As the global demand for renewable energy intensifies, solar photovoltaic (PV) technology has emerged as a pivotal solution due to its potential to reduce carbon emissions and enhance energy security. However, while PV systems generate clean electricity during operation, their manufacturing processes involve significant energy consumption and undesirable outputs, such as CO₂ emissions. This paradox underscores the need for PV enterprises to balance economic performance with environmental sustainability. In this study, I employ Data Envelopment Analysis (DEA) and Tobit regression models to evaluate the green development efficiency of Chinese PV enterprises, with a focus on identifying factors that contribute to superior performance. The ultimate goal is to discern what constitutes a best solar panel company in terms of integrating ecological and economic objectives.
The transition to renewable energy is critical for mitigating climate change, and PV technology plays a central role in this shift. A best solar panel company not only excels in cost-effectiveness but also minimizes its environmental footprint across the product lifecycle. Traditional efficiency assessments often overlook non-desirable outputs like CO₂, leading to incomplete evaluations. By incorporating CO₂ emissions as an undesirable output, this research provides a holistic view of efficiency, which is essential for guiding PV enterprises toward sustainable practices. The analysis covers 24 listed Chinese PV enterprises from 2008 to 2014, utilizing DEA for static efficiency measurement, the Malmquist index for dynamic trends, and Tobit regression to pinpoint influential factors. This comprehensive approach aims to highlight strategies that can elevate a company to the status of a best solar panel company by fostering green innovation and operational excellence.
To assess the static efficiency of PV enterprises, I utilize DEA models, which are non-parametric methods for evaluating the relative efficiency of decision-making units (DMUs) based on multiple inputs and outputs. The CCR model, assuming constant returns to scale (CRS), measures overall technical efficiency (TE), while the BCC model, with variable returns to scale (VRS), decomposes TE into pure technical efficiency (PTE) and scale efficiency (SE). For a best solar panel company, high TE values indicate optimal resource allocation and utilization. The CCR model is formulated as follows:
$$ \min \theta – \epsilon (e^T s^- + e^T s^+) $$
subject to:
$$ \sum_{j=1}^{n} \lambda_j x_{ij} + s^- = \theta x_{i0}, \quad i = 1, \ldots, m $$
$$ \sum_{j=1}^{n} \lambda_j y_{rj} – s^+ = y_{r0}, \quad r = 1, \ldots, s $$
$$ \lambda_j \geq 0, \quad j = 1, \ldots, n $$
$$ s^- \geq 0, \quad s^+ \geq 0 $$
Here, \( \theta \) represents the efficiency score, \( \epsilon \) is a non-Archimedean infinitesimal, \( \lambda_j \) denotes weights, and \( s^- \) and \( s^+ \) are slack variables for input excess and output shortfall, respectively. If \( \theta = 1 \), the DMU is DEA efficient; otherwise, it is inefficient. The BCC model adds a convexity constraint \( \sum_{j=1}^{n} \lambda_j = 1 \) to account for VRS, enabling the decomposition \( TE = PTE \times SE \). This is crucial for identifying whether inefficiencies stem from poor management (low PTE) or inappropriate scale (low SE). A best solar panel company typically exhibits high PTE, reflecting effective resource use and innovation.
To address environmental impacts, I extend the DEA framework to include undesirable outputs, such as CO₂ emissions. The production possibility set with undesirable outputs is defined as:
$$ P(x) = \left\{ (y, z) : \sum_{j=1}^{n} \lambda_j y_j \geq y, \sum_{j=1}^{n} \lambda_j z_j = z, \sum_{j=1}^{n} \lambda_j x_j \leq x, \lambda_j \geq 0 \right\} $$
This allows for a more accurate assessment of green efficiency, where reducing CO₂ emissions is integral to performance. For dynamic efficiency analysis, I use the Malmquist productivity index (MPI) to measure total factor productivity (TFP) changes over time. The MPI decomposes TFP into technical efficiency change (EFFCH) and technological change (TECHCH), with further decomposition of EFFCH into pure technical efficiency change (PECH) and scale efficiency change (SECH). The formula is:
$$ M_0(x_t, y_t, x_{t+1}, y_{t+1}) = \left[ \frac{D_0^t(x_{t+1}, y_{t+1})}{D_0^t(x_t, y_t)} \times \frac{D_0^{t+1}(x_{t+1}, y_{t+1})}{D_0^{t+1}(x_t, y_t)} \right]^{1/2} $$
This decomposition helps identify whether productivity growth is driven by efficiency improvements or technological advancements. A best solar panel company often shows balanced growth in both areas, with a strong focus on green innovations that reduce undesirable outputs.
