The global ecological crisis driven by carbon emissions has garnered widespread attention from governments worldwide, making green and low-carbon development a critical direction for productivity transformation. Solar energy is recognized as one of the most promising renewable energy sources, and China boasts exceptional natural endowments, including abundant annual solar radiation and rare earth resource reserves. With continuous improvements in photovoltaic technology efficiency and cost reduction, coupled with policy support under the “dual carbon” goals—such as research and development (R&D) expense deductions, investment tax credits, and subsidies for photovoltaic power generation projects—the photovoltaic industry is experiencing rapid growth. This presents significant investment opportunities, raising the question of how to scientifically evaluate the investment value of photovoltaic enterprises. This study develops a comprehensive evaluation model by integrating principal component analysis (PCA) and entropy weight TOPSIS methods to assess the investment value of photovoltaic enterprises, providing a robust framework for investors. The model incorporates both financial and non-financial indicators, such as R&D capability and market share, to identify the best solar panel company for investment.
Existing research on enterprise investment valuation primarily employs data dimensionality reduction techniques like PCA and factor analysis to evaluate financial and non-financial indicators. For instance, some scholars have used grey relational analysis to emphasize profitability and growth as key factors for investing in technology-based enterprises. Others have combined analytic hierarchy process (AHP) with factor analysis to assess intrinsic value, while complex neural networks and fuzzy evaluations integrate traditional financial indicators with non-financial metrics like market share and customer satisfaction. Richard proposed an investment strategy model based on seven dimensions: profitability, cash flow, development capability, ownership structure, operational risk, price changes, and valuation. Dynamic factor analysis has also been applied to panel data of listed companies to capture temporal and cross-sectional dynamics. However, most studies rely on单一的 financial indicators and fixed weights, with limited focus on the photovoltaic industry’s unique characteristics. This paper addresses these gaps by combining PCA and entropy weight TOPSIS, incorporating R&D and market share indicators tailored to the capital-intensive and technology-intensive nature of photovoltaic enterprises. This approach offers a more accurate investment valuation model, aiding investors in identifying the best solar panel company.
The theoretical foundation of this model begins with principal component analysis (PCA), a dimensionality reduction technique that transforms multiple correlated variables into a few uncorrelated components. Given a set of p original indicators \(x_1, x_2, \ldots, x_p\), the correlation matrix R is computed. The characteristic equation \(|\lambda I – R| = 0\) is solved to obtain eigenvalues \(\lambda_i\) and eigenvectors. The contribution rate of each principal component is calculated as:
$$\text{Contribution Rate} = \frac{\lambda_i}{\sum_{k=1}^{p} \lambda_k} \quad (i=1,2,\ldots,p)$$
The cumulative contribution rate is:
$$\text{Cumulative Contribution Rate} = \frac{\sum_{k=1}^{i} \lambda_k}{\sum_{k=1}^{p} \lambda_k} \quad (i=1,2,\ldots,p)$$
The principal component loadings are given by \(l_{ij} = \sqrt{\lambda_i} \cdot l_{ij}\), and the new variables (principal components) \(z_1, z_2, \ldots, z_m\) (where \(m < p\)) are derived as:
$$z_1 = l_{11}x_1 + l_{12}x_2 + \cdots + l_{1p}x_p$$
$$z_2 = l_{21}x_1 + l_{22}x_2 + \cdots + l_{2p}x_p$$
$$\vdots$$
$$z_m = l_{m1}x_1 + l_{m2}x_2 + \cdots + l_{mp}x_p$$
Next, the entropy weight method objectively determines weights based on information entropy. The principal component score matrix is non-negatively transformed, and the characteristic proportion \(P_{ij}\) for each indicator is computed as:
$$P_{ij} = \frac{y_{ij}}{\sum_{i=1}^{n} y_{ij}}$$
where \(y_{ij}\) is the normalized value. The entropy value \(e_j\) and weight coefficient \(w_j\) are then calculated:
$$e_j = -\frac{1}{\ln m} \sum_{i=1}^{m} P_{ij} \ln P_{ij}$$
$$w_j = \frac{1 – e_j}{\sum_{j=1}^{n} (1 – e_j)}$$
These weights are applied to the normalized indicator matrix for TOPSIS analysis. The positive ideal solution \(C^+\) and negative ideal solution \(C^-\) are identified. The distance from each evaluation object to the positive ideal solution is:
$$s_i^+ = \sqrt{\sum_{j=1}^{m} (b_{ij} – c_j^+)^2}, \quad i=1,2,\ldots,n$$
and to the negative ideal solution is:
$$s_i^- = \sqrt{\sum_{j=1}^{m} (b_{ij} – c_j^-)^2}, \quad i=1,2,\ldots,n$$
The relative closeness \(f_i\) to the ideal solution, which represents the comprehensive score, is:
$$f_i = \frac{s_i^-}{s_i^- + s_i^+}, \quad i=1,2,\ldots,n$$
This score serves as the final investment value metric, with higher values indicating better performance. The integration of PCA and entropy weight TOPSIS ensures a balanced consideration of multiple dimensions, crucial for identifying the best solar panel company.
