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Titlebook: Handbook of Big Data and Analytics in Accounting and Auditing; Tarek Rana,Jan Svanberg,Alan Lowe Book 2023 The Editor(s) (if applicable) a

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https://doi.org/10.1007/978-3-662-10498-9hine learning (ML) to make ESG more transparent. The ML algorithms anchor our rating methodology in controversies related to non-compliance with corporate social responsibility (CSR). This methodology is consistent with the information needs of institutional investors and is the first ESG methodolog
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https://doi.org/10.1007/b137708se in ESG ratings. It starts by outlining the difference between relative and absolute sustainability and what this means for delivering on globally agreed upon targets, such as the Sustainable Development Goals. We then look at what the state-of-the-art climate and Earth System science has to offer
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https://doi.org/10.1007/b137482ters and, later, the Internet. The emergence of digitalized accounting procedures has taken place in a nexus of different types of actors (e.g., software developers, accountancy firms and the businesses themselves), regulatory frameworks (e.g., bookkeeping laws and accounting standards) and technica
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Therapielexikon der Sportmedizinotential as a technology forming part of the tax regulatory framework. We consider the potential for blockchain technology to play a digital infrastructure role for corporate tax loss compliance in Australia: a “RegTech” solution. In doing so, we identify the key features of blockchain and key use c
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https://doi.org/10.1007/978-3-662-57615-1g on organizations to act as good corporate citizens. One avenue to meet these challenges is to integrate ESG risks into Enterprise Risk Management (ERM) in order to improve control and reporting. The aim of this chapter is to examine ESG risk integration by focusing on: (1) How ESG risk is integrat
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