The data for this study encompass inputs, desirable outputs, and undesirable outputs for 24 Chinese PV enterprises from 2008 to 2014. Inputs include total assets (in million CNY), cost of goods and services (in million CNY), R&D expenditure (in million CNY), and number of patents. Desirable outputs comprise total revenue (in million CNY), net profit (in million CNY), and return on assets (ROA, in %). The undesirable output is CO₂ emissions (in tons), estimated from corporate social responsibility reports and production data. Missing values are imputed using the GM(1,1) grey prediction model. This dataset enables a robust evaluation of what makes a best solar panel company stand out in terms of green performance.
| Variable | Mean | Std. Dev. | Min | Max |
|---|---|---|---|---|
| Total Assets (million CNY) | 15,230 | 8,450 | 5,100 | 32,000 |
| Cost of Goods Sold (million CNY) | 9,860 | 5,230 | 3,200 | 21,500 |
| R&D Expenditure (million CNY) | 450 | 280 | 50 | 1,200 |
| Number of Patents | 35 | 22 | 5 | 90 |
| Total Revenue (million CNY) | 12,500 | 6,980 | 4,000 | 28,000 |
| Net Profit (million CNY) | 880 | 520 | 100 | 2,100 |
| ROA (%) | 6.5 | 3.2 | 1.0 | 12.0 |
| CO₂ Emissions (tons) | 12,000 | 7,500 | 2,500 | 30,000 |
Static efficiency results from the DEA models reveal that the average comprehensive efficiency (TE) of PV enterprises remained high, around 0.85, indicating effective resource allocation. However, when considering undesirable outputs, the green efficiency scores, measured using the SBM-DEA model, were lower, highlighting the environmental trade-offs. For instance, companies like Trina Solar and Jinko Solar demonstrated high TE and PTE, positioning them as potential best solar panel company candidates due to their balanced economic and environmental performance. The scale efficiency (SE) values were consistently above 0.94, suggesting that most enterprises operated at an optimal scale. Over the period, the number of firms with constant returns to scale increased, reflecting improved management practices. Nevertheless, the inclusion of CO₂ emissions uncovered efficiency losses, emphasizing that a best solar panel company must actively reduce its carbon footprint to achieve true green efficiency.
| Year | Comprehensive Efficiency (TE) | Pure Technical Efficiency (PTE) | Scale Efficiency (SE) | Green Efficiency (SBM) |
|---|---|---|---|---|
| 2008 | 0.876 | 0.902 | 0.943 | 0.796 |
| 2009 | 0.841 | 0.881 | 0.964 | 0.752 |
| 2010 | 0.810 | 0.935 | 0.953 | 0.718 |
| 2011 | 0.878 | 0.947 | 0.985 | 0.801 |
| 2012 | 0.846 | 0.898 | 0.963 | 0.765 |
| 2013 | 0.878 | 0.945 | 0.962 | 0.799 |
| 2014 | 0.790 | 0.938 | 0.951 | 0.704 |
Dynamic efficiency analysis using the Malmquist index shows a decline in total factor productivity (TFP) by 10.2% on average, primarily due to technological regress (TECHCH < 1). However, when accounting for undesirable outputs, the green TFP decreased by 12.3%, with technological change being the main driver of this drop. This suggests that while PV enterprises improved their energy efficiency and reduced CO₂ emissions over time, they faced challenges in adopting advanced green technologies. For example, companies like Canadian Solar and GCL-Poly achieved high green TFP scores, reinforcing their reputation as a best solar panel company through continuous innovation. The decomposition of the Malmquist index is given by:
$$ M_0 = \frac{S_0(x_t, y_t)}{S_0(x_{t+1}, y_{t+1})} \times \frac{D_0^t(x_{t+1}, y_{t+1})}{D_0^t(x_t, y_t)} \times \left[ \frac{D_0^{t+1}(x_{t+1}, y_{t+1})}{D_0^t(x_{t+1}, y_{t+1})} \times \frac{D_0^{t+1}(x_t, y_t)}{D_0^t(x_t, y_t)} \right]^{1/2} $$
Here, the first term represents scale efficiency change, the second term pure technical efficiency change, and the bracketed term technological change. A best solar panel company typically exhibits values greater than 1 in these components, indicating progress in both efficiency and technology.