For empirical analysis, 25 photovoltaic enterprises listed in the A-share market were selected as samples, with data sourced from financial reports and databases like East Money Information. Indicators were averaged over the 2021–2023 period to ensure stability. The evaluation指标体系 includes financial capabilities (profitability, operational efficiency, solvency, and development capacity) and non-financial aspects (R&D capability and market share), as detailed in Table 1. Profitability is measured by return on equity (ROE), gross profit margin, and net profit margin; operational efficiency by total asset turnover, inventory turnover, and accounts receivable turnover; solvency by current ratio, quick ratio, and cash flow ratio; development capacity by total revenue growth rate and net profit growth rate; R&D capability by R&D intensity and technological level (logarithm of intangible assets); and market share by relative market share. This comprehensive set ensures a holistic view, essential for spotting the best solar panel company.
| Indicator Type | Indicator Name | Variable | Calculation Method |
|---|---|---|---|
| Profitability | Return on Equity | X1 | Net Profit / Equity |
| Gross Profit Margin | X2 | Gross Profit / Net Sales | |
| Net Profit Margin | X3 | (Total Profit – Income Tax) / Operating Revenue | |
| Operational Efficiency | Total Asset Turnover | X4 | Operating Revenue / Average Total Assets |
| Inventory Turnover | X5 | 360 × Average Inventory / Cost of Sales | |
| Accounts Receivable Turnover | X6 | Net Sales / Average Accounts Receivable | |
| Solvency | Current Ratio | X7 | Current Assets / Current Liabilities |
| Quick Ratio | X8 | Quick Assets / Current Liabilities | |
| Cash Flow Ratio | X9 | Operating Cash Flow / Current Liabilities | |
| Development Capacity | Total Revenue Growth Rate | X10 | Increase in Operating Revenue / Prior Period Revenue |
| Net Profit Growth Rate | X11 | Increase in Net Profit / Prior Period Net Profit | |
| R&D Capability | R&D Intensity | X12 | R&D Expenses / Operating Revenue |
| Technological Level | X13 | ln(Intangible Assets) | |
| Market Share | Relative Market Share | X14 | Operating Revenue / ∑ Sample Enterprises’ Revenue |
Prior to PCA, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.572, and Bartlett’s test of sphericity was significant (p < 0.01), confirming the suitability for PCA. Four principal components were extracted, explaining 84.938% of the total variance, as shown in Table 2. The communalities of all financial indicators exceeded 0.7, with most above 0.8, indicating minimal information loss. Varimax rotation was applied to simplify interpretation, resulting in distinct components: F1 (profitability and development capacity), F2 (solvency), F3 (profitability and solvency), and F4 (operational efficiency and solvency). The rotated component matrix in Table 3 illustrates high loadings for specific indicators, such as ROE and net profit margin on F1, and current ratio on F2. This decomposition helps in understanding the underlying factors driving investment value, particularly for identifying the best solar panel company based on financial health.
