The impact of the global stock and energy market on EU ETS: A structural equation modelling approach

The industrial revolution has brought about great development in the economy, but it has also increased the dependence on fossil energy. The emissions of CO2 and other greenhouse gases have contradicted economic development and the ecological environment. The establishment of the EU Emission Trading System (EU ETS) has improved the global carbon emission price mechanism, but as a new commodity, its price trend will affect buyers’ risk evaluation. Therefore, it is influential to master the driving factors behind carbon emission prices and make effective predictions. First, the paper points out that the driving factors are divided into macroeconomic risk factors and energy factors. Second, the Bayesian Network is used to select variables and make prediction of carbon prices. The results show that its accuracy exceeds other machine learning algorithms. Third, a structural equation model is used to study the impact of the selected markets on the carbon market. Finally, from the perspective of global carbon emission reduction, the relationship between driving factors and the carbon futures market is explained. The empirical results show that Cotation Assistée en Continu 40, natural gas and Brent crude oil will directly affect the yield of European Union Allowances and Certified Emission Reduction futures, and the Standard Poor 500 and Global Clean Energy Index will indirectly affect the yield of European Union Allowances and Certified Emission Reduction futures. The energy market will affect the carbon market through the intermediary effect of the stock market, in which the clean energy index is the most relevant factor. From the perspective of how to improve the carbon trading system, this paper proposes suggestions for the sustainable development of the world to promote the virtuous cycle of the global carbon emission market and the high-quality development of the global economy. •Multimarket Carbon Factor Selection Model.•Forecasting Carbon Market Price by Bayesian Network.•Markets Impacts Research and Path Analysis with Structural Equation Model.•Clean Energy Investment and Global Energy Structure Analysis.