English Language Teaching and Linguistics Studies ISSN 2640-9836 (Print) ISSN 2640-9844 (Online) Vol. 6, No. 5, 2024 www.scholink.org/ojs/index.php/eltls 43 Original Paper ACorpus-Based Analysis of Low-Carbon Image Construction in Chinese Commercial Banks Yishan Zhang1* 1 Guangdong University of Finance, Guangdong, China * Corresponding author Received: July 29, 2024 Accepted: August 25, 2024 Online Published: September 12, 2024 doi:10.22158/eltls.v6n5p43 URL: http://dx.doi.org/10.22158/eltls.v6n5p43 Abstract As global attention to sustainable development and environmental protection increases, corporate social responsibility (CSR) has become a crucial measure of corporate image and competitiveness. This study constructs a corpus of CSR reports from China's four major banks (ICBC, Bank of China, China Construction Bank and Agricultural Bank of China) and uses discourse analysis to explore how these banks convey low-carbon concepts through their CSR reports. First, the study outlines the background and purpose of the CSR reports, emphasizing the banks' roles in promoting low-carbon development amidst global climate change and China's dual-carbon strategy. Discourse analysis reveals how high-frequency words, clusters and key phrases in the reports contribute to building a responsible corporate image. The language strategies in the reports highlight the banks' commitment to environmental responsibility and their efforts in promoting green finance and supporting sustainable projects, illustrating the low-carbon image they aim to construct. Additionally, the analysis examines the different discourse strategies and attitudes of each bank, showing how they shape their low-carbon image and influence national policies and public perception. This study provides new insights into the role of China's banking sector in advancing social and environmental sustainability and offers CSR communication strategies for other enterprises. The thesis is organized into five sections. The first section introduces the study's background, purpose, significance and structure. The second section reviews the literature on low-carbon images, image construction, corpus research and discourse analysis. The third section presents the theoretical framework, data sources and the significance of corpus linguistics. The fourth section analyzes high-frequency words, clusters and key phrases in the CSR reports of China's four major banks, focusing on low-carbon-related discourse. The fifth section concludes the study. www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 44 Keywords Chinese Four Commercial Banks CSR reports, Corpus-based; Discourse Analysis, Image construction, low-carbon 1. Introduction This chapter primarily addresses the introduction of the study’s background, purpose and significance as well as the thesis’s organizational framework. 1.1 Background and Purpose of the Study China has committed to achieving carbon peaking by 2030 and carbon neutrality by 2060. The global economy is transitioning towards low-carbon development, following phases of industrialization and informatization. The adjustments in industrial structures and consumption patterns driven by the low-carbon economy have positioned carbon finance as a new competitive frontier in international finance (Guo, 2010). Commercial banks, as resource allocation hubs (Guo, 2010), actively participate in the carbon finance business. The realization of this ambitious goal is the need for commercial banks to respond to the international financial competition to grasp the right to speak on carbon finance so that the market status and brand image of these commercial banks actively participating in the carbon finance business will be significantly enhanced. A low-carbon image is crucial for a bank's corporate social responsibility. It showcases its commitment to environmental protection and sustainable development, highlighting the bank's proactive sustainability efforts and strengthening its public and market reputation. In addition, in-depth research on low-carbon image construction not only helps to broaden the scope of research in this field but also promotes the improvement and development of banks' low-carbon discourse systems, facilitating the wide dissemination and in-depth exploration of related research. This is of high reference value for enriching the theoretical system of bank image construction and improving the influence of international low-carbon discourse, which should not be ignored. 2. Method This thesis adopts a combined qualitative and quantitative approach. The following steps outline the analysis process: 1) Corpus Construction: A self-built corpus comprising CSR reports from China's three major commercial banks (2017-2022) will be utilized. 