A Three-Dimensional Discourse Analysis of SDGs Reports and Apple’s Environmental Progress Reports: A Corpus-Based Study
- DOI
- 10.2991/978-2-38476-222-4_27How to use a DOI?
- Keywords
- sustainability report; corporate reporting; critical discourse analysis; environmental progress
- Abstract
The importance of the United Nations’ Sustainable Development Goals (SDGs) is recognized globally. Analyzing sustainable development reports is a focus in quantitative linguistics. This study investigates the relationship between SDGs and corporate environmental progress reports using corpus-based and critical discourse analysis (CDA) methods. The analysis is conducted on SDGs Reports (SDGRs) and Apple’s Environmental Progress Reports (AEPRs) from 2015 to 2022. Fairclough’s three-dimensional analysis model is employed. The study examines high-frequency verbs, nouns, modal verbs, and outcomes of topic modeling. It explores discourse and social practices in the reports, highlighting companies’ proactive approach to SDGs. The paper introduces new research perspectives and methodologies to linguistics and discourse analysis.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Chao Cui PY - 2024 DA - 2024/03/26 TI - A Three-Dimensional Discourse Analysis of SDGs Reports and Apple’s Environmental Progress Reports: A Corpus-Based Study BT - Proceedings of the 3rd International Conference on Culture, Design and Social Development (CDSD 2023) PB - Atlantis Press SP - 229 EP - 234 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-222-4_27 DO - 10.2991/978-2-38476-222-4_27 ID - Cui2024 ER -