Effective criteria in the public-private partnership in developing countries to apply the sustainable development goals: GAN-based decision support system for the renewable electrical system, case study Syria

Abstract

The cost of generating electricity in developing countries surpasses the government's ability to sustain it, necessitating the involvement of the private sector in this service provision through public-private partnerships (PPPs) contracts. In Syria, the electricity system has been highly susceptible to damage as a result of the ongoing crisis, leading to frequent and prolonged blackouts. This research focuses on addressing the need for a comprehensive system that aids decision-making for PPPs contracts in the country. By employing a combination of studies, reports, and interviews with domain experts, significant general and exclusive factors that guide decision-makers in PPPs contracts are identified and organized into questionnaires. These questionnaires are then filled out by professionals engaged in PPPs contracts. The collected data is analyzed and validated using SPSS software. However, due to insufficient data collected, generative adversarial neural networks (GAN) are utilized to enhance the research data. Additionally, Expert Choice and the analytic hierarchy process are employed to calculate weights for each factor. Remarkably, the calculated weights for both general and exclusive factors align with real-life strategies. General factors primarily address the financial and commercial considerations associated with PPPs, while exclusive factors primarily focus on the operational aspects of the electrical power system. These factors are arranged in descending order of effectiveness, enabling stakeholders to determine whether the private sector should be engaged in the project or if it should remain within the public sector's purview. The proposed system has demonstrated its reliability and can serve as a promising starting point for PPPs contracts.

Keywords

• Effective criteria
• Private-public partnership
• Electrical system
• Sustainable energy
• Generative adversarial neural networks
• Sustainable development goals