This study applies an artificial neural network (ANN) to develop models for forecasting carbon emission intensity for Australia, Brazil, China, India, and USA. Nine parameters that play an essential role in contributing to carbon emissions intensity were selected as input variables. The input parameters are economic growth, energy consumption, R&D, financial development, foreign direct investment, trade openness, industrialisation, and urbanisation. The study used quarterly data which span over the period 1980Q1-2015Q4 to develop, train and validate the models. To ensure the reproducibility of the results, twenty simulations were performed for each country. After numerous iterations, the optimal models for each country were selected based on predefined criteria. A 9-5-1 multi-layer perceptron with back-propagation algorithm was sufficient in building the models which have been trained and validated. Results from the validated models show that the predicted versus actual values indicate approximately zero errors along with higher coefficients of determination (R2) of 0.80 for Australia, 0.91 for Brazil, 0.95 for China, 0.99 for India and 0.87 for USA. The Partial Rank Correlation Coefficient (PRCC) results reveal that for Australia, R&D has the highest sensitivity weight while for Brazil and the USA, urbanisation has the highest sensitivity weight. For China, population size has the highest sensitivity weight while energy consumption has the highest sensitivity weight in India. The ANN models presented in this study have been validated and reliable to predict the growth of CO 2 emission intensity for Australia, Brazil, China, India, and USA with negligible forecasting errors. The models developed from this study could serve as tools for international organisations and environmental policymakers to forecast and help in climate change policy decision-making. Image 1 • ANN models were developed to predict carbon emissions for five countries. • Stochastic gradient descent batching was employed to train the models. • Predicted versus actual carbon emissions shows approximately zero forecasting errors. • Sensitivity analysis shows significant contributory variables for each country. • A simplified closed-form formula for hands-on prediction of carbon emissions. [ABSTRACT FROM AUTHOR]