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It has been hoped that making abundant natural gas available by hydraulic fracturing (fracking) would reduce greenhouse gas emissions but now modelling shows that increased consumption will have limited effect on climate change. Fracking boom a minor influence on climate The development of hydraulic fracturing technologies has led to rapid growth in the use natural gas as an energy source. Some evidence has suggested that this growing adoption of natural gas might lead to a reduced greenhouse gas burden and consequent mitigation of climate change. This collaboration between five energy–climate modelling teams show that instead — under a scenario of abundant natural gas availability — increased consumption will have little or no impact on climate change. The authors suggest that expanded natural gas production and use is neither a substitute for a climate policy in the decades ahead nor a major new complication to the anthropogenic emissions problem. The most important energy development of the past decade has been the wide deployment of hydraulic fracturing technologies that enable the production of previously uneconomic shale gas resources in North America 1 . If these advanced gas production technologies were to be deployed globally, the energy market could see a large influx of economically competitive unconventional gas resources 2 . The climate implications of such abundant natural gas have been hotly debated. Some researchers have observed that abundant natural gas substituting for coal could reduce carbon dioxide (CO 2 ) emissions 3 , 4 , 5 , 6 . Others have reported that the non-CO 2 greenhouse gas emissions associated with shale gas production make its lifecycle emissions higher than those of coal 7 , 8 . Assessment of the full impact of abundant gas on climate change requires an integrated approach to the global energy–economy–climate systems, but the literature has been limited in either its geographic scope 9 , 10 or its coverage of greenhouse gases 2 . Here we show that market-driven increases in global supplies of unconventional natural gas do not discernibly reduce the trajectory of greenhouse gas emissions or climate forcing. Our results, based on simulations from five state-of-the-art integrated assessment models 11 of energy–economy–climate systems independently forced by an abundant gas scenario, project large additional natural gas consumption of up to +170 per cent by 2050. The impact on CO 2 emissions, however, is found to be much smaller (from −2 per cent to +11 per cent), and a majority of the models reported a small increase in climate forcing (from −0.3 per cent to +7 per cent) associated with the increased use of abundant gas. Our results show that although market penetration of globally abundant gas may substantially change the future energy system, it is not necessarily an effective substitute for climate change mitigation policy 9 , 10 .

The energy prices in Europe have in recent years surpassed unprecedented thresholds and varied in unexpected ways compared to previous years. This paper presents a study of the fuel markets in Italy, supplemented by insights from Sweden. Italy is heavily dependent on natural gas. The results show that natural gas demand changed only slightly in the period 2017–2022, but prices started to increase at the end of 2021. Notable spikes occurred at the beginning of the events in Ukraine, even though the baseline was already three times higher than the average price from 2017 to 2019. Distinct dynamics can be identified with the increase in demand for power generation, contrasted with a decrease in industrial natural gas demand after August 2022. The trends in coal and wood chip prices are consistent with those of natural gas, while oil prices appear to be less correlated. Additionally, events such as CO2 trading and the launch of the Fit for 55 program by the EU show some correlation with the trend in natural gas prices during 2021. Interestingly, the origin of the increase in natural gas prices during 2021–2022 cannot be simply attributed to the mismatch of supply and demand or any singular external event. This paper aims at starting a discussion on the topic by proposing some explanations.

China’s natural gas supply has been challenged in the past few years by non-traditional risks such as trading conflicts, the COVID-19 pandemic, and the country’s own emission policy. To ensure energy security and supply, conducting an up-to-date risk analysis of China’s natural gas supply status is crucial. This research utilized the Fuzzy-AHP method to compose a risk index and assessed the key links within China’s natural gas supply chain from the import side to the domestic side. The results indicate that (a) for China’s gas import, the most influential risks are the correlated dependence risk, international relation risk, and supplier internal stability risk. (b) While the dependence risk and transport risk have decreased sharply in the past decade, the import risk is still China’s major concern on natural gas supply. (c) Emissions-peaking and carbon neutrality targets are potential challenges, which the country would possibly face in the near future.

