Search Results (484)

In Poland, the gradual reduction in hard coal mining represents a cornerstone of the energy transition and economic restructuring strategy, with all mines scheduled to close by 2049 under the Social Agreement. Given Poland’s strong reliance on coal, this process has far-reaching implications for energy security, employment, regional development, and macroeconomic stability. The aim of this study is to assess the role and scale of the hard coal mining sector’s contribution to GDP and to examine the consequences of its gradual decline for the national energy mix. In the input–output framework, a reduction in domestic hard coal supply is modelled as a shock to the output of the disaggregated hard coal sector, affecting both intermediate demand and value added through inter-industry linkages. The analysis applies an inter-industry input–output framework based on a decomposed Input–Output Table of Poland, where the aggregated “hard coal and lignite” branch was disaggregated into thermal hard coal, coking coal, and lignite. Reduction Variants (WR25%, WR50%, WR75%, and WR100%) were combined with Substitution Variant WS2, which assumes replacement of domestic hard coal with imported coal, natural gas, and electricity under varying price scenarios (−40% to +40% relative to reference levels). The Migration Variant was also included to account for labour market effects. This approach generated a set of 100 scenarios, reflecting possible pathways of Poland’s energy transition. The results demonstrate that in every scenario, reducing domestic hard coal supply leads to a decline in GDP. Losses range from −0.175% to −0.25% under WR25% scenarios to between −0.775% and −1.1% under WR100%, depending on the relative prices of imported substitutes. Substitution patterns are highly sensitive to price dynamics: under low natural gas prices, gas dominates the replacement mix (over 57% share), while under high gas prices, imported coal prevails (70–90%). Electricity imports consistently remain marginal. These outcomes highlight Poland’s structural dependence on coal, the vulnerability of GDP to external price shocks, and the limitations of substitution options. This study concludes that the reduction in domestic coal mining, though inevitable in the context of the EU climate policy, will not be economically neutral. It requires careful management of substitution pathways, diversification of the energy mix, and socio-economic support for coal regions. The input–output framework used in this research offers a robust tool for quantifying both direct and indirect effects of the coal phase-out, supporting evidence-based policy for a just and sustainable energy transition. Full article

Biomethane plays a key role in the green transition, offering a renewable, carbon-neutral substitute for natural gas while enabling the storage and use of intermittent renewable energy. This work presents a techno-economic assessment of biomethane production through the Power-to-Biomethane concept, which combines electrolytic hydrogen from renewable electricity with the direct catalytic methanation of raw biogas from anaerobic digestion. The main objective of this study is to identify the optimal plant size and configuration, taking into account the different operational management strategies of the system’s constituting units. The analysis integrates thermochemical modeling with a techno-economic optimization procedure. Three different configurations for renewable energy production, photovoltaic-based, wind-based, and hybrid photovoltaic–wind, were evaluated for a case study in Southern Italy. Results show that the hybrid configuration provides the best techno-economic balance, achieving the highest annual biomethane output (≈2288 t) and the lowest levelized cost of biomethane (EUR 97.4/MWh). While current biomethane production costs exceed natural gas prices, the proposed pathway represents a viable long-term solution for renewable integration and climate-neutral gas supply. Full article

This study investigates the viability of using Ammonia as a carbon-free fuel for heat generation in terms of both reactive Nitrogen and Carbon emissions and production cost. As a carbon-free, environmentally friendly energy carrier, Ammonia has the potential to play a significant role in the sustainable, clean energy supply of the future. However, a major drawback of the steady combustion of ammonia for process heat generation is the extremely high levels of NO_x emissions it produces. In this pilot-scale study, the experimental results show that, through the oscillating combustion of NH₃, NO_x emissions can be reduced by as much as 80%. Production costs were compared to evaluate the economic feasibility of Ammonia-based heat; the results reveal the economic challenges associated with using Ammonia compared to natural gas, even when accounting for the development of CO₂ pricing. Only in terms of Carbon Capture and Storage requirements is Ammonia-based heat economically advantageous. This study also scrutinizes the economies of the production of gray and green Ammonia. Considering CO₂ certificate costs, the cost of green ammonia would be competitive in the near future. Full article

