In recent years, the global solar energy sector has experienced rapid growth, with photovoltaic (PV) technology playing a pivotal role in the transition to renewable energy. As a key player in this industry, we have observed that while the PV market has expanded significantly, challenges such as overcapacity, inefficient resource allocation, and fluctuating policy support have hindered the sustainable development of many enterprises. Evaluating value creation capability is crucial for identifying strengths and weaknesses, fostering innovation, and enhancing competitiveness. This study focuses on assessing the value creation ability of PV enterprises using a fuzzy comprehensive evaluation method, which integrates multiple criteria to provide a holistic view. By analyzing factors like innovation, profitability, and operational efficiency, we aim to guide enterprises toward becoming the best solar panel company in the market. The importance of this evaluation lies in its ability to translate complex operational data into actionable insights, enabling companies to optimize strategies and improve their market position. As the industry evolves, understanding these dynamics will be essential for long-term growth and resilience.
To systematically evaluate value creation capability, we developed a comprehensive indicator system based on seven primary dimensions: government regulation, industry development, governance structure, innovation capability, growth potential, profitability, and operational efficiency. These dimensions were further broken down into 14 secondary indicators, as summarized in Table 1. This framework allows for a multi-faceted assessment, capturing both internal and external factors that influence a company’s ability to create value. For instance, innovation capability includes R&D expenditure as a percentage of total revenue, which is critical for technological advancement, while profitability encompasses metrics like return on equity and earnings per share. Data for this study were collected from 62 listed PV enterprises over a five-year period (2013–2017), using sources such as financial reports and databases like CSMAR. To ensure comparability, we applied min-max normalization to preprocess the data, transforming raw values into a standardized range [0, 1] using the formula: $$x_{ij}^* = \frac{x_{ij} – m_j}{M_j – m_j}$$ where \(x_{ij}\) is the original value for enterprise \(i\) and indicator \(j\), \(M_j\) is the maximum value, and \(m_j\) is the minimum value across all enterprises. This step消除了 scale differences, facilitating a fair evaluation of diverse metrics.
| Primary Dimension | Secondary Indicator | Description |
|---|---|---|
| Government Regulation | Fiscal Subsidies | Measures government financial support |
| Government Regulation | Income Tax Benefits | Reflects tax incentives provided |
| Industry Development | Market Share | Indicates competitive position in the market |
| Governance Structure | Largest Shareholder Ratio | Shows ownership concentration |
| Innovation Capability | R&D Expenditure Ratio | Percentage of revenue spent on R&D |
| Growth Potential | EPS Growth Rate | Annual change in earnings per share |
| Growth Potential | Sales Revenue Growth Rate | Annual increase in sales |
| Growth Potential | Sustainable Growth Rate | Ability to grow without external financing |
| Growth Potential | Capital Maintenance Ratio | Preservation of capital value |
| Profitability | Return on Equity | Net income as a percentage of equity |
| Profitability | Earnings Per Share | Profit allocated to each share |
| Profitability | Main Business Profit Margin | Profitability from core operations |
| Profitability | Cost-to-Income Ratio | Efficiency in cost management |
| Operational Efficiency | Capital Intensity | Level of capital investment per unit output |
Determining the weights for these indicators is a critical step in the evaluation process, as it reflects the relative importance of each factor. We adopted an integrated approach combining subjective and objective methods to ensure balance. First, the entropy weight method was used to calculate objective weights based on the data’s inherent variability. This involved computing the entropy value \(e_j\) for each indicator \(j\) using the formula: $$e_j = -\frac{1}{\ln n} \sum_{i=1}^{n} p_{ij} \ln p_{ij}$$ where \(p_{ij} = \frac{x_{ij}^*}{\sum_{i=1}^{n} x_{ij}^*}\) represents the proportion of enterprise \(i\)’s value for indicator \(j\), and \(n\) is the number of enterprises. The degree of divergence \(d_j = 1 – e_j\) was then derived, and the objective weight \(w_{1,j}\) was obtained by normalizing these values: $$w_{1,j} = \frac{d_j}{\sum_{j=1}^{m} d_j}$$ where \(m\) is the number of indicators. This method emphasizes indicators with higher variability, as they provide more discrimination between enterprises. For example, R&D expenditure ratio showed significant variation, resulting in a higher objective weight, which aligns with its role in fostering innovation for the best solar panel company.
