Search Results (47)

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

Although carbon emissions trading systems are universally acknowledged as one of the most potent policy instruments for counteracting hazardous climate trends, and digitalization is seen as a favorable technological means to promote corporate green and low-carbon transformation, few studies have investigated the dark side of both. Using data on Chinese listed companies from 2011 to 2020 and adopting a multi-period DID methodology, this research reveals that, in response to the carbon emissions trading system, firms often adopt low-cost, strategic environmental governance behaviors—namely, carbon washing—to reduce compliance costs and maintain their reputation and image. Furthermore, the study reveals that the information advantages of digital transformation create conditions for the opportunistic manipulation of carbon disclosure. Digitalization amplifies the positive influence of the carbon trading system on corporate carbon washing behavior. Mechanism analysis confirms that the carbon emissions trading system increases the production costs of regulated firms, thereby increasing their carbon washing behavior. Economic consequence analysis confirms that firms engage in carbon washing to gain legitimacy and maintain their reputation and image, which may allow them to obtain opportunistic benefits in the capital market. Finally, this study suggests that the government should adopt supplementary policy tools, such as environmental subsidies, enhanced use of digital technologies to strengthen regulatory capacity, and increased media oversight, to mitigate the unintended consequences of the carbon trading system on corporate behavior. Full article

The energy transition toward greater electrification leads to incentives in public transportation fed by catenary-powered networks. In this context, emerging technological devices such as in-motion-charging vehicles and electric vehicle charging points are expected to be operated while connected to trolleybus networks as part of new electrification projects, resulting in a significant demand for power. To enable a significant increase in electric transportation without compromising technical compliance for voltage and current at grid systems, the implementation of stationary battery energy storage systems (BESSs) can be essential for new electrification projects. A key challenge for BESSs is the selection of the optimal converter topology for charging their batteries. Ideally, the chosen converter should offer the highest efficiency while minimizing size, weight, and cost. In this context, a modular dual-active-bridge converter, considering its operation as a full-power converter (FPC) and a partial-power converter (PPC) with module-shedding control, is analyzed in terms of operation efficiencies and thermal behavior. The goal is to clarify the advantages, disadvantages, challenges, and trade-offs of both power-processing techniques following future trends in the electric transportation sector. The results indicate that the PPC achieves an efficiency of 98.58% at the full load of 100 kW, which is 1.19% higher than that of FPC. Additionally, higher power density and cost effectiveness are confirmed for the PPC. Full article

Accurate, energy-efficient climate regulation is crucial for scaling smart greenhouse production. While nonlinear model predictive control (NMPC) can co-optimize yield and resource use, its efficacy hinges on short-range weather information and real-time solver feasibility. This paper investigates the performance of advanced NMPC strategies for smart greenhouse climate control, with particular emphasis on the roles of AI-driven disturbance prediction and warm-start initialization for real-time optimization. Six controller configurations, including feedback-only, LSTM-based forecast, and ideal disturbance models, each with and without warm-start, were tested in a 40-day simulation of a lettuce smart greenhouse. Performance metrics included final biomass, constraint violations, resource costs, profit, and solver time. Results show that feedback-only controllers maximize yield and profit, incurring higher CO₂ costs but lower heating costs, alongside greater constraint violations compared to the predictive strategies. Predictive and ideal disturbance-aware controllers effectively reduce resource consumption and improve constraint compliance at the expense of lower yields. Importantly, warm-start initialization significantly accelerates computation without affecting control quality. The study also demonstrates that penalty parameters, rather than economic weight settings, predominantly determine aggregate constraint violation. The findings provide actionable insights for designing and deploying NMPC-based greenhouse controllers, highlighting the importance of warm-start techniques and the trade-offs between productivity, resource efficiency, and environmental compliance. Full article