2) Corpus Analysis Tools: Tools such as AntConc will be employed to perform statistical analysis, ensuring objectivity and reliability. 3) Discourse Analysis: An in-depth examination of attitude and connotative meaning within the CSR reports will be conducted to understand the strategies and intentions behind low-carbon image construction. www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 45 The corpus-based study refers to a linguistic research method based on a corpus, which is grounded in a large collection of carefully compiled texts and also an effective approach to scrutinizing such discourses from various perspectives (Hameed, 2022). Corpus linguistics takes real language data as the object of study, analyzes a large number of language facts from a macroscopic point of view and searches for the laws of language use from them (Yang, 2002). Through probabilistic statistical methods, it concludes, providing not only statistical data and authentic language materials but also constructing new theories and verifying existing ones. This project primarily employs the "corpus-based" research method within corpus linguistics, that is, based on the self-built corpus of the Corporate Social Responsibility (CSR) reports from China's three commercial banks (2017-2022). It utilizes corpus retrieval and analysis tools such as Antconc to statistically identify strategies and processes, thereby ensuring the objectivity, authenticity and persuasiveness of the research results. Besides, based on the self-built small corpus, the project conducts an in-depth analysis of the application and method of discourse analysis in the CSR of three commercial banks. Through this research, we can more accurately understand the key role of discourse in image construction, reveal the intentions and attitudes behind it and thus provide a new research perspective and theoretical support for low-carbon discourse construction. In summary, this project innovates in the selection of research subjects, the application of research methods and the expansion of research perspectives. It is expected to inject new vitality into the research field of constructing a low-carbon image for China's banking industry and promote the in-depth development of this field. 2.1 Research Questions This study employs both quantitative and qualitative methods to investigate the low-carbon image of Chinese commercial banks. The research is designed to address the following questions: What type of low-carbon image have Chinese commercial banks established? How do Chinese commercial banks construct their low-carbon image? 2.2 Research Data The corpus collected for this study originates from the CSR reports on the official websites of the four major banks from 2017 to 2022, totaling 24 reports. By filtering and cleaning the raw data (such as merging duplicate texts, removing irrelevant content and deleting images), the final text was extracted in text format. On this basis, a self-built corpus of CSR reports from the four major banks (hereinafter referred to as the CSR Corpus) was created. The corpus retrieval software used in this study is AntConc 4.2.4. Statistical analysis reveals that the total number of word tokens in this specialized corpus is 754,321 words and the total number of word types is 24,533 words. 2.3 Research Instrument This study employs a corpus-based discourse analysis method to examine the low-carbon image constructed by China's four major commercial banks. First, the study utilizes the corpus software www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 46 AntConc 4.2.4 to retrieve the top 300 high-frequency content words and employs a word cloud visualization app to make the results more intuitive, thereby investigating the low-carbon discourse themes in the banks' CSR reports. Secondly, by observing and analyzing the collocates of the high-frequency content word "green" in the self-built corpus, the study further elucidates the characteristics of the low-carbon discourse-generated image of Chinese banks. Subsequently, the study analyzes the discourse in the CSR reports to examine its connotative meaning in the texts the attitudes of Chinese commercial banks towards constructing a low-carbon image and explores how the banks construct this low-carbon discourse image. 3. Result Combining corpus-based study and discourse analysis, this research finds that CSR reports of Chinese commercial banks tend to integrate financial strategy with low-carbon discourse to project a positive, environmentally conscious, policy-compliant image. The subsequent sections delve into the word list, clusters, KWIC (Key Word in Context), and discourse analysis, which more comprehensively reveal the linguistic features of the corpus and address the initial research questions: what kinds of low-carbon images have the commercial banks constructed and how these images are constructed. 