Stances on Environmental Sustainability: A Corpus-Assisted Study of Sustainability Reports by US and Chinese Energy Giants

This study investigates the stances of US and Chinese energy giants on environmental sustainability through a corpus-assisted analysis of their sustainability reports (2016–2023). Three stances are identified in both corpora, with subtle cross-cultural differences. First, both groups adopt a supportive stance on environmental governance. US companies emphasize international frameworks, whereas Chinese firms align more closely with national policies. Second, a confident stance on corporate competence is evident in both, but with differing rhetorical styles. US companies express confidence through personal and reasoned claims, whereas Chinese companies use assertive, target-driven language. Third, both display an implicit stance of obscuring environmental responsibility by deflecting blame, generalizing responsibility, and downplaying their own environmental impact. This study not only applies the corpus-assisted stance analysis in comparative, cross-cultural corporate environmental discourse, but also extends the theoretical scope of stance analysis by refining the analytical framework and identifying both explicit and implicit stances. It provides a practical tool for business communication scholars to critically evaluate corporate sustainability discourse.

Authors

Institutions

Publication Details

Journal
International Journal of Business Communication
Published
2026-02-18
DOI
https://doi.org/10.1177/23294884261418222
Primary Topic
Corporate Social Responsibility Reporting
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Stances on Environmental Sustainability: A Corpus-Assisted Study of Sustainability Reports by US and Chinese Energy Giants

Chenghui Wu, Jingyuan Zhang, Yang Zhang
International Journal of Business Communication
Corporate Social Responsibility Reporting
article

Stances on Environmental Sustainability: A Corpus-Assisted Study of Sustainability Reports by US and Chinese Energy Giants

Chenghui Wu, Jingyuan Zhang, Yang Zhang
article en

Abstract

This study investigates the stances of US and Chinese energy giants on environmental sustainability through a corpus-assisted analysis of their sustainability reports (2016–2023). Three stances are identified in both corpora, with subtle cross-cultural differences. First, both groups adopt a supportive stance on environmental governance. US companies emphasize international frameworks, whereas Chinese firms align more closely with national policies. Second, a confident stance on corporate competence is evident in both, but with differing rhetorical styles. US companies express confidence through personal and reasoned claims, whereas Chinese companies use assertive, target-driven language. Third, both display an implicit stance of obscuring environmental responsibility by deflecting blame, generalizing responsibility, and downplaying their own environmental impact. This study not only applies the corpus-assisted stance analysis in comparative, cross-cultural corporate environmental discourse, but also extends the theoretical scope of stance analysis by refining the analytical framework and identifying both explicit and implicit stances. It provides a practical tool for business communication scholars to critically evaluate corporate sustainability discourse.

International Journal of Business Communication
Peking University (CN), Institute of Linguistics (CN), University of Science and Technology Beijing (CN)
Life in Land, Responsible consumption and production
Openalex Percentile: Top 52%
Corporate Social Responsibility Reporting
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.