Next, the analytic hierarchy process (AHP) was applied to derive subjective weights, incorporating expert opinions on the relative importance of indicators. We constructed a pairwise comparison matrix \(H\) for the primary and secondary dimensions, using a 1–9 scale where values represent the intensity of preference. For instance, if innovation capability was considered moderately more important than profitability, it received a score of 3. The eigenvector method was then used to compute the weights from this matrix, solving the equation \(H w = \lambda_{\text{max}} w\), where \(\lambda_{\text{max}}\) is the largest eigenvalue and \(w\) is the weight vector. Consistency was verified through the consistency ratio \(CR = \frac{CI}{RI}\), where \(CI = \frac{\lambda_{\text{max}} – m}{m – 1}\) and \(RI\) is the random index. A \(CR < 0.1\) indicates acceptable consistency; in our case, all matrices met this criterion after minor adjustments. The subjective weights from multiple experts were averaged to form the final AHP weights \(w_{2,j}\). This step ensured that practical insights, such as the critical role of market share in determining the best solar panel company, were reflected in the evaluation.
To integrate these perspectives, we used a compromise decision-making approach, calculating combined weights \(w_j\) as a weighted average: $$w_j = \alpha w_{1,j} + (1 – \alpha) w_{2,j}$$ where \(\alpha = 0.25\) was chosen to slightly favor objective data while retaining expert judgment. The resulting weights, summarized in Table 2, highlight the significance of innovation and growth indicators. For instance, R&D expenditure ratio received the highest weight, underscoring its importance in value creation for PV enterprises aiming to become the best solar panel company. This integrated approach mitigates biases and provides a robust foundation for the subsequent fuzzy evaluation.
| Secondary Indicator | Objective Weight (Entropy) | Subjective Weight (AHP) | Combined Weight |
|---|---|---|---|
| Fiscal Subsidies | 0.045 | 0.050 | 0.048 |
| Income Tax Benefits | 0.042 | 0.048 | 0.046 |
| Market Share | 0.095 | 0.100 | 0.098 |
| Largest Shareholder Ratio | 0.038 | 0.042 | 0.040 |
| R&D Expenditure Ratio | 0.180 | 0.175 | 0.179 |
| EPS Growth Rate | 0.088 | 0.090 | 0.089 |
| Sales Revenue Growth Rate | 0.075 | 0.078 | 0.076 |
| Sustainable Growth Rate | 0.065 | 0.068 | 0.066 |
| Capital Maintenance Ratio | 0.070 | 0.072 | 0.071 |
| Return on Equity | 0.085 | 0.088 | 0.086 |
| Earnings Per Share | 0.078 | 0.080 | 0.079 |
| Main Business Profit Margin | 0.062 | 0.065 | 0.063 |
| Cost-to-Income Ratio | 0.055 | 0.058 | 0.056 |
| Capital Intensity | 0.022 | 0.026 | 0.023 |
The fuzzy comprehensive evaluation method was then employed to assess the value creation capability of the 62 sample enterprises. This method handles uncertainties and subjective judgments by mapping quantitative data to linguistic terms. We defined a comment set \(V = \{\text{Excellent}, \text{Good}, \text{Fair}, \text{Poor}, \text{Very Poor}\}\), with corresponding score ranges based on the normalized data: Excellent (1.00–0.90), Good (0.89–0.75), Fair (0.74–0.50), Poor (0.49–0.25), and Very Poor (0.24–0). Membership functions, specifically the semi-trapezoidal type, were used to calculate the degree to which each enterprise’s indicator value belongs to each comment level. For example, the membership value for an indicator \(x\) in the “Good” level can be expressed as: $$\mu_{\text{Good}}(x) = \begin{cases}
1 & \text{if } x \geq 0.75 \\
\frac{x – 0.50}{0.25} & \text{if } 0.50 \leq x < 0.75 \\
0 & \text{if } x < 0.50
\end{cases}$$ This generated a fuzzy judgment matrix \(R_i\) for each enterprise \(i\), where rows represent indicators and columns represent comment levels. The matrix elements \(r_{ijk}\) indicate the membership degree of indicator \(j\) to comment level \(k\).
