Search Results (179)

This paper proposes a physics-informed hybrid digital CO₂ emission-control technology for maritime transport, designed for adaptive ship speed optimization along a predefined geographical route between two ports, discretized into quasi-stationary segments and evaluated under forecasted metocean conditions, subject to economic and regulatory constraints associated with maritime decarbonization. The framework integrates two exact optimization methods, Backtracking (BT) and Dynamic Programming (DP), with a reinforcement learning approach based on Proximal Policy Optimization (PPO), operating on a unified physical, economic, and regulatory modeling core. By reducing propulsion fuel demand, the system acts as an upstream CO₂ emission-control mechanism for ship propulsion. This operational stabilization of the engine load creates favourable boundary conditions for advanced combustion processes and reduces the volumetric flow of exhaust gas, thereby lowering the technical burden on potential post-combustion carbon capture systems. Segment-wise speed profiles are optimized subject to propulsion limits, Estimated Time of Arrival (ETA) feasibility, and regulatory constraints, including the Carbon Intensity Indicator (CII), the European Union Emissions Trading System (EU ETS) and FuelEU Maritime. The physics-based propulsion and energy model is validated using full-scale operational data from four real voyages of an oil/chemical tanker. A detailed case study on the Milazzo–Motril route demonstrates that adaptive speed optimization consistently outperforms conventional cruise operation. Exact optimization methods achieve voyage time reductions of approximately 10% and fuel and CO₂ emission reductions of about 9–10%. The reinforcement learning approach provides the best overall performance, reducing voyage time by approximately 15% and achieving fuel savings and CO₂ emission reductions of about 13%. At the route level, the Carbon Intensity Indicator is reduced by approximately 10% for the exact methods and by about 13% for PPO. Backtracking and Dynamic Programming converge to nearly identical globally optimal solutions within the discretized decision space, while PPO identifies solutions located on the most favourable region of the cost–time Pareto front. By benchmarking reinforcement learning against exact discrete solvers within a shared physics-informed structure, the proposed digital platform provides transparent validation of learning-based optimization and offers a scalable decision-support technology for pre-fixture evaluation of fixed-route voyages. The system enables quantitative assessment of CO₂ emissions, ETA feasibility, and regulatory exposure (CII, EU ETS, FuelEU Maritime penalties) prior to transport contracting, thereby supporting economically and environmentally informed operational decisions. Full article

The analysis of water and emission efficiency in cropping systems is vital for sustainable agriculture in Mediterranean regions, which face increasing water shortages. This study offers a site-specific assessment of the Water Footprint (WFP) and Carbon Footprint (CFP) of organic cotton grown under Mediterranean conditions, integrating environmental indicator measurements with economic valuation of greenhouse gas (GHG) emissions via the EU Emissions Trading System (ETS) and the Social Cost of Carbon (SCC). Experiments were carried out at three sites with different soil types, testing two cultivars (Armonia and ST-318) under three irrigation scenarios: severe water deficit (I30), moderate water deficit (I70), and full irrigation (I100). The results reveal significant site-specific variability, with average WFP_lint values ranging from about 1.440 m³ per ton at the most productive site to over 4.100 m³ per ton at the least productive site. Similarly, CFP_lint is lower under high-yield conditions, emphasizing the strong influence of yield on mass-based indicators. At the Carboj and Primosole sites, shifting from (I30) to I100 results in roughly a 50% reduction in emissions, while at Buonfornello, increased irrigation does not consistently produce benefits. The cultivar response is key: Armonia shows greater resilience to water stress, while ST-318 performs best with full irrigation. Overall, the findings highlight that the sustainability of the Mediterranean cotton system depends on factors such as yield performance, site-specific conditions, and cultivar choice. Full article

