Renewable, clean and economically viable energy sources are needed as alternatives to fossil fueled energy in order to effectively reduce carbon dioxide emissions. Often, the facilities installation costs for generating renewable energy is higher than the cost of traditional power generating facilities. Thus, governments need effective policies, regulations, and incentives to promote the usage of renewable energy. The policies used for promoting specific categories of renewable energies, such as on-shore or off-shore wind farms and solar power plants, vary significantly. These policies' success depends on the policy goals, regulations, taxation, incentives and promotional schemes, which require careful evaluation and assessment using globally available energy/environment/economic (3E) data. The purpose of this study is to propose an intelligent and concurrent analytic platform for renewable energy policy assessment, which can automatically collect, update, integrate, and harmonize data from pre-authorized websites and online databases for analytic modeling. Further, data mining techniques (e.g., clustering analysis and self-organizing maps - SOM) are adopted to identify types of renewable energies, their attributes, economic factors, projected demands and supplies, and environmental benefits and impacts. The research achieves three outcomes. First, the intelligent and concurrent platform architecture is developed and the prototype system is implemented with data management, decision-support model management, and user interface modules. Second, the dynamic data retrieval technique from authorized online data sources for analytic modeling is depicted. Finally, the SOM intelligent model is built and linked to the database as an analytic tool in the model management module.