretic approach. J. Clean. Prod. 490, 144628 (2025).

    Article  Google Scholar 

51. Bhuwalka, K., Kirchain, R. E., Olivetti, E. A. & Roth, R. Quantifying the drivers of long-term prices in materials supply chains. J. Ind. Ecol. 27, 141–154 (2023).

    Article  Google Scholar 

52. Wang, H. et al. China’s electric vehicle and climate ambitions jeopardized by surging critical material prices. Nat. Commun. 14, 1246 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

53. Peng, K. et al. The global power sector’s low-carbon transition may enhance sustainable development goal achievement. Nat. Commun. 14, 3144 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

54. Muratori, M. et al. Cost of power or power of cost: a U.S. modeling perspective. Renew. Sust. Energ. Rev. 77, 861–874 (2017).

    Article  Google Scholar 

55. Kruitwagen, L. et al. A global inventory of photovoltaic solar energy generating units. Nature 598, 604–610 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

56. O’Neill, B. C. et al. Achievements and needs for the climate change scenario framework. Nat. Clim. Chang. 10, 1074–1084 (2020).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

57. Wang, C. et al. Looming challenge of photovoltaic waste under China’s solar ambition: a spatial–temporal assessment. Appl. Energy 307, 118186 (2022).

    Article  Google Scholar 

58. Li, C., Dong, F. & Nian, V. Global photovoltaic waste under ratcheting climate ambition: spatio-temporal distribution and future pathways. Resour. Conserv. Recycl. 221, 108388 (2025).

    Article  Google Scholar 

59. Renewable EnergyStatistics 2025 (IRENA, 2025); https://www.irena.org/Publications/2025/Jul/Renewable-energy-statistics-2025.

60. Weckend, S., Wade, A. & Heath, G. End-of-Life Management: Solar Photovoltaic Panels (IEA PVPS 2016, accessed 11 November 2025); https://iea-pvps.org/wp-content/uploads/2020/01/IRENA_IEAPVPS_End-of-Life_Solar_PV_Panels_2016.pdf.

61. Philipps, S. & Warmuth, W. Photovoltaics Report (Fraunhofer Institute for Solar Energy Systems, 2025); https://www.ise.fraunhofer.de/en/publications/studies/photovoltaics-report.html.

62. Li, J., Shao, J., Yao, X. & Li, J. Life cycle analysis of the economic costs and environmental benefits of photovoltaic module waste recycling in China. Resour. Conserv. Recycl. 196, 107027 (2023).

    Article  Google Scholar 

63. Anshassi, M. & Townsend, T. G. Improving waste systems in the global south to tackle international environmental impacts. Nat. Sustain. 8, 936–946 (2025).

    Article  Google Scholar 

64. Nominal GNI, Total and Per Capita, Annual (The United Nations Trade and Development Data Hub, 2023, accessed 15 December 2025); https://unctadstat.unctad.org/datacentre/dataviewer/US.GNI.

65. Dong, Z. et al. How international trade shapes critical material demand in electric vehicle batteries. Resour. Conserv. Recycl. 226, 108648 (2026).

    Article  Google Scholar 

66. ISO 668:2020 Series 1 Freight Containers—Classification, Dimensions and Ratings (ISO, 2020, accessed 22 December 2025); https://www.iso.org/standard/76912.html.

67. Verschuur, J., Koks, E. E. & Hall, J. W. Ports’ criticality in international trade and global supply-chains. Nat. Commun. 13, 4351 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

68. Wang, X. T. et al. Trade-linked shipping CO₂ emissions. Nat. Clim. Change 11, 945–951 (2021).

    Article  ADS  Google Scholar 

69. Helveston, J. P., He, G. & Davidson, M. R. Quantifying the cost savings of global solar photovoltaic supply chains. Nature 612, 83–87 (2022).

