Facile and optimal decision support model for development photovoltaic solar power toward sustainability: Four case studies for selection location, manufacturer, technology, and strategy

• We propose an intelligent decision support model for the sustainable development of solar PV power. • SWARA is used to obtain factor weights and Evaluation Based on Distance from Average Solution (EDAS) to order alternatives. • A spherical fuzzy set (SFS) framework is used with MCDM methods to deal with vague information. • The comparative study compared the SF-SWARA-EDAS procedure with other MCDM methods. • The outcomes of the comparative analysis show the SF-SWARA-EDAS procedure is effective compared with other MCDM approaches. Fossil fuels pose various risks to human health and the environment. Fossil fuels release carbon emissions, impact climate change, air pollutions harm human health, and water pollution harm communities. Solar is the best choice to convert from fossil fuel to clean, sustainable, inexpensive energy sources and solve all previous issues. Photovoltaic (PV) energy is the primary part of solar energy. PV is used to generate electricity. This paper proposes an intelligent decision support model for the sustainable development of solar PV power by introducing four stages. The multi-criteria decision-making (MCDM) procedure is used in this paper to deal with conflict criteria. Two MCDM methods are used such as Stepwise Weight Assessment Ratio Analysis (SWARA) to obtain factor weights and Evaluation Based on Distance from Average Solution (EDAS) to order alternatives. A spherical fuzzy set (SFS) framework is used with MCDM methods to deal with vague information. There are four stages in this paper applied to four applications in the solar PV field. In the first stage, the site selection application is employed using the SF-SWARA-EDAS procedure to select the best location. Then, in the second stage, after obtaining the best location, the manufacturer selection application is solved by choosing the best manufacturer. In the third stage, the technology selection application is solved after obtaining the best location and manufacturer. Finally, the strategy selection application is solved to select the best strategy to overcome issues in the development of solar power PV. In each stage, sensitivity and comparative analysis are employed. The main results show the location 10, manufacture 10, technology 1, and strategy 1 are the best alternatives. The sensitivity analysis shows the rank of alternatives in each stage is stable. The comparative study compared the SF-SWARA-EDAS procedure with other MCDM methods. The outcomes of the comparative analysis show the SF-SWARA-EDAS procedure is effective compared with other MCDM approaches. This study can aid governance in providing sustainability and renewable energy with multiple benefits such as clean energy production, reduced carbon emissions, minimized dependence on fossil fuels, and reduced costs.