Voluntary Carbon Reporting Prediction: A Machine Learning Approach.
In this paper we address the impact of the introduction of the National Greenhouse and Energy Reporting scheme on corporate carbon reporting, and subsequently identify factors that influence the level of voluntary carbon reporting. A review of the literature demonstrates a large number of potential...
| Publicado en: | Abacus Vol. 59; no. 4; pp. 1116 - 1167 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=173971599&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 173971599 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00013072 AUB jtl: Abacus issn: 00013072 maglogo: Y pubinfo: dt: Dec2023 vid: 59 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 173971599 10.1111/abac.12298 ppf: 1116 ppct: 51 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 1MB tig: atl: Voluntary Carbon Reporting Prediction: A Machine Learning Approach. aug: au: Frost, Geoffrey Jones, Stewart Yu, Muchen affil: Business School, University of Sydney, Sydney 2006, New South Wales, , Australia su: Machine learning Corporation reports Random forest algorithms Machine performance Social responsibility of business Proxy sug: subj: Machine learning Corporation reports Random forest algorithms Machine performance Social responsibility of business Proxy keyword: Corporate governance CSR performance Financial performance Gradient boosting machines Logit National Greenhouse and Energy Reporting scheme Random forests Voluntary carbon reporting ab: In this paper we address the impact of the introduction of the National Greenhouse and Energy Reporting scheme on corporate carbon reporting, and subsequently identify factors that influence the level of voluntary carbon reporting. A review of the literature demonstrates a large number of potential factors have been previously deployed to explain voluntary reporting practices; however, the analytical and empirical methods widely used in the literature have limiting statistical assumptions and confine analysis to a small number of explanatory factors. To address this limitation in prior research we apply advanced machine learning methods, such as gradient boosting machines and random forests, to identify predictive variables through analytical means. We compare the performance of machine learning methods with traditional methods such as logistic regression. We find that machine learning methods significantly outperform logistic regression and provide fundamentally different interpretations of the role and influence of different predictive variables on voluntary carbon reporting. While most variables were not statistically significant in the logit results, a number of key proxies for financial performance, corporate governance, and corporate social responsibility have out‐of‐sample predictive power of the level of voluntary carbon reporting in the machine learning analysis. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Abacus is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Abacus holder: Wiley-Blackwell dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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