    Article  ADS  CAS  PubMed  Google Scholar 

70. Elshurafa, A. M., Albardi, S. R., Bigerna, S. & Bollino, C. A. Estimating the learning curve of solar PV balance-of-system for over 20 countries: implications and policy recommendations. J. Clean. Prod. 196, 122–134 (2018).

    Article  Google Scholar 

71. Renewable Power Generation Costs in 2024 (IRENA, 2025); https://www.irena.org/Publications/2025/Jun/Renewable-Power-Generation-Costs-in-2024.

72. Inflation, GDP Deflator (Annual %) (World Bank, 2025, accessed 21 December 2025); https://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG.

73. Liu, C., Zhang, Q. & Wang, H. Cost-benefit analysis of waste photovoltaic module recycling in China. Waste Manage. 118, 491–500 (2020).

    Article  Google Scholar 

74. Ovaitt, S., Mirletz, H. M., Hegedus, A., Gaulding, A. & Barnes, T. PV evolution in the light of circular economy. In Proc. 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC) (2021); https://doi.org/10.1109/PVSC43889.2021.9518683.

75. Ansanelli, G., Fiorentino, G., Tammaro, M. & Zucaro, A. A life cycle assessment of a recovery process from end-of-life photovoltaic panels. Appl. Energy 290, 116727 (2021).

    Article  CAS  Google Scholar 

76. Latunussa, C. E. L., Ardente, F., Blengini, G. A. & Mancini, L. Life Cycle Assessment of an innovative recycling process for crystalline silicon photovoltaic panels. Sol. Energy Mater. Sol. Cells 156, 101–111 (2016).

    Article  CAS  Google Scholar 

77. Dai, T., Chen, B., Wang, T., Yue, Z. & Kang, Y. Carbon emissions accounting and reduction potential analysis of the whole industrial chain for recycling waste photovoltaic modules in China. Sustain. Prod. Consum. 59, 161–176 (2025).

    CAS  Google Scholar 

78. Wang, J., Feng, Y. & He, Y. The research progress on recycling and resource utilization of waste crystalline silicon photovoltaic modules. Sol. Energy Mater. Sol. Cells 270, 112804 (2024).

    Article  CAS  Google Scholar 

79. Crippa, M. et al. GHG Emissions of All World Countries—2025 Report (Publications Office of the European Union, 2025); https://edgar.jrc.ec.europa.eu/report_2025.

80. An, K., Wang, C. & Cai, W. Low-carbon technology diffusion and economic growth of China: an evolutionary general equilibrium framework. Struct. Change Econ. Dyn. 65, 253–263 (2023).

    Article  Google Scholar 

81. Probst, B., Touboul, S., Glachant, M. & Dechezleprêtre, A. Global trends in the invention and diffusion of climate change mitigation technologies. Nat. Energy 6, 1077–1086 (2021).

    Article  ADS  Google Scholar 

82. Bandara, P., Ray, R., Lu, J. & Gallagher, K. P. Developing countries locked out of low-carbon technology trade. Science 388, 248–250 (2025).

    Article  ADS  CAS  PubMed  Google Scholar 

83. World Energy Investment 2025 (IEA, 2025); https://www.iea.org/reports/world-energy-investment-2025.

84. Kruse, T., Dechezleprêtre, A., Saffar, R. & Robert, L. Measuring Environmental Policy Stringency in OECD Countries: An Update of the OECD Composite EPS indicator (OECD, 2022); https://www.oecd.org/en/publications/measuring-environmental-policy-stringency-in-oecd-countries_90ab82e8-en.html.

85. Yang, Y. et al. Mapping global solar technology diffusion and its pathways over the past 50 years. Sci. China Earth Sci. 68, 3325–3342 (2025).

    Article  ADS  Google Scholar 

86. Wang, M. et al. Breaking down barriers on PV trade will facilitate global carbon mitigation. Nat. Commun. 12, 6820 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

87. Komoto, K. & Lee, J. S. End-of-Life Management of Photovoltaic Panels: Trends in PV Module Recycling Technologies (IEA PVPS, 2018); https://iea-pvps.org/wp-content/uploads/2020/01/End_of_Life_Management_of_Photovoltaic_Panels_Trends_in_PV_Module_Recycling_Technologies_by_task_12.pdf.

