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.

In the Taihang Mountain Tourism Development Plan (2020–2035), the Taihang Mountain Expressway is included in the construction of the National Tourism Scenic Road around Taihang Mountain to promote the integrated development of regional transportation and tourism. The Lai-Qu Expressway is part of the Baoding section of the Taihang Mountain Expressway. Based on the data of traffic flow on the Lai-Qu Expressway, data of regional tourism resources, and data of regional economic and social development, this paper studies the interaction between the traffic and tourism space of the Lai-Qu Expressway by using spatial interaction, geographically weighted regression (GWR), and other geospatial analysis theories and methods. The results show that the traffic flow of the Baishishan Tollgate is directly correlated with the passenger flow of the Baishishan scenic spot. The spatial pattern of two tourism resource cluster centers and one sub-center, and one residential cluster center and one sub-center is expected to be formed along the Lai-Qu Expressway. The newly built traffic routes extend the influence of the traffic space and overlaps with the regional tourism space, not only providing new opportunities and possibilities for the development of regional tourism, but also promoting the change in the regional tourism spatial pattern and the cluster form of tourism resources. The research on the interaction between the traffic–tourism space in this paper can help to enrich the theoretical connotation of the research on the integration of transport and tourism, and can also be used to evaluate the tourism impact of newly built transport routes and serve the regional tourism development.

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.

Controlling air pollution from sea-going vessels is crucial to the sustainable development of maritime transportation. However, emissions of intermediate volatility organic compounds (IVOCs), an emerging aerosol precursor, remain poorly understood. This study developed a ship-type-, fuel-, and operating-mode-specific IVOC emission factor dataset based on existing real-world vessel measurements, and a ship-call-based IVOC inventory methodology tailored for regulatory applications. We quantified IVOC emissions from sea-going ships (excluding fishing and military vessels) entering or departing from the ports in the Economic Zone on the West Coast of the Taiwan Straits in 2014. The total IVOC emissions were 481.4 ± 220.0 t, with Xiamen Port contributing the highest share. Cargo and passenger ships accounted for 65% and 21% of emissions, respectively. While switching to low-sulfur and ultra-low-sulfur fuels increased IVOC emissions by 87% and 49% compared to high-sulfur fuels, the greater reductions in particulate matter and SO₂ emissions still yielded net environmental benefits. The ship IVOC emissions might have become more important in recent years due to enhanced port activity and fuel switching. Uncertainty analysis emphasizes the urgent need for IVOC emission testing on more vessel types. By providing a high-resolution profile of IVOC emissions from selected ports, this study underscores the urgency of adopting shore power and zero-emission vessels to mitigate organic aerosol pollution and offers a foundation for refining environmental impact assessments and efficient emission control policies to achieve sustainability in maritime transportation.