| Component | Initial Eigenvalues | Rotation Sums of Squared Loadings | ||
|---|---|---|---|---|
| Variance % | Cumulative % | Variance % | Cumulative % | |
| F1 | 34.481 | 34.481 | 28.797 | 28.797 |
| F2 | 27.890 | 62.371 | 21.730 | 50.527 |
| F3 | 13.823 | 76.194 | 17.248 | 67.775 |
| F4 | 8.744 | 84.938 | 17.163 | 84.938 |
| Variable | F1 | F2 | F3 | F4 |
|---|---|---|---|---|
| X1 | 0.942 | 0.066 | 0.095 | 0.206 |
| X2 | 0.367 | 0.235 | 0.853 | -0.021 |
| X3 | 0.900 | 0.115 | 0.197 | 0.193 |
| X4 | 0.201 | -0.240 | -0.676 | 0.510 |
| X5 | 0.016 | 0.324 | -0.083 | 0.661 |
| X6 | 0.261 | -0.317 | -0.164 | 0.774 |
| X7 | 0.136 | 0.946 | 0.182 | 0.032 |
| X8 | 0.094 | 0.949 | 0.220 | 0.076 |
| X9 | 0.133 | 0.210 | 0.615 | 0.707 |
| X10 | 0.576 | -0.452 | -0.414 | 0.075 |
| X11 | 0.822 | 0.110 | 0.019 | -0.009 |
The principal component scores were computed by multiplying the rotated matrix with standardized financial data. For R&D capability and market share, which are not part of the traditional financial framework but critical for photovoltaic enterprises, separate handling was necessary. R&D intensity (X12) and technological level (X13) reflect innovation potential, while relative market share (X14) indicates competitive positioning. These were incorporated into the entropy weight TOPSIS analysis after non-negative transformation. The entropy values, divergence coefficients, and weight coefficients are presented in Table 4. Market share (X14) had the highest weight (0.251), underscoring its importance in evaluating the best solar panel company. The positive and negative ideal solutions were derived, and relative closeness values (comprehensive scores) were calculated, as summarized in Table 5. Enterprises like Tongwei Co., Ltd. and LONGi Green Energy Technology Co., Ltd. achieved high scores (above 0.75), indicating strong overall performance and making them candidates for the best solar panel company. Notably, some firms with moderate PCA scores excelled in R&D and market share, highlighting the model’s ability to capture multifaceted value.
| Item | F1 | F2 | F3 | F4 | X12 | X13 | X14 |
|---|---|---|---|---|---|---|---|
| Entropy Value | -3.141 | -2.838 | -2.887 | -2.887 | -2.906 | -3.053 | -2.410 |
| Divergence Coefficient | 0.976 | 0.882 | 0.897 | 0.897 | 0.903 | 0.948 | 0.749 |
| Weight Coefficient | 0.024 | 0.118 | 0.103 | 0.103 | 0.097 | 0.052 | 0.251 |
| Positive Ideal Solution | 0.033618 | 0.163380 | 0.142609 | 0.142369 | 0.134041 | 0.071354 | 0.347247 |
| Negative Ideal Solution | 0.000034 | 0.000163 | 0.000142 | 0.000142 | 0.000134 | 0.000071 | 0.000347 |
| Enterprise | F1 | F2 | F3 | F4 | F (PCA Score) | Optimal Distance | Worst Distance | Comprehensive Score | Rank |
|---|---|---|---|---|---|---|---|---|---|
| LONGi Green Energy | 0.888 | -0.635 | -0.287 | 0.536 | 0.558 | 0.239 | 0.718 | 0.750 | 2 |
| Tongwei Co. | 0.909 | -1.036 | 0.315 | 2.522 | 2.273 | 0.224 | 0.810 | 0.784 | 1 |
| Sungrow Power | 0.822 | -0.316 | -0.392 | -0.879 | -0.627 | 0.334 | 0.578 | 0.634 | 11 |
| Trina Solar | 0.580 | -0.617 | -0.905 | -0.238 | -0.305 | 0.288 | 0.621 | 0.683 | 4 |
| CSG Holding | 0.092 | -0.885 | 1.586 | 1.818 | 1.594 | 0.353 | 0.673 | 0.656 | 7 |
| Risen Energy | 0.470 | -0.410 | -0.859 | -0.379 | -0.403 | 0.362 | 0.524 | 0.591 | 15 |
| TCL Zhonghuan | 0.667 | -0.790 | -0.142 | 0.589 | 0.505 | 0.271 | 0.695 | 0.719 | 3 |
