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Reducing emissions in the aviation industry remains a critical challenge for global low-carbon transition. Accurate fuel consumption prediction is essential to achieving emission reduction targets and advancing sustainable development in aviation. Aircraft fuel consumption is influenced by numerous complex factors during flight, resulting in significant nonlinear relationships between segment-specific variables and fuel usage. Traditional statistical and econometric models struggle to capture these relationships effectively. This article first focuses on the different characteristics of QAR data and uses the Adaptive Noise Ensemble Empirical Mode Decomposition (CEEMDAN) method to obtain more significant potential features of QAR data, solving the problems of mode aliasing and uneven mode gaps that may occur in traditional decomposition methods when processing non-stationary signals. Secondly, a dynamic multidimensional particle swarm optimization algorithm (DMPSO) was constructed using an adaptive adjustment dynamic change method of inertia weight and learning factor, which solved the problem of local extremum and low search accuracy in the solution space that PSO algorithm is prone to during the optimization process. Then, a DMPSO-LSTM aircraft fuel consumption model was established to achieve fuel consumption prediction for three flight segments: climb, cruise, and descent. The final proposed model was validated on real-world datasets, and the results showed that it outperformed other baseline models such as BP, RNN, PSO-LSTM, etc. Among the results, the climbing segment MAE index decreased by more than 40%, the RMSE index decreased by more than 38%, and the R² index increased by more than 6%, respectively. The MAE index of the cruise segment decreased by more than 40%, the RMSE index decreased by more than 40%, and the R² index increased by more than 5%, respectively. The MAE index of the descending segment decreased by more than 20%, the RMSE index decreased by more than 30%, and the R² index increased by more than 5%, respectively. The improved prediction accuracy can be used to implement multi-criteria optimization in flight operations: (1) by quantifying weight–fuel relationships, it supports payload–fuel tradeoff decisions; (2) enhanced phase-specific predictions allow optimized climb/cruise profile selections, balancing time and fuel use; and (3) precise consumption estimates facilitate optimal fuel-loading decisions, minimizing safety margins. The high-precision fuel consumption prediction framework proposed in this study provides actionable insights for airlines to optimize flight operations and design low-carbon route strategies, thereby accelerating the aviation industry’s transition toward net-zero emissions. Full article

With the escalating environmental crisis, electric vehicles have emerged as a key solution for emission reductions in logistics due to their low-carbon attributes, prompting significant attention and extensive research on the electric vehicle routing problem (EVRP). However, existing studies often overlook charging infrastructure (CI) capacity constraints and fail to fully exploit the synergistic potential of heterogeneous energy replenishment infrastructures (HERIs). This paper addresses the EVRP with HERIs under various capacity constraints (EVRP-HERI-CC), proposing a mixed-integer programming (MIP) model and a hybrid ant colony optimization (HACO) algorithm integrated with a variable neighborhood search (VNS) mechanism. Extensive numerical experiments demonstrate HACO’s effective integration of problem-specific characteristics. The algorithm resolves charging conflicts via dynamic rescheduling while optimizing charging-battery swapping decisions under an on-demand energy replenishment strategy, achieving global cost minimization. Through small-scale instance experiments, we have verified the computational complexity of the problem and demonstrated HACO’s superior performance compared to the Gurobi solver. Furthermore, comparative studies with other advanced heuristic algorithms confirm HACO’s effectiveness in solving the EVRP-HERI-CC. Sensitivity analysis reveals that appropriate CI capacity configurations achieve economic efficiency while maximizing resource utilization, further validating the engineering value of HERI networks. Full article

This study investigates the spatiotemporal distribution of carbon emissions and the decoupling relationship between emissions and innovation-driven urban development in six coastal provinces and municipalities in China from 2008 to 2022. The main questions of this paper are as follows: What are the spatial and temporal distribution characteristics of carbon emissions in the study area? What is the decoupling relationship between carbon emissions and innovation-driven urban development? What key variables contribute significantly to carbon emissions and urban development? Carbon emissions increased overall, with higher levels in northern regions such as Shandong, northern Jiangsu, and the Yangtze River Delta. Meanwhile, innovation levels rose but disparities widened, with northern cities leading and those in western Fujian and Guangdong lagging behind. The green economy and industrial transformation were key drivers of rapid development in some cities. To identify the driving factors, the SHAP (SHapley Additive exPlanations) model was employed to quantify the contributions of key variables, including energy structure, technological innovation, and industrial upgrading, to both carbon emissions and urban development. This study found that decoupling between carbon emissions and smart city development improved, transitioning from negative to strong decoupling, particularly in coastal cities. These insights can assist governments in formulating sustainable development strategies. High-emission cities should focus on integrating low-emission measures to mitigate their carbon footprint. High-carbon cities need to transition to low-carbon pathways, enhancing resource efficiency and reducing emissions. Low-emission cities should prioritize improving carbon sinks. Cities with weak economies but rich ecological resources should develop tertiary and ecological economies. Developed cities should optimize resource allocation, digitize industries, and pursue low-carbon growth. Additionally, adjustments in transportation and industry can further boost innovation and urbanization. Full article

