As a researcher focusing on corporate sustainability and innovation, I have chosen to analyze BYD Company Limited, a global leader in the automotive and clean energy sectors, due to its proactive approach to green technology innovation and transparent reporting practices. BYD has consistently published corporate social responsibility reports since 2010, providing valuable data for assessing its performance. The company emphasizes autonomous innovation, integrating green ecological design, adopting green manufacturing techniques, and developing environmentally friendly products to achieve cleaner, greener, and smarter production processes. In this study, I explore how green technology innovation impacts BYD’s performance, with a particular emphasis on its electric vehicles (EVs), such as the BYD EV models and BYD car lineup, which represent core components of its strategy. Traditional performance evaluations often prioritize financial metrics, overlooking environmental and social factors. Therefore, this research aims to develop a comprehensive and scientific evaluation framework that incorporates innovation, environmental, and social dimensions, using BYD as a case study to illustrate the mechanisms through which green technology innovation drives corporate success.
The growing emphasis on sustainability in the automotive industry, especially with the rise of electric vehicles like the BYD EV, underscores the need for such evaluations. BYD’s commitment to green technology is evident in its development of the BYD car series, which includes hybrid and fully electric models designed to reduce carbon emissions and promote energy efficiency. By examining BYD’s practices, this study not only contributes to academic literature but also offers practical insights for other firms seeking to enhance their green innovation performance. The methodology involves constructing a performance evaluation指标体系 using gray relational analysis, a technique that measures the degree of correlation between variables without requiring large datasets or strict assumptions. This approach allows for a nuanced understanding of how various inputs, such as R&D investments and environmental measures, influence outputs like patent acquisitions and revenue. Throughout this analysis, I will frequently reference BYD EV and BYD car initiatives to highlight their role in driving the company’s performance, ensuring that the discussion remains grounded in real-world applications.
In the literature on green technology innovation, numerous studies have explored its impact on industrial and economic levels, but fewer have delved into micro-level corporate mechanisms. For instance, some researchers argue that corporate performance is significantly influenced by inputs in R&D personnel and funding, while others propose evaluation frameworks based on economic, environmental, and social dimensions. A key distinction lies in the definition of green technology innovation; it is not merely about technical advancements but involves ecological considerations that differentiate it from conventional innovation. This perspective suggests that green technology, as applied in BYD EV production, focuses on advantages in sustainability, such as reduced resource consumption and lower emissions. Additionally, studies indicate that invention-oriented green innovations, like those seen in the BYD car development, have a more pronounced effect on carbon performance, especially when supported by digitalization. However, findings vary, and there is a gap in understanding how these elements interact within a single firm. My research addresses this by adopting a micro-level approach, categorizing BYD’s performance into innovation, environmental, and social aspects to build a tailored evaluation system and uncover the underlying mechanisms.
To construct the performance evaluation指标体系, I adhered to several design principles that ensure robustness and practicality. First, the principle of systematicity combined with feasibility emphasizes a hierarchical structure with clear logical connections between levels, while prioritizing data availability and alignment with BYD’s operational realities. For example, in assessing the BYD EV segment, indicators must reflect the entire supply chain, from R&D to production, without becoming overly idealized. Second, comprehensiveness paired with importance ensures that the指标体系 covers all critical aspects of green technology innovation, such as resource efficiency in BYD car manufacturing, but avoids redundancy by focusing on representative metrics. This balance is crucial given the multitude of financial and non-financial data involved. Third, comparability and objectivity require that indicators facilitate both internal and cross-industry comparisons, using relative measures to account for differences in corporate life cycles and strategies. For instance, metrics like R&D intensity or emission ratios allow for fair comparisons with other automakers. By integrating these principles, the指标体系 captures the multifaceted nature of BYD’s green technology efforts, particularly in areas like the BYD EV innovations, which are central to the company’s mission.
| Primary Indicator | Secondary Indicator |
|---|---|
| Innovation Input | R&D Personnel Ratio /% |
| R&D Expenditure Ratio /% | |
| New Energy Subsidy / million CNY | |
| Environmental Input | Natural Gas Total Consumption / 10,000 m³ |
| Gasoline Total Consumption / 10,000 L | |
| Diesel Total Consumption / 10,000 L | |
| Greenhouse Gas Total Emissions / t | |
| Hazardous Solid Waste Total / t | |
| COD Total / t | |
| VOCs Total / t | |
| Social Input | Public Welfare Donations / million CNY |
| Output Indicator | Green Technology Patents Obtained / units |
| Operating Revenue / million CNY |
The data for this evaluation were sourced from publicly available reports, including annual financial statements and corporate social responsibility documents, which provide reliable information on BYD’s activities, such as those related to the BYD EV and BYD car production. To analyze the performance, I employed gray relational analysis, a method that calculates the degree of correlation between a reference series (e.g., output indicators) and comparative series (e.g., input indicators). The gray relational coefficient is computed using the formula: $$\xi_i(k) = \frac{\min_i \min_k |x_0(k) – x_i(k)| + \rho \max_i \max_k |x_0(k) – x_i(k)|}{|x_0(k) – x_i(k)| + \rho \max_i \max_k |x_0(k) – x_i(k)|}$$ where \(x_0(k)\) represents the reference series (e.g., green technology patents or revenue), \(x_i(k)\) denotes the comparative series (e.g., R&D inputs or environmental metrics), and \(\rho\) is the distinguishing coefficient, set to 0.5 in this study to balance sensitivity and stability. The gray relational degree, which summarizes the overall correlation, is derived as: $$r_i = \frac{1}{n} \sum_{k=1}^n \xi_i(k)$$ where \(n\) is the number of data points. This approach allows for a quantitative assessment of how various factors influence BYD’s performance, with values closer to 1 indicating stronger relationships.
