Search Results (7,751)

The recent global energy crisis and the instability of the oil market have prompted scientists to explore innovative ways to produce alternative energy sources such as biodiesel. Current chemical processes for converting waste into biodiesel use catalyst-promoted conventional heating, ultrasonication, and magnetron-irradiated electromagnetic microwave irradiation, although developing more efficient, ecologically friendly methods remains challenging. The main goal of this research was to develop a novel, rapid, and efficient method for biodiesel production from waste cooking fats and oils (brown grease), using gyrotron-generated electromagnetic radiation. To achieve this goal, we investigated the effects of gyrotron radiation parameters, the heterogeneous catalyst WS₂, and the ratio of the reacting components on the efficiency of biodiesel production. Brown grease and its components, such as oleic acid, linoleic acid, triolein, and their mixtures, were explored as a source for biodiesel production. We selected promising conditions to develop a technological process for biodiesel production. As a result of our study, novel gyrotron-activated methods for biodiesel production using heterogeneous catalysts have been developed, and the production parameters have been improved. Full article

Emissions Trading Systems (ETSs) have become some of the most widely adopted market-based instruments for reducing greenhouse gas emissions. However, their environmental performance varies considerably across jurisdictions, suggesting that carbon pricing mechanisms operate under heterogeneous structural and institutional conditions. This study analyzes the factors influencing ${\mathrm{CO}}_2$ emissions performance in economies implementing ETSs. Grounded in Ecological Modernization Theory and Institutional Theory, the research combines a k-prototypes clustering model and an Artificial Neural Network (ANN). First, 58 ETSs across 53 countries were classified into four archetypes according to their institutional maturity, regulatory scope, and structural characteristics. Second, an ANN model was estimated using annual data from 2000–2021 to examine the influence of environmental, socio-demographic, economic, and development-related variables on ${\mathrm{CO}}_2$ emissions per capita. The results show that ETS performance depends not only on economic development levels but also on broader structural and institutional factors. Renewable energy consumption and renewable energy production emerge as the most influential drivers of lower ${\mathrm{CO}}_2$ emissions, particularly in developing economies. Conversely, urbanization, export-oriented activities, and governance weaknesses are associated with greater emissions pressures. Corruption also exhibits a stronger negative effect on environmental performance in emerging economies. Overall, the findings suggest that ETSs should not be viewed as standalone climate instruments; their effectiveness depends on complementary policies that promote renewable energy deployment, strengthen institutional quality, and address the pressures associated with trade and urbanization. Full article

In light of growing concerns about sustainable development and green innovation, the green market has progressively taken center stage in the financial markets. From the nonlinear information transmission angle, we look into the interconnectedness between the global banking sectors and the green markets using transfer entropy causal networks, containing the Dow Jones Green Bond Index (SPGB), Dow Jones Sustainability Index (DJSI), The S&P Global Clean Energy Index (SPCL), and MSCI World ESG Leaders Index (ESGL). We observe significant bidirectional causal relationships between two markets. The banking industries of developed nations and emerging economies like South Korea, Indonesia, and India are the most important, while four green markets are vital. Furthermore, using the CEEMDAN-SE-KM approach, this study also investigates the two markets’ heterogeneous performance at various time scales. The causal relationships between two markets exhibit heterogeneity at time scales, and that is most noticeable at the short-term scale. Additionally, after the COVID-19 pandemic and the conflict between Russia and Ukraine, there is an increase in the causal relationships between the two markets and a higher efficiency of information transmission. These results help regulatory bodies and green market players have a more thorough understanding of and dynamic regulation of the green market. Full article