Scientific, accurate, and interpretable carbon price forecasts provide critical support for addressing climate change, achieving low-carbon goals, and informing policy-making and corporate decision-making in energy and environmental markets. However, the existing studies mainly focus on deterministic forecasting, with obvious limitations in data feature diversity, model interpretability, and uncertainty quantification. To fill these gaps, this study constructs an interpretable hybrid system for carbon market price prediction by combining feature screening algorithms, deep learning models, and interpretable explanatory analysis methods. Specifically, this study first screens important variables from twenty-one multi-source structured and unstructured influencing factor datasets on five dimensions affecting carbon price using the Boruta algorithm. Immediately after that, this study proposes a hybrid architecture of bidirectional temporal convolutional network and Informer models, where a bidirectional temporal convolutional network is used to extract local spatio-temporal dependent features, while Informer captures long sequences of global features through the connectivity mechanism, thus realizing staged feature extraction. Then, to improve the interpretability of the model and quantify the uncertainty, this study introduces Shapley additive explanations to analyze the feature contribution in the prediction process, and the Monte Carlo dropout method is used to achieve interval prediction. Finally, the empirical results in China’s Guangdong and Shanghai carbon markets show that the proposed model significantly outperforms benchmark models, and the coverage probability of the obtained prediction intervals significantly outperforms the confidence level. The Shapley additive explanation analysis reveals regional heterogeneity drivers. In addition, the proposed model is also intensively validated in the European carbon market and the U.S. natural gas market, which also demonstrate an excellent prediction performance, indicating that the model has good robustness and applicability. Full article

While renewable energy deployment has accelerated in recent years, fossil fuels continue to play a dominant role in electricity generation worldwide. This necessitates the development of transitional strategies to mitigate greenhouse gas emissions from this sector while gradually reducing reliance on fossil fuels. This study investigates the potential of blending green hydrogen with natural gas as a transitional solution to decarbonize Jordan’s electricity sector. The research presents a comprehensive techno-economic and environmental assessment evaluating the compatibility of the Arab Gas Pipeline and major power plants with hydrogen–natural gas mixtures, considering blending limits, energy needs, environmental impacts, and economic feasibility under Jordan’s 2030 energy scenario. The findings reveal that hydrogen blending between 5 and 20 percent can be technically achieved without major infrastructure modifications. The total hydrogen demand is estimated at 24.75 million kilograms per year, with a reduction of 152.7 thousand tons of carbon dioxide per annum. This requires 296,980 cubic meters of water per year, equivalent to only 0.1 percent of the National Water Carrier’s capacity, indicating a negligible impact on national water resources. Although technically and environmentally feasible, the project remains economically constrained, requiring a carbon price of $1835.8 per ton of carbon dioxide for economic neutrality. Full article

Blue hydrogen technology, generated from natural gas through carbon capture and storage (CCS) technology, is a promising solution to mitigate greenhouse gas emissions and meet the growing demand for clean energy. To improve the sustainability of blue hydrogen, it is crucial to explore alternative feedstocks, production methods, and improve the efficiency and economics of carbon capture, storage, and utilization strategies. Two established technologies for hydrogen synthesis are Steam Methane Reforming (SMR) and Autothermal Reforming (ATR). The choice between SMR and ATR depends on project specifics, including the infrastructure, energy availability, environmental goals, and economic considerations. ATR-based facilities typically generate hydrogen at a lower cost than SMR-based facilities, except in cases where electricity prices are elevated or the facility has reduced capacity. Both SMR and ATR are methods used for hydrogen production from methane, but ATR offers an advantage in minimizing CO₂ emissions per unit of hydrogen generated due to its enhanced energy efficiency and unique process characteristics. ATR provides enhanced utility and flexibility regarding energy sources due to its autothermal characteristics, potentially facilitating integration with renewable energy sources. However, SMR is easier to run but may lack flexibility compared to ATR, necessitating meticulous management. Capital expenditures for SMR and ATR hydrogen reactors are similar at the lower end of the capacity spectrum, but when plant capacity exceeds this threshold, the capital costs of SMR-based hydrogen production surpass those of ATR-based facilities. The less profitably scaled-up SMR relative to the ATR reactor contributes to the cost disparity. Additionally, individual train capacity constraints for SMR, CO₂ removal units, and PSA units increase the expenses of the SMR-based hydrogen facility significantly. Full article

