The importance of mineral resources cannot be ignored in the country's economic and social development and is of vital significance in securing China's continued economic and social prosperity. Against the backdrop of the country's active promotion of the clean energy industry, the booming development of the photovoltaic industry has triggered a growing demand for its key raw materials, especially important metallic minerals. Based on the system dynamics theory, the article uses Vensim to construct a photovoltaic cell-key metal mineral simulation model to analyze the development of China's photovoltaic industry in depth and focuses on its far-reaching impact on the supply and demand relationship of key minerals. The results of the study show that (1) China's photovoltaic cells show strong growth; (2) recycling and technology substitution can significantly reduce the risk of copper and aluminum supply and demand imbalance; and (3) technology substitution is more effective than recycling in reducing the supply and demand imbalance of copper and aluminum. Based on the above-mentioned findings, the article puts forward corresponding policy recommendations.

IRENA

International Renewable Energy Agency

OECD

Organization for Economic Co-operation and Development

PV

photovoltaic

R&D

research and development

USGS

United States Geological Survey

With the urgent global demand for clean energy and the growing problem of climate change, solar photovoltaic (PV) technology, as a renewable and environmentally friendly form of energy, is gradually becoming a key force in promoting sustainable development and is widely recognized as the main option to meet the world's future continuously growing power demand (Klugmann-Radziemska , 2010; Jean , 2015). In terms of low-carbon emission reductions, the World Bank estimates that 3 × 109 tons of key mineral resources will be consumed in emission reductions in 2050 (Hund, 2023). Therefore, the rapid rise of the solar photovoltaic industry has not only injected new vitality into the transformation of the national energy structure but also triggered profound thinking about the supply and demand of the key minerals it requires.

Many countries have already made gains in photovoltaic power generation and, as a result, the PV market is expanding rapidly (Yi, 2014; Parida, 2011). By the end of 2022, China's installed solar power capacity will be about 390 × 106 kW, a year-on-year increase of 28.1%. In addition, “Minerals for Climate Action: Mineral Consumption Intensity for the Clean Energy Transition” and “The Role of Critical Mineral Resources in the Clean Energy Transition,” published by the World Bank and the International Energy Agency in 2020 and 2021, respectively, point out that demand for critical metal minerals will continue to rise in the coming decades and that security-of-supply concerns will intensify. The security risks in the supply chain of critical minerals are further reinforced by the irreplaceable nature of critical minerals in high-technology fields and their uneven distribution across the globe (McNulty and Jowitt, 2021). However, China's geological endowment of key metals is poor, and its low domestic self-sufficiency rate makes it difficult to meet the growing scale of consumption. At the same time, a large number of key metals and minerals rely on overseas imports, and the degree of external dependence is increasing year by year; once the supply of these overseas key metals and minerals is cut off, it will not only pose a threat to the pattern of resource security but also lead to the disruption of the clean energy industry chain, which will have a huge negative impact on economic development.

The widespread use of photovoltaic power generation will produce a large number of used batteries, photovoltaic cells contain a large number of precious metal mineral resources, including copper and aluminum, and used batteries if not recycled may lead to the generation of hazardous wastes (Chowdhury , 2020; McDonald and Pearce, 2010). By effectively recycling these precious metals, it not only reduces dependence on finite mineral resources but also helps to reduce the waste of resources and the burden on the environment (Fthenakis, 2009; Marwede and Reller, 2012; Kushiya, 2009; Sinha, 2013; and Klugmann-Radziemska, 2012). At the same time, the recycling of key metals and minerals in photovoltaic cells not only contributes to the sustainable use of resources but also provides a solid foundation for environmental protection and the establishment of resource recycling chains. Therefore, the recycling of key metals and minerals in photovoltaic cells is not only part of the sustainable use of resources but also a key link in environmental protection and sustainable energy development.

Therefore, it has become an urgent task to study how to efficiently recycle and reuse the key metal minerals in photovoltaic cells. The research objective of this paper is to predict the future growth trend of photovoltaic cell ownership as well as to simulate the changes in the supply and demand of metal minerals in China under different recycling rates as well as different technological substitutions by designing technological scenarios by constructing a predictive simulation model for photovoltaic cells-critical metal minerals, so as to provide a strategic guide to future sustainable development and to ensure the sustainable use of critical metal minerals. Compared with the existing literature, the contributions of this paper are the following two points: first, the previous literature has not organically combined the PV industry with the supply and demand of key metal minerals. By establishing a PV cell-critical metal minerals simulation model, this paper enriches the extended research on the impact of PV cell recycling on critical metal minerals and fills the research in this field. Second, the research method is based on system dynamics, and technology scenario analysis is added to enrich the research conclusions.

