During the Paris Agreement of 2015, 195 UNFCCC members pledged to improve the global response to climate change by reducing the carbon emissions and contributing to keeping the average global temperature increase well below 2° above the pre-industrial levels. The whole world is gradually shifting towards renewable resources but this transition to non-conventional energy sources looks very slow compared to what is required to prevent the extreme effects of global warming. Countries that have shown tremendous promise, out of some of the largest solar and wind energy producers, are India and China. Both these countries have shown astonishing growth rates and boast of huge world shares in 2017 for the cumulative installed Solar PV. The total installed wind energy capacities for India and China till 2017 are also very large. Even though these growth rates look very large and are a promising start towards the complete shift to renewable energy, they are still not adequate to reduce the global carbon emissions levels fast enough to keep the average temperature change to 1.5°C. Thus, in this thesis, we aim to identify and analyze the barriers to diffusion of Solar PV and Wind energy in India and China, using interpretive structural analysis (ISM) method, so that we can propose solutions and policy recommendations to further accelerate the diffusion of these technologies. We plan to use the Interpretive Structural Modeling (ISM) method to study the inter-relations between barriers and finally present a hierarchy among them, so that the most important ones can be solved first.