Despite the well-documented acceleration of climate-related economic losses in Europe, existing research has largely treated these damages as isolated stochastic events rather than as structurally embedded fiscal risks. This gap leaves EU fiscal governance frameworks inadequately prepared for the persistent, spatially concentrated, and temporally dependent nature of such losses. This study addresses this gap by investigating the systemic transformation of climate-related economic risks within the European Union, arguing that climate losses have evolved from unpredictable stochastic shocks into a persistent, structural burden on the European economy. Adopting a comprehensive multi-methodological approach, the research quantifies this transition by integrating spatial concentration metrics (HHI), advanced time-series modelling (OLS, ARIMA, ETS), and anomaly detection techniques to analyse loss patterns across the EU-27 from 1980 to 2023. The empirical results demonstrate three critical systemic dimensions: (1) a statistically significant upward shift in the baseline of economic damages; (2) a high geographical concentration of losses, with Germany, Italy, and France consistently bearing the largest share of climate-driven fiscal pressure; and (3) the emergence of volatility clustering, indicating that climate risks are becoming increasingly non-linear and embedded in the macroeconomic environment. The study contributes to the literature by reframing climate-related economic losses as a systemic fiscal phenomenon requiring structural governance reform, rather than ad hoc disaster response. The findings suggest that existing reactive policy frameworks are insufficient to address the scale of these structural risks. Consequently, the paper advocates for a paradigm shift in EU climate policy—moving toward anticipatory fiscal instruments, harmonised resilience financing, and monitoring systems designed to mitigate systemic volatility and cross-country economic asymmetry rather than merely responding to isolated disaster events. Full article

African swine fever (ASF) has been a persistent threat to Eurasian pig populations since its emergence in 2007. The disease has become endemic in numerous countries, including Poland, Germany, Romania, Hungary, Italy, the Philippines, and several others. Epidemiological data reveals that over 99% of outbreaks are attributed to a highly virulent hemadsorbing virus belonging to genotype II. Traditional genotyping methods, primarily relying on the B646L gene, have faced significant limitations in providing a comprehensive understanding of virus dissemination patterns. Previous attempts to identify a universal marker for tracking virus spread through analysis of the CVR locus of the B602L gene and the I73R/I329L locus failed to produce a coherent picture of the virus’s geographical distribution across Eurasia. To address these challenges, a comprehensive study was conducted involving the analysis of 250 ASFV isolates/strains from 25 countries across Europe and Asia between 2007 and 2024. This research led to the development of a novel sub-genotyping algorithm for ASFV genotype II. The study identified four topotypes: «CAU1», «EU1», «EU2», and «ASIA1». Within these topotypes, 31 genetic lineages were detected, each characterized by specific single-nucleotide polymorphisms (SNPs). Based on the comparison of two methods of sub-genotyping Eurasian ASFVs—the classification by Gallardo C. et al. (2023) based on genetic variations of 6 loci, and the proposed classification into topotypes and genetic lineages using whole-genomes—it was established that the multigenic approach has insufficient resolution. At the same time, significant differences were observed at the level of whole-genomes. The creation of a new spatiotemporal classification has significant applications in international surveillance of ASF outbreaks, local disease monitoring, and investigation of new infection cases. Full article

The transition towards circular economy is now a key strategy to address the environmental issues we are facing. Within this framework, biochar, a carbon-rich material derived from residual agricultural pyrolysis, can represent a sustainable and circular solution. This paper aims at evaluating the possibility of implementing a local biochar-production system as part of an economic and social strategy of the redevelopment of an abandoned rural site, Borgo di Perolla, in Tuscany, Italy. A cost–benefits analysis (CBA) was conducted to evaluate the economic feasibility of three different scenarios of production and strategies: Scenario 1 considers revenues solely from the production and sale of biochar and wood vinegar; Scenario 2 additionally includes potential income from the sale of voluntary carbon credits; and Scenario 3 incorporates biochar credits within the European Union Emission Trading System (EU ETS). For each scenario, three indicators were calculated: Net-Present Value (NPV), Internal Rate of Return (IRR), and Breakeven point (BEP). The most evident result that emerged is that the sale of biochar and its by-products alone is not sufficient to ensure the project’s economic sustainability, mainly due to high production costs. Only through carbon-credit-trading markets biochar becomes not only an environmentally strategic tool but also an economically rewarding one. In this sense, market infrastructures, such as the ETS, are essential for the dissemination of circular models, like biochar, that generate both environmental and economic benefits. Previous studies on biochar have largely focused on its application and associated benefits, while cost–benefit analyses have primarily examined its economic feasibility through the commercialization of biochar as a soil amendment, particularly within the United States context. The present work contributes to this literature in three main ways. First, it provides a site-specific and replicable CBA framework applied to a real territorial regeneration project (Borgo di Perolla), grounded in primary data collected through field surveys, stakeholder interviews, and expert validation. Second, the study explicitly compares multiple market-access scenarios within the same analytical framework, ranging from biochar-only sales to voluntary carbon markets, allowing for a clear identification of the economic thresholds at which biochar becomes financially sustainable. Third, and most importantly, the main contribution of this work lies in the explicit modeling of biochar integration into the EU Emissions Trading System. This paper extends the analysis to a regulated carbon market scenario, assuming the recognition of biochar-based carbon removals within the EU ETS framework. From a methodological perspective, the study quantitatively assesses how ETS price dynamics affect the profitability, internal rate of return, and break-even point of a biochar project over a long-term horizon. From a policy perspective, the analysis anticipates recent regulatory developments, such as the EU Regulation 2024/3012, on establishing a Union certification framework for permanent carbon removals, carbon farming, and carbon storage in products, by showing how biochar could function as a fully market-integrated climate technology. Full article

