Clean Hydrogen Is a Challenge for Enterprises in the Era of Low-Emission and Zero-Emission Economy

Borowski, Piotr F.; Karlikowska, Barbara

doi:10.3390/en16031171

Cited by 18 publications

(9 citation statements)

References 37 publications

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“…The findings are summarised within descriptive statistics analysis section. Finally, desk research (Borowski and Karlikowska 2023;Rahman et al 2014;Yang 2023) was used to identify significant factors to explain whether the world is ready to buy now and pay later for products using the internet.…”

Section: Methodsmentioning

confidence: 99%

Buy Now Pay Later—A Fad or a Reality? A Perspective on Electronic Commerce

Lupșa-Tătaru,

Nichifor,

Dovleac

et al. 2023

Economies

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The Millennials and Generation Z use online shopping for a holistic experience and buy more expensive or better-quality products with buy now pay later payment methods for their highly demanding needs. The authors aimed to deepen understanding of this phenomenon by finding related knowledge fields and discovering the type of economy that will represent an increasing market share for the method of domestic e-commerce payments. The methodology used combined computer-assisted review, descriptive statistics, and linear regression to explain the market share of 23 economies worldwide. Student credit card use, myopic consumer law, buying tendencies. and dark financial triangles were identified as related topics. Logistics performance, ease of doing business, and postal development were found to be significant factors. Finally, economies with medium ranks are inclined to adopt this kind of payment easily. Hence, major implications, both managerial and academic, must be addressed. High responsibility should be borne by industry associations, which should run information campaigns by collaborating with public institutions. From the point of view of theoretical implications, studying the buy now and pay later concept and its outcomes might deepen understanding of consumer behaviour, decision-making processes, risk perception mitigation, debt behaviours, and credit adoption.

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Section: Methodsmentioning

confidence: 99%

Buy Now Pay Later—A Fad or a Reality? A Perspective on Electronic Commerce

Lupșa-Tătaru,

Nichifor,

Dovleac

et al. 2023

Economies

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“…The latest reviews [20,21] have covered most types of hydrogen production from renewable sources of energy, as well as hydrogen production from fossil sources, the commercial methods of which are summarized in Table 1. This comparison indicates that not all hydrogen is created equal, but there is a whole rainbow of different ways to make it (gray, turquoise, pink, blue, and green) [22]. This arbitrary color-coding system for how hydrogen is produced despite the multiplicity of production sources.…”

Section: Current Hydrogen Productionmentioning

confidence: 99%

How Green Hydrogen and Ammonia Are Revolutionizing the Future of Energy Production: A Comprehensive Review of the Latest Developments and Future Prospects

Adeli,

Nachtane,

Faik

et al. 2023

Applied Sciences

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As the need for clean and sustainable energy sources grows rapidly, green hydrogen and ammonia have become promising sources of low-carbon energy and important key players in the transition to green energy. However, production and storage problems make it hard to use them widely. The goal of this review paper is to give a complete overview of the latest technology for the manufacture and storage of hydrogen and ammonia. This paper deals with hydrogen and ammonia synthesis and storage. It examines the most recent technological breakthroughs in areas such as electrolysis, reforming, C-ZEROS, HYSATA, DAE, sulfide, and SRBW, as well as novel storage techniques, such as solid-state storage, plasma kinetics, and POWERPASTE. This article examines the history of ammonia production and discusses some of the newer and more sustainable techniques for producing ammonia, such as electrochemical and biological approaches. This study also looks at how artificial intelligence (AI) and additive manufacturing (AM) could be used to revolutionize the way green hydrogen and ammonia are produced, with an emphasis on recent breakthroughs in AI-assisted catalyst design and 3D-printed reactors, as well as considering major investments in the shift to green energy, such as Moroccan government programs, and how they may affect future hydrogen and ammonia production.

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“…Their work has made an in-depth evaluation of policies and measures in promoting the sustainable energy pattern. Borowski and Karlikowska 14 solved the challenges faced by enterprises in adopting clean hydrogen in the era of low-emission and zero-emission economy. Their research emphasized the complexity and obstacles faced by integrating clean hydrogen in achieving sustainable energy goals.…”

Section: Recent Related Workmentioning

confidence: 99%

ESG guidance and artificial intelligence support for power systems analytics in the energy industry

Li,

Zou,

Zeng

et al. 2024

Sci Rep

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In order to increase the precision and effectiveness of power system analysis and fault diagnosis, this study aims to assess the power systems in the energy sector while utilizing artificial intelligence (AI) and environmental social governance (ESG). First, the ESG framework is presented in this study to fully account for the effects of the power system on the environment, society, and governance. Second, to coordinate the operation of various components and guarantee the balance and security of the power system, the CNN-BiLSTM power load demand forecasting model is built by merging convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Lastly, the particle swarm optimization (PSO) algorithm is used to introduce and optimize the deep belief network (DBN), and a power grid fault diagnostic model is implemented using the PSO technique and DBN. The model’s performance is assessed through experimentation. The outcomes demonstrate how the CNN-BiLSTM algorithm significantly increases forecasting accuracy while overcoming the drawback of just having one dimension of power load data. The values of 0.054, 0.076, and 0.102, respectively, are the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Effective processing of large-scale nonlinear data is achieved in the area of power grid fault diagnosis, resulting in prediction accuracy of 96.22% and prediction time of only 129.94 s. This is clearly better than other algorithms and increases fault prediction efficiency and accuracy. Consequently, the model presented in this study not only produces impressive results in fault diagnosis and load demand forecasting, but also advances the field of power system analysis in the energy industry and offers a significant amount of support for the sustainable and intelligent growth of the energy industry.

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Clean Hydrogen Is a Challenge for Enterprises in the Era of Low-Emission and Zero-Emission Economy

Cited by 18 publications

References 37 publications

Buy Now Pay Later—A Fad or a Reality? A Perspective on Electronic Commerce

Buy Now Pay Later—A Fad or a Reality? A Perspective on Electronic Commerce

How Green Hydrogen and Ammonia Are Revolutionizing the Future of Energy Production: A Comprehensive Review of the Latest Developments and Future Prospects

ESG guidance and artificial intelligence support for power systems analytics in the energy industry

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