3.1 Corpus Analysis in CSR Reports of Chinese four Major Banks Frequency statistics are the most direct method provided by corpus analysis (Qian, 2016). High-frequency words are the starting point for discourse research assisted by corpora and high-frequency content words often indicate the focus of the text (Zhou, 2023), serving as an external manifestation of core issues at the lexical level. Stubbs asserts, "Analyzing repeatedly occurring phrases is crucial for studying language and ideology" (Stubbs, 1996). Therefore, this study uses the word frequency (Word List) statistics function of the corpus retrieval software AntConc to identify the top 50 high-frequency content words (see Figure 8) to uncover key topics embedded in the low-carbon discourse corpus and observe the language operation mechanisms used by CSR reports when describing the image of Chinese commercial banks. Functional words, although they have typical collocation behaviors, mainly reflect grammatical structures and should not be the focus of research (Yang, 2002). Thus, after removing function words, the remaining open class content words are shown in the figure. "The core vocabulary includes words which occur not only frequently, but with a relatively even distribution across a wide variety of texts and text types" (Stubbs, 2005). As shown in the figure, the retrieved high-frequency words appear frequently and are widely distributed, occurring in all 24 files. Therefore, the 50 open-class words also constitute the core vocabulary in the reports. 3.1.1 The Analysis of High-Frequency Words of Linguistic Features in CSR Reports Word frequency statistics provide the most direct method for corpus analysis (Qian, 2016). High-frequency words serve as the starting point for discourse studies assisted by corpora, as these words often indicate the focal points of the text (Zhou, 2023), representing the core issues at the lexical www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 47 level. Stubbs posits that “analyzing recurrent phrases is crucial for the study of language and ideology” (Stubbs, 1996). Therefore, this study utilizes the word frequency statistics function of the corpus analysis software AntConc to identify the top 50 high-frequency content words (see Figure 8). This approach aims to uncover key topics embedded within the low-carbon discourse corpus and observe the linguistic mechanisms employed in depicting China’s national image. Although function words also exhibit typical collocation patterns, these patterns primarily reflect grammatical structures and should not be the focus of this study (Yang, 2002). Consequently, after removing function words, the remaining open class words are illustrated in the figure. The core vocabulary includes words that occur frequently and are evenly distributed across a wide variety of texts and text types (Stubbs, 2005). As shown, the retrieved high-frequency words appear frequently and are widely distributed, occurring across all 24 documents. Hence, the 50 open class words identified are considered core words within the reports. Four major categories of high-frequency words: Financial Operations: The words bank, financial, services, development, branch, finance, loans, customers, credit and business highlight banks' core focus on financial services, management, loans and customer relations. Terms like risk, control and system underscore their emphasis on managing financial risks and ensuring operational stability. Corporate Social Responsibility and Sustainability: Terms such as corporate, responsibility, management, report, social and support indicate banks' commitment to corporate governance, social responsibility and transparent reporting. They also prioritize employee training and customer service. Poverty Alleviation and Social Development: Terms like poverty, rural areas and alleviation demonstrate banks' efforts in poverty reduction and social development, particularly in underserved regions. Environmental Protection and Low Carbon Development: Words such as green, environmental, carbon, energy and protection signify banks' initiatives in promoting sustainable practices, managing environmental impacts and reducing carbon footprint. They also emphasize green product development and sustainable energy practices. Innovation and Future Development: Terms like products and innovation highlight banks' investments in developing new financial products and services. Reporting and information terms underscore their commitment to transparency and accountability through