From the economic perspective, China has made remarkable progress, yet environmental concerns represent an alarm to the country’s long-term prosperity. The associated relationship between green investment and the green economy has important implications for the environment. Whether there is an interactive relationship between green investment and green ecology, current views seem to be at odds with each other. This paper employs a panel vector autoregressive model to construct an empirical analysis of China’s provincial panel data from 2005 to 2019. Specifically, generalized moment estimation, impulse response function, variance decomposition, and other measurement methods were applied to study the interaction between green investment and green ecological development. The research results show the following: (1) the inertial development of the green investment system seriously restricts the progress of green investment levels, and the long-term development of green ecology has a significant self-reinforcing trend; (2) the two-way interaction between green investment and green ecology shows a positive spillover effect in the short term, but the positive effect gradually weakens in the long run; (3) the impact of green ecology on green investment is most significant in the interactive relationship, and the positive effect of green ecology on green investment in the western region is the most prominent. Therefore, the government should standardize green investment standards and use policy guidance to promote the regional transfer of green investment and green ecological resources. Financial institutions should appropriately lower the financing threshold for polluting enterprises and municipal construction and leverage more social funds to flow into long-term green technologies and green industries. Companies should raise awareness of environmental disclosure, ban outdated production capacity, and transition to cleaner production models to secure green funding.

Significant energy initiatives within the context of the “Belt and Road” are characterized by substantial financial transactions, intricate technological complexities, and prolonged construction timelines. Furthermore, the array of risk factors involved is diverse and continuously in flux. To identify pivotal risk indicators in investment projects, we employ the ISM‐MICMAC (interpretive structural modeling‐matriced impacts corise‐multiplication appliance classement) method. We integrate the Leaky Noisy‐OR gate model and dynamic Bayesian network (DBN) to formulate a dynamic model depicting the evolution of investment risk. This model enables us to trace the path of risk evolution at various temporal nodes through forward and backward reasoning. In a practical application of our methodology, we conduct an empirical analysis focusing on the M5 thermal power station in Belarus. The study findings reveal the establishment of a multidimensional and all‐encompassing index system. We identify primary factors contributing to project risk, notably including political instability and policy alterations. Additionally, we unveil the intricate interplay among risk factors within major energy projects and highlight specific risks at pivotal junctures in their development. Through the creation of a dynamic risk evolution model and the subsequent analysis of risk evolution paths, our work offers a foundational framework for conducting risk assessments and understanding the evolution of investments in energy projects within the Belt and Road initiative (BRI). The study’s findings have important policy implications for improving the management of risks associated with BRI major energy projects, including enhancing government macroeconomic policies, strengthening industry associations, and optimizing enterprise risk management.