Journal of Environmental Accounting and Management 13(3) (2025) 239--254 | DOI:10.5890/JEAM.2025.09.002

Arooba Arshad$^1$, A. Sohail$^2$, M. Jalal$^3$, Ying Zhang$^4$

$^1$ Department of Mathematics, COMSATS University Islamabad, Lahore Campus 54000, Pakistan

$^2$ School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia

$^3$ National University of Sciences and Technology (NUST) H12, Islamabad, Pakistan

$^4$ School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, 2006, Australia

Abstract

Urban safety is a critical concern for both states and individuals, yet it often remains vulnerable, compromised by the very citizens it seeks to protect. Effective urban safety management is crucial, and mental health stands out as a significant factor among the challenges. Poor mental health, often linked to unemployment, can lead to increased crime rates, driven by the frustration of those struggling to find stability. This manuscript explores, for the first time, the intricate relationship between urban safety, mental health, and unemployment. We propose a novel approach utilizing advanced machine learning techniques, particularly time series analysis with neural networks, to investigate these connections. By examining the influence of employment satisfaction on mental health and urban safety, we aim to uncover patterns and predictive indicators that can inform more effective urban management strategies. Our research aligns directly with the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) and SDG 11 (Sustainable Cities and Communities).

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