Overhauls in Water Supply Systems in Ukraine: A Hydro-Economic Method of Socially Responsible Planning and Cost Management

The poor technical conditions of the water supply systems complicate the quality of urban population life and create a burden on public budgets. We examine the water supply system factors in Ukraine and identify the main parameters that restrain their development. This study examines the development of overhaul planning in water supply systems in Ukraine, taking into account the network equipment-aging factor. We employ a mathematical technique for optimizing the planned costs for overhauls in water supply networks by engaging latent-semantic analysis (LSA). The LSA provides a novel approach to sustainable planning of several economic factors in water supply. The exhibited results underline prominent technical and economic similarities in Ukrainian water supply systems. These results allow us to reveal an unsustainable usage of public funding for overhauls. In addition, the overhaul planning model engaged, improves the Ukrainian water supply sector. It helps the country to abandon the current reactive water management approach and aims towards proactive and sustainable water management. This innovative approach could redefine the public managerial profile, calibrating it towards a socially responsible one, significantly raising the quality of the urban population’s living standards

An Intelligent Environmental Plan for Sustainable Regionalisation Policies: The Case of Ukraine

This paper introduces an environment-driven, artificial intelligence model for sustainable policymaking in European countries, with a focus on Ukraine. It develops regional clusters using artificial neural networking; then, it dynamically optimises budgeting allocations. It is a hybrid, environment-driven model that clusters regionalised-data using Kohonen’s self-organising map and optimises budget allocations using the simplex-modified distribution method (U-V MODI). Model benefits focus on regional public policies, environmental development, and core-periphery balanced growth. Results reveal an innovative plan that activates the participation of environmental stakeholders in public policymaking, reforms regions based on set sustainability criteria, and optimises regional funding. Keywords: Environmental planning, sustainable public policy, environment-driven regional policies, artificial neural network methodolog

Overhauls in water supply systems in Ukraine: A hydro-economic model of socially responsible planning and cost management

The poor technical conditions of the water supply systems complicate the quality of urban population life and create a burden on public budgets. We examine the water supply system factors in Ukraine and identify the main parameters that restrain their development. This study examines the development of overhaul planning in water supply systems in Ukraine, taking into account the network equipment-aging factor. We employ a mathematical technique for optimizing the planned costs for overhauls in water supply networks by engaging latent-semantic analysis (LSA). The LSA provides a novel approach to sustainable planning of several economic factors in water supply. The exhibited results underline prominent technical and economic similarities in Ukrainian water supply systems. These results allow us to reveal an unsustainable usage of public funding for overhauls. In addition, the overhaul planning model engaged, improves the Ukrainian water supply sector. It helps the country to abandon the current reactive water management approach and aims towards proactive and sustainable water management. This innovative approach could redefine the public managerial profile, calibrating it towards a socially responsible one, significantly raising the quality of the urban population's living standards

An intelligent environmental plan for sustainable regionalisation policies: The case of Ukraine

This paper introduces an environment-driven, artificial intelligence model for sustainable policymaking in the European countries, focusing on Ukraine. It develops regional clusters using artificial neural networking and then it dynamically optimises budgeting allocations. It is a hybrid, environment-driven model which: i) clusters regionalised-data, using Kohonen's self-organising map; and ii) optimises budget allocations, using simplex modified distribution method (U\u2013V-MODI). Model benefits focus on: i) regional public policies; ii) environmental development; and iii) core-periphery balanced growth. Results reveal an innovative plan that: i) activates participation of the environmental stakeholders in public policymaking ii) reforms regions based on sustainability criteria set; and iii) optimises regional funding