In the innovation input analysis, I examined the correlation between green technology patent acquisitions (as the reference series) and sub-series including R&D personnel ratio, R&D expenditure ratio, and new energy subsidies. After standardizing the data to eliminate dimensional differences, I calculated the gray relational coefficients and degrees. The results, presented in Table 2, show that R&D expenditure ratio has the highest relational degree (0.754), indicating it is the most influential factor in driving patent outputs for BYD. This underscores the importance of financial investments in green technology, particularly for advancements in the BYD EV sector. The R&D personnel ratio follows with a relational degree of 0.733, highlighting the role of human capital, while new energy subsidies, though beneficial, have a lower impact (0.610), suggesting that while government support aids innovation, internal R&D efforts are more critical for sustained performance in areas like the BYD car development.
| Evaluation Item | Relational Degree | Rank |
|---|---|---|
| R&D Personnel Ratio | 0.733 | 2 |
| R&D Expenditure Ratio | 0.754 | 1 |
| New Energy Subsidy | 0.610 | 3 |
For environmental input analysis, I used operating revenue as the reference series and sub-series such as natural gas consumption, gasoline consumption, diesel consumption, greenhouse gas emissions, hazardous solid waste, COD, and VOCs emissions. The gray relational degrees, computed with a distinguishing coefficient of 0.5, reveal inverse relationships; lower values indicate stronger negative correlations with revenue, meaning that reductions in these inputs correspond to higher revenue. As shown in Figure 1, diesel consumption has the lowest relational degree (0.58), implying that minimizing diesel use in processes, possibly in BYD car manufacturing, significantly boosts revenue. Conversely, VOCs emissions have a higher degree (0.76), suggesting that increased emissions are associated with lower revenue, emphasizing the need for pollution control measures. This analysis demonstrates that BYD’s efforts to reduce resource consumption and emissions, integral to its BYD EV production, positively impact financial performance by lowering costs and enhancing efficiency.
The social input analysis focused on public welfare donations as a sub-series, with operating revenue as the reference series. Using data from a five-year period, I calculated the gray relational coefficients and derived an overall relational degree of 0.695. This indicates a moderate positive correlation, meaning that increased charitable contributions, such as those supporting education and disaster relief, are associated with higher revenue. This aligns with BYD’s philosophy of integrating social responsibility into its business model, where initiatives like technological philanthropy for poverty alleviation not only benefit society but also enhance corporate reputation and, indirectly, financial performance. For instance, programs linked to the BYD EV ecosystem may foster community goodwill, driving sales and loyalty.
In a comprehensive analysis, I evaluated all indicators against operating revenue to assess their overall impact. The gray relational degrees, summarized in Table 3, show that green technology patent acquisitions have the highest relational degree (0.883), indicating a strong positive correlation with revenue. This highlights the direct economic benefits of innovation, particularly in the BYD car and BYD EV domains, where patents lead to competitive advantages and market growth. Other indicators, such as greenhouse gas emissions and VOCs, also show significant correlations, reinforcing the importance of environmental management. The results can be interpreted using the formula for gray relational degree, where higher values suggest that prioritizing certain inputs, like R&D for BYD EV technologies, yields substantial returns. This comprehensive view underscores the interconnectedness of innovation, environmental, and social factors in driving BYD’s performance.
| Primary Indicator | Secondary Indicator | Relational Degree | Rank |
|---|---|---|---|
| Innovation Input | R&D Personnel Ratio | 0.693 | 9 |
| R&D Expenditure Ratio | 0.673 | 11 | |
| New Energy Subsidy | 0.648 | 12 | |
| Environmental Input | Natural Gas Total Consumption | 0.797 | 4 |
| Gasoline Total Consumption | 0.719 | 7 | |
| Diesel Total Consumption | 0.681 | 10 | |
| Greenhouse Gas Emissions | 0.838 | 2 | |
| Hazardous Solid Waste | 0.774 | 5 | |
| COD Total | 0.711 | 8 | |
| VOCs Total | 0.833 | 3 | |
| Social Input | Public Welfare Donations | 0.743 | 6 |
| Output Indicator | Green Technology Patents | 0.883 | 1 |
The findings from this study reveal that green technology innovation plays a pivotal role in enhancing BYD’s performance, with distinct variations in the influence of different factors. Innovation inputs, particularly R&D expenditures, drive patent outputs and revenue, underscoring the value of sustained investment in technologies for the BYD EV and BYD car lines. Environmental inputs show that reducing resource consumption and emissions not only aligns with sustainability goals but also improves financial outcomes, as seen in the negative correlations with revenue. Social inputs, while less directly impactful, contribute to long-term reputation and stakeholder engagement. However, achieving optimal performance requires balancing these elements; for example, while BYD has made strides in green manufacturing for its BYD EV series, further efforts in emission reduction could yield additional benefits. The gray relational analysis provides a robust framework for such evaluations, enabling firms to prioritize actions based on empirical evidence.
In conclusion, this research demonstrates that a holistic approach to performance evaluation, incorporating innovation, environmental, and social dimensions, is essential for understanding the impact of green technology innovation in companies like BYD. The emphasis on BYD EV and BYD car initiatives illustrates how targeted innovations can drive both economic and ecological benefits. By continuously advancing green technologies, BYD not only strengthens its competitiveness but also contributes to broader societal goals, such as carbon neutrality and sustainable development. This study offers a model for other firms to assess and enhance their green innovation strategies, ultimately fostering a more sustainable industrial landscape. Future research could expand this framework to include dynamic analyses or cross-industry comparisons, further enriching the understanding of green technology’s role in corporate performance.