As environmental pressures intensify, the pursuit of renewable energy has become critical for alleviating energy crises and achieving low-carbon development. In this context, the solar industry emerges as a pivotal sector in the renewable energy landscape, offering substantial potential to reduce carbon emissions, mitigate pollution, and address energy demands. The financial performance of solar enterprises is essential for the coordinated growth of the新能源 industry, as it provides insights into operational efficiency and sustainability. Evaluating financial metrics helps stakeholders, including investors and managers, make informed decisions. This study employs the entropy-weight TOPSIS method to assess the financial performance of solar enterprises, focusing on identifying the best solar panel company through a comprehensive analysis of profitability, solvency, operational capacity, and growth potential. The integration of objective weighting via entropy and ranking through TOPSIS ensures a robust evaluation, minimizing subjectivity and highlighting areas for improvement.
The selection of financial indicators is grounded in established frameworks, such as the “Enterprise Performance Evaluation Standard Value 2022,” ensuring reliability and comprehensiveness. Four primary dimensions are considered: profitability, solvency, operational capacity, and development capability, with 12 secondary indicators derived from these categories. This multi-faceted approach captures the holistic financial health of solar enterprises, enabling a nuanced comparison. The entropy-weight method assigns weights based on the variability of each indicator, reflecting its relative importance, while TOPSIS ranks enterprises by their proximity to an ideal solution. This combination addresses limitations of other methods, such as information loss in principal component analysis, and provides a clear hierarchy of performance. By applying this model to data from 2019 to 2021 for eight A股-listed solar companies, this study aims to delineate the best solar panel company based on empirical results, offering actionable insights for industry advancement.
To construct the financial performance evaluation system, the following indicators were selected, ensuring alignment with industry-specific characteristics and data availability. The indicators are categorized into four dimensions, with their types (positive, moderate) specified to guide data normalization. Positive indicators are those where higher values denote better performance, while moderate indicators have an optimal range. For instance, solvency indicators like debt-to-asset ratio are treated as moderate, as excessively high or low values may indicate inefficiencies. This systematic selection facilitates a balanced assessment, crucial for identifying the best solar panel company that excels across multiple facets.
| Primary Indicator | Secondary Indicator | Symbol | Type |
|---|---|---|---|
| Profitability | Net Profit Margin | X1 | Positive |
| Profitability | Gross Profit Margin | X2 | Positive |
| Profitability | Return on Total Assets | X3 | Positive |
| Solvency | Debt-to-Asset Ratio | X4 | Moderate |
| Solvency | Quick Ratio | X5 | Moderate |
| Solvency | Operating Cash Flow Ratio | X6 | Positive |
| Operational Capacity | Total Asset Turnover | X7 | Positive |
| Operational Capacity | Inventory Turnover | X8 | Positive |
| Operational Capacity | Equity Turnover | X9 | Positive |
| Development Capability | Total Asset Growth Rate | X10 | Positive |
| Development Capability | Net Profit Growth Rate | X11 | Positive |
| Development Capability | Net Asset Growth Rate | X12 | Positive |
The entropy-weight TOPSIS model involves several steps to ensure objective evaluation. First, raw data are processed to handle moderate indicators. For moderate indicators like the debt-to-asset ratio, the optimal range is defined (e.g., 48.7–53.7 for X4), and values are normalized using specific formulas to convert them into positive indicators. The quick ratio (X5) is optimized around a value of 1. The transformation formulas are applied as follows: for an intermediate indicator with a best value \(X_{\text{best}}\), the normalization uses:
$$ X_{ij} = 1 – \frac{|X_{ij} – X_{\text{best}}|}{\max |X_{ij} – X_{\text{best}}|} $$
For interval-based moderate indicators with an optimal range [a, b], the formula is:
$$ X_{ij} = \begin{cases}
1 – \frac{a – X_{ij}}{\max\{a – \min(X_{ij}), \max(X_{ij}) – b\}}, & \text{if } X_{ij} < a \\
1, & \text{if } a \leq X_{ij} \leq b \\
1 – \frac{X_{ij} – b}{\max\{a – \min(X_{ij}), \max(X_{ij}) – b\}}, & \text{if } X_{ij} > b
\end{cases} $$
Subsequently, all indicators are standardized to eliminate dimensional effects. The standardized decision matrix \(Y\) is computed as:
$$ Y_{ij} = \frac{X_{ij}}{\sqrt{\sum_{i=1}^{m} X_{ij}^2}} $$
where \(m\) is the number of enterprises. The entropy weight method then determines the weights based on the information entropy of each indicator. The entropy \(e_j\) for indicator \(j\) is calculated as:
$$ e_j = -\frac{1}{\ln(m)} \sum_{i=1}^{m} P_{ij} \ln(P_{ij}) $$
where \(P_{ij} = Y_{ij} / \sum_{i=1}^{m} Y_{ij}\). The weight \(W_j\) for each indicator is derived from the entropy:
$$ W_j = \frac{1 – e_j}{n – \sum_{j=1}^{n} e_j} $$
with \(n\) being the number of indicators. These weights reflect the relative importance of each indicator in the evaluation. A higher weight indicates greater discriminative power, which is crucial for identifying the best solar panel company. The weighted decision matrix \(V\) is then constructed as \(V_{ij} = W_j \cdot Y_{ij}\).