The development of appropriate assessment methods of gas-pipeline functionality contributes to the reduction of failure consequences and helps engineers to make the right decisions as to the optimal solution choice for technical facilities, as well as provides procedures to protect their users and the surrounding environment. This paper presents methods for the assessment of gas network operation. Pipe failure data were collected from a gas distribution network. A statistical analysis of the failure of gas networks was made. An attempt was made to isolate seasonal and accidental fluctuations in the tested failure stream. The Poisson distribution was proposed as a model of failure distribution of gas networks. The conducted analysis allowed us to propose the forecasting method of acceptable failure consequences using the homogeneous Markov chain. The obtained results are valuable for supporting the management of urban gas networks, mainly in terms of the strategic modernization plans and the rehabilitation techniques.

China has been reforming its domestic natural gas market in recent years, while construction of storage systems is lagging behind. As natural gas accounts for an increasing proportion due to the goal of carbon neutrality, large-scale gas storage appears to be necessary to satisfy the needs for gas peak shaving and national strategic security. Additionally, the domestic gas production in China cannot meet consumption demands, and imports will play a significant role on the supply side. This paper developed a system dynamics (SD) model and applied it to simulate gas market behaviors and estimated China’s gas storage capabilities and import demands over the next 40 years. To achieve carbon neutrality, it is necessary for China to make great progress in its energy intensity and improve its energy structure, which have a great impact on natural gas consumption. Thus, alternative scenarios were defined to discuss the changes in the gas market with different gas storage goals and environmental constraints. The results show that under low and medium carbon price scenarios, natural gas demand will continue to grow in the next 40 years, but it will be difficult to achieve the goal of carbon neutrality. Under the high carbon price scenario, natural gas consumption will grow rapidly and reach a peak in approximately 2040, after which renewable energy will play a more important role to help achieve carbon neutrality. At the peak time, China’s gas storage demand will be 205.5 billion cubic meters (bcm) and import demand will reach 635.4 bcm, accounting for 72.8% of total consumption. We also identified the contradiction between the estimated storage capability, import demand and infrastructure planning. There will be a gap of 28.1–69.3 bcm between the planned storage capacity and simulated demand by 2030, while import facilities may partly strand assets. Finally, we provided some policy recommendations for constructing gas storage and import management and operation systems.

Safety and disturbance issues in system engineering have garnered substantial attention. This study focuses on the analysis of the distinct characteristics of emergency dispatch problems in Natural Gas Pipeline Networks (NGPS). Graph theory serves as a tool to transform the NGPS topology and establish an optimization model for NGPS emergency dispatch. The model also integrates user weights, satisfaction, and reduction factors into the user modeling approach. Its objective is to maximize overall system satisfaction while considering factors such as demand-side requirements and operational constraints. To solve this optimization model, the Particle Swarm Optimization (PSO) method is employed. An in-depth exploration of four unique disturbance scenarios provides solid evidence of the effectiveness and practicality of the PSO method. Compared to other methods, the PSO method consistently boosts overall user satisfaction and aligns more fluidly with the real-time demands of emergency scheduling, regardless of reduced supply capacity, complete supply interruptions, sudden surges in user demand, or pipeline connection failures. The developed emergency scheduling optimization method presents two key advantages. Firstly, it proficiently mitigates potential losses stemming from decreased supply capacity at local or regional levels. By adeptly adjusting natural gas supply strategies, it minimizes economic and production losses while ensuring a steady supply to critical users. Secondly, the method is superior at swiftly reducing the affected area and managing the increased demand for natural gas, thus maintaining NGPS stability. This research underscores the importance of considering user characteristics and demands during emergencies and demonstrates the effectiveness of employing the PSO method to navigate emergency scheduling challenges. By strengthening the resilience of the pipeline network and ensuring a sustainable natural gas supply, this study constitutes a significant contribution to energy security, economic development, and the promotion of clean energy utilization, ultimately propelling the achievement of sustainable development goals.