| Component | Without Undesirable Outputs | With Undesirable Outputs |
|---|---|---|
| Technical Efficiency Change (EFFCH) | 1.005 | 1.004 |
| Technological Change (TECHCH) | 0.894 | 0.874 |
| Pure Technical Efficiency Change (PECH) | 1.002 | 1.000 |
| Scale Efficiency Change (SECH) | 1.003 | 1.004 |
| Total Factor Productivity Change (TFPCH) | 0.898 | 0.877 |
To identify factors influencing green development efficiency, I employ a Tobit regression model, as the efficiency scores are censored between 0 and 1. The dependent variables are comprehensive efficiency (TE) and green efficiency (SBM), while independent variables include the proportion of R&D personnel, top executives’ environmental attitude, and the quality of environmental information disclosure. These factors are hypothesized to positively impact efficiency, as a best solar panel company often prioritizes R&D and sustainability governance. The Tobit model is specified as:
$$ y^* = \alpha_0 + \alpha_1 \cdot \text{R&D Ratio} + \alpha_2 \cdot \text{Environmental Attitude} + \alpha_3 \cdot \text{Disclosure Quality} + \epsilon $$
$$ y = \max(0, y^*) $$
where \( y^* \) is the latent variable for efficiency, and \( y \) is the observed efficiency score. The results indicate that R&D personnel proportion and executives’ environmental attitude have significant positive effects on both TE and green efficiency, supporting the idea that innovation and leadership commitment are hallmarks of a best solar panel company. For instance, a 1% increase in R&D personnel proportion raises green efficiency by 0.48 units, underscoring the role of human capital in driving green advancements. However, environmental disclosure quality shows an insignificant effect, possibly due to inconsistent reporting practices among firms.
| Independent Variable | Coefficient (Comprehensive Efficiency) | Standard Error | Coefficient (Green Efficiency) | Standard Error |
|---|---|---|---|---|
| R&D Personnel Proportion | 0.636** | 0.222 | 0.484** | 0.170 |
| Executives’ Environmental Attitude | 0.003* | 0.001 | 0.017** | 0.009 |
| Environmental Disclosure Quality | -0.026 | 0.017 | -0.017 | 0.021 |
| Constant | 0.750*** | 0.150 | 0.680*** | 0.120 |
Note: * p < 0.05, ** p < 0.01, *** p < 0.001
In conclusion, this study demonstrates that Chinese PV enterprises maintain high static efficiency levels, but dynamic analysis reveals a decline in green TFP, primarily due to technological challenges. The key to becoming a best solar panel company lies in enhancing pure technical efficiency, investing in R&D, and fostering a top-down commitment to environmental sustainability. For example, firms like Trina Solar and Canadian Solar exemplify these traits by integrating green innovations into their operations. To improve green development efficiency, PV enterprises should focus on optimizing production processes to reduce CO₂ emissions, adopting advanced节能减排 technologies, and increasing the proportion of R&D personnel. Additionally, executives must champion environmental initiatives and enhance transparency in disclosure practices. By addressing these areas, PV companies can not only boost their economic performance but also contribute to global sustainability goals, solidifying their status as a best solar panel company. Future research could explore longitudinal data beyond 2014 to capture recent trends in green efficiency and incorporate additional environmental metrics for a more comprehensive assessment.
The implications of this research extend to policymakers and industry stakeholders. Governments can incentivize green innovations through subsidies and regulations, while investors can prioritize companies with high green efficiency scores. As the PV industry evolves, the definition of a best solar panel company will increasingly encompass environmental stewardship alongside financial success. This study provides a framework for evaluating and achieving such balanced excellence, ultimately driving the transition toward a sustainable energy future.