CARBON EMISSION DISCLOSURES BY HIGHER EDUCATION INSTITUTIONS IN UK - \nDETERMINANTS, CARBON REDUCTION TARGET, VOLUMETRIC AND QUALITATIVE DISCLOSURE AND INSTITUTIONAL REPUTATION \n
Notice bibliographique
Résumé
This thesis investigates the determinants of the carbon emission disclosures (CED) in UK higher education institutions (HEI), relationship between such CED in terms of volume and quality and the role of such disclosures on HEIs’ green reputation. The study recognises that HEIs are distinct in characteristics from profit seeking organizations, which has been widely researched in literature. Generalizing the research studies on profit-oriented companies for the majorly publicly funded UK HEIs may mislead any outcome. This study examines three questions. First, what are the determinant factors for the CED by UK HEIs? (Based on stakeholder theory and institutional theory). Second, what is the relationship between CED volume and quality? (Based on stewardship theory). And finally, what is the impact of CED on institutional green reputation? (Based on signalling theory). An initial sample of all available UK HEIs in 2012 was taken to study the carbon emission disclosures made in annual reports. Carbon disclosures in standalone reports were also accounted for. \nThe first part of the research investigates the determinants of CED in annual reports of UK HEIs, with a special concern of the impact of the carbon reduction targets set by the Higher Education Funding Council of England (HEFCE) on such disclosures. A disclosure index was prepared to capture both disclosure categories and types. The relationship between CED and its determinants were examined using TOBIT linear regression analysis, associated by sensitivity tests. Carbon reduction targets by HEFCE were found to have significant positive impact on CED. The results also show that carbon audit and HEI region have significant impact in determining CED volume. \nThe second part of the study explores the relationship between quality and volume of CED in the UK HEIs, with a special concern of the impact of HEFCE carbon reduction target on such disclosures. CED volume has been criticised as being merely wordy and therefore is not good enough. This study explores the decision usefulness of the CED by HEIs i.e. whether the more CED means more useful it is. A framework was developed to measure the CED quality. The relationship between CED volume and quality were examined using Ordered PROBIT regression model. CED volume in annual reports and HEFCE carbon reduction target were found to have significant positive impact on CED quality. \nThe third part explores the impact of CED by UK HEIs on their environmental reputation. The study is distinct in investigating whether and how the HEI CED contributes towards the environmental reputation of the institution. The green score was found from the People and Planet organisation database. All universities having a score were entered into the initial sample. The relationship between green score and CED was examined using robust least squared regression model. CED, Carbon emission and audit were found to have significant impact on green reputation. This study clarifies the impact of CED to motivate the HEIs to engage in such disclosure. \nThis thesis contributes to the existing knowledge by presenting a framework for determinants and consequences of carbon emission disclosure with respect to UK HEIs. There exists a void in research with carbon disclosures by HEIs, which was widely researched for profit seeking organisations. The study adds to the earlier related studies by Godemann et al. (2011), Nejati et al. (2011) and Mazhar et al. (2014) by its own contribution to the disclosure literature. The thesis is distinct in finding causal determinants and impacts different from those found earlier for profit oriented companies and the relationship between the volume and quality of disclosures, which proves the worthiness of the study. Thus, the thesis findings open a fascinating area of investigation and expect to motivate further research in the area.
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 | 0,004 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 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 ».