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In recent years, promoting regional synergistic development has become the shared objective in city-regions during the new stages of development. This paper aims to uncover the dynamics of synergistic development in urban agglomerations by constructing an index measurement system based on four dimensions: industrial synergy, innovation synergy, infrastructure synergy, and institutional synergy. By comprehensively employing theories and methods such as "space of flow" big data analysis, network analysis, and interlocking network models, this study examines patterns and capacities of synergistic development in the Guangdong-Hong Kong-Macao Greater Bay Area. The main instances of industrial cooperation between cities are observed in Hong Kong-Shenzhen and Guangzhou-Shenzhen; capital flows primarily concentrate in Guangzhou-Shenzhen and Hong Kong-Shenzhen. Research paper and patent collaborations between cities are predominantly concentrated in Hong Kong-Shenzhen; joint laboratories are mainly established in Hong Kong-Macao and Hong Kong-Guangzhou; inter-city co-construction of higher education institutions is mainly focused on Guangzhou-Foshan and Shenzhen-Hong Kong. Infrastructure synergistic development performs best in Guangzhou-Shenzhen, Guangzhou-Foshan, and Shenzhen-Dongguan. Cross-border collaboration benefits from institutional advantages held by Hong Kong and Macao. Urban interactions with outstanding performance include Guangzhou-Shenzhen, Hong Kong-Shenzhen, Guangzhou-Foshan, Shenzhen-Dongguan. Additionally, increasing interaction trends can be observed between Guangzhou-Dongguan as well as Shenzhen-Zhuhai/Shenzen-Foshan partnerships. Understanding the typical characteristics and potential trends of synergetic development within the GBA holds significant practical value for establishing a first-class bay area along with a world-class urban agglomeration. • Concept and connotation of synergistic development in urban agglomeration area was examined. • Indicators and methods for evaluation of synergistic development in the GBA were conducted. • Patterns and capacities of synergistic development at urban scale in the GBA were explained. • Urban interaction with highest synergistic development level is Guangzhou-Shenzhen. [ABSTRACT FROM AUTHOR]

The train generates regenerative braking energy (RBE) during braking. When the RBE generated by the braking train is too large to be absorbed and utilized, the OCS voltage will rise rapidly. It may even exceed the rated voltage threshold, resulting in hidden dangers for driving safety. How to use RBE efficiently and reasonably has become a hot issue in recent years. This paper presents a metro timetable optimization method for multi-train operation to improve the RBE utilization efficiency of trains. Firstly, an energy-saving multi-objective model for multi-train operation is established, which takes the total energy consumption and travel time as the objective functions and the dwell time and departure interval as the constraints. Then, the genetic algorithm (GA) is used to generate the composition sequence of dwell time and departure interval, and the multi-node nonlinear model is used to calculate the total energy consumption of the metro line. Finally, Guangzhou metro line 7 is selected as an example to illustrate the effectiveness of the developed optimization model. The results show that the energy consumption of the whole line can be effectively reduced by adjusting the dwell time and departure interval. Meanwhile, the timetable optimization model with GA has better energy-saving effects by comparative analysis. • A metro timetable optimization method based on improved genetic algorithm is developed. • The multi-node nonlinear model is developed to calculate the total energy consumption. • The developed optimization model has better energy-saving effects than previous methods. [ABSTRACT FROM AUTHOR]

The energy crisis caused by climate change is the most significant challenge of the 21st century. Single-objective and multi-objective optimization studies for buildings often only consider the building itself, without considering the impacts of climate change. However, in the future, energy-efficient design and appropriate operating parameters will be crucial considerations in the face of global climate change. This paper aims to discuss the impact of climate change in the humid-hot region over the past 60 years on the energy consumption of office buildings and propose countermeasures. Firstly, the meteorological data of Guangzhou from 1960 to 2020 is statistically analyzed, and four typical meteorological years and corresponding air conditioning design days are established. Secondly, Energyplus is used to simulate the building's energy consumption, and the energy consumption factors are analyzed for sensitivity using jEplus. Finally, a multi-objective optimization is performed using jEPlus + EA with the Non-dominated Sorting Genetic Algorithm (NSGA-II). The results found that climate change led to a significant increase in the duration of high outdoor temperatures during summer. Specifically, the daily dry-bulb temperature for air conditioning design increased by 0.4–0.7 °C, and due to climate change, the energy consumption of the office building increased by about 4.6 %. The main factor affecting the cooling energy consumption in summer was the indoor cooling set point (8.29%–30%), infiltration air volume (1.0%–8.6%), fresh air volume (3.2%–16.06%), the glazing solar transmission rate (1.25%–9.0%), building orientation (0%–1.43%), and relatively small influence of envelope insulation performance. The operation scheme determined by multi-objective optimization can reduce the cooling energy consumption of the building by 10.6%–16.8%, which can mitigate the rise in energy consumption resulting from climate change to a greater extent. [Display omitted] • Discuss the impact of climate change over the past 60 years on the energy consumption of office buildings. • There was a significant increase in the duration of high outdoor temperatures during the summer. • Multi-objective optimization is performed using jEPlus + EA. • Climate change caused energy consumption to increase by about 4.6 %. • Optimal solutions can reduce the cooling energy consumption of the building by 10.6%–16.8%. [ABSTRACT FROM AUTHOR]

As the financialization of carbon emission right, developing effective carbon asset trading strategy is important for both investors and regulators. Traditional trading strategy based on technical analysis is hardly profitable due to high complexity inherent in carbon markets. In order to increase investment returns and reduce risk in carbon market, this paper introduces PSO-VMD-DRL, an innovative carbon asset trading strategy that integrates signal decomposition technology and deep reinforcement learning (DRL). Firstly, the carbon price is decomposed into multiple components by variational mode decomposition (VMD) optimized through particle swarm optimization (PSO) to extract frequency features and reduce noise. Then state representation of the environment (state space) is constructed based on the components and transaction data. Finally, deep reinforcement learning (PPO, A2C, DDPG) are performed separately based on state space to learn optimal trading strategy. Empirical studies based on the data from carbon markets of Hubei and Guangzhou validate that PSO-VMD-DRL can adapt to carbon asset with the better profitability and risk resistance. The PSO-VMD-DRL outperform the other comparison strategies with superiority in all evaluation indices, achieving annual return of 21.39%, 17.67%, 25.50% and 46.34%, 44.55%, 43.03% in carbon market of Hubei and Guangzhou. [ABSTRACT FROM AUTHOR]