| Jinko Solar | 0.628 | -0.715 | -0.805 | 0.221 | 0.099 | 0.299 | 0.602 | 0.668 | 6 |
| China Singyes Solar | 0.184 | 0.682 | 1.638 | 0.718 | 1.107 | 0.357 | 0.638 | 0.641 | 9 |
| Sunflower | 0.497 | 0.681 | 1.174 | -0.278 | 0.280 | 0.384 | 0.584 | 0.603 | 12 |
| Rongsheng Petro Chemical | -0.066 | 2.567 | -1.211 | 2.448 | 2.666 | 0.374 | 0.681 | 0.645 | 8 |
| Zongyi Co. | -0.426 | 2.049 | 1.226 | -0.927 | -0.216 | 0.377 | 0.658 | 0.636 | 10 |
| Yicheng New Energy | 0.080 | -0.401 | -1.028 | -0.448 | -0.618 | 0.398 | 0.464 | 0.538 | 22 |
| Topray Solar | 0.254 | -0.195 | 1.265 | -1.115 | -0.767 | 0.410 | 0.490 | 0.544 | 20 |
| Aikang Technology | -1.083 | -0.405 | -0.552 | -0.020 | -0.580 | 0.412 | 0.407 | 0.497 | 24 |
| Xiamen Amperex | 0.226 | 0.752 | -1.101 | -0.473 | -0.278 | 0.403 | 0.482 | 0.545 | 19 |
| Aerospace Electromechanical | -0.279 | -0.169 | -0.603 | -0.205 | -0.406 | 0.396 | 0.470 | 0.543 | 21 |
| Jinko Energy | 0.418 | -0.555 | -0.798 | -0.510 | -0.567 | 0.292 | 0.606 | 0.675 | 5 |
| Linyang Energy | 0.568 | 0.908 | 1.061 | -0.823 | -0.124 | 0.382 | 0.560 | 0.594 | 14 |
| Zhongli Group | -3.077 | -0.567 | -0.288 | 0.044 | -1.220 | 0.393 | 0.472 | 0.546 | 18 |
| GCL System Integration | -2.640 | -0.674 | -0.329 | 0.152 | -1.011 | 0.411 | 0.387 | 0.485 | 25 |
| Lucky Film | -0.067 | 2.164 | -0.995 | -0.353 | 0.134 | 0.389 | 0.590 | 0.603 | 13 |
| Jingyuntong Technology | 0.910 | -0.523 | 0.095 | -1.159 | -0.833 | 0.396 | 0.475 | 0.546 | 17 |
| Jiawei New Energy | -0.793 | -0.400 | 2.096 | -0.221 | -0.289 | 0.405 | 0.515 | 0.560 | 16 |
| ACETRON | 0.237 | -0.512 | -0.160 | -1.019 | -0.974 | 0.418 | 0.429 | 0.506 | 23 |
In conclusion, the integrated PCA and entropy weight TOPSIS model provides a scientifically robust framework for evaluating the investment value of photovoltaic enterprises under the “dual carbon” goal. By incorporating financial capabilities, R&D potential, and market share, it addresses the industry’s specificities, such as high capital and technology intensity. The results indicate that photovoltaic enterprises like Tongwei Co., LONGi Green Energy, TCL Zhonghuan, and Trina Solar exhibit high comprehensive scores (above 0.68), reflecting strong performance across multiple dimensions. These firms represent promising investment targets and potential candidates for the best solar panel company due to their balanced financial health, innovation capacity, and market dominance. For instance, Trina Solar, despite a moderate PCA score, demonstrated exceptional R&D investments and intangible assets, underscoring the importance of non-financial factors. Investors should prioritize enterprises with high scores in profitability, development capacity, R&D intensity, and market share, as these are critical for long-term growth in the evolving photovoltaic sector. The average ROE of sample enterprises increased from 4.26% in 2021 to 6.95% in 2023, signaling industry growth and reinforcing the investment potential. However, this study has limitations, including a small sample size and the exclusion of policy effects like subsidies and tax incentives, which could be integrated in future research. Overall, this model offers a practical tool for identifying the best solar panel company, enabling informed investment decisions in the context of sustainable development.