Sentiment Analysis of Sustainability Disclosures in the Tech Sector: An AI-Based Assessment of Environmental Accountability
-
Belgin Rana ÇARDAK Department of Accounting and Finance Management Faculty of Commercial Sciences, Başkent University, Ankara, Turkey
-
Hatice ÇARDAK Department of Accounting and Finance Management, Faculty of Commercial Sciences, Başkent University, Ankara, Turkey
-
Özge Sezgin ALP Department of Accounting and Finance Management, Faculty of Commercial Sciences, Başkent University, Ankara, Turkey
The technology acts as both a driver of environmental solutions through AI-driven climate modelling and a source of pressure via high energy consumption and carbon emissions. This study employs a BERT-based Natural Language Processing (NLP) architecture to conduct a sentiment analysis of environmental sustainability reports from leading technology firms. By evaluating the consistency of these disclosures and their alignment with sector-specific indicators, the research assesses the reliability of "green" narratives in high-growth sectors. The findings demonstrate that sentiment-based analysis can effectively identify patterns of reporting quality, which is essential for ensuring the financial sustainability and accountability of tech-driven economies. This study offers a structural framework for regulators and investors to utilize AI in verifying the integrity of sustainability reporting, particularly in transitional markets where transparency is a prerequisite for long-term economic stability.
Copyright© 2026 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
Article’s history: Received 29th of October, 2026; Revised 19th of January, 2026; Accepted 15th of February, 2026; Available online: 15th of March, 2026. Published as article in the Volume XXI, Special Issue, 1(91), 2026.
Çardak, B. R., Çardak H., Alp, Ö. S., Hazar, A., & Babuşcu, S. (2026). Sentiment Analysis of Sustainability Disclosures in the Tech Sector: An AI-Based Assessment of Environmental Accountability. Journal of Applied Economic Sciences, Volume XXI, Special Issue, 1(91), 217 – 235. https://doi.org/10.57017/jaes.v21.si.1(91).11
Acknowledgments/Funding: No financial assistance was received for this study.
Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. There are no patents and copyrights to be declared in relation to this work.
Data Availability Statement: The reports used in this work available on the company website.
Ethical Approval Statement: This study uses publicly available corporate sustainability and environmental reports as the primary data source. No human participants or confidential data were involved in the research process. Therefore, formal ethical approval was not required. All data were obtained from publicly accessible company reports and used solely for academic research purposes.
Abdullah, M., Sarker, P. K., Abakah, E. J. A., Tiwari, A. K. & Rehman, M. Z. (2024). Tail risk intersection between tech-tokens and tech-stocks, Global Finance Journal, Volume 61, 100989. https://doi.org/10.1016/J.GFJ.2024.100989
Akin, I., & Akin, M. (2025). Mandated ESG Disclosure and Its Effects on Earnings Quality and Cost of Capital: Evidence from European Stock Markets. Corporate Social Responsibility and Environmental Management, 1–14. https://doi.org/10.1002/csr.70280
Aman, A., & Reji, D. J. (2022a). Environmental due diligence data: A novel corpus for training environmental domain NLP models. Data in Brief, 45, 108579. https://doi.org/10.1016/j.dib.2022.108579
Aman, A., & Reji, D. J. (2022b). EnvBert: An NLP model for Environmental Due Diligence data classification. Software Impacts, 14, 100427. https://doi.org/10.1016/j.simpa.2022.100427
Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint. https://doi.org/10.48550/arXiv.1908.10063
Arunkumar, O. N., Divya, D., & Chandan. (2025). ESG efficiency analysis in the IT industry: a DEA based approach. Cogent Business & Management, 12(1). https://doi.org/10.1080/23311975.2025.2450092
Batool, Z., Ali, S., & Rehman, A. (2022). Environmental Impact of ICT on Disaggregated Energy Consumption in China: A Threshold Regression Analysis. Sustainability, 14(23), 15600. https://doi.org/10.3390/su142315600
Bingler, J. A., Kraus, M., Leippold, M., & Webersinke, N. (2024). How cheap talk in climate disclosures relates to climate initiatives, corporate emissions, and reputation risk. Journal of Banking & Finance, 164, 107191. https://doi.org/10.1016/j.jbankfin.2024.107191
