Effectiveness of the voluntary disclosure of corporate information and its commitment to climate change
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,178 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».