Accurate forecasting of the energy demand is crucial for the rational formulation of energy policies for energy management. In this paper, a novel ensemble forecasting model based on the artificial bee colony (ABC) algorithm for the energy demand was proposed and adopted. The ensemble model forecasts were based on multiple time variables, such as the gross domestic product (GDP), industrial structure, energy structure, technological innovation, urbanization rate, population, consumer price index, and past energy demand. The model was trained and tested using the primary energy demand data collected in China. Seven base models, including the regression-based model and machine learning models, were utilized and compared to verify the superior performance of the ensemble forecasting model proposed herein. The results revealed that (1) the proposed ensemble model is significantly superior to the benchmark prediction models and the simple average ensemble prediction model just in terms of the forecasting accuracy and hypothesis test, (2) the proposed ensemble approach with the ABC algorithm can be employed as a promising framework for energy demand forecasting in terms of the forecasting accuracy and hypothesis test, and (3) the forecasting results obtained for the future energy demand by the ensemble model revealed that the future energy demand of China will maintain a steady growth trend.

Aiming to identify the intellectual structure of risk management in foreign direct investment and its evolving trends, this paper introduces bibliometric analysis to systematically review the 1231 articles published between 1995 and 2022. Through the analysis of publication trends, influential indicators, cooperative networks, and citations, we draw the following important conclusions. From 1995 to 2021, the average annual growth rate of scientific production in the field reached 21.4%, with scholars from the United States and China contributing the most. Regarding the cooperation network, its structure is relatively dispersed, and the deep cooperation among a wide range of researchers has not yet been formed. As for research topics, the popularity of interest in environmental protection, carbon dioxide emissions, energy consumption, and climate change has increased significantly in recent years. Moreover, this paper points out that future research directions include new risks and challenges posed by the COVID-19 pandemic and the effects of climate risks on foreign direct investment. These results are helpful for scholars to systematically understand the current research status, research frontiers, and future trends of risk management in foreign direct investment.