Ning Mao

Given the increasing environmental concerns and energy consumption, the transformation of the new energy vehicle industry is a key link in the innovation of the energy structure. The shift from traditional fossil fuels to clean energy encompasses various dimensions such as technological innovation, policy support, infrastructure development, and changes in consumer preferences. Predicting the future ownership of electric vehicles (EVs) and then estimating the energy demand for transportation is a pressing issue in the field of new energy. This study starts from dimensions such as cost, technology, environment, and consumer preferences, deeply explores the influencing factors on the ownership of EVs, analyzes the mechanisms of various factors on the development of EVs, establishes a predictive model for the ownership of motor vehicles considering the penetration of electric vehicles based on system dynamics, and then simulates the future annual trends in EV and conventional vehicle (CV) ownership under different scenarios based on the intensity of government funding. Using energy consumption formulas under different power modes, this study quantifies the electrification energy demand for transportation flows as fleet structure changes. The results indicate that under current policy implementation, the domestic ownership of EVs and CVs is projected to grow to 172.437 million and 433.362 million, respectively, by 2035, with the proportion of EV ownership in vehicles and energy consumption per thousand vehicles at 28.46% and 566,781 J·km⁻¹, respectively. By increasing the technical and environmental factors by 40% and extending the preferential policies for purchasing new energy vehicles, domestic EV ownership is expected to increase to 201.276 million by 2035. This study provides data support for the government to formulate promotional policies and can also offer data support for the development of basic charging infrastructure.

To achieve global sustainability goals and meet the urgent demands of carbon neutrality, China is continuously transforming its energy structure. In this process, electric vehicles (EVs) are playing an increasingly important role in energy transition and have become one of the primary user groups in the electricity market. Traditional load prediction algorithms have difficulty in constructing mathematical models for predicting the charging load of electric vehicles, which is characterized by high randomness, high volatility, and high spatial heterogeneity. Moreover, the predicted results often exhibit a certain degree of lag. Therefore, this study approaches the analysis from two perspectives: the overall industrial park and individual charging stations. By analyzing specific load data, the overall framework for the training dataset was established. Additionally, based on the evaluation system proposed in this study and utilizing both Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) algorithms, a framework for machine learning-based load prediction methods was constructed to forecast electric vehicle charging loads in industrial parks. Through a case analysis, it was found that the proposed solution for the short-term prediction of the charging load in industrial park electric vehicles can achieve accurate and stable forecasting results. Specifically, in terms of data prediction for normal working days and statutory holidays, the Long Short-Term Memory (LSTM) algorithm demonstrated high accuracy, with R² coefficients of 0.9283 and 0.9154, respectively, indicating the good interpretability of the model. In terms of weekend holiday data prediction, the Multilayer Perceptron (MLP) algorithm achieved an R² coefficient of as high as 0.9586, significantly surpassing the LSTM algorithm’s value of 0.9415, demonstrating superior performance.

Promoting integrated development of transportation and energy is of great significance for achieving carbon peaking and carbon neutrality goals, deepening the nationwide battle to prevent and control pollution and accelerating the building of China’s strength in transportation. In the future, with the continuous growth of traffic demand, the acceleration and improvement of the comprehensive national transportation network and the continuous expansion of new infrastructure construction in the transport sector, the energy demand of transportation will continue to increase. At present, the institutions and mechanisms for the integrated development of transportation and energy are imperfect, supporting policies are not yet sound, and some key technologies are in urgent need of breakthrough. To further promote the integrated development of transportation and energy, we need to further clarify the strategy and pathway, optimize development policies and mechanisms, explore feasible business models, and strengthen scientific and technological innovation in this field.

The rapid expansion in shipping traffic, oil tankers, and oil field exploration in coastal and marine areas has, inevitably, resulted in the occurrence of many oil spill accidents. Oil spill accidents, which cause serious socio-economic, health, and environmental risks in coastal and marine areas, are a global concern. An oil spill pollution risk distribution map, combining multiple spill sources, is an effective tool by which to identify high-risk areas, which may help decision-makers in adopting contingency response and integrated coastal management. However, the assessment of oil spill distribution and risk assessment has been restricted, due to their heavy dependence on laboratory experiments and model simulations lacking reliable shipping data, which often derive inaccurate mapping results. This study combines the automatic identification system (AIS) and other data to precisely quantify the spatial extent of accident risk in coastal China. Based on oil quantity, oil spill rate, and accident probability, the ship, oil storage tank, submarine pipeline, and oil platform accidents spill risk index is analyzed. Next, combined with the sensitive degree of a coastal area, considering environmental and social issues, the oil spill environmental risk index is calculated. The oil spill pollution risk level is classified into five categories based on the oil spill pollution risk index, namely the low-risk zone, relatively low-risk zone, moderate-risk zone, relatively high-risk zone, and high-risk zone. The relatively high oil spill environmental risk concentration zone is located in the Bohai Sea, inter-border area between the Yellow Sea and Bohai Sea, the Yangtze River estuary, south of the Taiwan Strait, and the Pearl River estuary. The high-risk zone in the Bohai Sea is 36,018 km² in area, with an average risk value of 32.23, whereas the high-risk area in the Pearl River estuary is only 14,007 km². The high-risk area proportions in Tianjin are 23.5%, while those in Fujian, Hainan, Jiangsu, and Guangxi are very low. The low-risk area proportion in Hainan Province is 62%, while the value in Tianjin is only 2.9%. This study will be helpful in assisting decision-makers in mapping the influence area of oil spills and adopting the important strategies and effective management and conservation countermeasures for ship accidents in the coastal areas of China.