Search Results (63)

In this study, we develop a stochastic optimal control model for corporate carbon management that synergistically combines emission reduction initiatives with carbon trading mechanisms. The model incorporates two control variables: the autonomous emission reduction rate and initial carbon allowance purchases, while accounting for both deterministic and stochastic carbon pricing scenarios. The solution is obtained through a two-step optimization procedure that addresses each control variable sequentially. In the first step, the problem is transformed into a Hamilton–Jacobi–Bellman (HJB) equation in the sense of viscosity solution. A key aspect of the methodology is deriving the corresponding analytical solution based on this equation’s structure. The second-step optimization results are shown to depend on the relationship between the risk-free interest rate and carbon price dynamics. Furthermore, we employ daily closing prices from 16 July 2021, to 31 December 2024, as the sample dataset to calibrate the parameters governing carbon allowance price evolution. The marginal abatement cost (MAC) curve is calibrated using data derived from the Emissions Prediction and Policy Analysis (EPPA) model, enabling the estimation of the emission reduction efficiency parameter. Additional policy-related parameters are obtained from relevant regulatory documents. The numerical results demonstrate how enterprises can implement the model’s outputs to inform carbon emission reduction decisions in practice and offer enterprises a decision-support tool that integrates theoretical rigor and practical applicability for achieving emission targets in the carbon market. Full article

This study presents an integrated ex-post evaluation of a municipal window-retrofit program in Goyang, Republic of Korea (heating-dominated, Dwa). Using field surveys and pre- and post-utility bills for 36 dwellings, mainly pre-2000 low-rise reinforced-concrete buildings, we normalize climate with HDD and CDD and prices with CPI-deflated tariffs to isolate the intrinsic effect of window replacement. Area-normalized indicators ($∆e$, $\eta$, DPB, NPV, AC) were computed. Average annual savings were 30.2 kWh per m² per year ($\eta$ ≈ 16 percent), consisting of 10.6 kWh per m² per year of gas and 19.6 kWh per m² per year of electricity (n = 36). The median discounted payback was 7.0 years. Under a 50 percent subsidy, about 80 percent of projects recovered private investment within 15 years and showed positive NPV with a median of about USD 4944. The electricity-tariff multiplier had the largest influence on cash flows and payback. The median abatement cost was about USD 352 per tCO₂-eq. A portfolio view indicates that prioritizing low-cost cases maximizes total abatement, and that higher-cost cases merit design or cost review. Using the first post-retrofit year 2023, portfolio abatement is about 623 tCO₂-eq per year. The framework jointly normalizes climate and price effects and yields policy-relevant estimates for heating-dominated contexts. Full article

This paper aims to provide an analytical assessment of country-level experience in moving towards agricultural decarbonization—from ideas around decarbonization measures to assessment of their potential, including evaluations of political goals and practical implementation success. This paper is based on 10-year cycle that highlights the main steps in building decarbonization awareness using an approach that can monitor, quantify, and evaluate the contribution of agricultural practices to climate change mitigation. This approach is based on a marginal abatement cost curve (MACC), which serves as a convenient visual tool for evaluating the effectiveness of various greenhouse gas emission reduction measures in agriculture, as well as climate policy planning. This study reveals the experiences to date and the main directions for developing the MACC approach, which serves as a basis for analyzing the potential of moving towards decarbonization in agriculture for a specific European Union Member state, i.e., Latvia. The results of the study are of practical use for the development of agricultural, environmental, and climate policies or legal frameworks, policy analysis, and impact assessment. Additionally, the findings are useful for educating farmers and the public about measures to reduce GHG and ammonia emissions. Full article

The transport sector is a major contributor to global greenhouse gas emissions, making its decarbonization critical for climate change mitigation efforts. The Marginal Abatement Cost (MAC) curve is a vital tool that evaluates the cost-effectiveness of mitigation measures by comparing their emission reduction potential against their implementation costs. This paper conducts a literature review to analyze the application of the MAC curve in the transport sector, identifying common mitigation measures, comparing abatement costs, and assessing the tool’s role in shaping decarbonization policies. The findings reveal a predominance of technology-focused, bottom-up methodologies, with a significant research gap in the freight sector, which is largely overlooked compared with passenger transport. The results show that the abatement costs for similar measures vary considerably across geographical contexts, influenced by local factors such as fuel prices and gross domestic product (GDP). The analysis suggests that combining technological solutions with behavioral and structural changes creates synergistic effects, yielding greater benefits than isolated actions. The strong alignment observed between measures analyzed in the literature and subsequent national climate policies confirms the MAC curve’s strategic importance as an evidence-based instrument for policymakers to construct economically rational decarbonization pathways. Full article