Microbial-Enhanced Coalbed Methane (MECBM) is a technology that generates new methane gas in coal seams through the action of microorganisms, thereby improving the efficiency of coalbed methane development. In this study, low-temperature CO₂ adsorption, low-temperature N₂ adsorption, and isothermal adsorption experiments were conducted to systematically characterize the changes in the pore characteristics of low-rank coals in Xinjiang before and after degradation. The results show that microbial action increases the average pore diameter and enhances pore connectivity. Meanwhile, it reduces the fractal dimension of the pore surface and simplifies the complexity of the pore structure. The modification of the pore structure effectively promotes the efficiency of methane desorption and migration, thus improving the exploitation potential of coalbed methane. Microbial degradation avoids the risk of deterioration of reservoir physical properties through biological modification, and reduces carbon emissions and environmental pollution. This study provides an environmentally friendly solution for the low-carbon development of coal resources, and has important scientific significance for promoting the transformation of energy structures and achieving the goal of carbon neutrality. Full article

The decarbonization of the operational fleet through the implementation of renewable and low-carbon fuels (LCFs) is considered a key factor in achieving the regulatory greenhouse gas (GHG) reduction targets set by the IMO and the EU. In parallel with optimizing engine energy efficiency and emission characteristics during retrofitting for LCF operations, it is equally important to assess and ensure the reliability of engine components under permissible thermal and mechanical loads. This study investigated the key factors influencing thermal and mechanical stresses on the cylinder–piston assembly components as the engine’s operation shifts from diesel to biodiesel, natural gas, methanol, or ammonia. The methodological foundation of this research was an original comparative analysis method that evaluates the impacts of thermal stress and combustion cycle energy efficiency factors. The combustion cycle energy parameters were modeled using a single-zone mathematical model. The thermal load factor was determined based on the ALPHA (α_gas) coefficient of heat transfer intensity and the average combustion gas temperature (T_avg). The optimization of the combustion cycle during retrofitting was simulated without changes to the engine structure (or without “major” modernization, according to IMO terminology), with modifications limited to the engine’s combustion adjustment parameters. A key characteristic of the transition from diesel to LCFs is a significant increase in the maximum cycle pressure (Pmax), a factor influencing mechanical stresses: ammonia, +43%; LNG, +28%; methanol, +54–70%; biodiesel, no significant changes. This study confirms the adopted strategy to maintain thermal load factors for engine components equal to Dmax conditions. It is emphasized that, after ensuring Pmax-idem conditions, the thermal load during LCF operation aligns closely with the characteristic diesel level with minimal deviation. The thermal load reduction is associated with an increase in the excess air coefficient (λ) and a controlled reduction in the compression ratio within an allowable variation of ±1 unit. Based on statistical correlations, a rational increase in λ was identified, reaching up to 2.5 units. Considering the real-world operational load cycle structure of marine engines, further research will focus on analyzing thermal and mechanical stresses according to ISO 81/78, as well as E2 and E3 operational cycles. Full article