The literature on the impact of the development of the solar photovoltaic (PV) industry on the supply and demand relationship of key minerals focuses on the three areas.

Liu (2021) describes the materials required for the production of photovoltaic cells, discusses the potential environmental impacts of photovoltaic cells, and states that material recycling of photovoltaic cells is necessary. By recycling photovoltaic cells, the risk of pollution and waste disposal of photovoltaic cells can be solved and, at the same time, system costs can be further reduced (Binek, 2016). Furthermore, Corcelli (2017) used an energy-value analysis approach to assess the sustainability of the PV panel recycling process and noted that the recycling of PV cell materials can yield significant environmental benefits. Some scholars have also studied the influencing factors of photovoltaic cell recycling, for example, dos Reis Benatto (2017) conducted a life cycle assessment study on photovoltaic cells and pointed out that recycling of photovoltaic cells has a strong correlation with environmental impact. Malandrino (2017) provides an in-depth analysis of the technical and economic aspects related to end-of-life management of PV cells, highlighting ways to increase the recovery and recycling rates of PV cells in Italy and pointing out that increasing the awareness of the participants' involvement would have a positive impact on a greener PV energy cycle. Some scholars have also studied the recycling methods of photovoltaic cells; Zhang and Xu (2016) investigated the recycling of used PV modules from an environmentally friendly perspective, stating that nitrogen pyrolysis and vacuum decomposition can successfully recover useful organic components, glass, and gallium from solar cell modules. Zhang (2017) empirically analyzed the recycling efficiency of photovoltaic cells and pointed out that electrostatic separation technology is an effective and environmentally friendly recycling method and that the separation efficiency of silver and silicon will reach 96% and 98%, respectively. Yang (2017) empirically analyzed the recycling efficiency of photovoltaic cells, stating that silver can be extracted from solar cells using environmentally friendly and reusable methanesulfonic acid (MSA) and the addition of oxidizing agents and that the optimal ratio of extraction is 90:10. Søndergaard (2014) used a life cycle analysis approach to assess the most efficient method of silver extraction and the impact on the entire life cycle of a photovoltaic cell and noted that silver in the electrodes can be recovered in the form of silver chloride with a recovery rate of up to 95% and a 13% reduction in overall energy recovery time. Considering the low efficiency of elemental separation, insufficient component recovery, and environmental considerations in the recycling process of photovoltaic cells, Luo (2021) considered the recycling of photovoltaic cells by an integrated hydrometallurgical recycling method and showed that this method can recover valuable elements and capture the hazardous substance lead. Zante (2022) analyzed the methods of recovering silver from photovoltaic cells, stating that the use of brine to process the metal is a new method for obtaining high purity silver with a recovery rate of up to 95%.

Metallic minerals will play a key role in the move toward a low-carbon economy. As the demand for green energy technologies, such as solar panels and energy storage, continues to rise, so too does the demand for the minerals needed to develop and deploy these technologies. This growing demand is expected to create economic opportunities for countries with major reserves of key strategic minerals such as cobalt, lithium, and copper (Church, 2020). Compared with the strong demand for resources, the key mineral supply chain is affected by political intervention and distorted trade behavior, and this supply chain faces many challenges and poses a threat to its sustainability (Dou and Xu, 2023).

Some scholars have studied the supply of key metal minerals; for example, Zhou (2019) quantitatively analyses the short-term supply risk of 12 minerals predominantly used in clean energy technologies and identifies tin, cobalt, chromium, and nickel as having a high supply risk. Further, Valero (2018) assesses the supply risk of 31 key minerals involved in the development of wind, solar PV, solar thermal, and new energy vehicles globally over the period 2016–2050, noting that cadmium, chromium, cobalt, copper, gallium, indium, lithium, manganese, nickel, silver, tellurium, tin, and zinc carry very high supply risk. Jowitt (2020) analyzed global metal reserves and showed that the main source of risk to metal and mineral supply in the coming decades is likely to be environmental, social, and governance factors and that this threat is more serious than direct reserve depletion. Yu (2021) used stochastic multi-criteria acceptability analyses to assess the supply risk of China's 14 strategic metal mineral resources, pointing out that the level of copper, gold, tungsten, molybdenum, antimony, and lithium supply risk is increasing, the level of nickel supply risk is decreasing, and the fluctuation of iron, tin, chromium, and rare earth elements is irregular.

