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China, India, and the USA are the world's biggest energy consumers and CO 2 emitters. Being the leading contributors to climate change, these economies are also at the core of environmental solutions. This paper investigates the causal relationship among solar and wind energy production, coal consumption, economic growth, and CO 2 emissions for these three countries. To do so, we use an advanced methodology in Machine Learning to verify the predictive causal linkages among variables. The Causal Direction from Dependency (D2C) algorithm set CO 2 emissions as the target variable. The obtained results were disaggregated and estimated in a supervised prediction model. The findings, confirmed by three different Machine Learning procedures, showed an interesting output. While a reduction in overall carbon emissions is predicted in China and the US (resulting from the intensive use of renewable sources of energy), India displays critical predictions of a rise in CO 2 emissions. This indicates that curbing CO 2 emissions cannot be achieved without conducting a comprehensive shift from fossil to renewable resources, although China and the U.S. present a more promising path to sustainability than India. Being an emerging renewable energy leader, India should further enhance the use of low-carbon sources in its power supply and limit its dependence on coal. • Renewable energies, coal consumption, economic growth, and CO 2 we examine. • We investigate the causal predicted relationship on China, India, and the USA. • An advanced methodology in Machine Learning is used. • India has a prediction of an increase in emissions of CO 2. • China and the U.S. present a more promising path to sustainability than India. [ABSTRACT FROM AUTHOR]

Renewable energy has a critical role in limiting the greenhouse gas (GHG) emissions. This paper assesses the implication of aligning renewable energy deployment target with national emission reduction target for mitigation cost. The assessment methodology uses Asia-Pacific Integrated Assessment/computable general equilibrium (AIM/CGE) model to determine the mitigation cost in terms of GDP and welfare loss under alternative renewable targets in different climate-constrained scenarios. A range of country-specific emission constraints is taken to address the uncertainties related to global emission pathway and emission entitlement scheme. Comparative results show that China needs to increase its share of non-fossil fuel significantly in the primary energy mix to achieve the stringent emission reduction target compared to India. The mitigation cost in terms of economic and welfare loss can be reduced by increasing the penetration of the renewable energy to achieve the same emission reduction target. The modeling results show that coordinated national climate and renewable energy policies help to achieve the GHG emission reduction target in an efficient and cost-effective manner. [ABSTRACT FROM AUTHOR]