In our study, we conducted an in-depth ecological discourse analysis of Corporate Social Responsibility (CSR) reports from two leading新能源汽车 companies, Tesla and BYD. The comparison of Tesla vs BYD in their CSR disclosures reveals critical insights into how these firms construct their environmental narratives and position themselves in the global sustainability landscape. We employed Python-based text mining techniques to extract and analyze ecological vocabulary from their reports, focusing on frequency, co-occurrence, and divergence in terminology. This analysis not only highlights the strategic differences in their ecological discourse but also underscores the importance of international compliance and local policy alignment for corporate image building. The ongoing rivalry between BYD vs Tesla in the electric vehicle market extends beyond sales to encompass their environmental commitments, as reflected in their CSR communications. Through this research, we aim to provide a comprehensive understanding of how these companies leverage ecological language to enhance their global standing and influence.
Corporate Social Responsibility reports serve as vital tools for companies to communicate their environmental, social, and economic commitments to stakeholders. In the context of global governance and the international expansion of Chinese enterprises, analyzing the ecological discourse in CSR reports becomes crucial for understanding how firms like BYD and Tesla navigate sustainability agendas. Our investigation into Tesla vs BYD CSR reports focuses on identifying patterns in ecological vocabulary usage, which can shed light on their respective approaches to environmental stewardship. We collected English-language CSR reports from BYD covering the years 2016 to 2023 and from Tesla for 2019 to 2023, ensuring a robust dataset for comparative analysis. The significance of this study lies in its ability to inform best practices for CSR reporting, particularly for Chinese companies seeking to improve their international image through more effective ecological narratives.
To perform this analysis, we designed a research methodology centered on computational text analysis. We extracted textual data from the CSR reports of both BYD and Tesla, preprocessed the text by removing noise and standardizing formats, and then applied Python libraries such as Gensim, Numpy, and Pandas for natural language processing. Specifically, we used a Word2Vec model trained on the Google News corpus to generate 300-dimensional word vectors, enabling us to compute semantic similarities between terms. Ecological vocabulary was identified by calculating cosine similarity with a predefined set of seed words related to sustainability, and we set a threshold of 0.37 for inclusion. This approach allowed us to quantify the frequency and distribution of ecological terms, facilitating a direct comparison between Tesla vs BYD in their CSR discourse. The formula for cosine similarity is given by:
$$ \text{similarity} = \frac{\mathbf{A} \cdot \mathbf{B}}{\|\mathbf{A}\| \|\mathbf{B}\|} $$
where $\mathbf{A}$ and $\mathbf{B}$ are the word vectors for the terms being compared. This method ensured that we captured nuanced semantic relationships, such as near-synonyms, which are essential for understanding ecological narratives. Additionally, we employed statistical tests, including chi-square, to assess the significance of differences in term frequencies between the two companies. For instance, the chi-square statistic is computed as:
$$ \chi^2 = \sum \frac{(O_i – E_i)^2}{E_i} $$
where $O_i$ is the observed frequency and $E_i$ is the expected frequency under the null hypothesis. This rigorous methodology provided a foundation for identifying key trends and disparities in the ecological language used by BYD and Tesla, setting the stage for a detailed discussion of their CSR strategies.
The results of our analysis reveal substantial similarities and differences in the ecological vocabulary of Tesla vs BYD CSR reports. First, we present an overview of the corpus data in Table 1, which summarizes the scale of the reports and the occurrence of ecological terms. This table highlights the extensive use of environmental language in both companies’ disclosures, with BYD’s reports containing a larger overall word count but similar densities of ecological terms when normalized.
| Corpus | Time Period | Total Words | Ecological Words |
|---|---|---|---|
| BYD CSR Reports | 2016-2023 | 129,000 | 6,801 |
| Tesla CSR Reports | 2019-2023 | 83,985 | 5,016 |
Next, we examined the high-frequency ecological terms in both sets of reports. Table 2 lists the top 10 most frequently used ecological words for BYD and Tesla, respectively. This comparison underscores the core themes emphasized by each company, with terms like “energy” and “emissions” appearing prominently in both, but with varying frequencies that reflect their strategic priorities.
