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Climate change has emerged as one of the challenges of the global economy. Climate change economics has focused on the economic aspects of climate tradeoff. Studies have been conducted on the causal association of economic indicators and climate change indicators. However, for the sample of BRICS countries, that are important participants of global climate change, no study has attempted to identify whether causal connections apply to them. The study is an endeavor to identify the underlying causal connections between economic indicators and carbon emissions for BRICS economies. Six economic indicators, current account balance, inflation, foreign direct investment inflows, gross domestic product, real effective exchange rate, and trade openness, are selected for the sample period 2005-2019. Neural network analysis as a method of computational economics is applied for the superior methodology over standard statistical techniques. The outcome suggests that for BRICS economies, economic indicators have a significant relationship with carbon emissions, differing in intensity as per node strength of the neural network.

Economic Indicators and Climate Change for BRICS Economies in the Post–COVID-19 World: Neural Network Approach