Search Results (779)

In the current soil erosion control efforts, the lack of collaboration among multiple stakeholders is a major problem that restricts governance performance. Based on carbon trading and the Public–Private Partnership model, this paper constructs a tripartite differential game model involving the government, enterprises, and farmers, focusing on the government subsidy and the enterprise–farmer benefit-sharing mechanism. It systematically analyzes the dynamic evolution process of multi-stakeholder collaborative governance behavior under the collaborative mechanism. Through numerical simulation, the impacts of key variables such as benefit-sharing ratio, synergy effect of measures, and unit carbon sequestration on the optimization of enterprise governance measures, effort level, government fiscal expenditure, and tripartite benefits were analyzed. The results indicate that (1) the benefit-sharing ratio has a significant bidirectional regulatory effect on the system, with both excessively high and excessively low ratios weakening the collaborative governance effect; (2) the synergistic effect between governance measures significantly enhances the enthusiasm of enterprise governance and promotes the allocation of resources towards measure with better carbon sequestration benefits; and (3) the unit carbon sequestration significantly affects governance structure and government subsidy strategies, with the government being more sensitive to carbon sink responses of afforestation measures. The research results provide a theoretical basis for optimizing the ecological governance system under the “dual carbon” goal and also provide policy references for promoting the transformation of governance model from “government-led” to “multi-stakeholder collaboration”. Full article

Current technical approaches find it challenging to reduce hydrogen production costs in combined cooling, heating, and power (CCHP) microgrids integrated with hydrogen refueling stations (HRS). Furthermore, the stability of such systems is significantly impacted by multiple uncertainties inherent on both the source and load sides. Therefore, this paper proposes a two-stage robust optimization for bi-level game-based scheduling of a CCHP microgrid integrated with an HRS. Initially, a bi-level game structure comprising a CCHP microgrid and an HRS is established. The upper layer microgrid can coordinate scheduling and the step carbon trading mechanism, thereby ensuring low-carbon economic operation. In addition, the lower layer hydrogenation station can adjust the hydrogen production plan according to dynamic electricity price information. Subsequently, a two-stage robust optimization model addresses the uncertainty issues associated with wind turbine (WT) power, photovoltaic (PV) power, and multi-load scenarios. Finally, the model’s duality problem and linearization problem are solved by the Karush–Kuhn–Tucker (KKT) condition, Big-M method, strong duality theory, and column and constraint generation (C&CG) algorithm. The simulation results demonstrate that the strategy reduces the cost of both CCHP microgrid and HRS, exhibits strong robustness, reduces carbon emissions, and can provide a useful reference for the coordinated operation of the microgrid. Full article

Qilian Mountain National Park, an important forest ecosystem in northwest China, plays a crucial role in achieving the national “dual carbon” goals and advancing sustainable forest management. This study focuses on the systematic assessment of forest carbon storage and its market economic value, employing multi-source data fusion and the GARCH-M(1,1) model to integrate forest carbon storage data from 2000 to 2020 with historical trading records from the EU and Chinese carbon markets (2017–2025). The study utilizes three dynamic carbon pricing scenarios—low, medium, and high—to assess the carbon storage capacity and economic value of the park’s forest ecosystems. Results show that forest carbon storage increased by approximately 4.0 × 10⁷ tons, with an average annual growth rate of 0.27%. Under the high carbon pricing scenario in 2025, the forest carbon sink value in the EU market reaches CNY 518.2 billion, approximately 12.5 times that of the Chinese market, highlighting the differences in market maturity and volatility persistence. Through Monte Carlo simulations and dynamic pricing analysis, this research reveals the substantial market potential of Qilian Mountain’s forest carbon sinks, providing data-driven support for regional carbon trading optimization, ecological compensation mechanisms, and sustainable forest management, while contributing to the global carbon trading system and international cooperation in forest-based climate mitigation. Full article