With the continuous expansion in China’s vehicle fleet, emissions of CO₂ and air pollutants from the on-road transportation sector are widely projected to be rising, posing a challenge to realizing China’s targets of carbon peaking in 2030 and carbon neutrality in 2060, as well as the national target for air quality improvement. Therefore, vehicle electrification in the on-road transportation sector is urgently needed to reduce emissions of CO₂ and air pollutants, as it serves as a key pathway to align transportation development with sustainability goals. While vehicle electrification is supposed to be the primary solution, there is a research gap in quantifying the provincial, environmental, and economic impacts of implementing such a policy in China. To bridge this gap, we projected the provincial-level ownership of different types of vehicles based on historical trends, assessed the emission reduction potential for CO₂ and air pollutants using the LEAP model from 2021 to 2060, and predicted the provincial marginal abatement costs at different mitigation stages under various scenarios with different strategies of vehicle electrification and development patterns of electricity structure. Our results show that the implementation of vehicle electrification lowers the national carbon peak by 0.2–0.6 Gt yr⁻¹ and advances its achievement by 1–3 years ahead of 2030. The marginal abatement cost ranges from $532 to $3466 per ton CO₂ (tCO₂⁻¹) in 2025 and from −$180 to −$113 tCO₂⁻¹ in 2060 across scenarios. The provincial marginal abatement cost curves further indicate that China’s vehicle electrification should be prioritized in cost-effective regions (e.g., Shanghai and Guangdong), while concurrently advancing nationwide grid decarbonization to guarantee the supply of low-carbon electricity across the country. This optimized pathway ensures that transportation decarbonization aligns with both environmental and economic requirements, providing actionable support for China’s sustainable development strategy. Full article

The operation of modern power networks is increasingly exposed to overlapping climate extremes and volatile system conditions, making it essential to adopt scheduling approaches that are resilient as well as economical. In this study, a two-stage stochastic formulation is advanced, where indicators of system adaptability are embedded directly into the optimization process. The objective integrates standard operating expenses—generation, reserve allocation, imports, responsive demand, and fuel resources—with a Conditional Value-at-Risk component that reflects exposure to rare but damaging contingencies, such as extreme heat, severe cold, drought-related hydro scarcity, solar output suppression from wildfire smoke, and supply chain interruptions. Key adaptability dimensions, including storage cycling depth, activation speed of demand response, and resource ramping behavior, are modeled through nonlinear operational constraints. A stylized test system of 30 interconnected areas with a 46 GW demand peak is employed, with more than 2000 climate-informed scenarios compressed to 240 using distribution-preserving reduction techniques. The results indicate that incorporating risk-sensitive policies reduces expected unserved demand by more than 80% during compound disruptions, while the increase in cost remains within 12–15% of baseline planning. Pronounced spatiotemporal differences emerge: evening reserve margins fall below 6% without adaptability provisions, yet risk-adjusted scheduling sustains 10–12% margins. Transmission utilization curves further show that CVaR-based dispatch prevents extreme flows, though modest renewable curtailment arises in outer zones. Moreover, adaptability provisions promote shallower storage cycles, maintain an emergency reserve of 2–3 GWh, and accelerate the mobilization of demand-side response by over 25 min in high-stress cases. These findings confirm that combining stochastic uncertainty modeling with explicit adaptability metrics yields measurable gains in reliability, providing a structured direction for resilient system design under escalating multi-hazard risks. Full article

To address the challenges of inefficient path planning, discontinuous trajectories, and inadequate safety margins in autonomous spraying vehicles for greenhouse environments, this paper proposes a hierarchical motion control architecture. At the global path planning level, the heuristic function of the A* algorithm was redesigned to integrate channel width constraints, thereby optimizing node expansion efficiency. A continuous reference path is subsequently generated using a third-order Bézier curve. For local path planning, a state-space sampling method was adopted, incorporating a multi-objective cost function that accounts for collision distance, curvature change rate, and path deviation, enabling the real-time computation of optimal obstacle-avoidance trajectories. At the control level, an adaptive look-ahead distance pure pursuit algorithm was designed for trajectory tracking. The proposed framework was validated through a Simulink-ROS co-simulation environment and deployed on a Huawei MDC300F computing platform for real-world vehicle tests under various operating conditions. Experimental results demonstrated that compared with the baseline methods, the proposed approach improved the planning efficiency by 38.7%, reduced node expansion by 16.93%, shortened the average path length by 6.3%, and decreased the path curvature variation by 65.3%. The algorithm effectively supports dynamic obstacle avoidance, multi-vehicle coordination, and following behaviors in diverse scenarios, offering a robust solution for automation in facility agriculture. Full article