Against the dual-carbon background, civil aviation is in urgent need of low-carbon and green transformation. Carbon emissions from airports are one of the main environmental concerns in civil aviation, so the early realization of airport carbon peak and carbon neutrality will help accelerate the construction of green civil aviation and assist in the low-carbon transformation and upgrading of civil aviation. According to research, the terminal building, aircraft, and ramp area are the main sources of airport carbon emissions. Taking Harbin Taiping International Airport as an example, this study first measured the carbon emissions from the terminal building and ramp area of the airport in the past five years by using the emission factor method; then, we measured the carbon emissions from aircraft in the airport in the past five years by using the ICAO method and finally predicted the trend of carbon emissions from aircraft and the possibility of reaching the peak carbon emissions of the airport in the coming years by using scenario analysis and the Monte Carlo simulation method. The results show that the total carbon emissions of Harbin Taiping International Airport will be 458,800 tonnes in 2023 and up to 581,100 tonnes in 2035; under the scenarios of green development and technological innovation, the airport’s carbon emissions can reach their peak by 2035, which will be lower and reached earlier under the scenario of technological innovation; the airport can improve energy use efficiency, increase the utilization of renewable energy sources, establish a carbon emission monitoring system, and actively participate in the market for carbon emission monitoring systems. Airports can systematically build a development path for airport carbon peaking by improving energy efficiency, increasing the utilization rate of renewable energy, establishing a carbon emission monitoring system, actively participating in carbon trading in the market, etc., so as to reduce carbon emissions from airports and promote the transformation and upgrading of civil aviation into a green and low-carbon sector. Full article

Reasonable planning and scheduling in low-carbon parks is conducive to coordinating and optimizing energy resources, saving total system costs, and improving equipment utilization efficiency. In this paper, the optimization study of a distributed photovoltaic energy storage system considers the synergistic effects of the planning and operation phases. On the basis of the variable operating characteristics of the unit equipment and the complementary synergistic characteristics of the energy storage equipment, a two-layer optimization model combining planning and operation is adopted, with the minimum total cost and the minimum carbon emission content in the whole life cycle of the system as the optimization objectives and the upper layer of the planning equipment capacity and the configured capacity of each equipment in the system as the optimization variables, which are solved by using the multi-objective no-dominated-sorting genetic algorithm. The lower layer is the optimized operation mode, and the time-by-time operating capacity of each item of equipment is the optimization variable, which is solved by the interior point method. The upper layer optimization results are used as the constraint boundary conditions for optimization of the lower layer, and the lower layer optimization results provide feedback correction to the upper layer optimization results, which ultimately determine the energy system optimization scheme. The optimization results reflect that photovoltaic green power should be arranged in large quantities as a priority, and the synergistic effect of power and cold storage equipment on the system’s economy and low-carbon performance is positive. At the same time, by setting up four control scenarios of only cold storage, only electricity storage, no energy storage, and no two-tier optimization, the impacts of cold storage and electricity storage on the economic and environmental aspects of the system and the positive effect of mutual synergy are investigated, which concretely proves the validity of the two-tier optimization strategy, taking into account the operating characteristics of the equipment. Full article

With the rapid growth of global energy demand, the fossil fuel-dominated electric power industry has led to serious environmental problems. Tradable green certificates (TGC) and carbon emission trading (CET) have become key mechanisms for promoting sustainable development of the electricity market by serving as market-oriented policy tools. To deeply analyze the impact of TGC and CET on the sustainable development of China’s electricity market and provide a scientific basis for policymakers. This study uses system dynamics (SD) methods to construct a policy synergy analysis framework for TGC and CET. It explores the impact mechanism of dual policy incentives on the sustainable development of the electricity market. Firstly, the current application status of TGC and CET in China was reviewed. Based on the literature analysis, identify key factors that affect the sustainable development of the electricity market. Then, by deconstructing the interaction between TGC policy and CET policy, an SD model was established that includes multidimensional feedback such as policy, technology, funding, and market, and the dynamic functional relationships in the SD model were quantified. Finally, Vensim PLE software 7.3.2 was used to simulate the evolution of sustainable development in the electricity market under different policy scenarios. The research results indicate that (1) the adjustment of the TGC quota ratio can change the supply and demand mechanism to form a price leverage effect, effectively stimulate the growth of renewable energy generation capacity, and accelerate the low-carbon transformation of power enterprises; and (2) the CET market changes the cost structure of power generation through carbon price signals. When the carbon emission cap target tightens, CET prices quickly rise, leading to a significant trend of carbon reduction in the electricity market; (3) the application of policy combinations can significantly promote the sustainable development of the electricity market, but the unreasonable setting of policy parameters can trigger market risks. Therefore, policy design should focus on flexibility and implement appropriate policy combinations at different stages of electricity market development to promote green transformation while ensuring smooth market operation. This study innovatively reveals the synergistic effect of TGC and CET in the sustainable development of the electricity market from a systems theory perspective. The research results provide a scientific basis for decision-makers to formulate policy adjustment plans and have essential reference value for achieving the dual goals of energy structure transformation and electricity market stability. Full article

