Enforcing Double Materiality in Global Sustainability Reporting for Developing Economies: Reflection on Ghana’s Oil Exploration and Mining Sectors
Bibliographic record
Abstract
While the development of globally accepted sustainability reporting standards initiated by the IFRS Foundation has largely engaged stakeholders in developed economies, the stakes for developing economies could be compromised without an explicit consideration of their sustainability issues within this standard-setting framework. This paper examines the need to develop global sustainability reporting standards based on the principle of double materiality to warrant that both the target towards carbon net-zero by 2050 under the Paris Agreement and the subsequent promise to accelerate under COP26 are achieved with efficacy. Adopting a multiple-case study approach, this paper reveals the limitations of existing sustainability reporting in the absence of double materiality in a developing economy. Specifically, the analyses reveal limited climate-related disclosures among selected cases in Ghana. Available disclosures connote increasing GHG emissions over the period under consideration. This study also shows weak disclosure comparability across the companies following similar reporting standards. Overall, it argues that enforcement of double materiality to embrace sustainability issues impacting both developed and developing economies is necessary for an effective transformation towards a low-carbon global economy. It contributes to the existing body of knowledge by elucidating double materiality as a pertinent interdisciplinary concept and devising a holistic framework for the emerging global sustainability reporting system to underscore governance accountability for external costs to the environment. Global sustainability reporting standards with a myopic focus on conventional financial matters in the absence of double materiality remain a disclosure system with implausible impact on climate change.
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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 | 0.039 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".