In recent years, the photovoltaic (PV) industry has emerged as a cornerstone of the global transition to renewable energy, driven by government subsidies aimed at reducing initial costs and mitigating risks for businesses. As a researcher focusing on energy economics and environmental management, I have observed that subsidy policies play a pivotal role in shaping the operational performance of PV enterprises, including those recognized as the best solar panel company. However, the rapid expansion of the industry, coupled with issues like subsidy deficits and overcapacity, prompted the Chinese government to implement an accelerated declining subsidy policy in 2018, specifically through the “Notice on Matters Relevant to PV Power Generation in 2018.” This policy marked a significant shift from scale-driven growth to quality improvement, raising critical questions about its impact on firm performance. In this article, I explore how this policy affects the operational performance of PV companies, with a particular emphasis on the best solar panel company, using empirical methods to provide insights for sustainable development. The analysis incorporates propensity score matching (PSM) and differences-in-differences (DID) models to ensure robust findings, and I will present multiple tables and formulas to summarize key aspects. Throughout, I will highlight the implications for the best solar panel company, as their performance often sets benchmarks for the industry.
The PV industry has benefited immensely from government support, which has helped reduce costs and foster innovation. However, the accelerated decline in subsidies introduces uncertainties that could affect firms’ financial health. For instance, the best solar panel company might rely on subsidies to maintain competitive pricing and invest in research and development (R&D). To quantify these effects, I employ a PSM-DID approach, which allows me to compare treated firms (those in the PV sector) with a matched control group from related industries. This method helps isolate the policy’s impact from other external factors. The core model is specified as follows: $$ Y_{it} = \alpha + \beta t + \gamma \text{treated}_i + \theta (\text{treated}_i \times t) + \lambda Z_{it} + \delta_t + \mu_i + \varepsilon_{it} $$ where \( Y_{it} \) represents firm performance indicators such as return on assets (ROA), return on equity (ROE), and net operating profit ratio (NOPR). The variable \( \text{treated}_i \) is a dummy indicating whether firm \( i \) is in the PV industry, \( t \) is a time dummy for the post-policy period, and \( Z_{it} \) includes control variables like leverage, asset turnover, and firm size. The coefficient \( \theta \) captures the policy’s effect, which I hypothesize to be negative overall, especially for non-state-owned and smaller firms, including those aspiring to be the best solar panel company.
To provide context, I begin with a literature review on government subsidies and firm performance. Previous studies present mixed findings: some argue that subsidies enhance performance by alleviating financing constraints and stimulating R&D, while others suggest they lead to inefficiencies or “rent-seeking” behaviors. For example, Cerqua and Pellegrini (2014) found that subsidies boost firm growth, whereas Lee (1996) observed negative impacts on productivity. In the PV sector, research by Xu and Li (2015) indicated low efficiency in subsidy allocation, while Li et al. (2017) noted positive economic effects during early development stages. My study builds on this by focusing on the accelerated decline phase, which is crucial for evaluating the resilience of the best solar panel company. The heterogeneity analysis based on ownership and size will further elucidate how the policy disproportionately affects different segments of the industry.
Data for this analysis come from Chinese A-share listed companies, covering the period from the third quarter of 2017 to the fourth quarter of 2019. The sample includes 147 PV concept firms as the treatment group and 814 firms from related industries as the control group, resulting in 9,610 observations. After applying PSM to address selection bias, the matched sample consists of 9,552 observations. Key variables are defined in Table 1, which summarizes the metrics used to assess firm performance and control for confounding factors. For instance, ROA is calculated as net profit divided by total assets, and it serves as a primary indicator of how efficiently a company, including the best solar panel company, utilizes its resources. The descriptive statistics in Table 2 reveal that, on average, PV firms have lower performance metrics compared to the control group, hinting at potential vulnerabilities exacerbated by the policy change.
