Analysis of carbon credit trading (CCT) practices: a study of manufacturing organizations in British Columbia, Canada
Bibliographic record
Abstract
Purpose Factors that affect the use of carbon credit trading (CCT) by industries include as follows: avoiding carbon taxes, international expansion, venture capital, competitive advantage and clean technology. The impact of these factors is examined here in relation to the profile of 14 Canadian organizations to investigate factors that influence CCT practices. Design/methodology/approach This research involves a survey of 150 employees at 14 industries in British Columbia (BC) Canada to review and analyze their perceptions of factors that impact CCT. Findings Results demonstrate the potential for enhancing the use of CCT by organizations. It was shown that organizations perceive that CCT enhances their competitive advantage, which is an incentive that needs further investigation as having potential for encouraging CCT and greenhouse gas (GHG) reduction. Research limitations/implications Due to limited funding and workforce, as well as geographical constraints, only 14 industrial organizations were engaged in this research in BC Canada. The scope of future research needs to be enlarged by considering neighboring countries such as the USA and Mexico. This research regarding factors that impact organizations in adopting carbon crediting trading has the potential to provide and shape inter-continental comparisons. Practical implications This study illustrates how CCT has the potential to enhance competitive advantage and may impact the industry toward reducing GHG emissions through CCT. This concept adds a new environmental protection factor and dimension to trade and industry. As organizations plan to invest funds in different carbon reduction projects this may result in expanded employment opportunities. Social implications Organizations are interested in CCT but may hesitate in engaging in CCT as it can be a complex procedure. In addition to further research, workshops and seminars regarding CCT and dissemination of research should be organized by the universities, related authorities and government organizations to make CCT more known and feasible. This study shows that financial and non-financial benefits may be gained by any organization when involved in CCT. Larger advertising and information campaigns may motivate more organizations in this regard. Originality/value This study extends the study of Garg et al. (2017) regarding challenges for CCT practices. International Journal of Management, 10(1), 85–96. It contributes evidence that the size (revenue) of an organization does not affect the level of carbon credits traded and shows potential for smaller organizations to be encouraged to take part in CCT.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".