Under the framework of the Paris Agreement, carbon trading has emerged as a pivotal market-based instrument for achieving carbon neutrality. Following years of pilot programs, China has taken a critical step toward establishing a unified national carbon market. Consequently, accurate carbon price forecasting is essential for constructing a stable and effective carbon pricing mechanism. However, the 2017 reform of the EU Emissions Trading System (EU ETS) significantly altered the carbon price formation mechanism, exacerbating price volatility and uncertainty. This shift further underscores the urgent need for research into high-precision carbon price forecasting.Existing deep learning models struggle to simultaneously capture short-term high-frequency fluctuations and long-term evolutionary trends within complex carbon market data, a limitation that compromises their prediction accuracy and stability. To address these challenges, this paper proposes a Transformer-based carbon price forecasting model that incorporates an sLSTM structure. By enhancing sequence memory and state update mechanisms, this model effectively improves the capability to model both short-term volatility characteristics and long-term evolutionary patterns of carbon prices. In the data preprocessing phase, Variational Mode Decomposition (VMD) is employed to perform multi-scale decomposition of carbon price sequences, effectively mitigating the issue of overlapping fluctuations across different time scales. Furthermore, the Whale Optimization Algorithm (WOA) is utilized to optimize the number of decomposition modes and the penalty factor, thereby resolving the parameter sensitivity issues inherent in modal decomposition. Experimental results on real-world carbon price datasets demonstrate that the model achieves an average coefficient of determination ($R^2$) of 0.9862 and a Mean Absolute Percentage Error (MAPE) of only 0.5607%. These findings indicate that the proposed method possesses significant advantages in characterizing the complex dynamic features of time series, thereby effectively enhancing prediction accuracy.The proposed model can serve as a supportive tool for carbon-market risk monitoring and policy evaluation by identifying abnormal fluctuations and mitigating market inefficiencies caused by information asymmetry. This enhances the stability and predictability of carbon price signals as incentives for emissions reduction, enabling firms to plan abatement pathways and low-carbon investments, and strengthening the sustainable role of carbon markets in achieving carbon neutrality. Full article

The increasing regulatory pressure on the maritime sector to decarbonize, driven in part by market-based mechanisms at the European level, is accelerating the development of onboard carbon management and energy-efficiency solutions. In this context, this study evaluates an integrated architecture that combines a CO₂ liquefaction system with organic Rankine cycles. The system captures 66% of the total CO₂ emitted by ship engines and is capable of recovering up to 2600.8 kW of energy from onboard hot and cold sources. To identify the most suitable working fluids, an extensive screening of 208 low-GWP zeotropic mixtures is conducted, assessing their thermophysical behavior and energy recovery performance. A detailed thermo-economic assessment is undertaken, including the calculation of CO₂-equivalent savings, GHG abatement cost, and payback periods. To account for fuel price variability, probabilistic modelling based on Monte Carlo sampling is applied to estimate the distribution of discounted payback outcomes. The results demonstrate that Novec 649-based zeotropic mixtures combined with the proposed architecture reduce fuel consumption and enhance onboard CO₂ management while remaining safe and economically viable across a wide range of operating scenarios. Full article

