Reassessing the Impact of Foreign Direct Investment on Environmental Quality in 112 Countries: A Bayesian Quantile Regression Approach

ABSTRACT Studies on foreign direct investment (FDI) and environmental quality (EQ) have not reached a consensus and have, in fact, exacerbated disagreements about their relationship. Previous research on FDI and CO2 emissions still faces two key limitations. First, earlier studies have not examined this relationship at lower quantiles. Second, most approaches rely on traditional frequency‐based methods. As a result, this study adds to the literature by investigating how FDI affected EQ in 112 nations between 1990 and 2021. The Bayesian quantile regression (BQR) approach was used in this study, resulting in the fact that FDI improves the environment in countries with low to medium CO2 quantiles (0.05–0.45) but deteriorates the environment in countries with medium to high CO2 quantiles (0.50–0.95). A unique aspect of this study is that Bayesian regression provides the probability of an independent variable's impact (positive or negative) on the dependent variable. The findings show that the probability of FDI improving EQ in countries with low to medium CO2 quantiles is relatively high (up to 99.94% at the 0.25 quantile). On the other hand, the 99.05% chance that FDI will have a detrimental effect on the environment is most noticeable at the 0.70 quantile. The robustness of the BQR is assessed through trace plots and posterior distribution plots (histograms). The results demonstrate that all parameter plots in the model are reasonable, with uniform trace plots and normally shaped distributions, indicating the robustness of the BQR approach. On the basis of the findings, the author recommends increasing FDI inflows to countries with lower CO2 quantiles, particularly in Africa and Latin America. In order to attract high‐quality FDI inflows, host nations should also upgrade their infrastructure and release policies targeted at accomplishing sustainable development goals.