Some scholars have studied the demand of key metal minerals. Boubault and Maïzi (2019) used the TIMES integrated assessment model to project global demand for metal minerals under 2100, noting that there will be a dramatic increase in the use of graphite, aluminum, chromium, nickel, silver, gold, rare earth elements, or their substitutes due to the large-scale installation of low-carbon technologies. Sverdrup (2019) assesses the long-term supply sustainability of copper, noting that there will be no physical shortage of copper in the future, but that increased demand and declining resource quality will lead to a significant increase in prices. Ren (2021) used a combination of dynamic material flow analysis and exploratory energy scenarios to assess the induced demand for base metals (copper, steel, aluminum, nickel) and rare earth elements, with the study noting that, in China, annual demand for base metals for wind power is projected to increase by a factor of 12 by 2050 compared to 2018. Islam (2022) uses a cross-sectional autoregressive distributional lag approach to analyze panel data on renewable energy capacity in Organization for Economic Co-operation and Development (OECD) countries, and the results of the study validate that, in the long run, overall renewable energy production, including solar and wind installations, drives the demand for imports of aggregated and disaggregated minerals (e.g., copper and nickel).

Guo and Guo (2015) used a system dynamics approach to construct a simulation model about the development of PV power generation in China, emphasizing the rationality of China's incentive policies. In this context, Jeon (2015) combines system dynamics and real options modeling to propose a methodology for optimizing financial subsidies and public research and development (R&D) investments in PV technologies and suggests that the best way for governments to achieve PV diffusion goals should consider a dual strategy of increasing R&D funding and reducing financial subsidies. Zhao (2021) investigated the impact of R&D investment on China's PV power generation industry by constructing a system dynamics model and pointed out that R&D investment and FIT are favorable to the technological innovation of China's PV power generation industry. Zhang (2022) used a system dynamics model and a Weibull function model to assess the impact of China's PV industry on carbon neutrality and showed that PV power generation contributes 36.8% to China's zero-carbon emissions from electricity and 14.7% to society's carbon neutrality. Zhang (2022) developed a system dynamics model for the recycling of discarded PV modules to simulate the return on investment for benchmarks and reasonable scenarios with different subsidies, and the results of the study show that subsidy policies are needed to support and guide recycling. Heidari and Heravi (2023) proposed a system dynamics model for photovoltaic (PV) ecosystems and showed that the development of PV power generation can show an S-shaped growth if supportive policies are adjusted and canceled in a timely manner. Lags in policy changes, volatility in energy markets, and interactions of influencing factors may lead to overshooting or oscillations in the ecosystem.

At present, research has been carried out on PV cell recycling methods and the risk of imbalance between supply and demand of key metal mineral resources. However, there is a research gap on the impact of PV cell recycling on the supply and demand of key metal minerals. Therefore, the purpose of this paper is to analyze the development of China's photovoltaic industry by constructing a photovoltaic cell-key metal mineral system dynamics model and to focus on its far-reaching impact on the supply-demand relationship of key minerals.

In this paper, a PV cell-critical metal minerals simulation model is constructed by the system dynamics software Vensim, and the model runs from 2017 to 2050, in which 2017–2021 is the yearly interval for the model to run and historically test, and 2022–2050 is the forecast year for the system simulation to predict the future development of PV cells and the recycling trend of critical metal minerals. The simulation step is 1 year. The data sources for the article are mainly China Statistical Yearbook, China Customs, International Renewable Energy Agency (IRENA) database, and United States Geological Survey (USGS) database.

According to the defined system boundary, based on the analysis of the internal mechanism of the system and the interaction of various influencing factors, the flow diagram of photovoltaic cell-key metal mineral system is drawn (as shown in Fig. 1). The model includes three state variables, four rate variables, and the rest are auxiliary variables and shadow variables.

FIG. 1.

Flow chart of photovoltaic cell-key metal mineral system.

FIG. 1.

Flow chart of photovoltaic cell-key metal mineral system.

