Did ESG Affect the Financial Performance of North American Fast-Moving Consumer Goods Firms in the Second Period of the Kyoto Protocol?
Bibliographic record
Abstract
Many agreements and protocols in the global framework call on industries and businesses to respond to threats related to climate change. New terminologies such as environmental, social, and governance (ESG) scores address this issue and responsibility. This study investigates the impact of sustainability (environment (ENV), social (SOC), governance (GOV), and ESG) on the financial performance of firms in the fast-moving consumer goods industry from 2013 to 2020, the second commitment period of the Kyoto Protocol (SCKP). The study sample covers 113 firms in the North American region (the USA and Canada did not participate in SCKP). The results showed that ESG is not an influencer of financial performance, while ENV and SOC components negatively affect financial performance. On the other hand, GOV is the most significant influencer that positively impacts financial performance. Based on these findings, ESG and its components are not conducive to promoting financial performance during the SCKP period. However, fast-moving consumer goods are ahead of other sectors in terms of sustainability disclosure. Moreover, the highest positive impact of GOV is attributed to the advanced system with rules, standards, and regulations that foster the better and more efficient governance of firms from developed countries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".