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–V-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.

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

Gazzola P.;
2020-01-01

Abstract

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–V-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.
2020
Artificial neural network methodology; Environment-driven regional policies; Environmental planning; Sustainable public policy
Papagiannis, F.; Gazzola, P.; Burak, O.; Pokutsa, I.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2095450
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