Due to their potential to reduce greenhouse gas emissions, light-frame timber buildings (LFTBs) are widely used in seismically active regions. However, their construction in these areas remains limited, primarily due to the high costs associated with continuous anchor tie systems (ATSs), which are required to withstand significant seismic forces. To address this challenge, frictional seismic isolation offers an alternative by enhancing seismic protection. Although frictional base isolation is an effective mitigation strategy, its performance can be compromised by extreme ground motions that induce large lateral displacements, resulting in impacts between the sliders and the perimeter protection ring. The effects of these internal lateral impacts on base-isolated LFTBs remain largely unexplored. To fill this knowledge gap, this study evaluates the collapse capacity of a set of base-isolated LFTBs representative of Chilean real estate developments. Nonlinear numerical models were developed in the OpenSeesPy platform to capture the nonlinear behavior of the superstructure, including the impact effects within the frictional isolation system. Incremental dynamic analyses following the FEMA P695 methodology were performed using subduction ground motions. Collapse margin ratios (CMRs) and fragility curves were derived to quantify seismic performance. Results indicate that frictional base-isolated LFTBs can achieve acceptable collapse safety without ATS, even with compact-size bearings. Code-conforming archetypes achieved CMRs ranging from 1.24 to 1.55, indicating sufficient safety margins. These findings support the cost-effective implementation of frictional base isolation in mid-rise timber construction for high-seismic regions. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article

Since its development in 1904, radical prostatectomy (RP) has remained a fundamental surgical option in the management of localised prostate cancer. Over time, continuous advancements in surgical techniques have improved oncological outcomes while reducing functional complications. This narrative review explores the evolution of RP, depicting its progression from the traditional open approach to minimally invasive laparoscopic and robotic-assisted techniques. Key developments in RP techniques, including nerve-sparing, bladder neck-sparing and Retzius-sparing techniques as well as enhanced perioperative management, have contributed to reduced postoperative complications, namely incontinence and erectile dysfunction. Additionally, technological innovations such as augmented reality, utilising indocyanine green for improved visualisation of prostatic boundaries and illuminare-1 to easily identify nerves intraoperatively, artificial intelligence, and novel molecular imaging technologies such as PSMA PETs for improved margin assessment are shaping the future of RPs. Despite these advancements, challenges persist, including a steep learning curve associated with newer techniques, disparities in access due to cost considerations, and a lack of standardised outcome measures across different surgical approaches. This review provides insight into current trends, ongoing challenges, and future directions that may further refine surgical precision, enhance patient safety, and improve long-term treatment success in prostate cancer management. Full article

In contrast to brittle normal-strength concrete (NSC), high-performance steel-fiber-reinforced concrete (HPSFRC) provides better tensile and shear resistance, enabling enhanced bridge girder design. To achieve a balance between cost efficiency and quality, reducing conventional reinforcement is a viable cost-saving strategy. This study focused on the flexural behavior of a type of pre-tensioned precast HPSFRC girder without longitudinal and shear reinforcement. This type of girder consists of HPSFRC and prestressed steel strands, balancing structural performance, fabrication convenience, and cost-effectiveness. A 30.0 m full-scale girder was randomly selected from the prefabrication factory and tested through a four-point bending test. The failure mode, load–deflection relationship, and strain distribution were investigated. The experimental results demonstrated that the girder exhibited ductile deflection-hardening behavior (47% progressive increase in load after the first crack), extensive cracking patterns, and large total deflection (1/86 of effective span length), meeting both the serviceability and ultimate limit state design requirements. To complement the experimental results, a nonlinear finite element model (FEM) was developed and validated against the test data. The flexural capacity predicted by the FEM had a marginal 0.8% difference from the test result, and the predicted load–deflection curve, crack distribution, and load–strain curve were in adequate agreement with the test outcomes, demonstrating reliability of the FEM in predicting the flexural behavior of the girder. Based on the FEM, parametric analysis was conducted to investigate the effects of key parameters, namely concrete tensile strength, concrete compressive strength, and prestress level, on the flexural responses of the girder. Eventually, design recommendations and future studies were suggested. Full article

