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

Abstract

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.

Impact of Artificial Intelligence on Regional Green Development under China’s Environmental Decentralization System—Based on Spatial Durbin Model and Threshold Effect

“…Zhao et al (2022) found a remarkable “U-shaped” contribution of AI to GTFP and found that increasing AI levels in resource-rich areas improved GTFP [ 18 ]. The research on AI and government environmental governance systems is mostly explored in terms of the application of AI technologies in environmental modernization [ 19 , 20 , 21 ], ensuring that emerging technologies such as AI are fully utilized by building new frameworks, policies, and governance [ 22 ]. Current scholarship on GTFP still focuses on policy evaluation and production drivers [ 23 ].…”