R&D disclosure and carbon performance: a machine learning analysis of carbon‐intensive firms

Acheampong, A ORCID: https://orcid.org/0000-0002-9489-5751, Danso, A, Adu‐Ameyaw, E and Sakariyahu, R, 2026. R&D disclosure and carbon performance: a machine learning analysis of carbon‐intensive firms. Business Strategy and the Environment. ISSN 0964-4733

Text 2579881_Acheampong.pdf - Post-print Full-text access embargoed until 3 February 2028. Download (816kB)

Abstract

The growing urgency of climate change, alongside global sustainable development initiatives, has brought environmental priorities to the forefront of corporate strategy. This study explores how narrative disclosures related to research and development (R&D) predict carbon performance in European industries with high R&D intensity. Guided by the natural resource-based view (NRBV), the research examines how qualitative R&D narratives act as strategic tools for communicating innovation-driven environmental strategies. We introduce a novel methodological approach for analyzing unstructured textual data using advanced machine learning (ML) models, including neural networks (NNs), support vector machines (SVMs), and random forests (RFs). Our results show that firms with extensive and positively framed R&D disclosures are more effective in managing carbon emissions and in progressing toward major sustainability targets such as the Paris Agreement and the EU Green Deal. The findings also reveal that regulation and innovation shape distinct patterns in narrative disclosures across sectors, particularly in technology and pharmaceuticals. Moreover, the tone and thematic focus of these narratives offer strategic insights that go beyond traditional financial indicators, effectively linking innovation with sustainability objectives. This research advances the corporate disclosure literature by deepening our understanding of how sustainability and innovation intersect, while also offering practical
guidance for firms and policymakers.

Item Type:	Journal article
Publication Title:	Business Strategy and the Environment
Creators:	Acheampong, A., Danso, A., Adu‐Ameyaw, E. and Sakariyahu, R.
Publisher:	Wiley
Date:	3 February 2026
ISSN:	0964-4733
Identifiers:	Number Type 10.1002/bse.70594 DOI 2579881 Other
Rights:	This is the peer reviewed version of the following article: Acheampong, A., Danso, A., Adu‐Ameyaw, E., & Sakariyahu, R. (2026). R&D disclosure and carbon performance: a machine learning analysis of carbon‐intensive firms. Business Strategy and the Environment, which has been published in final form at https://doi.org/10.1002/bse.70594 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Divisions:	Schools > Nottingham Business School
Record created by:	Laura Borcherds
Date Added:	20 Mar 2026 11:51
Last Modified:	20 Mar 2026 11:51
URI:	https://irep.ntu.ac.uk/id/eprint/55449

Actions (login required)

Edit View

Statistics

Views

Downloads