The TOPSIS method involves identifying the positive ideal solution (PIS) and negative ideal solution (NIS). The PIS \(V^+\) consists of the maximum values for each indicator, while the NIS \(V^-\) comprises the minimum values. The Euclidean distances from each enterprise to the PIS and NIS are computed as:
$$ D_i^+ = \sqrt{\sum_{j=1}^{n} W_j (V_{ij} – V_j^+)^2} $$
$$ D_i^- = \sqrt{\sum_{j=1}^{n} W_j (V_{ij} – V_j^-)^2} $$
The relative closeness \(C_i\) to the ideal solution is given by:
$$ C_i = \frac{D_i^-}{D_i^+ + D_i^-} $$
Values of \(C_i\) range from 0 to 1, with higher values indicating better financial performance. This score allows for a clear ranking, facilitating the identification of the best solar panel company based on comprehensive financial metrics.
Empirical analysis was conducted using financial data from 2019 to 2021 for eight solar enterprises. The data were sourced from reliable financial databases, and averages were taken to mitigate annual fluctuations. After processing moderate indicators and standardizing the data, the entropy weights were computed. The results reveal the significance of each dimension and indicator in evaluating financial performance. Solvency and development capability emerged as the most influential dimensions, underscoring their importance in the solar industry’s financial health. The weights for primary and secondary indicators are summarized below.
| Primary Indicator | Weight | Secondary Indicator | Information Entropy | Weight |
|---|---|---|---|---|
| Profitability | 21.417% | Net Profit Margin (X1) | 0.791 | 7.177% |
| Profitability | 21.417% | Gross Profit Margin (X2) | 0.804 | 6.724% |
| Profitability | 21.417% | Return on Total Assets (X3) | 0.781 | 7.516% |
| Solvency | 36.099% | Debt-to-Asset Ratio (X4) | 0.760 | 8.246% |
| Solvency | 36.099% | Quick Ratio (X5) | 0.331 | 22.986% |
| Solvency | 36.099% | Operating Cash Flow Ratio (X6) | 0.858 | 4.867% |
| Operational Capacity | 17.356% | Total Asset Turnover (X7) | 0.892 | 3.695% |
| Operational Capacity | 17.356% | Inventory Turnover (X8) | 0.788 | 7.285% |
| Operational Capacity | 17.356% | Equity Turnover (X9) | 0.814 | 6.376% |
| Development Capability | 25.128% | Total Asset Growth Rate (X10) | 0.814 | 6.393% |
| Development Capability | 25.128% | Net Profit Growth Rate (X11) | 0.624 | 12.933% |
| Development Capability | 25.128% | Net Asset Growth Rate (X12) | 0.831 | 5.802% |
The quick ratio (X5) has the highest weight (22.986%), indicating its critical role in assessing short-term solvency, while the net profit growth rate (X11) follows with 12.933%, emphasizing the importance of growth in financial performance. In contrast, total asset turnover (X7) and operating cash flow ratio (X6) have lower weights, suggesting less variability and impact on the overall evaluation. This weighting scheme objectively prioritizes indicators that distinguish the best solar panel company from its peers.