comprehensive reporting practices. It is observed that the high-frequency words can be categorized into four main groups according to their lexical meanings, explaining the four main themes, namely finance and business operations, CSR and sustainable development, environmental protection and low-carbon development and innovation and future development, reflecting that the bank focuses on the development of its financial business while at the same time corresponding to the national call for a dual-carbon policy. The core words carbon and green are all mentioned in their CSR reports. They are evenly distributed, indicating that low carbon is the next word with the highest frequency and significant research value (Yang H.Z., www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 48 2002). Therefore, it can be concluded that four central commercial banks were willing to project the image of environmentally friendly, low-carbon advocates. Table 1. The Chart of 50 high-frequency Words High frequency Word in CSR Reports Type Rank Freq Range Type Rank Freq Range bank 1 8925 24 corporate 26 1459 24 financial 2 4711 24 information 27 1433 24 development 3 4066 24 responsibility 28 1422 24 management 4 3936 24 protection 29 1400 24 green 5 3614 24 training 30 1396 24 services 6 3178 24 products 31 1382 24 china 7 3101 24 banking 32 1380 24 rmb 8 2612 24 areas 33 1351 24 service 9 2608 24 rural 34 1305 24 branch 10 2593 24 loan 35 1295 24 finance 11 2401 24 customer 36 1293 24 social 12 2351 24 reporting 37 1270 24 new 13 2321 24 people 38 1266 24 employees 14 2283 24 total 39 1263 24 risk 15 2128 24 energy 40 1262 24 business 16 2099 24 environmental 41 1217 24 poverty 17 1936 24 billion 42 1206 24 credit 18 1856 24 building 43 1176 24 enterprises 19 1780 24 construction 44 1143 24 system 20 1714 24 key 45 1142 24 report 21 1700 24 control 46 1116 24 loans 22 1658 24 alleviation 47 1114 24 customers 23 1657 24 end 48 1106 24 support 24 1645 24 office 49 1094 24 million 25 1517 24 carbon 50 1093 24 3.1.2 The Corpus-based Analysis of Clusters in CSR Reports The word cluster statistics can verify the distribution and typical features of phrases, expressions and collocations in a corpus. By extracting and analyzing words with abnormally high frequencies in www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 49 discourse, as well as words or word groups with co-occurrence relationships, the theme of the discourse can be determined (Yang, 2002). Through statistical analysis, it was found that the word "green" related to the low-carbon topic had the highest frequency and meanwhile "low-carbon" is the theme word. Therefore, this article identifies "green" and "carbon" as the central node word and this study will focus on four-word clusters. This choice is because typically, there are a large number of three-word clusters extracted from the text and due to the presence of numerous function words, three-word clusters cannot fully reflect the essence of the text (Chen, Ding, & Ding, 2013). Setting the Cluster Size to 4, Search Term Position to on Left/Right, frequency to 10, Sort by Frequency, capturing all the clusters that appeared, finally generating a word cloud to visually highlight them, enabling readers to quickly grasp the essence of the text (Yuan, 2016), as shown in Figure 9. Corpora can compensate for the shortcomings of intuitive inference alone. Utilizing word frequency, keywords, collocation, word clusters, patterns and other information provided by corpora facilitates the analysis of discourse presentation (Qian, 2010). Optional patterns of collocation, colligation and semantic preference highlight relevant aspects of meaning and prosody can be searched for in the immediate context (Sinclair, 2004). Below are three main themes grouped by frequency: First group: High-frequency themes (high word frequency). From a macro perspective, banks attach great importance to the discourse construction of green development policies. "green," "low carbon," "development," and "balance" focus on green and low-carbon development, indicating that low-carbon development is a core issue in banks, emphasizing the importance of green development and the balance between development and environmental protection. Second group: Medium-frequency themes (moderate word frequency). Medium-frequency themes reflect banks, as commercial entities, integrating green into their financial characteristics while developing the financial industry. Related clusters such as "sustainable finance of green" and "green finance inclusive finance" demonstrate the role of green development in finance, with both complementing each other. "Responsibility Report Pursuing Green" focuses on corporate social responsibility and