Forecasting China’s CO2 emissions by considering interaction of bilateral FDI using the improved grey multivariable Verhulst model

Because of the harmful influence of CO2 emissions on the environment and humans, issues related to CO2 emissions have received considerable attention in recent years. Based on the pollution haven hypothesis and pollution halo effect, the uncertain effect of bilateral foreign direct investment (FDI) on CO2 emissions has recently been in focus. Moreover, because of the opposing capital flow of bilateral FDI, the interaction between inward FDI (IFDI) and outward FDI (OFDI) might have a trade-off effect on CO2 emissions. The accurate forecasting of CO2 emissions in China in light of effect of the bilateral FDI is important since the government can use it to regulate emissions’ reduction. The grey multivariable Verhulst model (GMVM) was formulated in this paper with the goal of forecasting CO2 emissions in China by considering the nonlinear, independent, and interaction-related effects of bilateral FDI on them. To enhance the accuracy of prediction, this paper used the Fourier series and the grey prediction model for residual modifications. The empirical results showed that the IFDI and the item of the interaction of bilateral FDI promoted CO2 emissions, whereas OFDI reduced them in China. These results also verified the higher precision of the improved GMVM relative to other models. This paper also used improved GMVM to further forecast CO2 emissions and provided suggestions for the Chinese government to plan for foreign investment, including selectively implementing bilateral FDI, and focusing on the trade-off in its interaction-related effects.