The adaption of climate change in public sector financial reporting: A comparative case study between United Kingdom and Norway
Bibliographic record
Abstract
Climate change is a serious issue. From melting polar ice, rising sea levels and water scarcity to name a few, action on climate change has become a key concern for policy makers, business leaders and individuals worldwide. While most research on climate change reporting is focused on the private sector to date, public sector reporting on UN Sustainable Development Goals and how public sector activities affects the environment remains limited. Adopting thematic analysis of Norwegian and UK central government financial reports along with semi-structured interviews, our aim is to explore how public sector financial reporting currently addresses climate change, the perceived uses and usefulness of climate change information contained in public sector financial reports and the perceived information needs for reporting information about climate change in public sector financial reports. Similar to private sector research, we find that central government engages in climate-related sense making in their annual reports, but not in the financial statements. Both countries make sense of climate change in different ways. Norway has a tendency to reflect on the social context with which it’s exploring, while the UK communicate via visual representations, demonstrating a continued commitment to new public management. Moreover, the interviews reveal a consensus between both countries that the current means to report on climate action within financial reports is limited in both uses and usefulness. Interviewees from both countries referred to the challenges of reporting for different stakeholders, ability to identify capability gaps and lack of guidance on how to use climate change information. The findings would suggest that at present, climate action that is communicated in financial reporting remains an accountability mechanism to comply with mandate for external legitimacy.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".