Wet grinding is an important operation in the technological process of ceramic tile production. The properties of the slurry obtained from grinding (slip) are conditioned by the raw materials (the type and characteristics of raw material in mixture, recipes for mixture), and by the operating parameters in grinding (technical characteristics of mill, type of grinding system, mill charge, grinding media body, grinding time, etc.). The optimal selection of these influential parameters results in satisfactory properties of slip, i.e., in efficient grinding as process operation, and, consequently, in smooth and efficient realisation of subsequent operations in the process, particularly spray drying. At the end of the technological process, the final goal is to obtain a ceramic tile of satisfactory quality. Multi-criteria decision-making (MCDM) is an increasingly applied tool for selecting optimal technological parameters for the purpose of optimisation, problem solving and improvement of technological processes. This paper presents the application of the symmetry point of criterion—ranking alternatives by perimeter similarity (SPC-RAPS) as an MCDM hybrid method for the selection of optimal technological parameters in grinding in the ceramic tile production process. The ranking and selection of alternatives (raw materials, grinding balls and grinding time) were performed according to various criteria. In addition to the technological parameters related to the characteristics of the products from the grinding (slip), and to the technical characteristics of the final product (ceramic tiles), the criteria also included economic parameters (the market price of raw material and specific energy consumption in grinding). The developed mathematical model enabled the selection of the best alternative as a solution for this problem. Full article

China’s power sector is accelerating its transition to spot-market clearing with increasing offshore wind penetration. This transition poses compounded operational and economic challenges, as the interaction between generation variability and price volatility affects both producer revenues and real-time system balancing costs. This study utilizes full-year hourly generation and spot price data from an offshore wind farm in eastern Guangdong, which represents the largest offshore wind industry cluster and a premier high-wind-resource area along China’s near-sea coasts. This empirical dataset provides significant value for characterizing real-world market behaviors under Guangdong’s dual-settlement framework. By employing a settlement-consistent Monte Carlo framework to quantify the joint effects of forecast errors, our results reveal that while downside risk is primarily driven by generation volume errors under normal conditions, the negative correlation between wind output and prices intensifies revenue volatility. Furthermore, under high-stress scenarios characterized by extreme market volatility and large deviations, price uncertainty emerges as the dominant driver of tail risk. Ultimately, these findings demonstrate that probabilistic forecasting for both prices and generation is essential not only for producer risk management but also for supporting dispatchable decision-making and reliable operation of power systems with high shares of renewable energy. Full article

Cities in the Majority World face a widening climate investment gap that is often attributed to the absence of suitable financing instruments. Green bonds promise to mobilise private capital for low-carbon urban infrastructure, yet they have diffused unevenly, leaving the economies with the greatest needs at the market’s margins. This study asks whether macroeconomic constraints—the cost of finance, monetary instability, and public indebtedness—systematically shape green bond issuance across emerging and developing economies. We assemble an original panel of 24 such economies over 2015–2024 (240 country-year observations) and estimate pooled ordinary least squares (OLS), random-effects, two-way fixed-effects, Tobit, and probit models with robust standard errors. The public debt-to-GDP ratio is positively associated with issuance in most specifications, though the strength of this relationship varies across estimators and it is not statistically significant in the preferred two-way fixed-effects model; the renewable energy share is consistently positive, while consumer price inflation shows no significant suppressive effect. A probit model of the extensive margin shows that public debt, the renewable energy share, and income per capita raise the probability of issuing among the economies for which the data permit estimation. The four lower-income Sub-Saharan economies in the sample fall outside this estimation owing to missing data, yet record no issuance whatsoever over the decade—a descriptive pattern consistent with the structural barriers the model identifies. The findings challenge the assumption that monetary stabilisation is a precondition for climate finance, pointing instead to capital-market depth and subnational fiscal capacity as the more binding constraints. Full article