Natural gas plays a key role in the low-carbon energy transition due to its clean and efficient characteristics, yet challenges remain in balancing economic efficiency, user behavior, and carbon emission constraints in demand-side scheduling. This study proposes a low-carbon economic operation model for terminal natural gas systems, integrating price elasticity and differentiated user behavior with carbon emission management strategies. To capture diverse demand patterns, dynamic time warping k-medoids clustering is employed, while scheduling optimization is achieved through a multi-objective framework combining NSGA-III, the entropy weight (EW) method, and the VIKOR decision-making approach. Using real-world data from a gas station in Xi’an, simulation results show that the model reduces gas supply costs by 3.45% for residential users and 6.82% for non-residential users, increases user welfare by 4.64% and 88.87%, and decreases carbon emissions by 115.18 kg and 2156.8 kg, respectively. Moreover, non-residential users achieve an additional reduction in carbon trading costs of 183.85 CNY. The findings demonstrate the effectiveness of integrating dynamic price signals, user behavior modeling, and carbon constraints into a unified optimization framework, offering decision support for sustainable and flexible natural gas scheduling. Full article

Different resources of energy are unevenly distributed around the world. Not every country has energy resources of its own. Those that have, may experience inadequacy in meeting domestic demand. Countries have to import the needed energy regardless of the size of worldwide price fluctuations and crises that come into existence in the global marketplace, which works in favor of energy-exporting countries. Foreign dependence on energy negatively affects the current account balance. Energy importation has an important share in the current account deficit of non-oil-exporting countries, such as Türkiye, in which as a developing country with a young and growing population, energy demand is increasing, and new natural gas and oil reserves as well as renewable energy sources are sought in order to remove foreign dependency. This study aims to determine the effect of making use of renewable energy sources on the current account deficit for a selected sample of countries which consist of 12 European Union (EU) and International Energy Agency (IEA) members including Türkiye. A balanced panel data regression analysis was conducted using the data of these countries for the period of 2000–2022. As a result of the analysis with the Driscoll–Kraay estimator, it was observed that as the share of renewable energy sources in total energy consumption increases, a reducing effect is achieved in terms of current account deficit. As renewable energy technologies develop, countries will have access to energy. This will reduce their foreign exchange expenditure, decrease their current account deficit, and strengthen price stability and growth performance. Full article

The global transition to renewable energy has intensified the focus on anaerobic digestion (AD) as a sustainable solution for organic waste management and biogas production. This study presents a comprehensive techno-economic analysis (TEA) of AD systems integrated with carbon capture and digestate treatment technologies, evaluated across four distinct operational scenarios. The research leverages an innovative AI-agent framework to streamline TEA, enabling stakeholders to conduct sophisticated analyses without specialized expertise. Key findings reveal that feedstock composition significantly impacts biogas yields, with maize and rye blends (mix2) outperforming maize-dominated mixes (mix1), achieving higher biogas production (26,029 m³/y vs. 23,182 m³/y). Membrane-based CO₂ separation and liquefaction technologies demonstrated superior economic viability compared to cryogenic methods, yielding lower energy consumption (2400 MWh/y vs. 3000 MWh/y) and higher net revenues (GBP 4.0 million/y vs. GBP 3.5 million/y). Financial metrics further underscored the advantages of membrane-based systems, with the mix2 configuration achieving a net present value (NPV) of GBP 19 million and an internal rate of return (IRR) of 36%, alongside a shorter payback period (3 years). Sensitivity analysis highlighted natural gas prices and tax rates as critical determinants of economic performance, while water costs had negligible impact. The study also evaluated digestate treatment methods, finding that base-case separation outperformed torrefaction in financial returns. Full article