The purpose of this study is to understand the impact of the thickness of Nafion membrane, which is a typical polymer electrolyte membrane (PEM) in Polymer Electrolyte Membrane Fuel Cells (PEMFCs), and relative humidity of supply gas on the distributions of H2, O2, H2O concentration and current density on the interface between a Nafion membrane and anode catalyst layer or the interface between a Nafion membrane and cathode catalyst layer. The effect of the initial temperature of the cell (Tini) is also investigated by the numerical simulation using the 3D model by COMSOL Multiphysics. As a result, the current density decreases along with the gas flow through the gas channel irrespective of the Nafion membrane thickness and Tini, which can be explained by the concentration distribution of H2 and O2 consumed by electrochemical reaction. The molar concentration of H2O decreases when the thickness of Nafion membrane increases, irrespective of Tini and the relative humidity of the supply gas. The current density increases with the increase in relative humidity of the supply gas, irrespective of the Nafion membrane thickness and Tini. This study recommends that a thinner Nafion membrane with well-humidified supply gas would promote high power generation at the target temperature of 363 K and 373 K.

Gas supply system risk assessment is a serious and important problem in cities. Existing methods tend to manually build mathematical models to predict risk value from single-modal information, i.e., pipeline parameters. In this paper, we attempt to consider this problem from a deep-learning perspective and define a novel task, Urban Gas Supply System Risk Assessment (GSS-RA). To drive deep-learning techniques into this task, we collect and build a domain-specific dataset GSS-20K containing multi-modal data. Accompanying the dataset, we design a new deep-learning framework named GSS-RiskAsser to learn risk prediction. In our method, we design a parallel-transformers Vision Embedding Transformer (VET) and Score Matrix Transformer (SMT) to process multi-modal information, and then propose a Multi-Modal Fusion (MMF) module to fuse the features with a cross-attention mechanism. Experiments show that GSS-RiskAsser could work well on GSS-RA task and facilitate practical applications. Our data and code will be made publicly available.

Natural gas is playing an important role in the reconstruction of the energy system of China. Natural gas supply and consumption indicators forecasting is an important decision-making support for the government and energy companies, which has attracted considerable interest from researchers in recent years. In order to deal with the more complex features of the natural gas datasets in China, a Grey Wavelet Support Vector Regressor is proposed in this work. This model integrates the primary framework of the grey system model with the kernel representation employed in the support vector regression model. Through a series of mathematical transformations, the parameter optimization problem can be solved using the sequential minimal optimization algorithm. The Grey Wolf Optimizer is used to optimize its hyperparameters with the nested cross-validation scheme, and a complete computational algorithm is built. The case studies are conducted with real-world datasets from 2003–2020 in China using the proposed model and 15 other models. The results show that the proposed model presents a significantly higher performance in out-of-sample forecasting than all the other models, indicating the high potential of the proposed model in forecasting the natural gas supply and consumption in China.

Analysis and assessment of the reliability and safety of a gas-supply system is a key issue, given its status as critical infrastructure. A gas-supply system is characterised by continuous operation and a consequent need to achieve a high level of operating reliability and safety. Such a system has its unique aspects, with particular elements having their different functions while also simultaneously interacting in the context of the integral whole. In such circumstances, risk analysis can prove useful in planning activity to prevent damage, and also in the devising of rescue scenarios. Thus, the purpose of the analysis presented here has been to supply the information that is necessary in decision-making relating to risk reduction. One of the most comprehensive assessment methods is based on the expected value of gas shortage. Basic formulae with which to determine a generalised indicator of system reliability are also presented, with risk viewed as synonymous with the unreliability of gas supply. This paper then proposes a method by which an indicator of the expected efficiency of operation may also be determined as the quotient of chance and absolute risk. The thinking in this article has been developed on the basis of data from a real gas-supply system, while the computational methods deployed allowed applications to draw conclusions regarding possible modification of the expected gas shortages method.