Digital twin (DT) technology could help photovoltaic manufacturers tackle “involution,” a term increasingly used in China’s solar industry to describe excessive competition, persistent price declines, and shrinking profit margins, according to a new study published in the Journal of Cleaner Production.
Digital twins are virtual representations of physical assets or processes that use real-world data, simulations, and artificial intelligence to monitor, predict, and optimize performance. In the PV industry, the technology has already been explored for plant monitoring, fault detection, predictive maintenance, and quality assurance. Other applications include drone-based inspections and energy-yield analysis, helping operators identify underperforming assets and improve operational efficiency.
In PV manufacturing and R&D, researchers have also investigated the use of digital twins to accelerate materials discovery and optimize organic solar cell production. The new study extends this approach beyond technical and operational optimization to explore broader economic and competitive challenges facing the PV industry.
In the paper, “Evolution path of anti-involution in photovoltaic enterprises driven by digital twin technology,” the researchers developed a digital twin-inspired simulation framework to assess how different corporate strategies could help PV manufacturers move away from destructive price competition and improve their financial performance.
“The dynamic and iterative nature of anti-involution strategies in the photovoltaic industry closely aligns with the core principles of digital twin technology, which creates virtual representations of physical systems to enable real-time monitoring, simulation, and optimization of decision-making,” corresponding author Fan Wang told pv magazine. “We incorporated the industry’s multidimensional real-time conditions, including competition intensity, profitability pressure, operational efficiency, and technological competition, into a structured information matrix, while defining price increases, increased R&D investment, and their combined effects as decision matrices.”
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The proposed framework combines industry information, decision modeling, iterative simulations, and weighted performance evaluation. It also incorporates a multidimensional involution index, developed using company-level data to distinguish destructive competition from other market conditions. The researchers then examined how pricing decisions and R&D-driven technological differentiation interact, aiming to identify strategies that could improve the industry’s long-term competitiveness.
The team analyzed financial and operational data from 98 Chinese PV companies between 2020 and 2024, covering four dimensions: profitability, operational efficiency, technological investment and scale, and external economic conditions. These were assessed using 13 indicators, including profit margins, inventory turnover, R&D spending, revenue, subsidies, accounts payable, and producer prices. The researchers noted that higher R&D intensity does not necessarily translate into greater profitability and may, in some cases, reflect defensive competition rather than productive innovation.
After normalizing the indicators, the researchers combined them into a composite involution index, which rose from 0.0447 in 2020 to 0.9612 in 2024, including a sharp increase of approximately 595% between 2023 and 2024. Over the same period, industry gross profit margins fell from 26.93% to 18.91%, indicating that intensifying competition coincided with declining profitability.
The team also calculated a revenue-weighted industry price indicator and standardized historical data to establish a baseline for the simulations. The indicators were subsequently integrated into a 4 × 3 information matrix, allowing the researchers to model different anti-involution strategies and evaluate their potential effects on industry performance.
Using this framework, the researchers compared four scenarios: increasing product prices, expanding R&D investment, combining both measures, and continuing without intervention. Each strategy was simulated over 300 days and evaluated against the four performance dimensions. The researchers assigned weights of 32.71% to technological investment and scale, 28.52% to operational efficiency, 22.53% to growth and external conditions, and 16.25% to profitability.
The simulations showed that a strategy combining a 10% price increase with a 60% rise in R&D investment delivered the best overall performance. It achieved a recovery rate of 29.5% relative to the no-intervention benchmark, compared with 9.8% for a 5% price increase alone and 9.3% for a 30% increase in R&D investment alone. The combined approach also generated a 12.85% additional benefit compared with the sum of the individual effects of the two measures.
According to the researchers, higher prices can provide immediate financial relief but do little to improve technological competitiveness. Greater R&D investment, meanwhile, can support long-term differentiation but puts additional pressure on short-term profitability. Combining the two measures could help address these limitations, with higher revenues supporting innovation and technological improvements making higher prices more sustainable. However, more aggressive combinations, involving price increases of 15% to 20% and R&D investment increases of 90% to 120%, produced weaker results because of higher costs and financial risks.
To assess the robustness of their findings, the researchers conducted sensitivity analyses covering simulation duration, market conditions, decision persistence, pricing behavior, and other parameters. The combined strategy ranked first in all 1,000 parameter combinations tested and remained the preferred option even under scenarios involving aggressive competitive responses to price increases.
The authors cautioned, however, that the results are based on simulations rather than observed corporate outcomes. The proposed 10% price increase and 60% R&D expansion should therefore be viewed as an indicative strategy under the model’s assumptions, rather than a universally applicable target for PV manufacturers.
“Under the proposed simulation framework, a strategy combining a 10% price increase with a 60% rise in R&D investment delivered the best overall results, achieving a comprehensive profit and loss value of L = 1.8524, a recovery rate of 29.5%, and a synergy coefficient of 1.15,” Wang explained. “By supporting short-term profitability while promoting long-term technological development, this approach emerged as the most effective anti-involution strategy under the model’s assumptions and calibrated parameters.”
“The study’s main theoretical contribution lies in extending digital twin technology beyond conventional equipment monitoring and engineering simulations to strategic decision-making. We developed the first matrix-based dynamic simulation framework for tackling industry involution, linking the identification of its underlying causes with the quantitative assessment of potential countermeasures,” he concluded.
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