Agriculture stands at a pivotal juncture in the twenty-first century, confronting the converging crises of climate change, biodiversity loss and rising food demand, even as it is increasingly recognised as part of the solution. This paper assesses the transformative potential of integrating three emerging paradigms—digital agriculture, regenerative agriculture and decommoditised agriculture—into a unified approach capable of delivering productivity, ecological restoration and economic viability. Digital agriculture deploys artificial intelligence, Internet of Things (IoT) networks and remote sensing to optimise inputs and sharpen decision-making. Regenerative agriculture seeks to rebuild soil function, enhance biodiversity and restore ecosystem processes through holistic, adaptive management. Decommoditised agriculture reorients value chains from bulk markets towards quality-differentiated systems that privilege direct producer–consumer relationships, value-added processing and regional market development, enabling price premiums and community resilience. We examine their convergence through the “3N” lens—net-zero greenhouse gas emissions, nature-positive outcomes and nutrition-balanced food systems. Integration creates clear complementarities: digital tools monitor, verify and optimise regenerative practices; regenerative systems provide the ecological foundation for sustainable intensification; and decommoditised models supply economic incentives that reward stewardship and nutritional quality. Persistent barriers include the digital divide, data governance, technical complexity and fragmented policy settings. Realising the benefits will require technology democratisation, interdisciplinary research, enabling regulation and farmer-centred innovation processes. We conclude that converging digital, regenerative and decommoditised approaches offers a credible and necessary pathway to resilient, sustainable and equitable agri-food systems. Full article

As a core clean energy source in the global energy transition, natural gas price fluctuations directly affect the energy market supply demand balance, industrial chain cost control, etc. Thus, accurate natural gas price prediction is crucial for market participants’ decision making and policymakers’ regulation. To tackle the issue that traditional single models fail to capture data patterns of the New York Mercantile Exchange (NYMEX) natural gas futures daily prices—due to their nonlinearity, high volatility, and multi-scale features—this study proposes a hybrid model: VMD-CNN-BiLSTM-attention, integrating Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and an attention mechanism. A one-step to four-step forecasting comparison was conducted using NYMEX natural gas futures daily closing prices, with the proposed model vs. CNN-BiLSTM-Attention and Autoregressive Integrated Moving Average (ARIMA) models. The empirical results show that the VMD-CNN-BiLSTM-attention model outperforms the comparison models in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), etc. Specifically, its four-step forecast MAPE stays ≤3.5% and $R^2$ ≥ 98%, demonstrating a stronger ability to capture complex price fluctuations, better accuracy, and stability than traditional single models and deep learning models without VMD, and provides reliable technical support for short-to-medium-term natural gas price prediction. Full article

The transition to low-carbon energy systems demands robust strategies that leverage existing fossil resources while integrating renewable technologies. In this work, a single-cycle Gaussian-based producibility model is developed to forecast natural gas production profiles, domestic consumption, export potential, hydrogen production and revenues, adaptive for untapped natural gas discoveries. Annual natural gas production is represented by a bell curve defined by peak year and maximum capacity, allowing flexible adaptation to different reserve sizes. The model integrates renewable energy adoption and steam–methane reforming to produce hydrogen, while tracking revenue streams from domestic sales, exports and hydrogen markets alongside carbon taxation. Applicability is demonstrated through a case study of Eastern Mediterranean gas discoveries, where combined reserves of 2399 bcm generate a production peak of 100 bcm/year in 2035 and deliver 40.71 billion kg of hydrogen by 2050, leaving 411.87 bcm of reserves. A focused Cyprus scenario with 411 bcm of reserves peaks at 10 bcm/year, produces 4.07 billion kg of hydrogen and retains 212.29 bcm of reserves. Cumulative revenues span from USD 84.37 billion under low hydrogen pricing to USD 247.29 billion regionally, while the Cyprus-focused case yields USD 1.79 billion to USD 18.08 billion. These results validate the model’s versatility for energy transition planning, enabling strategic insights into infrastructure deployment, market dynamics and resource management in gas-rich regions. Full article

