Vlerick Repository

• The relevance of climate-related information to analysts

  Brié, Bjarne; Stouthuysen, Kristof; Verdonck, Tim (2023)

  We investigate the usefulness of emission information and climate-related annual report disclosures to financial analysts. We extract climate disclosures from annual reports using a state-of-the-art machine learning model, ClimateBert, and automatically identify and quantify the underlying disclosure topics using a structural topic model. For a large international sample of firms subject to a European nonfinancial disclosure mandate, we find that analysts face greater uncertainty while forecasting future earnings for firms with greater emission intensities and for firms that show weaker commitment to reducing emissions. We also show that the aggregate level of climate disclosure does not significantly reduce this uncertainty. However, we do find that disclosure on a specific set of topics improves analysts’ ability to forecast future earnings. Nevertheless, for firms with greater emission intensities, this relationship reverses. These findings suggest that, overall, current climate reporting practices are not informative to analysts since they do not mitigate the uncertainty inherent to the potential impacts of climate change.
• Mandatory CSR reporting in Europe: A textual analysis of firms’ climate disclosure practices

  Brié, Bjarne; Stouthuysen, Kristof; Verdonck, Tim (2023)

  We introduce a machine learning approach that accurately captures disclosure quality by quantifying the thematic content of climate reporting in annual reports. We then use our approach to analyze firms’ climate reporting practices in the context of the widespread European Non-Financial Reporting Directive. For a large sample of annual reports from 2010 to 2020, we find that firms significantly changed their climate reporting narratives in the periods following the announcement and implementation of the mandate. Using the Task Force on Climate-Related Financial Disclosures’ framework as the benchmark for high-quality climate reporting, we show that these changes correspond with improvements in disclosure quality. We further show that the comparability of reporting improves over time and provide first descriptive evidence of a more pronounced comparability change in the years following the implementation of the mandate. Overall, our results highlight the validity of our model and provide further descriptive evidence on the disclosure impact of nonfinancial reporting regulation. Our study also adds to the growing body of research applying machine learning to analyze information from annual reports.
• Mandatory CSR reporting in Europe: A textual analysis of firms’ climate disclosure

  Brié, Bjarne; Stouthuysen, Kristof; Verdonck, Tim (2023)

  We introduce a machine learning approach that accurately captures disclosure quality by quantifying the thematic content of climate reporting in annual reports. We then use our approach to analyze firms’ climate reporting practices in the context of the widespread European Non-Financial Reporting Directive. For a large sample of annual reports from 2010 to 2020, we find that firms significantly changed their climate reporting narratives in the periods following the announcement and implementation of the mandate. Using the Task Force on Climate-Related Financial Disclosures’ framework as the benchmark for high-quality climate reporting, we show that these changes correspond with improvements in disclosure quality. We further show that the comparability of reporting improves over time and provide first descriptive evidence of a more pronounced comparability change in the years following the implementation of the mandate. Overall, our results highlight the validity of our model and provide further descriptive evidence on the disclosure impact of nonfinancial reporting regulation. Our study also adds to the growing body of research applying machine learning to analyze information from annual reports.
• A comparison of activity ranking methods for taking corrective actions during project control

  Vaseghi, Forough; Vanhoucke, Mario (Computers & Industrial Engineering, 2023)

  Monitoring and controlling projects in progress is key to support corrective actions in case of delays and to deliver these projects timely to the client. Various project control methodologies have been proposed in literature to include activity variability in the project schedule and measure the performance of projects in progress. Much of these studies rely on a schedule risk analysis to rank activities according to their time sensitivity and expected impact on the total project duration. This paper compares two classes of activity ranking methods to improve the corrective action process of projects under uncertainty. Each method ranks activities based on certain criteria and places the highest ranked activity in a so-called action set that is then used to take certain corrective actions. The first method is the analytical based ranking method which relies on exact or approximate analytical calculations to provide a ranking of activities. This analytical ranking method will be compared with a second simulation-based ranking method that relies on Monte Carlo simulations to measure the sensitivity of each activities. Results on a set of artificial projects show that the analytical ranking method and one specific simulation-based ranking outperform all other methods, not only for predicting the contribution of actions on the expected project duration and its variability, but also in the efficiency of the project manager’s control.
• Regulatory sandboxes: Do they speed up innovation in energy?

  Beckstedde, Ellen; Correa Ramírez, Mauricio; Cossent, Rafael; Vanschoenwinkel, Janka; Meeus, Leonardo (Energy Policy, 2023)

  Regulatory sandboxes are generally seen as an important tool to make policy and regulation evolve with the changes in our energy system and to create an equal playing field for new technologies and business models that arise with the energy transition. Although an increasing number of legal frameworks on regulatory sandboxes are being implemented in Europe, the pioneers in the Netherlands decided to close their sandbox program. These contradictory events lead to questions about the potential of regulatory sandboxes to bring innovation to the European energy sector. This paper contributes to this discussion by examining the experiences with regulatory sandboxes in Austria, Belgium, France, Germany, Great Britain, the Netherlands, Norway and Spain. We compare approved sandbox projects based on their scope and regulatory derogations to identify areas of innovation and regulatory learning brought by regulatory sandboxes. We also examine the legal frameworks of the concerned countries to evaluate the interaction between the implementation of the framework and its potential to bring innovation. In this way, we develop best practices on the topics of regulatory sandboxes and their implementation frameworks.

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