Close modal

The model parameters are determined by constant values, direct assignment, function assignment, regression analysis, and entropy value methods. Parameters that do not change significantly over time are taken as constants, such as the average lifetime of PV cells. The initial value of the horizontal variable is taken as direct assignment, and some quantities that change over time are taken as function assignment, such as table function assignment or delay function assignment. For synthetic indicators, entropy value method is used to confirm their specific weights, such as the growth rate. Due to the limitation of space, this paper only lists the functional relationship between the main parameters of the photovoltaic cell-key metal-mineral system dynamics model (as shown in Table I).

TABLE I.

Relationship between main parameters of the model.

Main parameter Functional relationship between main parameters
GDP  GDP = INTEG (average annual GDP increase, 830 946) 
Photovoltaic cell output  Photovoltaic cell output = base period output × (1 + growth rate) 
Photovoltaic cell reduction  Photovoltaic cell reduction = scrap + photovoltaic cell export quantity 
Recovery of photovoltaic cells  Recovery amount of photovoltaic cells = weight of photovoltaic cells× recovery rate  × scrap amount × 1 × 108 
Photovoltaic cell ownership  The number of photovoltaic cells = INTEG (new number of photovoltaic cells − decrease in photovoltaic cells, 276.096) 
Number of new photovoltaic cell additions  Number of new photovoltaic cell additions = output of photovoltaic cells+ import quantity of photovoltaic cells 
Growth rate  Growth rate = 0.194 529 × technical factor + 0.746 794 × use cost factor + 0.058 677 × electricity demand factor 
Scrap quantity  Scrap quantity = DELAY1I (new quantity of photovoltaic cells, average life of photovoltaic cells, 100) 
Metallic mineral supply  Metal mineral supply = metal mineral output + metal mineral recovery + metal mineral import 
Metallic mineral demand  Metallic mineral demand = export of metal minerals + consumption of metal minerals 
Main parameter Functional relationship between main parameters
GDP  GDP = INTEG (average annual GDP increase, 830 946) 
Photovoltaic cell output  Photovoltaic cell output = base period output × (1 + growth rate) 
Photovoltaic cell reduction  Photovoltaic cell reduction = scrap + photovoltaic cell export quantity 
Recovery of photovoltaic cells  Recovery amount of photovoltaic cells = weight of photovoltaic cells× recovery rate  × scrap amount × 1 × 108 
Photovoltaic cell ownership  The number of photovoltaic cells = INTEG (new number of photovoltaic cells − decrease in photovoltaic cells, 276.096) 
Number of new photovoltaic cell additions  Number of new photovoltaic cell additions = output of photovoltaic cells+ import quantity of photovoltaic cells 
Growth rate  Growth rate = 0.194 529 × technical factor + 0.746 794 × use cost factor + 0.058 677 × electricity demand factor 
Scrap quantity  Scrap quantity = DELAY1I (new quantity of photovoltaic cells, average life of photovoltaic cells, 100) 
Metallic mineral supply  Metal mineral supply = metal mineral output + metal mineral recovery + metal mineral import 
Metallic mineral demand  Metallic mineral demand = export of metal minerals + consumption of metal minerals 

1. Dimensional consistency test

The dimensional consistency test is also known as the unit consistency test. The basic principle of the measure consistency test is to check whether the measure of each equation in the established system dynamics model is consistent. This paper checks the measure of each equation one by one, so as to make sure that the variables in the equations are uniform in the measure of the equations, and the test function of Vensim has been used to check the measure of the equations. It has passed the test in running the model, so it is possible to ascertain that all the variables have passed the measure consistency test, and all the parameters in the model are reflected in the actual system with the significance of the real value.

2. Historical inspection

Historical testing is a method to verify the accuracy of a model, which assesses the quality of the model by comparing the simulation data generated by the model with the available historical data. In order to verify the validity of the model and the reasonableness of its structure, and to ensure that the constructed model is applicable to the development and change of the real situation, this paper selects the level variables in the model, including PV cell ownership, GDP, and the total population for the historical test, with 2017–2021 as the time range of the model run. First, the data of 2017 were used as the initial value, and these core indicators were assigned to the model for simulation, which was performed using the Vensim software. Then, the simulation results generated by the model were compared with the actual data to verify the accuracy and validity of the model, as shown in Table II.

TABLE II.

Test results of model history.