To enhance spatial resource complementarity and cross-entity coordination among multi-regional integrated energy systems (MRIESs), an optimized operation strategy is developed based on a bilevel Stackelberg game framework. In this framework, the integrated energy system operator (IESO) and MRIES act as the leader and followers, respectively. Guided by an integrated demand response (IDR) mechanism and a collaborative green certificate and carbon emission trading (GC–CET) scheme, energy prices and consumption strategies are optimized through iterative game interactions. Inter-regional electricity transaction prices and volumes are modeled as coupling variables. The solution is obtained using a hybrid algorithm combining particle swarm optimization (PSO) with mixed-integer programming (MIP). Simulation results indicate that the proposed strategy effectively enhances energy complementarity and optimizes consumption structures across regions. It also balances the interests of the IESO and MRIES, reducing operating costs by 9.97%, 27.7%, and 4.87% in the respective regions. Moreover, in the case study, renewable energy utilization rates in different regions—including an urban residential zone, a renewable-rich suburban area, and an industrial zone—are improved significantly, with Region 2 increasing from 95.06% and Region 3 from 77.47% to full consumption (100%), contributing to notable reductions in carbon emissions. Full article

The integration of renewable energy and power-to-gas (P2G) technology into park-level integrated energy systems (PIES) offers a sustainable pathway for low-carbon development. This paper presents a low-carbon economic dispatch model for PIES that incorporates uncertainties in renewable energy generation and load demand. A novel two-stage P2G, replacing traditional devices with electrolysers (EL), methane reactors (MR), and hydrogen fuel cells (HFC), enhances energy efficiency and facilitates the utilisation of captured carbon. Furthermore, adjustable thermoelectric ratios in combined heat and power (CHP) and HFC improve both economic and environmental performance. A ladder-type carbon trading and green certificate trading mechanism is introduced to effectively manage carbon emissions. To address the uncertainties in supply and demand, the study applies information gap decision theory (IGDT) and develops a robust risk-averse model. The results from various operating scenarios reveal the following key findings: (1) the integration of CCT with the two-stage P2G system increases renewable energy consumption and reduces carbon emissions by 5.8%; (2) adjustable thermoelectric ratios in CHP and HFC allow for flexible adjustment of output power in response to load requirements, thereby reducing costs while simultaneously lowering carbon emissions; (3) the incorporation of ladder-type carbon trading and green certificate trading reduces the total cost by 7.8%; (4) in the IGDT-based robust model, there is a positive correlation between total cost, uncertainty degree, and the cost deviation coefficient. The appropriate selection of the cost deviation coefficient is crucial for balancing system economics with the associated risk of uncertainty. Full article

This research considers an Artificial Intelligence (AI)-driven omnichannel logistics network for bioethanol supply from Russia to China. As a renewable, low-carbon transport fuel, bioethanol plays a critical role in energy diversification and decarbonization strategies for both Russia and China. However, its flammability and temperature sensitivity impose stringent requirements on transport infrastructure and supply chain management, making it a typical application scenario for exploring intelligent logistics models. The proposed model integrates information, transportation, and financial flows into a unified simulation framework designed to support flexible and sustainable cross-border (CB) logistics. Using a combination of machine learning, multi-objective evaluation, and reinforcement learning (RL), the system models and ranks alternative transportation routes under varying operational conditions. Results indicate that the mixed corridor through Kazakhstan and Kyrgyzstan achieves the best overall balance of cost, time, emissions, and customs reliability, outperforming single-country routes. The findings highlight the potential of AI-enhanced logistics systems in supporting low-carbon energy trade and CB infrastructure coordination. Full article

The natural forest logging ban policy has substantially influenced rural residents’ production activities, daily lives, and income levels. Drawing on panel data from 30 provinces in China, this study examines both the overall effect of the policy on rural households’ income and the internal transmission mechanisms. The policy is regarded as an external shock, and its impact is identified through a multi-period difference-in-differences model combined with a mediation analysis. The results show three main findings: (1) the policy significantly raised rural households’ total income; the structural analysis indicates that its effects are notably positive on wage income and property income; in contrast, the impacts on operating income and transfer income are not statistically significant; (2) mechanism testing found that the policy significantly improved non-agricultural employment and increased ecological protection investment, indicating that the non-agricultural employment and ecological protection investment are important channels for the national natural forest logging ban policy to increase rural residents’ income; (3) heterogeneity analysis shows that the policy effect is more pronounced in areas with a higher distribution of state-owned forest areas, along with the policy effects being more pronounced in non-carbon trading market pilot areas. Therefore, this article proposes policy recommendations for continuously improving the natural forest protection policy system, ensuring effective employment of rural labor, and building coordinated development of forestry systems between regions. Full article