The 83rd session of the IMO Maritime Environment Protection Committee (MEPC 83) approved a global pricing mechanism for the shipping industry, with formal adoption scheduled for October 2025. Proposed mechanisms include the International Maritime Sustainable Fuels and Fund (IMSF&F) and a combined approach integrating GHG Fuel Standards with Universal GHG Contributions (GFS&UGC). This study developed a model based on the marginal abatement cost curve (MACC) methodology to assess the cost-effectiveness of alternative fuels under both mechanisms. Sensitivity analyses evaluated the impacts of fuel prices, carbon prices, and the GHG Fuel Intensity (GFI) indicator on MAC. Results indicate that implementing the GFS&UGC mechanism yields higher net present values (NPVs) and lower MACs compared to IMSF&F. Introducing universal GHG contributions promotes a comparatively fairer transition to sustainable shipping fuels. Investments in zero- or near-zero-fueled (ZNZ) ships are unlikely to be recouped by 2050 unless carbon prices rise sufficiently to boost revenues. Bio-Methanol and bio-diesel emerged as the most cost-competitive ZNZ options in the long term, while e-Methanol’s poor competitiveness stems from its extremely high price. Both pooling costs and universal GHG levies significantly reduce LNG’s economic viability over the study period. MACs demonstrated greater sensitivity to fuel prices (P_fuel) than to carbon prices (P_carbon) or GFI within this study’s parameterization scope, particularly under GFS&UGC. Ratios of P_carbon%/P_fuel% in equivalent sensitivity scenarios were quantified to determine relative price importance. This work provides insights into fuel selection for shipping companies and supports policymakers in designing effective GHG pricing mechanisms. Full article

Data bias in geohazard artificial intelligence (AI) systems, particularly class distribution imbalances, critically undermines the reliability of landslide detection models. While active learning (AL) offers promise for mitigating annotation costs and addressing data biases, the interplay between landslide class proportions and AL efficiency remains poorly quantified; additionally, self-optimizing mechanisms to adaptively manage class imbalances are underexplored. This study bridges these gaps by rigorously evaluating how landslide-to-non-landslide ratios (1:1, 1:12, and 1:30) influence the effectiveness of a widely used AL strategy—margin sampling. Leveraging open-source landslide inventories, we benchmark margin sampling against random sampling using the area under the receiver operating characteristic curve (AUROC) and partial AUROC while analyzing spatial detection accuracy through classification maps. The results reveal that margin sampling significantly outperforms random sampling under severe class imbalances (1:30), achieving 12–18% higher AUROC scores and reducing false negatives in critical landslide zones. In balanced scenarios (1:1), both strategies yield comparable numerical metrics; however, margin sampling produces spatially coherent detections with fewer fragmented errors. These findings indicate that regardless of the landslide proportion, AL enhances the generalizability of landslide detection models in terms of predictive accuracy and spatial consistency. This work also provides actionable guidelines for deploying adaptive AI systems in data-scarce, imbalance-prone environments. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

Offshore wind power is one of the primary forms of utilizing marine green energy in China. Currently, near-shore wind power predominantly employs monopile foundations, with designs typically being overly conservative, resulting in high construction costs. Precise characterization of the interaction mechanisms between marine piles and surrounding soils is crucial for foundation design optimization. Traditional p-y curve methods, with simplified fitting functions, inadequately capture the complex pile–soil behaviors, limiting predictive accuracy and model uncertainty quantification. To address these challenges, this research collected 1852 empirical datasets of offshore wind monopile foundation pile–soil interactions, developing p-y curve and horizontal displacement prediction models using artificial neural network (ANN) expressions and comprehensive uncertainty statistical analysis. The constructed ANN model demonstrates a simple structure with satisfactory predictive performance, achieving average error margins below 6% and low to moderate prediction accuracy dispersion (26%~45%). In contrast, traditional p-y curve models show 30%~50% average biases with substantial accuracy dispersion near 80%, while conventional finite element methods exhibit approximately 40% error and dispersion. By strictly characterizing the probability cumulative function of the neural network model factors, a foundation is provided for reliability-based design. Through comprehensive case verification, it is demonstrated that the ANN-based model has significant advantages in terms of computational accuracy and efficiency in the design of offshore wind power foundations. Full article