In the urgent context of global climate change and carbon neutrality goals, effective carbon balance regulation is critical for achieving temperature control targets. Metropolitan areas encounter significant challenges in carbon emission reduction, energy transition advancement, and enhancement of sequestration capabilities. However, traditional carbon balance analysis methods have limitations in capturing dynamic changes and guiding precise regulation. Therefore, this study developed a dynamic–static classification system for carbon balance based on the Ecological Support Coefficient (ESC) and the Economic Contributive Coefficient (ECC). This system examined carbon emissions and carbon sequestration in China’s 14 national-level metropolitan areas from 2000 to 2020. The results showed that: (1) Carbon emissions showed an increasing trend, exhibiting a spatial distribution with higher levels in the north, moderate levels in the central region, and the lowest levels in the southeast. In contrast, carbon sequestration exhibited a spatial pattern with higher levels in the east, moderate levels in the central region, and lower levels in the west. (2) Static classification revealed that the ECC and ESC of metropolitan areas in the central and northern regions were relatively weaker than those in other regions. Dynamic classification further showed an upward trend in the economic and ecological capabilities of these central and northern metropolitan areas. In contrast, metropolitan areas along the coast and within the Yangtze River Economic Belt needed to optimize their economic–ecological coordination efficiency. Although southern coastal metropolitan areas demonstrated robust economic vitality, they encountered significant ecological support pressures. (3) Economic development level and ecological environmental quality were the predominant factors in metropolitan area classification. Regions with a higher ECC tended to exhibit an enhanced ESC, while regions with a stronger ESC prioritized economic growth. This classification system provided a solid scientific basis for formulating differentiated low-carbon transformation strategies, thereby supporting high-quality development in China’s metropolitan areas while maintaining a dynamic balance between economic and ecologic objectives. Moreover, it offered both theoretical foundations and practical guidance for optimizing sustainable development pathways in similar metropolitan areas globally. Full article

This article aims to investigate the mechanisms of farmer professional cooperative (FPC) operations and to understand their role in promoting low-carbon production among small-scale farmers in China. Agricultural carbon emissions account for 17% of the total carbon emission in China; therefore, reducing agricultural carbon emissions is important for China to achieve carbon neutrality. Small-scale farmers face many obstacles in achieving the low-carbon transition of agriculture, which therefore makes them a priority target for the implementation of low-carbon production systems in China. Participating in FPCs is an effective support mechanism for them to conduct low-carbon production. In this paper, a system dynamics model is used to simulate the methods of how FPCs assist small-scale farmers to adopt low-carbon production practices within the framework of China’s carbon trading system, through the year 2030. After attending the carbon transaction system, the agricultural carbon emissions are anticipated to decline by 10.21%, and FPCs’ net income could increase by 11.85%. In a scenario where the price of their agricultural products increases, the reduction of carbon emissions and the increase of FPCs’ net income will be beneficial. Under the operation of FPCs, the greatest profits will be generated from trading, and these will be distributed to small-scale farmers, thereby creating a positive feedback loop between carbon transactions and FPC operations. This article seeks determine the potential outcomes that can serve as a basis for informed decision-making within relevant policy-making agencies regarding agricultural carbon transactions by simulating the potential benefits to both small-scale farmers and FPCs from the integration of a carbon trading system. Full article

This study explores the driving factors of household energy consumption in high-density residential areas of Beijing and proposes targeted energy-saving strategies. Data were collected through field surveys, questionnaires, and interviews, covering 16 influencing factors across household, building, environment, and transportation categories. A hyperparameter-optimized ensemble model (XGBoost, RF, GBDT) was employed, with XGBoost combined with genetic algorithm tuning performing best. SHAP analysis revealed that key factors varied by season but included floor level, daily travel distance, building age, greening rate, water bodies, and household age. The findings inform strategies such as optimizing workplace–residence layout, improving building insulation, increasing green spaces, and promoting community energy-saving programs. This study provides refined data support for energy management in high-density residential areas, enhances the application of energy-saving technologies, and encourages low-carbon lifestyles. By effectively reducing energy consumption and carbon emissions during the operational phase of residential areas, it contributes to urban green development and China’s “dual carbon” goals. Full article