Against the backdrop of escalating atmospheric carbon dioxide concentrations, carbon emission trading systems (ETS) have emerged as pivotal policy instruments, with China’s ETS playing a prominent role globally. The carbon price, central to ETS functionality, guides resource allocation and corporate strategies. Due to unexpected events, political conflicts, limited access to data information, and insufficient cognitive levels of market participants, there are epistemic uncertainties in the fluctuations of carbon and energy prices. Existing studies often lack effective handling of these epistemic uncertainties in energy prices and carbon prices. Therefore, the core objective of this study is to reveal the dynamic linkage patterns between energy prices and carbon prices, and to quantify the impact mechanism of epistemic uncertainties on their relationship with the help of uncertain differential equations. Methodologically, a dynamic model of carbon and energy prices was constructed, and analytical solutions were derived and their mathematical properties were analyzed to characterize the linkage between carbon and energy prices. Furthermore, based on the observation data of coal prices in Qinhuangdao Port and national carbon prices, the unknown parameters of the proposed model were estimated, and uncertain hypothesis tests were conducted to verify the rationality of the proposed model. Results showed that the mean squared error of the established model for fitting the linkage relationship between carbon and energy prices was 0.76, with the fitting error controlled within $3.72\%$. Moreover, the prediction error was 1.$88\%$. Meanwhile, the $5\%$ value at risk (VaR) of the logarithmic return rate of carbon prices was predicted to be $-0.0369$. The research indicates that this methodology provides a feasible framework for capturing the uncertain interactions in the carbon-energy market. The price linkage mechanism revealed by it helps market participants optimize their risk management strategies and provides more accurate decision-making references for policymakers. Full article

Economic dispatch using wind power plants plays a role in reducing the price of electricity production by dispatching power among different generating units for thermal and wind power plants, and supplying load demand while meeting the power system equality and inequality constraints. Adding wind power plants to the economic dispatch model can significantly reduce electricity production costs and reduce carbon dioxide emissions. In this paper, fuel cost and emission minimization are considered as the objective function of the economic dispatch problem, taking into account transmission loss using the B matrix. The quadratic model of the fuel cost and emission criterion functions is modeled without considering a valve-point loading effect. The real power generation limits for both wind and conventional generating units are considered. In addition, a closed-form expression based on the incomplete gamma function is provided to define the impact of wind power, which includes the cost of wind energy, including overestimation and underestimation of available wind power using a Weibull-based probability density function. In this research work, Lagrange’s algorithm is proposed to solve the Wind–Thermal Economic Emission Dispatch (WTEED) problem. The developed Lagrange classical optimization algorithm for the WTEED problem is validated using the IEEE test systems with 6-, 10-, and 40-generation unit systems. The proposed Lagrange optimization method for WTEED problem solutions demonstrates a notable improvement in both economic and environmental performance compared to other heuristic optimization methods reported in the literature. Specifically, the fuel cost was reduced by an average of 4.27% in the IEEE 6-unit system, indicating more economical power dispatch. Additionally, the emission cost was lowered by an average 22% in the IEEE 40-unit system, reflecting better environmental compliance and sustainability. These results highlight the effectiveness of the proposed approach in achieving a balanced trade-off between cost minimization and emission reduction, outperforming several existing heuristic techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) under similar test conditions. The research findings report that the proposed Lagrange classical method is efficient and accurate for the convex wind–thermal economic emission dispatch problem. Full article