Indicator name Comparative value Result
2018 2019 2020 2021
Photovoltaic cell ownership (unit: 100 × 106 Actual value  372  436  541  654 
Predicted value  337  431  517  686 
Error rate (%)  −9.25  −0.99  −4.37  4.88 
GDP (unit: 109 Actual value  915 244  983 751  1 005 451  1 133 240 
Predicted value  922 350  1 005 360  1 075 740  1 097 250 
Error rate (%)  0.77  2.15  6.53  −3.28 
Populations (unit: 10 000)  Actual value  140 541  141 008  141 212  141 260 
Predicted value  140 792  141 324  141 794  141 999 
Error rate (%)  0.18  0.22  0.41  0.52 
Indicator name Comparative value Result
2018 2019 2020 2021
Photovoltaic cell ownership (unit: 100 × 106 Actual value  372  436  541  654 
Predicted value  337  431  517  686 
Error rate (%)  −9.25  −0.99  −4.37  4.88 
GDP (unit: 109 Actual value  915 244  983 751  1 005 451  1 133 240 
Predicted value  922 350  1 005 360  1 075 740  1 097 250 
Error rate (%)  0.77  2.15  6.53  −3.28 
Populations (unit: 10 000)  Actual value  140 541  141 008  141 212  141 260 
Predicted value  140 792  141 324  141 794  141 999 
Error rate (%)  0.18  0.22  0.41  0.52 

By comparing and analyzing the data in Table II, it can be seen that the error rates of PV cell ownership, GDP, and population forecasts for 2018–2021 are all within 10%, indicating that the model fits the reality relatively well and is able to respond more realistically to the structure of the PV industrial system and its changes and can be used to simulate the development of PV cell ownership as well as the future recycling and disposal situation.

In order to further simulate the future development trend, this paper uses the Vensim PLE software to simulate the state variables in the system, and the simulation results of each variable are shown in Figs. 1–5.

FIG. 2.

Development trend of photovoltaic cell ownership (unit: 100 × 106).

FIG. 2.

Development trend of photovoltaic cell ownership (unit: 100 × 106).

Close modal
FIG. 3.

Changes of copper supply and demand under different recovery rates (unit: 10 000 tons).

FIG. 3.

Changes of copper supply and demand under different recovery rates (unit: 10 000 tons).

Close modal
FIG. 4.

Changes of aluminum supply and demand under different recovery rates (unit: 10 000 tons).

FIG. 4.

Changes of aluminum supply and demand under different recovery rates (unit: 10 000 tons).

Close modal
FIG. 5.

Changes of copper supply and demand under technical substitution (unit: 10 000 tons).

FIG. 5.

Changes of copper supply and demand under technical substitution (unit: 10 000 tons).

Close modal

According to Fig. 2, China's PV cell ownership stood at just 27 609.6 × 106 units in 2017, yet this figure is expected to grow rapidly to 403 099 × 106 units by 2030. Of particular interest is the fact that the slope of the curve in the graph shows that this growth rate continues to rise, signaling a growing trend of a booming PV cell industry in China, which also reflects the country's excellence in the renewable energy sector. Looking ahead to 2050, China's PV cell ownership is expected to reach an impressive 181.5 × 109 units. This trend not only signals China's growing competitiveness in the global market but also opens up more opportunities and potential for the Chinese economy.

In this study, a technology scenario is designed, which consists of a recycling scenario and a technology substitution scenario. The recycling scenario focuses on the recycling side of PV cells and simulates different recycling rates to see how the supply and demand of copper changes. The technology substitution scenario focuses on the production side of PV cells, i.e., the demand side of copper. The substitution in this study is designed to reduce the dependence on key minerals and the consumption of expensive minerals, to reduce the risk of supplying the key minerals needed for PV cells, to compensate for the use of the key minerals through the level of technology, or to replace the existing key minerals with other minerals which are more plentiful and cheaper.

According to the data presented in Fig. 3, the four curves, respectively, represent the different effects of the change of photovoltaic cell recovery rate on the growth of supply–demand gap and are all compared with the copper supply–demand gap without recovery. When the recovery rate of photovoltaic cells is 0.3, that is, only 30% of waste photovoltaic cells are recycled, it can be observed that in 2017, the surplus between supply and demand of copper increased by 300 tons compared with the situation without recycling. With the passage of time, by 2030, the surplus of supply and demand of copper will increase to 1600 tons compared with the situation without recycling, and finally in 2050, the surplus of supply and demand of copper will increase to 6700 tons. This trend clearly reflects that even if the recovery rate is relatively low, the recovery treatment still has a positive impact on the balance between supply and demand in the copper market.