Food quality and safety are essential for ensuring public health, preventing foodborne illness, reducing food waste, maintaining consumer confidence, and supporting regulatory compliance and international trade. This has led to the emergence of many research works that focus on automating and streamlining the assessment of food quality. Electronic noses have become of paramount importance in this context. We analyze the current state of research in the development of electronic noses for food quality and safety. We examined research papers published in three different scientific databases in the last decade, leading to a comprehensive review of the field. Our review found that most of the efforts use portable, low-cost electronic noses, coupled with pattern recognition algorithms, for evaluating the quality levels in certain well-defined food classes, reaching accuracies exceeding 90% in most cases. Despite these encouraging results, key challenges remain, particularly in diversifying the sensor response across complex substances, improving odor differentiation, compensating for sensor drift, and ensuring real-world reliability. These limitations indicate that a complete device mimicking the flexibility and selectivity of the human olfactory system is not yet available. To address these gaps, our review recommends solutions such as the adoption of adaptive machine learning models to reduce calibration needs and enhance drift resilience and the implementation of standardized protocols for data acquisition and model validation. We introduce benchmark comparisons and a future roadmap for electronic noses that demonstrate their potential to evolve from controlled studies to scalable industrial applications. In doing so, this review aims not only to assess the state of the field but also to support its transition toward more robust, interpretable, and field-ready electronic nose technologies. Full article

Ports are vital nodes in global trade networks but are also significant contributors to greenhouse gas emissions. Their transition toward sustainable, nearly zero-energy operations require comprehensive and structured strategies. This study proposes a practical and scalable framework to support the energy decarbonization of ports through the phased integration of hybrid renewable energy systems (HRES) and energy storage systems (ESS). Emphasizing a systems-level approach, the framework addresses key aspects such as energy demand assessment, resource potential evaluation, HRES configuration, and ESS sizing, while incorporating load characterization protocols and decision-making thresholds for technology deployment. Special consideration is given to economic performance, particularly the minimization of the Levelized Cost of Energy (LCOE), alongside efforts to meet energy autonomy and operational resilience targets. In parallel, the framework integrates digital tools, including smart grid infrastructure and digital shadow technologies, to enable real-time system monitoring, simulation, and long-term optimization. It also embeds mechanisms for regulatory compliance and continuous adaptation to evolving standards. To validate its applicability, the framework is demonstrated using a representative case study based on a generic port profile. The example illustrates the transition process from conventional energy models to a sustainable port ecosystem, confirming the framework’s potential as a decision-making tool for port authorities, engineers, and policymakers aiming to achieve effective, compliant, and future-proof energy transitions in maritime infrastructure. Full article

Virtual machine (VM) placement in cloud datacenters is a complex multi-objective challenge involving trade-offs among energy efficiency, carbon emissions, and network performance. This paper proposes NCRA-DP-ACO (Network-, Cost-, and Renewable-Aware Ant Colony Optimization with Dynamic Power Usage Effectiveness (PUE)), a bio-inspired metaheuristic that optimizes VM placement across geographically distributed datacenters. The approach integrates real-time solar energy availability, dynamic PUE modeling, and multi-criteria decision-making to enable environmentally and cost-efficient resource allocation. The experimental results show that NCRA-DP-ACO reduces power consumption by 13.7%, carbon emissions by 6.9%, and live VM migrations by 48.2% compared to state-of-the-art methods while maintaining Service Level Agreement (SLA) compliance. These results indicate the algorithm’s potential to support more environmentally and cost-efficient cloud management across dynamic infrastructure scenarios. Full article

Against the backdrop of rapidly expanding international trade and escalating environmental challenges, the development of green ports has emerged as a pivotal element of sustainable development. This study addresses the critical issues of insufficient corporate motivation for transformation and inadequate regulatory mechanisms by establishing a tripartite evolutionary game model involving government, customs, and port enterprises. Key findings demonstrate that customs supervision significantly reduces enterprises’ transition costs and enhances environmental compliance willingness, though its effectiveness depends on complementary government policies including environmental taxation and fiscal incentives. When regulatory intensity is weak, enterprises persist with conventional practices; conversely, strengthened supervision accelerates strategic convergence toward sustainable governance. This research provides a theoretical foundation for policymakers to formulate green port initiatives while offering practical guidance for enterprises to align with sustainability objectives, thereby contributing to environmentally responsible port development. Full article