green goals. "Green credit," as part of green finance, emphasizes that the core of financial products lies in green, expressing the bank's emphasis on the research and development of green financial products. Third group: Low-frequency themes (low word frequency). This theme involves livelihood construction, upgrading of infrastructure, green industries and green finance reform, expressing the bank's emphasis on implementing the theme of green development in specific areas such as green infrastructure, industrial upgrading and financial reform. Considering their low frequency, however, it does exist in the report that proves the bank also pays attention to some low-carbon actions in the industry. These may be aspects that banks need to explore and implement in the process of green development. www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 50 Figure 1. Word Cloud of Clusters of Green Shifting the attention to the term "carbon," we'll conduct a similar analysis using the established parameters. The resulting clusters, depicted in Figure 9, prominently feature associations with "green," "low carbon," "carbon peak," "carbon neutrality," "emissions," "tons," and "energy conservation." Together, these clusters encapsulate a thematic emphasis on reducing carbon emissions, enhancing energy efficiency and advancing sustainable development. This underscores the banking sector's prioritization of energy conservation and its alignment with the contemporary discourse on carbon reduction. Upon closer examination of the Key Word in Context (KWIC) index, it becomes evident that the terms "carbon peak" and "carbon (neutrality)" are particularly concentrated within the 2021 Corporate Social Responsibility (CSR) report. Notably, 2021 marked the official introduction of China's "dual carbon" policy. This observation suggests that commercial banks in China are strategically positioning themselves at the forefront of topical issues, aligning with national policies such as the "dual carbon" strategic objectives, thereby projecting a distinct low-carbon image. Table 2. The Chart of Clusters of “Carbon” Cluster Rank Freq Range green and low carbon 1 87 18 carbon peak and carbon (neutrality) 2 43 4 www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 51 emissions tons of carbon 3 17 2 million tons of carbon 4 13 8 energy conservation and carbon 5 12 2 Therefore, it can be generated that Chinese banks highly value green and low-carbon development. Firstly, in the high-frequency themes, banks emphasize the importance of green development policies, considering green and low-carbon development as core issues and highlighting the balance between green development and environmental protection. Secondly, in the medium-frequency themes, banks integrate green into their financial features by promoting the development of sustainable finance and green financial products to support green development. Lastly, in the low-frequency themes, banks implement the green development theme in specific areas, including livelihood construction, infrastructure upgrading, green industries and financial reform. These efforts demonstrate the commitment of Chinese commercial banks to construct a low-carbon image closely aligned with contemporary hot issues and national policies. 3.2 The Discourse Analysis of KWIC of Linguistic Features in CSR Reports Every word tends to occur in clusters with other words, providing valuable insights for collocation, association and semantic resonance studies (Yang, 2002). These clusters, termed micro-contexts or "co-text" (Li & Pu, 2001), offer not just phrase and sentence-level usage contexts but also broader discourse contexts. To gain deeper insights into how Chinese commercial banks shape their low-carbon image, the author examines index lines related to carbon in the article. Indexing functions aid in interpreting speakers' and writers' attitudes, extending to the study of semantic prosody (Qian, 2010), which spreads connotational coloring beyond single-word boundaries (Partington, 1998) and reveals significant linguistic patterns in the text (Sinclair, 1991). Researchers can tailor criteria according to their needs. In this paper, "carbon" serves as the search word, with analysis covering attitude and connotative meaning. Analysis reveals that verbs collocated with "carbon" primarily convey positivity, such as "advocate," "advance," and "support," indicating banks’ proactive low-carbon actions and encouragement for participation. These verbs underscore the banks' leadership and responsibility in promoting low-carbon transformation. Other verbs, like "saved," "reduced," "provides," "supported," and "carry out," demonstrate concrete actions and support in low-carbon