The maritime industry is undergoing significant transformation, necessitating a reassessment of operational strategies, particularly for bulk carriers. Unlike container ships or ferries, which benefit from speed optimisation and real-time operational adjustments, bulk carriers face distinct challenges arising from rigid scheduling practices and the inherent complexities of cargo handling. Variability in loading and unloading processes, fluctuating discharge rates, and port congestion further constrain the practical implementation of Just-In-Time (JIT) arrival strategies in this segment. Through an exploratory analysis of major shipping segments, this study examines the structural challenges and operational limitations associated with the application of JIT port-arrival concepts in dry bulk shipping. Full article

This study examines how national electricity market structures condition the impact of carbon pricing on green innovation within the European Union. Using two-way fixed-effects panel models, we uncover a central paradox: although liberalized, price-signal markets exhibit the highest baseline levels of green innovation, the marginal effect of carbon pricing in these markets is weakest and often negative. This pattern points to an innovation-substitution effect, whereby market flexibility facilitates short-term compliance strategies, such as fuel switching, that crowd out investment in fundamental research and development (R&D) when carbon prices remain moderate. By identifying this mechanism, the study establishes electricity market structure as a pivotal moderating factor in the carbon pricing–innovation nexus and highlights a critical boundary condition for the Porter Hypothesis. The findings provide important insights for the design of sustainability policy mixes, demonstrating that institutional context plays a decisive role in translating economic instruments into sustained technological change. Effective climate policy therefore cannot be context-blind; instead, it must combine carbon pricing with tailored market design and direct support for long-term R&D to coherently advance the sustainability transition. Full article

Methane emissions from underground coal mines are a significant source of greenhouse gases (GHGs) and a major safety concern. In highly methane-prone operations, a large proportion of emissions comes from low-content abandoned mine methane (LCAMM) accumulated in post-mining goafs, where concentrations usually stay below 30% CH₄. Building on the Research Fund for Coal and Steel (RFCS) REM project, this paper presents a cost–benefit analysis of a comprehensive scheme for capturing, transporting, and utilising LCAMM from post-mining goafs for electricity generation. The concept involves long-reach directional boreholes drilled behind isolation dams, a dedicated methane-reduced drainage system connected to a surface methane drainage station, and four 2 MWe gas engines designed to run on a 20–40% CH₄ mixture. Greenhouse gas performance is evaluated by comparing a “business-as-usual” scenario in which post-mining methane is combusted in gas engines to produce electricity without further GHG cost–benefit consideration. The results indicate that the project can achieve a positive net present value, highlighting the role of LCAMM utilisation for methane-intensive coal mines. The paper also explores the monetisation of non-emitted methane using the European Union Emissions Trading System (EU ETS), as well as social cost benchmarks and penalty levels consistent with the emerging EU Methane Emissions Regulation (EU MER). Full article

Coastal shipping plays a critical role in meeting maritime decarbonization targets under the International Maritime Organization’s (IMO) Carbon Intensity Indicator (CII) and the European Union Emissions Trading System (EU ETS); however, operators currently lack robust tools to forecast route-specific carbon intensity and evaluate the causal benefits of fuel switching. This study developed a distribution-free causal forecasting framework for voyage-level Carbon Dioxide (CO₂) intensity using an enriched panel of 1440 real-world voyages across four Nigerian coastal routes (2022–2024). We employed a physics-informed monotonic Light Gradient Boosting Machine (LightGBM) model trained under a strict leave-one-route-out (LORO) protocol, integrated with split-conformal prediction for uncertainty quantification and Causal Forests for estimating heterogeneous treatment effects. The model predicted emission intensity on completely unseen corridors with a Mean Absolute Error (MAE) of 40.7 kg CO₂/nm, while 90% conformal prediction intervals achieved 100% empirical coverage. While the global average effect of switching from heavy fuel oil to diesel was negligible (≈−0.07 kg CO₂/nm), Causal Forests revealed significant heterogeneity, with effects ranging from −74 g to +29 g CO₂/nm depending on route conditions. Economically, targeted diesel use becomes viable only when carbon prices exceed ~100 USD/tCO₂. These findings demonstrate that effective coastal decarbonization requires moving beyond static baselines to uncertainty-aware planning and targeted, route-specific fuel strategies rather than uniform fleet-wide policies. Full article