This study examined the dynamic relationship between China’s carbon pricing (CP) and green technology innovation (GTI) using monthly data from August 2013 to February 2025 through sub-sample rolling-window Granger causality tests. The results revealed a time-varying bidirectional relationship where CP significantly promotes GTI during periods when innovation offset effects dominate (such as from July to October 2021 and October 2023 to March 2024), but inhibits GTI when compliance cost effects prevail (as observed from February to June 2022). Conversely, GTI alternately suppressed CP from June to November 2017 and enhanced it from February to July 2024. These patterns demonstrate that the interaction between CP and GTI is critically shaped by three key factors: policy synergy between carbon markets and complementary environmental regulations, international competitive pressures from carbon border mechanisms, and financial market capacity to support green investments. Based on these findings, we propose a comprehensive policy framework that includes expanding emissions trading to heavy industries, implementing dynamic CP stabilization mechanisms, introducing innovation-linked quota incentives with 1.1 to 1.5 multipliers, and developing integrated green financial instruments. This framework can effectively align CP with GTI to accelerate China’s low-carbon transition while maintaining industrial competitiveness. Full article

This paper presents a Location-Routing Problem (LRP) model for optimizing pickup operations in reverse logistics while incorporating drivers’ well-being constraints. The LRP is formulated as a Mixed-Integer Linear Programming (MILP) model, integrating collection center selection and vehicle routing to minimize total costs, including facility operation, vehicle fixed costs, travel expenses, and driver salary rates. A key contribution of this study is the inclusion of maximum driving time and mandatory break constraints to enhance drivers’ well-being, ensuring compliance with regulations and mitigating fatigue-related risks. We solve the problem using the MILP model in Gurobi and validate it with data from the literature. We test multiple instances to check the model’s performance and solution quality. The results show that the model effectively optimizes collection point allocation and routing while considering cost efficiency and drivers’ well-being. The inclusion of breaks leads to a trade-off between cost minimization and operational sustainability, highlighting the importance of incorporating social factors in logistics planning. Full article

Automotive air conditioning systems improve passenger comfort and safety while keeping pace with changing environmental and technological requirements. This review evaluates the historical development, technological progress, and future trends of automotive air conditioning systems, primarily focusing on passenger vehicles, where cabin comfort and individualized thermal control are essential. The analysis examines the transition from early, energy-intensive cooling systems typically operating at a coefficient of performance (COP) of around 1.5 to modern, environmentally friendly alternatives that achieve COP values of approximately 3.0 or higher, highlighting the impact of regulatory measures such as the Kigali Amendment. A particular focus is placed on comparing refrigerants, especially the transition from HFC-134a to HFO-1234yf, with a discussion of their ecological impact and compliance with regulations. Innovative technologies, including adsorption cooling, AI-enhanced climate control, and the integration of renewable energy, are being explored as potential solutions to current challenges. Initially, 121 articles were reviewed, with 84 chosen for detailed examination based on their relevance, methodological soundness, and contributions to the field. The results reveal the trade-offs among efficiency, cost, and sustainability, highlighting the need for ongoing innovation to balance energy usage and environmental stewardship. Future studies should focus on creating refrigerants with extremely low global warming potential, improving battery efficiency in electric vehicles, and utilizing AI for tailored climate control. By tackling these issues, the automotive sector can offer more sustainable and efficient air conditioning options that align with consumer expectations and environmental regulations. Full article