When the recovery rate of photovoltaic cells is increased to 0.5, the surplus between supply and demand of copper will increase by 500 tons in 2017, and by 2030, the surplus between supply and demand of copper will increase to 2700 tons. In 2050, the surplus between supply and demand of copper exceeded 10 000 tons, specifically 11 100 tons. This shows that the positive effect of improving the recovery rate on the relationship between copper supply and demand is gradually strengthened, and it also implies the importance of sustainable utilization of resources.

When the recovery rate of photovoltaic cells was further increased to 0.8, the surplus between supply and demand of copper in 2017 increased by 800 tons compared with the situation without recovery. In 2030, the surplus of supply and demand of copper increased to 4300 tons compared with the situation without recycling, and finally in 2050, the surplus of supply and demand of copper increased to 17 800 tons. This further verifies the positive effect of high recovery rate on copper market.

Finally, when the recovery rate of photovoltaic cells reaches 1, that is, all scrapped photovoltaic cells are recycled, it can be observed that in 2017, the surplus between supply and demand of copper increased by 0.1 × 106 tons compared with the situation without recycling. In 2030, the surplus between supply and demand of copper increased to 5400 tons compared with the situation without recycling. Until 2050, the surplus between supply and demand of copper exceeded 20 000 tons, specifically 22 200 tons. This once again emphasizes the positive impact of comprehensive recycling on the copper market.

A longitudinal comparison of the different recovery rates for a uniform year shows that when the recovery rate in 2017 is 0.3, 0.5, 0.8, and 1, the supply/demand surplus grows by 300, 500, 800, and 1000 tons, respectively, compared to the no-recovery scenario, with growth rates of 66.67%, 60%, and 25%, respectively. Similarly in 2030, the surplus of supply and demand over the no-recovery scenario increases by 1600, 2700, 4300, and 5400 tons, respectively, with growth rates of 68.75%, 59.26%, and 25.58%, while in 2050, the surplus of supply and demand over the no-recovery scenario increases by 6700, 11 100, 17 800, and 22 200 tons, respectively, with growth rates of 65.67%, 60.36%, and 24.72%, respectively. In summary, the comparison between the different recovery rate scenarios for a uniform year reveals a significant positive relationship between recovery rate and copper supply. This further emphasizes the importance of resource recovery, especially in the context of sustainable development, which will have far-reaching positive impacts on the copper market and economic health of the country.

As shown in Fig. 4, when the recovery rate of photovoltaic cells is 0.3, the supply and demand surplus of aluminum in 2017 increased by 1000 tons, that of aluminum in 2030 increased to 5400 × 106 tons and that of copper in 2050 finally increased to 22 200 tons. When the recovery rate of photovoltaic cells is 0.5, the supply and demand surplus of aluminum increased by 1700 tons in 2017, 9000 tons in 2030, and 30 000 tons in 2050. When the recovery rate of photovoltaic cells is 0.8, the supply and demand surplus of aluminum increased by 2700 tons in 2017, 14 500 tons in 2030, and 59 200 tons in 2050. When the recovery rate of photovoltaic cells is 1, that is, all scrapped photovoltaic cells are recycled, the supply and demand surplus of aluminum in 2017 increased by 3400 tons and that of copper in 2030 increased to 18 100 tons, until 2050, the supply and demand surplus of copper exceeded 70 000 tons, specifically 74 000 tons. After unifying the recovery rate and the year, we compare the supply and demand of aluminum and copper, and we can know that the supply and demand surplus of aluminum is obviously greater than that of copper. No matter from the long-term 2050 or the medium-term 2030, the imbalance between supply and demand of copper and aluminum will be significantly reduced, which further emphasizes the positive impact of resource recovery on China's aluminum market and economy.

According to Fig. 5, the two curves in the figure represent the changes of copper supply and demand when the copper content in photovoltaic cells is 0.3% and 0.1%, respectively, compared with the situation without technical substitution. When the copper content in photovoltaic cells decreased from 0.6% to 0.3%, the surplus between supply and demand of copper in 2017 increased by 762 tons compared with the situation without technical substitution. By 2030, the surplus between supply and demand of copper will increase to 5247 tons relative to the situation without technical substitution, and finally in 2050, the surplus between supply and demand of copper will increase sharply to 13 969 × 106 tons relative to the situation without technical substitution.