The transition towards decarbonised energy systems, often characterised by high photovoltaic penetration, imposes crucial challenges for operational security, flexibility and grid resilience. In this landscape, meteorological-data reliability has emerged as a strategic pillar to mitigate systemic risks arising from forecasting uncertainty, including grid imbalances, electricity-market volatility, and structural asset safety during extreme weather. This study provides a comparative analysis of four forecasting providers, evaluating their accuracy across atmospheric and solar irradiance parameters through heterogeneous datasets spanning diverse climatic zones and seasons. The analysis is performed by employing a dual-source validation framework that benchmarks every forecast against independent, real-world references rather than the providers’ own model-derived observations: first, the atmospheric variables are compared with real surface-station measurements; second, plane-of-array irradiance is benchmarked directly against on-site sensors at operational photovoltaic plants. This dual-source approach isolates systematic model biases relative to real-world environmental conditions, yielding a provider-independent estimate of accuracy against the true atmospheric and irradiance state. This work therefore proposes not merely a comparative analysis but a validation methodology that addresses a specific limitation of existing forecast-comparison approaches, offering actionable insights to minimise financial and operational risks while fostering a secure, resilient, and sustainable energy infrastructure. Full article

Copper is a strategically important commodity whose price dynamics are increasingly affected by structural changes, geopolitical shocks, and the global energy transition. These conditions create substantial challenges for forecasting models and provide a useful setting for evaluating the practical value of machine learning predictions. This study compares statistical and artificial intelligence-based forecasting models for copper price prediction under different market regimes and structural break conditions. Model performance is assessed using a multi-dimensional evaluation framework that combines statistical accuracy (MAPE), dynamic pattern reproduction (Taylor diagrams and time-lagged cross-correlation analysis), and the economic performance of forecast-driven trading strategies. The results reveal a consistent error–profit paradox: models with the highest statistical forecasting accuracy do not necessarily generate the best trading outcomes. In several cases, models with larger prediction errors achieve superior economic performance because they capture directional market dynamics more effectively. The analyses further show that structural breaks substantially alter model rankings and predictive usefulness, highlighting the importance of regime-aware evaluation. These findings suggest that forecast accuracy alone provides an incomplete assessment of model quality in financial and commodity forecasting applications. The study contributes to machine learning evaluation research by proposing an integrated framework that jointly considers predictive accuracy, temporal dynamics, model robustness, and economic utility, thereby offering a more comprehensive approach to assessing forecasting systems in real-world decision-making environments. Full article

Carbon fibers, whose origins are closely intertwined with precursor chemistry and processing conditions, have become indispensable structural lightweight materials due to their exceptional combination of low density, high tensile strength, and high stiffness. This review aims to provide a combined overview of the mechanical properties of carbon fibers by tracing their development from the historically dominant polyacrylonitrile (PAN) and mesophase pitch systems to emerging polyethylene (PE)-based alternatives. Based on decades of fundamental and applied research, this review outlines how precursor molecular structure, stabilization pathways, and carbonization conditions direct microstructural growth and thereby mechanical performance. Established structure/property relationships in PAN and mesophase pitch fibers are discussed alongside recent insights into the sulfonation, crosslinking, and carbonization behavior of PE-based precursor systems. Additionally, this review presents current knowledge on production costs, market dynamics, and the environmental impact of carbon fiber manufacturing, highlighting how energy-intensive processing remains a key barrier to broader industrial adoption. Combined, the findings presented in this review provide an integrated basis describing how precursor selection, processing strategy, and resulting morphology shape mechanical behavior and clarify the position of PE-based carbon fibers within the broader landscape of cost, performance, and sustainability. Full article

The rapid expansion of artificial intelligence (AI) is accelerating data center construction and creating downstream implications for households. This study examines how AI-era data center costs (comprising construction, energy, water, grid upgrades, cooling, and lifecycle management) move through service supply chains and affect household prices in healthcare, transportation, education, banking, and commerce. It also considers the productivity and welfare benefits that AI may transmit. This study identifies four pass-through channels: utility-rate socialization of energy costs, cloud-platform pricing, sectoral pass-through from AI-adopting industries, and indirect effects through supply chains and labor markets. It introduces the AI-inflated net good basket, defined as transmitted cost minus transmitted benefit, to show how AI reshapes the overall net cost of household consumption rather than simply inflating individual prices. The study develops the AI Infrastructure Net Cost Pass-Through Model (AI-NCPM), a four-layer conceptual framework tracing net cost flows from data center investment to sectoral allocation and household outcomes. The model’s parameters are analytically specified but not empirically calibrated; numerical examples are illustrative rather than representing estimated effects. Its main contribution is an integrative framework linking cost pass-through, infrastructure cost socialization, two-sided platform allocation, environmental externalities, and household expenditure incidence within a single net-cost account. Because these effects originate in the design, construction, energy and cooling systems, and lifecycle operation of data centers, the analysis connects AI infrastructure economics to the built environment. The framework suggests that low-income, minority, rural, older adult, and disability-affected households may face disproportionate net burdens, as costs fall heavily on essential services while benefits accrue more readily to affluent and digitally connected households. Full article

