Global fossil fuel consumption and carbon pricing: Forecasting and counterfactual analysis under alternative GDP scenarios

This paper demonstrates how the global vector autoregressive (GVAR) modelling framework can be used for producing conditional forecasts of global fossil fuel consumption and CO2 emissions, as well as for conducting counterfactual analysis related to carbon pricing, conditional on alternative GDP scenarios. The choice of the conditioning variable does not limit the generality of the approach. The proposed analysis can be useful in guiding and informing policy making as illustrated by our application, which conditions on two-year horizon GDP forecast trajectories by the International Monetary Fund. These trajectories are associated with the global economic shock due to the COVID-19 pandemic. Our model makes use of a unique quarterly data set of coal, natural gas, and oil consumption, output and exchange rates, including global fossil fuel prices for 32 major CO2 emitting countries. The results show that fossil fuel consumption and CO2 emissions are expected to return to their pre-crisis levels, and even exceed them, within the two-year horizon despite the large reductions in the first quarter following the outbreak. More robust growth is anticipated for emerging than for advanced economies. Recovery to the pre-crisis levels is expected even if another wave of pandemic occurs within a year. Results from the counterfactual carbon pricing scenario indicate that an increase in coal prices is expected to have a smaller impact on GDP than on fossil fuel consumption. Thus, the COVID-19 pandemic would not provide countries with a strong reason to delay climate change mitigation efforts.