The Sustainable Development Goals (SDGs): a rising tide lifts all boats? Global reporting implications in a post SDGs world
Bibliographic record
Abstract
Purpose We investigate the integration of the United Nation's Sustainable Development Goals (SDGs) into the Global Reporting Initiative (GRI)– based reporting thus exploring the factors that influence the adoption of the SDGs by organizations. Design/methodology/approach We analyzed the GRI dataset provided by the GRI data secretariat. We analyzed 14,308 reports provided by 9,397 organizations between 2016 and 2017. Findings Larger organizations are more likely to integrate the SDGs into their reporting than smaller organizations. Secondly, publicly listed firms are more likely to address the SDGs. Thirdly, industries with higher sustainability impacts are more likely to address the SDGs in their reporting. Fourthly, our data confirm a regional effect with regard to SDG reporting. Moreover, organizations that follow international sustainability guidelines and standards such as becoming a member of the GRI Gold Community or using the GRI Content Index services and having external assurance are more likely to report on the SDGs. Research limitations/implications Corporations play an essential role in the achievement of the SDGs, which shape the future of the world's sustainable development. Nevertheless, SDGs reporting needs more research to analyze the factors that can influence it. The study contributed to the academic literature on CSR and legitimacy theory by analyzing institutional and regional factors that impact SDGs reporting. Practical implications The study provides insights about the integration of the SDGs into organizational reporting and accounting, including the adoption of the SDGs by small and medium enterprises (SMEs) and the benefits of the SDGs as a framework for strategic corporate sustainability. Social implications A global sustainability framework, such as the SDGs can be integrated into organizations sustainability reporting and accounting in a meaningful way. Originality/value This is the first study that analyzes the integration of the SDGs into GRI-based reporting. The study contributes to legitimacy theory by highlighting the factors, which contribute to the legitimacy-based adoption of the SDGs, including organizational size, being publicly listed, being from high-impact industries and certain global regions, etc. SDG reporting can help firms increase their organizational legitimacy across their stakeholders.
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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 | 0.031 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".