When the copper content in photovoltaic cells decreased from 0.6% to 0.1%, the surplus between supply and demand of copper in 2017 increased by 1236 × 106 tons compared with the situation without technical substitution. In 2030, the surplus of supply and demand of copper increased to 8778 × 106 tons relative to the situation without technical substitution, and finally in 2050, the surplus of supply and demand of copper increased sharply to 23 348 × 106 tons relative to the situation without technical substitution.

Finally, we can compare the substitution of different technologies in the same year. In 2017, when the copper content was 0.3% and 0.1%, respectively, the surplus between supply and demand of copper increased by 762 and 1236 × 106 tons, respectively, with a growth rate of 62.2%. By 2030, the surplus between supply and demand of copper will increase to 5247 × 106 and 8778 × 106 tons, respectively, with a growth rate of 67.3%. In 2050, the surplus between supply and demand of copper increased sharply to 13 969 and 23 348 tons, respectively, with an increase rate of 67.14%. This also shows that technical substitution is helpful to reduce the risk of imbalance between supply and demand of copper.

As shown in Fig. 6, the three curves in the figure represent the changes of aluminum supply and demand when the aluminum content in photovoltaic cells is 1.5%, 1%, and 0.5%, respectively, compared with the situation without technical substitution. When the aluminum content dropped from 2% to 1.5%, the supply and demand surplus of aluminum in 2017 was 1236 tons, in 2030 it was 8778 tons, and in 2050 it rose rapidly to 23 347 tons. When the content of aluminum is 1%, the supply and demand surplus of aluminum in 2017 is 2372 tons, that in 2030 is 17 456 tons, and that in 2050 is 46 794 tons. When the aluminum content is as low as 0.5%, the surplus between supply and demand of aluminum in 2017 is 3607 tons, and in 2022 it will exceed 10 000 tons, specifically 11 603 tons. Finally, in 2050, the surplus between supply and demand of aluminum will increase rapidly to 70 142 tons.

FIG. 6.

Changes of aluminum supply and demand under technical substitution (unit: 10 000 tons).

FIG. 6.

Changes of aluminum supply and demand under technical substitution (unit: 10 000 tons).

Close modal

Finally, we can compare the substitution of different technologies in the same year. In 2017, when the aluminum content was 1.5%, 1%, and 0.5%, respectively, the surplus between supply and demand of aluminum increased by 1236, 2372, and 3607 tons, respectively, with the growth rates of 91.91% and 52.07%, respectively. By 2030, the surplus between supply and demand of aluminum will increase to 8778, 17 456, and 26 135 tons, respectively, with growth rates of 98.86% and 49.72%, respectively. In 2050, the surplus between supply and demand of aluminum increased sharply to 23 374, 46 794, and 70 142 tons respectively, with growth rates of 100.2% and 49.9%, respectively. This also shows that technical substitution significantly reduces the risk of imbalance between aluminum supply and demand.

In this study, a photovoltaic cell-critical metal-mineral prediction simulation model is constructed through system dynamics, which fills a relatively vacant state in this field. Despite the remarkable progress, however, the development of the photovoltaic industry still faces numerous uncertainties. These uncertainties mainly stem from various factors such as fluctuations in market demand, changes in the policy environment, and uncertainty in the speed of technological innovation. On the other hand, the development of the photovoltaic industry is also affected by a combination of changes in mineral-related industries, the level of sustained exploration of mineral resources, advances in recycling technology, and innovations in technological alternatives. These factors are intertwined, making it relatively difficult to accurately predict the future development of PV cells.

It is worth noting that this study faced some difficulties in data collection, and the limited nature of the study data also had an impact on the accuracy of the model. However, despite these challenges, every effort was still made to provide an in-depth analysis of the PV cell-critical metal-mineral system from a macroscopic point of view, which provides a new approach for research in this field. In order to further improve the accuracy of the prediction, future research can strengthen the in-depth mining of key data, expand data sources, and improve the reliability of the model. At the same time, close attention is paid to the development dynamics of the mineral market and related technologies in order to better grasp the future development trend of the PV industry. This will help provide more reliable information to policy makers, industrial decision makers, and researchers, and provide strong support for the sustainable development of the PV cell industry.