Chen, Y., & Mai, W. (2024). Investor attention and environmental performance of Chinese high-tech companies: the moderating effects of media attention and coverage sentiment. Humanities and Social Sciences Communications, 11, 1020. https://doi.org/10.1057/s41599-024-03489-1
Chen, S. & Wu, Z. (2025). Corporate ESG performance and stock pricing efficiency. The North American Journal of Economics and Finance, 79, 102440. https://doi.org/10.1016/j.najef.2025.102440
Chitty-Venkata, K. T., Mittal, S., Emani, M., Vishwanath, V., & Somani, A. K. (2023). A survey of techniques for optimizing transformer inference. Journal of Systems Architecture, 144, 102990. https://doi.org/10.1016/j.sysarc.2023.102990
Clark, K., Khandelwal, U., Levy, O., & Manning, C. D. (2019). What does BERT look at? An analysis of BERT’s attention. arXiv (Cornell University).https://doi.org/10.48550/arxiv.1906.04341
Deng, G., Deng, Q., Jingzhou, Y. & Xinyuan, L. (2026). ESG sentiment, investor behaviour, and corporate cost of equity capital. International Review of Financial Analysis, 109, 104800. https://doi.org/10.1016/j.irfa.2025.104800
Deng, Q., Ji, S., & Wang, Y. (2017). An examination of corporate sustainability reporting in IT sector. Journal of Information, Communication and Ethics in Society, Volume 15, Issue 2, 145–164. https://doi.org/10.1108/JICES-12-2016-0046
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short papers), pages 4171-4186. Minneapolis, Minnesota. Association for Computational Linguistics. https://aclanthology.org/N19-1423.pdf
Devika, M. D., Sunitha, C., & Ganesh, A. (2016). Sentiment analysis: a comparative study on different approaches. Procedia Computer Science, 87, 44-49. https://doi.org/10.1016/j.procs.2016.05.124
Egorova, A. A., Grishunin, S. V., & Karminsky, A. M. (2022). The Impact of ESG factors on the performance of Information Technology Companies. Procedia Computer Science, 199, 339-34. https://doi.org/10.1016/j.procs.2022.01.041
European Securities and Markets Authority (ESMA). (2024). Final Report on Greenwashing: Response to the European Commission’s request for input on greenwashing risks and the supervision of sustainable finance policies (ESMA 36-287652198-2699). https://www.esma.europa.eu/sites/default/files/2024-06/ESMA36-287652198-2699_Final_Report_on_Greenwashing.pdf
GlobeScan & ERM Sustainability Institute. (2024). Sustainability Leaders 2024 Report. GlobeScan Incorporated and ERM. https://www.globescan.com/sustainability-leaders-2024/
Goutte, S., Liu, F., Hoang Viet, L. & von Mettenheim, H-J. (2023). ESG Investing: A Sentiment Analysis Approach. http://dx.doi.org/10.2139/ssrn.4316107
Hasnaoui, A. (2025). ESG ratings and investment performance: evidence from tech-heavy mutual funds. Review of Accounting and Finance, Volume 24, 1, 59–70. https://doi.org/10.1108/RAF-02-2024-0069
Jones, P., & Wynn, M. (2021). The Leading Digital Technology Companies and Their Approach to Sustainable Development. Sustainability, 13(12), 6612. https://doi.org/10.3390/su13126612
Kavathatzopoulos, I. (2015). ICT and sustainability: skills and methods for dialogue and policy making. Journal of Information, Communication and Ethics in Society, 13(1), 13-18. https://doi.org/10.1108/JICES-12-2014-0063
Kang, H., & Kim, J. (2022). Analysing and Visualizing Text Information in Corporate Sustainability Reports Using Natural Language Processing Methods. Applied Sciences, 12(11), 5614. https://doi.org/10.3390/app12115614
Kumari, A., Nagina, R., Sheoran, V., Adsule, K., & Surkutwar, P. (2024). Sentiment Analysis for Sustainability Reporting in Banking Sector: GRI-based Quality Control Approach. In 2024 International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM) Singapore, 2024, 1-6.https://doi.org.10.1109/IIPEM62726.2024.10925712
Kowsher, M., Prottasha, N. J., & Yu, C. (2024). Does Self-Attention need separate weights in transformers? arXiv (Cornell University).https://doi.org/10.48550/arxiv.2412.00359
Lagasio, V. (2023). Measuring greenwashing: the greenwashing severity index. Available at SSRN 4582917. http://dx.doi.org/10.2139/ssrn.4582917
Lodhia, S., Farooq, M. B., Sharma, U., & Zaman, R. (2025). Digital technologies and sustainability accounting, reporting and assurance: framework and research opportunities. Meditari Accountancy Research, 33(2), 417-441. https://doi.org/10.1108/MEDAR-01-2025-2796
