Open this publication in new window or tab >>2020 (English)Report (Other academic)
Abstract [en]
The paper contributes to the debate if voluntary nonfinancial disclosures, such as greenhouse gas disclosures in corporate social responsibility reporting, exhibit accountability or are merely greenwashing. If firms exhibit accountability, does their actions translate into observable impacts, e.g., as country-level real changes in GHG emissions? How do contextual factors affect accountable disclosures in CSR reporting? To answer these questions, we develop a novel measure to classify accountable information of GHG disclosures in CSR reporting. We operationalize the measure using natural language processing tools, such as collocation analysis, regular expressions, and text mining. Statistical models were used to carry out aggregate analysis to detect real effects in GHG emissions reductions and firm-level analysis to investigate how institutional factors affect accountable GHG disclosures. We find that firms headquartered or reporting in a civil-law legal-environment disclose significantly higher accountable information compared to those in a common-law legal-environment. However, there is a negative trend in accountability worldwide, and firm-level accountability in GHG disclosures is not detectable in a country-level reduction of GHG emissions. The results are robust for various alternative model specifications, and operationalization of the developed measure achieved high concordance when investigated on random samples manually. Compared to most past studies, we work with a significantly larger sample of 4459 reports across 82 countries, thereby dealing with greater complexity and leading to better generalizability. In addition, developing an approach that is many folds scalable and makes replicability straightforward. This is also one of the few studies to move beyond the usual “bag-of-words” approach in classifying voluminous corporate disclosure using NLP techniques.
Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2020. p. 40
Series
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2020:02
Keywords
greenhouse gas, accountability, text mining, CSR, sustainability
National Category
Business Administration
Research subject
Research Profiles 2009-2020, Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-34935 (URN)
2020-09-022020-09-022021-11-12Bibliographically approved