With the goal of achieving “carbon peak in 2030 and carbon neutrality in 2060”, as clearly proposed by China, the transportation sector will face long–term pressure on carbon emissions, and the application of hydrogen fuel cell vehicles will usher in a rapid growth period. However, true “zero carbon” emissions cannot be separated from “green hydrogen”. Therefore, it is of practical significance to explore the feasibility of renewable energy hydrogen production in the context of hydrogen refueling stations, especially photovoltaic hydrogen production, which is applied to hydrogen refueling stations (hereinafter referred to “photovoltaic hydrogen refueling stations”). This paper takes a hydrogen refueling station in Shanghai with a supply capacity of 500 kg/day as the research object. Based on a characteristic analysis of the hydrogen demand of the hydrogen refueling station throughout the day, this paper studies and analyzes the system configuration, operation strategy, environmental effects, and economics of the photovoltaic hydrogen refueling station. It is estimated that when the hydrogen price is no less than 6.23 USD, the photovoltaic hydrogen refueling station has good economic benefits. Additionally, compared with the conventional hydrogen refueling station, it can reduce carbon emissions by approximately 1237.28 tons per year, with good environmental benefits.

Short-term load forecasting (STLF) is a core technical support for ensuring the safe and economic operation of power systems and efficient trading in electricity spot markets. To address the limitations of traditional forecasting models in handling the strong nonlinear and non-stationary characteristics of load data under electricity spot market conditions—where load is influenced by the coupling of multiple factors, such as meteorological conditions, electricity price signals, and seasonal patterns—we propose a hybrid forecasting model (VMD-PSO-LSTM-RF) that integrates Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), Random Forest (RF), and Particle Swarm Optimization (PSO) to enhance the forecasting accuracy and market adaptability. First, VMD is applied to adaptively decompose the half-hourly power load data of a comprehensive user in Ningbo, Zhejiang Province, from July 2024 to June 2025. The original load series was decomposed into three components, effectively avoiding the mode aliasing problem common in traditional decomposition methods and providing high-quality inputs for subsequent forecasting. Simultaneously, meteorological data and temporal features were incorporated to construct a multi-dimensional input feature set, meeting the requirements of electricity spot markets for considering multiple influencing factors. Second, the PSO algorithm was used to optimize the key hyperparameters of LSTM and RF with seasonal differentiation. With the optimization, we aimed to maximize the Coefficient of Determination (R²) on the validation set, ensuring that the model parameters precisely matched the load fluctuation characteristics of each season. Finally, based on the feature differences of various frequency components, LSTM and RF were used to construct sub-models, and the final load value was obtained through weighted integration of the prediction results of each component. The results fully demonstrate that the proposed model can accurately capture the multi-scale fluctuation characteristics of load in electricity spot market environments, with forecasting performance superior to traditional single models and basic hybrid models; furthermore, the proposed model achieves precise extraction of multi-scale load features and in-depth temporal pattern mining, providing reliable technical support for efficient electricity spot market operation, as well as empirical references for formulating scenario-specific forecasting strategies and managing trading risks in electricity markets.

With the development of the civil aviation industry, the passenger throughput of airports has increased explosively, and they need to carry a large number of passengers every day and maintain operations for a long time. These factors cause the large space buildings in the airport to have higher energy consumption than ordinary buildings and have energy-saving potential. In practical engineering, there are problems such as low accuracy of prediction results due to inability to provide accurate building parameters and design parameters, some scholars oversimplify the large space building load forecasting model, and the prediction results have no reference significance. Therefore, establishing a load forecasting model that is closer to the actual operating characteristics and laws of large space buildings has become a research difficulty. This paper analyzes and compares the building and load characteristics of airport large space buildings, which are different from general large space buildings. The factors influencing large space architecture are divided into time characteristics and space characteristics, and the influencing reasons and characteristics of each factor are discussed. The Pearson analysis method is used to eliminate the influence parameters that have a very low connection with the cooling load, and then the historical data that affect the cooling load parameters are input. The MATLAB software is used to select a variety of neural network models for training and prediction. On this basis, the particle swarm optimization algorithm is used to optimize the prediction model. The results show that the prediction effect of the gated recurrent neural network based on particle swarm optimization algorithm is the best, the average absolute percentage error is only 0.7%, and the prediction accuracy is high.

This paper addresses the synergy between environmental and economic benefits in the green power trading market by constructing a collaborative game model for environmental rights value and electricity energy value. Based on this, a model for maximizing the benefits of flexible resource operation is proposed. Through the combination of non-cooperative and cooperative games, the conflict and synergy mechanisms of multiple stakeholders are quantified, and the Shapley value allocation rule is designed to achieve Pareto optimality. Simultaneously, considering the spatiotemporal regulation capability of flexible resources, dynamic weight adjustment, cross-period environmental rights reserve, and risk diversification strategies are proposed. Simulation results show that under the scenario of a carbon price of 50 CNY/ton (≈7.25 USD/ton) and a peak–valley electricity price difference of 0.9 CNY/kWh (≈0.13 USD/kWh), when the environmental weight coefficient α = 0.5, the total revenue reaches 6.857 × 10⁷ CNY (≈9.94 × 10⁶ USD), with environmental benefits accounting for 90%, a 15.3% reduction in carbon emission intensity, and a 1.74-fold increase in energy storage cycle utilization rate. This research provides theoretical support for green power market mechanism design and resource optimization scheduling under “dual-carbon” goals.

The publishing industry, a major contributor to greenhouse gas emissions, produced approximately 730 Mt CO₂eq globally in 2020 during the paper production phase alone. Unlike other sectors, decarbonization in publishing requires systematic reforms across the supply chain, production efficiency, energy transitions, consumption patterns, and recycling processes, as reliance on renewable energy alone is insufficient. This study focuses on China’s physical publishing industry, developing a comprehensive, high-resolution carbon emissions dataset that spans multiple publication types, stages, and processes. It reveals the emission characteristics across the life cycle, aiming to quantify the emissions accurately and address the lack of life-cycle-based research. This study explores efficient, replicable, and scalable strategies to facilitate the industry’s low-carbon transformation and sustainable development. The findings are as follows. (1) Books are the primary carbon emissions source, contributing approximately 77.05% of the total emissions, while journals and newspapers account for 13.20% and 9.75%, respectively. (2) Annual carbon accounting across the life-cycle identifies paper production and printing as the most carbon-intensive stages, responsible for about 85% of the total emissions. (3) In terms of recycling efforts, carbon reductions of approximately 347,000 t CO₂eq per year can be achieved through measures such as waste paper and plastic packaging recycling, second-hand publication exchanges, and energy recovery from incineration.

As the global energy transition progresses, green electricity, which is crucial for low-carbon systems, has gained attention. However, the lack of effective market linkages hinders a full understanding of the price transmission effects across green markets. This study uses the Vector Autoregression (VAR) model and Granger causality tests to analyze the price transmission and lag effects between the carbon, green certificate, and China Certified Emission Reduction (CCER) Markets. The findings reveal complex price linkages, offering theoretical insights and policy recommendations for optimizing green electricity markets and environmental rights trading.