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This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

This paper aims to develop a methodology for the spatiotemporal analysis of urban household waste data and a geographic information system (GIS)-based dashboard for interactive outcomes that identifies emerging trends and spatial distribution.

The study visualized the emerging hotspot analysis of household waste data covering the waste in selected areas from 2014 to 2019 in New South Wales, Australia. Through analyses in ArcGIS Pro, multiple maps and diagrams can be created to display these results in ArcGIS Insights. To enable the spatial waste analysis outcomes accessible, a GIS-based dashboard including maps and charts, spatiotemporal visualization of household waste tonnage, and emerging hotspots was created.

Based on the development of the dashboard in the ArcGIS Suites, there is an accessible data pipeline from ArcGIS Pro to Insights. The cloud-mapping system in ArcGIS online serves as a foundation for temporary data storage. The results also show the emerging hotspots of recyclable, residual and organic (RRO) waste in the Greater Sydney Region, Wollongong, Newcastle and Tweed. This study found an emerging cold spot in Wagga Wagga.

A dashboard for monitoring waste streams can be developed to enable GIS specialists to use historical spatiotemporal datasets in ArcGIS suites easily. Policymakers, strategy developers, urban waste managers and organizations dealing with urban waste can utilize this analytical dashboard to identify the issues, patterns and trends concerning urban waste for better decision-making in allocating required resources to overcome the identified issues to make informed decisions and develop strategies to alleviate the trends and patterns of ongoing problems. Indeed, the GIS-based dashboard developed in this research provides deep analysis and insights from the spatial waste data, allowing them to understand the included insights at a glance quickly.

Deriving location information for urban household waste data is crucial for waste management since it offers a better understanding of urban household waste data patterns, issues and historical trends. Small-scale studies have examined spatial waste patterns, but the investigation of urban household waste focusing on RRO waste is limited. Moreover, there is a lack of GIS-based dashboard development to enable spatiotemporal waste analysis outcomes to be publicly accessible.