Table 7 Details study of sensitivity analysis statistics with variation index

Rane et al. (Current study)

Nashik, India

GIS-based integrated method namely MIF technique is used

According to the result, solar radiation, relative humidity, and elevation are more sensitive as well as the dominant factors for optimal site selection of Solar PV power plants in the study area

[25]

Kahramanmaraş, Turkey

Multi-criteria decision making (MCDM) technique namely AHP method

Four MCDM methods yielded effective results according to the proposed criteria, and most of the existing solar PV power plants match the convenient regions on the suitability map provided by AHP method

[75]

India

GIS and MCDM techniques

Rajasthan state in India has the highest suitable land for the installation of solar plants (20,881 km²) as well as wind farms (6323 km²). The proposed model can be used for the development of policies related to renewable energy resources and the assessment of suitability

[83]

Pakistan

AHP-fuzzy VIKOR method

The outcome of the sensitivity analysis revealed that obtained results are reliable and robust for the installation of solar PV power projects in Pakistan

[29]

China

GIS-based analysis

The results show that there is a large area suitable for solar power stations in the northwestern regions with sufficient radiation, sparse surface vegetation and gentle surface gradient

[80]

Mumbai, India

GIS-based image analysis of sample satellite images

Large scale deployment of Rooftop Solar PV Systems can provide 12.8–20% of the average daily demand and 31–60% of the morning peak demand for different months, even with median conversion efficiency panels. This method can be used to obtain the PV potential for any region

[91]

Southern England

MCDM framework including AHP approach

This method can be used to assist appropriate site selection for onshore renewable energy projects across large geographical areas, helping to minimise their environmental impacts