This paper proposes a bi-level game-theoretic framework for coordinating large-scale electric vehicles (EVs) in regional power grid deep peak regulation under a demand response mechanism. The upper-level model formulates a non-cooperative game among the deep peak regulation market operator (DPRMO), electric vehicle aggregators (EVAs), and distributed generation units to optimize electricity pricing and scheduling strategies. The lower-level model adopts an evolutionary game based on the logit protocol to describe the bounded rational decision-making process of EV users in charging and discharging strategy selection. Through iterative interaction between the two layers, a Nash equilibrium of the overall system is achieved. Case studies demonstrate that the proposed method effectively improves system performance and economic efficiency. The total operating cost is reduced to 278,642.53 CNY, while the renewable energy utilization rate reaches 96.73%. The peak–valley load difference is reduced to 12,480.52 kW, indicating significant load-smoothing capability. In addition, EV participation generates 12,684.37 CNY in net benefit, while the profits of the distributed energy supplier and EV aggregator reach 94,836.71 CNY and 30,118.52 CNY, respectively. Furthermore, the consumer surplus of flexible loads increases to 417,863.29 CNY, reflecting enhanced demand-side participation. Comparative results show that the proposed bi-level game framework outperforms benchmark strategies in terms of economic cost reduction, renewable energy accommodation, and peak regulation capability. The results verify the effectiveness of the proposed coordinated optimization approach for multi-stakeholder energy systems with large-scale EV integration. Full article

This study examines whether daily supply-chain stress and geopolitical risk improve the forecasting of strategic commodity and clean energy market returns. Using daily data on aluminum, copper, nickel, and clean energy from 10 February 2015 to 27 February 2026, the analysis compares a baseline forecasting model based on conventional market controls with augmented specifications that incorporate supply-chain stress, geopolitical risk, and their joint effects. The empirical framework combines multiple machine-learning algorithms with SHAP-based explainability to evaluate both forecast performance and the relative importance of predictors. Formal Diebold-Mariano tests are also used to assess whether the forecasting gains from augmented specifications are statistically significant. A Model Confidence Set analysis is further used to identify statistically superior model groups across the full set of algorithm-specification combinations. The results show that disruption-related predictors contain asset-specific forecasting information, while the comparison across algorithms indicates that no single model uniformly dominates across all assets and loss functions. The forecasting gains from disruption-related predictors, however, are strongly asset-specific and statistically uneven. For aluminum returns, augmented specifications that include supply-chain stress and/or geopolitical risk significantly improve forecast accuracy relative to the baseline. For copper returns, the evidence is weaker and mainly associated with geopolitical risk. For nickel returns, the joint inclusion of supply-chain stress and geopolitical risk provides the greatest improvement. By contrast, clean energy returns remain more closely tied to conventional macro-financial conditions, with no statistically significant incremental gains from disruption-related variables. SHAP evidence further indicates that predictor importance is asset-specific rather than dominated by a single market factor across all assets. The findings highlight the importance of combining flexible forecasting methods with economically interpretable tools when evaluating disruption-sensitive commodity and clean energy markets. Full article