While tariff liberalization under regional trade agreements has progressed, non-tariff measures (NTMs) have emerged as a significant impediment to the realization of full trade potential, particularly in the agriculture sector where NTMs are especially prevalent and in the Southern African Development Community (SADC) where intra-regional trade is low. Despite the extensive available literature on this subject, the impact of NTMs on trade efficiency in the SADC has hardly been explored. Against this background, this study estimates the impact of NTMs on the efficiency of South Africa’s bilateral agricultural trade with 11 SADC member states using data from 2011 to 2022 and a stochastic frontier gravity model. The average efficiency is found to be 45.6 percent, implying that more than half of South Africa’s agricultural trade potential remains unrealized in the region due to inefficiencies. NTMs are found to be a source of inefficiency, the effect of which is larger than that of tariffs by a factor of 6. This result emphasizes an urgent need for harmonizing NTMs across SADC member states to reduce compliance costs which are associated with trade inefficiency. The study contributes to the literature by treating NTMs as man-made trade resistances that affect trade efficiency rather than trade volumes. Full article

In response to the International Maritime Organization’s (IMO) 2020 sulfur cap and China’s stricter emission control policies, this study investigates the strategic interaction between port authorities and shipowners concerning air pollution emissions from ships in port areas. Using supervisory game theory, we construct a model that captures the cost–benefit trade-offs between inspection efforts by regulators and compliance behavior by ship operators. Empirical data from Guangzhou Port in 2020—including government inspection costs, fuel substitution costs, subsidy schemes, and fine levels—are incorporated into the model to simulate equilibrium outcomes. Results indicate that while the current level of inspection has a significant deterrent effect, the probability of full compliance remains low at 34.36%, highlighting the importance of a balanced regulatory approach combining inspection, fines, and subsidies. Policy implications suggest that increased financial incentives and stronger penalties can reduce both regulatory costs and non-compliance risks. This study contributes to the literature on maritime environmental governance by providing a quantitative supervisory framework grounded in real-world port data. Full article

Maritime transport accounts for approximately 3% of global greenhouse gas (GHG) emissions, a figure projected to rise by 17% by 2050 without effective mitigation measures. Achieving zero-emission shipping requires a comprehensive strategy that integrates regulatory frameworks, alternative fuels, and energy-saving technologies. However, existing studies often fail to provide an integrated analysis of regulatory constraints, economic incentives, and technological feasibility. This study bridges this gap by developing an integrated model tailored for international maritime transport, incorporating regulatory constraints, economic incentives, and technological feasibility into a unified framework. The model is developed using a predictive approach to assess decarbonization pathways for global shipping from 2018 to 2035. A multi-criterion decision analysis (MCDA) framework, coupled with techno-economic modeling, evaluates the cost-effectiveness, technology readiness, and adoption potential of alternative fuels, operational strategies, and market-based measures. The results indicate that technical and operational measures alone can reduce emissions by up to 44%, while market-based measures improve the diversity of sustainable fuel adoption. Biofuels, particularly BISVO and BIFAME, emerge as preferred alternatives due to cost-effectiveness, while green hydrogen, ammonia, and biomethanol remain unviable without additional policy support. A strict carbon levy increases transport costs by 46%, whereas flexible compliance mechanisms limit cost increases to 14–25%. The proposed approach provides a robust decision-support framework for policymakers and industry stakeholders, ensuring transparency in evaluating the trade-offs between emissions reductions and economic feasibility, thereby guiding future regulatory strategies. Full article

The maritime industry, a cornerstone of global trade, faces mounting pressure to decarbonize and align with international greenhouse gas (GHG) reduction targets set by the International Maritime Organization (IMO). This study investigates how project management frameworks and techno-economic analysis (TEA) can jointly address the technical, economic, and strategic challenges of retrofitting maritime vessels for alternative fuels. A mixed-methods approach was employed, combining systematic literature synthesis, case study analysis, and theoretical modeling. Key findings highlight the pivotal role of project management in mitigating retrofitting risks, optimizing lifecycle costs, and aligning retrofitting projects with organizational objectives, including sustainability and regulatory compliance. The study also identifies best practices, such as leveraging interdisciplinary collaboration and smart energy management systems, to enhance retrofitting outcomes. By integrating TEA with project management, the research contributes actionable insights to advance the maritime industry’s energy transition and decarbonization efforts. Full article