88. Andeobu, L., Wibowo, S. & Grandhi, S. Informal E-waste recycling practices and environmental pollution in Africa: what is the way forward? Int. J. Hyg. Environ. Health. 252, 114192 (2023).

    Article  CAS  PubMed  Google Scholar 

89. Cheng, J. et al. Trade risks to energy security in net-zero emissions energy scenarios. Nat. Clim. Change 15, 505–513 (2025).

    Article  ADS  Google Scholar 

90. Wang, Z. & Fan, W. Economic and environmental impacts of photovoltaic power with the declining subsidy rate in China. Environ. Impact Assess. Rev. 87, 106535 (2021).

    Article  Google Scholar 

91. Wang, G., Yuan, M. & Xu, H. The impact of subsidy and preferential tax policies on mobile phone recycling: a system dynamics model analysis. Waste Manage. 152, 6–16 (2022).

    Article  Google Scholar 

92. Special Management Measures for Central Budgetary Investments in Energy Conservation and Carbon Reduction (National Development and Reform Commission, 2024, accessed 20 December 2025); https://www.ndrc.gov.cn/xxgk/zcfb/ghxwj/202404/t20240408_1365534.html.

93. Wang, Y., Chang, X., Chen, Z., Zhong, Y. & Fan, T. Impact of subsidy policies on recycling and remanufacturing using system dynamics methodology: a case of auto parts in China. J. Clean. Prod. 74, 161–171 (2014).

    Article  CAS  Google Scholar 

94. Notice on the Implementation of the Large-Scale Equipment Renewal and Consumer Goods Trade-In Policy (National Development and Reform Commission, 2025); https://www.ndrc.gov.cn/xxgk/zcfb/tz/202501/t20250108_1395564.html.

95. World Energy Outlook 2024 (IEA, 2024); https://www.iea.org/reports/world-energy-outlook-2024.

96. State and Trends of Carbon Pricing 2025 (World Bank, 2025); https://www.worldbank.org/en/publication/state-and-trends-of-carbon-pricing.

97. Chen, W. Data for “Towards an equitable future of global photovoltaic waste recycling”. Zenodo https://doi.org/10.5281/zenodo.215067969 (2026).

98. Chen, W. Codes for “Towards an equitable future of global photovoltaic waste recycling”. Zenodo https://doi.org/10.5281/zenodo.20715379 (2026).

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Author notes

1. These authors contributed equally: Chen Wang, Jian Zuo, Xinyu Chen

Authors and Affiliations

1. Institute of Blue and Green Development, Shandong University, Weihai, China

   Chen Wang, Pengfei Yuan, Yu Xin, Xi Liu, Peipei Tian & Jiashuo Li

2. School of Civil Engineering and Construction Management, Adelaide University, Adelaide, South Australia, Australia

   Jian Zuo & Ruidong Chang

3. School of Management, State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China

   Xinyu Chen

4. Department of Geography, The University of Hong Kong, Hong Kong, China

   Kuishuang Feng

5. College of Economics and Management, Taiyuan University of Technology, Taiyuan, PR China

   Jing Li

6. Department of Management and Engineering, Linköping University, Linköping, Sweden

   John Laurence Esguerra

Contributions

Jiashuo Li designed the research and coordinated the project. C.W., J.Z., X.C., Jing Li and Jiashuo Li collected and compiled the data. C.W., J.Z., R.C., X.C., P.Y. and Jiashuo Li performed the analyses and interpreted the results. C.W., J.Z., R.C., X.C., P.Y. and Jiashuo Li wrote the paper. X.C., Y.X., X.L., P.T., J.L.E., K.F. and Jiashuo Li reviewed and revised the manuscript.

Corresponding author

Correspondence to Jiashuo Li.

Competing interests

The authors declare no competing interests.

Peer review information

Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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