Effectiveness of the voluntary disclosure of corporate information and its commitment to climate change
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although governments and companies have been implementing various measures, such as technological innovation, new emissions regulations, and policies to reduce greenhouse gas emissions, it seems that global warming is not decreasing. In order to reduce greenhouse gas emissions, the commitments of companies were considered to be the key for climate change. However, since the Paris Climate Agreement, there has not been an accurate evaluation of the efforts and contributions of companies toward emission reductions. This study investigated the effectiveness of companies in Climate Action and tested its impact on greenhouse gas emissions at the country and per capita levels.METHODS: This study focuses on companies of the countries from the main Latin American economies (Mexico, Chile, Brazil, Colombia, and Argentina) and their major trading partners (the United States of America, Canada, China, Korea, Germany, and Japan). There are 894 companies from Latin America and 3680 companies that represent their trading partners of referred countries in Climate Action. This study used two data sources, the commitment of companies from Global Climate Action and the annual greenhouse gas emissions levels of each country from an open-access data platform called Our World in Data.FINDING: The findings demonstrate a significant and positive relationship between changes in greenhouse gas emissions from 2021 and 2020 and the number of companies participating in Global Climate Action (Pearson = .718*, significance = .013) and per capita (Pearson = 0.827** significance = 0.002). Correlations indicate there is a higher level of commitment to climate action but with marginal contributions to greenhouse gas emissions reduction. Previous expectations were that greater corporate involvement in climate action would reflect a link to greenhouse gas reductions, but this was not the case. Additionally, the reduction in greenhouse gas emissions during the pandemic was due to the economic slowdown and was not necessarily because of the climate action efforts of companies and governments to reduce emissions. The findings demonstrated a negative and significant correlation at the country level during the pandemic (Pearson = −0.629 significance = .038). The lack of effective results for reducing (from 2020 and 2021) greenhouse gas emissions justifies the relevance of increasing transparency and accountability for both companies and countries. The acceleration of the production system reflected in an increase in greenhouse gas emissions is not keeping pace with the commitments and the reported achievements on Global Climate Action.CONCLUSION: This study contributed to justifying efforts for a better way to follow up international efforts to reduce greenhouse gas emissions. Transparency and accountability are key to effectively achieving greenhouse gas reductions and curbing the impending climate crisis.
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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 | 0.033 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".