The transportation of prefabricated components is challenged by the particularity of large cargo transport and urban road conditions, restrictions on parking, height, and weight. To address these challenges and to promote low-carbon logistics, this paper investigates the transportation of prefabricated components by leveraging separable fleets of trucks and trailers. Focusing on real-world constraints, this paper formulates the capacitated truck and trailer routing problem with time windows (CTTRPTW) incorporating carbon emissions, and designs a dynamic adaptive hybrid algorithm combining simulated annealing with tabu search (DASA-TS) to solve this model. The efficiency and robustness of the methodology are validated through two computational experiments. The results indicate that the DASA-TS consistently demonstrates excellent performance across all evaluations, with significant reductions in both transportation costs and carbon emissions costs for prefabricated components, particularly in large-scale computational instances. This study contributes to promoting the optimization of low-carbon transport for prefabricated components, offering guidance for routing design involving complex and large cargo, and supporting the sustainable development of urban logistics. Full article

The rapid global shift toward renewable energy necessitates innovative solutions to address the intermittency and variability of solar and wind power. This study presents a comprehensive review and framework for deploying Integrated Energy Storage Systems (IESSs) to enhance grid efficiency and stability. By leveraging a Multi-Criteria Decision Analysis (MCDA) framework, this study synthesizes techno-economic optimization, lifecycle emissions, and policy frameworks to evaluate storage technologies such as lithium-ion batteries, pumped hydro storage, and vanadium flow batteries. The framework prioritizes hybrid storage systems (e.g., battery–supercapacitor configurations), demonstrating 15% higher grid stability in high-renewable penetration scenarios, and validates findings through global case studies, including the Hornsdale Power Reserve (90–95% round-trip efficiency) and Kauai Island Utility Cooperative (15,000+ cycles for flow batteries). Regionally tailored strategies, such as Kenya’s fast-track licensing and Germany’s H2Global auctions, reduce deployment timelines by 30–40%, while equity-focused policies like India’s SAUBHAGYA scheme cut energy poverty by 25%. This study emphasizes circular economy principles, advocating for mandates like the EU’s 70% lithium recovery target to reduce raw material costs by 40%. Despite reliance on static cost projections and evolving regulatory landscapes, the MCDA framework’s dynamic adaptation mechanisms, including sensitivity analysis for carbon taxes (USD 100/ton CO₂-eq boosts hydrogen viability by 25%), ensure scalability across diverse grids. This work bridges critical gaps in renewable energy integration, offering actionable insights for policymakers and grid operators to achieve resilient, low-carbon energy systems. Full article

Our research investigated whether the carbon emissions trading pilot policy (CET), while mitigating environmental pollution externalities and fostering green economic and social transformation, can also enhance China’s new quality productivity (NQP) as a key driver of economic growth. This study addresses a research gap by examining the CET from an integrated perspective of economic development and environmental protection. We have developed an NQP evaluation indicator system based on three productivity factors, revealing that the CET can elevate NQP levels in pilot provinces through the advancement of green finance (GF) and industrial structure upgrading (ISU). Furthermore, we analyzed the relationship between the CET and NQP from the perspective of low-carbon energy consumption (LCEC), demonstrating that the level of LCEC can reinforce the CET’s positive impact on NQP and moderate the path before and after the mediating process. Our findings offer valuable insights into leveraging market-based environmental regulation tools to support NQP development, thereby facilitating its cultivation and enhancement. Full article

As environmental pollution has become a global concern, regulations on carbon emissions from maritime activities are being implemented, and interest in using renewable energy as fuel for ships is growing. Hydrogen, which does not release carbon dioxide and has a high energy density, can potentially replace fossil fuels as a renewable energy source. Notably, storage of hydrogen in a liquid state is considered the most efficient. In this study, a 0.7 m³ liquid hydrogen fuel tank suitable for small vessels was designed, and a structural analysis was conducted to assess its structural integrity. The extremely low liquefaction temperature of hydrogen at −253 °C and the need for spatial efficiency in liquid hydrogen fuel tanks make vacuum insulation essential to minimize the heat transfer due to convection. A composite insulation system of sprayed-on foam insulation (SOFI) and multilayer insulation (MLI) was applied in the vacuum annular space between the inner and outer shells, and a tube-shaped supporter made of a G-11 cryogenic (CR) material with low thermal conductivity and high strength was employed. The material selected for the inner and outer layers of the tank was STS 316L, which exhibits sufficient ductility and strength at cryogenic temperatures and has low sensitivity to hydrogen embrittlement. The insulation performance was quantitatively assessed by calculating the boil-off rate (BOR) of the designed fuel tank. Structural integrity evaluations were conducted for nine load cases using heat transfer and structural analyses in accordance with the IGF code. Full article