The Hurst parameter H plays a critical role in modeling long-memory behavior in financial time series, particularly within the framework of the fractional Black–Scholes model (fBSM). While the Method of Moments (MOM) provides a fast, closed-form estimator for H, it suffers from increasing negative bias, especially as H grows beyond 0.6. This paper proposes a bias-corrected version of the MOM estimator based on a quadratic regression fit derived from simulation data. The corrected estimator substantially reduces estimation error while retaining computational efficiency. Through extensive simulations, we quantify the impact of MOM bias on option pricing and demonstrate how our correction method leads to more accurate pricing under the fBSM. We apply the methodology to real financial assets—including Natural Gas, Apple, Gold, and Crude Oil—and show that the corrected Hurst estimates reduce option pricing error by up to USD 0.47 per contract relative to the uncorrected estimator, depending on the asset’s volatility structure. These results underscore the importance of accurate Hurst parameter estimation for derivative pricing, particularly in volatile markets such as energy and commodities, while also remaining relevant to equities and precious metals. The corrected estimator thus offers practitioners a simple yet effective tool to improve financial decision-making. Full article

Accurate natural gas price forecasting is vital for risk management, trading strategies, and policy-making in energy markets. This study proposes and evaluates four hybrid deep learning architectures—CNN-LSTM-Attention, CNN-BiLSTM-Attention, TCN-LSTM-Attention, and TCN-BiLSTM-Attention—integrating convolutional feature extraction, sequential learning, and attention mechanisms. Using Henry Hub and NYMEX datasets, the models are trained on long historical periods and tested under multi-step horizons. The results show that all hybrid models significantly outperform the traditional moving average benchmark, achieving R² values above 95% for one-step-ahead forecasts and maintaining an accuracy of over 87% at longer horizons. CNN-BiLSTM-Attention performs best in short-term prediction due to its ability to capture bidirectional dependencies, while TCN-based models demonstrate greater robustness over extended horizons due to their effective modeling of long-range temporal structures. These findings confirm the advantages of deep learning hybrids in energy forecasting and emphasize the importance of horizon-sensitive evaluation. This study contributes methodological innovation and provides practical insights for market participants, with future directions including the integration of macroeconomic and climatic factors, exploration of advanced architectures such as Transformers, and probabilistic forecasting for uncertainty quantification. Full article

This study investigates the feasibility of green hydrogen as an alternative to natural gas for power generation. In doing so, it contributes to the broader discourse surrounding hydrogen’s potential role in the transition of the energy sector. Our case study is of Ontario, Canada, where natural gas serves as the sole remaining carbon-emitting energy source for the generation of electricity. Through this, we present a practical reference and methodology that energy planners, policymakers, and researchers can use to analyze fuel consumption patterns and their costs. Our research involves estimating the volume of hydrogen required to support the conversion of natural gas-powered turbines. At present, electrical power in Ontario generated by natural gas may reach 39 TWh annually by 2035. Our findings suggest that the cost of hydrogen to generate that volume of electricity will range between USD 1.8 billion and USD 23.2 billion, contingent upon turbine efficiency and fluctuations in hydrogen prices. Moreover, if hydrogen prices remain elevated (up to USD 8/kg), the annual premium for hydrogen-generated electricity compared to natural gas could reach USD 20.604 billion, a significant deterrence for energy planners in Ontario from adopting hydrogen at scale. Thus, the added costs of hydrogen, along with challenges related to infrastructure requirements, safety, and technological considerations, render a potential transition to hydrogen, and to green hydrogen specifically, a complex undertaking. Ultimately, insights derived here enhance understanding of hydrogen’s potential within the context of power generation and may be applicable to other regions considering similar transitions toward hydrogen-based energy systems. Full article