practices, enhancing the bank's credibility and environmental image. Verbs like "advanced," "develop," "facilitated," and "integrate" signify continuous improvement and development in low-carbon actions, reflecting the banks' strategic vision and perseverance. Verbs such as achieving and reaching highlight the banks’ low-carbon achievements, emphasizing specific results in the transformation process. Lastly, verbs like "control", "measure", "formulate" and "underwrite" showcase compliance and standardization in low-carbon management www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 52 and financial products, reflecting regulatory operations and responsible attitudes. In conclusion, through positive verbs and discourse prosody in CSR reports, banks portray themselves as leaders and practitioners in low-carbon transformation and green finance. By advocating concepts, providing action support, achieving goals and adhering to standards, banks convey commitment and practical contributions to environmental protection and sustainable development, enhancing trust and support. Table 3. Part of the KWIC www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 53 4. Discussion The forthcoming sections will conclude the specific knowledge gained from analyzing Chinese commercial banks' CSR reports, identifying the study's constraints, and proposing directions for future research. These findings will have broader implications for promoting environmentally responsible images in the banking industry, emphasizing the study's importance in China's financial and environmental initiatives, notably the dual-carbon strategy. 4.1 Major Findings CSR reports of Chinese commercial banks are essential for foreign readers to understand the current state of China's financial development, Chinese discourse construction and Chinese banks' images. This paper finds that corpus-based study and discourse analysis play essential roles in constructing banks with low-carbon images by analyzing relevant high-frequency words, clusters and KWIC in CSR reports. 4.2 Limitation and Direction of Further Study At the same time, there are shortcomings in this study, as not all examples can be fully covered due to time and space limitations and there are other relevant aspects that can be studied. More details can be found in the CSR reports than in the examples provided here, allowing future researchers to read more and conduct more case studies from various angles. Furthermore, since this paper applied a quantitative method to analyze the corpus, many words that might be relevant to studying low-carbon images need to be explored. Words are occasionally employed to research specific sentences due to the large corpus volume. However, the author must select it more concisely based on scientific techniques. 4.3 Implications The study has implications for outreach translations and suggests using corpus-based study and discourse analysis theory in bank image construction. It recommends analyzing more CSR reports regularly using these methods to improve the dissemination of the low-carbon image of Chinese commercial banks and make further progress in Chinese low-carbon finance by balancing financial development and low-carbon implementation. This will involve choosing appropriate financial strategies and keeping abreast of Chinese dual-carbon policy to establish Chinese commercial banks as the performers and advocates of low-carbon. References Alexander. (2011). Framing Discourse on the Environment: A Critical Discourse Approach. https://doi.org/10.4324/9780203890615 Chabot, M., Bertrand, J. L., & Courquin, V. (2023). Climate Interconnectedness and Financial Stability. Finance, Pub. Anticipées, (0), 1-50. https://doi.org/10.3917/fina.pr.024 Gee, J. P. (2014). An introduction to discourse analysis: Theory and method. Routledge. https://doi.org/10.4324/9781315819679 Guo, Q. M. (2010). A Strategic Study on Commercial Banks' Participation in Carbon Finance. www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 54 Southwest Finance, (06), 65-67. Guo, X. (2010). China's national environmental image: A case study of New York Times risk reporting on the global climate change. Journalism & Communication, (4), 18-30. Hameed, A. A. (2022). Corpus-Based Investigation of Saudi Corporate Discourse for Environmental Concerns. Eurasian Journal of Applied Linguistics, 8(2), 259-271. Han, J. L. (2010). Authorship Construction and Self-Promotion in Academic Papers. Beijing: Foreign Language Teaching and Research Press. Hu, X. B. (2021). A Comparative Study of Chinese and American Media's Construction of China's National Image in the New Crown Epidemic: A Corpus-Assisted Three-Dimensional Discourse Analysis. Journal of Tianjin Foreign