This study provides a comprehensive analysis of the impact of different marine fuels such as heavy fuel oil (HFO), methane, methanol, ammonia, or hydrogen, on energy efficiency and pollutant emissions in maritime transport, using a combined application of the Energy Efficiency Design Index (EEDI), Energy Efficiency Operational Indicator (EEOI), and Carbon Intensity Indicator (CII). The results show that methane offers the most balanced alternative, reducing CO₂ by more than 30% and improving energy efficiency, while methanol provides an intermediate performance, eliminating sulfur and partially reducing emissions. Ammonia and hydrogen eliminate CO₂ but generate NO_x (nitrogen oxides) emissions that require mitigation, demonstrating that their environmental impact is not negligible. Unlike previous studies that focus on a single fuel or only on CO₂, this work considers multiple pollutants, including SO_x (sulfur oxides), H₂O, and N₂, and evaluates the economic cost of emissions under the European Union Emissions Trading System (EU ETS). Using a representative model ship, the study highlights regulatory gaps and limitations within current standards, emphasizing the need for a global system for monitoring and enforcing emissions rules to ensure a truly sustainable and decarbonized maritime sector. This integrated approach, combining energy efficiency, emissions, and economic evaluation, provides novel insights for the scientific community, regulators, and maritime operators, distinguishing itself from previous multicriteria studies by simultaneously addressing operational performance, environmental impact, and regulatory gaps such as unaccounted NO_x emissions. Full article

Accurately forecasting the dynamic linkages among carbon, energy, and stock markets is essential for effective risk management and the design of energy transition strategies. This study proposes a BOHB-optimized Multivariable Graph Neural Network (BOHB-MSGNN) framework to forecast dynamic correlations derived from a DCC-GARCH model. Using data from the EU ETS market and related energy and stock markets, we document strong and persistent interconnectedness across markets, with the carbon market exhibiting the closest linkage to natural gas, followed by coal, stocks, and oil. Moreover, the proposed BOHB-MSGNN model significantly outperforms benchmark models in predicting dynamic risk correlations across multiple error metrics, owing to its ability to capture both intra-series and inter-series dependencies. Minimum-variance portfolios based on predicted correlations achieve returns similar to those using realized correlations. Forecasts also suggest a moderate decline in future correlations, highlighting diversification opportunities. These results offer practical implications for portfolio allocation, risk management, and carbon market policy. Full article

In the aviation industry, momentum for reducing emissions has rapidly increased in recent years. From international systems like the EU ETS and CORSIA, to the introduction of new fuels such as electricity and SAF as alternatives to conventional fuels, various approaches are being considered. Within this context, there is a further movement to reduce aviation emissions through a modal shift from air to high-speed rail. In this research, a Systematic Literature Review is undertaken to detail the nature of the modal shift from air to rail, uncovering energy policy and economic considerations. While research targeting China has increased recently, prior studies focus on Europe, leaving some regions understudied. From an emissions reduction perspective, the power source supplying rail is a critical factor. Capacity constraints on rail are also a key challenge to be addressed. Future research should address the need for additional regional studies. In the age of modal shift movements, the aviation industry is attempting to reduce emissions through the introduction of alternative low-carbon fuels. Policies to reduce emissions must consider this. Discontinuing flights could lead to unintended emissions. A synergistic approach combining modal shift and internal decarbonization is likely to be the most economically feasible and sustainable approach. Full article

Africa has always been among the top geopolitical priorities for the EU due to the continent’s close geographical proximity and long-standing economic ties. The agro-food trade between the EU27 and Africa is extremely important for both subjects and not only in terms of food security, as it is also a useful tool to secure a long-term partnership between the two continents, making them true and reliable allies ready to give support to each other, especially in the current unstable global situation. The analyzed data were taken from the official publications of the Eurostat (ESTAT). The time frame under analysis is 23 time periods—from 2002 to 2024 inclusive. Such methods and tools of scientific research as textual and tabular methods, empirical, statistical and comparative analyses, as well as the logical method, comprising deductive and inductive reasoning, time series analysis, modelling and forecasting, methods of time series data decomposition, etc. were used while conducting the research presented in the given article. The results for the time series analysis, modelling and forecasting assume the projections for the next four time periods for the EU27 to Africa agro-exports to be around their last observed value, slightly fluctuating or increasing with a delicateslope. The EU27 from Africa agro imports for the next four time periods are projected to increase, with a rather sharp slope. The research and its results can be of great help for public administrators, decision makers, academic community representatives, statisticians, and data analysts. Full article