Mercury Disclosure Practices of Major Emitting Companies: A Qualitative Analysis
Bibliographic record
Abstract
Mercury is one of the world’s most toxic elements. Corporate mercury reporting in Pollution Release and Transfer Registers (PRTRs) is compulsory in some countries but disclosure in companies’ annual or sustainability reports is not mandatory. Therefore, mercury disclosure in these reports differs among the companies. This paper aims to identify the current quality of corporate mercury reporting and propose best practice mercury disclosure by analyzing the current disclosure practices of the largest mercury emitting companies.Major corporate mercury emitters were identified from Pollution Release and Transfer Registers (PRTRs) of the USA, Australia, Canada, the UK and the EU. Qualitative content analysis was used to identify and analyse the contents of 2013 mercury disclosures in the annual report, sustainability report, environmental performance report, or company website. Few companies disclosed mercury information. For disclosing companies the volume and dimensions of mercury disclosure significantly differed. Major dimensions of mercury disclosures include: extent of mercury emissions, sources of emissions, variation in emissions and its reasons, health safety and mercury impacts, mercury control, technologies for mercury reduction, mercury monitoring, and management. Companies from the USA and Australia disclosed more mercury information than companies from other countries. Policy implications Guidelines are proposed to assist regulators regarding policy development and enable mercury emitting companies to benchmark their mercury reporting practices. Researchers in social and environmental accounting may use the issues raised in this article for more comprehensive studies in mercury disclosure practices.Though there are many studies on mercury from scientific, physiological or environmental perspectives no studies have been conducted focusing on mercury accounting or disclosure or social and environmental accounting perspective.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".