| BYD CSR Reports | Frequency | Tesla CSR Reports | Frequency |
|---|---|---|---|
| energy | 792 | emission(s) | 586 |
| green | 296 | energy | 436 |
| products | 277 | supplychain(s) | 241 |
| emission(s) | 263 | solar | 218 |
| environmental | 237 | products | 175 |
| water | 195 | environmental | 150 |
| waste | 191 | water | 140 |
| electric | 177 | ghg | 135 |
| csr | 174 | sustainable | 110 |
| innovation | 155 | ghgemissions | 108 |
In terms of co-occurring ecological vocabulary, we identified terms that appear in both BYD and Tesla reports, as shown in Table 3. This table includes selected high-frequency words and their normalized frequencies per 10,000 words to account for differences in report length. The data indicate that both companies prioritize energy and sustainability topics, but with distinct emphases. For example, “energy” is used more frequently by BYD, while “emissions” is more common in Tesla’s reports. The normalized frequency difference for “energy” is calculated as:
$$ \text{Normalized Frequency} = \frac{\text{Raw Frequency}}{\text{Total Words}} \times 10,000 $$
This normalization allows for a fair comparison between the two corpora. The similarities in co-occurring terms suggest a shared commitment to global sustainability agendas, but the differences in frequency point to varying narrative strategies in the Tesla vs BYD discourse.
| Word/Phrase | BYD Frequency | Tesla Frequency | Normalized Difference |
|---|---|---|---|
| energy | 792 | 436 | +356 |
| emission(s) | 263 | 586 | -323 |
| green | 296 | 3 | +293 |
| innovation | 155 | 12 | +143 |
| electric | 177 | 47 | +130 |
| supplychain(s) | 106 | 241 | -135 |
| waste | 191 | 68 | +123 |
| solar | 100 | 218 | -118 |
| carbonneutral | 8 | 2 | +6 |
| sustainable(-ability) | 173 | 175 | -2 |
| renewable(s) | 41 | 88 | -47 |
| recycle(s) | 9 | 19 | -10 |
| recycling(-able,-ed) | 66 | 113 | -47 |
The divergent ecological vocabulary between Tesla vs BYD is particularly revealing. Table 4 lists terms that are unique or significantly more frequent in one company’s reports compared to the other. For instance, BYD frequently uses terms like “green,” “innovation,” and “carbon neutrality,” which align with Chinese national policies such as the dual-carbon goals. In contrast, Tesla emphasizes technical terms like “ghg” (greenhouse gases) and “supplychain,” reflecting its focus on international standards and compliance. The statistical significance of these differences was assessed using chi-square tests with a significance level of α=0.05. For example, the term “green” shows a normalized frequency difference of 63.75 times higher in BYD’s reports (χ²=180.9, p<0.001), indicating a substantial divergence in discourse strategy.
