AI-Enabled Sustainability Reporting and Assurance for the Circular Economy: Aligning Real-Time Supply Chain Data with Global Sustainability Reporting Frameworks.

Introduction

The recent global pressure on organisations to accelerate the transition towards a circular economy reflects an urgent need to decouple economic growth from resource depletion and environmental harm (Kirchherr, Reike & Hekkert, 2018; Geissdoerfer et al., 2017).

This shift places intense demand for timely, accurate, and credible information on material and product flows, as well as their associated environmental impacts, across global supply chains (Opferkuch et al., 2022).

However, existing sustainability reporting, whether aligned with the GRI, the IFRS (S1/S2) or the CSRD/ESRS, remains essentially periodic and backwards-looking (Adams & Abhayawansa, 2022; Bebbington & Unerman, 2020). As such, most firms continue to compile and release annual or semi-annual disclosures based on manually aggregated data, creating a significant time lag between operational reality and reporting. This time lag limits the decision-usefulness of sustainability reports and may create space for greenwashing and selective disclosure (Cho et al., 2015; Pizzi et al., 2024).

Meanwhile, advances in artificial intelligence (AI), and the Internet of Things (IoT) technologies create new opportunities to close this gap. While many studies focus on creating models, frameworks, approaches, and solutions (Lee et al., 2012; Ma et al., 2018), few test their usability, application, or generalisability. This gap could be addressed by using real-world data to evaluate the proposed items. For instance, AI techniques such as artificial neural network (ANN), product -life cycle management (PLM), with multi-agent systems (MASs) and machine learning algorithms can capture, analyse and forecast real-time supply-chain data on emissions, energy use, material loops and product life cycles. ANNs models can detect anomalies, forecast resource needs, and map circular material flows, while blockchain and other distributed ledger technologies can help provide time-stamped evidence trails (Li et al., 2024).

Yet, the accounting and assurance implications of embedding such high-frequency machine generated data within recognised sustainability reporting frameworks remain underexplored (Quattrone, 2022). Traditional audit and assurance methods, designed for low-volume, human-recorded transactions, are ill-equipped to cope with the variety and veracity of AI-driven data streams (Li et al., 2024). This creates a critical gap at the intersection of sustainability reporting, accounting theory and digital assurance which this project aims to address.

This PhD project will address that gap

By designing and testing an AI-enabled sustainability reporting and assurance model that aligns real-time supply chain data with global reporting frameworks (GRI, IFRS S1/S2, CSRD/ESRS) using innovative methodologies and interdisciplinary approaches. The student will work in an interdisciplinary environment, supervised jointly by AI specialists from the computing department (Dr Vahid Yazdanpanah) and sustainability accounting and reporting experts from the Centre of Excellence in Sustainability Accounting and Reporting (CESAR) at the University of Southampton Business School (Dr Ishmael Tingbani and Dr Renata Konadu). It will aim to recruit an exceptional candidate with the technical ability and skills needed to deliver the project as scheduled.

Research objectives:

1. To design and develop an AI-enabled framework for sustainability reporting and assurance that can recognise and determine reporting and compliance anomalies, process, track and disclose real-time supply-chain data in compliance with global standards such as GRI, IFRS S2, and CSRD/ESRS.

2. Develop and evaluate AI-driven assurance protocols for sustainability reporting – (Create and trial new audit procedures, including automated exception testing and blockchain-secured evidence trails to verify high-frequency AI data and strengthen stakeholder trust in sustainability disclosures).

3. To empirically investigate, via multi-case empirical research with SMEs across sectors and supply-chain positions, how AI-enabled data capture (IoT), analytics (ML/ANN) and tamper-evident evidence (e.g., blockchain) change SMEs’ sustainability reporting and assurance capabilities.

References:

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