Studies University, 28(04), 74-86+159. Kainiemi, L., Karhunmaa, K., & Eloneva, S. (2020). Renovation Realities: Actors, Institutional Work and the Struggle to Transform Finnish Energy Policy. Energy Research & Social Science, 70, 101778. https://doi.org/10.1016/j.erss.2020.101778 Koteyko, N. (2012). Managing carbon emissions: A discursive presentation of “market-driven sustainability” in the British media. Language & Communication, 32(1), 24-35. https://doi.org/10.1016/j.langcom.2011.11.001 Kotler, P. (2011). Philip Kotler’s contributions to marketing theory and practice. Rev. Mark. Res., 8, 87-120. Li, W. Z., & Pu, J. Z. (2001). Corpus indexing in foreign language teaching. Journal of PLA College of Foreign Languages, 2001(02), 20-25. Lou, B. C., & Wang, L. (2020). Self-referents and Authorship Construction in Learners' Academic English Writing. Journal of PLA College of Foreign Languages, (01), 93-99. Lu, J. Y. (2021). How to do a good job in the international communication of carbon peak, carbon neutral China program. External Communication, 301(10),67-70. Mo, F. (2010). Commitment to the First Low-Carbon Bank in China - Pudong Development Bank Builds a Green Financial Image. Chinese Financier, (02), 119-120. https://doi.org/10.1108/S1548-6435(2011)0000008007 Partington, A. (1998). Patterns and Meanings: Using Corpora for English Language Research and Teaching. Amsterdam: John Benjamins. https://doi.org/10.1075/scl.2 Qian, L. H., Fang, Q., & Lu, Z. (2020). What does carbon neutrality mean for banks?. China Banking, (12), 97-99. Qian, Y. F. (2010). Corpus and critical discourse analysis. Foreign Language Teaching and Research, (3), 198-202. Qian, Y. F. (2016). Discourse Construction of Low Carbon Economy in British Mainstream Newspapers. Foreign Language and Foreign Language Teaching, (2), 25-35. Qian, Y. F. (2020). Low-carbon discourse and the construction of China's image. Frontiers of Corpus Research, 2020(00), 83-92. www.scholink.org/ojs/index.php/eltls English Language Teaching and Linguistics Studies Vol. 6, No. 5, 2024 Published by SCHOLINK INC. 55 Rahimivand, M., & Kuhi, D. (2014). An exploration of discoursal construction of identity in academic writing. Procedia Social and Behavioral Sciences, 98(9), 1492-1501. https://doi.org/10.1016/j.sbspro.2014.03.570 Ren, Y., Chen, J. S., & Ding, J. (2013). Word Clusters in British Gothic Novels - A Corpus-Based Study of Literary Stylistics. Journal of PLA College of Foreign Languages, 36(05), 16-20+127. Sinclair, J. (1991). Corpus, Concordance, Collocation. Oxford: OUP. Sinclair, J. (2004). In R. Carter (Ed.), Trust the Text: Language, Corpus and Discourse (1st ed.). Routledge. https://doi.org/10.4324/9780203594070 Stubbs, M. (1996). Text and Corpus Analysis: Computer-Assisted Studies of Language and Culture. Oxford: Blackwell Publishers Ltd. Stubbs, M. (2005). Computer-Assisted Text and Corpus Analysis: Lexical Cohesion and Communicative Competence. The handbook of discourse analysis, 304-320. https://doi.org/10.1002/9780470753460.ch17 Svartzman, R., Bolton, P., Despres, M. et al. (2021). Central banks, financial stability and policy coordination in the age of climate uncertainty: A three-layered analytical and operational framework. Climate Policy, 21(4), 563-580. https://doi.org/10.1080/14693062.2020.1862743 Wu, G. Q. (2013). A Comparative Study of Authorship Construction in English-Chinese Academic Papers. Hangzhou: Zhejiang University Press. Yang, H. Z. (2002). Introduction to Corpus Linguistics. Shanghai: Shanghai Foreign Language Education Press. Yang, M., & Shi, Y. J. (2021). The Discourse Construction of American “Legitimization” in the US-China Trade War: A Corpus-Based Discourse-Historical Analysis. Foreign Language Studies, 38(03), 7-13. Yang, X. R. (2015). Self-reference and authorship construction in second language academic writing. Foreign Language and Foreign Language Teaching, (04), 50-56. Yuan, Z. C. (2016). The application of visualization tool “word cloud” in English teaching. Chinese Journal of Education, 2016(S1), 102-103. Zhou, X. C. (2023). The construction of China's national image in the discourse of “double-carbon” in domestic mainstream media: a corpus-based positive discourse analysis. Journal of Southwest Jiaotong University (Social Science Edition), 24(04), 12-25. Zuo, Y. N. (2017). Discourse Construction of the Belt and Road Initiative in the U.S. Mainstream Media: A Critical Discourse Analysis Based on Corpus. Journal of Henan Engineering College (Social Science Edition), 32(04), 55-59. A Corpus-Based Analysis of Low-Carbon Image Constr Yishan Zhang1* 1 Guangdong University of Finance, Guangdong, Chin Abstract Keywords Table 1. The Chart of 50 high-frequency Words Table 3. Part of the KWIC