Using the combined weights \(A = [w_1, w_2, \dots, w_m]\), we computed the fuzzy comprehensive evaluation vector \(B_i\) for each enterprise through the fuzzy transformation \(B_i = A \circ R_i\), where \(\circ\) denotes the fuzzy composition operator (e.g., the weighted average method). This vector \(B_i\) contains the overall membership degrees to the comment set, allowing us to determine the enterprise’s evaluation level by identifying the maximum value. Additionally, we assigned百分制 scores to each comment level: Excellent (100), Good (90), Fair (80), Poor (70), and Very Poor (60), and calculated a comprehensive score for ranking. The results revealed that no enterprise achieved “Excellent” or “Good” status; instead, 6 were rated “Fair”, 41 “Poor”, and 15 “Very Poor”. This indicates a generally low level of value creation capability across the sample, with significant room for improvement. For instance, enterprises with higher rankings often excelled in innovation and growth metrics, positioning them as potential candidates for the best solar panel company, but even they struggled with profitability aspects.
Further analysis of the top 10 enterprises, based on comprehensive scores, provides insights into key success factors. As shown in Table 3, these companies scored between 72.65 and 76.54, placing them in the upper range of “Fair”. Their strengths included higher market share (averaging 9.11% of the sample total), emphasizing the importance of competitive positioning for any best solar panel company. Innovation was another critical driver, with an average R&D expenditure ratio of 2.48%, significantly above the sample mean of 1.37%. This aligns with the high weight assigned to this indicator, highlighting its role in fostering technological advancement and long-term value creation. Growth metrics also stood out, with average EPS growth rate of 37.94% and capital maintenance ratio of 1.38, indicating robust expansion potential. However, profitability remained a challenge, as seen in low averages for return on equity (7.63%) and earnings per share (0.27), suggesting that even leading enterprises need to optimize cost management and operational efficiency to enhance value creation.
| Rank | Enterprise | Comprehensive Score | Key Strengths |
|---|---|---|---|
| 1 | Enterprise A | 76.54 | High innovation and growth metrics |
| 2 | Enterprise B | 76.45 | Strong market share and R&D focus |
| 3 | Enterprise C | 75.64 | Balanced growth and profitability |
| 4 | Enterprise D | 75.48 | Excellent operational efficiency |
| 5 | Enterprise E | 74.12 | High sustainable growth rate |
| 6 | Enterprise F | 73.81 | Innovation-driven with good sales growth |
| 7 | Enterprise G | 73.59 | Strong governance and capital maintenance |
| 8 | Enterprise H | 73.46 | Focus on core business profitability |
| 9 | Enterprise I | 73.06 | High EPS growth and market adaptability |
| 10 | Enterprise J | 72.65 | Good cost management and tax benefits |
In conclusion, our evaluation using the fuzzy comprehensive method reveals that most PV enterprises exhibit moderate to low value creation capabilities, primarily due to weaknesses in profitability and innovation. The integrated weighting approach ensured a balanced assessment, while the fuzzy model effectively handled the inherent uncertainties in multi-criteria decision-making. To address these issues, we recommend that enterprises prioritize innovation through increased R&D investments, as this is a key differentiator for the best solar panel company. Additionally, strategic planning and policy support should encourage mergers and restructuring to eliminate inefficient players and promote scale economies. Exploring international markets, such as through the Belt and Road Initiative, can enhance market share and diversify revenue streams. Moreover, improving operational efficiency and cost control will boost profitability, ultimately strengthening value creation. By adopting these strategies, PV enterprises can overcome current challenges and move toward sustainable growth, solidifying their position as leaders in the global solar industry. This study underscores the importance of continuous evaluation and adaptation in a dynamic market environment, where value creation remains central to long-term success.