| BYD CSR Reports | Frequency | Tesla CSR Reports | Frequency |
|---|---|---|---|
| csr | 174 | ghg | 135 |
| congestion | 49 | ghg emissions | 108 |
| park | 49 | mpg | 29 |
| traffic congestion | 41 | mine | 27 |
| poverty alleviation | 31 | affordability | 18 |
| interaction | 26 | scrap | 12 |
| noise | 25 | cleaner | 12 |
| photovoltaic | 22 | particulates | 10 |
| dioxide emissions | 22 | artisanal | 10 |
| competitiveness | 21 | forestry | 10 |
| emission reduction | 20 | – | – |
| carbon neutrality | 19 | – | – |
| garbage | 14 | – | – |
| ecological | 14 | – | – |
| sewage | 14 | – | – |
| traffic jams | 13 | – | – |
| eco friendly | 11 | – | – |
| ammonia nitrogen | 10 | – | – |
Our discussion of these findings centers on the implications of the ecological discourse strategies employed by BYD and Tesla. In the comparison of Tesla vs BYD, it is evident that both companies leverage sustainability narratives to align with global trends, but their approaches differ significantly. Tesla’s frequent use of internationally recognized terms like “ghg” and “emissions” enhances its credibility and compliance with global environmental standards, such as those set by the CDP. This aligns with Tesla’s strategy to position itself as a leader in the global market, emphasizing technical rigor and transparency. Conversely, BYD’s discourse is heavily influenced by domestic policies, such as China’s dual-carbon goals, leading to a higher frequency of terms like “green” and “carbon neutrality.” This local policy orientation helps BYD resonate with national stakeholders but may limit its appeal in international contexts where more standardized terminology is expected.
Moreover, the analysis of BYD vs Tesla reveals how ecological vocabulary shapes corporate image. Tesla’s focus on supply chain management and specific emissions metrics (e.g., “ghg emissions”) demonstrates a commitment to detailed, data-driven reporting, which can foster trust among global investors and regulators. In contrast, BYD’s use of broader terms like “innovation” and “poverty alleviation” reflects a holistic approach to CSR that integrates social and environmental goals, consistent with Chinese corporate practices. However, this may lead to perceptions of vagueness in international forums. The statistical significance of these differences, as shown by chi-square tests, underscores the robustness of our findings. For example, the term “emission(s)” has a normalized frequency difference of 3.42 times higher in Tesla’s reports (χ²=315.74, p<0.001), highlighting its central role in Tesla’s ecological narrative.
To further illustrate the semantic relationships in the ecological discourse, we can model the similarity between key terms using vector representations. The cosine similarity formula mentioned earlier allows us to quantify how closely related terms are in the semantic space. For instance, the similarity between “sustainable” and “renewable” can be computed as:
$$ \text{similarity} = \frac{\mathbf{S} \cdot \mathbf{R}}{\|\mathbf{S}\| \|\mathbf{R}\|} $$
where $\mathbf{S}$ is the vector for “sustainable” and $\mathbf{R}$ is the vector for “renewable.” This approach reveals that in Tesla’s reports, terms like “solar” and “energy” have high similarity, indicating a focused narrative on renewable energy sources. In BYD’s reports, terms like “green” and “innovation” show stronger associations, reflecting its policy-driven agenda. These semantic patterns enrich our understanding of the Tesla vs BYD discourse dynamics and how they construct their ecological identities.
In conclusion, our ecological discourse analysis of Tesla vs BYD CSR reports demonstrates that both companies actively engage with sustainability issues, but through distinct linguistic strategies. Tesla emphasizes international compliance and technical specificity, which bolsters its global authority, while BYD adopts a more localized approach aligned with Chinese policies. This comparison of BYD vs Tesla underscores the importance of balancing global standards with regional narratives for companies seeking to enhance their international image. As the competition between Tesla and BYD intensifies, their CSR reports will continue to be a key battleground for ecological storytelling. We recommend that companies like BYD incorporate more internationally recognized terminology and detailed disclosures to improve their global appeal, without sacrificing their local relevance. Future research could expand this analysis to include other新能源汽车 manufacturers or longitudinal studies to track evolving discourse trends.
Ultimately, the rivalry between Tesla vs BYD in the ecological domain reflects broader trends in corporate sustainability. By leveraging advanced text mining techniques, we have uncovered nuanced differences in their CSR communications that can inform both academic research and practical corporate strategies. The insights from this study highlight the power of language in shaping environmental narratives and the need for companies to adapt their discourse to diverse audiences. As the world moves towards a greener future, the ecological discourse in CSR reports will play an increasingly vital role in defining corporate leadership in the Tesla vs BYD era.