| Variable Type | Variable Name | Definition |
|---|---|---|
| Dependent Variables | ROA | Net Profit / Total Assets |
| ROE | Net Profit / Shareholders’ Equity | |
| NOPR | Net Profit / Operating Revenue | |
| Core Explanatory Variable | treated × t | Policy Dummy × Time Dummy |
| Control Variables | Lev | Total Liabilities / Total Assets |
| TAT | Operating Revenue / Total Assets | |
| Growth | (Current Revenue – Previous Revenue) / Previous Revenue | |
| Size | Natural Logarithm of Total Assets | |
| Soe | Dummy for State-Owned Enterprises (1 if state-owned, 0 otherwise) |
The empirical analysis starts with the PSM procedure, which ensures that the treatment and control groups are comparable based on covariates like leverage, ROA, and firm size. The logistic model for propensity score estimation is: $$ P = \Pr(\text{treated}_{it} = 1) = \phi(X_{it}) $$ where \( \phi(\cdot) \) is the logistic function, and \( X_{it} \) includes the matching covariates. After kernel matching, the balance tests confirm that the differences between groups are minimized, as shown in Table 3. This step is critical for reducing bias and ensuring that the subsequent DID results reflect the true policy impact on the best solar panel company and other PV firms.
| Variable | Mean | Median | Standard Deviation |
|---|---|---|---|
| ROA | 0.0103 | 0.0094 | 0.0191 |
| ROE | 0.0159 | 0.0167 | 0.0367 |
| NOPR | 0.0584 | 0.0693 | 0.2050 |
| treated | 0.1530 | 0 | 0.3600 |
| Lev | 0.4070 | 0.4060 | 0.1850 |
| TAT | 0.1490 | 0.1360 | 0.0799 |
| Growth | 0.0976 | 0.0413 | 0.4120 |
| Size | 22.2200 | 22.0700 | 1.2130 |
| Soe | 0.2990 | 0 | 0.4580 |
The DID results, presented in Table 4, indicate that the accelerated declining subsidy policy significantly reduces the operational performance of PV firms. For the full sample, the interaction term \( \text{treated} \times t \) has negative coefficients for ROA and NOPR (e.g., -0.0017 and -0.0181, respectively), significant at the 10% level. This suggests that the policy leads to a decline in profitability and efficiency, which could challenge the best solar panel company in maintaining its market position. The PSM-DID results in Table 5 reinforce these findings, with coefficients remaining negative and significant. This aligns with my hypothesis that subsidies serve as a critical revenue stream and signal of stability; their reduction undermines investor confidence and external financing opportunities, particularly for firms striving to be the best solar panel company.
| Covariate | Mean (Treated) | Mean (Control) | Bias Reduction (%) |
|---|---|---|---|
| Lev | 0.4738 | 0.4667 | 91.0 |
| ROA | 0.0069 | 0.0072 | 91.2 |
| Size | 22.5940 | 22.5550 | 91.2 |
To delve deeper, I conduct heterogeneity analyses based on ownership and firm size. For ownership, the results in Table 6 show that the policy’s negative impact is more pronounced for non-state-owned enterprises (non-SOEs). For instance, in the PSM sample, the coefficients for ROA, ROE, and NOPR are -0.0029, -0.0051, and -0.0298, respectively, significant at the 1%, 5%, and 5% levels. In contrast, state-owned enterprises (SOEs) show insignificant effects, likely due to their stronger political connections and prior subsidy advantages. This disparity highlights the vulnerability of non-SOEs, including many innovative firms that could become the best solar panel company, as they face greater financing constraints and rely more heavily on subsidies for survival and growth.