Using the TOPSIS method, the distances to the PIS and NIS were calculated, and the relative closeness scores were derived. The rankings based on these scores provide a clear performance hierarchy. The enterprise with the highest score is deemed the best solar panel company in terms of financial performance. The results are presented below, showcasing the distances and normalized scores.
| Enterprise | Distance to PIS (D+) | Distance to NIS (D-) | Non-normalized Score | Normalized Score | Rank |
|---|---|---|---|---|---|
| Enterprise A | 0.133 | 0.452 | 0.772 | 0.220 | 1 |
| Enterprise B | 0.402 | 0.175 | 0.304 | 0.086 | 8 |
| Enterprise C | 0.373 | 0.225 | 0.377 | 0.107 | 5 |
| Enterprise D | 0.314 | 0.269 | 0.461 | 0.131 | 3 |
| Enterprise E | 0.408 | 0.210 | 0.340 | 0.097 | 7 |
| Enterprise F | 0.317 | 0.274 | 0.463 | 0.132 | 2 |
| Enterprise G | 0.387 | 0.215 | 0.357 | 0.101 | 6 |
| Enterprise H | 0.331 | 0.264 | 0.444 | 0.126 | 4 |
Enterprise A achieves the highest normalized score (0.220), indicating it is the best solar panel company based on financial performance, as it is closest to the ideal solution and farthest from the negative ideal. Enterprises F and D follow closely, highlighting their strong financial standings. In contrast, Enterprise B ranks last, suggesting areas for significant improvement. To delve deeper, the performance across individual dimensions is analyzed below, providing insights into strengths and weaknesses.
| Enterprise | Profitability Score | Profitability Rank | Solvency Score | Solvency Rank | Operational Capacity Score | Operational Capacity Rank | Development Capability Score | Development Capability Rank |
|---|---|---|---|---|---|---|---|---|
| Enterprise A | 0.363 | 1 | 0.141 | 4 | 0.093 | 6 | 0.461 | 1 |
| Enterprise B | 0.221 | 2 | 0.028 | 8 | 0.096 | 5 | 0.046 | 7 |
| Enterprise C | 0.035 | 7 | 0.123 | 6 | 0.149 | 2 | 0.090 | 4 |
| Enterprise D | 0.168 | 3 | 0.167 | 1 | 0.090 | 7 | 0.092 | 3 |
| Enterprise E | 0.001 | 8 | 0.121 | 7 | 0.117 | 4 | 0.040 | 8 |
| Enterprise F | 0.100 | 4 | 0.145 | 3 | 0.243 | 1 | 0.085 | 5 |
| Enterprise G | 0.072 | 5 | 0.125 | 5 | 0.081 | 8 | 0.047 | 6 |
| Enterprise H | 0.042 | 6 | 0.149 | 2 | 0.131 | 3 | 0.139 | 2 |
Enterprise A excels in profitability and development capability, with the highest scores in these dimensions, reinforcing its position as the best solar panel company. Its net profit margin and growth rates are notably superior, indicating efficient cost management and robust expansion. However, its operational capacity score is lower, suggesting potential inefficiencies in asset utilization. Enterprise D leads in solvency, supported by optimal debt levels and quick ratios, but its profitability is moderate. Enterprise F demonstrates strong operational capacity, with high inventory and asset turnover, yet its development capability is average. Conversely, Enterprise B struggles with solvency and development, likely due to suboptimal debt management and slow growth, highlighting the need for strategic adjustments. The variability in scores across dimensions underscores the importance of a balanced approach to financial management in the solar industry.
The analysis reveals that solvency and development capability are the most weighted dimensions, significantly influencing overall financial performance. This aligns with the solar industry’s capital-intensive nature, where debt management and growth potential are crucial for sustainability. Enterprises with high solvency scores, such as Enterprise D and H, effectively leverage debt without compromising stability, while those with strong development capabilities, like Enterprise A, exhibit impressive growth trajectories. The best solar panel company typically balances these aspects, as seen in Enterprise A’s top rankings in profitability and development. However, common weaknesses include low operating cash flow ratios across enterprises, indicating inadequate cash generation from operations, which could hinder liquidity and investment capacity. Additionally, disparities in operational efficiency suggest that some companies may benefit from optimizing inventory and asset management to enhance turnover rates.
In conclusion, the entropy-weight TOPSIS method provides a robust framework for evaluating the financial performance of solar enterprises, objectively identifying the best solar panel company based on multi-dimensional metrics. The results highlight that Enterprise A stands out as the top performer, excelling in profitability and development, though all enterprises have room for improvement, particularly in solvency and operational aspects. To foster long-term growth, solar companies should prioritize maintaining healthy financial indicators, such as by optimizing capital structure to improve solvency and investing in innovation to boost profitability. Moreover, focusing on cost reduction through technological advancements and strengthening stakeholder relationships can enhance competitiveness. As the industry evolves under the “dual carbon” goals, continuous financial performance evaluation will be vital for navigating market dynamics and achieving sustainable development. By addressing identified weaknesses and leveraging strengths, solar enterprises can position themselves as leaders in the renewable energy sector, ultimately contributing to global environmental objectives.