Does GRI Reporting Impact Environmental Sustainability? An Industry-Specific Analysis of CO2 Emissions Performance between Reporting and Non-Reporting Companies
Bibliographic record
Abstract
The stated goal of the Global Reporting Initiative (GRI) reporting framework is two-fold: to make it easier for organizations to communicate their sustainability performance to stakeholders, and to drive companies to become more sustainable. Our aim in this paper is to assess if GRI-reporting has any direct and positive impact on sustainability performance, and more specifically on CO2 emissions of the reporting companies. This study is the first that attempts to answer this question in a quantitative and systematic manner. We analyze the CO2 emissions data from 40 A-level GRI-reporting companies, over a period of six years and across five industry sectors, comparing them with a control group of 24 non-reporting companies, to assess any direct impact of reporting on emissions. We perform an industry-specific analysis of the CO2 emissions of both reporting and non-reporting companies for each industry sector. We find that amongst all reporting companies and industries, only the Utilities industry exhibits a dramatic decrease in emission intensity between 2007-2012, while the others show only minimal reductions, while the overall absolute emissions levels have grown significantly for both sets of companies. On the more qualitative side, we also note, based on our own experience in undertaking this study, that the GRI reports are not conducive to providing stakeholders with a coherent, user-friendly or transparent structure of a company’s sustainability performance in general, or improvement thereof, concluding that neither of the GRI stated goals are currently attained. Finally, we provide constructive recommendations on how the GRI reporting process could better achieve its stated purpose. Academics, investors and analysts alike might find the review, the analysis as well as the recommendations of this paper useful, as they directly address the core objectives of the GRI reporting process and how it could be improved to have the desirable impact.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".