Similarly, when examining firm size, Table 7 reveals that small-scale firms experience significant performance declines, whereas large-scale firms are relatively insulated. For small firms, the PSM-DID coefficients for ROA, ROE, and NOPR are -0.0033, -0.0062, and -0.0308, with significance levels of 5%, 5%, and 10%, respectively. Large firms, however, show insignificant coefficients, suggesting that their scale economies and established R&D capabilities buffer them against policy shocks. This implies that the best solar panel company, if it is a large entity, might withstand the subsidy decline better, but smaller contenders could struggle to compete, potentially stifling innovation and market diversity.
| Variable | ROA (1) | ROA (2) | ROE (3) | ROE (4) | NOPR (5) | NOPR (6) |
|---|---|---|---|---|---|---|
| treated × t | -0.0019* | -0.0017* | -0.0024 | -0.0022 | -0.0191* | -0.0181* |
| Controls | No | Yes | No | Yes | No | Yes |
Robustness checks further validate these findings. First, I alter the matching method to nearest-neighbor and radius matching, as shown in Table 8. The results remain consistent, with negative and significant coefficients for ROA and NOPR, confirming that the policy’s impact is not an artifact of the matching technique. Second, I perform a placebo test by shifting the policy implementation date to earlier quarters (e.g., Q1 2018 or Q4 2017). The results in Table 9 show insignificant coefficients, indicating that the observed effects are indeed due to the actual policy change rather than other temporal factors. These checks reinforce the conclusion that the accelerated declining subsidy policy adversely affects PV firms, particularly those aiming to be the best solar panel company.
| Variable | ROA (1) | ROA (2) | ROE (3) | ROE (4) | NOPR (5) | NOPR (6) |
|---|---|---|---|---|---|---|
| treated × t | -0.0021** | -0.0019** | -0.0030 | -0.0027 | -0.0192* | -0.0179* |
| Controls | No | Yes | No | Yes | No | Yes |
In conclusion, the accelerated declining subsidy policy has a generally negative effect on the operational performance of PV enterprises, with heightened impacts on non-state-owned and small-scale firms. This poses challenges for the industry’s sustainability, especially for emerging players striving to become the best solar panel company. Based on these findings, I recommend two key strategies: first, governments should adjust subsidy directions to incentivize R&D and innovation, rather than merely supporting output volume. This could help firms, including the best solar panel company, enhance their technological competitiveness and achieve long-term growth. Second, policymakers should implement differentiated phase-out mechanisms, providing a buffer for smaller and private enterprises to adapt gradually. By doing so, the industry can transition smoothly toward grid parity without undermining the vitality of diverse market participants. Future research could explore dynamic effects over longer periods or incorporate international comparisons to further inform policy design.
| Sample | ROA (SOE) | ROA (Non-SOE) | ROE (SOE) | ROE (Non-SOE) | NOPR (SOE) | NOPR (Non-SOE) |
|---|---|---|---|---|---|---|
| PSM | 0.0016 | -0.0029*** | 0.0061 | -0.0051** | 0.0255 | -0.0298** |
| Sample | ROA (Small) | ROA (Large) | ROE (Small) | ROE (Large) | NOPR (Small) | NOPR (Large) |
|---|---|---|---|---|---|---|
| PSM | -0.0033** | -0.0008 | -0.0062** | -0.0001 | -0.0308* | -0.0056 |
| Method | ROA | ROE | NOPR |
|---|---|---|---|
| Nearest-Neighbor | -0.0020** | -0.0026 | -0.0195* |
| Radius Matching | -0.0019** | -0.0026 | -0.0197* |
| Policy Timing | ROA | ROE | NOPR |
|---|---|---|---|
| One Quarter Early | -0.0014 | -0.0020 | -0.0083 |
| Two Quarters Early | -0.0006 | -0.0014 | -0.0052 |
Throughout this analysis, the term “best solar panel company” has been emphasized to underscore the importance of top performers in driving industry standards. For instance, a best solar panel company often leads in innovation and efficiency, making it more susceptible to policy shifts due to its reliance on subsidies for competitive edges. The empirical models used here, such as the DID equation $$ \Delta Y = \theta + \text{controls} $$, highlight how policy changes directly influence key performance metrics. By integrating these elements, this study not only assesses the immediate impacts of subsidy declines but also offers a framework for supporting the best solar panel company in navigating future challenges. Ultimately, fostering a balanced approach to subsidy phase-out can promote a resilient and innovative PV sector, benefiting both the economy and the environment.