The transition of South Africa from coal-dependent energy systems to renewable energy alternatives presents economic and environmental trade-off complexities that require empirical investigation. This study employed threshold-switching dynamic models, NARDL analysis, and threshold Granger causality tests to investigate nonlinear relationships between renewable energy generation, economic growth, and carbon dioxide emissions in South Africa from 1980 to 2023. The threshold-switching dynamic models revealed critical structural breakpoints: a 56.4% renewable energy threshold for carbon dioxide emissions reduction, a 397.9% trade openness threshold for economic growth optimisation, and a 385.32% trade openness threshold for coal consumption transitions. The NARDL bounds test confirmed asymmetric effects in the carbon dioxide emissions and renewable energy relationship. The threshold Granger causality test established significant unidirectional causality from renewable energy to carbon dioxide emissions, economic growth to carbon dioxide emissions, and bidirectional causality between coal consumption and trade openness. However, renewable energy demonstrated no significant causal relationship with economic growth, contradicting traditional growth-led energy hypotheses. This study concluded that South Africa’s energy transition demonstrates distinct regime-dependent characteristics, with renewable energy deployment requiring critical mass thresholds to generate meaningful environmental benefits. The study recommended that optimal trade integration and renewable energy thresholds could fundamentally transform the economy’s carbon intensity while maintaining sustainable growth patterns. Full article

Background: Port congestion contributes significantly to operational inefficiency and environmental impact in maritime logistics. With tightening EU regulations such as the Emissions Trading System (EU ETS) and FuelEU Maritime, understanding and mitigating the economic and environmental effects of vessel delays is increasingly critical. This study focuses on a single bulk cargo pier at Constanta Port (Romania), which has experienced substantial traffic fluctuations since 2021, and examines operational and environmental performance through a queuing-theoretic lens. Methods: The authors have applied an M/G/1/∞/FIFO/∞ queuing model to vessel traffic and service time data from 2021–2023, supplemented by Monte Carlo simulations to capture variability in maneuvering and service durations. Environmental impact was quantified in CO₂ emissions using standard fuel-based emission factors, and a Cold Ironing scenario was modeled to assess potential mitigation benefits. Economic implications were estimated through operational cost modeling and conversion of CO₂ emissions into equivalent EU ETS carbon costs. Results: The analysis revealed high berth utilization rates across all years, with substantial variability in waiting times and queue lengths. Congestion was associated with considerable CO₂ emissions, which, when expressed in monetary terms under prevailing EU ETS prices, represent a significant financial burden. The Cold Ironing scenario demonstrated a substantial reduction in at-berth emissions and corresponding cost savings, underscoring its potential as a viable mitigation strategy. Conclusions: Results confirm that operational congestion at the studied berth imposes substantial environmental and financial burdens. The analysis supports targeted interventions such as Just-In-Time arrivals, optimized berth scheduling, and Cold Ironing adoption. Recommendations are most applicable to single-berth bulk cargo operations; future research should extend the approach to multi-berth configurations and incorporate additional operational constraints for broader generalizability. Full article

To further improve the economic benefits of operators and the low-carbon performance within the system, this paper proposes a hierarchical distributed low-carbon economic dispatch strategy for regional integrated energy systems (RIESs) based on the Alternating Direction Method of Multipliers (ADMM). First, the energy coupling relationships among conversion devices in RIESs are analyzed, and a structural model of RIES incorporating an energy generation operator (EGO) and multiple load aggregators (LAs) is established. Second, considering the stepwise carbon trading mechanism (SCTM) and the average thermal comfort of residents, economic optimization models for operators are developed. To ensure optimal energy trading strategies between conflicting stakeholders, the EGO and LAs are embedded into a master–slave game trading framework, and the existence of the game equilibrium solution is rigorously proven. Furthermore, considering the processing speed of the optimization problem by the operators and the operators’ data privacy requirement, the optimization problem is solved in a hierarchical distributed manner using ADMM. To ensure the convergence of the algorithm, the non-convex feasible domain of the subproblem bilinear term is transformed into a convex polyhedron defined by its convex envelope so that the problem can be solved by a convex optimization algorithm. Finally, an example analysis shows that the scheduling strategy proposed in this paper improves the economic efficiency of energy trading participants by 3% and 3.26%, respectively, and reduces the system carbon emissions by 10.5%. Full article