Lu, J., & Jagoda, K. (2023). Sentiment analysis in sustainability accounting reporting: does the tone reveal future environmental performance? International Journal of Accounting and Finance, 11(3), 202-219. https://doi.org.10.1504/IJAF.2023.10060039
Marsdenia. (2018). The Digital Role in Environmental Sustainability: Corporate Social Responsibility Disclosure Performance and Quality of Earnings. In Proceedings of the 3rd International Conference on Vocational Higher Education (ICVHE 2018) https://doi.org/10.2991/assehr.k.200331.134
Mehedințu, A., & Șoavă, G. (2023). Approach to the Impact of Digital Technologies on Sustainability Reporting through Structural Equation Modelling and Artificial Neural Networks. Electronics, 12(9), 2048. https://doi.org/10.3390/electronics12092048
Mohammadrezaei, M., Marques, J. C., & Huq, A. (2024). Use of text mining and natural language processing techniques in analysing sustainability reports: A systematic literature review and assessment. http://dx.doi.org/10.2139/ssrn.5071032
Rodrigue, M., Cho, C. H., & Laine, M. (2015). Volume and Tone of Environmental Disclosure: A Comparative Analysis of a Corporation and its Stakeholders. Social and Environmental Accountability Journal, 35(1), 1–16. https://doi.org/10.1080/0969160x.2015.1007465
Saxena, A., Santhanavijayan, A., Shakya, H. K., Kumar, G., Balusamy, B., & Benedetto, F. (2024). Nested Sentiment Analysis for ESG Impact: Leveraging FinBERT to Predict Market Dynamics Based on Eco-Friendly and Non-Eco-Friendly Product Perceptions with Explainable AI. Mathematics, 12(21), 3332. https://doi.org/10.3390/math12213332
Stander, Y. S. (2025). Climate sentiment analysis on the disclosures of the corporations listed on the Johannesburg Stock Exchange. Journal of Risk and Financial Management, 18(9), 470. https://doi.org/10.3390/jrfm18090470
Stocker, V., Mariotte, N., Ullrich, A., & Rejeski, D. (2024). ICT Sustainability Reporting Strategies of Large Tech Companies: Changes in Format, Scope, and Content. Scope, and Content. In the 52nd Research Conference on Communications, Information and Internet Policy (TPRC52/2024). https://ssrn.com/abstract=4927128
Sun, Y., Zhao, D., & Cao, Y. (2024). The impact of ESG performance, reporting framework, and reporting assurance on the tone of ESG disclosures: Evidence from Chinese listed firms. Journal of Cleaner Production, 466, 142698. https://doi.org/10.1016/j.jclepro.2024.142698
Tetlock, P.C. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The Journal of Finance, 62, 1139-1168. https://doi.org/10.1111/j.1540-6261.2007.01232.x
Zheng, P., Wang, H., Sang, Z., Zhong, R. Y., Liu, Y., Liu, C., ... & Xu, X. (2018). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 13(2), 137-150. https://doi.org/10.1007/s11465-018-0499-5
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. arXiv preprint. https://doi.org/10.48550/arxiv.1706.03762
Villacampa-Porta, J., Coronado-Vaca, M., & Garrido-Merchán, E. C. (2025). Impact of EU non-financial reporting regulation on Spanish companies’ environmental disclosure: a cutting-edge natural language processing approach. Environmental Sciences Europe, 37(1). https://doi.org/10.1186/s12302-025-01067-z
Webersinke, N., Kraus, M., Bingler, J. A., & Leippold, M. (2021). Climatebert: A pretrained language model for climate-related text. arXiv preprint. https://doi.org/10.48550/arXiv.2110.12010
Wilkho, R. S., Chang, S., & Gharaibeh, N. G. (2023). FF-BERT: A BERT-based ensemble for automated classification of web-based text on flash flood events. Advanced Engineering Informatics, 59, 102293. https://doi.org/10.1016/j.aei.2023.102293
Wu, Y., Jin, Z., Shi, C., Liang, P., & Zhan, T. (2024). Research on the application of deep learning-based BERT model in sentiment analysis. arXiv Preprints. https://doi.org/10.48550/arxiv.2403.08217
Yang, Y., Uy, M. C. S., & Huang, A. (2020). Finbert: A pretrained language model for financial communications. arXiv Preprint. https://doi.org/10.48550/arXiv.2006.08097
Yu, H., Liang, C., Wang, W., & Liu, X. (2025). Does environmental, social, and governance news coverage affect the cost of equity? A textual analysis of media coverage. Frontiers in Public Health, 13, 1509167. https://doi.org/10.3389/fpubh.2025.1509167
Zhang, T., Luo, B., & Wang, G. (2024). Improving vision transformers by overlapping heads in Multi-Head Self-Attention. arXiv Preprints. https://doi.org/10.48550/arxiv.2410.14874