  1. Photovoltaic cells in China show a strong growth trend. The number of photovoltaic cells has increased from 27.6096 × 109 in 2017 to 403.099 × 109 in 2030, and it is expected to reach 1.815 × 109 by 2050. The rapid increase in the number of photovoltaic cells reflects the importance attached by China government to renewable energy and the rapid development of technology and industrial chain in this field. This trend has brought great opportunities and potential to China's economy and laid a solid foundation for it to play a leading role in global energy transformation.

  2. Recycling and technical substitution can significantly reduce the risk of imbalance between supply and demand of copper and aluminum. The wide application of recycling technology not only helps to effectively use waste materials and reduce excessive dependence on primary resources but also is expected to reduce the environmental impact of production links. Technical substitution, such as the research and development of new alloys and the substitution of other surplus and cheap minerals, is expected to reduce the demand for limited resources such as copper and aluminum and promote the innovative development in the field of materials science.

  3. Technical substitution is more effective than recycling in reducing the imbalance between supply and demand of copper and aluminum. Through the introduction of new-generation alloys, advanced production processes, and efficient material science innovations, technology alternatives are expected to achieve more efficient use of traditional copper and aluminum, thereby reducing dependence on finite resources. The strategic deployment of technological substitution will not only provide the industry with more flexible and sustainable solutions but also offer new breakthroughs in resource efficiency in the production chain. This innovation-driven substitution is expected to improve material performance while reducing raw material demand, resulting in a more balanced and stable copper and aluminum market.

By the above summary, our study not only emphasizes the novelty of the research methodology but also highlights the dual contribution of this innovative research to the government's sustainable development goals and the improvement of industrial competitiveness of PV firms. Not only does it provide comprehensive insights for governments to help them formulate more precise and sustainable energy policies, but it also provides key decision support for PV companies to lead them to achieve competitive advantages in the market.

  1. Strengthen policy support and incentives. The government should continuously formulate and improve policies and measures to support the photovoltaic cell industry, including financial incentives, tax incentives, and subsidies. The formulation of this series of policies aims to actively guide enterprises to increase investment and promote innovation activities. Through these measures, it is expected to encourage more enterprises to actively participate, thus promoting the continuous improvement of photovoltaic cell technology level, effectively reducing production costs, and further promoting the upgrading of the entire industrial chain. The government can consider establishing a more long-term and sustainable incentive mechanism to stabilize the investment expectation of enterprises and encourage them to make more breakthroughs in technology research and development and production technology improvement. In addition, in order to better cope with market changes and technological development, the flexibility of policies should also be strengthened to adapt to the ever-changing photovoltaic cell industry environment.

  2. Strengthen the construction of recycling system and support the development of technology substitution and innovation. First of all, the government should formulate and implement stricter waste management regulations to encourage and regulate the metal recycling behavior of enterprises and individuals. Second, the government can provide incentives, such as tax incentives or incentive schemes, to encourage enterprises to adopt and improve advanced recycling technologies. Finally, the government can set up a special fund to support new alloys, alternative materials, and other innovative research to reduce the demand for limited resources, and at the same time formulate incentive policies to encourage enterprises to invest in R&D and promote Industry–University–Research's cooperation, so as to accelerate the application of new technologies in industries and improve the sustainability of industries.

  3. Promote international cooperation and standard setting. Actively participating in international cooperation is one of the key factors to promote the development of renewable energy technology. Therefore, it is suggested that China deepen cooperation with other countries and international organizations, share advanced technologies and successful experiences, and jointly meet global energy challenges. Advocate active participation in the process of formulating international standards to ensure that China plays a more active leading role in formulating standards in the field of photovoltaic cells. This move will help to standardize industry development, improve product quality, accelerate technological innovation, and provide strong support for China enterprises to win a larger share in the international market. Through more active participation in the international arena, China is not only expected to share its advanced experience in the field of photovoltaic cells with the international community but also promote the progress of global green energy technology. This strategic measure of international cooperation and standard setting will strongly promote the sustainable development of global photovoltaic cells and contribute to the construction of a clean, green, and sustainable global energy system in China.

This research is supported by the National Social Science Fund of China (No. 21CJL007).

The authors have no conflicts to disclose.

Qing Guo: Conceptualization (lead); Funding acquisition (lead); Methodology (lead); Supervision (lead); Writing – original draft (lead); Writing – review & editing (lead). Kaiyi Wu: Formal analysis (supporting); Software (supporting); Writing – original draft (supporting); Writing – review & editing (supporting).

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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