China considers the healthy and sustainable development of the natural gas market to be crucial for achieving carbon neutrality and has been actively reforming its natural gas market to optimize the energy and economic structure. While previous studies have discussed the effectiveness of policy measures, there is a lack of simulation research and quantitative analysis on the impact of reform measures in China. This study addresses this gap by constructing a numerical simulation model based on China’s long-distance pipeline network and natural gas market, calibrated to 2019 as a pre-reform baseline and extended to the partially reformed market of 2024–2025. The model includes a base scenario to replicate real market conditions and two policy scenarios that incorporate market-oriented reform measures such as deregulation of prices, pipeline separation and interconnection. The effects of these measures on gas allocation and social welfare are examined. The results indicate that (1) pipeline separation and price deregulation increase social welfare, with welfare shifting from consumers to producers and distributors; (2) natural gas consumption and price changes exhibit pronounced regional heterogeneity, even though prices rise overall, reflecting the complexity of policy effects; (3) the reform follows a path of diminishing returns, and the current stage policy already captures about half of the welfare gain and most of the price adjustment, while shielding consumers from much of the surplus loss. Finally, we conclude with policy implications for China’s natural gas market reform. As these reform measures are now being implemented, the quantified welfare and price effects also offer a timely, model-based reference for assessing China’s ongoing gas-market marketization. Full article

Ports and industrial regions increasingly call clean-fuel projects “hubs,” although the market functions behind those projects are often still being developed. In Ulsan, this language has shifted from the Northeast Asian Oil Hub to an oil–gas hub and then to a hydrogen–ammonia vision. This article does not ask whether the infrastructure matters. It asks whether the “hub” label is supported by publicly visible evidence. It develops a public-evidence framework for calibrating claims against evidence and adds a public-label validation layer to sustainability-transition and port-governance analysis. The framework is applied to public documents on Ulsan’s oil, oil–gas, and hydrogen–ammonia projects. The oil-hub narrative began with the use of stockpiling assets and tankage leasing. The North Port later became an oil/LNG terminal through project vehicles, terminal-use agreements, EPC contracts, financing, and commercial operation. These records confirm terminal implementation, not the existence of a trading hub. The South Port is better understood as a low-carbon infrastructure vision, while the April 2026 ammonia-bunkering operation is treated as a port-system fuel-supply milestone. By linking public labels to evidence of implementation, recurring use, coordination routines, market institutions, and external recognition, the framework treats hub naming as a sustainability–accountability issue tied to SDG-related infrastructure claims and ESG disclosure integrity, rather than merely as project branding. Full article

This study evaluates operational decarbonisation options for container vessels operating under the European Union Emissions Trading System (EU ETS) and FuelEU Maritime. This study analyses a 5000 TEU post-Panamax container vessel on the Rotterdam–Limassol route under nine operational scenarios combining three operating speeds (24, 21, and 19 kn) with three fuel configurations (HFO, MGO, and a B30 biofuel blend). The assessment included voyage-level fuel consumption, CO₂ emissions, Energy Efficiency Operational Indicator (EEOI), Tank-to-Wake (TTW) greenhouse-gas intensity, fuel cost, EU ETS exposure, Pareto trade-off analysis, and Monte Carlo uncertainty evaluation. Pareto analysis reduced the nine evaluated scenarios to three representative low-speed operating configurations corresponding to minimum cost (HFO, 19 kn), intermediate emissions reduction (MGO, 19 kn), and minimum emissions (B30, 19 kn). Among these configurations, the B30 case produced the lowest TTW GHG-intensity and EEOI values, whereas the HFO case remained the least-cost option under current market conditions. The break-even assessment indicates that B30 becomes cost-competitive with HFO slow steaming only at carbon prices of approximately 754 EUR/tCO₂ under reference market conditions—substantially above current EU ETS levels (50–80 EUR/tCO₂). Sensitivity analysis shows that reducing the B30 price from 900 USD/t to 720 USD/t lowers this threshold to approximately 406 EUR/tCO₂. The results quantify the current economic gap for transitional biofuels under the EU ETS alone and highlight the complementary role of FuelEU Maritime lifecycle incentives. Key limitations include TTW-based emissions accounting and steady-state operational assumptions. Full article