Global benchmarking and modelling of installed solar photovoltaic capacity by country

Setting solar photovoltaic capacity targets and implementing supportive policies is a widespread strategy among nations aiming to achieve decarbonisation goals. However, policy implementation without a thorough understanding of the intricate relationship between social, economic, and land-use factors and solar photovoltaic deployment can lead to unintended consequences, including over- or underdeployment and failure to reach targets. To address this challenge, an investigation was conducted into the relationship between 36 factors and solar photovoltaic deployment across 143 countries from 2001 to 2020 using correlation analysis and principal component analysis. From these factors, five key variables were identified that collectively explain 79% of the year-to-year variation in photovoltaic capacity. Using these variables, a neural network model was constructed, enabling the estimation of capacity additions by country with an error of less than 10%. Additionally, a solar photovoltaic deployment index was developed, serving as a benchmark for comparing a country’s actual historical photovoltaic deployment to similar nations. Furthermore, the model’s utility in evaluating the impact of solar photovoltaic policies was explored. Through three distinct use cases—forecasting solar photovoltaic capacity additions, developing a solar photovoltaic deployment index, and assessing the impact of solar photovoltaic policies—the model emerges as a potentially powerful tool for governments and policy makers to assess solar photovoltaic deployment effectively and formulate strategies to promote sustainable solar energy growth.