Abstract
Economic regulations for sustainable development improve sharing and sustainability through diverse approaches. Market changes, stock values, and investor ideas are taken into consideration to achieve high sustainability. Multiple points across regulations are mandatory for adaptable improvements. Considering this feature, a conservative regulation approach (CRA) using artificial intelligence (AI) is introduced. The proposed approach relies on convolutional learning to improve economic sharing and sustainability. This approach takes in market values and economic sharing factors to estimate stability. The stability is validated using recurrent knowledge and non-tractable regulations. The proposed method was trained using current economic sharing and restrictions were applied. The learning process was prepared based on the available sharing information and development recommendations. This training improvises the changes and adaptations necessary for development and sustainability in economic sharing scenarios. The proposed approach’s performance is validated through metrics recommendation, data analysis, sustainability features, and economic sharing ratio.
Funder
National Social Science Fund
Henan Provincial Innovation Team
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Cited by
4 articles.
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