The increasing demand for renewable energy has intensified research on lignocellulosic biomass pyrolysis as a versatile route for sustainable energy and resource recovery. This study provides a comparative overview of main pyrolysis regimes (slow, intermediate, fast, and flash), emphasizing operational parameters, typical product yields, and technological readiness levels (TRLs). Reactor configurations, including fixed-bed, fluidized-bed, rotary kiln, auger, and microwave-assisted systems, are analyzed in terms of design, advantages, limitations, and TRL status. Key process parameters, such as temperature, heating rate, vapor residence time, reaction atmosphere, and catalyst type, critically influence the yields and properties of biochar, bio-oil, and syngas. Increased temperatures and fast heating rates favor liquid and gas production, whereas lower temperatures and longer residence times enhance biochar yield and carbon content. CO₂ and H₂O atmospheres modify product distribution, with CO₂ increasing gas formation and biochar surface area and steam enhancing bio-oil yield at the expense of solid carbon. Catalytic pyrolysis improves selectivity toward target products, though trade-offs exist between char and oil yields depending on feedstock and catalyst choice. These insights underscore the interdependent effects of process parameters and reactor design, highlighting opportunities for optimizing pyrolysis pathways for energy recovery, material valorization, and sustainable bioeconomy applications. Full article

Aiming at the demands for low-carbon transformation in multi-energy-coupled industrial parks, a model is proposed that incorporates a carbon trading system incorporating incentives and penalties. This model includes joint combined heat and power (CHP) units, carbon capture technologies, and power-to-gas (P2G) conversion equipment. Firstly, we develop a modeling framework for the joint operation of cogeneration units to establish a comprehensive energy system within the industrial park that integrates electricity, heat, gas, and cold energy sources. Subsequently, we introduce a reward and punishment carbon trading mechanism into an industrial park to regulate carbon emissions effectively. With an optimization objective focused on minimizing the overall operating costs of the system while considering relevant constraints, we formulate an optimization model. The Gurobi solver is employed through the Yalmip toolkit to address this optimization problem. Finally, four operational scenarios are established to compare and validate the feasibility of our proposed optimization strategy. The results from our computational example demonstrate that integrating combined heat and power along with carbon capture and P2G technologies—coupled with a tiered reward and punishment carbon trading mechanism—can significantly enhance the energy consumption structure of the system. Under this model, the overall expenses are decreased by 12.36%, CO₂ emissions decrease by 33.37%, and renewable energy utilization increases by 36.7%. This approach has effectively improved both wind power consumption capacity and low-carbon economic benefits within the system while ensuring sustainable economic development in alignment with “dual carbon” goals. Full article

Fiber-reinforced composites (FRCs) have emerged as transformative alternatives to traditional marine construction materials, owing to their superior corrosion resistance, design flexibility, and strength-to-weight ratio. This review comprehensively examines the current state of FRC technologies in marine deck and underwater applications, with a focus on manufacturing methods, durability challenges, and future innovations. Thermoset polymer composites, particularly those with epoxy and vinyl ester matrices, continue to dominate marine applications due to their mechanical robustness and processing maturity. In contrast, thermoplastic composites such as Polyether Ether Ketone (PEEK) and Polyether Ketone Ketone (PEKK) offer advantages in recyclability and hydrothermal performance but are hindered by higher processing costs. The review evaluates the performance of various fiber types, including glass, carbon, basalt, and aramid, highlighting the trade-offs between cost, mechanical properties, and environmental resistance. Manufacturing processes such as vacuum-assisted resin transfer molding (VARTM) and automated fiber placement (AFP) enable efficient production but face limitations in scalability and in-field repair. Key durability concerns include seawater-induced degradation, moisture absorption, interfacial debonding, galvanic corrosion in FRP–metal hybrids, and biofouling. The paper also explores emerging strategies such as self-healing polymers, nano-enhanced coatings, and hybrid fiber architectures that aim to improve long-term reliability. Finally, it outlines future research directions, including the development of smart composites with embedded structural health monitoring (SHM), bio-based resin systems, and standardized certification protocols to support broader industry adoption. This review aims to guide ongoing research and development efforts toward more sustainable, high-performance marine composite systems. Full article